on the bene ts and costs of microgridsdigitool.library.mcgill.ca/thesisfile114561.pdf · this...

On the Benefits and Costs of Microgrids

By Gregory B. C. Weyrich Young Morris

Master of Engineering

Department of Electrical and Computer Engineering

McGill University

Montreal, Quebec

10 December, 2012

A thesis submitted to McGill University in partial fulfilment of the requirementsfor the degree of Master of Engineering in Electrical Engineering.

©Copyright 2012, Gregory B. C. Weyrich Young Morris.

Abstract

This thesis examines the benefits that Microgrids can provide to a variety of

stakeholders and considers their costs. A flexible framework is proposed in which to

consider Microgrid stakeholders, benefits, and benefit allocation. A methodology is

presented for evaluating several key benefits, namely: reliability improvement, ancil-

lary service provision, investment deferral resulting from both peak load reduction

and ancillary service provision, as well as emissions reduction. Finally, several Micro-

grid case studies are evaluated as business cases using the methodology presented in

order to illustrate benefit estimation and allocation, and to better understand the

interaction between the parameters that define a Microgrid project and the resultant

benefits seen by each stakeholder.

i

Abrege

Cette these etudie les avantages que peuvent fournir aux interesses les micro-

reseaux, et propose une approche a l’evaluation des couts et benefices. Un cadre

flexible est propose pour classer les interesses, les avantages, et la repartition des

avantages. Une methodologie est presentee pour evaluer quelques avantages cles,

incluant : amelioration de fiabilite, fourniture des services auxiliaires, possibilite de

differer les investissements requis par l’augmentation de la charge par la reduction

de la charge de pointe, et la reduction des emissions perturbatrices. Enfin, quelques

etudes de cas micro-reseaux existants sont presentees, sous la forme de cas d’affaires

a l’aide de la methodologie presentee. Ceci est fait afin d’illustrer l’estimation et

l’allocation des avantages, et pour une meilleure comprehension de l’interaction en-

tre les parametres qui definissent un projet de micro-reseau et les avantages dont

beneficient chacun des interesses.

ii

Acknowledgements

First and foremost I thank my supervisor, Prof. Geza Joos, for his support and

guidance throughout my degree. In addition to research supervision, the opportuni-

ties he made possible for me to travel, make connections, and disseminate my work

have been invaluable. Thanks are also due to Drs. Chad Abbey of Hydro-Quebec

and Steven Wong of CanmetENERGY for their valuable advice and feedback as well

as their support as co-authors of published work resulting from this research.

Thanks to my parents, Greg and Ellen Weyrich Morris. Their unwavering faith

in my abilities and constant encouragement have had no small part in giving me the

confidence and determination that has been essential in my studies and work.

I would also like to thank my fellow students for their support, advice, and oc-

casional entertainment during an otherwise serious and studious period. This group

is too large to enumerate in full, but I would be remiss not to acknowledge Davy

Zhuang, Hamed Golestani, Andra St. Quintin, and Carl Muller-Romer. And many

thanks are due especially to Michael Ross who provided mentorship and friendship

throughout my degree (and who made a particularly excellent travelling partner).

And finally thanks to Katie, my wife and accomplice, for her companionship,

her support, and (perhaps most importantly during this time) her longsuffering.

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Table of Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Abrege . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Useful Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

Nomenclature Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

1 Introduction and Literature Review . . . . . . . . . . . . . . . . . . . . . 1

1.1 Microgrid Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Control of Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Microgrids and Markets . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3.1 Regulatory Environment . . . . . . . . . . . . . . . . . . . 61.4 Previous Efforts in Benefit Quantification and Related Work . . . 81.5 Thesis Scope and Contributions . . . . . . . . . . . . . . . . . . . 121.6 Thesis Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Framework and Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2 Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3 Impacts and Benefits . . . . . . . . . . . . . . . . . . . . . . . . . 192.4 Benefit Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3 Cost-Benefit Analysis Methodology . . . . . . . . . . . . . . . . . . . . . 29

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

iv

3.2 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . 293.3 The Base Case and Context . . . . . . . . . . . . . . . . . . . . . 313.4 The Infrastructure and Functionality of the Microgrid . . . . . . . 333.5 Simulation and Analysis . . . . . . . . . . . . . . . . . . . . . . . 353.6 Economic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 353.7 Alternatives Comparison . . . . . . . . . . . . . . . . . . . . . . . 363.8 Analysis Software . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.9 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4 Benefit Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.2 Reduced Energy Purchased Cost and Energy Exchange . . . . . . 384.3 Reduced System Loading . . . . . . . . . . . . . . . . . . . . . . . 41

4.3.1 Loss Reduction . . . . . . . . . . . . . . . . . . . . . . . . 434.4 Improved Reliability . . . . . . . . . . . . . . . . . . . . . . . . . 454.5 Ancillary Services . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.5.1 Frequency or Active Power Support . . . . . . . . . . . . . 534.5.2 Voltage or Reactive Power Support . . . . . . . . . . . . . 564.5.3 Black Start Support . . . . . . . . . . . . . . . . . . . . . . 58

4.6 Reduced Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . 604.7 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5 Business Cases and Case Studies . . . . . . . . . . . . . . . . . . . . . . 63

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.2 Community Microgrid . . . . . . . . . . . . . . . . . . . . . . . . 63

5.2.1 Base Case and Context . . . . . . . . . . . . . . . . . . . . 645.2.2 Microgrid Alternative Case . . . . . . . . . . . . . . . . . . 655.2.3 Impacts and Modelling . . . . . . . . . . . . . . . . . . . . 665.2.4 Economic Evaluation . . . . . . . . . . . . . . . . . . . . . 67

5.3 Commercial Microgrid . . . . . . . . . . . . . . . . . . . . . . . . 725.3.1 Base Case and Context . . . . . . . . . . . . . . . . . . . . 725.3.2 Microgrid Alternative Case . . . . . . . . . . . . . . . . . . 735.3.3 Impacts and Modelling . . . . . . . . . . . . . . . . . . . . 745.3.4 Economic Evaluation . . . . . . . . . . . . . . . . . . . . . 75

5.4 Isolated Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . 815.4.1 Base Case and Context . . . . . . . . . . . . . . . . . . . . 815.4.2 Microgrid Alternative Case . . . . . . . . . . . . . . . . . . 82

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5.4.3 Impacts and Modelling . . . . . . . . . . . . . . . . . . . . 835.4.4 Economic Evaluation . . . . . . . . . . . . . . . . . . . . . 84

5.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

6 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 89

6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 896.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 906.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

A Useful Principles of Economics . . . . . . . . . . . . . . . . . . . . . . . . 95

A.1 Cash flow diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . 95A.2 Annual and Present Worth of a Project . . . . . . . . . . . . . . . 97

A.2.1 Interest, Inflation, and Tax Rates . . . . . . . . . . . . . . 98A.2.2 Annual and Present Worth . . . . . . . . . . . . . . . . . . 99

A.3 Internal Rate of Return . . . . . . . . . . . . . . . . . . . . . . . . 100A.4 Benefit-Cost Ratios . . . . . . . . . . . . . . . . . . . . . . . . . . 101

B Sensitivity to Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 103

C Analysis Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

C.1 A Comparison of Available Analysis Software . . . . . . . . . . . 108C.1.1 DER-CAM . . . . . . . . . . . . . . . . . . . . . . . . . . . 108C.1.2 RETScreen . . . . . . . . . . . . . . . . . . . . . . . . . . . 112C.1.3 HOMER . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

C.2 The Author’s Software . . . . . . . . . . . . . . . . . . . . . . . . 118

D Useful Data and Simulation Parameters from Literature . . . . . . . . . 125

D.1 Network Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125D.1.1 Reliability Figures . . . . . . . . . . . . . . . . . . . . . . . 125D.1.2 Demand Growth Rate . . . . . . . . . . . . . . . . . . . . . 126

D.2 Component Operating Data . . . . . . . . . . . . . . . . . . . . . 126D.3 Investment costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

D.3.1 Distributed Generation, etc. . . . . . . . . . . . . . . . . . 127D.3.2 Microgrid Infrastructure and Controller Costs . . . . . . . . 128D.3.3 Transformers and Substations . . . . . . . . . . . . . . . . 129D.3.4 Capacitor Banks . . . . . . . . . . . . . . . . . . . . . . . . 129

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D.3.5 Distribution Feeders . . . . . . . . . . . . . . . . . . . . . . 129D.3.6 Interconnection Cost . . . . . . . . . . . . . . . . . . . . . 129

D.4 Commodity Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . 130D.4.1 Electricity prices . . . . . . . . . . . . . . . . . . . . . . . . 130D.4.2 Natural Gas prices . . . . . . . . . . . . . . . . . . . . . . . 131D.4.3 Ancillary Service Prices . . . . . . . . . . . . . . . . . . . . 131

D.5 Emission Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131D.5.1 Carbon Emissions . . . . . . . . . . . . . . . . . . . . . . . 131D.5.2 Non Carbon Gaseous Emissions . . . . . . . . . . . . . . . 132D.5.3 Particulate Emissions . . . . . . . . . . . . . . . . . . . . . 132

D.6 Reliability Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132D.6.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132D.6.2 Residential . . . . . . . . . . . . . . . . . . . . . . . . . . . 132D.6.3 Commercial . . . . . . . . . . . . . . . . . . . . . . . . . . 133D.6.4 Industrial . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133D.6.5 Small Commercial and Industrial . . . . . . . . . . . . . . . 133D.6.6 Medium and Large Commercial and Industrial . . . . . . . 133

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

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List of TablesTable page

2–1 Microgrid Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2–2 Summary of Microgrid Benefit Functions . . . . . . . . . . . . . . . . 22

2–3 Function: Reduced Energy Costs . . . . . . . . . . . . . . . . . . . . . 22

2–4 Function: Reduced Loading . . . . . . . . . . . . . . . . . . . . . . . . 23

2–5 Function: Improved Reliability . . . . . . . . . . . . . . . . . . . . . . 24

2–6 Function: Ancillary Services . . . . . . . . . . . . . . . . . . . . . . . 25

2–7 Function: Reduced Emissions . . . . . . . . . . . . . . . . . . . . . . . 26

3–1 Microgrid Valuation Parameters . . . . . . . . . . . . . . . . . . . . . 34

5–1 Case Study 1 Input Parameters . . . . . . . . . . . . . . . . . . . . . . 65



D–1 Ancillary Services Market Clearing Price (average hourly $/MW,2004) [57] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

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List of FiguresFigure page

2–1 Overview of Relationships between Microgrid Benefit Functions. . . . 21

4–1 The net present cost of a future equipment investment relative tothe future cost of the upgrade (on the y-axis), calculated usingEq. 4.1. This is shown as a function of the power level at which theinvestment is required, normalized with respect to the Microgridpeak demand (on the x-axis). The Microgrid reduces peak load tohalf its base case value in the first year and a 2% annual growth inpeak demand is assumed. . . . . . . . . . . . . . . . . . . . . . . . 44

4–2 Real and reactive power outputs and required power overratings for avariety of power factors. . . . . . . . . . . . . . . . . . . . . . . . . 59

5–1 Case 1 net values for key stakeholders. . . . . . . . . . . . . . . . . . 70

5–2 Net annual costs in the Microgrid Case relative to the Base Case forkey stakeholders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5–3 Variation in net annual Microgrid benefits over the Base Case forreasonable parameter ranges. . . . . . . . . . . . . . . . . . . . . . 71



5–6 Variation in net annual benefits of the Microgrid over the Base Casefrom the perspective of each stakeholder group in Case 2. . . . . . . 77



ix

5–9 Variation in net annual benefits of the Microgrid over the Base Casefrom the perspective of the utility and customers in Case 3. . . . . 86

A–1 Total cost and benefit flows for a Microgrid project over an N yearlifespan. Note that in reality, total costs and benefits will be dividedamongst the various Stakeholders in their own unique cost-benefitflow diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

A–2 Cost and benefit flows for each stakeholder in a Microgrid. . . . . . . 97

B–1 Example of a “Tornado diagram” showing the sensitivity of Microgridowner annual net revenues to changes in various project parameters.The diagram is centred on the middle estimate value of $60,000. . . 104

B–2 The benefits provided by systems that operate independently maybe analyzed using a “separated approach”. In this case, the benfitprovided by System i is found directly as Bi. . . . . . . . . . . . . . 106

B–3 Microgrids consist of interdependent systems, which, in general,cannot be analyzed independently, but must be analyzed using a“combined approach”. In this case, benefits come bundled togetheras BΣ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

B–4 A “subtractive approach” may be used to estimate the benefit providedby the whole Microgrid less an individual system, BΣ/i. . . . . . . . 106

B–5 An “incremental approach” may be used to estimate the incrementalbenefit provided by an individual Microgrid System parameter,BΣ(Pij++). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

C–1 The Configuration worksheet of the author’s analysis tool. . . . . . . 120

C–2 A screenshot from the CostsAndEnergyEx worksheet of the author’sanalysis tool, showing data entry fields for general project parame-ters and the base case. . . . . . . . . . . . . . . . . . . . . . . . . . 121

C–3 A screenshot from the CostsAndEnergyEx worksheet of the author’sanalysis tool, showing data entry fields for the second Microgridcase under consideration. . . . . . . . . . . . . . . . . . . . . . . . . 122

C–4 A screenshot from the Resources worksheet of the author’s analysistool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

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C–5 A screenshot from the OtherBenefits worksheet of the author’s analysistool, showing data entry fields for improved reliability benefits. . . 123

C–6 A screenshot from the Results worksheet of the author’s analysis tool,showing summarized output values. . . . . . . . . . . . . . . . . . . 123

C–7 A screenshot from the results worksheet of the author’s analysis tool,showing outputs from various benefit calculations. . . . . . . . . . . 124

D–1 Costs of capacitor banks by reactive power rating. Based on valuesgiven by NEPSI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

xi

Useful Abbreviations

The following is a list of abbreviations used in this document and related liter-

ature, which the reader may find helpful.

AV Annual ValueDER Distributed Energy ResourcesDG Distributed Generation or Distributed Generator (See also MS and µG)DNO Distribution Network Operator (See also DSO)DR Demand ResponseDSO Distribution System Operator (See also DNO)EPS Electric Power SystemESS Energy Storage SystemGC Grid CustomerGHG Greenhouse GasHHV Higher Heating ValueIPP Independent Power ProducerISO Independent System OperatorLHV Lower Heating ValueLP Load PointMG Microgrid (See also µG)NDE Non-Distributed EnergyNPV Net Present ValuePCC Point of Common CouplingPF Power FactorPoA Probability of AdequacyPV Present ValueRES Renewable Energy SourceTOU Time of Use (e.g. TOU Tariff)µG Microgrid(See also MG)µGC Microgrid Customer

xii

Nomenclature Used

The following is a list of nomenclature used in this thesis, listed in order of

appearance in the text.

PV Present value of an investmentCi,t Cost of asset i in time period td Interest or discount rate on an investmentY Year in which an investment is madeh Planning horizonn Number of investments required in time step tPL Real power lossesQL Reactive power lossesM Number of nodes in a networkVi Voltage at node iIi Current injection at node i~V Column vector containing all nodal voltages~I Column vector containing all nodal current injectionsNi Number of customers affected by an interruption iNT Total number of customersNk Number of customers at load point kλk Failure rate at load point kri Restoration time of an interruption, iUk Average interruption duration or unavailability at load point kLC Average demand of load point Cγ Set of all feeders in the MicrogridPL Probability that a failure in any feeder will cause the entire Microgrid to

shutdownPM Probability that islanding will not occur correctlyTaL Restoration time after an internal shutdownTaup Restoration time after an upstream outagePoA Probability of Adequancy of the DG in an islanding MicrogridS Proportion of load that is shed during an islanding eventGr Set of network components that must be repaired before power can be

restored to the MicrogridGi Set of network components that do not cause an outage when they failGc Set of network components that must be bypassed when they fail

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Pi|k Probability that failure of component i will cause an outage inload point k

ti|ks Time required to isolate component i from the load point andrestore power to load point k

ti|kc Time required to reconfigure the network to restore power to loadpoint k

rs(t) Net revenue from a contract or an accepted bid for service s duringtime step t

πs(t) Price for service s at time txs(t) Amount of service s made available at time step tC(xs) Cost of making amount x of service s availableRs Total net revenue from a provision contract for service sΠs Fixed price for service sXs Fixed quantity for service sTf Length of the contract or projectxT (t) Total output of a generatorλ(t) Instantaneous reserve utilization factor at time txE(t) Energy produced for energy exchange∆BE∆E

Marginal net benefit from selling energy∆RE∆E

Marginal revenue from selling energy∆CO&M

∆EMarginal O&M cost from selling energy

Po Real power output of a deviceQo Reactive power output of a deviceVr Voltage rating of a deviceIr Current rating of a deviceSor Required equipment apparent power overratingPF Power factorEe Total quantity of emission exσ Dispatched by source σxGP Power purchased from the gridxGS Power sold to the gridεe σ The average rate of emission e from source σ

xiv

CHAPTER 1Introduction and Literature Review

The Microgrid concept was defined by the Consortium for Electric Reliabil-

ity Technology Solutions (CERTS) in 2002 as “[A]n aggregation of loads and mi-

crosources operating as a single system providing both power and heat.” [60]

Since that definition was proposed, Microgrids have been suggested as a means

to improve the reliability, power quality, environmental impact, and efficiency associ-

ated with electric power provision [33,44,45,66,85]. Microgrids integrate distributed

generation (DG) and loads into one system, allowing for both greater flexibility and

autonomy in how power is used. At a time of widespread concern over the environ-

mental impacts of polluting sources of energy, Microgrids can potentially use Energy

Storage Systems (ESS) and controllable loads to facilitate integration of renewable

energy sources (RES) into the power system while maintaining or improving stan-

dards of power quality and reliability (PQR) [26,98,106].

Certain technical benefits have been demonstrated in a number of Microgrid

pilot projects, and additional benefits have been identified, but in order to make

informed decisions about whether to invest in Microgrids, the value of these benefits

to Microgrid stakeholders must be well understood. Work has been done to identify

and quantify a number of individual benefits, but the diversity of Microgrid charac-

teristics naturally complicates attempts to quantify benefits and to form a business

case around them. This thesis develops a framework and a methodology to identify,

1

quantify, value, and assign potential Microgrid benefits to stakeholders. This allows

the overall costs and benefits to be readily understood and interpreted as they per-

tain to investment decisions by utilities, customers, or independent power producers

(IPPs).

It should be recognized that the costs of new smart grid technologies, of which

the Microgrid is arguably a subset, remain highly uncertain [32], so the emphasis of

this work will be on methodology rather than precise values or specific recommen-

dations.

