1268631/fulltext01.pdf · -ii- master of science thesis trita-itm-ex 2018:612 on the market...

Master of Science Thesis

KTH School of Industrial Engineering and Management

Energy Technology EGI- TRITA-ITM-EX 2018:612

Division of Heat and Power Technology

SE-100 44 STOCKHOLM

On the Market Potential of

Modular Stirling CSP Systems

With Storage in the MENA

Youssef Benmakhlouf Andaloussi

-II-

Master of Science Thesis TRITA-ITM-EX

2018:612

On the Market Potential of Modular Stirling

CSP Systems With Storage in the MENA

Youssef Benmakhlouf Andaloussi

Approved

Examiner

Björn Laumert

Supervisor

Rafael Guédez

Commissioner

Contact person

Abstract

Given the intermittent nature of renewable energy sources, integrated storage solutions are necessary to

accomplish the energy shift necessary for sustainable development. In the case of solar, PV-BESS tend to

be highly capital intensive, especially for long storage hours most needed to guarantee stable electricity

production day and night. This study presents a methodology to quantify the market potential for a novel

distributed CSP technology with cost competitive thermal energy technology, where the cost target is 30%

cheaper than PV-BESS. The system in question is similar to the one developed by Cleanergy AB, where a

13 kW Stirling engine is powered by heat collected from a heliostat field and stored in an integrated latent

heat storage unit. Morocco, Tunisia, Egypt, Jordan and Saudi Arabia are chosen as representative countries

of the MENA for the study. The study is done by detailed investigation of the macro-environment of each

country, developing a methodology to rank identified business opportunities. Said opportunities are

restricted to companies within the industrial sector, based on the assumption that such customers would be

interested in a solution guarantying stable electricity production. First, a techno-economical optimisation is

done to find optimal plant configurations to service a particular energy need for each business opportunity.

Second, the multi-criteria analysis scores and ranks the latter with respected to different criteria that can be

conflicting. Finally, the top business opportunity identified by the MCA in each country are compared

through a scenario analysis, assuming different rates at which the electricity generated by the system can be

sold. With a global market potential above 40 GW in the whole MENA, industrial sectors such as mining

and cement hold the best prospects in terms of market share. The achievable costs of generation vary

depending on the DNI of the sites considered but prove to be lower compared with conventional distributed

generation (diesel gensets or PV-BESS). However, several countries in the MENA, although having high

DNI resource, still offer low electricity utility prices to industrial customers for distributed CSP to become

competitive with on-grid electricity procurement. Hence, Jordan is ranked first with the MCA, both because

of the high DN in the country, and its high electricity rates, despite having the smallest market share in

terms of capacity to install. The amount of subsidies necessary for the technology to be profitable and cons

competitive were found respectively. Except in Jordan where the system is competitive with utility rates, all

other countries needs to implement feed-in-tariffs schemes for distributed CSP with storage to become

viable. The observed trend of increasing electricity prices in the MENA however, coupled with decreasing

LCOE values due to high volumes of production indicate that economic viability in the countries with low

present rates can be achieved in the future.

-III-

Sammanfattning

Eftersom förnybara energikällor har en oförutsägbar energiproduktion krävs välutvecklade

energilagringssystem för att samhället ska gå över till förnybara energi. Solenergi kräver PV-BESS, vilket

tenderar att vara kapital intensivt, speciellt vid energilagring över lång tid som krävs för stabil

energiproduktion under nattetid. Denna studie tar fram en metodologi för att kvantifiera

marknadspotentialen för nya distribuerade CSP teknologier med termisk lagring. Kostnadsmålet för sådan

termisk lagring är 30% lägre än för PV-BESS. Som exempel för CSP systemet används tekniken utvecklat

av Cleanergy AB, vilket består av en 13 kW Stirlingmotor som är driven av hettan från ett heliostatfält och

lagrat i en integrerad latent värmelagringsenhet. Marocko, Tunisien, Egypten, Jordanien och Saudi Arabien

används för att representera länder från MENA i denna studie. Analysen består av en djupgående forskning

av makroekonomiska faktorer som används för att identifiera och ranka affärsmöjligheter. Dessa

affärsmöjligheter är begränsade till den industriella sektorn som kräver stabil energiproduktion. Först görs

en teknologisk och ekonomisk optimering för att hitta den bästa konfiguringen av energianläggningen för

kunden. För det andra poängterar multikriterieanalysen (MCA) och rankar kunderna med respekt för olika

kriterier som kan vara motstridiga. Slutligen jämförs de bästa affärsmöjligheterna som identifierats av MCA

i varje land genom en scenarioanalys, förutsatt att det är olika priser för elektricitet. Med en global

marknadspotential på över 40 GW i hela MENA, har industrisektorer som gruv och cement de bästa

utsikterna när det gäller marknadsandelar. De uppnådda LCOE varierar beroende på de undersökta

platsernas DNI men är ändå lägre jämfört med alternativa distribuerad generation (dieselgeneratorer eller

PV-BESS). Men flera länder i MENA , trots att de har en hög DNI-resurs, fortfarande erbjuda låga

elverktygspriser till industrikunder för distribuerad CSP för att bli konkurrenskraftiga med elförsörjning på

nätet. Därför rankas Jordan först med MCA, både på grund av den höga DN i landet och höga elpriser,

trots att den minsta marknadsandelen. ängden subventioner som är nödvändiga för att tekniken ska vara

lönsam och konkurrensbegränsad hittades. Förutom i Jordanien där systemet är konkurrenskraftigt med

nyttjandepriser måste alla andra länder genomföra inmatningstullsystem för distribuerad CSP med lagring

för att bli lönsam. Den observerade trenden med att öka elpriserna i MENA, i kombination med minskande

LCOE-värden på grund av stora volymer av produktion tyder på att ekonomisk lönsamhet i länder med

låga priser kan uppnås i framtiden.

-IV-

Acknowledgements

My first line of acknowledgments is bound to be addressed to Rafael Guédez, my supervisor, who played

a central part in giving me the opportunities and experience I have today. Such small recognition cannot

get right how grateful I am to him. To that effect, an additional appendix is needed in this report, so I can

enumerate all the things he taught me. Naturally, Jonas Wallmander comes next in this thank you note, who

directed me during the internship, and played a big role in the professional opportunities which came with

it. Very much thanks also to Monika Topel, who always opened (literally) the door of her office to me. The

laughter and good discussions we had there contributed a lot to this modest work. Special mention also to

Osama Zaalouk in whom I found a precious ally against the Venezuelan mafia of the Energy department.

Various reasons almost pushed me not to pursue this double degree master in KTH, but at the

end, I am glad I went with it. Some of my closest friends now are people I met during these two

years, and I am grateful to all one of them. Although Sweden is known for its cold and dark winters, my

overall experience was one of warm memories. Finally, thanks to my family and mother most notably, whose

unconditional love and words of wisdom will always resonate with me.

شكر كلمة

البحت هذا في ما فهم بإمكانهم يكن لم فإن. أمي و أبي لشكر صغيرة، كانت لو و حتى فقرة، أخصص أن علي الواجب من

بقدر مؤلفوه فهم لذلك. مثله فهم حتى أو نشره، بإمكاني كان لما تربيتي، في وتضحياتهم دعمهم ولوال فلوالهم، اللغة، بحكم

المستقبل في مكانة أوأي اليوم، مكانتي في لهم يرجع الفضل كل. مالكه أنا ما .

مرة، كل في. المشوار هذا لحظات أصعب في بجانبي كانت واللتي فرنسا، في تمدرسي دعم في السباقة كانت التي أمي، إلى

لمؤانستي المسافات أطول قطع حبها .

في لرغبتي إليه، يرجع العلمي بالمجال اهتمامي. دراستي خطوات كل وثبت الحياة في التدبر كيفية علمني الذي أبي، إلى

إلدهاشه الرياضيات مادة في الدرجات أعلى على الحصول أحاول كنت حين صغري، مند وذلك بي، فخورا جعله

.

-V-

Abbreviations

AFEX Arab Future Energy Index

BESS Battery Electricity Storage Systems

CAPEX Capital Expenditure(s)

CSP Concentrated Solar Power

CT Central Tower

DNI Direct Normal Irradiance

DS Dish Stirling

GDP Gross Domestic Product

GIS Geographic Information System

HTF Heat Transfer Fluid

IPP Independent Power Producer

IRENA International Renewable Energy Agency

IRR Internal Rate of Return

LCOE Levelized Cost of Electricity

LF Linear Fresnel

MCA Multi Criteria Analysis

MENA Middle East North Africa

MGT Micro Gas Turbine

NPV Net Present Value

O&M Operation and Maintenance

OPEX Operational Expenditure(s)

PCM Phase Changing Material

PT Parabolic Through

PV Photovoltaic

RE Renewable Energy

RES Renewable Energy Systems

SAM Serviceable Achievable Market

SOM Serviceable Obtainable Market

STEALS Investor-Owned Utility

TAM Total Addressable Market

TES Thermal Energy Storage

WACC Weighted Average Cost of Capital

-VI-

List of figures

Figure 1 : Cleanergy Stirling dish demonstration plants, Dubai (right), Mongolia (left) [3] .............................. 2

Figure 2 DESERTEC project map. The red squares represent the area need for solar power plant to power

the whole world, Europe and Germany [7] .............................................................................................................. 4

Figure 3 PV installed capacity growth [10] ................................................................................................................ 5

Figure 4 CSP installed capacity growth [10] .............................................................................................................. 5

Figure 5 Flow diagram of a typical CSP plant [11] .................................................................................................. 6

Figure 6 CSP technologies [14] ................................................................................................................................... 6

Figure 7 CSP market trends [15] ................................................................................................................................. 8

Figure 8 247Solar Plant [18] ......................................................................................................................................... 9

Figure 9 Vast Solar CSP system [19] ........................................................................................................................10

Figure 10 Design configuration of STEALS [22]...................................................................................................11

Figure 11 Cleanergy's Alpha type Stirling engine [27] (adapted)..........................................................................12

Figure 12 Cleanergy's initial target market (2021-2025) for the TES system design ........................................13

Figure 13 Model of one modular Cleanergy CSP unit with the three main components: concentrator,

receiver with storage (10 hours) and Heat Engine (Stirling) ................................................................................14

Figure 14 PV-BESS LCOE in 2021 .........................................................................................................................15

Figure 15 Market size estimation [34] ......................................................................................................................16

Figure 16 Selection methodology for business opportunities [36] ......................................................................17

Figure 17 AFEX Renewable Energy 2016 [37] ......................................................................................................18

Figure 18 Techno-economical analysis process ......................................................................................................22

Figure 19 LCOE vs Reflective area ..........................................................................................................................23

Figure 20 NES 2030 targets [59] ...............................................................................................................................28

Figure 21 Electricity market Morocco [62] .............................................................................................................29

Figure 22 RE National Program 2017-2020, Tunisia [86] ....................................................................................32

Figure 23 Electricity market, Tunisia [85]................................................................................................................32

Figure 24 Government power generation expansion plans [91] ..........................................................................35

Figure 25 Egypt power market structure .................................................................................................................36

Figure 26 RE projects in Jordan 2016 [108] ............................................................................................................39

Figure 27 Jordan's electricity market [110] ..............................................................................................................40

Figure 28 Long-term renewable energy targets, Saudi Arabia [117]....................................................................42

Figure 29 Power market structure, Saudi Arabia [25] ............................................................................................43

Figure 30 RE private Investment Increase (2013-2016) [37] ...............................................................................48

Figure 31 LCOE vs TES size (Cleanergy’s cost functions) ..................................................................................49

Figure 32 LCOE vs TES size (STEALS cost data) ...............................................................................................50

Figure 33 Site positioning map, Morocco ...............................................................................................................51

Figure 34 company positioning map MENA (Industry).......................................................................................52

Figure 35 Scaling up the SAM to the MENA .........................................................................................................54

Figure 36 SAM in the MENA region by country ...................................................................................................55

Figure 37 Cleanergy's CAPEX breakdown .............................................................................................................55

Figure 38 LCOE sensitivity analysis .........................................................................................................................56

Figure 39 NPV(k€) sensitivity analysis .....................................................................................................................57

Figure 40 MCA country score (1-10) .......................................................................................................................59

Figure 41 Multi Criteria Analysis – Ranking of business opportunities (1-10) .................................................60

Figure 42 Expected utility electricity price for industry 2021 (€/MWh) ............................................................66

Figure 43 IRR vs Power price ...................................................................................................................................67

Figure 44 NPV vs Power price .................................................................................................................................68

Figure 45 NPV vs Power price (zoom) ...................................................................................................................68

Figure 46 Normalized LCOE evolution ..................................................................................................................69

Figure 47 Case 1 ..........................................................................................................................................................99

-VII-

Figure 48 Case 2 ..........................................................................................................................................................99

Figure 49 Case 3 ..........................................................................................................................................................99

Figure 50 Case 4 ....................................................................................................................................................... 100

Figure 51 Case 5 ....................................................................................................................................................... 100

Figure 52 Case 6 ....................................................................................................................................................... 100

Figure 53 Case 7 ....................................................................................................................................................... 101

Figure 54 Case 8 ....................................................................................................................................................... 101

List of tables

Table 1 CSP technology comparison [15] ................................................................................................................. 7

Table 2 Entry modes categories [31] ........................................................................................................................19

Table 3 Financial model inputs .................................................................................................................................21

Table 4 Industry electricity rates ...............................................................................................................................25

Table 5 Attractiveness scoring table .........................................................................................................................25

Table 6 Morocco generation units 2015 [58] ..........................................................................................................27

Table 7 High voltage industry general rate, Morocco [51] ...................................................................................30

Table 8 Identified industry companies, Morocco ..................................................................................................30

Table 9 Identified industry companies, Tunisia......................................................................................................33

Table 10 Identified industry companies, Egypt ......................................................................................................37

Table 11 Identified industry companies, Jordan.....................................................................................................41

Table 12 Electricity rate, Saudi Arabia [121] [120] .................................................................................................44

Table 13 Identified industry companies, Saudi Arabia ..........................................................................................45

Table 14 Countries Performance under International Indices [124] [125] [126] ..............................................46

Table 15 Business model country comparison .......................................................................................................46

Table 16 Country score ranking ................................................................................................................................48

Table 17 Market potential for the MENA (industry), with optimum configuration ........................................52

Table 18 SAM in the MENA, industry (grid connected, VHV-HV-MV) ..........................................................53

Table 19 Market potential for the MENA (industry), with optimum configuration using STEALS cost data

........................................................................................................................................................................................56

Table 20 Most competitive business cases under the MCA (per country).........................................................58

Table 21 Country score, by criterion (1-10) ............................................................................................................61

Table 22 Country score, additional criteria .............................................................................................................62

Table 23 Weighting factors case definition .............................................................................................................63

Table 24 MCA sensitivity (top 5 business opportunities) .....................................................................................63

Table 25 Scenario analysis results, Morocco (MM31), WACC = 4,5% ..............................................................64

Table 26 Scenario analysis results, Tunisia (TC11), WACC = 4,8% ...................................................................64

Table 27 Scenario analysis results, Egypt (EM21), WACC = 4,9% ....................................................................64

Table 28 Scenario analysis results, Jordan (JCh31), WACC = 4,8% ...................................................................65

Table 29 Scenario analysis results, Saudi Arabia (SC11), WACC=5% ...............................................................65

-VIII-

1 Contents

Abstract .......................................................................................................................................................................... II

Sammanfattning .......................................................................................................................................................... III

Acknowledgements .................................................................................................................................................... IV

Abbreviations ........................................................................................................................................................... V

List of figures ......................................................................................................................................................... VI

List of tables ......................................................................................................................................................... VII

1 Introduction .......................................................................................................................................................... 1

1.1 Cleanergy AB ............................................................................................................................................... 1

1.2 Objectives ..................................................................................................................................................... 2

1.3 Thesis structure ........................................................................................................................................... 3

2 Theoretical Framework ....................................................................................................................................... 4

2.1 Solar energy overview ................................................................................................................................. 4

2.2 Solar CSP technologies .............................................................................................................................. 6

2.3 Stirling-based CSP systems ........................................................................................................................ 9

2.3.1 Small scale CSP .................................................................................................................................. 9

2.3.2 Cleanergy CSP systems ...................................................................................................................11

2.3.3 New design with TES .....................................................................................................................12

3 Methodology .......................................................................................................................................................16

3.1 Geographical limitation ............................................................................................................................17

3.2 Market analysis/entry ...............................................................................................................................18

3.3 Financial model .........................................................................................................................................19

3.4 Techno-economical analysis ....................................................................................................................21

3.5 Multi-criteria analysis ................................................................................................................................24

3.6 Scenarios definition...................................................................................................................................26

4 National environment, industry and firms’ specifics ....................................................................................27

4.1 Morocco .....................................................................................................................................................27

4.1.1 Energy context .................................................................................................................................27

4.1.2 Electricity market .............................................................................................................................28

4.1.3 Industrial companies .......................................................................................................................30

4.2 Tunisia .........................................................................................................................................................31

4.2.1 Energy context .................................................................................................................................31



4.3 Egypt ...........................................................................................................................................................34

4.3.1 Energy context .................................................................................................................................34


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4.4 Jordan ..........................................................................................................................................................38

4.4.1 Energy context .................................................................................................................................38



4.5 Saudi Arabia ...............................................................................................................................................42

4.5.1 Energy context .................................................................................................................................42



4.6 Country comparison .................................................................................................................................45

5 Comparison/Analysis ........................................................................................................................................49

5.1 Optimum configurations .........................................................................................................................49

5.2 Potential/Serviceable achievable market ...............................................................................................52

5.2.1 Results ................................................................................................................................................52

5.2.2 Sensitivity analysis ............................................................................................................................55

5.3 Multi Criteria Analysis ..............................................................................................................................57

5.3.1 Results ................................................................................................................................................57


5.4 Scenario analysis ........................................................................................................................................63

5.4.1 Results ................................................................................................................................................63


6 Conclusions .........................................................................................................................................................70

7 Appendixes ..........................................................................................................................................................72

Bibliography .............................................................................................................................................................. 102

-1-

1 Introduction

Being one of our era’s most critical problems, securing electricity access in a cost-effective way and without

harming the environment is a big challenge that countries worldwide are trying to tackle. Consequently,

electricity generation based on renewable sources is on the rise and has seen rapid growth in the last decade

globally. Investments and technological innovations are the drivers of the shift in the current energy system,

going away from centralized generation stations to decentralized and distributed units that can

accommodate different configurations. Among the multiple technologies and renewable sources that can

be used, solar energy ranks in the top due to its huge potential in providing electricity access on a global

scale. The DESERTEC initiative suggested that in only six hours, the African desert receives as much energy

from the sun as humankind consumes in a whole year [1].

However, and by definition, renewable and sustainable energy sources are intermittent, which hinders their

full adoption and penetration into the grid. Solar energy is no exception to that, being available only when

the sun shines, and unavailable after, period where the demand profiles are the highest. Specifically,

intermittent solar production cannot fit around the clock the clock load profiles. The solution is then to

couple the solar technology with a storage system that would generate electricity on demand, later in the day

when there is no sun. Storage systems can refer either to chemical batteries in the case of PV plants, or

Battery Energy Storage Systems (BESS), or thermal energy storage (TES) in the case of CSP systems. BESS

are highly capital intensive and are only viable when considering large scale projects, but are not suitable,

nor competitive to smaller distributed systems with large storage requirements [2] . Likewise, CSP systems,

such through or tower with TES are proven to cost competitive in large scale sizes, and do not fit for

distributed generation as their efficiencies drop when dealing with smaller systems.

Cleanergy AB, a privately owned Swedish company, is in the crossroad of all these considerations, offering

a new modularized designed on-demand electricity production technology with distributed energy storage

that can provide a high efficiency solar power plant and be built cost efficient in any size from the range of

10kW to hundreds of MW. This Master thesis, done in the form of an internship at Cleanergy AB, aims at

elaborating business strategies for the company that wishes to develop its new technological solar innovation

in the MENA and Sub-Saharan African regions where the solar resource is abundant and perfect for its

product (DNI greater than 2000 kWh/m²/year)

1.1 Cleanergy AB

Cleanergy is a privately held Swedish company that was founded in 2008 and that focuses on Stirling engine-

based renewable energy solutions. The company is a small and medium sized enterprise that has two sites

of production, Uddevalla and Åmål, alongside offices in Stockholm and Gothenburg [3]. Its core expertise

is the production and manufacture of Stirling engines which convert heat into electricity. The first target

segment market was gas-fuelled power production that started in 2008 with the GasBox. This product is

fully commercialized today in different countries (United Kingdom, Norway, Sweden…) [3]. In parallel,

Cleanergy started developing solutions for CSP systems with the modified version of the Stirling engine

called SunBox. The company proposes a modular CSP dish Stirling unit, comprising of a parabolic dish

capable of tracking the sun that uses SunBox for electricity generation. While the generation is caped at

13kW per unit, the Cleanergy SunBox unit is well suited for large utility scales ranging from kW to MW

scales thanks to its modular and autonomous design. No water is consumed in the power production cycle,

which is a key competitive advantage over other CSP technologies such as linear trough and tower systems,

especially in areas of high ambient temperature where high levels of Direct Normal Irradiation are usually

to be found and water resources are usually scarce. It also holds the highest conversion efficiency from sun-

to-electricity among all solar technologies, reaching 30% [3]. Currently, Cleanergy has commissioned 3

demonstration plants: 110 kW installed capacity in both Mongolia and Dubai seen in Fel! Ogiltig s

jälvreferens i bokmärke., and a 13kW unit in Ouarzazate, Morocco. The company is now focused on

-2-

developing a thermal energy storage system of 10 hours to be coupled with its CSP unit, allowing for on-

demand electricity production, increasing the grid stability, its flexibility and thus its attractiveness.

1.2 Objectives

The CSP system the company is developing is best suited for locations with a DNI greater than 2000

kWh/m²/year, which is why Cleanergy wishes to study the potential opportunities laying in the MENA.

Indeed, most of the countries in the MENA are considered to be part of the Sunbelt, where solar radiation

is well above the threshold mentioned [4]. More specifically, the countries studied in this thesis are: Morocco,

Tunisia, Egypt, Jordan and Saudi Arabia. The choice of these 5 countries is strategic, as they all have enough

resemblance to draw conclusions and strategies to be applied to MENA in general, but present in the same

time enough differences for them to stand out and challenge the company in its way of doing business.

The main goal of this thesis is to identify how a small scale distributed CSP system with TES, such as

Cleanergy’s, can enter a specific marketplace and propose strategies the company should develop for its

modular technology to successfully penetrate the MENA. Detailed prospective customer profiles are

identified in each country, and for each customer-type a techno-economic analysis has been carried out to

determine best configuration (in terms of key component size and operation) that would minimize the

generation costs of the system in order to ultimately assess the competitiveness of a business case based on

such a technology when compared to the current way of procuring electricity. At last, specific

recommendations for technology developers and potential off-takers are provided.

As follows, this work needs to:

• Present a comprehensive and detailed market profile for each of the countries selected. This

includes description of the electricity sector, generation capacities, regulatory framework in place

for the power sector, future trends in terms of energy policy and generation.

• Identify potential customers in each of the countries selected, by validating the company’s

assumptions on the market application, sizing the total addressable market, in terms of MW to

install, in each one and identifying which customers are in need of dispatchable renewable

electricity.

• Carry out a techno-economic analysis for each customer to determine best configuration (in terms

of key component size and operation) that would minimize the generation costs of the system.

• Set up business cases for selected companies/customers based on cash flow calculations to outline

the attractiveness of Cleanergy’s technology for them and its competitiveness when compared to

current way of procuring electricity, but also provide proof of business profitability for Cleanergy.

Figure 1 : Cleanergy Stirling dish demonstration plants, Dubai (right), Mongolia (left) [3]

-3-

• Suggest business development strategies to achieve successful market entry in the MENA region.

The go-to market strategies to be developed will be based on the analysis of the countries selected

and comparison between the customer identified. It will also include suggestions on how to build

communication channels and strategic partnerships with the most promising customers.

1.3 Thesis structure

The report is constructed around the above objectives and follows the structure detailed below:

• Section 1, Theoretical framework: An overview of all the theory behind the concepts to be

developed and used throughout the report, describing in detail Cleanergy’s value proposition and

outlining the different solar technologies that compete with it, reviewing the market research theory

and detailing the financial metrics that will be used as key performance indicators to rank and qualify

the market.

• Section 2, Methodology: A step by step explanation of the methods followed to perform this

research work, ranging from the data mining approach used, description of the economic model

built to the actual market analysis and comparative approach applied.

• Section 3, National environment, industry and firm’s specifics: Acts as market analysis of the

different countries reviewed, by setting up country profiles detailing market status and regulations,

understanding how macro-environmental factors will help or hinder Cleanergy’s business,

quantifying the total addressable market by identifying potential industrial customers, and carrying

out techno-economical assessment for each.

• Section 4, Comparison/Analysis: Cross-country, cross-customer comparison and analysis to

identify the most promising and profitable market for Cleanergy. The analysis will be based on

individual business cases built for selected industrials and firms to understand the market behaviour

and need, by considering different market shares scenarios for the future and different localization

estimates for Cleanergy’s product.

• Section 5, Go-to market strategies: This section draws strategies and suggestions for Cleanergy

to develop its business in each of the countries selected based on the market analysis done and

delimits risks inherent in each one of them.

• Section 6, Conclusions: A summary of all the findings of the research work, and suggestion on

how to proceed in the future.

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2 Theoretical Framework

2.1 Solar energy overview

With regards to the shift in energy systems currently deployed around the world, solar energy technologies

are well prioritized thanks to their numerous advantages. The old-fashioned way of producing electricity

relying on fossil fuel brings many problems, from environmental concerns such as damage to the earth,

pollution of the atmosphere and water, but also socio-political issues when considering a country’s need to

secure energy access and the shrinking availability of fossil fuels and volatility of their prices. Relying on the

sun for electricity production solves above issues, as the yearly received energy from the sun is 1500 times

largen than the world energy use [5]. In fact, the DESERTEC project suggested that solar power plants

located in the African desert covering 0,3% of its area, could power up all nations around the world given

the right transmission infrastructure built [6]. Figure 2 shows the area requirement of such a project

Figure 2 DESERTEC project map. The red squares represent the area need for solar power plant to power the whole

world, Europe and Germany [7]

While solar energy is usually associated with PV power generation, the sun’s irradiance also delivers its

energy in the form of heat that can be used for power generation in CSP systems. In 2016, 76,6 GW of solar

were installed, making the total solar capacity reach 306,5 GW, representing a 33% increase compared to

2015. Asia leads the solar market, dominating with 48% of the total install capacity making it the largest

solar powered region. The global forecasted capacity to be installed in 2017 is 387 GW, while this figure will

surpass 700 GW after 2030 [8]. The evolution of the global installed solar capacity throughout the years

from 2006 till 2016, for both PV and CSP can be seen in Figure 3 and Figure 4. Solar CSP power plants are

capital intensive, needing huge investments for their erection compared to PV plants, which explains the

difference between the installed capacities of both [9].

-5-

Figure 3 PV installed capacity growth [10]

Figure 4 CSP installed capacity growth [10]

-6-

2.2 Solar CSP technologies

Unlike photovoltaic cells or flat plate solar thermal collectors, CSP power plants do not use the global solar

irradiation, namely disregarding the diffuse part which results from scattering of the direct sunlight by

clouds, particles and molecules in the air because they cannot be concentrated. The CSP technology is based

essentially on the direct solar radiation, which is collected through a concentrator to a receiver. This makes

CSP power plants best suited for locations with high percentage of clear sky days, which do not have smog

or dust. The concentrated heat is then used to run a power conversion cycle to produce electricity. A

schematic of a conventional CSP plant is given in Figure 5.

Figure 5 Flow diagram of a typical CSP plant [11]

The most common CSP systems are shown in Figure 6 .A brief overview of each is given below, while Table

1 compares the technological specifications of each [12] [13].

Figure 6 CSP technologies [14]

• Parabolic trough (PT): It is the most deployed CSP technology. Trough-shaped mirrors concentrate

sunlight into a linear focus on a receiver tube that follows the parabola’s focal line. The mirror and

receiver tube structure are mounted on a frame that follows the daily sun movement on one axis,

while the seasonal movement of the sun are tracked with lateral movements of the line focus. The

heat is collected from the receiver tubes via a heat transfer fluid and is used to feed a power block

for electricity generation.

-7-

• Linear Fresnel reflectors (LF): Variation of the parabolic trough collectors. Their main difference

from parabolic trough collectors is that, instead of using parabolic bent mirrors to concentrate

sunlight, they use several parallel flat mirrors to concentrate it onto one receiver, which is located

several meters above the primary mirror field. The secondary mirror structure is necessary to

account for the astigmatism distortion caused by the optical principles of Fresnel collectors.

• Centrale receiver tower (CT): This design contains an array of heliostats (large mirror structures

with double axis tracking) that concentrates the solar radiation into a central receiver mounted on

the top part of a tower. This configuration gives high efficiency energy conversion into the large

receiver point, yielding higher concentration ratios compared to linear focusing systems. It permits

the power cycle to work at higher temperatures with reduced losses.