1.1 Microgrid Definitions

In the ten years since the CERTS definition was proposed, Microgrid researchers

have developed a number of alternative definitions and criteria. For example, Hatziar-

gyriou et al. [44] have proposed a definition that does not require cogeneration and

they suggest that Microgrids must connect to a wider electric power system (EPS)

at a point of common coupling (PCC), implicitly eliminating the possibility of iso-

lated Microgrids. As mentioned, Microgrids are often considered to be a subset of

the “Smart Grid”, with at least one researcher referring to them as a “pillar” of the

Smart Grid paradigm (the other pillars being better use of existing infrastructure

and more effective interaction between energy suppliers and consumers) [67]. With

the active control that is implied by the Smart Grid, others define Microgrids as nec-

essarily containing dispatchable resources such as storage devices and controllable

loads that confer the ability to intentionally disconnect from the main power grid

and to operate in a disconnected state (islanding mode) [28,86].

2

It is also interesting to note that Marnay et al. have introduced the concepts

of “milligrids” and “nanogrids” as complements to the Microgrid paradigm [67].

Milligrids are defined as community microgrids that operate on an existing section

of the distribution system and must adhere to conventional operating regulations.

“Microgrids”, use automation in individual customer networks. “Nanogrids” are

systems such as telecom or Ethernet that can supply many small devices at low

voltage and power with high reliability and power quality.

A broad definition that will be used in this thesis can be stated as follows: A

Microgrid is a system in which Distributed Energy Resources (DER)–potentially in-

cluding distributed generation (DG), energy storage systems (ESS), renewable energy

sources (RES), and demand response (DR)–are connected to loads and that makes

use of smart grid or active distribution network technology in a local control scheme

in a way that is compatible with the existing “macrogrid” infrastructure. Optional

characteristics are as follows: Microgrids may provide heat as well as power to loads,

they may be centrally controlled or control may be distributed, and they may be

connected to a larger EPS with the ability to disconnect from it and island, or they

may be isolated.

1.2 Control of Microgrids

Of some importance in the evaluation of the impacts and benefits of Microgrids

is the issue of control. Control of Microgrids may be performed centrally by a Micro-

grid Central Controller (MGCC) that communicates with Microsource and Load

Controllers (MCs and LCs, respectively), or it may be performed in a decentralized

manner using techniques such as droop control [60,87].

3

In a system with centralized control, the MGCC is responsible for co-ordinating

and optimizing the Microgrid’s operation and resource dispatch based on avail-

able information–potentially including current energy prices, fuel prices, and load

requirements–as well as reacting to various system contingencies–as during island-

ing or black start operations [70, 87]. This control scheme may attempt to optimize

operation to maximize various techincal, economic, or environmental benefits of in-

terest [86,97]. By some definitions, co-ordinated control is central to the meaning of

the term “Microgrid”, allowing DG to be operated in conjunction with energy stor-

age devices and controllable loads in ways that are not possible in an unco-ordinated

distribution system [87]; however, other Microgrid definitions do not specify the re-

quirement for centralized control [60], and in fact, decentralized control may allow for

more robust operation, as it is not dependent on any single unit for operation [44].

Furthermore, it has been noted that a Microgrid owned exclusively by a particu-

lar “non-independent” stakeholder (e.g. the utility or customer), may tend to restrict

benefits to its owner, whereas a free-market model operated by an IPP according to

various real-time price signals and objectives may offer a more fair and transparent

distribution of benefits [97]. For example, if upstream technical demands required

additional power from the Microgrid, in a free-market model additional incentives

could be added to the price signal, encouraging the DG to produce more power and

the controllable loads to reduce demand. Similar incentives could be introduced for

maximizing other benefits through other services in local markets.

4

It should also be noted that a hybrid control model, in which the MGCC does

not have total control of the system (such as in certain multi-agent control schemes),

may provide certain infrastructure cost reductions and operational benefits [29].

1.3 Microgrids and Markets

Markets can play a significant role in determining Microgrid benefit value and

to whom the benefits are accrued. Depending on how “favourable” markets and

regulations are in a particular jurisdiction, a Microgrid may be able to provide energy

to its internal loads strictly without exporting power to the utility system, it may be

able to export power at wholesale prices, or it may be able to export power at retail

prices [87]. Additionally it may be able to sell a variety of other services, as will be

discussed in Chapter 3.

The degree to which provision of these services is permitted and compensated

can vary widely from one jurisdiction to another. In general, there are four pro-

curement methods for ancillary services: compulsory provision, bilateral contracts,

tendering, and a spot market. Remuneration may be non-existent, or it could be

based on a regulated price, a bid by the service provider, or a common market clear-

ing price [58, 82]. Remuneration structures may be composed of a fixed payment or

payment based on service availability (whether a service is used or not), a utilization

payment or payment based on the frequency of utilization, or a payment based on

lost opportunity cost (for the opportunity of providing energy, for example).

Services that are needed in varying quantity over a short period of time such as

primary frequency regulation are often procured in spot markets, whereas services

for which needs do not vary significantly tend to be procured via long-term contracts

5

[58]. Primary frequency regulation, for which the DG units in Microgrids may be

best suited [103], is typically remunerated based on availability, whereas secondary

frequency control can be remunerated based on availability or as a combination of

availability and utilization. Reactive power or voltage control is often a requirement

of interconnection, and as such it is often not remunerated. When it is remunerated,

it is typically done at a fixed rate or based on availability [81,82].

Some authors have mentioned the potential effects of Microgrids on market

prices [59]; i.e. if Microgrids bring a significant amount supply to the market, com-

modity prices will be pushed down. This would only be the case with a large number

of Microgrids offering these services; individual Microgrids will probably not have

significant market power, and thus, take commodity prices as given (i.e. they are

“price takers”). Also note that while these commodities may be bought and sold

on the spot market, contracts may be in place to maintain price stability [58]. For

example, an Independent Power Producer (IPP) may have a contract to provide a

certain amount of power to consumers, and it may sell excess power on the spot

market.

1.3.1 Regulatory Environment

Microgrids can only offer benefits to stakeholders if local regulations allow them

to provide energy and services. Indeed, regulation may be a critical issue for Micro-

grid development in the near future.

The interconnection of distributed resources to an electric power system has been

given considerable attention in the past 10 years by standards-producing bodies such

as the IEEE, IEC, and CSA, and also by individual utilities and various regulatory

6

bodies. Less attention has been given to Microgrids explicitly, and so it should

be noted that while much of the regulation regarding DG units might apply to

Microgrids, in some cases there may be a grey area.

Perhaps the most widely referenced standard on distributed resource intercon-

nection is the IEEE 1547 standard, first approved in 2003 (IEEE 1547-2003) and

reaffirmed in 2008. This standard deals with the interconnection of distributed re-

sources of 10 MVA aggregate capacity or less with electric power systems [53]. It

has broadly influenced utility policies and regulations regarding DG interconnection–

including the relevant standards from the Canadian Standards Association (CSA)

(CAN/CSA-C22.3 NO. 9 and CAN/CSA-C22.2 NO. 257) [49,68].

The IEEE 1547 standard is focused on ensuring that if distributed resources

are allowed to connect to the grid, they will not interfere with the operation of a

system not designed to accommodate them. As such, the standard restricts certain

operations that have been proposed as being marketable services that Microgrids

could provide. Notably, distributed resources are explicitly forbidden from [53]:

• Engaging in voltage regulation, or

• Reconnecting to the EPS before power has been restored and voltage and

frequency been stabilized (black starting),

both of which have been proposed as potentially beneficial Microgrid operations.

These restrictions may be a challenge for Microgrid stakeholders who might otherwise

benefit from providing these services. It should also be noted that the original IEEE

1547 standard was not clear on whether DG units could intentionally disconnect

part of an EPS from a larger area EPS and energize the section to which they were

7

attached (i.e. intentional islanding). Since this original publication, an additional

standard has been published, IEEE 1547.4, approved in 2011, which clarifies the

regulations regarding intentional islanding [54].

Outside the United States, utilities are not obliged conform to the IEEE 1547

standard, and it is reasonable to assume that as utilities gain more experience dealing

with DG units and Microgrids and as they develop a better understanding of how

the systems interact, they may become more willing to allow DG units or Microgrids

to provide some of these additional services. As an example, BC Hydro has allowed

cases of intentional islanding since at least 2006 [12].

The work in this thesis does not assume a particular market or regulatory en-

vironment, but leaves the issue open, allowing it to be treated on a case-by-case

basis.

1.4 Previous Efforts in Benefit Quantification and Related Work

Direct, energy transaction-based benefits may be significant in Microgrids, de-

pending on the market environment and regulations. In many cases, however, Micro-

grid business cases require a more complete picture of the benefits and services that

Microgrids can provide in order to be profitable. A cost-benefit analysis is a tool

for comparing the relative desirabilities of various courses of action, especially in

cases where market prices (which ideally act as signals to optimize investment) do

not fully or correctly account for various externalities [76]. This thesis will apply

the principles of cost-benefit analysis to Microgrids, considering a number of benefits

that may not be currently valued in markets, or where direct economic value may

not represent the full benefit Microgrids can provide to stakeholders.

8

Since the birth of the Microgrid concept a decade ago, a number of benefits have

been identified, and valuation of some benefits has been reported in the literature.

It should be recognized that Microgrid benefits inevitably overlap with benefits from

DG alone as well as with the benefits of Smart Grids or Active Distribution Net-

works (ADN). However, many of these overlapping benefits can be improved by the

potential for resource co-ordination in the Microgrid, and the ability to intentionally

island or separate from the distribution network.

An oft-cited benefit of Microgrids is the ability to island during an upstream

outage or disturbance, improving the reliability of service provision to Microgrid

customers. Approaches to reliability valuation in Microgrids usually follow a de-

terministic formulation, favoured by utilities, and a number of papers propose and

apply these formulations as they apply to both DG and Microgrids, primarily in

radial systems with sectionalisers [24, 26, 35, 47]. Costa et al., however, generalize

this approach in evaluating the reliability improvement brought by a Microgrid to

a wider meshed system [26]. In this approach the Microgrid is assumed to be able

to provide power during an outage to customers not normally considered part of the

Microgrid, thereby improving reliability indices on the wider system. It is interest-

ing to note that Hlatshwayo et al. have demonstrated reasonable agreement between

analytic and stochastic (i.e. Monte Carlo) approaches to evaluating the potential

contributions of Microgrids to reliability [47].

Provision of ancillary services such as frequency support, voltage support, peak

load reduction, and black start support have been proposed as a viable source of

benefits for multiple stakeholders, especially given the history of compensation for

9

these services in some jurisdictions [82,89]. It has been noted that the fast response

of Microgrid resources (potentially including DG, ESS units, and Demand Response)

and the presence of control could make Microgrids or collections of Microgrids ideally

suited to provide frequency and voltage support services [40, 103, 104]. The focus of

researchers studying this area tends to be optimal control of bids for reserve, and

this issue has been treated in detail by a number of papers as well as the textbook

by Kirschen and Strbac on power system economics [6, 40,58,84,104].

The possibility that Microgrids may offer Black Start support services has also

been investigated–the stage being set by Fink et al. in 1995 when they surveyed

system restoration strategies with an interest in software-based control of the pro-

cess [34]. The aptness of Microgrids to assist with the complex process of service

restoration or black start after a major outage has not been overlooked. It has been

proposed that they would be able to use the resources at their disposal (including

their internal generation, load-balancing, and co-ordinated control) to restore power

to external, low voltage loads at the same time as the restoration of high voltage

transmission corridors was happening higher in the network. This would reduce

down time and partially mitigating decreases of reliability indices in the event of

a major blackout [18, 70, 78]. The benefits of this type of black start support can

be accounted for using the powerful approach for reliability evaluation discussed by

Costa et al. [26].

Distributed generation has been recognized as a means to reduce peak system

loads by providing power sources close to power sinks, thereby mitigating network

10

congestion, voltage drop, and losses, and delaying the necessity of certain infras-

tructure investments. A depreciation-based valuation method of this benefit was

proposed by Gil and Joos in 2006 [39], and further developed by several other re-

searchers, [2, 79, 100], including considerations for long-term planning and system

security improvements [99]. It should be noted that Microgrids, with their broader

availability of resources to control power flows, and their potential for co-ordinated

control of those resources, may be even better suited for dependable peak load reduc-

tion than DG alone, especially when combined with market incentives to do so [97].

Reduced system loss is closely related to reduced peak loading, in that Micro-

grids can effectively co-ordinate resources to reduce losses (as though levelling out im-

port and export across the PCC), and to improve system efficiency (as through com-

bined heat and power (CHP) or combined cooling heat and power (CCHP)) [9,27,65].

This improved efficiency along with the integration of renewable energy sources into

the Microgrid can lead to a reduction in pollutant emissions and generate other

environmental and social benefits.

A number of papers deal with direct economic evaluation and optimization of

Microgrids. These are often focused on minimizing energy cost, but may include

optimization of a number of technical, environmental, and economic benefits [5,

28, 86, 86, 106]. Although the majority of such research focuses on grid-connected

Microgrids, a few researchers recognize and deal specifically with Microgrid benefits

and operation in remote areas [1, 20], which is of interest to utilities with rural

communities without a connection to a large grid, as is the case in much of northern

Canada.

11

In addition to individual papers, larger projects on broad smart grid evaluation

methodologies that take into account a number of smart grid benefits have been

reported on by EPRI and NETL/DOE [32,33,92]. The More Microgrids project has

been aimed at developing an understanding of the benefits of Microgrids and Multi-

Microgrid systems from a European perspective, and researchers from that group

have contributed a significant body of work describing many individual Microgrid

benefits, many of which have been published as individual papers already highlighted

[85,87,89].

1.5 Thesis Scope and Contributions

Despite advances in the valuation of various individual benefits of Microgrids

and Distributed Generation, a cohesive framework tied to a combined, comprehensive

evaluation methodology is needed to combine these potential benefits into clear busi-

ness cases with financial merit. This framework must outline not only benefits and

beneficiaries, but the actual quantified flows of benefits to each stakeholder. Further-

more, the impacts of the Microgrid must not only be quantified in this methodology,

but where possible, their economic value to each stakeholder must be clearly defined.

This is necessary to spur investment in Microgrids.

In meeting these challenges, this thesis develops a framework to illustrate the

division of benefits between stakeholders (Chapter 2), details a methodology to quan-

tify and evaluate those benefits (Chapters 3 & 4), describes a number of business

cases to be made for Microgrids in various conditions, and evaluates case studies

based on established or proposed Microgrid projects (Chapter 5).

12

This thesis does not attempt to answer specific questions regarding the current

economic viability of Microgrid development, as it is recognized that specific invest-

ment and commodity costs can vary significantly with time, and price forecasting

is outside the scope of this work1 . Similarly, the methodology presented assumes

no specific technologies, but rather focuses on functionalities of the Microgrid, pre-

serving its generality. As a further scope limitation, it should be emphasized that

the functionalities and potential benefits detailed here are believed by the author to

comprise a set of the most significant, but they are by no means complete. Many

other benefits could potentially be derived from Microgrids, as briefly discussed in

Chapter 2.

1.6 Thesis Summary

In Chapter 1, an overview of Microgrids, their operation, relevant regulations,

and market characteristics is given. A literature review is described, including an

overview of previous work in the area of Microgrid benefit identification and quan-

tification. The scope and contributions of the thesis are discussed, and a chapter-

by-chapter summary of the thesis is provided.

Chapter 2 describes the tools and framework on which the methodology is based.

It develops the use-case-based approach taken by the author to organize Microgrid

stakeholders and benefit flows. It identifies key stakeholders and the types of benefits

1 In Appendix D, useful model parameters have been listed with year of publica-tion.

13

that accrue to them, and it describes each of the major benefits addressed in the rest

of the thesis.

The methodology for benefit quantification is described in Chapters 3 & 4.

Chapter 3 describes a general methodology for evaluating Microgrid case studies

relative to a base case through a cost-benefit analysis. The steps are outlined from

defining cases to performing the analysis to comparing alternatives. Chapter 4 follows

this general analysis description with specific, detailed descriptions of quantification

methodologies for the major benefits treated.

Chapter 5 applies the framework and methodology provided in the preceding

chapters to three business cases based on actual Microgrid installations, a community

Microgrid, a commercial Microgrid, and an isolated, utility-owned Microgrid. In

each of these cases additional information has been furnished beyond the publicly

available data, with the intention of illustrating key concepts and operation of the

methodology.

Chapter 6 summarizes the work and explains what conclusions can be drawn

and where additional work is needed.

14

CHAPTER 2Framework and Tools

2.1 Introduction

As mentioned in Chapter 1, a number of potential technical, economic, and social

benefits have been ascribed to Microgrids. A few of these benefits are commonly

recognized and have been studied in some detail including: locality and selectivity

benefits (economic); the provision of ancillary services, Power Quality and Reliability

(PQR) improvements, and reduced peak loading and system losses (technical); and

reduced emissions (social). In addition to these common benefits (which will be

termed “major” benefits in this thesis), there may be a number of less significant or

less direct “minor” benefits, many of which are also associated with the Smart Grid,

including (but not limited to):

• Reduced dependence on external sources of oil [33],

• Reduced natural resource usage,

• Reduced power restoration costs [33],

• Reduced congestion cost [33,69],

• Reduced meter reading costs [33],

• Increased local employment [101].

It is clear that depending on the level of detail required in an analysis, its

structure could easily become quite complicated. In order to organize and structure

the analysis of Microgrid costs and benefits, which may include a large and variable

15

number of benefits, which in turn may apply to a large number of stakeholders,

a scalable, modular framework has been developed that maps the distribution of

benefits to the various stakeholders [102], which will be described in this chapter.

This framework was inspired by the Universal Modelling Language (UML) Use Case

paradigm, which is used to describe systems in terms of their functions or interactions

with users or other systems. In the Microgrid framework, Microgrids are described

in terms of “stakeholders”, “impacts”, and “functions”. These will be described in

the following sections.

The key advantage of this modular framework is that any additional benefit or

stakeholder can be added, removed, or combined with another.

2.2 Stakeholders

Stakeholders are all parties with some interest in a Microgrid, whether it is

through its economic, technical, or social impacts. Stakeholders include the following

(or combinations thereof):

• The end-use Microgrid Customers (µGCs),

• The Microgrid Owner or Independent Power Producer (IPP),1

• Utilities, which may include generation utilities, System Operators (SOs) or

Bulk Energy Suppliers (BESs),

• Customers outside the Microgrid (herein referred to as “Grid Customers”

(GCs)), and

1 Note that these categories may overlap. For example, the Microgrid Customermay also be the Microgrid Owner.

16

• Society.

Each stakeholder has different types of benefits that accrue to it.

Microgrid customers are energy consumers to whom power will be provided

through the Microgrid infrastructure. They can benefit from reductions in electrical

energy costs, and improvements in PQR [38, 92]. Energy cost reductions can result

from a reduction in consumption, for example through increased efficiency, or from

reductions in peak charges and cost per energy consumed.

Independent Power Producers (IPPs) or Microgrid Owners own and operate

the Microgrid, and are responsible for meeting any contractual obligations for terms

of provision of energy or other services. They could benefit from sale of energy,

from the proceeds of contractual agreements for provision of other services, or from

participation in other markets such as for ancillary services.

Utilities are entities that supply electrical energy in large quantities. They could

benefit from reduced Operation and Maintenance (O&M) costs, deferred investment

and upgrade costs, reductions in contractual compensation or fines for poor PQR,

and possibly from reduced or avoided energy or service purchases [26, 38,92].

Grid Customers (GCs) are energy consumers not directly connected to the

Microgrid infrastructure and who may not have any kind of direct contractual agree-

ment or financial interactions with the Microgrid Owner, but can be impacted by

the Microgrid through the distribution system. Despite this arms-length interaction,

GCs may benefit from improved reliability, for example, if the Microgrid is able to

provide power to an adjacent section of the external power system that has expe-

rienced loss of power from the upstream network [26]. They may also benefit from

17

improved power quality, for example, if the Microgrid is able to inject reactive power

as part of a voltage support service.

Society represents all entities impacted by the Microgrid outside those already

listed. Externalities not directly impacting other stakeholders accrue to Society.

These may include reductions in emissions, resource use, infrastructure footprint,

and increases in local employment [92,101].

A summary of these stakeholders and the benefits that accrue to them is shown

in Table 2–1. It should be noted that in some cases there may be no IPP, and the

Microgrid will be owned and operated by either the customer(s) or by the SO or

utility. Similarly, in some jurisdictions there may be a separate SO or BES rather

than a monolithic utility. In this case the functions and accrued benefits of each

stakeholder would be separated.

Table 2–1: Microgrid StakeholdersActor Name Brief Description Benefits Accrued

Microgrid Customers(µGCs)

Residential, commer-cial, or industrial loadswithin the Microgrid.

Cost reductions, PQR im-provements.

Grid Customers (GCs) Loads outside theMicrogrid.

PQR improvements.

Microgrid Owner or IPP Owner of Microgrid. Profit from energy and ser-vice sales.

Utilities The entities outside theMicrogrid that supplypower.

O&M reduction, reduction offines or fees for PQR viola-tions, deferred or avoided in-vestments.

Society Anyone who might beaffected by Microgridexternalities.

All externalities incl. reducedemissions.

18

2.3 Impacts and Benefits

The benefits of a Microgrid are the results of the changes that it effects in

the electric power system (EPS) and in the economic system in which it is framed.

These changes or “impacts” may be thought of as “proto-benefits”; they are the

direct, measurable results of a Microgrid’s operation that have yet to be transformed

into the benefits that accrue to the various stakeholders. Impacts may include highly

technical and easily quantifiable measures such as reduced peak loading as well as

more nebulous concepts such as increased local employment. “Benefits” on the other

hand, must directly apply to Stakeholders and must fit into the categories of benefits

accepted by each Stakeholder. This distinction has been drawn from EPRI’s work

on Smart Grid benefits [33].

A further distinction between two types of impacts arises in an analysis in that

some impacts must be known a priori before engaging in an analysis, and some must

be extracted or “discovered” during an analysis or simulation of Microgrid operation.

Examples of these two types of impacts are:

• Load Growth Rate (Known)

• Emissions Rates of DG units (Known)

• Energy consumed from each source (Discovered)

• Peak currents through equipment (Discovered)

Note that in order to apply a cost-benefit methodology in an economic frame-

work, it becomes further necessary to economically quantify all benefits insofar as

possible. This has been demonstrated in the fourth chapter of this thesis.

19

2.4 Benefit Functions

Given these definitions of the stakeholders and parameters, the benefits of Mi-

crogrids can be viewed in terms of “functions”, a concept borrowed from the UML

Use Case paradigm. These benefit functions provide a value to stakeholders based

on the technical, economic, and environmental/social impacts that result from the

characteristics and operation of the Microgrid along with other system parameters.

Thought of another way, benefit functions map Microgrid impacts onto Stakeholder

benefits. They are the central component of the cost-benefit evaluation methodology.

As a very simple example of how a benefit function would work, suppose that a

Microgrid reduced the total carbon emitted to the atmosphere in providing energy

to Microgrid customers by 100 t per year, and suppose that customers were charged

by their carbon use at a rate of $20 per tonne. In this case, the benefit function

would find the benefit to the customer as:

100t/yr × 20$/t = 2000$/yr.

In practice, however, benefit functions may be significantly more sophisticated,

as described in the next chapter.

A representative sample of these functions is summarized in Table 2–2 and

described in more detail in Tables 2–3 through 2–72 . The connections between

stakeholders and functions are summarized in Figure 2–1. Note that Reduced Energy

2 Note that this work has been reported in a publication by the author [102].

20

Purchased Cost combines the effects of selectivity and locality benefits into one

function.

Figure 2–1: Overview of Relationships between Microgrid Benefit Functions.

21

Table 2–2: Summary of Microgrid Benefit Functions

Category Function Name Stakeholder(s)Receiving Benefit

Table

Economic Reduced Energy Purchased Cost µGCs, IPP 2–3

TechnicalReduced System Loading IPP, Utility 2–4Improved Reliability IPP, Utility, µGCs 2–5Ancillary Service Provision IPP, Utility, µGCs 2–6

Social Reduced Pollutant Emissions MGCs, GCs, IPP,Utility

2–7

Table 2–3: Function: Reduced Energy CostsFunction Name Reduced Energy Purchased CostStakeholder(s) Re-ceiving Benefit

µGCs, IPP

Description The presence of internal DG sources allow local provisionof energy to loads (locality benefit) avoiding energy pur-chase from the grid, the controllability of the Microgrid’sresources can allow energy to be purchased when pricesare low and sold when prices are high (selectivity benefit),and the presence of efficiency improving measures such asCHP to reduces the total energy that is consumed. To-gether these reduce the total cost of meeting MicrogridCustomer loads.

Required Impacts (D) Amount of energy purchased, from whom, and at whatprice both with and without the Microgrid

QuantificationMethodology

The operation of the Microgrid is simulated given resourceand load profiles, commodity prices, and a dispatch orcontrol strategy. Depending on regulations, energy may beexchanged with the grid. The value of energy sales and thecost of energy production are accrued to the appropriateStakeholders (IPP or utility), and energy purchasing costsare accrued to customers.

22

Table 2–4: Function: Reduced LoadingFunction Name Reduced LoadingStakeholder(s) Re-ceiving Benefit

µGCs, IPP, Utility

Description The reduction of peak loading may extend the life of cer-tain network equipment, allowing upgrade or replacementinvestments to be deferred. This provides value to theutility through the present value of money not spent. Inaddition to this, if peak charges are in place, the IPP andµGCs may benefit from a reduction in that charge. Fur-thermore, the reduction in average system loading can re-duce losses, which is of primary benefit to the utility orSO, as it pays for energy dissipated in its system.

Relevant Impacts (K) Load growth rate; (K) Planned infrastructure invest-ments (e.g., future substation upgrades); (D) Peak andaverage power through equipment of interest both withand without the Microgrid


Peak loading is found through simulation with and with-out the Microgrid. The upgrade timelines and consequentpresent value of investments can be calculated from knowndemand growth and interest rates. The difference betweenthe present values of investments in each case provide thebenefit to whichever entity is responsible for making theinvestment.

23

Table 2–5: Function: Improved ReliabilityFunction Name Improved ReliabilityStakeholder(s) Re-ceiving Benefit

µGCs, GCs, Utility, IPP

Description Microgrids can reduce the impact of outages experiencedby internal loads by islanding from the EPS in the event ofa fault or disturbance and prioritizing supply to more im-portant loads. Depending on the network configurationand degree of automation, Microgrids can also provideemergency power to outside customers during a contin-gency, and they can reduce outage durations in the eventof a major system outage.

Relevant Impacts (D) Expected outage frequency, duration, times, and Non-Delivered Energy (NDE) both with and without the Micro-grid; (K) Value of customers’ power reliability and relatedfees and fines to the utility


Reliability is primarily quantified using a few standardmetrics–chiefly, Non-Delivered Energy (NDE) and SAIFI,and SAIDI. Reliability indices can be found analyticallyor simulated using stochastic methods. These are thencompared to a status quo base case. The economic valueof reliability is often customer-dependent, and may beassigned through contractual arrangement or more com-monly through regulatory oversight.

24

Table 2–6: Function: Ancillary ServicesFunction Name Ancillary Service ProvisionStakeholder(s) Re-ceiving Benefit

Utility, IPP

Description Ancillary services that can be provided by Microgrids areprimarily voltage and frequency support, although blackstart support may also be considered. These services mayimprove local PQR and reduce the costs associated withmeeting PQR requirements.

Relevant Impacts (K) Contracted ancillary services use; (K) Contracted an-cillary services value; (K) Load growth rate; (K) Plannedinfrastructure investments to ensure power quality; (D)Deferral time of power quality investments


The value of ancillary services are usually determinedthrough market prices or through contracts [57,74]. Theirvalue to a utility can be through deferral of investmentsneeded to maintain power quality through means otherthan the Microgrid. If service provision is determinedthrough a bidding system, bidding behaviour should besimulated along side energy exchange.

25

Table 2–7: Function: Reduced EmissionsFunction Name Reduced GHG and non-GHG EmissionsStakeholder(s) Re-ceiving Benefit

Society, IPP, µGCs

Description Microgrids that possess low emitting or highly efficientDER (as through CHP) may produce less emissions thanare produced to meet demand in the base case. The emis-sions of certain pollutants are harmful to Society.

Relevant Impacts (K) Emissions rates of each source; (K) Cost of Emissions;(D) Energy consumed from each source


The amount of emissions from energy use within theMicrogrid is found through simulation, and this is com-pared to the base case of national emissions per kilowatthour of electricity production. Greenhouse Gas (GHG)emission reduction can be economically valued using typi-cal GHG or carbon tax rates as a guide. Valuation of otherpollutant reduction is indirect, but could be based on Soci-etal costs of medical expenses and agricultural losses frompollutants.

26

2.5 Chapter Summary

The framework in which to consider Microgrid benefits has been described. This

includes a description of the key stakeholders and the types of benefits that accrue

to each, namely, Microgrid Customers, the Microgrid Owner or Independent Power

Producer, the utilities, Customers outside the Microgrid, and Society. The major

benefits analyzed in this thesis were introduced and described, namely energy ex-

change benefits, the provision of ancillary services, PQR improvements, and reduced

peak loading, reduced system losses, and reduced emissions. Furthermore, the re-

lationships between the stakeholders and benefits was outlined. The stage has now

been set for the next chapter, in which the benefit quantification methodology will

be described in detail.

27

CHAPTER 3Cost-Benefit Analysis Methodology

3.1 Introduction

This chapter begins by describing a general methodology to evaluate Microgrid

costs and benefits in a general sense, and concludes with a series of detailed de-

scriptions of individual benefit evaluation based on the framework described in the

previous chapter.

As mentioned in the previous chapter, the direct, measurable “impacts” of Mi-

crogrids do not necessarily have an inherent value, and to consider them in an

economic analysis, they must be mapped onto “benefits” and, ideally, economi-

cally quantified. Efficient benefit allocation is necessary to ensure optimal decision-

making, as regards investing in Microgrids and additional DG [38]. This includes

incentives for carbon offset, etc.

The Microgrid evaluation methodology presented here will focus on evaluating

the merit of single Microgrids (as opposed to networks of multiple Microgrids or

“Multi-Microgrids”) for their net benefits to stakeholders. In principle, the flexible

Use Case-based approach could be extended to take into account broader Smart Grid

benefits and the benefits from Multi-Microgrids.

3.2 Methodology Overview

As stated in Chapter 1, cost-benefit analysis is a tool for comparing the relative

desirabilities of various courses of action, especially in cases where market prices do

29

not fully or correctly account for various externalities [76]. The primary feature of

interest in cost benefit analyses is usually the net gain each alternative is expected to

provide to stakeholders relative to a “base case” (usually “business as usual”). This

can be expressed using metrics such as Net Present Value (NPV), Internal Rate of

Return (IRR), or simply a Benefit-Cost Ratio (BCR), as explained in Appendix A.

Note that if alternatives are compared using an economic metric, this metric

can only account for benefits that are assigned an economic value.

An outline of a cost-benefit methodology that can be applied to Microgrids

is given here. In general, the purpose of the analysis is to convert a set of data

and assumptions about the Microgrid and the base case (known impacts and other

data inputs) into benefits for each Stakeholder, and to use benefits that can be

economically valued to find an approximation of the relative value of the Microgrid

(or a set of Microgrid alternatives) to each Stakeholder as compared with the base

case.

1. Establish the context and determine base case.

2. Determine the Microgrid infrastructure and the functionality that will be added

to the base case in each alternative.

3. Estimate the discovered impacts in each case.

4. Perform the economic analysis.

5. Compare alternatives.

The details of this methodology will be explained throughout this chapter.

Note that in many cases, analysis inputs are not known with a high degree

of precision or certainty. As such, sensitivity analysis can be performed on these

30

parameters within their probable ranges to indicate a probable range of benefits. A

brief treatise on sensitivity analysis can be found in Appendix B.

3.3 The Base Case and Context

Benefit-Cost Analysis is essentially a measure of the difference between cases.

Therefore it is critical that the Base Case or Control Case is accurately defined

[33, 36]. Typically it would consist of the operating data and the topology of the

network in a “business as usual” situation without any of the Microgrid functionality.

It would incorporate costs associated with status quo operation, including energy

costs, service costs, and any infrastructure upgrade costs without the Microgrid

(for example, to mitigate system constraints associated with rising demand). The

additional costs and benefits associated with each alternative case are compared

against this base case.

It is clear that careful selection of a valid base case is important. For example,

if a data centre were interested in the potential reliability improvements brought

by a Microgrid, a possible base case might consist of a direct EPS connection, and

the alternative cases of an Uninterruptible Power Supply (UPS) and a Microgrid

could be compared with that basis. However, if the data centre already had a UPS

installed, the true costs of the Microgrid over the UPS would not be reflected. It

would be better to consider the UPS-installed case as the base case, and consider

the Microgrid case on top of that, including the considerations of uninstalling and

selling or scrapping the UPS.

Ideally, real life data collected from the system in operation would be available,

otherwise, simulated or forecast data will have to be used. In some cases forecast

31

data may be preferred, for example, in considering the effects of load growth, as for

reduced peak loading [33]. The data should be taken over a long enough time period

to be representative of actual operation for a legitimate comparison. For example, if

reliability improvement is being considered, data should be taken over a period with

a representative number of outages. Resource data (e.g. wind or hydro resource time

series) should be included in this data, if necessary.

The economic context must be defined. Market characteristics and economic

parameters must be determined. These are often the most critical components of an

analysis, and small errors in these values can lead to large errors in results. These

include:

• The IPP’s cost of capital, and the expected project length.

• The cost of electricity and the costs and values assigned to other services. This

also includes whether energy or ancillary services can be sold by the Microgrid,

whether electricity is purchased at a fixed or varying rate, and whether other

tariffs are applied, for example on peak loading.

• The cost of any penalties for reliability or power quality infractions.

As explained in Appendix A, it is not always useful to consider tax and inflation

rates, but if required, these should also be determined.

Finally, the stakeholders and equipment owners must be determined in order

to determine where costs and benefits accrue. This answers the following questions:

Who owns what before and after Microgrid installation? Is there an IPP or is the

Microgrid customer owned or utility owned? Does the Microgrid have to pay any

32

fee to the utility to use the distribution system? Is the SO independent or part of a

monolithic utility?

3.4 The Infrastructure and Functionality of the Microgrid

In this step the analyst must determine the remaining parameters that define

Microgrid functionality and any remaining “known impacts” and inputs that must

inform the simulation of the Microgrid to extract the “discovered impacts”. The

analyst must decide on exactly what technologies will be used and with what specifi-

cations and costs. Aside from the direct installation and O&M costs of the Microgrid,

certain other costs may apply, notably the DSO may have to invest in network re-

inforcement upgrades to deal with large amounts of DG power injection [97], staff

retraining, or possibly even protection reconfiguration.

At this point it is helpful to acknowledge that there are many different types

of Microgrids, and quantification of Microgrid benefits is highly dependent on the

various characteristics that define the Microgrid. For example, the type or types of

Distributed Generation used can be quite diverse, and may include: wind turbines,

solar photovoltaic panels, hydro turbines, fuel cells, and various small thermal gen-

eration units. A list of the characteristics that can fundamentally affect the process

of valuation is summarized in Table 3–1 [102]. Parameters with purely numerical

effects on valuation, for example fuel prices, average wind speeds, or interest rates,

are not included in this list.

Service provision contracts or service bidding strategies should be determined

at this stage. This includes ancillary service contract terms or expected bid prices

33

Table 3–1: Microgrid Valuation ParametersParameter Description

CHP Integration Whether Combined Heat and Power (CHP) is used inMicrogrid (µG).

DER Mixture The combination of Distributed Energy Resources (DER)used in µG. For example, are Microturbines or RenewableEnergy Sources (RES) used?

Load Mixture The mixture of load types in µG. Are dispatchable orcritical loads included?

Market Characteris-tics

Whether energy or ancillary services can be sold to theDNO, whether electricity is purchased at a fixed or vary-ing rate, and whether other tariffs are applied, for exam-ple to reduce peak loading.

Isolation Whether the µG is connected to the EPS during normaloperation, or instead operates exclusively independently.Note that by some definitions, an isolated Microgrid isnot a “true” Microgrid.

Capable of Islanding Whether the µG is capable of disconnecting from the EPSin the event of a fault or other contingency. Converselyto isolation, islanding capability may be a requirement ofthe definition of “Microgrid”.

and requirements, as well as PQR requirements and incentives. These parameters

will vary from one jurisdiction to another.

The operational strategy of the Microgrid also needs to be ascertained. For

example: How will dispatchable generation be controlled? If demand response is

used, what is the aggregated load demand curve? What about ESS? Will service

provision be incentivised in the controller?

34

3.5 Simulation and Analysis

At this point, discovered impacts are found for each alternative case through

simulation and through direct, analytic calculation (applying benefit functions). De-

tails of this step are explained for a variety of benefits in the following chapter.

Note that impacts should be determined over a common period–for example annual

impacts may be used.

Software tools, as described in Section 3.8, are helpful in this step, but unfor-

tunately, most publicly available analysis software is limited in scope, especially as

regards Microgrids. This software is primarily limited to simulating distributed gen-

eration without additional Microgrid functionality (such as islanding). Therefore the

author created a new software tool to calculate benefits derived from a Microgrid

project for various stakeholders and compare them with a base case. This software

is described in the latter half of Appendix C.