• Parabolic dishes (DS): Similarly to the trough design, dish systems rely on the geometric properties

of a three-dimensional paraboloid to concentrate direct solar radiation to a point focus receiver,

reaching in optimum condition temperatures over 1,000ºC, similar to tower systems. The latter

gives them the advantage of having the highest solar conversion efficiency, since they always have

the aperture facing the sun and avoid the cosine loss effect. These systems have a power conversion

unit, namely Stirling engine, that transforms the concentrated heat into electricity. This will be

further explained in the following sections, as it is Cleanergy’s key product.

Table 1 CSP technology comparison [15]

Technology PT LF DS CT

Typical size (MW) 10 – 280 1 – 125 1 10 – 135

Concentration

Factor 70 – 80 25 – 100 600 – 4000 600 – 1200

Capacity Factor

(%) 30 – 50 20 – 30 20 – 30 40 – 70

Operation

Temperature (ºC) 293 – 393 140 – 275 250 – 700 290 – 565

Sun-to-Electricity

efficiency (%) 16 – 18 9 – 11 12 – 25 16 – 20

Installed worldwide

(MW) 4336 319 3 689

Use of land

(MWh/(ha·year)) 600 – 1000 600 – 1000 400 – 800 400 – 800

Maturity Commercial Commercial Demo Commercial

Reflector Parabolic

mirror

Flat/curved

mirror

Paraboloid

mirror Curved mirror

Receiver

Absorber tube

w/vacuum

cover

Absorber tube

w/concentrator

Stirling

engine/Gas

turbine

External /

Cavity

HTF

Thermal oil

Saturated steam

Air

Molten salt /

Water-steam

TES

Molten salts,

indirect

Steam

accumulator

N/A

Molten salts,

direct / steam

accumulator

TES capacity 4 – 12 hours < 1 hour N/A 6 – 14 / < 1

hours

Hybridization Yes, existing Yes Unlikely Yes

-8-

In terms of market deployment, parabolic through systems dominate the CSP plants in operation globally

as of 2016, followed by central tower systems. This is due to the huge solar investments Spain made in the

past regarding solar CSP, considering that at that time trough design was the most developed and proven

technologically. However, and as it is seen in Figure 7 , the majority of planned projects, as well as the ones

under development are central tower systems, proving that there is a shift in the market justified by better

efficiency due to the higher temperatures achieved in the receiver, compared to through systems. Central

tower systems capitalize on that as well with regards to heat storage in molten salts, as the high temperatures

reached in the receiver allow for a reduced cost of energy storage unlike trough systems [16]. Figure 7 also

shows which players are investing the most in solar energy. While Spain lead the market in the early 2000s,

near zero projects are planned or developed there in 2016. This is mainly due to the fact that the country

suffers from a huge electricity tariff deficit that pushed officials to halt capital intensive projects. MENA

countries, on the other hand, are investing heavily in solar and renewable energy in general, acknowledging

their need for sustainable energy access and security, and capitalizing on the perfect renewable resources

they have. It is suggested that PV alone has a potential of 7GW by 2020 in the MENA, and 27 GW by 2030

[4]. Nevertheless, the increasing number of large-scale solar projects planned and developed in that region

make it a promising market for companies like Cleanergy, as its countries show strong commitment to

energy transition.

Figure 7 CSP market trends [15]

-9-

2.3 Stirling-based CSP systems

2.3.1 Small scale CSP

Whereas conventional CSP technologies are competitive in large-scale parks, they have yet to become

attractive for small-scale distributed and dispatchable generation (< 5MW). On the other hand, PV-BESS

plant can be seen as best suited for that type of use, as they are more competitiveness to diesel generators

[2], which they mostly compete with. Both alternatives can offer on-demand electricity production and

represent a stable input to the existing grid generation. But PV-BESS joys from several advantages, in the

sense that they are not subject to volatile oil prices, or CO2 abatement policies and restrictions, that often

put extra financial hurdle on fossil fuel generation. In spite of that, the current cost of chemical batteries for

PV systems is too high and render it not competitive, nor its 2030 future projections [2].

As a result, a number of companies are currently developing modular and distributed cost-effective CSP

systems with TES. For instance, 247Solar develops a dispatchable 300 kWe system consisting of a heliostat

field-tower system, a micro gas turbine (MGT), and a brick-based dry TES. Specifically, a small heliostat

field concentrate solar power into a receiver mounted in a 35 m tower. The receiver heats air passing through

it to about 980 °C that in turn warms up turbine's compressor air. The microturbine is then powered by the

super-heated compressor air, thus spinning a generator to produce electricity. The system uses no

water/steam, salts, oils, hydrogen or helium. Not all the hot air from the receiver is used from power

generation, and serve as heating source to the TES, in the form of firebricks or small pieces of ceramic.).

When the sun isn't shining, air is blown through the hot TES to heat the turbine's compressor air. Natural

gas or biofuels (e.g., from landfills) provides backup power when there is not enough solar power, or during

nighttime [17]. An illustration of the 247Solar system is given in Figure 8.

Figure 8 247Solar Plant [18]

The same idea is reprised by Australian company Vast Solar, which already constructed three CSP research

and demonstration facilities, where a small heliostat field concentrates sunlight into a 30 m tower and

receiver, for electricity to be generated through a small steam turbine. Storage is achieved thanks to molten

salts. A pilot demonstration plant (6MWth, 1,1 MWe with 3 hours of storage) was commissioned in 2016,

and later connected to the Australian grid, making it the first CSP plant with storage connected in Australia.

The plant consists of five solar array modules. Each module consists of one tower of approximately 30m, a

thermal energy receiver and about 700 heliostats. Modules connect to a central energy storage tank with

molten salts, and from there the stored thermal energy is passed through a steam generator to make steam

for a small (1.1MWe) turbine and electricity generator [19]. The schematic of the plant is given in Figure 9.

-10-

Figure 9 Vast Solar CSP system [19]

A similar concept was proposed by AORA-Solar, a developer in solar-biogas hybrid power technology that

specializes in small-scale off-grid solutions. The design relies on the same heliostat field/tower

configuration, but the electricity is produced by a Stirling engine instead of a MGT. AORA-Solar however

do not propose TES, relying on natural gas, biofuels or diesel to offer around the clock electricity generation.

The lack of updated information and literature about the company and its projects means most likely that it

went bankrupt, as of today, its website is shut-down [20] [21].

Subsequently, promising alternatives are based on solar-powered Stirling engines integrated with TES,

leveraging from both the modularity (10-40 kW) and the high efficiency of the engine (e.g. when compared

to MGTs, and even to conventional cycles in large CSP plants). Indeed, a recent study performed by several

U.S. research institutions and funded by the U.S. Department of Energy [22] studies the performance

modelling and economical viability of a CSP tower small scale system with latent storage, referred to as

STEALS. The research study reprises the heliostat field design, that reflect sunlight on top of a tower, where

the entire thermal system is located. The cavity receiver heats up the bottom of the TES tank (and its PCM),

while sodium heat pipes extend vertically from the bottom of the tank to the top, distributing heat in the

storage material. A thermosyphon-based thermal valve acts as an interface between the TES tank and the

Stirling engine used for electricity generation. The heat flow from the storage to the power block is

controlled by a valve, as the Stirling engines considered in the study range from 0.1 to 1 MW. This modular

design results in minimal balance of system costs and enables high deployment rates with a rapid realization

of economies of scale, where generation costs reach values well below 100 €/MWh [22]. A schematic of the

whole system is given in Figure 10.

-11-

Figure 10 Design configuration of STEALS [22]

Against this background, previous research have investigated the potential small distributed CSP systems

can have, be it with storage or without [23] [24]. These studies provide summary information about market

application for such a design, and are narrow in scope as they don’t go beyond a general classification of

sector application: industries, villages…, or give a quantification of the potential, in terms of MW to install.

Neither have these proposed a business model that would allow small scale CSP systems with TES to secure

its market niche, based on the risks hindering its market entry. On the other hand, when it comes to analysis

work that measures the potential of a new technology, and specifically CSP in different countries, one can

name the HYSOL project work package 2 [25], which studies the economic feasibility and market

penetration of an innovative configuration for a fully renewable hybrid CSP plant. The study is carried out

for four selected countries: Kingdom of Saudi Arabia, Chile, Mexico and South Africa. It first assesses

regulatory and policy framework regarding renewable energies in each to understand the power market,

renewables energy targets…, to then carry out a corporate economic assessment for the decision-making

process based on metrics such as LCOE, NPV and IRR for the different countries. Based on the mentioned

analysis, it finally draws conclusions on the potential of the new technology in each prospective. Another

project tutored by Apricum, the strategy consultancy firm specialized on renewable energies deals with the

assessment of business opportunities present in the solar industry for Saudi Arabian companies. It

represents a prefeasibility analysis for a Saudi Company to enter the solar industry, by analysing the solar

market’s value chain and performing a multi criteria analysis of different business opportunities. Two

representative business cases are presented afterwards to showcase the value of the best identified

opportunities [26].

2.3.2 Cleanergy CSP systems

2.3.2.1 Legacy product

The company’s CSP solution “SunBox” is based on one core competence and component, which is the

Stirling engine. Cleanergy modified this two-hundred-year-old technology to better suit solar applications,

converting concentrated solar radiation to electricity with the engine [27]. The Stirling cycle is a closed cycle

that contains a unique and fixed volume working fluid (gas) that is heated, expanded, cooled, and

compressed, thus driving a piston for power generation [28]. Cleanergy’s system relies on an air cooled,

Alpha type Stirling engine. This configuration, shown in Figure 11, is characterized by two different pistons

in two separated cylinders. They are connected in series through a heater, a regenerator and a cooler. By

doing so, one piston acts as the “hot” part of the engine, and the other as the “cold” part. The heat coming

from the parabolic dish and receiver heats up the working fluid (hydrogen), causing it to expand, pushing

-12-

the hot piston and driving the crankshaft to create momentum. As a result, the cold piston is compressed,

moving the working gas into the cold heat exchanger and regenerator. The generator connected to the

moving crankshaft generates electricity, while warm air is rejected through the air cooling system [27] [3]

[28].

Figure 11 Cleanergy's Alpha type Stirling engine [27] (adapted)

As mentioned before, Cleanergy is currently developing a Stirling based CSP solution with TES. However,

the company had a previous design based on the Dish Stirling configuration. The latter will be referred to

as “legacy product” in this report, since the company no longer focus on it. Dish Stirling systems use

parabolic shaped mirrors to concentrate solar radiation into a receiver. The heat in the receiver feeds the

Stirling engine to produce electricity as explained above. “SunBox” refers to the unit Cleanergy produces,

which contains its modified version of the Stirling engine alongside the receiver. With a nominal capacity of

13kW, the SunBox unit is mounted onto the parabolic dish structure that can track sun movement

throughout the day. The main advantage of this system is its modularity, that allows it to operate individually

in remote locations such as small villages or off-grid locations. It also offers the possibility for medium to

large scale application by associating multiple Dish Stirling systems. With regards to the technical

specifications and comparison made in Table 1, the demonstration unit installed in Dubai reached a

conversion efficiency of 30%, record value for any solar technology. The system is best suited in hot arid

climate zones as mentioned before, where there is high level of direct normal solar irradiance. The water

scarcity characterizing these regions does not hinder Cleanergy’s system operation, as no water is required

for power generation. Moreover, the majority of the components of the system have near zero degradation

over a period of twenty-five years, rendering Cleanergy’s product sustainable, efficient, robust and the

perfect alternative for power generation during the day [3] [29] [27]. The addition of a thermal storage unit

would allow for dispatchable electricity, which is being developed at Cleanergy, and will be further detailed

below.

2.3.3 New design with TES

As seen in both Figure 3 and Figure 4, the installed capacity of PV power plants exceeds by far CSP. The

reason for this is the cheap price of PV cells and modules compared to solar thermal. As a point of reference,

PV power plants’ bidding price reached 30$/kWh in 2016, while CSP tower bidding price was 94.5$/kWh

[30]. However, while PV is attractive for its cheap and simple design, it becomes less appealing when the

sun falls, period when electricity demand is on the rise and PV output fading. The solution is to couple the

photovoltaic panels with storage batteries to produce reliable electricity like CSP systems do with TES. Li-

ion battery is most often the best choice, but offsets PV’s greatest advantage of being cheap. As of 2017,

the cost of Li-on battery pack was around 230$/kWh, but degradation considerations make it even more

costly: it may be needed to replace batteries 4 to 5 times during a PV power plant lifetime [30]. TES does

-13-

not present these limitations, and Cleanergy plans to capitalize on that to compete with PV and other CSP

technologies.

Cleanergy’s target market and strategy can be seen in Figure 12. In a nutshell, while PV technology is

definitely cheaper than any other renewable alternative from small scale installations(residential) to utility

scale plants, dispatchability and storage features make it way more expensive than the other solar systems,

especially in medium and large-scale projects needing storage capabilities greater than 4 hours. On the other

hand, CPS tower and through systems with TES systems are commercially viable for large installed capacities

of over 50 MW as was seen previously. Cleanergy then positions itself in markets of installed capacity from

100kW to 50 MW with long hours of storage above four hours. This market segment is a niche, meaning

that it is a blank spot where no product with competitive added value has been proposed yet.

Figure 12 Cleanergy's initial target market (2021-2025) for the TES system design

To do so, the company started the R&D on a new CSP Stirling based system that incorporates TES of 10

hours or more. Figure 13 shows the proposed design for the new system. In this configuration, a solar field

of small heliostats concentrate sunlight into a receiver mounted onto the top of a 10 m tower. The receiver

is linked to the TES system, which uses a phase changing material (PCM). The PCM transfers the heat

collected with a heat transfer fluid (HTF) that powers up the Stirling engine through a heat exchanger.

-14-

This new design is currently being researched and developed with the following technical specifications:

• Each modular system (solar field, tower and Stirling engine) will have a rated power of

13kW. This way the technology can still be used for distributed configurations but allows

for large scale deployment as well.

• The TES should have a capacity of 10 hours or more, storing enough heat for electricity

production when the solar resource is not available, but to also contribute to grid stability

by providing a firm electrical output. A storage utilization fraction of 90% is targeted.

• The Stirling engine’s performance depends to a great extent on the input temperature, the

change operated in the system (compared to the old dish configuration) should not affect

the thermal to electricity efficiency of 30%.

Based on these technical objectives, the new product must be competitive with other technologies on the

market, namely PV-BESS, but also conventional power generation system (Diesel based generators).

Cleanergy’s value proposition, especially in the niche mentioned above, must have the lowest cost of

electricity generation. More specifically, the company aims for a 25-30% lower cost of electricity production

compared to PV-BESS and Diesel Gensets based on forecasted generation costs for said technologies in

2021. Figure 14 shows the LCOE evolution of PV-BESS with storage hours placed in Ouarzazate, Morocco

with a DNI of 2630 kWh/m²/year. The costs used are 2021 projections based on several forecast sources

(Lazard [32] , IRENA [2] , NREL [33]). The target LCOE of the company should be lower than a similar

PV-BESS or diesel generator system with the same amount of storage hours, i.e.. 10 hours or more for a

similar location. The long-term goal is to reach a LCOE of around 35€/MWh in the year 2030 with the

setting (location, storage size, rated power). The reduction in cost is expected to be driven by higher volumes

Figure 13 Model of one modular Cleanergy CSP unit with the three main components: concentrator, receiver with storage (10 hours) and Heat Engine (Stirling)

-15-

of production, as well as reduced costs of installation, engineering and O&M. The reported learning rate for

CSP technologies with storage is 30% for the period 2010-2022 [31].

Figure 14 PV-BESS LCOE in 2021

0

20

40

60

80

100

120

140

2 3 4 5 6 7 8 9 10 11 12 13 14

LC

OE

(E

UR

/M

Wh

)

Storage (h)

IRENA PV-BESS Lazard PV-BESS NREL PV-BESS

-16-

3 Methodology

The present work aims at estimating the market potential of a dispatchable small scale CSP system with

TES, such as the one Cleanergy is currently developing, limited in the spatial boundary of the MENA. In

other words, the size of such a market must be quantified in relevant figures, such as MW to install, or units

(solar field, tower and Stirling engine) to deploy. Market size determination relies generally on three key

concepts as visually depicted in Figure 15 [34] :

• Total Addressable Market (TAM): represents the size of the market if the product analysed were to

meet all the demand, disregarding any kind of competition. In Cleanergy’s case, the TAM would be

the number of plants to install in the MENA region to meet all of its electricity demand.

• Serviceable Achievable Market (SAM): represents the portion of the TAM that the product assessed

is actually targeted to and geographically reachable, excluding any competition. In the scope of this

work, business opportunities to potential industrial companies (or any other local company) that

would be interested in the product. Industry accounts for 42% of the electrical consumption

globally [35], needing most often continuous electricity supply for its processes and activities. It is

then a perfect sector Cleanergy targets to seek business opportunities.

• Serviceable Obtainable Market (SOM): is the selected business opportunities within the SAM that

a company targets first to grow and develop its product.

Figure 15 Market size estimation [34]

At this stage, Cleanergy is most concerned with understanding its SAM, sizing its magnitude and numbering

all business opportunities within that area. From that, the company can clearly sees which of them are the

most promising, and that will represent its SOM. Thus, this work needs to present a way to accurately

estimate the SAM for Cleanergy, and propose a way to sort the best cases forming its SOM. To do so, the

approach depicted in Figure 16 is followed. The business opportunities are investigated in representative

countries of the MENA: Morocco, Tunisia, Egypt, Jordan and Saudi Arabia. Business opportunities refer

here to potential industrial companies (or any other local company) that would be interested in the product.

Industry accounts for 42% of the electrical consumption globally [35], needing most often continuous

electricity supply for its processes and activities. The methodology revolves around 2 steps:

• Filtering: In each country, a market analysis is done, where the main electricity intensive industrial

companies are identified, and regrouped by sector: mining, cement, chemical, metallurgy,

agriculture. Based on publicly available data, the electricity consumption of each company is

estimated and broken down to each of their consumption sites, for which exact location coordinates

and respective weather data are gathered. For each of these sites, a techno-economical analysis is

carried out based on the company’s simulation model. Different plant configurations, in terms of

installed capacity, storage size, and mirror area were evaluated, from which the optimal

configuration able to reduce the LCOE is selected. The knock-out criteria to filter the business

opportunities in this step is LCOE, and the lowest value it can reach for each site. Through this

-17-

step, a quantification of the SAM is made in each country, alongside identification of companies

that could populate the SOM of Cleanergy. The choice of which will Cleanergy should effectively

engage with will be the result of the second step.

• Scoring: The above-mentioned optimal configurations are regrouped by country in order to

estimate the country-specific potential in terms of installed capacity. With such information, a multi-

criteria analysis (MCA) is performed in order to be able to compare amongst the different markets

(by country), considering not only the potential for installed installations in MW, but also the lowest

cost at which parks could be built, macro-environmental factors in the country, and existing

infrastructure, among others. The MCA is used to score each business opportunity, to finally select

the top ones. The latter represent the lower bound of the SOM, while its higher bound is the SAM

which is also sized using global industry electricity consumption.

• Scenario analysis: Once top business opportunities are identified with the MCA, three scenarios are

defined to further the comparison between the opportunities and countries, in terms of profitability

for Cleanergy.

Figure 16 Selection methodology for business opportunities [36]

Cleanergy aims at positioning itself in the solar energy market as technology provider. In other words, its

business model will repose solely on its ability to find customers interested in owning and operating their

own power generation facilities. However, for such a novel technology like Cleanergy’s, it will be hard to

garner the desired market interest, due to it being unknown and not yet proven. Consequently, taking an

active part in the first projects to be deployed, e.g. being a co-developer can be more strategic. Doing so

will help introduce the product to the market, by showcasing that Cleanergy is equity shareholder in the

power plants based on its technology, thus build general trust in its product. In that sense, the analysis to

be performed will assume a different business model for Cleanergy, which will act as Independent Power

Producer (IPP) and operate the first projects under a Build-Own-Operate scheme. For such models,

producers compete in a liberalized power market, where off-takers decide freely the source of their electricity

procurement, which can be the national electricity utilities, other IPPs (wind, solar PV…) or even invest in

their own generation capacity. Hence, all calculations and discussions are done assuming the IPP model for

Cleanergy, rather being just a supplier, to assess the economic viability of the projects (primarily on a LCOE

basis). Indeed, although Cleanergy’s interest as suppliers is to sell as much systems as possible, the project

perspective matters in reality to the company as only a viable/competitive project will make the technology

be chosen. Conversely, while the IPP models encompasses the opportunities laying in the grid-connected

market, it lacks including off-grid users, as by definition that business model is not valid for such setting.

For that purpose, and not to disregard the potential business opportunities of users not connected to the

grid, the highest scores/weight are given to such projects in the MCA.

3.1 Geographical limitation

As mentioned previously, the study revolves around the MENA, with a special focus on Morocco, Tunisia,

Egypt, Jordan and Saudi Arabia. The choice of these 5 countries is motivated by is considered to be strategic,

as they all have enough resemblance to draw conclusions and strategies to be applied to MENA in general,

but present in the same time enough differences for them to stand out and challenge the company in its

-18-

way of doing business. Beyond that, those specific markets were included in the AFEX (Arab Future Energy

Index) Renewable Energy survey, ranking among the top 10 countries with most potential in RE

investments as seen in Figure 17. The AFEX is a policy assessment and benchmark tool that provides a

detailed comparison of renewable energy development in 17 countries of the Arab region on more than 30

different indicators. Such indicators include market structure, policy framework, private investment

regulations… [37].

Figure 17 AFEX Renewable Energy 2016 [37]

Hence, Morocco, Jordan and Egypt are representative of countries with the most potential when it comes

to renewables. Saudi Arabia, though ranked 10th, is also investigated in this work considering its market size,

and the strategic role it plays in the region, and in the world as a dominant oil producer. Tunisia is included

as well, as a representative under-developed country transitioning to renewables.

Once the SAM in these 5 markets is estimated, a scaling up process is necessary in order to have the same

figure for the whole MENA region. This can be achieved by considering the contribution of the industrial

sector as added value in each country’s GDP and interpolating the corresponding SAM as a function of the

latter. This is made possible because the SAM sizing deals exclusively with the industrial sector electricity

consumption. The data obtained for the 5 countries mentioned above serve as the basis for the interpolation.

The choice to include Saudi Arabia as country to research gets then even more meaning as it is the number

one ranking industrial economy in the MENA [38] [39], thus facilitating the scaling up process and make it

somewhat more precise.

The MENA is a region encompassing approximately 22 countries in the Middle East and North Africa.

While there is no standardized list of which countries are included in the MENA, the following are typically

included in MENA: Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco,

Oman, Qatar, Palestine, Saudi Arabia, Syria, Tunisia, United Arab Emirates, and Yemen [40]. Subsequently,

the SAM in these locations needs to be estimated to give a general figure about the MENA.

3.2 Market analysis/entry

To complete the task defined above, a comprehensive study of the five countries is carried out to assess

their macro-environment, but also to identify potential industrial customers. The latter are individually

researched by analysing their electricity need in terms of power capacity Cleanergy can install to cover their

demand. This data mining step is crucial, as it is the basis of the analysis to be carried out later on and that

will contribute in building the go-to market strategies. The information for each company is gathered

through research of publicly available studies, governmental report, utility annual activity reports, journal

articles… However, the data looked for, namely electricity consumption figures, load profiles, is most often

not made public, or hard to find, and was estimated based on several assumptions. This may lead to multiple

inaccuracies in the analysis done. As a result, finding ways to validate it is also primordial and is investigated.

-19-

Appendix 2 represents one way of doing so: a questionnaire made for the Moroccan companies, and that

was designed to help assess and validate the information collected.

Beyond that, once an optimal prospective list of customers defined, there is a need to understand the

environment around those customers, and how the company can do business in the desired markets.

Renewable policy targets, regulatory framework, business risk and fraud are all factors that need to be

considered and understood. Thereafter, an entry mode needs to be chosen to actively penetrate new

markets. The choice of entry mode into a foreign market is a very important decision for companies whose

activities are directed toward the international and wishes to expand there. Entry modes are based on the

firm’s involvement level, or the degree of influence and confidence it has over its operations but are also

based of equity of ownership and control [41]. Different motives explain a company’s choice of an entry

mode over another. For example, a firm will find that entry mode yielding the higher percentage of return

on investments the best suitable, while another would prefer an entry mode guaranteeing close to zero risk

[42]. Entry modes fall into three different categories as detailed in Table 2. A company can choose between

them when entering a new foreign market. Those categories depend on the firm’s level of control, and are

differentiated by their types of arrangements [43]

Table 2 Entry modes categories [31]

Categories Entry modes Arrangements

High Control Modes Wholly Owned Subsidiaries

The owner of the parent

company has full control over

the business in the new market.

Intermediate Modes

Strategic Alliances Partners agree to share

technology, jobs, and resources

& provide support to each other

during the agreed time. Joint Ventures

Low Control Modes

Indirect Export

The parent company use

independent organization located

in the home country/ third

country

Direct Export

The parent company sells directly

to a distributor, agent or importer

based in the market

3.3 Financial model

The power price that is set during RES tenders is the key point for a project to be awarded. These prices

are based on the cost of energy and quantifies the profits the projects stakeholders will make over the

lifetime of the power plant. They are determined by doing financial analysis that include all factors and risk

predictions to yield the best profits. The key metrics used to rank electricity production technologies are the

Levelized Cost of Electricity (LCOE), Net Present Value (NPV) and Internal Rate of Return (IRR). It is

important to be rigorous on their definition as various methodologies (inflation, nominal rates, taxes

issues…) are used in the industry and academia, and it can mislead decision makers when comparing projects

whose indicators do not follow the same method of calculation. Appendix 1 is a report done prior to this

thesis work, which compiles and explains in detail the methodologies and calculations of different metrics

used to financially valuate solar power plants. Below is a brief description of the above-mentioned metrics,

but the extensive explanations, definitions, terminology and symbols used in Appendix 1 will be referenced

in this research work.

-20-

Levelized Cost of Electricity

The levelized cost of electricity is the most frequently used economic performance metric for power

generation plants. LCOE is used to assess/compare the performance and profitability of any form of

generation technology, and not only concerns solar or renewable sources [44]. It is defined as the constant

per unit cost of energy which over the system’s lifetime will bring all the project cash flows to zero. In other

words, it is the ‘break even’ constant sale price of energy [45]. Another way to view the LCOE is it being

the price at which the electricity must be sold to recover all the costs incurred during the lifetime of the

project. Equation 1 gives the general formula for calculating the LCOE. All used symbols and terms are

explained in Appendix 1

𝐿𝐶𝑂𝐸 =

∑𝐶𝐴𝑃𝐸𝑋∗𝐸𝑞%

𝑁𝑐𝑜𝑛𝑠×(1+𝑅𝑂𝐸)𝑡𝑁𝑐𝑜𝑛−1𝑡=0 −∑

𝐷𝐸𝑃×𝑇

(1+𝑅𝑂𝐸)𝑡

𝑁𝑐𝑜𝑛+𝑁𝑑𝑒𝑝−1

𝑡=𝑁𝑐𝑜𝑛+∑

𝐼𝑁𝑇𝑡×(1−𝑇)

(1+𝑅𝑂𝐸)𝑡 +∑𝑃𝑅𝐼𝑁𝑡

(1+𝑅𝑂𝐸)𝑡𝑁𝑐𝑜𝑛+𝑁𝐿−1𝑡= 𝑁𝑐𝑜𝑛

𝑁𝑐𝑜𝑛+𝑁𝐿−1𝑡= 𝑁𝑐𝑜𝑛

∑𝐸𝑡

(1+𝑅𝑂𝐸)𝑡𝑁𝑡=0

+

∑𝑂𝑃𝐸𝑋×(1−𝑇)

(1+𝑅𝑂𝐸)𝑡𝑁𝑐𝑜𝑛+𝑁𝑜𝑝−1

𝑡= 𝑁𝑐𝑜𝑛+∑

𝐷𝑒𝑐𝑜

𝑁𝑑𝑒𝑐×(1+𝑅𝑂𝐸)𝑡𝑁−1𝑡= 𝑁𝑐𝑜𝑛+𝑁𝑜𝑝

∑𝐸𝑡

(1+𝑅𝑂𝐸)𝑡𝑁𝑡=0

(1)

Net Present Value and Internal Rate of Return

The Net Present Value (NPV) of a proposed project is most often used as the primary absolute metric to

compare/assess investments and serves as a base for decision making [46]. The NPV is the sum of the

discounted cash-flows over the lifetime of the project using an appropriate discount rate as discussed above.

The cash-flows represent the yearly difference between the revenues and costs incurred each year. It is then

linked primarily to the CAPEX, OPEX, decommission costs, the yearly energy yield or output and finally

the price at which the electricity is sold. Equation gives the general formula for calculating the NPV. All

used symbols and terms are explained in Appendix 1.