3.6 Economic Analysis

It is in this step that benefit functions are applied to find the benefits distributed

to each Stakeholder from the list of known and discovered impacts for each alternative

case. Where possible, benefits should be economically quantified so that they may

be included in an economic comparison as described in Appendix A. The details of

this step are described along with the details of the preceding step for a number of

specific benefits in the remainder of this chapter.

35

3.7 Alternatives Comparison

In this final step the approaches described in Appendix A can be used to compare

the Microgrid to alternative courses of action. These include Net Present Worth

estimations, Rate of Return calculation, and Benefit-Cost Ratio calculation.

If the analyst is trying to optimize Microgrid investment, as opposed to compar-

ing predefined Microgrid investments, parameters of interest can be adjusted, and

this methodology can be iterated.

3.8 Analysis Software

It should be noted that there are a number of software packages designed to

aid in analysis of the costs and energy production of Distributed Generation and

in some cases Microgrids. Determining the relative merits of each software package

may improve the efficiency and ease with which analysis of a Microgrid’s costs and

benefits is carried out.

Three such software packages are known at present to the author, namely:

the Distributed Energy Resources Customer Adoption Model (DER-CAM), cre-

ated and maintained by Micheal Stadler at Lawrence Berkley National Laboratory

from 2000 - present [61]; Renewable-energy and Energy-efficient Technologies Screen

(RETScreen), created in 1997 and maintained by National Resources Canada [73];

and Hybrid Optimization Model for Electric Renewables (HOMER), created for the

National Renewable Energy Laboratory in the United States in 1997, and maintained

by HOMER Energy LLC [48]. For a detailed comparison of these software packages,

please see Appendix C.

36

All three have the ability to consider long-term DER investments, Combined

Heating and Power (CHP), Combined Cooling, Heating, and Power (CCHP), emis-

sions, and sales and purchases from the grid. They vary greatly in their ease of use

and in the DER types and system types that may be considered. Two universal

weaknesses are the implicit assumption of customer-owned DER, and the inability

to consider DER effects on feeder voltage or distribution losses.

Given the fact that the three applications only consider the economics of energy

exchange using DER units, the methodology in this thesis still requires additional

information from other sources to perform a full cost-benefit analysis. Therefore, a

special-purpose program was developed by the author to incorporating the benefit

functions mentioned in the previous section with an economic analysis. This tool

has been used to generate the data presented in Chapter 5.

3.9 Chapter Summary

This chapter has described a cost-benefit analysis procedure in detail, including

determining the base case and context of the Microgrid, determining the Microgrid

functionality, estimating the discovered impacts, performing the economic analysis,

and comparing alternatives. Software tools that can aid the analyst are also briefly

described. The results of this chapter and Chapter 2 provide the framework into

which the quantification of individual benefits fit, as described in the following chap-

ter.

37

CHAPTER 4Benefit Quantification

4.1 Introduction

This chapter provides detailed descriptions of how to quantify the major benefits

outlined in Chapters 1 and 2, namely:

• Reduced Energy Purchased Cost

• Reduced System Loading

• Improved Reliability

• Ancillary Service Provision

• Reduced Pollutant Emissions

The quantification methodologies described here fit within the framework de-

scribed in Chapter 2, and inform the cost-benefit analysis described in Chapter 3.

4.2 Reduced Energy Purchased Cost and Energy Exchange

Energy-based transactions can represent a significant source of income for Mi-

crogrids. There are two primary benefits that Microgrids can provide in terms of

energy transactions, “locality benefit” and “selectivity benefit” [87].

Locality benefit is derived from the ability of the Microgrid to sell power di-

rectly to its customers, bypassing the wider transmission and distribution system

and avoiding the losses and fees associated with them. Retail prices charged to

energy consumers can be significantly higher than wholesale prices paid to energy

suppliers, with added charges for network usage, service fees, market charges, retail

38

charges, and taxes. Without the need to sell power into a central wholesale mar-

ket and without many of those additional charges, Microgrids are able to sell power

to their customers at a rate that is higher than wholesale price and lower than re-

tail price. This provides direct economic benefit to both the Microgrid owner and

customers [97].

In addition to the reduced fees and losses enabled by local provision of power,

the proximity to the customer makes feasible the provision of Combined Heat and

Power (CHP) as well as Combined Cooling Heat and Power (CCHP), also known as

co-generation and tri-generation, respectively [65]. These services typically use the

waste heat from thermal-based DG units such as microturbines or Solar Photovoltaic

with Thermal (PVT) to heat customers directly and to cool them via an absorption

chiller. The use of waste heat increases the effective efficiency of DG units to as high

as 80% [9,11]. This can further reduce the cost for the IPP to provide energy to the

customers and can be thought of as an extension of locality benefit.

In jurisdictions where Microgrids are allowed to import and export power, their

flexibility typically allows them a degree of selectivity regarding when they import

and when they export, potentially taking advantage of energy resources such as

dispatchable generation (thermal and hydro, primarily), energy storage, and demand

response. In jurisdictions with time- or market-based fluctuations in energy prices,

this can allow Microgrids to “buy low and sell high”; that is they can import power

(if needed) when energy prices are low, and they can export power (if allowed) when

energy prices are high, potentially reducing costs or increasing income significantly.

This increase has been termed “selectivity benefit” [87].

39

Note that in some jurisdictions, Microgrids with renewable energy sources (RES)

may be given special treatment by the utility or system operator in that a feed-in

tariff (FIT) will be paid to the Microgrid Owner for power exported to the grid at a

higher rate than wholesale.

These energy transaction-related benefits can be effectively estimated using a

time-series simulation of Microgrid operation, taking into account load variations

(including thermal loads if either CHP or CCHP is included), energy prices and price

variations (if applicable), resource variation (if applicable), and the control strategy

of the Microgrid. This type of simulation is able to find the energy transactions

(the impacts in this case) and the economic values can be compared to the cost

of energy in the base case to find the combined benefits of selectivity and locality.

These benefits manifest in simulation as differences in energy price paid in each case,

and it is probably inutile to separate the value of these benefits beyond finding the

relative impacts of different market characteristics.

Analysis of these transactions is the primary use of the software tools mentioned

in Chapter 2. However, in all three software tools studied, the Microgrid (actually

the DG in most cases) is assumed to be owned by the customer. The analyst must

separately account for the financial transactions between the Microgrid owner and

the customer if they should be separate entities in the Microgrid under analysis.

This can be easily calculated from the energy consumption found in simulation and

assumed prices for energy between the IPP and the customer.

40

4.3 Reduced System Loading

In general, demand for energy is constantly increasing, and much of that de-

mand is for electrical power. This increasing demand naturally means that more

power must flow through transmission and distribution systems, bringing operat-

ing conditions closer to network limits on branch currents (congestion) and voltage

drop [85]. If unmitigated, this can lead to blackouts and failures, and as infrastruc-

ture support can be expensive, even mitigation measures can lead to higher energy

prices [2].

Typically, as an operating limit is approached it must be mitigated by support-

ing, upgrading, or replacing constrained infrastructure. A Microgrid can support

network infrastructure by reducing the power that must be supplied to Microgrid

customers through the network, both by providing power locally and by curtailing

demand during peak network loading (if load control or demand response is present).

Note that a Microgrid must be able to dispatch power (or reduce load) in order to

be able to provide certain peak load reduction with the degree of certainty required

in power system security analysis.

Reducing peak power requirements has the added effect of potentially extending

the lifetimes of otherwise strained network components, thereby deferring or elimi-

nating the need for other infrastructure investment. This has an associated financial

benefit to the utility [2, 38, 79, 99]. If charges are in place for peak demand, reduc-

tion of this peak will also have a direct financial benefit to the IPP and Microgrid

customers. Reducing power requirements more generally has the benefit of reducing

network losses [27].

41

In the case of network upgrade deferral, the magnitude of the benefit is de-

pendent on two factors, which must be determined for the base case as well as all

alternative (Microgrid) cases: the value of the necessary investment to maintain net-

work security, and the time until the investment is required. In the simplest case,

we can consider a single upgrade which is deferred to some time in the future by the

Microgrid. The present value of a single future investment costing Ci in year Yi at

interest rate d is

PV =C

(1 + d)Y. (4.1)

If the presence of the Microgrid allows the investment, C, to be deferred from year

YBC to YµG, the net benefit will be the difference between the present value in the

base case, PVBC and that in the Microgrid case PVµG, i.e. [79]

B = PVBC − PVµG

= Ci ×(

1

(1 + d)YBC− 1

(1 + d)YµG

).

(4.2)

As required investments may not only be deferred, but may actually be changed

or eliminated within the planning horizon, the most comprehensive way of calculating

investment deferral benefits is to compile a list of required network upgrades in each

case based on network component limits, peak power flows, and assumed levels of

peak load growth [38, 99]. In this case, the cost in each case can be found by the

more general equation

Inv.Cost =h∑t=1

n∑i=1

Ci,t(1 + d)t

(4.3)

where t is the time step from 1 to the end of the planning horizon, h, n is the number

of investments required in time step t, Ci,t is the cost of asset i in time period t, and d

42

is the interest rate [99]. This cost can be applied to the utility or system operator in

each case, and the benefit will be the difference between this cost in each alternative

case and the cost in the base case.

Note that in some cases, infrastructure investment may be needed sooner or to

a greater extent with the presence of a Microgrid. This would happen, for example,

if the Microgrid were expected to export a very large amount of power to the grid.

In such a case, investment deferral would be negative, creating an additional cost,

rather than a benefit.

The thoroughness warranted by an investment deferral analysis will depend

on how close present operating parameters are to infrastructure limits. If operating

limits on infrastructure will not be approached for a long time in any case, investment

deferral will have less value in the present, as shown in Fig. 4–1, and that compounded

with the larger error associated with predicting needs and costs in the distant future

will render deferral calculations effectively meaningless. Focus should instead be

given to investments required in the near future.

4.3.1 Loss Reduction

Reduced losses can be found through direct calculation, taking into account

power injections determined at each time step in an energy exchange simulation.

Losses in each case can be found in the network of interest as [45]

PL + jQL =M∑i=1

Vi · Ii = ~V T ~I∗, (4.4)

43

Figure 4–1: The net present cost of a future equipment investment relative to thefuture cost of the upgrade (on the y-axis), calculated using Eq. 4.1. This is shownas a function of the power level at which the investment is required, normalized withrespect to the Microgrid peak demand (on the x-axis). The Microgrid reduces peakload to half its base case value in the first year and a 2% annual growth in peakdemand is assumed.

where where PL and QL are the real and reactive losses in the network, Vi and Ii

are the nodal voltages and currents, and ~V and ~I are column vectors containing all

nodal voltages and currents.

Energy loss outside of the Microgrid is energy purchased or produced by the util-

ity that will not be sold. Loss within the Microgrid is energy produced or purchased

by the Microgrid Owner that will not be sold to customers or exported upstream. In

each case being analyzed, the cost of upstream losses should be valued at marginal

wholesale price and applied as cost to the system operator, and the cost of internal

44

Microgrid losses should be applied to the IPP, being valued at the cost of energy

import or the value of energy export, as appropriate in each time step depending on

whether the Microgrid is importing or exporting.

4.4 Improved Reliability

Improved reliability may be one of the most significant benefits Microgrids can

provide. An improvement in reliability indices is primarily accomplished by the abil-

ity of a Microgrid to operate disconnected from the EPS (islanded) [26, 35]. This

means that in the event of a fault or disturbance in the upstream network, a Micro-

grid can disconnect from the upstream network and continue providing power to

its customers (this is known as intentional islanding). Note that in some cases a

Microgrid can also provide power to customers outside the Microgrid. Furthermore,

if there are multiple Microgrids in a network, they may be able to use controllable

network switches and sectionalizers to reconfigure a network to restore power to as

many customers as possible [26]. This approach will focus on the effects from a single

Microgrid without any expectation of external automation.

Reliability is typically measured using certain indices,1 based on underlying re-

liability statistic information collected and reported by electric utilities, as required

in North America by compliance with the North American Electric Reliability Cor-

poration (NERC) [71]. Three of the most commonly referenced reliability indices are:

1 An excellent description of reliability indices can be found in Chowdhury’s andKoval’s reference on distribution system reliability [22], and in the IEEE standard1366-2012 [52].

45

the System Average Interruption Frequency Index (SAIFI) and the System Average

Interruption Duration Index (SAIDI), respectively measuring the average number of

interruptions experienced by customers and the average duration of each interrup-

tion, and Non-Delivered Energy (NDE), a measure of demand not met due to outage,

usually on an annual basis. SAIFI and SAIDI can be described mathematically as

SAIFI =

∑iNi

NT

=

∑k λkNk

NT

(4.5)

SAIDI =

∑i riNi

NT

=

∑k UkNk

NT

(4.6)

where Ni is the number of customers affected by each interruption, summed over

the number of interruptions per year, and NT is the number of customers, λk is the

failure rate at load point k, Nk is the number of customers at load point k, ri is the

duration of each interruption, and Uk is the average interruption duration at load

point k [47, 52]. If we view the Microgrid as a single load point, these equations

simplify to SAIDI = λµG and SAIFI = UµG.

Many Microgrids are constructed in radial distribution feeders, and much work

has been done to analyze the impacts of Microgrids in such feeders [24, 26, 47]. The

load point indices (failure rate, λC , unavailability, UC , and non-delivered energy,

NDEC) for a load point C in a radial distribution feeder section f of a larger series

of feeders, γ, can be found as follows (adapted from Costa et al. [26])

λC =∑i∈f

λi + λup (4.7)

UC =∑i∈f

λiri + Uup (4.8)

46

NDEC =

(∑i∈f

λiri + Uup

)LC (4.9)

where λi and ri are the failure rate and restoration time of section i of the feeder,

λup and Uup are the failure rate and unavailability of the upstream network, and LC

is the average demand of load point C. The load point indices in a Microgrid case

can be described as follows (adapted from Costa et al. [26])

λC =∑i∈f

λi +∑i∈γ\f

λiPL + λupPM (4.10)

UC =∑i∈f

λiri +∑i∈γ\f

λiPLTaL + λupPMTaup (4.11)

NDEC =

∑i∈f

λiri +∑i∈γ\f

λiPLTaL + λupPMTaup

LC (4.12)

where γ is the set of all feeders in the Microgrid, PL is the probability that a failure

in any feeder will cause the entire Microgrid to shutdown, PM is the probability

that islanding will not occur correctly, TaL is the restoration time after an internal

shutdown, and Taup is the restoration time after an upstream outage. Note that

PM is dependent not only on the probability of correct transition from connected to

islanding state, but also on the availability and probability of adequacy (PoA) of the

DG, i.e. the probability that all load can be served by the DG [24,26,35]2 .

2 Some of these parameters may be difficult to extrapolate from typical outagedata, and they must therefore be estimated from known factors. Values can befound in the literature for some simulation conditions [24,26,35]. As with all param-eters that are not known to a high degree of certainty, it is important to conduct

47

It is clear that not only is sufficient power needed to meet demand during is-

landing, but sufficient dispatchable resources are needed in order to balance load

variations and regulate frequency in an islanded Microgrid. If the amount of power

available from Microgrid DER is less than the demand (taking into account regular

load control or demand response), certain loads or sections of the Microgrid will have

to be selectively disconnected or shed, or the Microgrid will not be able to island. If

load shedding is used, the shed load point indices will become [26]

λCS =∑i∈f

λi +∑i∈γ\f

λiPL + λup (4.13)

UCS =∑i∈f

λiri +∑i∈γ\f

λiPLTaL + Uup (4.14)

NDECS =

∑i∈f

λiri +∑i∈γ\f

λiPLTaL + Uup

LCS (4.15)

And the indices for the whole loadpoint will be

λCT = λC(1− S) + λCSS,

UCT = UC(1− S) + UCSS,

NDECT = NDEC +NDECS,

(4.16)

a sensitivity analysis on these estimated reliability parameters to find a reasonablerange of results for reliability impacts of the Microgrid.

48

where S is the proportion of load shed at load point C. If the loadpoint under

analysis is unable to shed only a fraction of its load but must instead shed all or

none, S can take values of either 0 or 1.

From the load point indices, λ and U , SAIDI and SAIFI can be calculated using

(4.5) and (4.6), and total NDE can be found.

In more complicated feeders with branches, meshes, or additional circuit break-

ers this radial approach may be inadequate. A more general approach to reliability

modelling can be based on categories of component failures as follows [26]:

• Gr: Failures of components in this category must be repaired before power

can be restored to the load point. An example is a branch upstream of the

loadpoint in a radial feeder.

• Gi: Failure of components in this category do not directly cause an outage

to the load point, but they must be isolated from it. Examples include a

downstream component, or a component in a different feeder branch, separated

from the loadpoint by at least one protection device.

• Gc: Failure of components in this category cause an outage to the load point,

but power can be routed around them to restore service to the load point

before the component is repaired. An example would be an upstream branch

in a ring-main network, where power can be directed to the load point by

closing a normally open disconnection point that connects to an unaffected

parallel feeder branch.

49

All network components of interest are assigned one of the preceding categories

for each load point being analyzed. From this, load point indices can be found as

λk =∑

i∈Gr∪Gc

λi +∑i∈Gi

λiPi|k, and

Uk =∑i∈Gr

λiri +∑i∈Gi

λiPi|kti|ks +∑i∈Gc

λiPi|kti|kc ,

(4.17)

Where i ∈ Gx is the set of all components in set Gx, Pi|k is the probability that

failure of component i will cause an outage in load point k, ri is the repair time of

component i, ti|ks is the time required to isolate component i from the load point and

restore power to load point k, and ti|kc is the time required to reconfigure the network

to restore power to load point k. The upstream network is not expressed explicitly,

as it can be thought of as another “component” that can fail in this formulation. The

expected value of non-delivered energy (NDE) can be found as NDEk = Uk × Lk,

where Lk is the expected demand at the load point.

The reliability improvement function of the Microgrid is effectively threefold:

to move components into categories in which they have a less negative effect on

reliability indices, to reduce the probability that a component failure will cause a

load outage (reducing Pi|k), and reducing reconfiguration and isolation times, tic and

tis.

It should be noted that in addition to analytic methods of reliability calculation,

as just given, stochastic methods such as Monte Carlo simulation may be used to

obtain similar reliability figures (within some error) [47]; however analytic methods

are generally favoured in power system analysis due to their transparency and low

process error, despite their comparatively high computational demand.

50

Once the reliability indices are known for the base case as well as each Microgrid

case, they can be compared directly to find the reliability improvement in terms of

index improvement, but they can only be used in the economic analysis by first

economically quantifying the reliability improvement.

The cost of outages is often valued based on NDE, and depending on the types

of customers, the time of year, and the duration of each interruption, NDE can have

drastically different values [22]. NDE has been estimated to range from less than

1$/kWh in the case of a long duration interruption to a residential customer, to more

than 1600$/kWh in the case of an instantaneous interruption to a small commercial

or industrial customer [90]. Typical values range between about 1.5 $/kWhNDE to

3.5 $/kWhNDE, the lowest values corresponding to residential loads, and the highest

industrial and commercial loads [8, 15, 26, 90]. A list of such values from literature

can be found in Appendix D.