𝑁𝑃𝑉 = ∑(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡

(1 + 𝑟)𝑡

𝑛

𝑡=0

= 0 (2)

Another fundamental economical metric that is used to rank projects and get a hold of their profitability is

the internal rate of return or IRR [44]. The internal rate of return is the discount rate that would be used in

an NPV calculation and would make it equal to zero, as seen in Equation 3.

𝑁𝑃𝑉 = ∑(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡

(1 + 𝐼𝑅𝑅)𝑡

𝑛

𝑡=0

= 0 (3)

The IRR is then the interest rate that would break even the project accounting for the costs incurred and

revenues generated during the lifetime of the plant. It is a measure of the profitability of a project and is

used mainly by developers and financial institutions to base their investments decisions. Each company has

its own predictions on how much profit can be made of a project and has usually a target return on

investments. If the IRR is higher than that required target, the project is financially acceptable. To compare

different projects and financing opportunities, the higher the IRR the better [44].

The input data for the financial model are described in Table 3 . The specific component costs (solar field,

receiver, TES, Stirling engine) are confidential to Cleanergy, but cost values from a similar technology

concept, known as STEALS and described in a recent study [47]. The latter are given, and used as a reference

to compare the results, in terms of LCOE values and so on. It should be noted though that cost projections

for the STEALS project are optimistic, and represent idealized future system cost, rather than the cost of a

system that could be built today. Not all manufacturing costs were considered for example and consider

large scale production rate. Moreover, heliostats are the equipment that weigh the most in the project’s

CAPEX, and the STEALS study considers a low value compared to existing plans. As underlined by the

-21-

project report, it is assumed that improvements will be made by the greater CSP community to reach such

low values, especially considering that heliostats are used in many systems [47].

Table 3 Financial model inputs

Parameter Value

Stirling engine cost 808 €/kWe

Heliostat cost 61 €/m²

Tower/receiver cost 90 €/kWe

TES cost 25 €/kWh

OPEX cost 25 €/kWe

Project lifetime 30 years

Inflation 0%

Power price escalation factor 0%

Equity financing 25%

Equity IRR 8%

Debt financing 75%

Cost of debt 5%

Debt amortization 15 years

Depreciation 25 years

Corporate tax rate

Morocco: 31%,

Tunisia: 25%

Egypt: 22,5%

Jordan: 25%

Saudi Arabia: 20%

Naturally, LCOE figures inherent to Cleanergy’s system cannot be shown due to confidentiality reasons.

However, since the analysis done revolves to great extent on that metric, normalized values of the calculated

LCOE will be shown later in the report. In each data set to be calculated, the normalization will be done

taking as a reference the minimum value of said data set.

3.4 Techno-economical analysis

As explained previously, the analysis to be carried out consists of identifying potential off-takers in the form

of industrial companies and trying to assess the electricity consumption on each of their sites, exact location

coordinates and respective weather data, which serve as input data for the simulation model the company

developed. The latter calculates the system size (MW) of Cleanergy’s product needed to service that

electricity consumption. This in turn serves as input data for the financial model described above, that

calculates the LCOE. Different plant configurations, in terms of installed capacity, storage size, and solar

field area are evaluated, from which the optimal configuration able to reduce the LCOE is selected. A

schematic of the process is shown Figure 18.

-22-

Figure 18 Techno-economical analysis process

DNI resource estimation

The online tool developed by IRENA called Global Atlas for Renewable Energy was used [48]. This GIS

tools comprises solar radiation maps, among other resources, of different countries around the world.

Practically, once a site was determined and its location coordinates were found, the tool was used to obtain

the DNI prevalent in that area. The obtained figure was further confirmed with the solar maps accessible at

SolarGIS for each of the countries analysed [49].

TES size

The underlying premise of the storage design is that the system is cost-effective at long storage hours, ie. 10

hours and more. Thus, the storage size is varied for each location from 10 to 14 hours to validate that with

actual LCOE figures. Also, load profiles and exact demand requirements from specific off-takers are still

unknown, so the analysis is made on a range of TES sizes to consider all possibilities, and consequently,

decide on which is optimal. Moreover, the optimisation of the storage size can have an effect on the total

solar field area: with varying solar resource, charging a TES of 14 hours for example can require more or

less mirror area, hence affect the total system costs, and the capacity installed.

Mirror area

For each 13kW Stirling engine, a solar field concentrates sunlight into its correspondent receiver/tower.

The area of the solar field, or reflective area of the mirrors is varied from 150 m² to 220 m² with a 10 m²

increment, in order to see how performance evolves with that change, but more importantly, to understand

the impact on the LCOE, and later help in the design criteria to be chosen. The natural course of thought

would be to consider that a higher reflective area per field would lead to higher CAPEX and LCOE figures.

However, increasing the solar field size will yield in better energy output, capacity factors, which in turn

contributes in decreasing the LCOE. The effect of the mirror area needs then to be investigated in detail.

Figure 19 shows how the LCOE varies with increasing solar field area, different DNI values and over the

range of storage hours considered.

-23-

Figure 19 LCOE vs Reflective area

In this figure, the scattered points refer to LCOE values obtained for all possible storage sizes considered

when varying the field mirror area, with respect to several DNI conditions, highlighted by the colours in the

graph. Correspondent coloured doted lines represent the general trend said scattered points follow, giving

a more precise depiction of the behaviour. As it can be seen, minimal LCOE numbers are always obtained

for the maximal reflective area allowed (220 m²), whatever for all storage sizes. Hence, the latter value is

used in the analysts and results sections of the report.

Electricity consumption estimation

The electricity consumption figures are estimations based on annual reports, goods production rate per year

and referenced electricity intensities. For example, if a cement company produces annually 0,85 MT in a

particular location, given the referenced energy intensity of cement production [50] (110 kWh/Tons), the

electricity consumption of that site is found. As a result of this approach, the numerical data used are not

completely accurate with regards to the real consumption rates, since the energy intensities used may not be

the same for all companies, due to different production process, better/worse efficiency measures, etc.…

However, the figures are valid enough to provide a first quantification of the specific electricity consumption

in each identified site, and that may need to be adjusted in the future.

In practical terms, Cleanergy provided yield simulations for 1 unit (13kW), for 6 DNI values

(kWh/m²/year): 1913, 1987, 2196, 2467, 2640 and 2728. Hence, this data needs to be interpolated to be

able to compute the yield for a range of DNIs in between 2000 kWh/m²/year and 3000 kWh/m²/year.

Moreover, thanks to the modularity of the system design, once the yield of 1 unit is known, and given the

electricity consumption in a particular site, it is easy to determine the number of units needed to produce

that exact energy yield, thus find how much capacity is required to be installed to service a particular site.

Finally, and in order to ease the comparison later on, especially when dealing with all 5 countries of the

analysis, each location or site is given a name code depending on the country, the company and the type of

industry. The nomenclature of the code is as follow: XX##

• X: First letter of the country dealt with: Morocco, Tunisia, Egypt, Jordan, Saudi Arabia

80

90

100

110

120

130

140

150

140 150 160 170 180 190 200 210 220 230

LCO

E (€

/MW

h)

Field reflective area (m2)

1915

2015

2115

2215

2315

2415

2515

DNI (kWhh/m²/year)

......Trend lines

-24-

• X: First letter of the industry type: Mining, Cement, Agriculture, Metallurgy, Chemical

• #: Rank of the company in each sector

• #: Rank of the site for each company

3.5 Multi-criteria analysis

Once a preliminary list of potential business opportunities is clearly defined, each option is evaluated, scored

and then ranked based on criteria jointly selected with Cleanergy. The selected criteria are both numerical

and cognitive. This work attempts to give a quantitative approach to a qualitative assessment, and this is

done by attributing weights to each of the criteria, and then scoring the opportunities on a scale of 1-10

based on the criteria. The qualitative and quantitative criteria used to perform the assessment of all business

opportunities are listed below, alongside their respective weights and scoring method. Table 5 represents a

summary of the following:

• Criteria 1: Potential (MW)

Cleanergy’s interest, which represent how much units of its product it can install. In other words,

how much electrical power capacity the industrial company uses to run all its factories. This

approach is conservative, since it is hard to define clearly how much an industrial would want

replaced by a CSP system, but it gives an idea about the potential of the market. The input figures

here are taken directly from the results of the electricity consumption estimation defined earlier.

The scoring method used is linear, with a maximum capacity of 50 MW given the highest score 10.

Anything below gets a linearly decreasing score.

• Criteria 2: DNI resource (kWh/m²/year)

As explained previously, Cleanergy’s CSP system relies on good solar conditions, the higher the

better the electricity is, and the better the business case will be. When screening the potential

customers, all locations with a DNI less than 2000 kWh/m²/year were disregarded. The input data

here are taken from the market analysis performed for each country.

The scoring method is linear here as well, with the score increasing between 0 and 10 linearly from

the values 2000 and 3000.

• Criteria 3: LCOE (€/MWh)

LCOE: This metric is very much location and project dependent, but it will be the primary figure

industrials will evaluate to make a decision on whether or not adopt Cleanergy’s system. If the

technology has a generation price higher than solar PV for example or conventional fuel-based

generation, adopting it would have no financial sense. It is also a relevant decision metric for

Cleanergy, as the company should target customers with attractive business cases, eg. low LCOE

and cost competitive with other alternatives, such as PV-BESS or diesel gensets. The input data are

taken here directly from the results of the techno-economic analysis.

The LCOE is scored in comparison with two price signals: P1, which is the local average utility

price (the values used are reported in Table 4) and P2, which is Cleanergy’s LCOE target in 2021.

Hence, there are two cases:

• If P2>P1, then

o If P1≤LCOE≤P2 then the score attributed is in the range [10:5]

o If P2 ≤LCOE≤P2+10 then the score attributed is in the range [5:0]

• If P1>P2, then

o If P2≤LCOE≤P1 then the score attributed is in the range [10:5]

o If LCOE>P1, then the score is 0

-25-

Table 4 Industry electricity rates

Average utility price for the industry (€/MWh) Source

Morocco 91 [51]

Tunisia 59 [52]

Egypt 53 [53]

Jordan 160 [54]

Saudi Arabia 41 [55]

• Criteria 4: Grid access

An off-grid site represents more opportunity in terms of business case to Cleanergy, as its modular

CSP system is best suited for remote locations, where grid access is inexistent. Industry owners are

often reluctant to pay for grid-connection due to its high capital costs, and the relatively low return

on investment it brings. Using diesel generators is the alternative they go by in such cases, but

Cleanergy’s product can bring great added value, and profit in the long term.

A score of 10 is given to off-grid sites, and 5 for grid-connected locations

• Criteria 5: Macro-environmental factors

Represents a country’s attractiveness over another, due to political, societal, legal factors… The

country analysis to be performed will include insights about regulatory framework, future prospects

in terms of RE policies, targets and major stakeholders in place. This description will serve then as

basis to score the attractiveness of each country, in terms of opportunities and appeal it presents

for a company like Cleanergy to engage with in business. The country score (1-10) is given

considering the following sub-criteria, that in turn are given a score with associated weights:

• Business models in place for RE projects (50% weight). An emphasis is made on the ability for

IPPs to engage in sells contracts with large consumers, and the permissibility of self-

consumption. The presence of a scheme rewarding excess electricity sells to the grid is a plus

• Business environment (25%). based on three international indices: World Bank Ease of Doing

Business, BTI status score and Global Competitiveness Index

• Participation share of the private sector in RE energy projects. (25%)

Table 5 Attractiveness scoring table

Criteria Relative importance (1-10) Scoring method

Potential (MW) 7 Linear, the higher the better

DNI (kWh/m²/year) 9 If <2000, 0

If ≥ 3000, 10

linear in between

LCOE (EUR/MWh) 10 Related to P1 and P2

P1: local average utility price

P2: Cleanergy’s target

Grid access 10 If off-grid, 10

If not, 5

Macro-environmental factors 5 See above

-26-

3.6 Scenarios definition

The MCA analysis ranks business opportunities according to the terms explained above, and indicates which

site Cleanergy should engage in with first. The criteria used in the MCA, while encompassing a number of

aspects primordial to Cleanergy’s business approach (costs, macro-environmental factors…), do not include

profitability, or the notion of NPV and IRR. As a matter of fact, and referring to the terminology of

Appendix 1, the LCOE used in the MCA is the ‘’societal perspective’’ LCOE, which does not account for

financing costs related to debt, interest payment nor tax issues. The choice to use the simplified version of

the LCOE in the MCA lies behind the fact that each country differs in corporate taxation as seen in Table

3, or utility electricity prices reported in Table 4, which would have made the overall comparison not

accurate. If the ‘’developer perspective’’ LCOE would have been used, tax related costs/benefits would

change from country to country, impacting the LCOE, and the comparison would have not been fair. An

intrinsic comparison based solely on technology costs, ie. simplified LCOE was then preferred for the MCA.

However, and not to disregard these aspects, the best business opportunity in each country are further

investigated with regards to profitability, namely NPV and IRR, to assess their real economic feasibility. In

doing so, a better insight of the investment profitability in each country is quantified and gives a better

country comparison. To calculate the NPV, a power price must be set, at which the electricity generated

will be sold. Electricity prices are uncertain as they are often linked to oil prices, but even more so for

renewables, as the presence or not of incentives can have a huge impact on the viability of a project. Hence,

the best business opportunities identified with the MCA in each country are analysed in the light of three

scenarios:

• Zero subsidy: the hypothesis here is that there will not be any kind of support mechanism for

renewables (in the form of feed-in tariff). The power price at which the electricity from Cleanergy’s

plant will be sold is assumed to be equal to the price national utilities sell their power, as described

in Table 4.

• Break-even subsidy: this scenario evaluates the power price at which the project would guarantee

a NPV equal to zero over its lifetime. That value can be regarded as the minimum amount of

subsidy the project needs to break even and recuperate all the costs, and reach the profits expected.

• Utility competitive: in this scenario, the analysis is performed to find the right input parameters

that would guarantee Cleanergy technology to be competitive, e.g. that the resulting LCOE would

be 20-30% lower than the power price set. The difference between the power price identified in

this case and the utility price represents the surplus that must be paid on top of the average national

power price to make investments in CSP profitable. This can be relevant when assessing the

feasibility of Cleanergy’s technology if regulatory/support frameworks are announced in the

countries studied.

-27-

4 National environment, industry and firms’ specifics

4.1 Morocco

4.1.1 Energy context

Unlike any other neighbouring North African country, the Kingdom of Morocco has close to zero natural

oil resources, making it highly dependent on fossil fuel imports for its energy needs. Petroleum products

account for 41% of the primary energy supply, while crude oil coal and peat account for 31%, 17% and 4%

respectively [56]. Morocco has also an electricity interconnection with Spain of 1.4 GW [56] and another

exists with Algeria, which is only used for grid balancing purposes [56]. Due to a rapid economic growth of

5% per year [56], the energy demand, and therefore the primary energy supply increased in Morocco,

reaching 17,7283 kTOE in 2015, increasing by 0.56 TOE against 0.36 TOE in 2002. This trend was logically

the same in the electricity sector, where the average growth rate for electricity consumption is 7% since

2002, with residential and industrial sectors growing the fastest at 8% and 7.4% respectively. Together, these

two classes account for more than 75% of the national electricity consumption, which amounted to 34,413

GWh at the end of 2015 [57]. The high rural electrification rate, which jumped from 18 % in 1996 to 99%

nowadays contributes also to the electricity demand growth. The electricity demand is met by a variety of

sources as detailed in Table 6. The total generation capacity stood at 8160 MW in 2015 [57], with hydro and

solar-wind accounting for 22% and 12% respectively. During the same year, renewables generated around

14% of the total electricity produced, but Morocco has an ambitious sustainable development program that

aims at increasing renewables share in the generation mix in the future

Table 6 Morocco generation units 2015 [58]

Generation units in 2015 MW %

Classical Hydro 1306 17%

STEP 464 6%

Total Hydro 1770 22%

Private wind farms (13-09 & Auto) 241 3%

IPP Wind farms CED + Tarfaya 352 4%

CCGT Ain Beni Mathar (Solar) 20% 0.2%

Ouarzazate Solar Power Plant (Noor 1) 160 2%

Total wind & solar 979 12%

Total thermal 5411 66%

The country joys from perfect solar irradiation conditions, with annual average DNI values reaching 2700

kWh/m² in some locations [56], as well as favourable wind resources in its northern and southern parts.

The country capitalized on that when revealing its renewable energy targets for 2020 and 2030, aiming for

42% and 52% respectively of the total installed capacity [59]. With this National Energy Strategy (NES),

Morocco led the way in 2009 in terms of sustainable development in order for the country to secure its

energy access from renewables and be independent for its supply. The strategy relies on two programs [59]:

the solar program Noor aims to reach 2000 MW installed solar power capacity by 2020, and around 4800

by 2030. The Moroccan integrated Wind programs aims at achieving 2000 MW of installed wind power

capacity by 2020, and up to 5000 MW by 2030 as shown in Figure 20. The Moroccan Agency for Sustainable

Energy (MASEN) oversees the development of such projects.

-28-

Figure 20 NES 2030 targets [59]

4.1.2 Electricity market

ONEE is the national electricity utility in Morocco and is the main player in its power sector. It is under

administrative and technical control of the Ministry of Energy, Mines, Water and Environment. The

ministry has also under its umbrella the following institutions that deal with renewable energy [56]:

• MASEN: Previously known as the Moroccan Agency for Solar Energy, MASEN became in 2016

the Moroccan Agency for Sustainable Energy, after it was decided that it will oversee the

development of all kind of renewable projects. Under the PPA tariff scheme, the agency has in the

pipeline 3000 MW renewable projects by 2020 and 6000 MW by 2030 as mentioned above. This

consolidation of all types of renewables under one umbrella helps MASEN optimize the generation

cost of electricity, especially when dealing with CSP. By auctioning the projects it seeks to

implement, MASEN has reached competitive electricity costs for its parks: NOOR 1 (CSP) 150

EUR/MWh, NOOR 4 (PV) 40 EUR/MWh [57].

• AMEE: Previously known as the National Agency for the Development of Renewable Energy and

Energy Efficiency and established in 2010, it became in 2016 the Moroccan agency for Energy

Efficiency, with the goal of focusing only on energy efficiency [57]. This move of focus is strategic

as it allows the agency to allocate all its resources to energy efficiency programs that were not

sufficient compared with the untapped potential efficiency measures can have on reducing the

overall consumption in the country [56]

• EIS: The Energy Investment Corporation was created in June 2009 to boost the development of

renewable projects and has a national interest capital of MAD 1 billion [60]. It is mainly involved

with small and medium scale projects, such as the use of PV for street lightning.

• IRESEN: The Institute for Solar Energy and New Energy was established in February 2011.

IRESEN aims to consolidate the needs of different stakeholders and to ensure the implementation

and enhancement of various research projects [57].

ONEE is the single buyer of power produced across the country as a governmental entity, and acts

throughout the whole value chain of the electricity market (generation, transmission and distribution; for

the latter, local utilities operate in some parts of the country as well), but the country is slowly liberalizing it

[56]. The following legislative developments were taken to achieve that liberalization, which gives the market

structure depicted in Figure 21:

• Act No. 16-08: Law on self-production that authorizes for the firs time any natural or legal person

to produce electricity for its own consumption. It is however subject to some conditions and

authorizations, such as a capacity limitation set to 50 MW, an obligation to use the produced

-29-

electricity for the exclusivity of the producer. As of today, only large industrial groups in the cement

or the mining sector like OCP or Lafarge use this ability [61].

• Law No. 13–09: Dubbed the renewable energy law, it served as the legal base for the NES of

Morocco. Law 13-9 allows any natural or legal person to produce energy from renewable sources.

This concerns both self-production for own needs’ service and production intended to be injected

into the high/medium voltage network and sold to buyers, provided with the right grid connections.

The law sets out also a power generation scheme based on the capacity of the renewable power

plants. However, the law also determines that the supply of electricity has to be undertaken through

the national grid with the exception of electricity generated for export or due to formal agreements

with ONEE [59]

• Law No. 58-15: Amends Law 13-09. The main changes reside in the fact that it makes it possible

to sell the surplus of electricity production from renewables, up to a certain level (no more than

20% of the annual production, and only to the high/very-high voltage grid) [62]. The law also lays

the ground for the liberalization of the medium/low voltages market as the decree of its application

has yet to be completed. This prevents large-scale decentralized injection by private individuals or

small businesses [61]. The decree aims at gradually opening the medium voltage network for

renewables, smoothening the effects and establishing a transparent framework for the investors

[62].

• Law No. 48-15: Related to the regulation of the electricity sector and the creation of the ANRE,

the National Agency for Electricity Regulation. With the goal of fully liberalizing the power market

and opening the access to the low and medium voltage grids, the law establishes a regulatory

authority to manage conflicts between operators, producers and networks users Moreover, and in

order to prevent discrimination against new independent producers , the ONEE high-voltage

network will be administered independently of energy [61].

Figure 21 Electricity market Morocco [62]

While there are no directs subsidies for renewable electricity production such as feed-in tariff for privates,

some institutions in Morocco are present to help finance and invest in renewable projects. Such institution

is MorSEFF, the Morocco Sustainable Energy Financing Facility, which is a 110 million euros credit facility

dedicated to financing energy efficiency and small-scale renewable energy investments of private companies

in Morocco [63]. MorSEFF offers bank financing, loans, free technical assistance and investment incentives

to improve quality equipment, reduce operating costs and improve competitiveness. To be eligible, projects

must abide by some criteria, such a carbon reduction targets, energy saving targets or addition of small-scale

renewable energy systems, but they have to also use equipment from pre-approved suppliers by the

-30-

institution. The MorSEFF also offers loans to suppliers of energy efficiency and renewable energy

equipment, with the goal of distribution and production capacity expansion [63].

4.1.3 Industrial companies

Industry stands for more than 40% of the national electricity consumption in Morocco [57]. More

specifically, ONEE sold around 4000 GWh to industrial connected to the high/very-high voltage grid. The

mining industry presented the highest consumption, with a total share of 29.5%, followed by the metallurgy

sector with 20.6% and the cement sector with 19.1% [64]. It was then only natural to further the

investigation in these sectors and make them a priority when identifying potential industrial companies that

would be interested in investing in a technology such as Cleanergy’s for self-production. Electricity rates for

the industrials sector in Morocco vary depending on the tariff scheme subscribed. For example, a super

peak tariff can be offered to push industrials to reduce consumption during peak periods. Table 7 details

the general high voltage tariff scheme. However, based on discussions with local industry players, the

average price paid for electricity procurement is around 0.8 MAD/ kWh. In comparison, the average

generation cost of diesel gensets in remote areas with difficult grid access is around 2.7 MAD/ kWh.

Table 7 High voltage industry general rate, Morocco [51]

Fixed rate (MAD/kVA/year) 494.09

Peak period, 17h-22h (MAD/kWh) 1.3645

Regular period, 7h-17h(MAD/kWh) 0.9736

Off-peak period, 22h-7h(MAD/kWh) 0.7131

Table 8 summarizes all the data gathered for the identified companies in Morocco that are heavy consumers

of electricity, and that represents potential customers for Cleanergy. The data was estimated on par with the

methodology described in 3.4. The list is not exhaustive, as the companies were sorted based on data

availability.

Table 8 Identified industry companies, Morocco

Company Site code Location DNI Energy consumed

(MWh) Source

OCP MM11 32.880157, -6.918677 2115 501441

[65] MM12

32.196702, -8.262906

2115 167147

MANAGEM

MM21 29.393149, -8.247911

2215 184581

[66] [67]

MM22 30.517327, -6.907685

2315 6243

MM23 30.366155, -6.463931

2415 149890

MM24 30.641392, -5.105671

2315 102285

MM25 31.413536, -8.400378

2115 315166

Maya MM31 30.777097, -7.790089 2515 14366 [68] [69]

Kasbah Ressources MM41 33.521702, -5.814462 2215 63208 [70]

LafargeHolcim MC11

27.149969, -13.202520

2215 22000 [50] [71]

[72] MC12 29.925619, -9.228238 2215 93500

-31-

MC13 30.482480, -8.879743

2115 93500

Ciments du Maroc

MC21 30.068723, -9.152878

2215 242000

[73] [50] [71]

MC22 32.300810, -9.226861

1915 11000

MC23 31.630052, -7.981257

2015 154000

MC24 27.149969, -13.202520

2215 55000

CIMAT MC31

34.775521, -4.529098

2015 17600 [74] [50]

[71] MC32

32.362069, -6.383365

2015 17600

CIMSUD MC41 27.149969, -13.202520

2215 55000 [75] [50]

Univers Acier MMe11 32.917874, -7.270166

2015 173448

[76] [77] [78]

Cosumar MA11 32.003820, -6.578683

2015 21600 [79]

Lesieur Cristal MA21 32.229224, -7,923771

2115 1095 [80]

4.2 Tunisia


Tunisia is an energy-dependent country with modest oil and gas reserves. The primary energy consumption

more than doubled in Tunisia between 1990 and 2015, rising from 4,5 kTOE to 9,4 kTOE, while fossil fuel

production stagnated at 7 kTOE during the whole period. This rising imbalance between production and

consumption created lots of pressure on the Tunisian energy system [81]. The natural gas production in the

country covers only about 53% of the primary energy consumption, while Algerian gas imports ensures the

rest, where 73% of the total natural gas consumption is dedicated to electricity generation. Moreover, the

national oil production covers about 40% of the primary energy consumption while the rest is imported.

This situation leaves Tunisia very dependent on imports, not securing its energy access nor independency

[81]. By the end of 2016, the total installed capacity amounted to 5224 MW. Natural gas power plants

accounts for almost 95% of the installed capacity, while the remaining 5% are shared between 68 MW of

hydropower, 254 MW of wind and around 15 MW of residential solar PV systems [82]. The installed capacity

is expected to reach 7500 MW by 2021 as a response to the imbalances the electricity system suffers,

especially in the residential and industrial sectors which stand for more than half of the national electricity

consumption [83] . Additionally, the country’s electrification rate improved over the years, reaching 99,9%

in 2012 compared to 95% in 2000. This can partially explain the increase in electricity consumption in

Tunisia, coupled with the general economical and demographical growth the country has witnessed [82].

Higher standards of living, especially in urban areas, mean that people tend to use more electrical appliances

in households and cities, and that is meant to be tackled by the energy efficiency and conservation plans of

the country [82]. Nevertheless, aware of the necessity to shift from fossil fuels to secure its energy

procurement, Tunisia is actively transitioning from conventional power generation to renewables. DNI

values can reach 2600 kWh/m²/year in the best locations of the country, and the wind potential of the

country is estimated to 8 GW [84]. More specifically, Tunisia launched in November 2016 the Renewable

Energy Action Plan 2030, which aims integrating wind and solar PV in its generation mix, with the goals of

-32-

12% by 2020 and 30% by 2030 (of total TWh produced). The plan sets also targets in terms of renewable

capacity to be installed, including 1000 MW for 2017-2020, and the addition of 1250 MW during the period

2021-2030 [85]. The gradual suppression of energy subsidies in the country, that began in 2013, can push

private investors take the leap and contribute to the overall country energy transition [84].

Figure 22 RE National Program 2017-2020, Tunisia [86]


The Tunisian power market has a simple and coherent market structure as depicted in Figure 23. Three

main players are involved [82] :

• The Ministry of Industry, Energy and Mines: Legislative authority when it comes to the energy

sector. The Ministry elaborates governmental policies to promote research and exploitation of its

natural resources, promotes the usage of a clean source of energy by setting the legislative basis for

the energy transition

• Société Tunisienne d’Electricité et de Gaz (STEG) : The national electricity utility in Tunisia. It is

responsible of the management of the production, transportation and the distribution of electricity

and gas in Tunisia. It owns up to 80% of electricity generation facilities in the country, managing

power facilities from diverse sources such as thermic, hydraulic and wind. STEG handles also the

transport of electricity and the development of high-tension grid lines, as well as the distribution

and the administration of medium to low voltage lines.

• STEG-ER: Acts as the renewable subsidiary of STEG. Its main activities consist of realizing the

goals and targets set by the Ministry of Industry, Energy and Mines, when it comes to the energy

transition, and the effective realization of the RE National Plan. As such, it operates in all the value

chain of renewable energy projects: development with feasibility studies, realization by setting the

guidelines for ownership, supervision…, and finally exploitation and maintenance

• Agence National pour la Maitrise de l’Energie (ANME): Under the administration of the Ministry

of Energy, the ANME’s role consists of applying Tunisia’s energy management policies.

Figure 23 Electricity market, Tunisia [85]

-33-

As seen in Figure 23, independent power production is permissible, and is regulated by law 62-08 and law

96-27, which allow the generation of electricity for self-consumption and to sell the surplus to STEG, but

authorizes also IPP concessions of power generation for exclusive sale to STEG by a PPA [82].

Subsequently, and on par with its RE National Plan, Tunisia has issued several law texts and decrees to

regulate the production of electricity from renewables and have a comprehensive framework detailing the

guidelines and obligations for the establishment of such projects. Law 2015-12 defines the legal framework

for the realization of installations of electricity production from renewable energies. It was later detailed in

Decree 2016-1123, that laid down the terms and conditions for the realization of projects and sales of

electricity production from renewable energy sources [85]. The projects to be developed must fall under

four different “regimes” that are shown in Figure 22 :

• Large-scale projects, subject to concession (tender process)

• Small-scale projects, subject to authorization

• Self-production projects, also subject to authorization

• Export projects, subject to concession

The distinction between large and small-scale projects is a capacity threshold, that depends on the type

of generation: 10 MW for solar PV and solar CSP, and 30 MW for wind energy. For the first phase of

the RE National Plan, all projects will be built under the Build, Own, Operate scheme (BOO), with all

the electricity sold exclusively to STEG according to 20 years PPAs agreements. The first phase

projects will follow the authorization regime [85].


Industry stands for more than 35 % of the total electricity consumption in Tunisia. Construction

industries such as cement lead the way with 20 %, flowed by chemical and metallurgy industries.

Electricity sales prices are not high enough to cover the costs of generation and distribution. As a result,

STEG is heavily subsidized from the government. However, the government plans to gradually remove

the latter, until they disappear in 3 to 6 years. In parallel, the government has planned a yearly increase

in electricity rates. For example, the tariff for the cement industry grew by 35% in 2014.

Following the same method described in 3.4, to identify potential industrial companies that could become

customers for Cleanergy, Table 9 was built

Table 9 Identified industry companies, Tunisia

Company Site code Location DNI Energy consumed

(MWh) Source

SCG TC11 33.874221,9.993171 2015 137500 [87] [50]

Sotacib TC21 34.937597,8.529963 2015 66000 [87] [50]

CPG TM11 34.213960, 8.606903 2015 94900 [87] [65]

GCT

TCh11 34.294721, 10.070836 2015 13200 [87] [88] [89]

TCh12 34.294721, 10.070836 2015 15000 [87] [88] [89]

TCh13 34.389871, 8.746074 2015 18600 [87] [88] [89]

TCh14 33.927670, 10.083885 2015 56400 [87] [88] [89]

TCh15 33.927670, 10.083885 2015 19500 [87] [88] [89]

CPG TCh21

33.916571,10.097558

2015 56400 [87] [88] [89]

TCh22 34.702332,10.724291 2015 15600 [87] [88] [89]

-34-

Tunisian

Indian

Fertilizers

TCh31 34.349608,10.149897

2015

43200

[87] [88] [89]

4.3 Egypt


Egypt is the largest non-OPEC oil producer and the second largest natural gas producer in Africa and plays

a major role in the energy market trades of the MENA, through the operation of the Suez Canal and the

Suez-Mediterranean (SUMED) Pipeline. The country is also the largest oil and natural gas consumer in

Africa. In 2013, the country accounted for about 20% of petroleum and other liquids consumption, and

40% of dry gas consumption in Africa. As such, Egypt has been dependent on oil and natural gas for 91%

of its energy needs, the remaining 8% coming from the Aswan High Dam (2100 MW installed capacity),

and solar and wind for the last 1%. Nevertheless, the country switched from net oil producer to net oil

importer after 2013 when the local oil production became insufficient to meet the demand. The country

especially experiences multiples power shortages in summer periods. At its peak, the energy demand in 2014

was 30 GW, while generating facilities shad a capacity of 26 GW [90]. The industrial and residential sectors

lead the way in terms of electricity consumption, representing more than 70% of the national consumption,

which grows at a 6% rate. The latter is seen by the government as challenge to overcome in its reforms [91]

[92]. The stress and pressure the energy sector in Egypt suffers is the consequence of multiples factors,

among them historical ‘mal-planning’, and the political turmoil following 2011 revolution [93]. Heavily

subsidized energy prices have for instance contributed to a constant growth of the energy demand, alongside

rising state deficit [94]. As such, the government announced that all energy subsidies would be halted

gradually by 2029, and that further electricity laws would allow the liberalization of the market, opening

competition, brining investment in the electricity sector and crucially making renewables more competitive

[95]. In that sense, Egypt is building its renewable energy plans on the country’s well perfect renewable

natural resources. Average wind speeds approach 11m/s along the Suez Gulf, and DNI values are between

2000 and 3000 kWh/m²/year, as Egypt is considered a “sun belt” country. The Integrated and Sustainable

Energy Strategy till 2035 was issued to find out the necessary approach to restructure supply mix of

electricity and envisions the addition of 42 GW of large scale and distributed on-grid renewable capacity by

2030, to reach 52 GW added by 2035 [96]. On the medium term, Egypt plans to supply 22% of the total

electricity generation from renewables by 2022, with wind accounting for 12%, hydro 5,8% and solar 2,2%.

In terms of installed capacity, its evolution till 2022 can be seen in Figure 24. Egypt aims to install 2,8 GW

of PV and 700 MW of CSP by 2027 as part of its solar energy plan [97]. Plans after 2022 include also coal

(12 GW) and nuclear (1,2 GW) power plants [98].

-35-

Figure 24 Government power generation expansion plans [91]


The Ministry of Electricity and Renewable Energy (MOERE) is in charge of developing and implementing

the country’s energy strategy by setting the targets, framework…, and governs all players of the Egyptian

power market Its main. The main stakeholders are [95] :

• Egyptian Electricity Holding Company (EEHC): Owns and operates almost all generation facilities,

alongside transmission and distribution networks through its multiple subsidiaries. It is state owned.

• Egyptian Electricity Production Company (EEPC): Affiliate company of EEHC. Owns 6

regionally-based companies.

• Egyptian Electric Transmission Company (EETC): Affiliated to EEHC, it manages and operates

and maintains the transmission network across the country. It is the major off-taker and PPA party

of wind and solar power projects under the FIT scheme. EEHC also issues renewable power plans

tenders

• Egyptian Distribution Company (EDC): Owns 9 distribution companies that serve the residential

customers with electricity.

• Egyptian Electric Utility and Consumer Protection Regulatory Agency (EgyptERA): Oversees

regulation and supervision of the generation, transmission and distribution systems. EgyptERA

license to private entities, set electricity tariffs and sets the requirement for renewable energy FIT

programs

• New and Renewable Energy Authority (NREA): Public entity in charge of the operational

implementation of national renewable energy policies, through tenders and own capital

investments. NREA is also closely involved with EgyptERA in the application of the FIT, and with

EETC when it comes to the implementation of competitive bidding, and the development of solar

and wind projects with EPC tender schemes.

• General Authority for Investment and Free Zones (GAFI): Governmental authority to regulate and

facilitate investment. It is mainly involved with the FIT scheme.

As seen in Figure 25 the Egyptian power market follows a single buyer model, embodied in the EETC.

EEHC owns 90% of generation capabilities, while the private sector participates with 3 long term BOOT

contracts with PPA. The NREA procures small IPPs and wind farms. The single off-taker, EETC, is

licensed for VHV and HV electricity transmission, and sells the electrical energy to the distribution

companies. It also handles direct contract with about 100 consumers directly connected to the VHV and

-36-

HV networks [95]. However, Egypt’s plan of liberalizing the market are slowly changing its structure. In

that sense, the government plans to create a two-tiered electricity market. First tier will be competition

based, and concerns only HV customers, who will independently choose electricity generation suppliers

based on bilateral contracts and negotiated electricity prices. Second tier will be more regulated, and will

concern MV and LV customers, who will pay a regulated tariff for electricity, procuring from the distribution

companies supplied by a single Wholesale Public Trader [91].

Figure 25 Egypt power market structure

The above-mentioned transition and transformation of the Egyptian electricity market was initiated in 2015

with the passing of the “Electricity Law”, and the establishment of its regulations in 2016 [97]. The other

most noticeable regulatory and law text concerning renewable energy is the “Renewable Energy Law”

enacted end of 2014. This law establishes NREA as responsible for launching renewable projects EPC

tenders and operating them thereby, but also introduces 3 private : development schemes [99] [100] :

• Competitive bidding: EETC launches tenders to establish and operate renewable power generation

plant, with the investor agreeing on terms with EETC to sell the electricity produced. Project size

here is above 100 MW. As of 2018, 4 BOO tenders have already begun: in Kom Ombo with

200MW PV, West of Nile area with 200 MW PV, 250 MW wind and 100 MW CSP [101]

• Merchant or IPP scheme: Based on terms agreed with EETC, IPPs can use the distribution and

transmission network to enter into direct bilateral contract with private off-takers to sell sale

electricity generated from renewable sources. This is particularly useful for energy intensive

industries such a cement.

• Feed-in-Tariff (FIT): Here, pre-qualified investors may establish, own and operate renewable power

plants, with the produced electricity sold to ETTC, based on 25 years PPAs and in consideration

for a predeterminant tariff fixed for the term of the agreement. Large scale 20 MW to 50 MW

renewable energy projects are found under the FIT program. Roof top and small scale solar power

generation is also included in the FIT, for installed capacities not exceeding 500 kW. A net-metering

process was introduced by EgyptEra in 2013, encouraging distributed renewable power generation.

Circular No.1/2013 allows small scale projects to feed in electricity to the grid by discounting the

surplus from the balance through the net metering process.

Off-grid renewable projects, especially solar are not widespread in Egypt, but are highly encouraged by

EETC. When they do exist, they often rely on PV generation, thus lacking reliability and certainty because

of its intermittent nature. BESS are not common in Egypt, but storage considerations in general are expected

to become prevalent in the private sector with the gradual removal of fuel/electricity subsidies that will

make conventional generation less appealing and render renewable electricity generation cost competitive.

In that sense, the previously mentioned CSP tender includes a TES [100]. Concerning the FIT program, the

price signals relevant to foreign investors are as follow : Large scale projects mentioned above were entitled

to US$14.34/kWh, reduced to US$8.40/kWh, while wind projects of the same capacity were paid a tariff

between US$4.60/kWh and US$11.48/kWh, reduced to a tariff between US$4/kWh and US$7.96/kWh

depending on the maximum operating hours of the wind plant [100]. Even though prices are announced in

-37-

dollar USD, the renumeration is paid in EGP. The investor will have to bear a part of the foreign currency

risk according to the formula used. However, the FIT program will not see a third tariff phase and was

announced to an end in July 2017. Reasons for this include disputes with the government over financing

and arbitration locations [102] In addition, and following its desire to boost private participation in the

renewable shift of the country, Egypt’ Investment law of 2017 grants investment incentives to renewable

projects. Such financial relief consist of a 30% deduction of the net taxable profit for the first 7 years of

renewable electricity generation projects. Similarly, Egypt encourages foreign investor to establish renewable

projects there. To do so, they are required to set up a project company in Egypt without any shareholding

nationality requirements. Moreover, the government backs up the import of renewable energy equipment

and machinery required to the erection of plants, with a unified customs rate of 2% (5% is the typical rate)

[100].

Similarly to MorSEFF in Morocco, the Egypt Sustainable Energy Financing Facility (EgyptSEFF) is a credit

line dedicated to energy efficiency and renewable energy investments in Egypt. The credit line is developed

by the European Bank for Reconstruction and Development (EBRD) and is available to clients in Egypt

through the National Bank of Egypt (NBE). It offers loans and credit facility to the nation’s energy

conscious business community to develop their sustainable energy projects. The maximum loan amount is

USD 5 million with a repayment period of up to five years [103].


Industry is the second largest electricity consumer in Egypt, after the residential sector. Metallurgy, cement

and mining are among the top electricity intensive sub-sectors. As mentioned before, electricity prices were

heavily subsidized in the past, but the country is effectively and gradually lifting the subsidies, which makes

electricity procurement more and more expensive, especially for industrial companies. Fossil fuels are also

concerned with the subsidy lift, which will represent an increased pressure on privates and entities relying

on diesel generators for their electricity supply. Similarly to 3.4 Table 10 was built.

Table 10 Identified industry companies, Egypt

Company Site

code Location DNI

Energy

consumed

(MWh)

Source

Lafarge Cement EC11 29.804432,32.089027

2315 979000

[50]

[104]

CEMEX (Assiut cement) EC21 27.170898,31.016183

2215 627000

Suez Cement

EC31 29.918392,31.533223

2015 165000

EC32 29.922371,31.288751

2015 374000

EC33 29.822361,31.308686

2015 330000

EC34 28.301581,30.746935

2215 33000

Sinai Cement company EC41 30.723626,33.774297

2515 363000

Arabian Cement Company EC51 29.796459,32.147373

2415 550000

-38-

Egyptian Iron and Steel Co EMe11

29.775674,31.315963 2015 272000

[104]

[77]

[78]

Misr National Steel EMe21

29.913912,32.448197 2115 51000

[104]

[77]

[78]

Kandil Steel EMe31

30.284382,31.793107 2115 34000

[104]

[77]

[78]

Ezz steel

EMe41 29.692945,32.319054

2215 170000 [104]

[77]

[78] EMe42 30.247967,31.740243

2115 85000

EgyptAluminum EM11 25.989233,32.331704

2215 4500000

[104]

[105]

Centamin EM²1 24.959837,34.712845 2415 282201 [106]

4.4 Jordan


Jordan is considered to be a low-middle income country, with its population reaching 9,5 million capita at

the end of 2015. The country suffers a scarcity in natural resources, including water, fossil fuels and

commercial minerals. Historically, the nation has been almost entirely relying on oil imports from Iraq at

discounted rates, but the 2003 war on Iraq shook the Jordanian energy system, and the country had to

procure its energy elsewhere. Subsequently, Jordan put in place the National Energy Strategy which aimed

at securing energy access of the kingdom, by studying its domestic sources, both renewable and non-

renewable Indeed, the Kingdom has huge shale oil reserves among other resources that can be exploited,

such as uranium. In parallel of that development, Jordan signed an agreement with Egypt, which became

the natural gas supplier to the Kingdom, supplying the country with all the quantity it needs to produce

electricity and distribute it domestically at discounted prices. As a result, the period from 2003-2010 saw

Jordan relying again almost completely on an external source for its energy procurement, and not taking any

concrete step of the national energy strategy. However, as stated in 4.3.1, Egypt experienced a drastic

reduction in its natural gas production that started in 2010, which put again Jordan’s energy security at risk.

Jordan stopped importing from Egypt in 2014, but initiated again a short-term energy plan, with the

objectives of importing liquefied natural gas (LNG) for the period 2015-2025, but in the same time heavily

investing in renewables, targeting 7% in the primary energy mix in 2015, and 10% by 2020 [107] [108] [109].

As of 2016, the annual growth of primary energy demand was 7%, while electricity demand growth rate was

2,5%. The total electricity consumption amounted to 16843 GWh in 2016, with households representing

43% and the industry 24% [108] [107]. The total installed capacity was 4644 MW the same year, of which

544 MW are split between solar and wind. The peak load was 3250 MW to which renewable electricity

generation contributes to approximatively 5,44%. Figure 26 shows the breakdown of the existing and

upcoming renewable energy projects occurring in Jordan for the year 2016. The later are either wind or

solar, as the nation enjoys class quality solar and wind energy resources: the country lays in the sun-belt

zone, with 5-7 kWh/m²/day and 300 sunny days per year; while wind speeds reach up to 9m/s in the best

locations. Consequently, the National Energy Strategy aims at securing a 10% renewable capacity by 2020,

but the farms and parks outlined in Figure 26 will represent 22% of the total generation capacity, therefore

surpassing the initial target. This shows the strong commitment the country has to diversifying its energy

capabilities, but also showcases the interest and confidence foreign investors put into the country’s potential

-39-

and attractiveness [108]. In Figure 26, all solar projects are based on the PV technology, as it is the most

mature in Jordan for the time being. The government acknowledges that CSP and CPV need further

development and conducted together with the World Bank a feasibility study on CSP. The results indicated

that CSP can be viable in the Kingdom starting 2023, especially capitalizing on dispatchability advantages

[108].

Figure 26 RE projects in Jordan 2016 [108]


The Jordanian power sector is structured in a single buyer model (NEPCO), as seen in Figure 27. The main

stakeholders are [107]:

• Ministry of Energy and Mineral Resources (MEMR): The MEMR was established in 1984, it is

entrusted with administering and organizing the energy sector, so it achieves the national objectives.

In light of the restructuring process of the energy sector, the responsibilities of the Ministry were

amended to include the comprehensive planning process of the sector. They also set the general

plans and ensure implementation in a way that achieves the general objectives of the energy sector.

• National Electrical Power Company (NEPCO): has an independent financial and administrative

existence. It regulates the electricity sector in Jordan, with respect to power generation and

transmission.

• Electricity Regulatory Commission (ERC): ERC was established based on the Council of Ministers

decision issued on January 15, 2001. The ERC’s objective is to ensure the rights of consumers and

to resolve any complaints that may occur between the consumer and Electricity companies

• National Research Energy Center (NERC): was established in Amman - Jordan for the purposes

of research, development and training in the fields of new and renewable energy. This research

center is considered a specialized science and technological center working under the umbrella of

the Higher Council for Science and Technology.

• Generation companies:

o Central Electricity Generating Company (Cegco), 40% state-owned, nominal capacity

~1,669MW

o o Samra Electric Power Generation Company (Sepgco), state-owned company, nominal

capacity ~880MW

o o Jordan has currently 4 IPPs

• Distribution companies:

o Jordan Electric Power Company -Middle areas- (Jepco) private company under concession

agreement

o o Electricity Distribution Company -Southern and Eastern areas- (Edco), state-owned

company

o o Irbid District Electricity Company (Ideco) -Northern areas- state-owned company

-40-

Figure 27 Jordan's electricity market [110]

Concerning the regulatory framework surround the energy shift in the country, Jordan has produced several

text laws applicable to renewable energy projects. The main ones are:

• Law No. 13 of 2012, Renewable Energy & Energy Efficiency Law: Sets up a Renewable Energy

and Energy Efficiency Fund, and regulated project development. The law establishes two different

business models for large-scale RE generation facilities (above 5MW):

o Direct proposals: On a BOO basis, investors can freely choose the site and propose the

project to MEMR, following ERC’s Direct Proposal guidelines. The proposals must not

exceed the price ceiling set by the ERC (Wind: 85 Fils/kWh; CSP: 135 Fils/kWh, PV: 120

Fils/kWh, with a 15% increase for plants with Jordanian origin). MEMER approves or not

the project upon consideration

o Government tenders(EPC): Nepco and MEMR tender projects together in pre-selected

sites that are part of a Land-use-list. The awarded projects have a PPA agreement with

Nepco.

• By-Law No. 10 of 2013, tax exemption for RE and EE, whereby RE equipment is exempted from

custom duties and sales tax (incl. products needed for manufacture, spare and wear parts, and

measurement)

• Regulations 3579 and 3583 on transmission for RE: Sets the framework for renewable electricity

transmission. The costs of connection to the grid are bared by the TSO Nepco, but are refunded

later by project developers. Although there is no direct priority dispatch in the grid code, Nepco

has the obligation to purchase all the power produced.

As stated previously, NEPCO is the single buyer of all the electricity produced, under any regime. Therefore,

power projects are not allowed to sell the energy produced directly to large consumers. Nevertheless, net-

metering and wheeling schemes are present for small and medium scale projects. The latter should not

exceed a total installed capacity of 5MW. In practice, these options were introduced by the Renewable

Energy & Efficiency law that allows electricity consumers who operate renewable systems (mostly PV in

Jordan) to self-consume, but also receive energy credits for any excess electricity their system generates

within a billing period. The difference between the self-consumption and self-production fed to the grid is

credited to a later time, where there is not enough production from the renewable system [111].

The large deployment of renewable the Kingdom of Jordan envisions will put a lot of pressure of the existing

transmission grid for two reasons. First because the renewable potential of the country is located in the

south, while loads are in the northern regions. Thus, the country plans for grid extension corridors (400KV).

Second, because of the intermittent nature of solar and wind. To this matter, MEMER is actively

-41-

investigating storage solutions to ensure grid stability, and store re excess production for later use. For

example, MEMER has announced a 30 MW with two hours storage system, followed by a 70 MW projects

with up to 4 hours. These plants will not be coupled with wind or PV as the goal is to test and see how they

can stabilize the network. Ultimately, they will serve a ramp up control solutions to both solar PV and wind,

enable energy shift of curtailed renewable energy. Battery storage, together with pump hydro and CSP with

TES are all being considered, assessing their potential to decide in the future on which is the most beneficial

and cost competitive [108]. In order for Jordan to boost international private investment and participation

in the renewable projects of the country, the Renewable Energy & Energy Efficiency law stipulates that all

RE equipment and systems will be exempted from customs and sales taxes. Even more, sales of energy

from RE systems are not taxable for the first 10 years of the project [112].


Industries in Jordan rely heavily on the national grid for their electricity procurement, as self-generation is

rare, and is mostly used as backup, instead of primary electricity source. Industry represents about 25% of

the total electricity consumption in Jordan, with a average growth rate of 4,14%. Regarding firms that have

self-generation capacity, most of them use either diesel reciprocating generators, or steam generators that

use fuel oil or coal. The later are more present as they can be utilized for co-generation as well. Hence,

renewable energy penetration in the industrial sector is still slow, with only few projects coming up by

selected companies: the Arab Pothash company plans for a 33 MW PV plant. Jordanian industries can

largely benefit from renewables, as they will secure their electricity generation cost, and not be subject to

fluctuating utility rates linked to the volatile oil price [113] .

Up until 2012, the average electricity selling price was below generation and transmission cost, with NEPCO

having to cover the difference in price. This situation led to a non-negligible deficit of JD 2,3 billion, which

pushed the MEMER to propose a plan that foresees the adjustment of electricity tariffs, aiming that by

2017, NEPCO would be able to cover all its cost [111]. However, prices stay closely linked to the average

market oil price [114]. Similarly, to 3.4, Table 11Table 10 was built.

Table 11 Identified industry companies, Jordan

Company Site code

Location DNI Energy

consumed

Sourc

e

Jordan Cement Company JC11 31.998069,35.781927 2215 8700

[115]

[116]

JC12 30.678506,35.631543 2515 98600

Al-Hadeetha Cement Company

JC21 - 2315 100000

Al-Rajihi Cement Company JC31 32.214893,36.202212 2415 144100

Quatrana Cement Company JC41 31.333889,36.129281 2415 74700

JC42 31.184202,36.073883 2515 74700

El-Hasa Phosphate JM11 31.184202,36.073883 2515 39400

Sheidiyah Phosphate JM21 29.912817,36.183768 2715 33300

Potash Co. JM31 31.042041, 35.488194 2015 165550

JM32 29.537524, 35.006558 2515 165550

Fertilizer Company JCh1

1 29.529386, 35.007802 2315 47600

Indo-Jordan Chemicals Company

JCh21

29.912817,36.183768 2715 26100

Indo-Jordan Fertilizer Company

JCh31

29.912817,36.183768 2715 61300

Jordan Petroleum Refinery Company

JCh41

31.720732, 35.993288 2415 105300

-42-

4.5 Saudi Arabia


Saudi Arabia has a tremendous amount of natural energy sources, both renewable and non-renewable. The

country is one of the world’s largest producers of oil, and with an estimated 267 BBO proved reserves it’s

also the country owning the largest oil resource all over the globe. Despite that, Saudi Arabia is facing an

energy crisis, as the increasingly growing pressure of energy demand risks to put the economy of the country

at risk, by halting its oil exports in the future. In fact, following present growth trends, domestic energy

consumption could reach 9,3 MBOE/day by 2028, which will force oil exports to stop, thus weakening the

country’s main revenue source. Two reasons fuel the rapid growth of energy demand in Saudi Arabia: a

rising population, with an expected 30 % rise between 2010 to 2030, greater than China’s expected increase

of 7%, or even India’s with 23%. Second reason can be seen as the effect of the very low and cheap energy

prices prevalent in Saudi Arabia, which attracts large investment in energy intensive industries, together with

wasteful consumption due to extremely low electricity rates. The later do not exceed 0,07 USD7kWh all

sectors included, with industry caped at 0,032 USD/kWh, regardless of the electricity consumption rate

[25]. The extreme aridity of the region contributes to the country’s high energy intensity, as it has an

enormous desalination program that processes more than 3,5 million cubic meters of seawater per day.

Parallel t being one of the world’s largest oil producers, Saudi Arabia is also a large oil consumer, ranking as

5th in 2016, with a consumption rate of 3,2 million BBL/day, of which a large proportion is used in power

plants. The electricity system of the KSA is the largest by capacity in the Arab world, with total installed

capabilities of nearly 70 GW, and a peak load of 54 GW in 2013. Electricity is supplied mainly by gas turbines

(40%), followed by steam turbines (32%) and coined cycles. All power plants are fuelled either by the

country’s own natural gas production, or petroleum products [25]. The sectorial distribution of electricity

demand is 51%, 13%, 13%, 19% and 4% for residential, governmental, commercial, industrial and other

sectors respectively. The industrial sector is experiencing an aggressive development, which results in a

growing electricity demand [117]. Due to its geographical location and climate, Saudi Arabia joys from

perfect renewable energy sources. Solar insolation can reach 28000 kWh/m²/year, with 3000 hours of

sunshine per year with clear skies. Moreover, there are two vast wind regions in the kingdom, along the

Arabian Gulf and the Red Sea coastlines, where average annual speeds can exceed 6 m/s. Acknowledging

this untapped potential, the government established King Abdullah City of Atomic and Renewable Energy

(KACARE), that seeks to utilize the country’s indigenous energy resources. Its strategic plan concerning

renewables is to have 72 GW installed capacity of renewable energy by 2032, with solar PV (16 GW), solar

CSP (25 GW), wind (9GW), nuclear (17,6 GW), waste-to-energy (3GW) and geothermal (1GW). The

expected evolution in the installed capacity can be seen in Figure 28. It was announced in 2015 that these

targets will be pushed back to 2040 [117].

Figure 28 Long-term renewable energy targets, Saudi Arabia [117]

-43-


The Ministry of Energy, Industry and Mineral Resources (MOEIMR) is the governmental entity handling

all policy planning in the power sector. The latter is organized around the Saudi Electricity Company (SEC),

as seen in Figure 29. SEC is a government owned entity that owns most of the generation facilities of the

country, with a generation capacity of 68 GW in 2015 (4 generation subsidiaries). It is also responsible for

the transmission (NGSA) and distribution (DISTCO). Besides, the other main players in the Saudi Arabia

power market are [25] :

• Saudi Armaco: Government entity in charge of Saudi Arabia’s oil and gas production. Alongside

SEC, it manages also power generation.

• Saline Water Conversion Corporation (SWCC): is a government corporation that

operates desalinization plants and power stations in Saudi Arabia. It is the second largest electrical

provider in the country.

• Electricity and Co-Generation Regulatory Authority (ECRA): Independent regulatory entity for

Saudi’s Arabia energy sector.

• King Abdullah City of Atomic and Renewable Energy (KACARE): A 2010 royal decree established

KACARE, with the tasks of focusing on nuclear and renewable energy, as well as technology

localization for renewables.

• Power and Water Utility Company (MARAFIQ): Government owned company that services most

of the electricity to the two industrial cities in the KSA: Jubail and Yanbu.

• Sustainable Energy Procurement Company (SEPC): a separate standalone government-guaranteed

entity, responsible for administering the procurement and executing and managing the power

purchase agreements (PPA).

Figure 29 Power market structure, Saudi Arabia [25]

The ECRA launched in 2011 a plan to unbundle the vertically integrated electricity market, to promote a

competitive environment for private investments in the future. Called the “Development of the Electricity

Industry Restructuring Plan”, it will help create an independent transmission company that will guarantee

unselective access to the grid for all producers and large consumers. It will also establish competition in

distribution, by creating several local distribution companies. A “Principle Buyer” entity will also be created

in order to manage the electricity industry income, independently of generation and transmission

management [25].

When it comes to renewables, and the regulatory framework setting the business models for projects, Saudi

Arabia has the following [118]:

https://en.wikipedia.org/wiki/Desalinization

https://en.wikipedia.org/wiki/Power_station

https://en.wikipedia.org/wiki/Saudi_Arabia

-44-

• KACARE program (IPP tenders): main procurement way to reach the national RE penetration

targets. KACARE issued in 2013 a white paper detailing the approach of bids and tenders to

projects developers, structured around 3 rounds, the first one (overall capacity of 500-800 MW)

will be located in pre-packaged sites, while the subsequent ones will not be bound to a specific

location, and will cover a capacity up to 7000 MW. The negotiated PPAs will be payed in Saudi

Riyals, but currency adjustments to cover exchange rate (US-KSA) is considered, but not regulated

in detail. Although not a prerequisite in the first round, local content and localization plays a major

role in the national RE program. In the first round, bidders with high content localization will be

more advantageous. Comparatively, developers who use less Saudi national equipment may be

subject to a fine and may be not eligible for future rounds. The minim set by KACARE is 25%

local content. Developers bear the cost of connection to the grid, while the TSO eventually

upgrades the grid beyond existing connection point

• IPPs selling to large consumers: Saudi Arabia’s law concerning bilateral contract between SEC,

IPPs and large electricity consumers does not distinguish between renewable and conventional

generation. It is practically difficult to establish projects of that nature because they are outside

KACARE’s scope of operation

• Self-production: The Saudi Arabian Electricity Law allows self-production. Moreover, a net-

metering scheme set to come in force starting July 2018 will foster private investment in renewable

energy. Targeted at small scale renewable energy systems (< 2MW), it will allow prosumers to

operate their own generation systems and export the unused excess to the national grid with a cash

payment [119].