Regarding the costs of this reliability improvement, given the application, we can

observe that typical utility service is very reliable, American utility networks typically

have SAIFIs on the order of 1-2 outages per year and SAIDIs of approximately

two hours per year [52]. Therefore, given the relatively small amount of energy a

Microgrid would have to supply during outages in a typical year, we can assume that

the impact of islanding on the IPP’s cost of providing energy is negligible. Beyond

the necessary infrastructure costs (which may, for example, include an energy storage

system to ensure the ability to balance power with the presence of intermittent,

renewable DG in an islanded Microgrid), fixed costs might include necessary fuel

storage and staff training beyond that required without islanding considerations.

51

Also note that while thermal DG sources are suitable for frequency regulation and

balancing in an islanding Microgrid, they can only perform these tasks when they are

operating. This means that if thermal DG is not operating when a sudden upstream

outage occurs, there will be some delay associated with start-up before power can be

balanced. In this case, installation of a fast response energy storage system may be

beneficial to bridge the time between frequency regulation via the upstream network

and from within the Microgrid using thermal DG units.

4.5 Ancillary Services

Ancillary services may offer some of the most promising sources of benefits, as

in many jurisdictions, procurement and compensation policies are already in place

for some of these services [81,82].

The term “ancillary services” generally refers to a set of services used to sup-

port the grid’s operation beyond simple energy provision. This can include frequency

support, voltage support, black start or system restoration support, peak load sup-

port (dealt with in Section 4.3), and balancing services [58, 87]. Depending on the

jurisdiction, the services may be procured through compulsory provision, bilateral

contracts, tendering, or a spot market, and they may be remunerated by a fixed pay-

ment, a payment for service availability, a use-based payment, or a payment based

on lost opportunity cost (for example, the missed opportunity of providing energy,

which provision of other services may partially preclude) [58,82].

A critical observation is that utilities or system operators must, by one means or

another, ensure that power quality experienced by customers be maintained within

certain standards. This may be through infrastructure investment or purchase of

52

power quality services from other entities. Thus, if a Microgrid provides these ser-

vices, it can have a benefit to the utility in the form of investment deferral (the value

of which can be found in exactly the same way as described in Section 4.3, and it

will likely not have substantive benefit to customers. The remaining benefit is the

flow of compensation to the IPP for provision of ancillary services.

It should be also be noted that depending on the islanding criteria of a Micro-

grid controller, it may disconnect from the area EPS in the event of an upstream

disturbance, possibly rendering the reliability of its power quality services less than

would be expected from a standard generating station. As such, this criteria must

be balanced with the obligation to provide power quality support in the event of

such a disturbance [87]. Furthermore, due to the relatively limited contribution that

Microgrids can provide toward the systems ancillary service needs, system operators

may be reluctant to accept these services from Microgrids. Some researchers have

suggested that a co-ordinated group of Microgrids (a “multi-microgrid”) could pro-

vide the services, effectively as a single entity from the perspective of the system

operator [87].

4.5.1 Frequency or Active Power Support

Microgrids can use the dispatchable resources at their disposal to help regulate

frequency in a network. This could be in the form of primary, secondary, or tertiary

frequency support, although it has been noted that due to their relatively small

capacity and small time constants, single Microgrids may be best suited to provide

primary frequency support, and that will be the focus of this analysis [81, 103, 104].

Of course, some degree of load balancing will inevitably occur if there is an effort

53

to reduce peak loading, as described in Section 4.3, or in exercising energy exchange

selectivity if time-of-use pricing is in effect.

In many jurisdictions, primary frequency support is remunerated based on avail-

ability rather than usage, and it is procured primarily via bilateral contracts or

through a tendering process [82]. In any case, the income to the IPP can be found

during each time step as [58,103]:

rPR(t) = πPR(t)xPR(t)− C(xPR(t)) (4.18)

where rPR(t) is the net revenue from a contract or an accepted bid during time step

t, πPR(t) is the price for reserves at time t, xPR(t) is the amount of reserve made

available at time step t, and C(xPR(t)) is the cost of making the reserve available.

This cost may include lost opportunity, as described in the previous section, as well

as any additional fuel or O&M cost that may apply.

The total net revenue can then by found as:

RPR =∑t

πPR(t)xPR(t)− C(xPR(t)). (4.19)

If a fixed quantity contract is in place, this can be simplified to

RPR = ΠPRXPRTf − C(XPR)Tf . (4.20)

where ΠPR is the fixed reserve price, XPR is the fixed reserve quantity, and Tf is the

length of the contract or project, as the case may be.

As stated, there may be two significant costs from frequency support provision:

increased O&M costs (including fuel), and lost opportunity costs. Increased O&M

54

costs are related to increased utilization of equipment and resources. Variable O&M

cost can be assumed to be a positive monotonic (usually linear or quadratic in the

case of fuel-based DG units), usage-dependent function, as CO&M(xT (t)), where xT (t)

is the total output of a generator. Increased usage resulting from frequency support

can be modelled using the instantaneous reserve utilization factor, λ(t) ∈ [0, 1], which

represents the proportion of reserve used at time t [104], giving total energy resource

usage as:

xT (t) = xE(t) + λ(t)xPR(t), (4.21)

where xE(t) is the energy produced for energy exchange (this includes energy for

both provision to customers and for exporting to the grid).

A critical observation is that for primary frequency support, the expected pri-

mary reserve utilization, λ is essentially equal to zero [104]. This is due to the fact

that it requires many small active power adjustments of only a short duration, and

furthermore, those adjustments can tend to increase as well as decrease active power

output in equal proportion. Ultimately, this means that the expected increase to

variable O&M costs from primary reserve provision is also effectively zero. This is

an approximation of course, as variable O&M cost is not truly linear, but when re-

serve usage is less than energy generation usage, it is very close, the straightforward

proof by way of Taylor series approximation is left to the reader.

Since we can effectively rule out increased O&M cost, income from reserve pro-

vision can be simplified to

RPR = ΠPRXPRTf . (4.22)

55

Note that this principle also applies to hydroelectric DG with a reservoir. Pri-

mary frequency support should not tend to deplete the reservoir.

Lost opportunity cost is a bit more insidious, as it is a hidden cost resulting

form DG units’ necessarily operating below full capacity in order to provide reserve;

meaning that they cannot produce and sell as much energy as they otherwise could,

if local retail markets are in place [87]. Indeed, energy provision often appears more

lucrative based on price alone, but when fuel and other variable O&M costs are taken

into account, such a conclusion may be less clear. The marginal benefit in this case

will be

∆BE

∆E=

∆RE

∆E− ∆CO&M

∆E, (4.23)

where ∆RE∆E

is the marginal revenue from selling energy, and ∆CO&M

∆Eis the marginal

O&M cost (including fuel, if applicable). The cost of the additional O&M will depend

heavily on the type of DER used, and the overall benefit of providing reserve instead

of energy will depend on the market prices. It should be noted that controllable loads

may be able to offer some degree of reserve, and in this case, reserve price would

need to be balanced against cost of NDE.

4.5.2 Voltage or Reactive Power Support

The issues of reactive power support are closely related to those of active power

support, except that reactive support may be less lucrative for the IPP. In many

jurisdictions voltage support is either not allowed for small generators (as most DG

units), or it is an uncompensated requirement of interconnection due to its perceived

negligible cost and the important role that voltage support can play in power quality

56

and network stability [13, 51, 82]. For these reasons, voltage support is only given a

brief treatment here.

For small generators, the system operator often requires reactive power to be

injected in a fixed quantity, but it may act in a range of values as part of a local

voltage support scheme [7, 13, 51]. When it is compensated, reactive power support

is most often remunerated either at a fixed rate or based on availability, although

markets for voltage support are not unheard of [82, 93]. In either case, revenue to

the IPP can be calculated in the same way as for active power support, as detailed

in the preceding section.

The two primary costs of reactive power support are: additional equipment costs

and reduced active power output.

Additional equipment costs arise from the need for equipment with variable

reactive power control. This is usually in the form of an induction machine or DC

source interfaced through a power electronic converter or a synchronous machine.

Microgrids with the ability to island must be able to balance reactive power during

islanding in any case, and therefore there is no additional cost.

The second cost arises from device current limitations. Even though reactive

power support requires no additional energy input in principle, it reduces the ac-

tive power that can be generated, as power through a component must obey the

relationship

P 2o +Q2

o ≤ 3(VrIr)2, (4.24)

where Po and Qo are the real and reactive power outputs, and Vr and Ir are the

three phase voltage and current ratings of the equipment. In some devices additional

57

limitations may apply. For example, synchronous machines must conform to limits

on rotor field current and stator field current as well as a maximum real power limit,

and a steady-state stability limit [40,41].

The reduction in active power capacity is minimal for power factors close to

unity (where synchronous machines are subject to a real power limit in any case),

but, as shown in Figure 4–2, for more severe power factor settings, the reduction

can become significant. One solution to this unwanted reduction in active power

capacity is to install devices that are rated to take the reactive power requirement

into account. The required overrating is equal to Sor = 1/PF in per unit, where PF

is the required Power Factor. Note that calculating the cost of this loss (or required

overrating) may not be useful in a case where voltage support is a requirement of

interconnection, as there is no base case to compare against. Also note that in some

cases Microgrids may be required to provide reactive power to internal loads during

normal operation, which may further affect the cost of providing reactive power

upstream [87].

4.5.3 Black Start Support

Most large generating stations require external power to start from a shutdown

state, as blowers, pumps, and other infrastructure must be operating in order for

a boiler to function. After a large blackout, this bootstrapping process requires a

starting point, a power source that will be available to support the restarting of

these large generating stations. This is usually what is referred to as a “Black Start

support service”.

58

Figure 4–2: Real and reactive power outputs and required power overratings for avariety of power factors.

Typical black starting procedures, as described by Fink et al. [34], generally

restore power from the top-down. They begin by ensuring the safe operation of

large power plants, then transmission corridors are energized, and power is restored

to critical loads, followed by less critical loads, and finally the remainder of system

loads in a process that can take up to twelve hours or more. Microgrids may be well

suited to help mitigate this long outage duration, especially for loads that are a low

priority in black starting, by providing bottom-up black start support [70]. This has

the potential to reduce SAIDI, and this reliability improvement can be evaluated

using the methodology described in Section 4.4.

59

In jurisdictions where Black Start support is treated as a remunerable service,

it is remunerated at a flat rate for maintaining service availability, although use-

based compensation also occurs [55, 56, 80]. As such, the benefit to the IPP can be

calculated in the same way as for other ancillary services, i.e.

RBS = ΠBSXBSTf − C(XBS)Tf , (4.25)

where RBS is the revenue to the IPP for black start support availability, ΠBS is the

fixed black start price, XBS is the amount of black start support that is available for

the time period, and Tf is the lifetime of the contract in appropriate units (probably

either months or years). Compensation can be designed to cover the costs of provid-

ing Black Start support as a necessary service, or it may be based on a competetive

bid process [3,80]. If the Microgrid is providing bottom-up Black Start support, the

impact is primarily an improvement in reliability indices, and as such, this service

could be compensated by the utility in the same way as reliability improvement.

The expected utilization rate of Black Start support is low, and as with primary

frequency support, the impact on variable O&M costs of making Black Start support

available are probably not significant. Costs are primarily installation costs for any

infrastructure necessary, certain staff training costs, and fuel storage costs which may

not otherwise apply [80].

4.6 Reduced Emissions

Microgrids can offer reduced emissions of certain pollutants based on non-

emitting (e.g. renewable) or low-emission (e.g. natural gas-based) DG as well as

60

through improved efficiency (as mentioned in the discussion of reduced loading, Sec-

tion 4.3). Emissions to consider may include greenhouse gasses (such as carbon diox-

ide (CO2), methane (CH4), and nitrous oxide (N2O)), as well as emissions that may

may cause health effects, damage to vegetation and materials, and visible smog (such

as particulate matter (PM), sulphur dioxide (SO2), and nitrogen oxides (NOX)) [23].

The reduction of an emissant, e, can be found as:

Ee = Ee BC − Ee µG

=T∑t

[xGP (t)εe G]

∣∣∣∣∣BC

−(4.26)

T∑t

[~xDG(t)T · ~εe DG + (xGP (t)− xGS(t))εe G

]∣∣∣∣∣µG

where Ee BC and Ee µG are the emissions of e in the base case and Microgrid

case, respectively, t is the time step (in hours), xGP and xGS are the power purchased

from and sold to the grid, εe G is the average rate of emissions from the grid (e.g.

in tCO2/kWh), ~xDG(t) is a vector containing the power produced by each DG unit

in time step t, and ~εe DG is a vector containing the emission rate of each DG unit.

Note that this approach takes into account offset grid emissions. The power figures

can be obtained from the simulation described in Section 4.2.

In power systems, emission rates usually vary by time of day and time of year

and demand, and renewable resource availability fluctuates. These considerations

should be taken into account for an accurate result [91]. In some cases this level of

information may not be available, and in any case, average rates may be sufficient

for estimation of emission reductions.

61

Emission reductions primarily benefit society, and the estimated value of emis-

sions can vary significantly according to the method and comprehensiveness of esti-

mation. Some jurisdictions may place a value on emissions (especially carbon), most

commonly through a tax on emissions [14, 83]. In these cases, reduction of carbon

emissions can have a financial benefit to the utility or IPP through either a direct

reduction of this cost or through compensation for reducing the cost paid by other

parties. Also, premium prices are sometimes paid to renewable energy producers

in the form of feed-in-tariffs, and in some cases consumers are willing and able to

pay a premium for renewable power, both of which could support Microgrids with

renewables-based DG units [17,50].

Costs include the cost for ensuring sufficient dispatchable resources in addition to

renewable resources so as to permit islanding and control. This can be accomplished

using energy storage systems, thermal generating units, and possibly load control.

4.7 Chapter Summary

This chapter has described the quantification of major individual Microgrid

benefits. This quantification methodology informs the cost-benefit analysis described

in Chapter 3 and fits into the benefits framework described in Chapter 2. Now

the stage has been set to demonstrate how actual Microgrid business cases can be

analyzed for all Stakeholders, which will be shown in the next chapter.

62

CHAPTER 5Business Cases and Case Studies

5.1 Introduction

This chapter describes the evaluation of a number of Microgrid case studies

using the framework and methodology described in the preceding chapters. This will

demonstrate the value of a full cost-benefit analysis that takes into account a diverse

array of benefits.

The cases considered are:

1. An IPP-owned community Microgrid, which will demonstrate benefits from

reduced emissions, reliability improvement, and investment deferral resulting

from improved power quality;

2. A customer-owned commercial Microgrid based on a remote ski resort, which

will demonstrate benefits from improved reliability, improved efficiency associ-

ated with cogeneration, and investment deferral resulting from reduced peak

loading;

3. A utility-owned isolated Microgrid, which will demonstrate the benefits from

reduced emissions and reduced fuel consumption that can arise from using

Microgrid technology to integrate renewable power into a system.

5.2 Community Microgrid

This case is based on an IPP-owned Microgrid installed near a growing commu-

nity at the end of a weak transmission line for which voltage support will be needed

63

in the near future. The Microgrid option provides energy and voltage support based

on hydroelectric DGs.

5.2.1 Base Case and Context

The input parameters are based in part on a few real Microgrid projects, notably

the Boston Bar project, constructed by BC Hydro in Western Canada in the 1990s

[37].

The load variability is based on the IEEE Reliability Test System 1996 standard

(RTS-96) [42], scaled for a 3 MVApk load. The Base Case is assumed to require

a 2 MVAR capacitor bank installation in the second year at an installed cost of

$50 /kVAR1 .

Base Case energy rates follow the Ontario TOU rates (10.8¢/kWh on-peak,

9.2¢/kWh mid, and 6.2¢/kWh off peak [77]). Additionally, a carbon emission tax of

$15 /tC , and a Societal carbon cost of $30 /tC2 are assumed.

An upstream failure rate of 1 failure per year and an average outage time of 6

hours are assumed along with an internal failure rate of 0.3 failures per year with an

associated average outage duration of 4 hours. An average penalty for non-delivered

energy (NDE) of $1200 /MWNDE is attributed to the utility.

1 Please see justification of this cost in Appendix D.

2 This societal cost may correspond to a conglomeration of factors resulting fromthe impacts of global warming, potentially including threats to ecosystems, foodproduction, and human security. For the sake of this example, it has been assumedto be similar to the value assigned to carbon tax. For a discussion of the impacts ofclimate change, see the first chapter of Harvey’s first volume on Energy in the 21st

century [43].

64

The Base Case in this example is the “do nothing” status quo case, in which there

is no DG, no Microgrid functionality, and the voltage support problem is mitigated by

installation of a switched capacitor bank. Relevant data are summarized in Table 5–

1.

Table 5–1: Case Study 1 Input Parameters

Parameter Value Parameter ValueBase Case Parameters Investment Deferral

TOU Rates10.8 ¢/kWh pk. Capacitor Bank $100,0009.2 ¢/kWh mid Reliability6.2 ¢/kWh off Outage

Frequency1 f/yr upstream

Interest Rate 8% 0.3 f/yr internalProject Life 25 Years Avg. Outage

Duration6 hrs upstream

Peak Load 3.0MW (Winter) 4 hrs internalMicrogrid Case Parameters Util. Penalty for

NDE$1200 /MWNDE

IPP Rate to Cus-tomer

6.5 ¢/kWh IPP AvoidedNDE Incentive

$600 /MWNDE

DG Type Small Hydro Cust. Cost ofNDE

$750 /MWNDE

DG Size 2×3.46MW Emissions ReductionDG Cost $16.5M Base Case CO2

Intensity200 gCO2/kWh

Microgrid Cost $687,500 Carbon Tax $15 /tCO&M Costs 2% p.a. Societal Carbon

Cost$30 /tC

5.2.2 Microgrid Alternative Case

In the Microgrid case, the IPP installs two 3.46 MW run-of-the-river hydro DG

units serving the local community. The water resource peaks in the summer, and is

based on data from Environment Canada’s Water Survey of Canada [31]. The DG is

used to supply 2 MVAR of reactive power, obviating the need for installation of the

65

capacitor bank, needed in the Base Case. A DG unit availability of 95% is assumed

in reliability calculations.

The cost of the DG is $16.5M, and the equipment required to enable Microgrid

functionality (intentional islanding in this case) adds $687,5003 . A fixed annual

O&M cost of 2% of the installation price is assumed on all assets.

In order to ensure customer buy-in, the IPP reduces customer charge from typ-

ical Ontario TOU rates to a constant rate of 6.5 ¢/kWh. Furthermore, to provide

incentive to the IPP to ensure reliability, the utility pays $600 for every MWh of

non-delivered energy that is avoided or prevented due to the presence of the Micro-

grid.