The high localization content the Kingdom is aiming for can be a challenge for foreign investors wishing to

develop renewable projects in Saudi Arabia. The country has strong incentives and detailed programs to

localize the electricity industries, such a preferential price, priority over foreign companies… In that sense,

foreign investments are regulated, with the Saudi Arabia Investment Authority (SAGIA) facilitating the

investment process for non-Saudi investors. Beyond that, the extreme low electricity prices prevalent in

Saudi Arabia can represent a hurdle to enter the market. As mentioned previously, electricity prices are

heavily subsidized. The wealth redistribution resulting from the country’s oil natural resources make

electricity generation extremely cheap, among the lowest in the world. Still, aware of the burden this situation

will have on the energy demand in the future, the KSA announced late 2017 that electricity tariffs will

increase in the beginning of 2018. This move comes together with the country’s ambition to rationalize

renewable energy investment and make them cost-competitive with attractive payback period for financers

[120].


A large chunk of the country’s electricity consumption is dedicated to the residential sector, and AC systems

in building as a result of the hot temperatures. Historically, electricity prices were really cheap, all sectors

included with 0,08 USD7kWh while industry was capped at 0,048 USD/kWh. However, as stated above,

the KSA is changing is tariff strategy to lessen stress on future energy demand and promote competitiveness

of renewable technologies. The announced rise in price can be seen in Table 12. Similarly to 3.4, Table 13

was built.

Table 12 Electricity rate, Saudi Arabia [121] [120]

Prior to 2018 (Halala/kWh) 2018 (Halala/kWh)

Commercial

1-4000 kWh/month 10 16

40001-8000 kWh/month 18 24

>8000 kWh/month 24 30

Industrial All brackets ¨12 18

-45-

Table 13 Identified industry companies, Saudi Arabia

Company Site

code Location DNI

Energy

consumed

(MWh)

Source

MAADEN

SM11 31.502471,39.922940 2315 153300 [65]

[122]

SM12 31.502471,39.922941 2315 164250

SM13 23.781456,45.073226 2315 44203

[123]

[122]

SM14 24.983122,41.598602 2315 44203

SM15 23.501225,40.866544 2415 44203

SM16 19.981040,42.012451 2415 44203

Industrial Minerals - Al

Ghazalah Mine SM21 27.252438,40.500585 2015 240000

[122]

[67] Al Masane Al Kobra Mining

Company SM31 18.135165,43.859965 2315 40500

Tabuk Cement Company

(TCC) SC11 27.531459,35.538953 2715 143000

[122]

[50]

Al Jouf Cement Company

(JCC) SC21 31.414052,38.683258 2515 187000

Yanbu Cement Company

(YCC) SC31 24.270600,37.561681 2315 154000

Al Safwa Cement Company SC41 22.553124,39.435419 2115 220000

Qassim Cement Company

(QCC) SC51 26.491166,43.962721 2115 220000

Southern Province Cement

Company (SPCC) SC61 19.519343,42.538318 2115 198000

4.6 Country comparison

RE business models

-46-

Table 15 presents a comparison of the business models applicable to renewable electricity generation in the

selected markets. Small scale generation is generally not much developed, with all 5 countries heavily

focusing on large scale project development schemes, by setting up detailed and concise project guidelines

for establishing tenders and PPAs. Moreover, and although all the markets considered rely on a single buyer

model for the electricity, most often embodied by the TSO, unbundling the power market and liberalizing

electricity sales are underway. Morocco, Egypt and Saudi Arabia make it possible to offset the TSO to sell

electricity directly to large consumers. Those PPAs agreements are not completely unrelated to the TSO

though, as it is through its grid that electricity is transmitted. Jordan and Tunisia still lack this ability. This is

an important consideration for a smalls scale CSP system with TES like Cleanergy’s, as the company should

promote its technology to local developers and showcase that there is a market need (large industrials with

high energy intensity), and that the framework in place allows said developers to sell electricity directly to

those customers. There is a strategic added value Cleanergy can offer to developers with its technology,

namely cost-competitive on demand electricity generation. Similarly, all 5 countries allow privates to self-

produce from renewables for their own energy procurement. Net metering options are offered in all of

them, with variation depending on the project size. This last treat is also desirable as it will facilitate market

entry for Cleanergy, in the sense that the company can go directly contact selected industrial firms and

propose them cost-competitive offers to secure their energy access with no grid or state dependence, while

in the case of surplus production, inject it to the grid and gain a premium.

Business environment

The business environment of a market can play a pivotal role in the failure or success of a new product

since external factors (to the company, i.e., Cleanergy) such as political stability, security of investment,

competitive landscape, currency rate fluctuations… can hinder its activities To that purpose, three

international indexes help analyse these particular factors. First, the World Bank Ease of Doing Business

Index, the BTI status score and the Global competitiveness index, as seen in Table 14.

Table 14 Countries Performance under International Indices [124] [125] [126]

Morocco Tunisia Egypt Jordan Saudi Arabia

World Bank Ease of Doing Business 69 88 128 103 92

BTI Status score 4,61 6,27 4,28 5,22 4,27

Global competitiveness Index 71 95 100 65 30

Table 15 Business model country comparison

Morocco Tunisia Egypt Jordan

Saudi

Arabia

IPP selling

to single

buyer

IPPs can sell to

ONE with

PPAs.

For projects

under the wind

and solar plan,

the PPA is

granted as a

result of a tender

process referring

Permissible for

large scale

projects under

the concession

scheme (tender

process),

through BOO

and 20 years

PPAs with

STEG

Competitive

bidding

organized by

EETC,

responsible for

the PPAs

Government

tenders (EPC):

in pre-selected

sites. PPA

signed with

Nepco

Direct

proposals:

BOO basis, on

freely selected

locations

KACARE

white papers

details

several

tendering

rounds for

IPPs. The

PPA is

signed with

SEPC

-47-

to pre-selected

sites

IPP selling

to large

consumers

Law 13-09

allows RE IPPs

to sell to large

consumers or a

group thereof.

Regulatory

development for

access to

medium voltage

is currently in

progress and will

make this option

easier to

implement.

No, STEG is

the single buyer

Merchant IPP

scheme with

bilateral

contracts

between IPP,

large consumer

and EETC (for

grid use)

No, Nepco is

the single

buyer

According

to the Saudi

Arabian

Electricity

Law, IPPs

can sell to

large

consumers

Self-

production

Current

regulation allows

self-production,

20 % of annual

production can

be sold to the

grid

Permissible and

subject to

authorization.

The surplus can

be sold to

STEG (up to

30% for large

and medium

scale)

Egypt’s

electricity law

allows private

production of

electricity for

self-

consumption

and third party

sales

Existing

regulation

allows energy

consumers to

produce their

own electricity

and to use the

transmission

grid.

Self-

production

is allowed,

with no

distinction

for

renewables

Support

mechanism

medium-

small scale

RE

Medium and low

voltage RE

integration

underway for

IPPs

Net metering

scheme, with no

monetary

transfer, only

energy flows

between billing

periods

FIT

mechanism

that was

stopped

middle 2017.

Only net

metering still

in place

Net metering

and wheeling

projects (<5

MW)

Net

metering

scheme to

begin in

2018 for

capacities

under 2 MW

First, the Ease of Doing Business index outlines the ability of establishing and operating a commercial

enterprise (taxes, bureaucracy, construction permits…). This index ranks economies from 1 to 190 [124].

Among the countries vetted, Morocco ranks first, followed by Tunisia, Saudi Arabia, Jordan and finally

Egypt. Although Egypt is last, the country experiences every year small upgrade (ranked 131 in 2016).

Second, the Bertelsmann Stiftung Transformation (BTI) Status Index (1-10) which assesses political

participation, social integration, stability of institutions, organization of the market and competition [125].

Political and economical transformations are both investigated by this index. Within the countries analysed,

-48-

it puts Tunisia on the forefront with its stabilizing political landscape and healthier economic state. Jordan

ranks second, followed by Morocco, Egypt and finally Saudi Arabia.

Third is the Global Competitiveness Index. Developed by the World Economic Forum, it looks into the

factors determining a country’s level of productivity. The latter include macroeconomic environment,

institutions infrastructure, labour market…. This index ranks 137 economies, and puts Saudi Arabia first

among the countries investigated, followed by Jordan, Morocco, Tunisia and finally Egypt.

Private sector participation

Private participation in renewable projects investments is a mandatory criterion to quantify in order to

decide on which market to target first. For a foreign company like Cleanergy, countries that are able to

promote and attract private investments and contribution to the renewable energy sector should be the

priority. Previous sections somewhat detailed how each of the countries deal with foreign and private

investment, in the light of policies in place, financial facilities, etc. The efficiency of such governmental

decisions are shown in Figure 30, where the increase of private investment in RE projects between 2013

and 2016 is shown. Morocco was the only country with private actors in 2013 and is the one with the highest

increase in private financing, achieving nearly 18% growth. The strong regulatory framework backing up

IPPs, and the ongoing liberalization of the electricity market play a significant role in catalysing private

investments. Jordan follows next, as it experienced a 10% increase. Although the country still relies on a

single buyer model for large scale electricity generation plants, the small-medium scale schemes (net

metering, wheeling) push forward the participation of the private sector. The last three countries, Egypt,

Tunisia and Saudi Arabia respectively didn’t achieve more than 2% increase. For Egypt, this can be explained

by the halt the FIT tariff program saw as a result of payments delays at the end of its first round, and

payments disputes with the government. As a result, a general unwillingness to invest rose in the country.

Finally, Saudi Arabia ranks last with its difficult private financing environment, favouriting local Saudi

companies over international ones.

Figure 30 RE private Investment Increase (2013-2016) [37]

Country rank

The final score of each country is given in Table 16, and will serve later for the MCA.

Table 16 Country score ranking

Morocco Tunisia Egypt Jordan Saudi Arabia

RE Business Models 8 6 8 6 6,5

Business environment 7 8 5 7 6

Private participation 9 4 5 8 3

Final score 8 6 6,5 6,75 5,5

0,00%

5,00%

10,00%

15,00%

20,00%

Morocco Jordan Egypt Tunisia Saudi arabia

2013 2016

-49-

5 Comparison/Analysis

5.1 Optimum configurations

The results of the techno-economic optimization can be seen in Appendix 3Appendix 3. As a reminder the

optimization searches for the lowest LCOE achievable by varying several parameters: installed capacity,

mirror area and storage hours. Appendix 3 presents for each identified site, the optimal technical parameters

of a fictive power plant that should service the energy need of said site, at the lowest LCOE possible. LCOE

numbers are given in normalized values. Although the storage size was varied during the optimization from

10 to 14 hours, only a handful of configurations have 14 hours as optimum, while the initial guess would be

the larger the storage, the higher the energy production, thus the lower LCOE. As a matter of fact, most

locations have 10 to 11 hours TES. When plotting all possible values of the LCOE with regards to the TES

size for all sites as shown in Figure 31, the optimal conditions for lowering the LCOE can be deducted. As

it can be seen, the lowest LCOE point is achieved for 14h of storage in the case of high DNI, eg. 2715

kWh/m²/year. The more the DNI decreases, the farther on the left that point gradually shifts, from the

maximal storage capacity to the lowest (13h for 2515 and 2415 kWh/m²/year, 12h for 2315kWh/m²/year

and so on). Below 2215 kWh/m²/year, the lowest LCOE is always obtained for a 10h TES. Hence, the

benefits of having a larger storage system, in terms of yearly extra power output, are not capable to offset

the capital investment of such a large TES, solely due to inadequate DNI conditions, and it is more

economically viable to opt for smaller storage hours. For such cases, hybridization with PV can represent

an alternative, and shortcut the high electricity cost by having the PV system produce during the day, and

the CSP-TES system acting as a storage for the PV and generating power during the night. Conversely,

high DNI locations allow to recuperate high storage capital, which contributes in lowering the LCOE.

Figure 31 LCOE vs TES size (Cleanergy’s cost functions)

Furthering that idea, the same plot as in Figure 31 is shown Figure 32, using the cost data from the STEALS

report instead of Cleanergy’s, as given in 3.3. The corresponding optimal configurations for that case are

found in Appendix 4. As it can be seen in this case, only a TES of 14 hours is sorted out as optimum, since

the higher the storage, the lower the LCOE. Obviously, having a lower capital investment will give lower

generation costs, but also makes the use of a large TES rational and profitable, regardless of the DNI

1

1,05

1,1

1,15

1,2

1,25

1,3

1,35

1,4

1,45

8 9 10 11 12 13 14 15 16

No

rmal

ized

LC

OE

TES size (hours)

1915

2015

2115

2215

2315

2415

2515

2715

......

Trend lines

-50-

condition, and leverages on the benefits of large storage hours to produce more electricity and lower its

generation cost.

Figure 32 LCOE vs TES size (STEALS cost data)

The data from Appendix 3 can be rearranged to form a map that gives valuable insight on where Cleanergy

should position itself in a chosen country, or the MENA as a whole. Figure 33 is a chart with such purpose,

that positions each site in Morocco (bubble) with regards to its target LCOE (y axis), energy consumed (x

axis) and capacity installed (size of the bubble), while the colour indicates the industry type. It is then a way

to identify which sites or industrial companies to target first when entering the market, but also which type

of industry holds the highest potential in terms of size. Obviously, the sites positioned on the far right of

the chart represent the most profitable cases to Cleanergy, since they have the largest energy consumption,

hence the largest capacities to install. For example, the site indexed MM11 (owned by OCP) consumes

annually 501,4 GWh. By installing a 85 MW park with 10 hours storage nearby its location, OCP will be

able to procure its electricity at a normalized LCOE of 0.66. Comparatively, a smaller consumption site such

as MC22 with 11GWh can be serviced with a smaller park size of 2 MW and 10 hours TES, but not

necessarily lower LCOE, as in that case, the normalized cost of electricity generation is 0.98. This is mainly

due to the weather conditions of each site, and their respective DNIs : the higher, the better business case,

independently of the system size. However, it may be more strategic to target first companies and sites with

low LCOE values, such as MM31 (Maya Gold&Silver), since it will be easier for Cleanergy to prove its

storage solution’s added value. This is an important consideration to make, as for any new product that is

unknown to a market, there might be relatively high resistance and lack of confidence in its attributes, which

will hinder its adoption by players in said market. The way to-go should be then to target the companies

whit the most strategically profitable business case (lowest LCOE) to quickly attract them. Those first

customers might be small in terms of market share, but once few players adopt the new technology, it will

be easier for Cleanergy to engage with more prominent companies, that might be more traditional in their

way of doing business. In fact, Cleanergy should capitalize on the competitiveness of its target LCOEs and

stress out the benefits of on-demand electricity production, especially with the small awareness there is

about CSP in Morocco, or in the MENA in general when it comes to small/medium scales.

1

1,1

1,2

1,3

1,4

1,5

1,6

8 9 10 11 12 13 14 15 16

No

rmal

ized

LC

OE

TES size (hours)

1915

2015

2115

2215

2315

2415

2515

2715

......

Trend lines

-51-

Figure 33 Site positioning map, Morocco

The positioning map regrouping all five countries representative of the MENA is given in Appendix 5. For better clarity, the same map is shown Figure 34, but positions this time the companies, instead of their individual sites. The chart axes remain the same but refer hereafter to the average values of all sites within a company, while the bubble colour refer to the country. Along the companies and countries identified, it appears that Egypt has the highest potential in terms of market size, especially within its mining companies. This last observation holds for most of the countries, as all bubbles to the right, all countries included, belong either to mining or cement companies. These two segments are the most energy intensive, with round the clock load profiles that can greatly benefit from a dispatchable CSP system. Mines are often isolated or have weak grid access which makes Cleanergy’s offer the more appealing, as it can represent a better alternative than diesel generators that these can of companies tend to use on their sites. As stated for Morocco before, locations with high DNI tend to have low LCOE values, which is the case for Jordan, where the cheapest electricity generation cost is achieved for the mining company Sheidiyah Phosphate. A 4 MW plant with 14 hours of storage can be built in one of its sites, with close to 2700 kWh/m²/year DNI and produce enough electricity to cover all its energy needs with a normalized LCOE of 0.01. Likewise, the Saudi Tabuk Cement Company can be proposed a 18 MW park with 14 hours of storage at the targeted price of 0.01 in one of its facilities located in similar weather conditions (around 2800 kWh/m²/year).

MM11MM12

MM21

MM22

MM23

MM24

MM25

MM31

MM41

MC11 MC12

MC13

MC21

MC22

MC23

MC24

MC31

MC32

MMe11MA11

MA21

1,05

1,1

1,15

1,2

1,25

1,3

1,35

1,4

0 100000 200000 300000 400000 500000 600000

No

rmal

ized

LC

OE

Energy consummed (MWh)

Mining

Cement

Mettalurgy

Agriculture

-52-

Figure 34 company positioning map MENA (Industry)

5.2 Potential/Serviceable achievable market

5.2.1 Results

A total of 58 industrial companies was investigated in this research work (11 for Morocco, 11 for Tunisia,

15 for Egypt, 12 for Jordan and 9 for Saudi Arabia), each broken down to their respective sites, which lead

to 76 different sites. These companies belong to one of the following industrial segments : mining, cement,

metallurgy, chemical and agriculture, which are the biggest industrial sub-sector in each country Table 17

shows the market size for a technology such as Cleanergy’s with regards to the 58 customers identified,

together with the respective optimum configuration found for the best business case among all sites considered

in each country, including its target LCOE and industry type. A total market potential of 2672 MW is

estimated, with Egypt and Saudi Arabia in the forefront. It would be possible to reach LCOE values below

Cleanergy’s target for each of the most promising sites in each country (expect Tunisia), thus making such

a distributed CSP technology highly competitive.

Table 17 Market potential for the MENA (industry), with optimum configuration

Market

size

(MW)

Most competitive business case (optimum configuration and industry type)

Index Normalized

LCOE

DNI

(kWh/m²/year)

Park

Size

(MW)

TES

(hours)

Mirror

Area

(m²)

CF

(%)

Industry

type

Morocco 403 MM31 1,07 2425 2 13 220 84% Mining

Tunisia 94 TChe11 1,27 2000 2,3 10 220 65% Chemical

Egypt 1400 EC41 1,07 2415 50 13 220 85% Cement

-53-

Jordan 165 JM21 1 2680 40 14 220 92% Mining

Saudi

Arabia 610 SC11 1,01 2700 17 14 220 92% Cement

While this approach gives an initial idea about the market size in the MENA for the identified countries, it

can be argued that it is not complete since it is limited to the 58 companies vetted. The real potential is

bigger as there are several other companies (both in the industrial segments considered and other smaller

ones) that are part of the industry and the economy of those markets, hence unaccounted for by this method.

This inaccuracy can be explained by a lack of data, meaning that the analysis considered only companies

that had enough data (namely energy consumption estimates, site location) to run the tecno-economical

model, and disregarded all others for which those information were unavailable. Companies that publish

annual reports and reference their production values and site locations are most often established industrials,

with big enough market share to be representative of their representative segments. Nonetheless, and as a

result of this method, the market size obtained in each country represents a fraction of the real potential,

which renders the country comparison somewhat useless: under this approach, Egypt may seem having the

lion share in terms of market share, followed by Saudi Arabia, but in reality, it can be the opposite when

accounting for all industrial companies in both. As a matter of fact, Saudi Arabia is likely to have the lion’s

share of the real SAM, because of the cheap electricity prices in the Kingdom that push for bad consumption

behaviours, energy waste, inefficient industrial processes… This is particularly relevant, as Saudi Arabia’s

electric consumption per capita is 9 times greater than Morocco’s, showcasing a fairly different energy use

culture in those two countries [127]. Hence, the companies that were investigated represent potential

customers that can populate the SOM area of Cleanergy in the future, while the market potential estimated

based on them (2672 MW) represents a lower bound of that space.

A possible method to quantify the SAM in those 5 countries would be to study the total electricity

consumption of the industrial sector as a whole in each and find the optimum technical configuration to

service that electricity need, assuming an average DNI for the whole country. This method is not as precise

as previously, especially since it relies on input parameters not completely accurate (the DNI in Egypt varies

between 2000 kWh/m²/year and 3000 kWh/m²/year, and assuming a 2500 kWh/m²/year for all possible

locations favours some over the other) but is sufficient in providing a first-hand quantification of the market

opportunity present in each country’s industrial sector. The results of such approach are given in Table 18,

where the market size shown correspond to the needed capacity of Cleanergy’s technology to produce the

indicated electricity consumption, with the average DNIs shown for each country. A total SAM of 16,2 GW

is estimated in this approach, with Saudi Arabia being the largest market (8 GW), followed by Egypt (6 GW)

as suspected before. Morocco is third with 1,2 GW, followed closely by Tunisia (0,86 GW). Jordan on the

other hand is the smallest market, with a 205 MW market size. These SAM values consider all kind of

industry types, including ones that do not necessarily need a continuous electricity supply, or that would be

interested in a storage solution. The previous method however accounted for industry segments and actors

that could have a benefit from adoption a small scale CSP system with TES. As such, both methods are

complementary, since the second one compares the countries in terms of total electricity need in the

industrial sector, while the first one targets individual companies and sorts out the most promising ones in

each market.

Table 18 SAM in the MENA, industry (grid connected, VHV-HV-MV)

Industry

electricity

consumption

(GWh)

Average DNI

(kWh/m²/year) SAM (MW) Source

Morocco 6972 2115 1200 [64]

-54-

Tunisia 4915 2015 860 [128]

Egypt 38310 2315 6000 [129]

Jordan 1383 2515 205 [115]

Saudi Arabia 56240 2515 8000 [117]

The market size embodied in the SAM figures represents how much Cleanergy would have to install of its

technology in order to supply all the electricity needed in the industrial sector of each of the 5 countries. As

a result, to scale up the SAM in the MENA region, these figures are plotted against the industry added value

in the GDP of each country [39]. The resulting graph Figure 35 shows how that the higher the contribution

of the industrial sector as a whole to a country’s economy, the more likely it will be energy intensive, hence

results in a higher SAM. A polynomial trendline was used to interpolate the 5 set of points to get the

respective SAM sizes of each MENA country.

Figure 35 Scaling up the SAM to the MENA

The results of such approach can be seen in Figure 36. As a whole, the MENA region has a market potential

in the industrial sector of approximatively 47,2 GW. Saudi Arabia leads the way naturally, followed by Iran,

Egypt, the Emirates and Iraq. Interestingly, 4 countries of the latter are OPEC members, having their own

fossil fuel resources. Egypt, up until 2013 was an important gas exporter in the region, which explains its

presence in the top 5 countries. As stated previously in the case of Saudi Arabia, the abundance of fossil

fuel assets pushes for an inefficient energy use, which drives the electricity consumption up. Even more,

electricity prices in these countries tend to be very low, which represents a barrier for Cleanergy, as it will

be in direct competition with grid connected solutions since most often, industrial are grid connected and

care only about the price paid. In brief, even if large energy intensive countries represent have the biggest

market size for Cleanergy, there is a trade-off to make considering energy prices in said markets.

The small difference in numbers between Table 18 and Figure 36 for Saudi Arabia, Egypt, Morocco, Jordan

and Tunisia is a result of the interpolation method used.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 100 200 300 400 500 600 700 800 900

SAM

(M

W)

Industry added value in GDP (billion USD)

-55-

Figure 36 SAM in the MENA region by country

5.2.2 Sensitivity analysis

The general breakdown of the CAPEX of Cleanergy’s system is shown Figure 37. As it can be seen, the

solar field account for nearly 40% of the capital investment, followed by the thermal storage and the Stirling

engine unit. The repartition sown is for a 8 MW system with 10 hours of storage and 220m² mirror reflective

area, configuration determined to be the optimal for this plant located around 2000 kWh/m²/year DNI. As

discussed briefly earlier, few optimal configurations have 14 h of TES, showcasing that the added value in

terms of additional kWh produced cannot offset the high investment cost linked to the storage size. Also,

the cost data from the STEALS report (which are lower than Cleanergy’s) validate that, as all of the optimal

designs rely on a 14h TES: the extra energy produced thanks to higher storage capacity (compared to 10-13

hours) make the use of 14 TES economically viable, but also have an effect on another parameter of the

configurations, namely the system capacity. Indeed, having the maximum TES size makes the system smaller

in terms of MW to install, compared with a site with the exact same DNI resource, electricity consumption,

but smaller TES (10h to 13 hours). As a reminder, the optimal configurations are determined by having as

an input the DNI and electricity consumption of a particular site, and by varying TES size and the mirror

area.

Figure 37 Cleanergy's CAPEX breakdown

Furthermore, this can be seen when exanimating the SAM with the STEALS cost data in Table 19, where

although the previously identified optimal configurations in Table 17 stayed the same, but the market size

in each country, is lower, thus impacting the total potential, that decreases from 2672 MW to 2430 MW.

045

178322346

508610

143714551487

29533045

38054341

53515925

74117984

PalestineSyria

YemenLebanon

JordanBahrainTunisia

LibyaIsrael

OmanMorocco

QuatarKoweitAlgeria

IrakEmirates

EgyptIran

Saudi Arabia

SAM (MW)

-56-

The same reasoning can be made when estimating the SAM for each country as explained before, that would

result in a lower value than 16,2 GW by calculating it with the STEALS cost data.

Table 19 Market potential for the MENA (industry), with optimum configuration using STEALS cost data

Market

size

(MW)

Most competitive business case (optimum configuration and industry type)

Index LCOE

(EUR/MWh)

DNI

(kWh/m²/year)

Park

Size

(MW)

TES

(hours)

Mirror

Area

(m²)

CF

(%)

Industry

type

Morocco 380 MM31 48,2 2425 2 14 220 86% Mining

Tunisia 88 TChe11 59,7 2000 3 14 220 69% Chemical

Egypt 1220 EC41 48,1 2415 15 14 220 85% Cement

Jordan 160 JM21 44,7 2680 4 14 220 92% Mining

Saudi

Arabia 582 SC11 44,7 2700 17 14

220 92% Cement

This leads to the conclusion that a change in the system costs does not affect the optimum sites identified

previously, but rather has a slight effect on the parameters of the configurations, thus making the general

ranking not price sensitive, nor the MCA. However, the sensitivity analysis should be conducted to analyse

inputs’ effect on individual configurations. Here, the most competitive business case (based on the

LCOE) identified earlier will be used to perform the analysis: JM²1 (see Table 17). The parameters chosen

for the sensitivity analysis are: the discount rate, and selected CAPEX components (storage cost, solar

field cost and Stirling engine cost). Each parameter is varied within the range -20% to 20% to understand

the changes it has on the LCOE (Figure 38) and NPV (Figure 39) of the JM21 site. To compute the NPV,

the zero-subsidy scenario is followed, assuming that the electricity is sold at the utility price of Jordan.

Figure 38 LCOE sensitivity analysis

-0,3 -0,2 -0,1 0 0,1 0,2 0,3

Stirling engine

Solar field

Storage

Discount rate

+20%

-20%

-57-

Figure 39 NPV(k€) sensitivity analysis

As it can be expected, the change in LCOE values follows closely the changes in CAPEX components such

as the storage and solar field, but not so much of the Stirling engine: an increase of 20% in the latter pushes

the normalized LCOE up by almost 5%, while the same increase for the storage and solar field costs nearly

pushes the normalized LCOE by 20% up. The same trend is observed with the NPV changes, however in

a smaller order of magnitude. The effect these two parameters have both on the NPV and LCOE can be

traced back to their importance in the CAPEX breakdown from Figure 37, accounting for more than 60%

together. Conversely, the changes in the discount rates have more effects on the NPV than the LCOE,

compared with specific components costs: achieving 20% decrease in the cost of capital yields a greater

NPV (more than 20% surplus) than with combining the same cost reduction of all three components

previously described. This leads to the conclusion that acquiring cheap financing may be more beneficial

and profitable than investing in R&D to decrease costs. The latter however contributes more in lowering

the LCOE, rendering the project more competitive.

5.3 Multi Criteria Analysis

5.3.1 Results

Each business opportunity (indexed by its site code) is given a score under the criteria mentioned in 3.4, as

seen in Appendix 6. The final score (1-10) obtained is shown in Figure 41. Under this model, the most

promising site to approach first for Cleanergy is MM31 with a score of 7.15, followed by EM21 with a score

of 6.92. These sites both correspond to mining companies who have their mines in off-grid locations, which

contributed in pushing them on top of the list of all prospective sites. Business opportunities with a score

over 5 all belong to either the mining or cement sector, confirming the appeal these segments should have

for Cleanergy when entering any specific market. Alternatively, while the previous results suggested that

Egypt, Saudi Arabia and Morocco are the most interesting markets because of their size, the MCA suggests

that Jordan is attractive as well, namely because of the low generation cost Cleanergy’s technology can

achieve there. Indeed, the high DNI in Jordan allows for cheap prices (average of 74 EUR/MWh) below

the 77 EUR/MWh target, and the high industry electricity price proposed by the national utility (160

EUR/MWh) puts Jordanian companies among the top business opportunities to consider. This is furthered

by the fact that even though Saudi Arabia has similar DNI conditions, several of its indexed sites have a

score below 5 due to the low industry utility rate in place (41 EUR/MWh) which makes Cleanergy’s offer

for them not very attractive. A similar case can be made for Egypt, although having two sites scored above

5 (EM21, score 6,92 and EC51, score 6,02), has most of its sites in the lower bound of the ranking because

of the cheap electricity price (53 EUR/MWh) compared to the average achievable LCOE of 81 EUR/MWh.