5.2.3 Impacts and Modelling

As mentioned in Section 3.8, the impacts were calculated using a software tool

created by the author. The program simulates the energy exchange that occurs in

a single year of each modelled case, and it performs the steps of converting impacts

derived from this simulation into economically-valued benefits.

5.2.3.1 Base Case Results

In the base case, the utility provided 16,028 MWh annually to the community

at a total cost to the customers of $1,335,820 and an average cost of $83.3 /MWh

(8.33 ¢/kWh). The utility emitted 3206 t of carbon in providing this energy. SAIFI

3 This is based on the actual installation costs of the Boston Bar project (withinflation).

66

was calculated to be 1.30, SAIDI was 7.20, and the expected annual NDE was 13.2

MWh. The capacitor bank was installed at a cost of $100,000.

5.2.3.2 Microgrid Case Results

In the Microgrid case, the IPP-owned DGs produced 31,474 MWh annually,

exporting 16,942 MWh to the utility (worth $1,347,002 to the IPP). Due to the

variation in the water resource, 1,496 MWh needed to be imported annually (at cost

to the IPP of $133,502) to ensure the load is always served. The annual O&M costs

were $330,000, and the annual amortized investment cost was $1,610,104 (paid by

the IPP). The total energy cost to customers was $1,041,815 (set at a fixed cost of

6.50 ¢/kWh).

The utility emitted 299 t of carbon annually in supplying the Microgrid loads,

the DGs emitted none, and the exported DG energy offset 3388 t of carbon annually

(for a net utility emission of –3089 tC , meaning that operation of the Microgrid

reduced overall utility emissions otherwise emitted from supplying loads outside the

Microgrid). SAIFI was reduced to 0.47, SAIDI was reduced to 2.22, and expected

annual NDE was reduced to 4.1 MWh. In addition to this, the Microgrid provided

17,520 MVAR-hr (or 2 MVAR-yr) of reactive power support to the utility.

5.2.4 Economic Evaluation

The costs of carbon emissions in the base case were $48,084 to the utility and

$96,168 to society. The cost of outages was $9,880 to the utility, and the present

value of the capacitor bank installation in the second year was –$85,734 (amortized

annual value of –$8,031). The total Annual Value (AV) to the customer was just the

cost of energy, –$1,335,820, with a Present Value (PV) of –$14,259,577 over the 25

67

year project lifespan. The total annual to the utility was –$56,115, for a 25 year PV

of –$599,020, and the annual value to Society was –$96,168, for a PV of –$1,026,572.

In the Microgrid case, the value of carbon emissions were $12,455 for the utility

(i.e. a positive annual benefit), $33,883 for the IPP, and $92,767 for Society. The

value of reduced NDE (9.11 MWh annually) was $10,933 to the utility, of which

half ($5,467) was paid to the IPP as a reliability incentive. Finally, the injection of

reactive power provided by the Microgrid enabled the utility to forgo the 2 MVAR

capacitor bank installation (for an effective amortized annual savings of $8,031 over

the Base Case). The AV for the utility is $17,922, and the PV is $191,310. The AV

for the IPP is $1,964,664 and the PV is $20,972,353, without taking into account the

installation costs (having an amortized value of $1,610,104). Taking into account the

installation costs, the net AV to the IPP is $354,560, and the net PV is $3,784,853.

The AV for the customer is –$1,041,815, and the PV is –$11,121,139. The AV for

Society is $92,767, giving a PV of $989,300. These costs are compared to the costs

in the Base Case in Figs. 5.2.4 and tabulated in Table 5–1. Note that the payback

period for the IPP is 8.75 years.

5.2.4.1 Sensitivity Analysis

In order to understand the sensitivity of results to variations in input parame-

ters given the imperfect confidence in parameter accuracy, a sensitivity analysis was

performed on certain key parameters.

The price that the IPP would need to charge to the customer to just break even

is 4.288 ¢/kWh. The project lifespan required for the IPP to break even is 16 years.

68

If instead of a varying time of use rate, the utility energy exchange rate is set

at the load-weighted mean value of 8.33 ¢/kWh, the customer sees no difference in

costs, but the IPP sees an increase in annual income from exchange from $1,347,002 to

$1,411,238 (bringing the net AV to $427,719). This is because the hydro production

is relatively constant throughout any 24 hour period, while the load tends to be

greater during peak charge hours, meaning that more energy is exported at night

when energy is valued less in a TOU-tariff framework. If this constant cost is applied,

the average value paid for exported energy will increase.

The impacts of varying a number of other parameters within their likely ranges

are shown in Figs. 5–3

69

Figure 5–1: Case 1 net values for key stakeholders.

70

(a) Net annual value including installationcosts for Case 1.

(b) Net annual value for Case 1 if the amor-tized investment costs are disregarded.

Figure 5–2: Net annual costs in the Microgrid Case relative to the Base Case for keystakeholders.

Figure 5–3: Variation in net annual Microgrid benefits over the Base Case for rea-sonable parameter ranges.

71

5.3 Commercial Microgrid

This case study is based on a small, remote ski resort with significant heating as

well as electricity needs. It will demonstrate improvements in reliability for a business

that places a high value on reliability during the winter operating season, when

outages are most likely to occur. It will also demonstrate the improved efficiency

that can result from using co-generation with thermal-based DG units.


The peak electric load is assumed to be 100 kWe, and the peak heating load

is estimated at 78 kWh4 . The utility electricity rate is assumed to be a constant

$80 /MWh. The facility operates from mid-December to the end of April, and

during this time, the customer places a very high value on reliability, in this case,

$300,000 /MWhNDE5 . Outside this operating season, energy usage is assumed to

be negligible. The facility is heated by a natural gas-fuelled boiler operating at

85% seasonal efficiency. Natural gas prices were assumed to be constant at 2 ¢/kWh

(LHV)6 . The emissions rate of the electric utility is 200 gCO2/kWhe7 (54 gC/kWhe

8 ),

4 This is based on an assumed floor space of 650 m2, and a high peak specificheating load of 120 W/m2.

5 This is in line with values given by Sullivan et al. for small commercial loads [90].

6 Please see Appendix D for more information.

7 This is the mean Canadian value, taken from Environment Canada [30].

8 This is calculated based on the known atomic weights of carbon (12.01 g/mol)and oxygen (16.00 g/mol); one gram of carbon dioxide is equivalent to ( 12.01

12.01+2×16.00=

0.273g of pure carbon.

72

and the emissions rate of the boiler is 237 gCO2/kWhh (65 gC/kWhh). A carbon tax

of $15/tC is assumed, and a Societal carbon cost of $30/tC . To illustrate the benefits

of investment deferral, a utility-side line upgrade (cost of $200,000) is assumed to be

required when the peak import power reaches 120 kW. Planned expansion at the ski

resort is expected to increase peak power consumption at an average rate of 2.5%

per year.

Given the remote location and severe winter climate, reliability was assumed to

be relatively low. An average upstream outage rate of 2 failures per year with an

average outage time of 16 hours, and an internal outage rate of 1 failure per year

with an average outage time of 6 hours were assumed9 .


The Microgrid case employs two 60 kW combined cycle microturbines with syn-

chronous generators and heat recovery to enable CHP. At $2000 /kW10 , the invest-

ment cost for this DG is $240,00011 . A ballpark figure of $25,000 is assumed for the

Microgrid infrastructure, including appropriate controllers and disconnect switches

9 These values were taken from the real values in Boston Bar, the location of aremote community Microgrid, with some modification to the internal outage factors[37].

10 See Appendix D.

11 Note that due to the pre-existing gas supply and heating infrastructure, thereis little expected additional cost for the gas supply or heating infrastructure in theMicrogrid beyond the additional costs of the Microturbines.

73

to allow intentional islanding in the event of an upstream outage12 . Electricity was

not permitted to be sold to the utility.

Each Microturbine has a minimum loading level of 20%, and a baseline electrical

efficiency of 30%. For each kWh of electricity produced, 1.75 kWh of heat energy

can be recovered, bringing the overall efficiency up to 82.5%. The emissions rate is

675 gCO2/kWhe = 184 gC/kWhe.

This input data is summarized in Table 5–2.


In the base case, the facility used 201 MWh of electricity at a rate of $80 /MWhe

for a total annual cost of $16,106, and it required 157 MWh of heating energy at a

cost of $23.3 /MWhh for a total annual cost of $3,664. The utility emitted 11.0 t

of carbon in providing energy to the customer, and 10.1 t were emitted for heating

purposes for a combined total emission of 21.1 t. SAIFI for this customer was found

to be 3 failures per year, and SAIDI was 38 hours per year. Expected annual NDE

was 870 kWh. The line upgrade will be needed in year 7.

In the Microgrid case, the DG units supplied the full electrical (201 MWhe) and

heating (157 MWhh) demand at a combined annual cost of $51,581. The DG units

emitted 37.0 t of carbon, and the utility emitted none. SAIFI was improved to 1.11

failures per year, and SAIDI was improved to 7.76 hours per year. Expected NDE

12 This is roughly based on the costs reported in the Boston Bar project, scaledto the size of this system. It is believed by the author that some of the Microgridinfrastructure costs reported in the literature (see Appendix D) may be unrealisticallylow at this time.

74

dropped to 180 kWh. Given the reduced loading, the line upgrade will not be needed

until well past the planning horizon of 15 years (at the assumed rate of load growth,

the power limit would be reached by approximately year 35).


In the base case, the annual carbon tax levied on the utility was $165 for energy

provided to the customer, and $152 was levied on the customer for heating-related

emissions. The present value of the line upgrade is –$113,303 for the utility, giving

an amortized AV of –$13,237 over the life of the project. The annual cost of carbon

emissions to Society was $634. The annual cost of NDE applied only to the customer

in this case, and was estimated at $261,995. This results in a total annual value of

–$13,402 to the utility, for a present value of –$114,713 over the 15 year project; an

AV of –$281,917 to the customer, for a PV of –$2,413,061; and an AV of –$634 to

Society, giving a PV of –$5,426.

In the Microgrid case, the annual carbon tax on the utility was $0, as no energy

was purchased from the utility, and the tax on the customer was $556. The cost

of carbon emissions to Society was $1,111–actually higher than in the Base Case.

The value of the line upgrade was also $0, as it will occur at some point beyond

the planning horizon. The annual cost of NDE was $53,493. This results in an

AV and PV of $0 to the utility; an AV of –$105,630 to the customer, for a PV of

–$904,136; and an AV to Society of –$1,111, for a PV of –$9,512. It is notable that

the annual fuel cost in this case is $13,421, significantly less than the energy costs

in the Base Case ($19,770 annually), but the annual O&M costs of $7,200 and the

amortized installation costs of $30,960 push up the effective energy cost so as to

75

be untenable for this purpose alone. The value of reliability, however, makes the

Microgrid quite economically attractive, providing a net annual benefit of $176,287

to the customer for a present value over the course of the project of $1,508,924

including the investment costs. Not including the amortized investment cost, this

is $207,247 annually ($1,773,924 PV), giving an exceptional payback period of just

over one year.

These costs are tabulated in Table 5–4, and the net benefits of the Microgrid

over the Base Case are shown in Figs. 5.3.4.


76




Figure 5–6: Variation in net annual benefits of the Microgrid over the Base Casefrom the perspective of each stakeholder group in Case 2.


A sensitivity analysis was performed on key parameters in this case. The main

driver of this case for the customer/owner is the high value placed on reliability.

The value placed on reliability at which the customer would just break even over the

77

lifetime of the project is $46,350 /MWhNDE. The project lifespan required for the

IPP to break even with the assumed reliability value is just 2 years.

If only one Microturbine unit is purchased (60 kW capacity), the reliability

improvement is significantly less, resulting in 210 kWh of expected NDE per year,

valued at $62,788, and 21 MWh of energy would need to be imported at a cost of

$1,694. The investment cost also reduces, to –$145,000 (which amortizes to –$16,940

annually). This changes the net annual benefit for the IPP over the Base Case to

$184,387, with a PV of $1,578,261–actually greater than the two Microturbine case.

The IRR changes from 66% in the two Microturbine case to an astounding 127%

in the single unit case, and the incremental benefit of the second Microturbine is

found to be negative; that is, it would be more fiscally prudent to only purchase one

turbine.

This is a very interesting result, and suggests that the Microgrid does not need

to be able to protect against outages at all times to provide a significant net benefit,

but merely most of the time.

If the Customer is allowed to sell energy to the grid at wholesale costs (assumed

here to be fixed at 70% of the retail price), there is no change in benefits, as the

price of $54 /MWh is below the cost of the Microturbines to produce electricity, even

with the cost savings of CHP accounted for. If the exchange rate is set to retail price

($80 /MWh), the two Microturbine case exports 187 MWh of electricity with a value

of $14,946, and an increase in fuel cost of $12,375 (net gain of $2,571), increasing the

net annual benefit to $178,346–a negligible improvement. In the single Microturbine

case, 17 MWh are exported annually at a value of $1,390, increasing the fuel cost by

78

$1,158 from $12,010 to $13,168, resulting in a net gain of only $232 per year. Energy

sales would therefore have an insignificant effect on the benefits in this case.


are shown in Figs. 5–6

79


Parameter Value Parameter ValueBase Case Parameters ReliabilityUtility Elec.Rate

8 ¢/kWh OutageFrequency

2 f/yr upstream

Natural GasPrice

3 ¢/kWh 1 f/yr internal

Interest Rate 8% Avg. OutageDuration

16 hrs upstreamProject Life 15 Years 6 hrs internalPeak Elec. Load 100 kW (Winter) Util. Penalty for

NDE$1200 /MWNDE

Peak Heat Load 78 kW (Winter) Cust. Cost ofNDE

$300,000 /MWhNDE

Boiler Efficiency 85% Emissions ReductionMicrogrid Case Parameters DG CO2 intensity 675 gCO2/kWhDG Type Thermal Base Case CO2

Intensity200 gCO2/kWh

DG Size 2×60 kW Carbon Tax $ 15 /tCDG Efficiency 30% Societal Carbon

Cost$ 30 /tC

Recoverable Heat 1.75 kWh/kWe Investment DeferralDG Cost $240,000 Avg. Peak

Growth2.5%

Microgrid Cost $25,000 TransmissionLimit

120 kW

O&M Costs 3% p.a. Transmission Up-grade

$200,000

80

5.4 Isolated Microgrid

Analysis of an isolated Microgrid can be tricky, as (depending on definition)

the base case is actually a Microgrid itself, and there is no wider grid with which

to exchange and balance energy. The alternative case here will be focused on using

additional Microgrid technologies to allow integration of renewable power (wind)

into an isolated grid with limited balancing capacity outside of a diesel generator.

Load control and energy storage will allow the Microgrid to minimize the use of the

diesel generator to balance wind, thereby reducing fuel costs and carbon emissions.

Supply of power (including installation and operation of the Microgrid) is the duty

of a utility.


The base case is loosely based on the Ramea remote community in Newfound-

land13 . The annual peak load is 1 MW, the existing generation consists of 3 ×

945 MW diesel generators (note that the diesel generation is drastically oversized for

the load–this is common in remote communities). The cost of diesel fuel is assumed

to be 85 ¢/L and the efficiency of the diesel generators is assumed to be 3.7 kWh/L

(with 30% minimum loading), resulting in a net power cost of 23.0 ¢/kWh. The cost

to customers is set at 20 ¢/kWh.

The emissions rate of the diesel generators is 710 gCO2/kWh = 194 gC/kWh. A

carbon tax of $20 /tC is considered to be in place (with a cost to Society of $30 /tC).

13 Relevant data on this community has kindly been provided to the author byNatural Resources Canada (NRCan).

81

If the availability of the diesel generators is assumed to be 85% each, this results in

a combined availability of 99.6%. Hence, reliability may not be a serious concern,

and it is not treated in this case.

The Microgrid is utility-owned, and as such the interest rate is set deliberately

low (3%), as the cost of capital to a large, government-owned utility is typically quite

low. Project life is taken to be 20 years.


The Microgrid case will involve the installation of three 300 kW wind turbines at

a cost of $4,500 /kW (900 kW and $4,050,000 total), load control capability at a cost

of $1,000 per customer,14 300 customers for a total of $200,000, and a 1 MW, 3 MWh

energy storage system at a cost of $625 /kWh + $625 /kW (including the inverter)

for a total cost of $2,500,000. Thus, the total investment cost will be $6,750,000.

Note that the wind resource is based on measurements available to the author from

an area with high wind (capacity factor of 46.3%). In order to encourage customers

to agree to load control, the utility reduced energy costs by 5% to 19 ¢/kWh.

The value settings of demand response are broken down as follows:

• 30% of loads place a very low value on reliability (water heaters, laundry driers,

municipal pumps, etc.) ($90 /MWh)

• 50% of loads place an intermediate value on reliability (most other residential

loads) ($2,000 /MWh)

14 This is in line with costs estimated by EPRI [32].

82

• 20% of loads place a higher value on reliability (hospitals, businesses, etc.)

($10,000 /MWh)

In practice, only the lowest block of loads will be used for Demand Response,

the others placing too high a value on reliability to be inconvenienced except possibly

in extreme cases.

The overall case study parameters are shown in Table 5–3.


Parameter Value Parameter ValueBase Case Parameters Microgrid Case ParametersRate to Cus-tomers

20 ¢/kWh Rate to Cus-tomers

19 ¢/kWh

Diesel GeneratorSize

3×925 kW Wind Capacity 3×300 kW

DG Efficiency 3.7 kWh/L Wind Cost $4,050,000Diesel Price 85 ¢/L Microgrid Cost $200,000Interest Rate 3% ESS Size 1 MW, 3 MWhProject Life 20 Years ESS Cost $2,500,000Peak Load 1 MW (Winter) O&M Costs 2% p.a.

Demand Re-sponse

Dispatched at $90 /MWh

Emissions ReductionDiesel CO2 inten-sity

710 gCO2/kWh

Carbon Tax $20 /tCSocietal CarbonCost

$30 /tC


In the base case (diesel generators only), the generators produced 4382 MWh

of electricity at a cost of $1,005,705 annually. Customers were charged $876,431

annually. Emissions were 850.1 tC .

83

In the Microgrid case, 1509 MWh was produced by the diesel generators, and

2870 MWh was produced by the wind. The net annual cost of energy provision was

$481,232. A charge of $901,128 was applied to customers. A total of 293.2 tC was

emitted.


In the Base Case, the net annual value for the utility (which is also the Microgrid

owner) was –$129,274 for power exchange and –$17,003 for carbon tax, giving a total

annual loss of $146,276 and a 20 year present value of –$2,176,222. The total for the

customers is composed of the energy cost alone, giving an AV of –$876,431 and a

PV of –$13,039,080. The cost of emissions to Society was $25,504 for a 20 year PV

of –$379,437.

In the Microgrid case, the annual value of energy exchange for the utility was

$350,690 (the annual cost of diesel fuel was $346,232, and the additional O&M cost

of the Microgrid system (incl. DG and ESS) was $135,000), and the amortized

annual cost of the additional infrastructure was $453,706. The utility’s cost of carbon

emissions was $5,854, bringing the total AV for the utility to –$108,869 and the PV to

–$1,619,703. The cost to customers is $831,922 (energy costs only), giving a PV over

the project life of –$12,376,897. The cost to Society is –$8,795 annually (emissions

costs only) for a PV of –$130,843. Note that while the utility is still taking a loss, the

Microgrid case represents a saving over the Base Case of $37,407 annually ($491,113

without investment costs), and the payback period is 13.75 years, which is reasonable

for a large, government-owned utility.

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These costs are tabulated in Table 5–7, and the net benefits of the Microgrid

over the Base Case are shown in Figs. 5.4.4.



In this case, key parameters of the system are the presence of ESS and Load

Control. Without Load Control, investment costs are reduced by $200,000, but

increases the necessary Diesel dispatch from 1509 MWh annually to 2025 MWh,

with a corresponding increase in fuel costs from $346,232 to $464,812, and overall

the net annual value to the utility over the Base Case changes from a gain of $37,407

to a loss of $65,423. The absence of ESS reduces investment costs by $2,500,000,

and increases diesel dispatch to 2784 MWh annually, with a corresponding increase

85




Figure 5–9: Variation in net annual benefits of the Microgrid over the Base Casefrom the perspective of the utility and customers in Case 3.

in fuel costs to $639,033. This dramatic increase is primarily due to the fact that

without an ESS, at least one diesel generator must always be operating so as to be

able to provide operating reserve and frequency support. In this case, the net annual

value to the utility over the Base Case becomes –$41,723; that is, the Microgrid case

ends up costing more than the Base Case. If both ESS and DR are dispensed with,

86

necessary diesel production increases still more to 3030 MWh, increasing fuel costs

to $695,527. With the reduced investment costs, this results in a net AV for the

utility of –$81,624.


are shown in Fig. 5–9. It is interesting to note that when the capacities of the ESS are

increased, though the benefit from the ESS may increase, so too does its investment

cost, and as a consequence, there is actually a decrease in net benefit at this point

compared with a case with a lower ESS rating. The optimal ESS ratings could easily

be found, but this is not the purpose of this example. Also note that the cases in

which the available controllable load is varied, this has no corresponding change in

investment cost; rather it is assumed that each load point simply allows more of its

extant load to be controlled.

5.5 Chapter Summary

This chapter has described three Microgrid case studies based on actual Micro-

grid installations, and it has demonstrated application of the methodology described

in Chapters 2 - 4. Business cases have been developed, and the contributions of

individual benefits to each Stakeholder’s value have been illustrated. As stated in

Chapter 1, this work does not attempt to prove the current economic viability of

Microgrid development, but rather it provides a general, scalable tool-set for analyz-

ing Microgrid business cases and this chapter has demonstrated its use.

87

CHAPTER 6Summary and Conclusions

6.1 Summary

Chapter 1. Chapter 1 establishes the context of this work by describing the

composition and operation of Microgrids, relevant regulatory and market considera-

tion, and surveys work done to the present in the area of Microgrid and Distributed

Generation benefit analysis. It also describes the motivation behind this work, which

is to provide a basis for the calculation of Microgrid benefits such that business cases

may be made to justify the development of Microgrids that provide a net benefit to

all stakeholders.

Chapter 2. Chapter 2 provides a framework in which to consider certain key

Microgrid benefits and the stakeholders to whom they accrue. The benefits described

are locality and selectivity benefits; the provision of ancillary services, power quality

and reliability (PQR) improvements, and reduced peak loading and system losses;

and reduced emissions. These benefits are described in terms of “benefit functions”,

which transform Microgrid impacts into benefits seen by stakeholders. The stake-

holders identified are the end-use Microgrid Customers, the Microgrid Owner or

Independent Power Producer (IPP), the System Operator(s) (SO), the generation

utilities or Bulk Energy Suppliers (BESs), Customers outside the Microgrid, and

Society.

89

Chapter 3. Chapter 3 describes a methodology to quantify the benefits de-

scribed in Chapter 2. A cost-benefit analysis methodology is given with specific

application to Microgrid business case evaluations, comparing a base (or status quo)

case.

Chapter 4. Chapter 4 provides specific methodologies to evaluate the ben-

efits described in Chapter 2. These methodologies effectively complete the picture

of “benefit functions” introduced in Chapter 2 by providing a means to transform

impacts into quantified benefits that can be analyzed in a financial business case.

Chapter 5. Chapter 5 applies the framework and methodology to three case

studies and develops business cases from them. The cases analyzed were intended to

illustrate viable business cases based on: an IPP-owned Microgrid with benefits to

a community of stakeholders; a customer-owned Microgrid that illustrated benefits

to a commercial business; and a utility-owned Microgrid that illustrated benefits for

an isolated community.

6.2 Conclusions

Energy cost-related transactions form the backbone of economic analysis on

Microgrids. However, even taking these benefits into account, it is difficult for Mi-

crogrids to compete with large, centralized generation on the basis of cost of energy

alone. Depending on the jurisdiction, Microgrids may be able to increase benefits to

all stakeholders through provision of a variety of other services, including ancillary

services (such as frequency and voltage support), peak load reduction, and reliability

improvement [82,89,97]. If economically valued, these additional services can signif-

icantly improve the business case for Microgrids, and they can provide demonstrable

90

benefits to all other stakeholders, helping to increase public and private support for

Microgrid projects.

Making business cases attractive is critical to attaining investor and stakeholder

buy-in necessary for Microgrid development. Furthermore, if Microgrids are devel-

oped with the intention of providing benefits for all stakeholders, this will create a

successful project history that can provide a tool in pushing forward future Micro-

grid developments. One illustration of this effect is how added reliability can have

positive effects for utilities in terms of public image, technical consequences, polit-

ical consequences, and even health and safety [22]. This can make utilities treat

Microgrids more favourably in future projects, and it may encourage them to share

investment costs with developers.

The real value of Microgrids is not necessarily an ability to provide any single

service better or cheaper than some alternatives, but their strength is their flexibility

to provide a variety of different services to meet the diverse needs of an array of

stakeholders.

It should be noted that the methodology outlined in this thesis focused on the

costs and benefits of individual Microgrid projects, and while the approach used

should be scalable to include virtually any costs and benefits resulting from a single

project, large-scale deployment of Microgrids may result in additional impacts and

benefits. For example, certain network-dependent Smart Grid benefits may mate-

rialize, co-ordination between multiple Microgrids may be used to provide certain

ancillary services, and factors relating to increasing DG penetration may become

more problematic [33, 104].

91

6.3 Future Work

This thesis has served to compile a broad set of methodologies for benefit cal-

culation, and any single methodology could be delved into further. Notably, there is

room for improvement in the techniques used in reliability calculations, in that the

technique actually used applies only to simplistic radial feeders. This could be ex-

panded to more complex topologies. The calculation of power quality improvements

derived from ancillary service provision could be more comprehensive, explicitly ac-

counting for feeder power flows and voltage profiles. At present load growth is only

accounted for in calculations of investment deferral. It would be prudent, however,

to take growth into account in calculation of all benefits, especially in calculation of

energy exchange. For example, with 2% load growth, after 20 years a load would be

nearly 50% larger than at the beginning.

Control techniques have the potential to play a major role in benefit creation,

especially where there is some form of period-to-period carryover of energy, as with

an ESS, with Load Control, and with hydroelectric power. The control strategies

employed in this research have been fairly simplistic, and they have the potential to

be improved–perhaps with significant results.

An interesting extension of this methodology would be the use of optimization

techniques such as Multi-Criteria Decision Analysis (MCDA) to optimize the decision

variables of each project alternative to maximize benefits for different stakeholders.

For example, in developing a Microgrid proposal an IPP may want to weight its

own benefits highly (for profit), but it may also want to value the benefits to the

Customer (to “sell” the project), as well as society (to secure grant funding).

92

Additional benefits should also be considered, such as the minor benefits listed

in Chapter 2, including:

• Reduced dependence on external sources of oil,

• Reduced natural resource usage,

• Reduced power restoration costs,

• Reduced congestion cost,

• Reduced equipment failures,

• Reduced meter reading costs, and

• Increased local employment.

93

APPENDIX AUseful Principles of Economics

In performing an economic analysis with any degree of realism and accuracy,

some basic principles of economics must be understood. In this appendix, the basics

of cash flow diagrams, annual and present worth of a project, interest and inflation

rates, cost of capital, rates of return, and benefit-cost ratios will be explained with

respect to the Methodology developed. Except where noted, information for this

appendix has been taken from Global Engineering Economics: Financial Decision

Making for Engineers, 4th ed. by Fraser, et al. [36]. It is suggested that the interested

reader consult this book for further details, or, for information on the economics that

apply to the power system specifically, Fundamentals of Power System Economics

by Kirschen and Strbac is also recommended [58].

A.1 Cash flow diagrams

The cash flow diagram is the fundamental tool of economic analysis of a project.

It consists of a horizontal line, representing the project timeline, and a series of up-

ward and downward pointing arrows, representing receipts (positive cash flows) and

disbursements (negative cash flows), respectively, at each period. [36] This diagram

aids in visualisation of the overall economic “picture” of a project, and informs the

use of other economic tools to complete the analysis, described below. It also helps

ensure that no project receipts or disbursements are overlooked.

95

It is notable that cash flow diagrams can be extended to represent costs and

benefits in the more general sense used in the rest of this document. In this “cost-

benefit flow diagram”, upward arrows would indicate benefits to a stakeholder, while

downward arrows would indicate costs. An example of a typical overall cost-benefit

flow in a Microgrid is shown in Fig. A–1. In this example there is a large initial

cost (typically to the IPP and possibly the DNO; this is found in step 2 of the

Methodology), there are annual costs and benefits for all parties throughout the

lifetime of the project (found in steps 3 and 4 of the Methodology), and at the end of

the project there is a certain amount of scrap or salvage value that the used equipment

has, and a cost for dismantling or disposing of the remaining equipment. Clearly,

each individual stakeholder would have its own unique cost-benefit flow diagram, as

shown in Fig. A–2, and a cost for one stakeholder might be a benefit for another

(for example, in the case of money paid from the Customer to the IPP for power

provided by the IPP).

Figure A–1: Total cost and benefit flows for a Microgrid project over an N yearlifespan. Note that in reality, total costs and benefits will be divided amongst thevarious Stakeholders in their own unique cost-benefit flow diagram.

96

Figure A–2: Cost and benefit flows for each stakeholder in a Microgrid.

Once cost and benefit flows have been determined, they must be collected into

a form that can be used to compare with other projects. Four principal methods

will be described for doing that, Annual Worth, Present Worth, Rate of Return, and

Benefit-Cost Ratio. In these sections, costs and benefits will be primarily discussed

in terms of a single stakeholder, but it is to be understood that this same approach

applies to each stakeholder.

A.2 Annual and Present Worth of a Project

Perhaps the most intuitive way to value a project is by finding its annual worth.

This is the average annual net benefit (or cost) that a project provides to a given

stakeholder. Closely related to annual worth, is the present worth of a project, which

is the total net benefit (or cost) of the project over its lifetime, expressed in today’s

97

dollars. In order to fully explain these approaches, it is necessary to explain interest

and inflation rates.

A.2.1 Interest, Inflation, and Tax Rates

An interest rate is the difference in an amount of money borrowed and the

amount later repaid to a lender [36]. This document assumes exclusively annual

interest rates, in which the total amount that must be repayed to the lender is

F = P (1 + i)N ,

where F is the repayment or future amount, P is the amount borrowed or present

value at time 0, i is the interest rate, and N is the number of years of the loan. Note

that the present value can be determined from the future amount using the inverse

of this formula, i.e.

P =F

(1 + i)N.

This principle is of vital importance in Microgrid projects, since an IPP typically

funds initial purchased through investors, to whom the money must be repaid with

interest. Note that in many cases, project funding may be obtained from multiple

sources. The weighted average cost of capital is an average interest rate that a

company must pay to all lenders [36].

An inflation rate measures the increase in the cost of goods and services from

year to year [36]. In Canada, the interest rate is typically kept between 1% and 3%

per year, with a target of 2% [10].

Interest and inflation rates work together to mean that money a year from now

has less value than money today. The combined “real discount rate” describes the

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amount by which the value of money should be discounted from one year to the

next. It is found as i′ = 1+i1+f− 1, where f is the inflation rate. It is noteworthy that

distribution planners typically use constant value dollars, that is they ignore the

effects of inflation and assume that it does not affect present worth calculations [2].

This has merit if one assumes that all prices increase (inflate) by the same amount.

Taxes and incentives also affect the value of money to a company, but they

will not be covered in this section, since each corporation will deal with the tax

aspects of projects differently depending on jurisdiction, company size and type, and

whether the company is making an overall profit, among other factors [36]. Incentives

are similar, and therefore the author’s approach is to develop a methodology that

indicates whether a Microgrid project can stand on its own economic merits, and

allow individual companies to determine the details of extraneous economics on an

individual basis.

A.2.2 Annual and Present Worth

In order to find the annual and present worths of a project, there is one more

important formula in addition to the future worth formula mentioned in the previous

section, the “series present worth factor”, which converts an annual cash flow into a

present value and its inverse, the so-called “capital recovery factor”, which converts

a present value into an annual cash flow series, based on a known interest rate. The

capital recovery factor is

P/A =(1 + i)N − 1

i(1 + i)N,

where A is the amount of each annual receipt or disbursement, P is the present worth

of that annual series, N is the number of years over which the annual series occurs,

99

and i is the interest rate paid to lenders each year. Clearly, a positive result means

that the project provides a net benefit to the stakeholder in question.

Now, given the interest rate and the Cost-Benefit flow diagram for a stakeholder

for a particular Microgrid project, which may have an initial cost, an annual net

cost or benefit, and a final net cost or benefit, the present worth and annual worth

can be calculated. First, it is advisable to put all values in terms of present worth.

The initial cost is already in terms of present worth, the annual net cost or benefit

can be put in terms of present worth using the series present worth factor, and the

final net costs or benefits can be put in terms of present worth using the formula,

P = F(1+i)N

. These three values can be summed to find the total net present worth of

the Microgrid project to a particular stakeholder. From there, the annual worth to

the stakeholder may be found using the capital recovery factor. This approach must

be repeated for each stakeholder’s cost-benefit flow.

A.3 Internal Rate of Return

Perhaps the “best” method of comparing projects is that of rate of return.

The internal rate of return (IRR) is the interest rate, i∗ that would result in zero net

benefit if all cost-benefit flows were discounted at i∗ [36]. The minutiae of calculating

the IRR will not be discussed in detail here, but let it suffice to say that in many

cases, it is best found through numerical approximation methods.

Internal rate of return analysis allows comparison of projects of different lengths,

and of different scales. Furthermore, it can also be used to find the rate of return of

an incremental investment. For example, given the cost-benefit flows for a Microgrid

project containing a WTG without an ESS, and the cost-benefit flows for a Microgrid

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project with both a WTG and an ESS, the incremental rate of return can be found,

which indicates the value of the additional investment in the ESS. Many companies

have a minimum annual rate of return (MARR), which all potential projects must

meet or exceed in order to be considered desirable investments [36].

A.4 Benefit-Cost Ratios

Benefit-Cost Ratios are typically used in government project evaluations [36].

They have the advantage of being independent of the value of money, which can be

an advantage in evaluation of Microgrid projects. Traditionally they are made using

either annual or present worth valuations of benefits to users, and costs to sponsors,

as

BCR =PW (Users’ Benefits)

PW (Sponsors’ Costs).

This approach is appropriate for evaluating projects that benefit society and are paid

for by a government sponsor, but to acommodate the larger number of stakeholders

in the case of Microgrids, it is better to consider BCRs for each stakeholder, as

BCRi =PW (Benefitsi)

PW (Costsi),

for stakeholder i. Clearly, Benefit-Cost Ratios of greater than unity indicate that a

project has more benefit than cost [36]. In addition to the simple Benefit-Cost Ratio,

the Modified Benefit-Cost Ratio,

MBCR =PW (Benefits)− PW (Operating Costs)

PW (Investment Costs),

101

effectively provides a value for net gain per dollar invested. The MBCR is not

necessarily useful for Microgrid evaluation, since the brunt of the investment cost is

borne by few stakeholders (typically just the IPP).

It should be noted that some ambiguity may exist in the construction of Benefit-

Cost Ratios, in that certain effects may be seen as benefits or as reductions in cost.

This ambiguity will not affect whether a BCR is greater or less than unity, but it

will affect the absolute magnitude of the BCR, and therefore BCRs should be used

to compare different projects only with great care [36].

102

APPENDIX BSensitivity to Uncertainty

All parameters in an analysis have a certain degree of uncertainty associated

with them. In many cases, parameter uncertainty can create uncertainty in the

results of an analysis. Sensitivity analysis is used to help understand and quantify

this relationship.

This can be accomplished through simple parameter variation where an output

is calculated based on low, high, and middle estimates for each parameter of interest

[33]. This is often illustrated using a so-called “tornado diagram”, which shows

the outputs that result from variation of each parameter in descending order of

variability. As the output variations are ordered by degree of variation, with large

variations plotted at the top of the diagram and small variations at the bottom, and

plotted about the middle estimate for all parameters, a tornado-like figure emerges,

as shown in Fig. B–1. This approach is related to a “break-even analysis”, which

attempts to find the value of each parameter that causes a project to just “break-

even”, that is, it finds the value of the parameter of interest at which the project’s

NPV evaluates to zero. Alternatively a sensitivity analysis could be a stochastic,

Monte Carlo type approach that varies inputs within their probable ranges to find a

probability distribution for benefits.

Beyond parameter variation, it may be of interest to consider each component

of the Microgrid in isolation in order to determine its contribution to the overall

103

Figure B–1: Example of a “Tornado diagram” showing the sensitivity of Microgridowner annual net revenues to changes in various project parameters. The diagramis centred on the middle estimate value of $60,000.

Microgrid benefit to the stakeholders, and therefore the return on each individual

investment. Using this approach, shown in Fig. B–2, the impacts, Ii, and benefits, Bi,

of each system, i, can be found individually. In general, this approach is difficult to

apply to Microgrids, since many components of a Microgrid do not work in isolation,

or at least do not provide full benefit unless they are combined with other Microgrid

systems. They must, therefore be considered as a whole, in a combined approach,

shown in Fig. B–3. In this case, only the combined impacts, IΣ, and benefits, BΣ,

can be found directly. Nonetheless, in order to better understand investment in a

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Microgrid project, methods of approximating the impact and benefit added by an

individual component must be used. There are two methods for estimating the value

of individual systems, a subtractive approach, and an incremental approach.