40000 45000 50000 55000 60000

Stirling engine

Solar field

Storage

Discount rate

+20%

-20%

-58-

Morocco, on the other hand ranks quite well in all indexed sites, thanks to comparatively cheaper prices

than the utility (82 EUR/MWh average against 91 EUR/MWh), and a strong ecosystem in the country that

favourites renewable adoption. Finally, Tunisia’s sites rank the lowest with high LCOE figures (86

EUR/MWh average), which is not offset by its small market size or low DNI values.

Table 20 presents the most competitive business cases with regards to the MCA findings, in each country.

Interestingly, the sites that the MCA concludes are the most competitive for Egypt and Jordan (EM21 and

JCh31) are not the same when looking only at the LCOE as decision criteria. Table 17 previously showed

that EC41 and JM21 were the top sites for these two countries. Moreover, both locations have a lower

LCOE than the ones indicated by the MCA. The Multi Criteria Analysis is built in a fashion that compares

locations to several parameters, and not only pure cost effects, and tries to determine a trade-off between

the importance of each parameter : EC41 might generate a lower LCOE than EM21 but the fact that the

latter is off-grid is more important as it makes more sense to propose a modular CSP technology with

storage to a mine disconnected from the electrical grid than to a cement company that sources its electricity

needs from the local utility. In parallel, JM21 and JCh31 present an infinitesimal difference in LCOE figures,

while JCh31 is twice JM21 in terms of park size (7,6 MW against 4 MW). Clearly, having a small difference

margin in LCOE numbers is less important than capacity to install, thus making JCh31 better to target than

JM21.

Table 20 Most competitive business cases under the MCA (per country)

Index MCA score Normalized

LCOE

DNI

(kWh/m²/year)

Park

Size

(MW)

TES

(hours)

Mirror

Area

(m²)

CF

(%)

Industry

type

Morocco MM31 7,15 1,07 2425 2 13 220 84% Mining

Tunisia TC11 2,67 1,28 2000 24 10 220 65% Cement

Egypt EM21 6,92 1,12 2415 40 13 220 85% Mining

Jordan JCh31 6,31 1 2715 8 14 220 92% Chemical

Saudi

Arabia SC11 5,67 1,01 2700 17 14 220 92% Cement

The results of the MCA are regrouped by country (average score of all sites in each) and represented in

Figure 40. Jordan is the market with the highest score, followed by Morocco, while Egypt and Saudi Arabia

share more or less the same rank, and Tunisia is the last. Following this, Cleanergy should first target markets

where its product can be the most competitive, not only with other renewable technologies (PV-BESS,

diesel gensets), but also to the electricity utility price as most often, industrial are already connected to the

grid, and do not necessarily see as added value an independent power generation system, that can produce

electricity on demand. This means that it may not be strategic to engage the biggest companies in terms of

size, but the focus should be put on choosing locations that showcase to the maximum the benefits of the

TES Cleanergy’s product has, which enables it to generate electricity at attractive prices. These optimum

business opportunities are mostly defined by: their grid access, the DNI available but also the conventional

power cost paid in the country.

-59-

Figure 40 MCA country score (1-10)

2,79

4,33

4,46

5,26

6,26

Tunisia

Saudi Arabia

Egypt

Morocco

Jordan

-60-

Figure 41 Multi Criteria Analysis – Ranking of business opportunities (1-10)

2,232,232,242,252,252,292,332,382,382,41

2,522,67

2,872,97

3,173,23

3,513,55

3,703,823,823,863,953,953,994,014,01

4,134,134,16

4,264,404,404,49

4,584,584,64

4,955,045,055,055,065,095,10

5,275,275,315,335,375,415,475,475,48

5,635,675,69

5,795,855,905,905,965,976,046,046,06

6,196,216,216,226,286,306,316,386,39

6,927,15

TF11TF12TF22TF13TF15

MC22TF31TF14TF21TC21

TM11TC11EI31EI21EI42

EC31EC34

SM21SC61SC41SC51EI11

EC32EC33

SM31SM13SM14MC31MC32MA11

EI41SM15SM16MA21SM11SC31

SM12MC23MC13EC21

EM11MMe11

JC11MC11MC24MC41

MM41JM31SC21

MM22MM12MC12JCh11EC11SC11JC21JC41

JM11JCh41

MM24MM21

JC42JC31EC51JC12

JCh21MM25MM11

JM21MC21JM32JCh31EC41

MM23EM21

MM31

-61-


5.3.2.1 Scoring method

In Table 21, the individual scores of each country are given, unweighted for the 5 considered criteria. The

LCOE criteria appears to have the highest contribution in setting the ranking result, given the disparities

between the country score for given criterion, but also because of the small difference the scores in the

other criteria have for each country. How the LCOE criteria is scored with regards to each site (and hence

country) seems to influence to the results to the highest degree. As a result, another scoring approach is

proposed below, to quantify how sensitive the results to the method followed.

Table 21 Country score, by criterion (1-10)

Size DNI LCOE Macro-

environmental factors

Grid access

Morocco 3,3 2,4 7,8 6,0 5,2

Tunisia 1,7 1,0 0,0 8,0 5,0

Egypt 7,0 2,7 2,7 6,5 5,2

Jordan 2,4 5,0 9,9 6,8 5,0

Saudi Arabia

4,9 3,7 3,9 5,5 5,0

As described in 3.5, the LCOE of each studied site is compared to two figures: Cleanergy’s targeted market

price and the average utility price for industry in each country. These two price signals are unrelated to each

other, the first being a goal Cleanergy is set to reach, while the other is directly linked to the specific electricity

market conditions of each country. A more relevant way of comparison would be to quantify the difference

the LCOE has with each, and scoring them independently in the MCA. Thus, the new scoring method will

decouple the latter, by replacing the LCOE criterion with two:

• Price target, which will act as an indicator of how realistic Cleanergy’s price target is compared to

the generation costs the company is able to reach in each specific country. Practically, the value of

this criteria for each site is calculated with equation 5, and the scores are attributed according to:

𝐷𝑖𝑓𝑓%1 = 𝑇𝑎𝑟𝑔𝑒𝑡 − 𝐿𝐶𝑂𝐸

𝑇𝑎𝑟𝑔𝑒𝑡∗ 100 (4)

o If −𝐷1 ≤ 𝐷𝑖𝑓𝑓%1 ≤ 𝐷1, then the score is: 5 +𝐷𝑖𝑓𝑓%1

10. 𝐷1 is the margin for which the

price difference with regards to Cleanergy’s target is still considered acceptable. Several

values of 𝐷1 are considered in the sensitivity to underatnd its impact on the MCA.

o Else if 𝐷𝑖𝑓𝑓%1 > 𝐷1 the score is 10, otherwise the attributed score is 0

• Price hedge, which will measure how much Cleanergy’s technology will protect the potential

customer from the variations in electricity prices. Often, national utilities set their electricity price

following crude oil and natural gas price variations, or as seen previously, changes the prices

following subsidies and environmental policies. These puts several risks on their revenues and

operations. Companies will often invest in solutions to shield them from such fluctuations, in the

form of future contracts, options and so on. An investment in a self-generating renewable power

technology can then be considered a hedge alternative. Hence, the value of this criteria will be

determined by equation 6, and the scores are attributed according to :

𝐷𝑖𝑓𝑓%2 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑚𝑎𝑟𝑘𝑒𝑡 𝑝𝑟𝑖𝑐𝑒 − 𝐿𝐶𝑂𝐸

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑚𝑎𝑟𝑘𝑒𝑡 𝑝𝑟𝑖𝑐𝑒∗ 100 (5)

-62-

o If −𝐷2 ≤ 𝐷𝑖𝑓𝑓%2 ≤ 𝐷2, then the score is: 5 +𝐷𝑖𝑓𝑓%1

10. 𝐷2 is the margin for wich the

hedge investment can be considered profitable. In this case, 𝐷2 will be equal to the

forecasted growth rates of electricity prics in each country.

o Else if 𝐷𝑖𝑓𝑓%2 > 𝐷2 the score is 10, otherwise the attributed score is 0

Table 22 shows the average score by country, for the new criteria defined. Jordan and Morocco are the only

countries where Cleanergy can propose generation costs that will prove to be profitable, even when

considering that electricity prices will grow over time. Specifically, Jordan’s utility rates are so high compared

to the average price Cleanergy can propose on the Jordanian sites that all of them score 10, showcasing the

competitiveness of CSP with storage in that particular market. On the other hand, the price target criteria

ranking shows different results, depending on the margin 𝐷1 choosen. For a price tolerance from 5% to

10%, Jordan, Saudi Arabia and Egypt are the top 3 countries where Cleanergy can approach its desired cost

target. This in turn reveals how much that target is relevant to the specific markets, ie. that it is not suitable

in Tunisia for example, where the score is 0. However, for a 15% margin, all countries have the same score,

meaning that a there is a maximal 15% difference (in absolute terms) between what Cleanergy’s target is and

its actual value proposition, all sites considered. Factoring in these two new criteria in the MCA, the new

weighted country rankings are show in Appendix 7. As it can be seen, even by changing the scoring

methodology, Jordan and Morocco still lead in terms of countries hiding the most potential for business.

This is mainly due to the fact that in the other countries, electricity price are still low for Cleanergy to

compete with, even when considering the project rise in prices.

Table 22 Country score, additional criteria

Price target, 𝑫𝟏= 5% (case 1)



Price hedge

Morocco 2,73 3,63 4,99 8,64

Tunisia 0 0,00 4,99 0

Egypt 3 3,66 4,99 0

Jordan 7,14 5,72 5 10

Saudi Arabia 4,37 5,00 5 0

5.3.2.2 Weighting factors

The weights chosen for the MCA favour off grid locations with high DNI and low LCOE. As explained

before, off-grid configurations are where Cleanergy’s offer is the most competitive, as usually the alternatives

require either costly grid connection or diesel generators. Much emphasis is put on the LCOE and the DNI

is a consequence of the infant stage of the technology. Being an innovation that has still to enter the market

and gather attention, Cleanergy’s best interest is to consider customer’s where generation costs are the

lowest. As a result, a lower weight is attributed to the system size. Yet, another set of weighting factors can

be chosen in the case the goal of the MCA was to sort the best opportunities in size. To that effect, a

sensitivity analysis of these factors is carried out to see if they affect the general ranking obtained, and more

specifically, which are the most critical weighting factors. The previous section showed the extent to which

the LCOE affects the final results. It is then interesting to offset that criteria in the MCA (from here

onwards, said criterion is given 0 as a weight) and vary the other factors in the sensitivity, as depicted in

Table 23.

-63-

Table 23 Weighting factors case definition

Potential DNI LCOE Macro-environmental factors Grid access

Case 4 5 5 0 5 5

Case 5 10 5 0 5 5

Case 6 5 10 0 5 5

Case 7 5 5 0 10 5

Case 8 5 5 0 5 10

The results of the weights variation by case can be seen in Appendix 7. In case 5, although Jordan performs

better in the DNI score from Table 21, Egypt is on the lead. Similarly, Egypt ranks first in case 6, although

it is far behind in the macro-environmental factor score from Table 21, where Morocco is leader. The criteria

where Egypt is first compared to Morocco and Jordan is the Potential criteria, which means it is the second

most critical assumption when attributing the weight for the MCA, after the LCOE. As a result, it is safe to

assume that the other criteria (DNI, macro-environmental factors, grid access) participate in determining

the ranks in a smaller order of magnitude. Naturally, the business opportunities individual ranks also change

depending on the case studied. Table 24 shows the 5 first business opportunities identified by the MCA in

each of the cases considered. While the ranks follow the logic described in the previous paragraphs, where

first 3 cases are most influenced by the LCOE criteria of each site, and the remaining focus on a different

one depending on the case, it is worth noticing which business opportunities hold the most occurrence all

cases considered. These locations represent the first companies to approach for Cleanergy, since they rank

among the top for different focus parameters (criteria of the MCA). Business opportunity MM31 stands out

first, as it is present in 5 of the cases studied, both where the emphasis is put on the price attractiveness

(case 1 to case 3), and the other criteria (other cases). Comes then business opportunity EM21, also present

in 5 of the 8 cases, but only when the price criteria was offset. Both EM21 and MM31 are off-grid, proving

the necessity of such criteria for Cleanergy’s best interest. None of the business opportunities identified in

Saudi Arabia belong to the top 5, even though the country joys of high solar irradiation. The extremely low

utility electricity price, coupled with the harsh business environment of the kingdom explains that absence.

Table 24 MCA sensitivity (top 5 business opportunities)

Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8

Top 5 business

opportunities

MM31

JM32

JCh31

JM21

JCh21

JCh31

JM21

JCh21

MM31

MM11

MM31

MM11

MM25

MC21

JM32

EM21

EC41

EC51

EC11

MM25

EM21

EC41

EC51

EC11

MM25

EM21

EC41

EC51

MM31

EC11

EM21

EC41

MM25

MM11

EC51

EM21

MM31

EC41

EC51

EC11

5.4 Scenario analysis

5.4.1 Results

Following the scenarios defined in 3.6, the 5 top business opportunities in each country, as described in

Table 20, are analysed by looking at their pure financial viability. As a reminder, the assessment of the

economic feasibility of a project relies often on the NPV and IRR, for which a power price (the price

Cleanergy’s electricity will be bought) needs to be set to be able to calculate the yearly cash flows. Against

that, the LCOE (developer perspective) changes since tax costs/benefits vary with varying power prices,

and affect also the discount rate used (see Appendix 1). Table 25 to Table 29 present the results of such

analysis for the optimal business cases per country, for each scenario:

-64-

• Zero subsidy: takes as input the current industry electricity utility price to calculate the cashflows

for the projects, considering the equity IRR (8%) and cost of capital of the project.

• Break-even subsidy: determines the necessary power price Cleanergy will sell its production in

order to reach 0 as a NPV value after the end of the project’s lifetime, hence making the Equity

Rate of Return (ERR) equal to the equity IRR (8%, desired value).

• Utility competitive: determines the power price at which Cleanergy’s technology can be considered

competitive, ie 20% to 30% cheaper than the utility rates.

It has to be noted that in the tables below, ERR and IRR need to be separated. The former is linked to the

equity party profitability (when accounting for tax costs, loan repayments…), while the latter is the intrinsic

project IRR, that stems from cashflow calculations that concern only pure technology costs. In the first

case, the discount rate used is the equity IRR (thus finding its value when the NPV = 0), while in the second,

the WACC is used.

Table 25 Scenario analysis results, Morocco (MM31), WACC = 4,5%

Scenario Normalized

power price NPV (k€) ERR (%) IRR (%)

Normalized

LCOE

Zero subsidy 1 -525 7,08 7,09 1,03

Break-even

subsidy 1,05 0 8 7,6 1,05

Utility

competitive 1,53 4938 17,5 12,6 1,2

Table 26 Scenario analysis results, Tunisia (TC11), WACC = 4,8%

Scenario Normalized


Normalized

LCOE

Zero subsidy 1 -71117 -1,9 1 1,77

Break-even

subsidy 1,9 0 8 7,4 1,9

Utility

competitive 2,7 55372 17 11,9 2,1

Table 27 Scenario analysis results, Egypt (EM21), WACC = 4,9%

Scenario Normalized


Normalized

LCOE

Zero subsidy 1 -122104 -1,4 1,3 1,7

Break-even

subsidy 1,82 0 8 7,3 1,82

Utility

competitive 2,45 81362 15,3 10,9 1,96

-65-

Table 28 Scenario analysis results, Jordan (JCh31), WACC = 4,8%

Scenario Normalized


Normalized

LCOE

Zero subsidy 1,83 37595 27 16 1,2

Break-even

subsidy 1 0 8 7,4 1

Utility

competitive 1,37 16911 16 8,4 1,1

Table 29 Scenario analysis results, Saudi Arabia (SC11), WACC=5%

Scenario Normalized


Normalized

LCOE

Zero subsidy 1 -65105 -3 0,1 1,99

Break-even

subsidy 2,1 0 8 7,1 2,1

Utility

competitive 2,7 31205 14 10,2 2,2

An initial takeaway is that given the current level of power price, Cleanergy’s technology would not be

profitable in any of the countries, besides Jordan. In all 4 other countries, both NPV and IRR are negative,

or below the 8% equity rate of return expected, rendering investing under that scenario not viable. Power

prices are still too small to recuperate all the investment costs and profits. Jordan represents an exception,

as the very high-power prices of the country not only break even the project (JCh31 site) but make highly

profitable with 27% IRR. Under this scenario, Jordan would be the only country where Cleanergy’s

investment would make financial sense.

The break-even subsidy scenario does not concern Jordan since the current power prices already make it

profitable. In fact, for the JCh31 business case to reach a null NPV, electricity price musts drop by nearly

50%, which is unlikely to happen in the future, deeming Jordan to always be a profitable location for

investments. In the other countries, this scenario implies that power prices must increase in various ranges

to break-even the projects: by 5% in Morocco, more than 80% in Tunisia, Egypt and more than double in

Saudi Arabia The LCOE results of this scenario suggest that the generation cost of Cleanergy and power

price are equal, but in reality, the LCOE is always higher. This can be explained by the inflation rate set to

zero in the financial model (Table 3) in all countries. As detailed in Appendix 1, the power purchase price

and LCOE are linked, especially in the presence of inflation, and power price escalation factors, which are

often equal. In reality, each country experiences inflation in a different fashion with varying rates. Egypt for

example, with the deregulation of its currency, has seen inflation more than double, situation not present in

any of the other countries considered. For this reason, the analysis did not consider inflationary effects. As

such, under the break-even subsidy scenario, the LCOE is always higher than the power price calculated to

make the projects profitable, thus not competitive.

Similarly to the second scenario, Jordan is not concerned with the third scenario results: it was already

competitive with no support scheme as seen in the first case. Hence, for the JCh31 business case to be only

20-30% competitive with the utility price, the latter should decrease by 25%. For Morocco, Tunisia, Egypt

and Saudi Arabia, the power price must increase (compared to the utility prices) by more than 50%, 140%,

150% and 160% respectively in order for the business opportunities in each country to be one third cheaper

than what utilities offer. These premiums must be paid on top of the power price of the concerned countries

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to make CSP with storage competitive at a modular level. Interestingly so, electricity prices are on the rise

in the MENA region. Based on current announced regulations in the electricity sector of each country, and

the expected rise in crude oil prices, the forecasted industry electricity price in each country are found and

presented in Figure 42. As it can be seen, Jordan will still witness high utility rates, while the subsidies to the

electricity sector will gradually phase out to increase prices in the other 4 countries. The expected rise in

power price will bring most of the projects closer to the conditions of the second scenario, but further

subsidies from the government to renewables, and CSP in particular in the form of FIT must be introduced

to make them profitable (break-even subsidy scenario) and cheaper than the national offer (Utility

competitive scenario).

Figure 42 Expected utility electricity price for industry 2021 (€/MWh)


The power price being the unique input in the scenario-based study described above, its variation effects on

the projects IRR and NPV are found. In Figure 43, the IRR of the 5 best optimum projects per country

(according to the MCA) is plotted against varying power prices. The higher the requested rate of return, the

higher the power price must be, following a polynomial fashion. For the 5 projects to reach the same IRR,

different power prices must be offered by Cleanergy, but whichever business opportunity reaches the IRR

with the lowest power price would then be the best for investment. As it can be seen, Saudi Arabia’s SC11

fulfils that requirement, making it the safest project to consider. Moreover, whatever power price

considered, that location seems to be always profitable, as the IRR is each time higher than the 4 other

projects. The IRR representing how fast an investment recuperates its incurred costs and generates revenue,

deciding to approach the Tabuk Cement Company (SC11) could be of a profit to Cleanergy. This is

interesting to note as the SC11 and JCh31 projects are smaller in size than EM21 (17 MW and 8MW against

40 MW), but yet yield a better rate of return for all electricity prices.

180

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59

Jordan Morocco Egypt Saudi Arabia Tunisia

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Figure 43 IRR vs Power price

Naturally, the NPV of the projects increases also with increasing power prices but following this time a

linear trend as shown in Figure 44. It may seem strange that the NPV of 5 different projects is compared,

but the MCA suggested that among all considered business opportunities, those 5 were the optimal. Hence,

the pure financial viability of the latter should also be taken in consideration to contribute in the decision-

making process of prioritizing a project over another. At first glance, mining project EM21 brings the

highest profits to Cleanergy, mainly due to its size of 40 MW, the highest among the 5 considered sites.

While this appears to contradict the IRR results stated above, a closer look at the NPV graph presented in

Figure 45, (power prices range here between 50 €/MWh and 150€/MWh) conveys the same message as

before. More specifically, although EM21 is bigger in capacity to install (and thus revenues of energy sold),

for power prices in between 85€/MWh and 110€/MWh, the Saudi cement project SC11 garners higher

positive cashflows over its lifetime than the Egyptian mining project. Even more so, this still holds after the

break-even point of EM21 located at a power price of around 96€/MWh. Additionality, just as for the IRR,

JCh31presents higher NPV values than EM21 for prices between 85€/MWh and 100€/MWh, while there

is a 5-time difference in system size. The same can be said about JCh31 and TC11: even if the Tunisian

cement plant presents a high potential for Cleanergy in terms of capacity to install, it lags in cash-flow

generation compared to the Jordanian chemical project JCh31 for power sold in the range of 85€/MWh to

130€/MWh. However, after a certain point (110€/MWh for EM21 and 130€/MWh for TC11), the large

size/revenue is able to offset the high rate of return of the competing projects, resulting in better economic

performance with increasing power prices.

Considering the above, the main take way from the scenario analysis is that in general, the technology has

yet to be competitive, mirrored with current (and even forecasted) grid prices. However, a decrease in

investment costs over time is expected due to several reasons. Factors including economies of scale, high

volumes of production and risk depreciation (linked to the ability of building strong partnerships when

developing first projects) will all contribute into making the technology described viable in said markets. In

Figure 46, such evolution is shown, where the LCOE decreases gradually as the technology gets momentum

and is increasingly adopted. As such, and to experience this high product adoption over time, a larger

opportunity for market introduction may be present in countries that face grid unreliability issues, or where

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0%

20%

40%

60%

80%

100%

0 100 200 300 400

IRR

Power Price (€/MWh)

EM21

JCh31

MM31

TC11

SC11

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the share of off-grid users is consequent. Indeed, modular CSP with long hours of storage close to the end

user hedges against unreliability of the grid and can retrofit the use of diesel generators.

Figure 44 NPV vs Power price

Figure 45 NPV vs Power price (zoom)

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0 50 100 150 200 250 300 350 400

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Figure 46 Normalized LCOE evolution

0,4

0,5

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2020 2022 2024 2026 2028 2030 2032

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6 Conclusions

This research work revolved about studying the market potential a modular CSP technology with storage

can have in selected countries of the MENA region: Morocco, Tunisia, Egypt, Jordan and Saudi Arabia,

from which conclusions about the whole region can stem later on. Cleanergy, a Swedish company specialized

in solar power production, was taken to illustrate the market entry case of an innovative CSP technology,

comprised of a Stirling engine, a field of heliostats, and a latent-heat TES. Specifically, the work aimed at

sizing the SAM of the company, and pinpointing at the most promising customers to potentially populate

its SOM. To do so, a methodology was developed to compare markets, countries and business

opportunities, with the goal of identifying the optimal ones that holds the highest potential for Cleanergy.

The technology the company is developing relies on a TES of high magnitude (above 10 hours), thus the

potential customer screening was limited to the industrial sector, which is often very energy intensive with

round the clock electricity demand. An extensive market review of the 5 countries has been carried out,

aiming at understanding their respective power market eco-systems, and identifying industrial companies

prone to be interested in Cleanergy’s value proposition. The electricity consumption of each company was

estimated based on referenced electricity intensities needed to produce the product of each industrial. It was

also broken down to each of their consumption sites, for which exact location coordinates and respective

weather data were gathered. Through techno-economical analysis, optimal plant configurations for each

were determined to reduce the LCOE, in terms of installed capacity, storage size, and mirror area. With

such information, a multi-criteria analysis, where each criterion is scored differently, was performed in order

to be able to compare amongst the different markets (by country), considering not only the potential for

installed installations in MW, but also the lowest cost at which parks could be built, macro-environmental

factors in the country, and existing infrastructure. First ranking business opportunities in each country, with

respect to the MCA, were further investigated to assess their economic feasibility, computing NPV and IRR

figures for each in the three scenarios considered.

The market size for such a technology exceeds 40GW in the MENA, albeit it is limited to the industry

sector. Within the latter, mining and cement are the two sub-sectors that hold the most opportunities in

terms of size, all countries considered. Most notably, mining companies can prove to be very attractive for

Cleanergy for their frequent off-grid design, making Cleanergy’s technology a way to off-set the often

expensive and volatile fuel prices, in the case of diesel gensets: the two best business opportunities from the

MCA are both mines, in Morocco and Egypt respectively. All three different analysis carried out (optimal

configurations screening, MCA, scenario analysis) make Jordan the country Cleanergy must prioritize, even

though it is small in size of capacity to install. Two reasons explain this result: the country joys from

extremely good solar resource, which de facto brings generation costs down and rationalizes the use of large

storage capacity, but also because of the high electricity price Jordanian industrial currently pay. Investing

in a dispatchable CSP system with long storage hours is the perfect hedging option in the Jordanian market

conditions. The latter is protecting risk averse industrial from the continual rise in electricity rates,

independently secure their energy procurement while making profits, considering the avoided cost of

conventional procurement. Following this rational, Morocco ranks second as market to pursue, followed by

Egypt and Saudi Arabia. Interestingly, even though the former two have better DNI conditions than

Morocco, thus reach low LCOE values, and are bigger in market share, Cleanergy has yet to be competitive

in those markets due to low electricity prices. Unlike Saudi Arabia and Egypt, Morocco has no fossil fuel

resource on its own, which drives rates up. In general, countries in the MENA are well on the way for the

renewable energy transition, with detailed political framework and goals, including gradual removal of fossil

fuel subsidies. Yet, they lack for the most part targeted support mechanism for private small-to-medium

scale generation in the form of feed in tariffs or carbon tax that would allow Cleanergy’s technology to be

competitive, as all seem to favour large bid projects. This represents an additional hurdle for Cleanergy,

especially considering its technology as an innovation that has yet to diffuse in and gain momentum. In

markets with no targeted subsidies and low utility electricity price, the gap in price is too high to make

industrials consider an unknown technology such as Cleanergy’s economically viable, thus preventing them

from crossing the innovation chasm. On the matter of subsidies, the scenario analysis quantified the support

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tariffs necessary for the system to be profitable and utility competitive in each country. Being profitable

doesn’t necessarily mean being competitive with the utilities, as the LCOE of the best cases in the break-

even scenario was higher than the purchase power price. In Morocco, Tunisia, Egypt and Saudi Araba, the

generated electricity with Cleanergy system has to be sold at prices double the utility rates to make the

generation cost 30% cheaper. The opposite case is made for Jordan as a result of the high electricity price

which make projects there always profitable and competitive. The latter highlights that it may be optimal to

target unreliable grid countries for market introduction, before addressing regions with stable grid as in the

MENA, where it will become viable as lower costs are attained. In these regards, Cleanergy should seek

different partners, partnerships and joint ventures when setting up projects to achieve competitive cases.

The goal being to elaborate innovative business models that would make the technology economically

viable. Wheeling schemes, ie. using the grid connection in exchange of a fee, can represent such approach.

The power plant to be built can be located in the highest DNI location possible of the country, while the

energy produced is wheeled to the off-taker. Doing so would allow leveraging on the location, hence land

cost, but also on the capacity to be installed, since there would be the possibility to bundle different off-

takers power needs into one site, but have separate power purchase agreement with each, while benefitting

from economies of scale. Even more, this business model can be more relevant considering that the analysis

done assumed a very low cost of capital, only accessible through international funding instrument, that are

often reluctant approving small isolated projects.

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7 Appendix 1

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KTH ROYAL INSTITUTE OF TECHNOLOGY

Master of Science in Sustainable Energy Engineering

SUMMER INTERNSHIP REPORT

Metrics and methods for financial valuation of solar power plants

PhD Professor MSc Student

Rafael Guédez Youssef Benmakhlouf

August 2017

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1 Table of content

Introduction ................................................................................................................................................................. 75

Deliverables ............................................................................................................................................................. 75

Review of solar tender process ................................................................................................................................. 75

Financial terminology ................................................................................................................................................. 76

Discount rate/Inflation ......................................................................................................................................... 76

Taxes ......................................................................................................................................................................... 76

Depreciation ............................................................................................................................................................ 77

Financing- Debt/Equity ........................................................................................................................................ 77

Risk management indicators ................................................................................................................................. 78

Economical metrics ..................................................................................................................................................... 78

Capital expenditures (CAPEX) ............................................................................................................................ 79

Operational Expenditures (OPEX) ..................................................................................................................... 79

Levelized Cost of Electricity (LCOE) ................................................................................................................. 80

Net Present Value (NPV) and Internal Rate of Return (IRR) ........................................................................ 81

LCOE: different approaches ..................................................................................................................................... 82

Societal perspective ................................................................................................................................................ 83

Business/developer perspective ........................................................................................................................... 85

LCOE for PPA projects ........................................................................................................................................ 86

Bibliography ................................................................................................................................................................. 87

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1.1 Introduction

The first year of the sustainable energy master at KTH was an eye-opener for me, as after each course taken,

I realized how much decision making and planning had great effects on the energy landscape of a country.

Furthermore, I had the chance to take part in the course ‘’Large Scale Solar Power’’ given by Rafael Guédez,

PHd professor at KTH. The main take away from that course was that even if a 100% efficient renewable

technology existed, the capitalistic nature of our society dictates that if no profits can be made from said

technology, no one would invest in it. Profit is what drives investments, and as such, I realized that it is

important to have a full grasp on the financial aspect of renewable energy projects, to be able to optimize

gains later on. Unfortunately, I did not have any economical financial knowledge prior, considering my

engineering background but wanted to learn and improve my knowledge in that field. The energy

department at KTH has developed several tools for techno-economical optimization of solar power plants,

and my supervisor Rafael Guédez, suggested this internship, as a way for me to deepen my knowledge in

this field, and a mean for the department to have a report summarizing and comparing how financial

calculations are done. Indeed, there are several ways of calculating financial metrics for renewable energy

projects (LCOE for example), and the literature and studies done do not often precise the methodology

followed or the assumptions taken.

1.2 Deliverables

As deliverables, the following report, summarizing and comparing the different ways present in the literature

for doing financial calculations. The discounted cash flow method is thoroughly followed and explained. As

mentioned, I have no economical background whatsoever. This report is written then in a way that explains

basic notions from scratch and can serve as an introduction to whomever interested in financial of renewable

projects, but lacks the proper knowledge. For the most introverted reads however, they can skip the first

sections and jump in directly into the most advanced parts. Another deliverable is an Excel spreadsheet that

takes as input financial structure of a project and returns cash flow analysis of revenues and costs, as well as

the necessary indicators needed for project valuation. This report focuses on solar power plants, but the

presented information can be applied to all kinds of renewable energy projects.

2 Review of solar tender process

Renewable energy tenders are somewhat a new way for governments to acquire renewable electricity

production [1]. When a government or entity tenders, it invites bids for a specified project, and based on

predefined criteria, the most competitive bid is chosen to carry put the project [2]. In the case of renewable

energy, tendering schemes are a competitive method for allocating and securing financial support to RES

projects. The tenders are foremost based on the cost of electricity production, to ensure the best cost-

efficient way of energy production [3].To enter a RES tenders, bidders need to comply a certain number of

criteria that make them qualified for the participation in the project. These requirements include references,

financial solidity, etc.., but also technical and commercial liability related to RES projects.

The procedure of bidding is done in the form of a reverse multi-unit auction: The sole buyer (usually the

government) ranks the bids based on their unit price starting from the lowest. Tenders can concern either

RES capacity in MW or units of produced electricity in MWh. The submitted bidding prices can be known

by all actors, or they can stay undisclosed, so that each bidder does not know competitors’ offers and react

to them. This latest form of bidding, sealed bid slows down competition as it is a static auction [3].Once the

bids are submitted and the choice made by the buyer, the price determination can either be pay-as bid (the

utility is paid the indicated price in its bid) or common price for all bidders, meaning that the awarded price

is the one of the most expensive successful bid (marginal price). Bid bond guarantees are often required by

successful bidders to protect themselves from eventual delays caused by the RES investor, and that hurdles

the accomplishment of the project [3].

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Tenders are more and more used as an allocating mechanism for new solar power plants [4]. Example of

recent tenders in Saudi Arabia of a 300MW solar PV power plant [5] or the 2000MW solar thermal plant

with storage tender in Dubai [6] show the global preference for tenders to governments wiling to promote

renewable energy. This is mainly due to the fact that the tendering mechanism stimulates competition

between the different operators and bidders, thing that lead to achieving record low energy prices, as seen

lately in the Dubai tender : The Dubai Electricity and Water Authority (Dewa) announced the prices from

the auction, that hit the low record of $US94.50/MWh, which represents the lowest price for solar thermal

and storage [6].Tendering contributes also in revealing the true cost of RES and sets off overcompensation,

this way, the governmental financial support goes exclusively to the best performing plants [4].

The bidding price is the essential criteria on which tenders are centered, and serve as a tool for the RES

investor to award the project to a specified utility or contractor. It is then obligatory to have a full grasp on

how this price is defined and set. The following sections present all the factors necessary to determine power

purchase agreement price, and all the metrics related to it.

3 Financial terminology

As said above, the power price that is set during RES tenders is the key point for a project to be awarded.

These prices are based on the cost of energy and quantifies the profits the projects stakeholders will make

over the lifetime of the power plant. They are determined by doing financial analysis that include all factors

and risk predictions to yield the best profits. It is therefore important to have an overview of the basic

concepts and elements of economic and financial analyses that will be needed to understand how the cost

of energy is defined.

3.1 Discount rate/Inflation

All power plants and energy projects are long time investments that require multiple years of construction,

operation and decommissioning. When doing a financial analysis however, all predicted cash flows of the

project must be expressed in the present so that the investor can make an accurate decision based on these

predictions. Indeed, whenever an investment is made, a certain return is expected, and the time that return

occurs is important: A dollar received today is worth more than a dollar received tomorrow since the dollar

today can be invested to earn interest immediately. The discount rate acts then as a measure of this time

value, and expresses the profit that is expected to occur considering the time value of money. Discount rates

are also indicators of how much risk is tolerated by an investor, and how much premium is expected as a

result of that risk [7].

On the other hand, inflation represents the rate at which the level of prices for goods and services is rising

over time [8]. When forecasting cash flows and benefits, it must be specified if inflation is taken into account

or not, as money can be expressed in “nominal” dollars or “real” dollars. “Nominal” dollars cash flows

represent the actual number of dollars required in the year the payment or cost occurs. “Real” dollars refer

to the number of dollars that would have been required if the payment or cost was paid in the base year of

the financial study.

Since the discount rate is related to the time value of money, it is also linked to the inflation rate. As a result,

two discount rates should be differentiated: real discount rate that includes inflation, and nominal discount

rate that excludes inflationary effects [7].

3.2 Taxes

From an investor’s point of view, accounting for taxes in the financial study is primordial as taxable income

is a non-negligible cost the company endures. Since in the case of energy production, the income is related

to the units of energy sold, taxes related cost take a considerable portion of the balance sheet, depending

on the taxes scheme in place. As such, it is important to express all cash flows as after-tax flows, and all

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analysis should be based on them. Each country sets the corporate tax according to its legislations and

voting decisions. For example, the United States uses a progressive taxe system, where the tax rate increases

with the taxable income [9].On the other hand; the taxable income can be reduced by necessary expenses a

business occurs. In the case of RES power plants, the yearly operation costs are fully tax deductible. Taxable

income can also be reduced when financing is done through debt and interest payments. Moreover, and as

an encouragement for renewable energy investments, taxable income can be further reduced with

Renewable Energy Tax Credits. The latter refer to an immediate reduction in income taxes equal to a

percentage of the installed cost of a new investment. Of course, the taxing schemes and way of renewable

support vary

from country to country, and there is no general rule to follow. Dependent on the project’s location, a

different tax plan and tax support will be put in place.

3.3 Depreciation

Depreciation is the process by which a company allocates an asset's cost over the duration of its useful life.

Each time a company prepares its financial statements, it records a depreciation expense to allocate a portion

of the cost to the current fiscal year. The purpose of recording depreciation as an expense is to spread the

initial price of the asset over its useful life [10]. Depreciation is a mean of reducing the taxable income, as it

is an expense included in the income statement of the company. Since the tax rate in related to the income,

the higher the depreciation expenses, the lower the amount of taxes owed to the government.

There are different ways of setting depreciation expenses related to the capital expenditure. The easiest one

being dividing the CAPEX cost by the number of years the power plants is running. This simple method is

called linear or straight-line depreciation, but others exists such as double declining method and non-linear

methods [11]. Here again, each government sets the rules for how depreciation should be used as a tax relief.

For instance, accelerated depreciation in India is a major incentive for solar as it offers a tax break in the

first year of operation, and enables great deductions in the early life of the asset. The higher the deductions,

the lower the overall tax burden [12].

3.4 Financing- Debt/Equity

Investing in solar energy projects requires large sums of money: indeed, the initial investment or capital

expenditure, CAPEX, represents the majority of all the costs related to the project, and securing funding

for it is the key for securing the construction and development.

A company can finance its assets either by equity, debt, or a combination of both. Equity financing means

that the company has enough capital to invest and cover the whole initial cost of the project. Stakeholders

of the company are then investing in the project and are owners, each one with its share, and each one

assumes the risk of the project failure: If the power plant construction does not occur, there are no back

payments to recover the failure. On the other hand, profits, if any, are shared and distributed among all

stakeholders [13].

Debt financing on the other hand means that the company developing the project seeks loans and debts in

order to cover the initial cost. This method of financing comes with annual loan payments linked to the

interest rate that the financial institution has set at the beginning. The advantage of debt financing compared

to equity financing is that the company does not share ownership of the project with multiple foreign

investors, and is fully in control. Another benefit is the tax shield debt financing represents, as the interest

payed on the loan is tax deductible, and thus lowers the company’s tax liability each year [14].

Nevertheless, when it comes to energy related investments and power plants construction, most of the

companies use a combination of debt and equity to finance their businesses. This means that the initial

capital expenditure needed at the beginning of the project is secured by a share of equity funding from the

company, and the complementary share by external loans and debts. Each of the investors, equity and debt

expect a return on the investment based on their desired profit and risk assessment of the project. This is

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translated by the cost of equity and the cost of debt, e.g. the discount rate mirroring the profits each type

of investor seeks to make from a particular investment. As such, the overall cost of capital of the project

capital is derived from a weighted average of all capital sources, widely known as the weighted average cost

of capital (WACC). Since the cost of capital represents a hurdle rate that a company must overcome before

it can generate value, it is extensively used in the capital budgeting process to determine whether the

company should proceed with a project [15].

The amount of equity invested in a project is dependent on the company’s financial assets and its ability to

attract external investors. An optimal debt to equity ratio is one that covers the project and investors

exposure to risk while simultaneously minimizing the cost of capital [16]. But a low equity share may not be

a good choice as it shows potential investors that the company is not willing to put its own money on the

table. Why should they do it then? A trade off must be chosen then. Many solar tenders and projects report

a 80-20 or 70-30 debt to equity ratios, as stated by professionals working in the field [17] [16].

3.5 Risk management indicators

When dealing with large financial investments that occur over a long period of time, risk is a crucial element

to consider when planning financing and operations. It is crucial in the sense that it is what encourages or

prohibits lenders and debt investors to take part in the project. A successful project must handle the risk

incurred and must guarantee that the operations (electricity production in the case of power plants) will

generate enough revenues to cover for the expected return, premiums and credit risk. Renewable sources

projects are often the subject of high risks that lenders and investors must take into account [18]: country

risk, political risk, foreign exchange risk, inflation risk, interest rate risk, appraisals, availability of permits

and licenses, operating performance risk, fuel prices, force majeure risk, and legal risk.

Looking at all these potential sources of risks for the completion of the project, the lender and investor will

judge the project ability to withstand them by investigating different ratios, called debt service coverage

ratios, which are function of the specific project risk. DSCRs analyze the financial expenses of the project

with retrospect to its ability to cover them. Usually, when debt investing is involved, the annual loan

payments should be as close as possible to the projects ability to generate cash, since the lenders want to be

assured that over the lifetime of the project, the revenues can service the debt. This is done with the annual

debt service coverage ratio (ADSCR) which is the ratio of yearly after-tax cash flow to the amount of debt

(principal and interest) payment incurred each year. Lenders require that the ADSCR should not be lower

than a minimal value usually comprised between 1.2 and 1.5, but the latter can vary according to the project

specifications. The loan life coverage ratio LLCR is also an indicator assessing the project’s ability to

withstand the risk. The LLCR is the ratio of the present value of cash available over the projects lifetime to

the outstanding debt. As such, the LLCR looks at the financial vitality of the project under the period it is

required to pay off the loan [18]. The following equations present the general form of these indicators, but

their components are detailed later in the report.

𝐴𝐷𝑆𝐶𝑅(𝑡) =

𝐴𝑓𝑡𝑒𝑟 𝑡𝑎𝑥 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤𝑡

𝐿𝑜𝑎𝑛 𝑝𝑎𝑦𝑚𝑒𝑛𝑡𝑡

(6)

𝐿𝐿𝐶𝑅 =

𝑁𝑒𝑡 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤𝑠

𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑓𝑖𝑛𝑎𝑐𝑖𝑎𝑙 𝑑𝑒𝑏𝑡

(7)

4 Economical metrics

In order to assess the profitability of a solar power plant, and determine the costs and revenue generated by

its exploitation, economical performance indicators must be defined. This section outlines the most

economical metrics used by the industry and the academic field. [17].

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4.1 Capital expenditures (CAPEX)

A capital expenditure is incurred when a business spends money either to buy fixed assets or to add to the

value of an existing fixed asset with a useful life extending beyond the taxable year [19]. Accounting for the

largest part of the solar investment, capital expenditures or CAPEX refers to all the investment occurred

during the development and construction of the project, including all direct and indirect costs. Purchase

and installation of equipment is direct cost while all other expenses spent during the years of construction,

such as taxation and project development, are called indirect costs. A traditional breakdown of the CAPEX

of a CSP power plant can be seen in figure 1. As stated above, equipment accounts for the biggest part of

the initial investment, the solar field here accounting for 27% of the total CAPEX.

Figure 1 : CAPEX Breakdown

As seen in the pie chart, the CAPEX regroups multiple costs made in the beginning of the project for plant

erection. Particularly, Sales Tax expenses are worth mentioning as they are often subject to confusion, or

are disregarded. They simply refer to the additional cost paid when a certain component is imported from

a country different than the one where the plant is built. Indeed, considering the complexity of the CSP

technology, all the equipment needed is often manufactured all around the world and is bought from

different parties. For example, the true price of a power block manufactured by Siemens would be its first

hand price plus a fraction of that price as a sale tax paid when it is imported.

There are different ways of estimating the CAPEX at the early stages of the project. The cost model reported

in [17] uses cost functions for component cost scaling based on cost values from reference plants and

respective material and labor cost multipliers, to ensure that results are sensitive to the specific location

considered. The CAPEX is then the sum of all the components cost. The scaling factors are a sensitive

choice, as they are linked to the equipment, technology but also the location. For this reason, it is mandatory

for the decision maker to be fully aware of the model used, as it influences to a big extent the resulting

CAPEX.

4.2 Operational Expenditures (OPEX)

An operating expense results from the ongoing costs a company pays to run its basic business [20]. OPEX

is then a recurrent cost, that the utility pays every year to run its day to day operations related to all

operational and maintenance (O&M) during a typical year. Typical refers to a year with no unexpected power

plant shortages or failures, and a normal operation of the power plant [17]. The breakdown of the OPEX

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cost of a CSP power plant can be seen in figure 2. OPEX covers all costs related to utility costs, service

costs, labor costs, insurance costs and other miscellaneous.

Figure 2 OPEX Breakdown

Similarly, to the CAPEX estimation, scaling methods are used to get a first assessment of the operation and

maintenance costs [17]. Here again, reference prices are taken, and then the actual values are obtained by

applying the corresponding scaling factors for each one of the parts of the OPEX, which is at the end is the

sum of all the latter.

4.3 Levelized Cost of Electricity (LCOE)

The levelized cost of electricity is the most frequently known economic performance metric for power

generation plant. LCOE is used to assess/compare the performance and profitability of any form of

generation technology, and not only concerns solar or renewable sources [17]. It is defined as the constant

per unit cost of energy which over the system’s lifetime will bring all the project cash flows to zero. In other

words it is the ‘break even’ constant sale price of energy [150].Another way to view the LCOE is it being

the price at which the electricity must be sold to recover all the costs incurred during the lifetime of the

project.

𝐿𝐶𝑂𝐸 =

𝑇𝑜𝑡𝑎𝑙 𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑐𝑜𝑠𝑡

𝑇𝑜𝑡𝑎𝑙 𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛

(8)

As explained above, the major cost of a solar power plant is the initial investment, plus the annual OPEX

cost to cover for the plant’s operations, and decommissioning costs at the end of operational lifetime of the

plant. Unlike fossil fuel, renewable sources projects do not account as a big cost fuel, as by definition, the

fuel is free, be it a solar or wind farm. The rest of this report will detail the various components and ways

of calculation the LCOE.

Previous sections outlined the size taxation can have in a companies’ balance sheet, whether as a cost in the

form of tax on income, or as a tax shield in the case of depreciation. However, from a pure societal

perspective, it can be argued that tax issues can be left out of the LCOE. But, from a plant

owner/business/commercial entity owning a system perspective, the prevailing assumption is that, in order

to break even, it must be assumed that energy produced is taxed at the standard corporate tax rate. Against

this, interest, depreciation and operating costs are tax deductible [150]. The reason for this is that at the

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societal level, some cash flows such as subsidies and income taxes are only a representation of a

redistribution and reallocation of resources to the government, and hence should be excluded. From the

business point of view, these issues affect balance sheets and profitability and are fundamentally included

[22].

The LCOE is the price that would break the project even in the present, but is based on costs and payments

that occur in the future, and as explained previously, inflation effects change the value of the money over

the time. The OPEX is usually the metric affected by inflation, as it happens each year of the plant’s life,

the CAPEX being paid in the very beginning of the project. Hence, LCOEs can be in real (inflation

independent) or nominal terms. A nominal LCOE represents a hypothetical income that declines in real

value year by year, whereas a real LCOE has a constant ‘value’. the nominal LCOE will be the higher of the

two. Real LCOEs are typically used for future long-term technology projections, whereas nominal ones are

often used for short-term actual projects [150].

4.4 Net Present Value (NPV) and Internal Rate of Return (IRR)

The Net Present Value (NPV) of a proposed project is most often used as the primary absolute metric to

compare/assesses investments, and serves as a base for decision making [23]. The NPV is the sum of the

discounted cash-flows over the lifetime of the project using an appropriate discount rate as discussed above.

The cash-flows represent the yearly difference between the revenues and costs incurred each year. It is then

linked primarily to the CAPEX, OPEX, decommission costs, the yearly energy yield or output and finally

the price at which the electricity is sold.

𝑁𝑃𝑉 = ∑

(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡

(1 + 𝑟)𝑡

𝑛

𝑡=0

(9)

Equation 4 is a simplified version of the NPV, where r is the discount rate, and n the lifetime of the project.

The costs part will be detailed further down, but the revenues are simply the amount of money generated

by the sale of electricity at the decided price, as seen in equation 5 where Et is the net annual electrical output

and PPA is the power purchase agreement price.

𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 = 𝐸𝑡 × 𝑃𝑃𝐴 (10)

The discount rate that is often used if NPV and LCOE calculations is the WACC, or weighted average cost

of capital [17]. As explained in the definitions section, financing RES projects requires different types of

investors, and involves frequently debt/equity financing. The WACC is in that case the appropriate discount

rate that reflects the expected return of the investment from all the parties involved in financing the project.

It can be calculated by means of equation 6, where Eq%/Debt% is the equity debt ratio, or how much of

the project is financed by equity/debt. IRR% and idebt are the discount rates of expected return by both

the equity financers and debt financers, and Tcorp is the corporate taxes. As mentioned before, debt

financing is attractive for large utility projects as it is tax deductible, and that is reflected in the equation by

having the complementary (1 − Tcorp) as a factor in the debt weight.

𝑊𝐴𝐶𝐶 = 𝐸𝑞% × 𝐼𝑅𝑅% + 𝐷𝑒𝑏𝑡% × 𝑖𝑑𝑒𝑏𝑡 × (1 − 𝑇𝑐𝑜𝑟𝑝) (11)

A financially viable project would yield at the lowest a null NPV or a positive one. A null NPV means that

the present value of revenues and cost are equal with regards to the expected profit and risk embodied in

the WACC.

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Another fundamental economical metric that is used to rank projects and get a hold of their profitability is

the internal rate of return or IRR [17]. The internal rate of return is the discount rate that would be used in

an NPV calculation and would make it equal to zero, as seen in equation 7.

𝑁𝑃𝑉 = ∑

(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡

(1 + 𝐼𝑅𝑅)𝑡

𝑛

𝑡=0

= 0 (12)

The IRR is then the interest rate that would break even the project accounting for the costs incurred and

revenues generated during the lifetime of the plant. It is a measure of the profitability of a project and is

used mainly by developers and financial institutions to base their investments decisions. Each company has

its own predictions on how much profit can be made of a project and has usually a target return on

investments. If the IRR is higher than that required target, the project is financially acceptable. To compare

different projects and financing opportunities, the higher the IRR the better [17] .

It has to be noted that the IRR used in equation 7 is not the same as the equity IRR mentioned in equation

6. The latter is the internal rate of return of the equity financers, or the discount rate reflecting the expected

profits and risk premium of the equity investor. The IRR of the project is the discount rate that set the NPV

of the project to zero.

Limitations of the NPV can be see as it requires a known discount rate, and assumes that this rate will be

stable over the life of the project, which is not true in real life. It also assumes that cash revenues will be

reinvested at the same discount rate. Here again, this may not hold in reality when interest rates in the market

are fluctuating. As for the IRR, while not holding the same limitations since it is purely a function of the in

and out cashflows of a particular investment, it does not give a quantified picture of the financial impact the

investment will have on the firm. It is only a target to meet [24].

The paper [23] acknowledges these limitations and others related to financing during the construction time,

and proposes another metric, 𝑁𝑃𝑉% or annualized and normalized NPV and defined as seen in equation 8.

𝑁𝑃𝑉% =

𝑁𝑃𝑉𝑝𝑟𝑜𝑗

𝐶𝐴𝑃𝐸𝑋 × 𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒

(13)

𝑁𝑃𝑉%, the resulting quantity, is a normalized measure of profitability expressed as a rate (%/year). It can

be compared directly with market rates of interest or anticipated returns from projects with similar levels of

risk. 𝑁𝑃𝑉𝑝𝑟𝑜𝑗 can be found by means of equation 9. To understand𝑁𝑃𝑉𝑝𝑟𝑜𝑗, it is necessary to remember

that in constructing power plants and operating them, a life span of 25-30 years is necessary. Considering

present as year 0, decisions are made in the years preceding the construction time. NPV evaluated at the

time of project initiation, i.e., 𝑁𝑃𝑉𝑝𝑟𝑜𝑗represents the potential increase in net worth of the company if a

prospective project is undertaken. It is obtained by projecting 𝑁𝑃𝑉0 back to the time a decision is made to

start the project [23].

𝑁𝑃𝑉𝑝𝑟𝑜𝑗 = 𝑁𝑃𝑉0 × (1 + 𝑑𝑖𝑠𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑁𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 (14)

5 LCOE: different approaches

As stated in the definition of the LCOE, depending on the perspective taken, societal or business owner,

costs can vary and as a result, the LCOE. Hence, there are various types of ways to calculate the LCOE,

added to the real/nominal LCOE. This section will explain in detail how to calculate the LCOE, and the

different methodologies followed.

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5.1 Societal perspective

Tax related costs or benefits are disregarded in this approach, and therefore, the only costs that need to be

taken into account in the LCOE calculations are CAPEX, OPEX and decommissioning costs. Following a

discounted cash flow method, and considering that the LCOE is the price at which electricity needs to be

sold to make costs and revenues equal over the lifetime of the project, the formula of the LCOE is given

by equation 11, as a rearrangement of equation 10 [17] [18]. The costs included here are derived from the

NPV formula reported in [17].

∑ (

𝐿𝐶𝑂𝐸

(1 + 𝑊𝐴𝐶𝐶)𝑡× 𝐸𝑡) = ∑

𝐶𝑡

(1 + 𝑊𝐴𝐶𝐶)𝑡

𝑁

𝑡=0

𝑁

𝑡=0

(15)

𝐿𝐶𝑂𝐸 = ∑

𝐶𝐴𝑃𝐸𝑋𝑁𝑐𝑜𝑛𝑠 × (1 + 𝑊𝐴𝐶𝐶)𝑡

𝑁𝑐𝑜𝑛𝑠−1𝑡=0 + ∑

𝑂𝑃𝐸𝑋(1 + 𝑊𝐴𝐶𝐶)𝑡

𝑁𝑐𝑜𝑛𝑠+𝑁𝑜𝑝−1

𝑡=𝑁𝑐𝑜𝑛𝑠+ ∑

𝐷𝑒𝑐𝑜𝑁𝑑𝑒𝑐 × (1 + 𝑊𝐴𝐶𝐶)𝑡

𝑁−1𝑁𝑐𝑜𝑛𝑠+𝑁𝑜𝑝

∑𝐸𝑡 × (1 − 𝑆𝐷𝑅)𝑡

(1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑡=0

(16)

In equation 11, 𝑁𝑐𝑜𝑛𝑠 stands for years of construction, 𝑁𝑜𝑝 year of operation, 𝑁𝑑𝑒𝑐 decommission years

and N is the lifetime of the plant. 𝐷𝑒𝑐𝑜 is the decomissiong cost, 𝐸𝑡the annual energy yield multiplied by

the system degradation rate SDR, which is often negligible for CSP [17]. Equation 11 does not account for

inflation, and therefore the LCOE expressed is a nominal LCOE. When the inflation rate is known, the

nominal LCOE can be calculated by means of the same equation, with replacing the WACC by the nominal

WACC, and applying inflation to the OPEX. To calculate the nominal WACC, equation is 6 is used, where

the interest rates 𝐼𝑅𝑅% and 𝑖𝑑𝑒𝑏𝑡 are nominal, instead of real. To switch from a real discount rate to a

nominal one when inflation 𝑖 is known, equation 12 is used [7].

𝑑𝑟𝑒𝑎𝑙 =

(1 + 𝑑𝑛𝑜𝑚𝑖𝑛𝑎𝑙)

(1 + 𝑖)− 1

(17)

Equation 11 appears to be a bit complex, but it is a straightforward method using basic discounted cash

flows for the determination of the LCOE. In [17] [154] however, a simplified version of this formula is

reported, and that does not require utilizing annual cash flows, but instead relies on annualized costs and

energy production. Equation 13 gives then another method for calculating the LCOE.

𝐿𝐶𝑂𝐸 =

𝛼 × 𝐶𝐴𝑃𝐸𝑋 + 𝑂𝑃𝐸𝑋 + 𝛽 × 𝐷𝑒𝑐𝑜

𝐸𝑛𝑒𝑡

(18)

The annual electricity output 𝐸𝑛𝑒𝑡 used in the above formula is the net annual electricity output estimated

for the first year of operation, often calculated from performing dynamic power plant simulations using

typical meteorological year (TMY) data [17]. Since this method uses annual values of the costs and energy

yield, the long term costs like CAPEX and decommissioning need to be transformed into annual payment.

This is done through the α and β factors. The capital recovery factor α determined by equation 14, can be

seen as the amount of equal (or uniform) payments to be received for n years such that the total present

value of all these equal payments is equivalent to a payment of one dollar at present [26]. It is a function of

the discount factor d, 𝑁𝑜𝑝 years of operation and the annual plant insurance rate.

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𝛼 = 𝑓𝑐𝑜𝑛 ×

𝑑 × (1 + 𝑑)𝑁𝑜𝑝

(1 + 𝑑)𝑁𝑜𝑝 − 1+ 𝑘𝑖𝑛𝑠

(19)

As seen in equation 11, the CAPEX is discounted over the course of the plant construction, as it is not an

overnight payment that happens instantly. To reflect this, the 𝑓𝑐𝑜𝑛 factor is used in equation 14. It translates

the fact that during erection of the power plant, interest begins to accumulate on the money that has been

borrowed to finance the construction. The longer it takes to build the plant, the more interest that

accumulates and the greater the total revenue that needs to be generated [154]. Equation 15 gives the

formula for 𝑓𝑐𝑜𝑛.

𝑓𝑐𝑜𝑛 =

(1 + 𝑑)𝑁𝑐𝑜𝑛 − 1

𝑁𝑐𝑜𝑛 × 𝑑

(20)

The other factor 𝛽 serves the same purpose, but applied to the decommissioning costs. By means of

equations 16 and 17, 𝛽 is determined. The additional factor𝑓𝑑𝑒𝑐 takes into account the number of years𝑁𝑑𝑒𝑐

that it takes to decommission the power plant. Longer decommissioning times allow part of the costs to be

pushed further into the future, reducing the impact of the decommissioning costs on the overall levelized

cost of electricity [154].

𝛽 = 𝑓𝑑𝑒𝑐 ×𝑑

(1 + 𝑑)𝑁𝑜𝑝 − 1

(21)

𝑓𝑑𝑒𝑐 =

(1 + 𝑑)𝑁𝑑𝑒𝑐 − 1

𝑑 × 𝑁𝑑𝑒𝑐 × (1 + 𝑑)𝑁𝑑𝑒𝑐−1

(22)

Beside the fact that the two mentioned methods of calculating the LCOE differ by their definitions, the

second method incorporate an insurance rate materialized by the means of 𝑘𝑖𝑛𝑠 in the capital recovery

factor. This adds a cost that is disregarded in the first methodology. However, it can easily be corrected in

equation 11, by changing the first summation in the numerator to ∑(1+𝑘𝑖𝑛𝑠)×𝐶𝐴𝑃𝐸𝑋

(1+𝑊𝐴𝐶𝐶)𝑡 .

The social perspective does not account for tax related costs as seen in equation 11. Nevertheless, the use

of the WACC calculated by means of equation 6 in this LCOE calculation implies accounting for tax benefits

related to the debt financing. As explicated above, the complementary tax factor in equation 6 translates the

benefit of tax deduction financing with loans has on the project. It can be argued therefore that in this

method, a simplified WACC should be used instead, where there is no factor related to corporate tax.

However, keeping it the calculation that way approaches the real cost of electricity and gives an idea of the

impact of debt financing on the performance of the plant. The simplified version of the LCOE presented

above, which is generally used to have a quick grasp on the performance of power plants, can keep the tax

in the WACC, since it gives only an idea about the value of the LCOE, and not the actual one that should

be determined with the discounted cash flow method.

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5.2 Business/developer perspective

To fully evaluate the LCOE of a solar power plant, cash flow determination needs to be precise and has to

embody all key issues that can be encountered. Typically, these are [150] :

• As debt financing is usually involved, the loans may be paid off over a different timescale than equity investments.

• Tax benefits linked to debt vary according to jurisdictions.

• Depreciation and its tax shield may have a shorter time than the plant’s operations.

• As construction of the plant takes several years, the interest rate for finance during those years is higher

• System output may take some time to stabilize as commissioning processes proceed after first start-up

• Major plant upgrade expenditures may be predicted at certain times in addition to overall continuous O&M

• Various inputs may be subject to different escalation rates

Naturally, these considerations are very project and location dependent, but also on the developers status

and choice of technology. Nevertheless, equation 18 can be constructed for the LCOE in this perspective.

It is implied in this formula that the LCOE is nominal, with the WACC being nominal, and the OPEX

increasing yearly with inflation. As [150] states, it is a somewhat simplified approach that allows sufficient

complexity to allow issues of tax, cost of equity and cost of debt to be examined

𝐿𝐶𝑂𝐸 =

∑𝑇𝐶𝐼

𝑁𝑐𝑜𝑛𝑠 × (1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑐𝑜𝑛−1𝑡=0 − ∑

𝐷𝐸𝑃 × 𝑇(1 + 𝑊𝐴𝐶𝐶)𝑡

𝑁𝑐𝑜𝑛+𝑁𝑑𝑒𝑝−1

𝑡=𝑁𝑐𝑜𝑛+ ∑

𝐼𝑁𝑇𝑡 × (1 − 𝑇)(1 + 𝑊𝐴𝐶𝐶)𝑡

𝑁𝑐𝑜𝑛+𝑁𝐿−1𝑡= 𝑁𝑐𝑜𝑛

∑𝐸𝑡


+

∑𝑂𝑃𝐸𝑋 × (1 − 𝑇)

(1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑐𝑜𝑛+𝑁𝑜𝑝−1

𝑡= 𝑁𝑐𝑜𝑛+ ∑

𝐷𝑒𝑐𝑜𝑁𝑑𝑒𝑐 × (1 + 𝑊𝐴𝐶𝐶)𝑡

𝑁−1𝑡= 𝑁𝑐𝑜𝑛+𝑁𝑜𝑝

∑𝐸𝑡


Equation 18 [150] [27]build on the previous formula shown for LCOE by adding up tax related elements:

- TCI as explained in [23] is the true “up-front” capital requirements, including interest that would

be charged since construction takes multiples years.

- The second summation represents the tax deduction due to the use of asset depreciation, with Ndep

being the number of years of depreciation, and DEP the amount of yearly depreciation.

- Third term refers to the tax shield gained by having a portion of the capital investment financed by

debt. NL stands for the duration of loan repayment and INT is the annual interest payment. The

tax benefit concerns only the interest of the loan, and not the principal. Repaying off the principal

is not included in the costs for the LCOE as from the project’s point of view, the cost of securing

debt is the interests paid. The principal is the company’s money to run the project.

- Interest payment during construction period results in higher loans because of no generated

revenue during that period. Therefore the CAPEX rises by, for example two or three years of

interest payment. CAPEX including the loan payments during the construction period

(construction loan with a construction interest rate 𝐶𝑅) are the total capital investment (TCI) [23],

and can be calculated with equation 19 in the example of a construction time of 3 years. How

depreciation is done varies from jurisdiction to jurisdiction, and therefore a simplified and linear

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model is shown in equation 20. It simply depreciates the CAPEX over the years of operation of

the plant. Other models include accelerated depreciation with various percentages each year [28].

Finally, the interest of the loan payed yearly 𝐼𝑁𝑇𝑡 is determined by calculation of the yearly total

debt payment (principal+interest) 𝐷𝑡𝑜𝑡 by means of the loan constant 𝑐𝑙𝑜𝑎𝑛, then subtracting the

principal payment each year. This is done with equations 21 to 24. The NPV calculation in this

perspective is the same, with the only difference that now, tax deduction occurs in the revenues

each year, so equation 5 needs to be multiplied by the complementary factor of the corporate tax

rate [29] [16].

𝑇𝐶𝐼 = 𝐶𝐴𝑃𝐸𝑋 × (1 + 𝐶𝑅)2 + 𝐶𝐴𝑃𝐸𝑋 × (1 + 𝐶𝑅)1 + 𝐶𝐴𝑃𝐸𝑋 × (1 + 𝐶𝑅)0 (23)

𝐷𝐸𝑃 = 𝐶𝐴𝑃𝐸𝑋/𝑁𝑜𝑝 (24)

𝑐𝑙𝑜𝑎𝑛 =

𝑖𝑑𝑒𝑏𝑡

1 − (1 + 𝑖𝑑𝑒𝑏𝑡)−𝑁𝐿

(25)

𝐷𝑡𝑜𝑡 = 𝑐𝑙𝑜𝑎𝑛 × 𝐶𝐴𝑃𝐸𝑋 × 𝐷𝑒𝑏𝑡% = 𝐼𝑁𝑇𝑡 + 𝑃𝑅𝐼𝑁𝑡

(26)

𝐼𝑁𝑇𝑡 = 𝑖𝑑𝑒𝑏𝑡 × 𝐷𝑡 (27)

𝐷𝑡 = 𝐷𝑡−1 − 𝑃𝑅𝐼𝑁𝑡−1 𝑎𝑛𝑑 𝐷0 = 𝐷𝑒𝑏𝑡% × 𝐶𝐴𝑃𝐸𝑋 (28)

5.3 LCOE for PPA projects

While the LCOE is the price at which electricity needs to be sold to recover all the costs of the project, the

PPA price is the power purchase bid price for projects involved in a RES tender, where project developers

sell electricity at a price negotiated through a power purchase agreement (PPA). As mentioned in the

beginning, those tenders base mainly their decision to award the project to a developer on that price.

The PPA price is determined in a way to meet the specified internal rate of return of the developer. This is

done by solving equation 7, iteratively finding the price of electricity that would bring the NPV to a null or

positive value. This way, a minimal value of the PPA price is found that would produce an NPV greater or

equal to zero [27].

Typically, the PPA for a power plant project will escalate with a certain percentage each year, primarily to

account for inflation or to comply with an agreed rate with the generation off-taker. This annual value of

the PPA tariff (PPAt) can be expressed by using the first-year PPA tariff (PPA1) and an annual escalation

rate (resc) [27], so that

𝑃𝑃𝐴𝑡 = 𝑃𝑃𝐴1 × (1 + 𝑟𝑒𝑠𝑐)𝑡−1 (29)

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The relationship between the PPA and the LCOE can be seen in equation 26, where the LCOE stands for

the amount the project must receive for each unit of energy to cover the projects costs and the additional

revenue required to meet the target internal rate of return [30]. This offers a way to determine the PPA once

the LCOE is known, and vice versa.

𝐿𝐶𝑂𝐸 = ∑

𝑃𝑃𝐴𝑡 × 𝐸𝑡

(1 + 𝑊𝐴𝐶𝐶)𝑡𝑡=𝑁𝑡=0

∑𝐸𝑡

(1 + 𝑊𝐴𝐶𝐶)𝑡𝑡=𝑁𝑡=0

(30)

Figure X points this relationship, by comparing the LCOE and PPA price with regards to inflation and

escalation rates. The calculated values are for a 64 MW sample wind farm that generates 176 GWh of

electricity in its first year with a total installed cost of $2,000/kW and a 2.2 cent/kWh production tax credit.

The shades of color in the table show the relative magnitude of the values (higher values are darker than

lower values)

More specifically, the following can be mentioned [31] :

When the inflation rate and PPA price escalation rate are both zero, the PPA price, nominal LCOE and real

LCOE are equal.

When the inflation rate is zero, the real and nominal LCOE are equal.

When the PPA price escalation rate is zero, the PPA price and nominal LCOE are equal.

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Figure 3 LCOE and PPA Relationship [30]

-88-

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-91-

7 Appendix 2

Cleanergy is a Swedish cleantech company specialized in renewable solutions based on the Stirling engine technology. The Sunbox is a 13 kW concentrated solar power (CSP) system in which a Stirling engine is powered with energy from the Sun, collected by an array of mirrors. Cleanergy and the Moroccan Agency for Sustainable Development (MASEN) have recently entered in a Cooperation Framework Agreement to jointly develop a Thermal Energy Storage system that shall be coupled to Cleanergy’s CSP-Stirling based solar electricity technology. The agreement also seeks to jointly identify business opportunities for such a novel technology in the Kingdom of Morocco. The following questionnaire consists of a set of 10 questions which are aimed at providing a better understanding of the Moroccan market. Please note that no third parties will be given access to individual company data. Data will be analyzed anonymously and used for research purposes only.

1. Please rank, from cheapest to most expensive, the electricity providers powering your sites.

ONEE Independent Power Producer (IPP ) Self production

2. What is the average electricity price paid by the company?

☐ Following ONEE tariff scheme ☐ Other

3. In the case of self-production. when there is excess electricity, do you sell it back to the grid?

☐ Yes. Specify the rate ☐ No

Compared to the other solar technologies, the Sunbox has the highest sun-to-electricity efficiency (30%), it is modular and can be located near consumption sites where the energy is needed, thus saving grid infrastructure cost. The thermal energy storage system will enable electricity production on- demand in a more cost-effective way than through the use of electrical batteries, especially for large storage requirements. The integrated solution with storage will be demonstrated in 2019.

4. In the future, how is the company planning to answer its growing electricity needs?

☐ Expand ONEE contracts ☐Expand/Use IPP contracts ☐Invest in self-production

5. Is your company interested in renewable energy through IPPS/self-production?

☐ Yes. Rank by order of likelihood: Solar Wind Biomass

☐ No

6. In the future, how likely will the company be open to invest in solar CSP?

☐ Likely to invest ☐Technology neutral ☐ Not likely to invest

7. Considering its advantages, would you contemplate purchasing electricity from the SunBox?

☐ Yes ☐ No

8. If yes, what is the maximum price you would be willing to pay for as an off-taker?

☐ Less than 0,6 DHS/kWh ☐ 0,6-0,9DHS/kWh ☐More than 0,9 DHS/kWh

9. In the case one of the demo-plants of Cleanergy AB is located near one of your sites, would you be interested in becoming off-taker after verification and validation is completed?

☐ Yes ☐ No

10. Would you like to receive updates about the technology development and its demonstration?

☐ Yes ☐ No

-92-

8 Appendix 3

Site Size (MW) Normalized LCOE Mirror area (m²) TES size (hours) CF (%)

MM11 84,3 1,239 220 10 68%

MM12 28,1 1,239 220 10 68%

MM21 29,2 1,194 220 11 72%

MM22 1,0 1,158 220 11 74%

MM23 21,2 1,121 220 13 81%

MM24 15,3 1,158 220 12 76%

MM25 53,0 1,239 220 10 68%

MM31 1,9 1,071 220 13 84%

MM41 10,0 1,195 220 11 72%

MC11 3,5 1,193 220 11 72%

MC12 14,8 1,194 220 11 72%

MC13 15,7 1,239 220 10 68%

MC21 38,3 1,194 220 11 72%

MC22 2,0 1,361 220 10 62%

MC23 26,9 1,286 220 10 65%

MC24 8,7 1,194 220 11 72%

MC31 3,1 1,287 220 10 65%

MC32 3,1 1,287 220 10 65%

MC41 8,7 1,194 220 11 72%

MMe11 30,3 1,287 220 10 65%

MA11 3,8 1,286 220 10 65%

MA21 0,2 1,243 220 12 71%

TC11 24,0 1,287 220 10 65%

TC21 11,5 1,286 220 10 65%

TM11 16,6 1,287 220 10 65%

TF11 2,3 1,286 220 10 65%

TF12 2,6 1,286 220 10 65%

TF13 3,2 1,287 220 10 65%

TF14 9,8 1,287 220 10 65%

TF15 3,4 1,289 220 10 65%

TF21 9,8 1,287 220 10 65%

TF22 2,7 1,288 220 10 65%

TF31 7,5 1,287 220 10 65%

EC11 146,6 1,158 220 12 76%

EC21 99,2 1,194 220 11 72%

EC31 28,8 1,287 220 10 65%

EC32 65,3 1,287 220 10 65%

EC33 57,6 1,287 220 10 65%

EC34 5,2 1,195 220 11 72%

EC41 49,2 1,072 220 13 84%

EC51 78,0 1,121 220 13 81%

EI11 47,5 1,287 220 10 65%

-93-

EI21 8,6 1,239 220 10 68%

EI31 5,6 1,239 220 11 69%

EI41 26,9 1,194 220 11 72%

EI42 14,3 1,239 220 10 68%

EM11 177,8 1,194 220 11 72%

EM21 40,0 1,121 220 13 81%

JC11 1,4 1,197 220 10 70%

JC12 13,4 1,073 220 13 84%

JC21 15,0 1,158 220 12 76%

JC31 20,4 1,121 220 13 81%

JC41 10,6 1,121 220 13 81%

JC42 10,1 1,073 220 13 84%

JM11 5,3 1,073 220 13 84%

JM21 4,1 1,000 220 14 92%

JM31 28,9 1,287 220 10 65%

JM32 22,4 1,073 220 13 84%

JF11 7,1 1,157 220 12 76%

JF21 3,2 1,000 220 14 92%

JF31 7,6 1,001 220 14 92%

JF41 14,9 1,121 220 13 81%

SM11 23,0 1,157 220 12 76%

SM12 24,6 1,157 220 12 76%

SM13 6,6 1,158 220 12 76%

SM13 6,6 1,158 220 12 76%

SM15 6,3 1,121 220 13 81%

SM15 6,3 1,121 220 13 81%

SM21 41,9 1,287 220 10 65%

SM31 6,1 1,159 220 12 76%

SC11 17,7 1,000 220 14 92%

SC21 25,4 1,073 220 13 84%

SC31 23,1 1,158 220 12 76%

SC41 37,0 1,239 220 10 68%

SC41 37,0 1,239 220 10 68%

SC61 33,3 1,239 220 10 68%

9 Appendix 4

Site Size (MW) Normalized LCOE Mirror area (m²) TES size (hours) CF (%)

MM11 78,68 1,269 220 14 73%

MM12 26,23 1,269 220 14 73%

MM21 27,65 1,212 220 14 76%

MM22 0,90 1,173 220 14 79%

MM23 20,87 1,127 220 14 82%

MM24 14,76 1,167 220 14 79%

-94-

MM25 49,46 1,269 220 14 73%

MM31 1,91 1,077 220 14 86%

MM41 9,47 1,212 220 14 76%

MC11 3,30 1,211 220 14 76%

MC12 14,00 1,211 220 14 76%

MC13 14,67 1,270 220 14 73%

MC21 36,25 1,211 220 14 76%

MC22 1,92 1,415 220 14 65%

MC23 25,43 1,335 220 14 69%

MC24 8,24 1,213 220 14 76%

MC31 2,91 1,339 220 14 69%

MC32 2,91 1,339 220 14 69%

MC41 8,24 1,213 220 14 76%

MMe11 28,64 1,335 220 14 69%

MA11 3,57 1,335 220 14 69%

MA21 0,18 1,319 220 12 71%

TC11 22,70 1,335 220 14 69%

TC21 10,90 1,335 220 14 69%

TM11 15,67 1,336 220 14 69%

TF11 2,18 1,339 220 14 69%

TF12 2,48 1,336 220 14 69%

TF13 3,07 1,338 220 14 69%

TF14 9,31 1,335 220 14 69%

TF15 3,22 1,335 220 14 69%

TF21 9,31 1,335 220 14 69%

TF22 2,58 1,335 220 14 69%

TF31 7,13 1,336 220 14 69%

EC11 141,29 1,168 220 14 79%

EC21 93,91 1,211 220 14 76%

EC31 27,24 1,336 220 14 69%

EC32 61,75 1,335 220 14 69%

EC33 54,48 1,335 220 14 69%

EC34 4,94 1,212 220 14 76%

EC41 48,28 1,076 220 14 86%

EC51 76,59 1,127 220 14 82%

EI11 44,91 1,335 220 14 69%

EI21 8,00 1,270 220 14 73%

EI31 5,34 1,269 220 14 73%

EI41 25,46 1,212 220 14 76%

EI42 13,34 1,269 220 14 73%

EM11 134,80 1,211 220 14 76%

EM21 39,30 1,127 220 14 82%

JC11 1,30 1,215 220 14 76%

JC12 13,11 1,077 220 14 86%

JC21 14,43 1,168 220 14 79%

-95-

JC31 20,07 1,127 220 14 82%

JC41 10,40 1,127 220 14 82%

JC42 9,94 1,076 220 14 86%

JM11 5,24 1,078 220 14 86%

JM21 4,12 1,000 220 14 92%

JM31 27,33 1,335 220 14 69%

JM32 22,02 1,076 220 14 86%

JF11 6,87 1,169 220 14 79%

JF21 3,23 1,000 220 14 92%

JF31 7,58 1,001 220 14 92%

JF41 14,66 1,127 220 14 82%

SM11 22,13 1,167 220 14 79%

SM12 23,71 1,168 220 14 79%

SM13 6,38 1,167 220 14 79%

SM13 6,38 1,167 220 14 79%

SM15 6,16 1,127 220 14 82%

SM15 6,16 1,127 220 14 82%

SM21 39,63 1,335 220 14 69%

SM31 5,85 1,168 220 14 79%

SC11 17,68 1,000 220 14 92%

SC21 24,87 1,076 220 14 86%

SC31 22,23 1,168 220 14 79%

SC41 34,52 1,269 220 14 73%

SC41 34,52 1,269 220 14 73%

SC61 31,07 1,269 220 14 73%

-96-

10 Appendix 5

MM11

MM12

MM21

MM22

MM23

MM24

MM25

MM31

MM41

MC11

MC12

MC13

MC21

MC22

MC23

MC24

MC31MC32

MC41

MMe11MA11

MA21

TC11

TC21

TM11

TF11

TF12

TF13 TF14TF15

TF21

TF22

TF31

EC11

EC21

EC31

EC32EC33

EC34

EC41

EC51

EI11

EI21EI31

EI41

EI42

EM11

EM21

JC11

JC12

JC21

JC31

JC41

JC42JM11

JM21

JM31

JM32

JF11

JF21 JF31

JF41

SM11SM12

SM13

SM13

SM15SM15

SM21

SM31

SC11

SC21

SC31

SC41SC41

SC61

1,000

1,050

1,100

1,150

1,200

1,250

1,300

1,350

0 200000 400000 600000 800000 1000000 1200000

No

rmal

ized

LC

OE

(€/M

Wh

)

Energy consumed (MWh)

Morocco

Tunisia

Egypt

Jordan

Saudi Arabia

-97-

11 Appendix 6

Potential (MW)

DNI (kWh/m²/year)

Normalized LCOE

Macro-environmental

factors (%)

Grid access

Final score

Weighting factors

7,00 9,00 10,00 5,00 10,00 41,00

Value Score Value Score Value Score Score Score

MM11 84,33 10,00 2115 1,95 1,239 7,71 8,00 5,00 6,21

MM12 28,11 5,62 2115 1,95 1,239 7,72 8,00 5,00 5,47

MM21 29,20 5,84 2215 2,86 1,194 8,79 8,00 5,00 5,96

MM22 0,96 0,19 2315 3,77 1,158 9,67 8,00 5,00 5,41

MM23 21,25 4,25 2415 4,68 1,121 10,00 8,00 5,00 6,39

MM24 15,32 3,06 2315 3,77 1,158 9,66 8,00 5,00 5,90

MM25 53,00 10,00 2115 1,95 1,239 7,71 8,00 5,00 6,21

MM31 1,95 0,39 2515 5,59 1,071 10,00 8,00 10,00 7,15

MM41 10,00 2,00 2215 2,86 1,195 8,78 8,00 5,00 5,31

MC11 3,48 0,70 2215 2,86 1,193 8,83 8,00 5,00 5,10

MC12 14,79 2,96 2215 2,86 1,194 8,79 8,00 5,00 5,47

MC13 15,72 3,14 2115 1,95 1,239 7,71 8,00 5,00 5,04

MC21 38,29 7,66 2215 2,86 1,194 8,79 8,00 5,00 6,28

MC22 2,03 0,41 1915 0,14 1,361 0,00 8,00 5,00 2,29

MC23 26,89 5,38 2015 1,05 1,286 6,57 8,00 5,00 4,95

MC24 8,70 1,74 2215 2,86 1,194 8,79 8,00 5,00 5,27

MC31 3,07 0,61 2015 1,05 1,287 6,55 8,00 5,00 4,13

MC32 3,07 0,61 2015 1,05 1,287 6,55 8,00 5,00 4,13

MC41 8,70 1,74 2215 2,86 1,194 8,79 8,00 5,00 5,27

MMe11 30,29 6,06 2015 1,05 1,287 6,57 8,00 5,00 5,06

MA11 3,77 0,75 2015 1,05 1,286 6,59 8,00 5,00 4,16

MA21 0,18 0,04 2115 1,95 1,243 7,62 8,00 5,00 4,49

TC11 24,01 4,80 2015 1,05 1,287 0,00 6,00 5,00 3,00

TC21 11,53 2,31 2015 1,05 1,286 0,00 6,00 5,00 2,57

TM11 16,57 3,31 2015 1,05 1,287 0,00 6,00 5,00 2,75

TF11 2,31 0,46 2015 1,05 1,286 0,00 6,00 5,00 2,26

TF12 2,62 0,52 2015 1,05 1,286 0,00 6,00 5,00 2,27

TF13 3,25 0,65 2015 1,05 1,287 0,00 6,00 5,00 2,29

TF14 9,85 1,97 2015 1,05 1,287 0,00 6,00 5,00 2,52

TF15 3,41 0,68 2015 1,05 1,289 0,00 6,00 5,00 2,30

TF21 9,85 1,97 2015 1,05 1,287 0,00 6,00 5,00 2,52

TF22 2,72 0,54 2015 1,05 1,288 0,00 6,00 5,00 2,27

TF31 7,54 1,51 2015 1,05 1,287 0,00 6,00 5,00 2,44

EC11 146,62 10,00 2315 3,77 1,158 4,54 6,50 5,00 5,66

EC21 99,20 10,00 2215 2,86 1,194 3,31 6,50 5,00 5,15

EC31 28,81 5,76 2015 1,05 1,287 0,20 6,50 5,00 3,27

EC32 65,31 10,00 2015 1,05 1,287 0,20 6,50 5,00 4,00

EC33 57,63 10,00 2015 1,05 1,287 0,19 6,50 5,00 4,00

-98-

EC34 5,22 1,04 2215 2,86 1,195 3,28 6,50 5,00 3,62

EC41 49,22 9,84 2515 5,59 1,072 6,00 6,50 5,00 6,38

EC51 77,96 10,00 2415 4,68 1,121 5,32 6,50 5,00 6,04

EI11 47,50 9,50 2015 1,05 1,287 0,20 6,50 5,00 3,91

EI21 8,58 1,72 2115 1,95 1,239 1,79 6,50 5,00 3,17

EI31 5,59 1,12 2115 1,95 1,239 1,78 6,50 5,00 3,07

EI41 26,90 5,38 2215 2,86 1,194 3,30 6,50 5,00 4,36

EI42 14,30 2,86 2115 1,95 1,239 1,80 6,50 5,00 3,37

EM11 711,93 10,00 2215 2,86 1,194 3,31 6,50 5,00 5,15

EM21 40,00 8,00 2415 4,68 1,121 5,32 6,50 10,00 6,92

JC11 1,41 0,49 2215 2,86 1,197 9,78 6,75 5,00 5,14

JC12 13,37 2,67 2515 5,59 1,073 10,00 6,75 5,00 6,17

JC21 14,98 3,00 2315 3,77 1,158 9,94 6,75 5,00 5,81

JC31 20,43 4,09 2415 4,68 1,121 10,00 6,75 5,00 6,21

JC41 10,59 2,12 2415 4,68 1,121 10,00 6,75 5,00 5,87

JC42 10,13 2,03 2515 5,59 1,073 10,00 6,75 5,00 6,05

JM11 5,34 1,07 2515 5,59 1,073 10,00 6,75 5,00 5,89

JM21 4,12 0,82 2715 7,41 1,000 10,00 6,75 5,00 6,25

JM31 28,91 5,78 2015 1,05 1,287 9,42 6,75 5,00 5,56

JM32 22,45 4,49 2515 5,59 1,073 10,00 6,75 5,00 6,48

JF11 7,13 1,43 2315 3,77 1,157 9,95 6,75 5,00 5,54

JF21 3,23 0,65 2715 7,41 1,000 10,00 6,75 5,00 6,22

JF31 7,58 1,52 2715 7,41 1,001 10,00 6,75 5,00 6,37

JF41 14,93 2,99 2415 4,68 1,121 10,00 6,75 5,00 6,02

SM11 22,96 4,59 2315 3,77 1,157 4,54 5,50 5,00 4,61

SM12 24,60 4,92 2315 3,77 1,157 4,54 5,50 5,00 4,67

SM13 6,62 1,32 2315 3,77 1,158 4,51 5,50 5,00 4,04

SM13 6,62 1,32 2315 3,77 1,158 4,51 5,50 5,00 4,04

SM15 6,27 1,25 2415 4,68 1,121 5,21 5,50 5,00 4,40

SM15 6,27 1,25 2415 4,68 1,121 5,21 5,50 5,00 4,40

SM21 41,91 8,38 2015 1,05 1,287 0,20 5,50 5,00 3,60

SM31 6,07 1,21 2315 3,77 1,159 4,50 5,50 5,00 4,02

SC11 17,68 3,54 2715 7,41 1,000 6,34 5,50 5,00 5,67

SC21 25,36 5,07 2515 5,59 1,073 5,67 5,50 5,00 5,37

SC31 23,06 4,61 2315 3,77 1,158 4,54 5,50 5,00 4,61

SC41 37,00 7,40 2115 1,95 1,239 1,79 5,50 5,00 4,02

SC41 37,00 7,40 2115 1,95 1,239 1,79 5,50 5,00 4,02

SC61 33,30 6,66 2115 1,95 1,239 1,80 5,50 5,00 3,89

-99-

12 Appendix 7

Figure 47 Case 1

Figure 48 Case 2

Figure 49 Case 3

3,31

4,29

6,45

2,33

5,41

Tunisia

Egypt

Saudi Arabia

Morocco

Jordan

3,51

4,35

6,24

2,50

5,47

Tunisia

Egypt

Saudi Arabia

Morocco

Jordan

3,87

6,07

4,50

3,13

5,49

Tunisia

Saudi Arabia

Egypt

Morocco

Jordan

-100-

Figure 50 Case 4

Figure 51 Case 5

Figure 52 Case 6

3,44

4,74

4,77

4,78

5,38

Tunisia

Morocco

Saudi Arabia

Jordan

Egypt

3,09

4,30

4,46

4,81

5,71

Tunisia

Jordan

Morocco

Saudi Arabia

Egypt

2,96

4,27

4,55

4,83

4,84

Tunisia

Morocco

Saudi Arabia

Jordan

Egypt

-101-

Figure 53 Case 7

Figure 54 Case 8

3,95

4,92

5,17

5,39

5,61

Tunisia

Saudi Arabia

Jordan

Morocco

Egypt

3,75

4,819

4,824

4,84

5,37

Tunisia

Saudi Arabia

Jordan

Morocco

Egypt

-102-

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