In a case where employment of a certain technology or system is a binary decision

(i.e. it cannot be scaled or implemented in part; either it is implemented or it is not,

for example in the case of Demand Response), a subtractive approach can be used to

find its benefit contribution. This involves finding the benefit provided by the whole

Microgrid, BΣ, and the benefit provided by the Microgrid without the system in

question, BΣ/i, and comparing the two, Fig. B–4. That is, the approximated benefit

provided by system i is the difference between the two benefit totals, Bi = BΣ−BΣ/i.

In a case where a technology may be scaled, for example in the case of an

Energy Storage System, which may have different power flow and energy storage

capacities, an incremental approach may be used, Fig. B–5. In this case, a param-

eter of interest of the system, Pij, may be incremented (in the positive or negative

direction), and the resultant incremented benefits, BΣ(Pij++), can be compared to

the case of the non-incremented parameters. The incremental approach can be used

to find the local marginal value of investment in a particular Microgrid component,

as dBidICi

= dBidPij

(dICidPij

)−1

, where dBidPij≈

BΣ(Pij++)−BΣ

(Pij++)−Pij is the incremental benefit of in-

creasing parameter Pij, and dICidPij

is the incremental investment cost of increasing

parameter Pij.

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Figure B–2: The benefits provided by sys-tems that operate independently may beanalyzed using a “separated approach”. Inthis case, the benfit provided by System iis found directly as Bi.

Figure B–3: Microgrids consist of interde-pendent systems, which, in general, can-not be analyzed independently, but mustbe analyzed using a “combined approach”.In this case, benefits come bundled to-gether as BΣ.

Figure B–4: A “subtractive approach”may be used to estimate the benefit pro-vided by the whole Microgrid less an indi-vidual system, BΣ/i.

Figure B–5: An “incremental approach”may be used to estimate the incrementalbenefit provided by an individual Micro-grid System parameter, BΣ(Pij++).

106

APPENDIX CAnalysis Software

In analyzing Microgrid impacts and benefits, computer simulations have the po-

tential to be helpful in finding the impacts of the presence of Distributed Generation

in the Microgrid (for example, in step 3 of the methodology presented in this thesis).

The author is aware of three tools commonly used for used for analysis of the costs

and energy production of Distributed Generation:

• Distributed Energy Resources Customer Adoption Model (DER-CAM), cre-

ated and maintained by Micheal Stadler at Lawrence Berkley National Labo-

ratory from 2000 - present [61];

• Renewable-energy and Energy-efficient Technologies Screen (RETScreen), cre-

ated in 1997 and maintained by Natural Resources Canada [73]; and

• Hybrid Optimization Model for Electric Renewables (HOMER), created for

National Renewable Energy Laboratory in the United States in 1997, and main-

tained by HOMER Energy LLC [48].

A comparison was carried out between these three software packages, to deter-

mine their capabilities and relative mertis, and upon concluding that each was lacking

in certain functionality that enabled a full Microgrid cost-benefit analysis, the author

designed and built his own software package, which was used in the calculation of

the results of this thesis.

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C.1 A Comparison of Available Analysis Software

The three software packages considered, DER-CAM, RETScreen, and HOMER,

all have the ability to consider long-term DER investments, Combined Heating and

Power (CHP), Combined Cooling, Heating, and Power (CCHP), emissions, and sales

and purchases from the grid. They vary greatly in their ease of use and in the

DER types and system types that may be considered. Two universal weaknesses are

the implicit assumption of customer-owned DER, and the inability to consider DER

effects on feeder voltage or distribution losses.

N.B.: Given the fact that the three applications only consider the economics

of energy exchange using DER units, the methodology presented in this thesis still

requires additional information from other sources to perform a full cost-benefit

analysis.

C.1.1 DER-CAM

DER-CAM is primarily a text-based optimization and analysis tool within a

basic user interface that essentially serves to organize and explain the data-entry

process. What it lacks in user-friendliness, it makes up for in comprehensiveness,

including functionality for considering the effects of Demand Response and some

support for Microgrid-related improvements in service reliability. An additional,

interesting feature is the ability to request the optimization tool to ensure a system

consumes “Zero Net Energy” (ZNE), which means that the system “produces at least

as much emissions-free renewable energy as it uses from emissions-producing energy

sources” [94], however, this feature does not appear to be fully supported as yet (see

below regarding support for renewable DG).

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The heart of the DER-CAM software package, its raison d’etre, is a powerful

optimization tool that allows the user to find the best DER configuration for his or

her system with few prior assumptions. The user can define a number of different

DER options, the system load profiles, energy costs, and various other parameters,

as well as the objective of minimization (overall cost, emissions, or a combination of

the two), and DER-CAM computes the best system based on the given information.

The user can also force the program to invest in a certain DER configuration and

thereby output the result from a simulation with no optimization.

As powerful as this optimization tool is, however, it is dependent on the CPLEX

Mixed Integer Program (MIP) solver for GAMS, which may be seen as a weakness in

a cash-strapped research context, since the academic licence needed to run the solver

costs over $1200 on top of the $640 cost of the basic GAMS package. This deficit has

been largely addressed by Stadler, however, by offering a free, web-based DER-CAM

service at no cost. DER-CAM also offers a very limited sensitivity analysis tool,

which only serves to vary capital investment costs by a set amount.

Being an original, stand-alone user interface, there are some issues with data

manipulation (in stark contrast with RETScreen, which is based on Microsoft Excel,

and borrows that application’s highly-developed data-manipulation functionality).

For example, there is no apparent data import or export option, and the copy &

paste functionality has a few bugs. This is especially problematic since the process

of entering data by hand is compounded by the difficulty of navigating DER-CAM

datasets with the keyboard. DER-CAM is also entirely lacking in documentation

and help files.

109

Unlike the other two applications, DER-CAM does not appear to have the ability

to consider isolated Microgrids, and although the current version of the program deals

with solar Photovoltaic and solar thermal power in quite a sophisticated way (taking

into account daily insolation rates amongst other parameters), it offers no wind or

hydro power. This is a significant drawback. It does, however, have the ability to

integrate Electric Vehicles (EVs) in the analysis–a unique trait amongst the three

programs considered.

Other strengths of the DER-CAM application include excellent integration of

energy transfer technologies, e.g. heat pumps and absorbtion chillers, as well as

Combined Heat and Power (CHP), Combined Cooling, Heat, and Power (CCHP),

and its support of microturbine “sprint capacity”.

The method by which results are obtained involves calculating system variables

for each hour of several different types of days in different seasons, i.e. weekday,

weekend. This allows the simulation results to be output in a very comprehensive

yet relatively concise manner, since the results only need to be described for each

hour of each day type.

Types of DER that can be considered with DER-CAM:

• Electricity and Heat Sources

◦ Natural Gas (NG)- and Diesel-based Combustion

◦ Natural Gas-based Fuel Cells

◦ Heat Pumps and Absorbtion Chillers

◦ Solar PV

◦ Solar Thermal

110

• Load Types

◦ Electricity only

◦ Cooling

◦ Refrigeration

◦ Space Heating

◦ Water Heating

◦ Natural gas only

• Energy Storage

◦ Electrical Storage (both regular battery and flow battery options)

◦ Thermal Storage

◦ Electric Vehicles

Relative Strengths:

• Powerful, versatile DER optimization with few required assumptions,

• Very detailed output,

• Demand response functionality,

• Inclusion of Electric Vehicles.

Relative Weaknesses:

• Lack of renewable DG options,

• Cannot analyze isolated systems,

• Limited user interface,

• Difficult data manipulation, import/export,

• Limited sensitivity analysis,

• Need GAMS & CPLEX (at a combined cost of nearly $2,000) to run locally.

111

C.1.2 RETScreen

RETScreen is a Microsoft-Excel-based application, which takes advantage of

Excel’s considerable data manipulation power, provided at no cost by the Cana-

dian government. It has an excellent user interface, and provides built-in access to

international climate data and a substantial “real life” product database.

The type of analysis to be performed may be selected from among “Power”,

“Heating”, and “Cooling” analysis types or various combinations of the three (along

with “Energy Efficiency Measures” and “User-Defined”). Furthermore, in most data

entry steps, the user has the ability to choose amongst different “methods”, which

take advantage of different information the user might have available. For example,

when defining the characteristics of a wind turbine generator, the user has the op-

tion to specify: peak capacity and capacity factor; average wind speed and other

environmental data, a wind power curve, losses, and availability; or monthly wind

speed averages, a wind power curve, losses, and availability.

The software is very easy to use, the user being guided through a number of very

clearly explained data-entry forms. The documentation is excellent, even including

a number of instructional videos on the RETScreen website. If there is one great

weakness, it is that even with the ability to change the “methods” of data entry, the

application is relatively inflexible in terms of allowing the user freedom to define his

or her own system; the architecture is already largely assumed in the way the data

entry forms are structured. This is the cost of ease of use.

Other weaknesses of the RETScreen application include the lack of integrated

Time Of Use (TOU) analysis (although a separate tool is provided to calculate

112

TOU energy costs based on a table of given rates and a table of loading), and the

inability to consider energy storage apart from in isolated systems. It also lacks

a DER optimization tool and does not provide the detailed energy use output of

the other two applications (especially DER-CAM). A subtle deficiency is the lack

of individual component lifetimes, which the other two applications use to calculate

periodic capital investments.

Strengths of RETScreen include: a useful Monte Carlo-based sensitivity and risk

analysis tool in which the user can define the variability on expected values of certain

parameters, and ranges of results are given; the ability to consider numerous fuel

types including natural gas, propane, and biogas, among many others; and numerous

supplementary tools and options, not considered as part of the main analysis, such

as the afore mentioned TOU tool. Furthermore, if the user already has Microsoft

Office installed, installation of RETScreen is free.

Types of DER that can be considered with RETScreen:


◦ Fuel Cells

◦ Gas Turbines

◦ Geothermal

◦ Hydro Turbine

◦ Ocean Current Power

◦ Photovoltaic

◦ Reciprocating Engines

◦ Solar Thermal

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◦ Steam Turbines

◦ Tidal Power

◦ Wave Power

◦ Wind Power

• Load Types

◦ Electricity only

◦ Heating

◦ Cooling

• Energy Storage

◦ Limited to battery storage in off-grid systems

Relative Strengths:

• Ease of use,

• Excellent user’s manual and instruction,

• Flexible methods of data entry, depending on the user’s available data,

• Comprehensive product database,

• Integrated climate data,

• Choice of units,

• Offered in 34 languages,

• A tremendous selection of DG types and fuels,

• Sensitivity and risk analysis,

• Relatively sophisticated economic analysis, providing rates of return, net present

value and benefit-cost ratio,

• Numerous supplementary options and tools.

114


• Lack of integrated TOU tariff,

• Limited energy storage options, which are only useable in certain conditions,

• Does not take individual component lifetimes into account,

• No optimization tool,

• Inflexible system architecture.

C.1.3 HOMER

Unlike the other two applications, HOMER is an entirely stand-alone, very

specialized software package for DER analysis. The focus of its design is apparent

in its user interface, which is straightforward, relatively intuitive, and includes an

iconographic representation of the system under consideration. A great strength

of this software is the excellent visualization it provides at every opportunity, for

example, showing daily and yearly graphs of system loading based on user-entered

data.

HOMER is a compromise between the other two software packages in that it

balances flexibility with ease of use. It contains an optimization tool, like DER-CAM

(although not as powerful), as well as a sensitivity analysis tool, like RETScreen. It

also offers a device characteristic database, although it is not as comprehensive as

that of RETScreen. The documentation is complete and quite helpful.

HOMER’s optimization tool relies on user-entered assumptions for DER options

and discrete sizes, and it solves the optimization problem through the brute force

method of calculating the results of each possible system configuration. This does

have the advantage of allowing the user to compare all the different configurations

115

under consideration, however. HOMER automatically discards infeasible configura-

tions (for example, cases in which a converter is too small to convert power from

a PV array), and suggests if the DER parameter search space is not big enough.

The sensitivity tool works in precisely the same way as the optimization tool, but in-

stead of varying system configurations and DER parameters, the tool varies economic

parameters.

HOMER can integrate a number of renewable technologies into the analysis,

including PV, wind, hydro, and generic NG-powered generators. It can also take

multiple water pumping, hydrogen electrolysis, and natural gas reforming loads into

account. Furthermore, it considers the basic topology of the system and requires the

user to place a converter between the AC and DC busses.

HOMER has a number of unique features including the ability to determine the

break-even distance for connecting power lines to an isolated system. The user can

also use HOMER to synthesize wind datasets from key parameters and a level of

randomization. This wind synthesis tool is especially useful when combined with

HOMER’s import & export data feature.

Weaknesses of the software include some inconsistency in units (for example,

in some cases O&M costs must be input in $/hr and in other cases in $/year), the

discrete optimization and sensitivity analysis, as described, and the upfront cost of

$99.

Types of DER that can be considered with HOMER:


◦ Photovoltaic

116

◦ Generic (combustion)

◦ Wind Turbine

◦ Hydro

• Load Types

◦ Electrical primary load

◦ Deferrable electrical load

◦ Thermal load

◦ Hydrogen load

• Energy Storage

◦ Flywheel

◦ Hydrogen Tank

◦ Battery

• Other

◦ Electrolyzer

◦ Gas reformer

◦ Converter

Relative Strengths:

• Outstanding user interface with good visualizations,

• Good user’s guide and help files,

• Device database,

• Deferrable or reschedulable load,

• Sensitivity analysis and optimization,

• Suggests increasing search space for optimization when necessary,

117

• Power line connection break-even analysis,

• Ability to easily import and export datasets,

• Ability to synthesize certain datasets.


• Some inconsistency in units,

• Commercial software, costs $99,

• User must explicitly define the (discrete) optimization search space.

C.2 The Author’s Software

Based on analysis of the relative strengths and weaknesses and collective defi-

ciencies of the three available software packages, the author designed a tool based

in Microsoft Excel with which to conduct Microgrid cost-benefit analyses. A brief

overview of the tool and its operation is given here.

The tool is technology-independent (any thermal or renewable DG-type may

be used) and it allows a variety of different Microgrid configurations. In addition, it

incorporates the key benefits listed in this thesis in its structure, such that all benefits

can be easily calculated for all Stakeholders provided the right software parameters

are set. A disadvantage is that the tool was developed using Visual Basic macros

embedded in an Excel workbook, to ensure portability and transparency, but this

has consequently made it very large and slow to operate.

The workbook in which the tool is based is divided into 10 worksheets: Configu-

ration, CostsAndEnergyEx, Resources, Loads, MarketData, OtherBenefits, Results,

Calc, Rel, and HiddenSheet. These have three key functions, divided as:

• Function: Data Entry and Configuration

118

◦ Configuration

◦ CostsAndEnergyEx

◦ Resources

◦ MarketData

◦ OtherBenefits

• Function: Data Output and Display

◦ Results

• Function: Calculation and Back-End

◦ Calc

◦ Rel

◦ HiddenSheet

Worksheet 1: “Configuration”. The configuration sheet, shown in Fig. C–

1, sets the major parameters that define the analysis, including which benefits will

be considered, how many cases, the owner of the Microgrid (to whom Microgrid costs

and revenues accrue), whether the Microgrid is isolates, and whether heating loads

are considered.

Worksheet 2: “CostsAndEnergyEx”. The CostsAndEnergyEx sheet al-

lows the user to set general project parameters and parameters related to energy

exchange in each of the cases considered. This includes the tariff scheme in place,

the load and resource profiles, DER available, Microgrid functionalities, and equip-

ment costs. Screenshot from this sheet are shown in Figs. C–2 & C–3

Worksheet 3: “Resources”. This worksheet defines resources for use in

renewable energy-based DG units. Some pre-defined time-series are included, but the

119

Figure C–1: The Configuration worksheet of the author’s analysis tool.

greatest utility of this feature comes from the ability to enter user-defined resource

time-series. A screenshot is shown in Fig. C–4

Worksheet 4: “Loads”. This worksheet is similar to the Resources work-

sheet, in that it defines time-series used in simulations of the various cases. In this

case, load time-series are defined, and the user can enter parameters used for demand

response and reliability calculation (the value of various proportions of lost load).

Worksheet 5: “MarketData”. Again, this worksheet allows the user to

enter time-series data to be used in simulations. In this case, the value of various

market commodities can be defined.

Worksheet 6: “OtherBenefits”. This worksheet is where the user config-

ures parameters used for calculation of the non-energy exchange-related benefits.

120

Figure C–2: A screenshot from the CostsAndEnergyEx worksheet of the author’sanalysis tool, showing data entry fields for general project parameters and the basecase.

The benefits available are based on reduction of peak loading, ancillary services pro-

vision, reduced emissions, and improved reliability. An example of the entry fields

for reliability are shown in Fig. C–5

Worksheet 7: “Results”. This worksheet displays and compiles the results

for the user in tabular and graphical form. Representative screenshots are shown in

Figs. C–6 & C–7.

Worksheets 8 - 10: Calculation and Back-End Functions. These tabs

are not to be entered by the user, but are used to assemble data from the other tabs

to interface with the Visual Basic code that drives the analysis.

121

Figure C–3: A screenshot from the CostsAndEnergyEx worksheet of the author’sanalysis tool, showing data entry fields for the second Microgrid case under consid-eration.

Figure C–4: A screenshot from the Resources worksheet of the author’s analysis tool.

122

Figure C–5: A screenshot from the OtherBenefits worksheet of the author’s analysistool, showing data entry fields for improved reliability benefits.

Figure C–6: A screenshot from the Results worksheet of the author’s analysis tool,showing summarized output values.

123

Figure C–7: A screenshot from the results worksheet of the author’s analysis tool,showing outputs from various benefit calculations.

124

APPENDIX DUseful Data and Simulation Parameters from Literature

This appendix presents data that has been collected from literature that can

inform analysis parameters. Years have been provided for cost information.

D.1 Network Data

D.1.1 Reliability Figures

• 0.3 probability of unsuccessful isolation of a Microgrid in a fault [25,26]

• 0.04 failures/km/yr reliability of MV distribution line [26]

• 0.04 failures/km/yr reliability of LV distribution line [26]

• 0.09 failures/mi/yr = 0.055 failures/km/yr reliability of 3phase urban LV dis-

tribution line [22]

• 0.12 failures/mi/yr = 0.075 failures/km/yr reliability of 3phase rural LV dis-

tribution line [22]

• 0.012 failures/yr reliability of 3phase urban padmount transformer [22]

• 0.010 failures/yr reliability of 3phase rural padmount transformer [22]

• 0.4 - 1.6 failures/yr reliability of upstream network [25]

• 1.10 - 1.75 outages per year (middle two quartiles SAIFI for US utilities) [52]

• 0.8 - 4.5 outages per year (outer two quartiles SAIFI for US utilities) [52]

• 30 hrs average repair time for a MV distribution line fault [26]

• 20 hrs average repair time for a LV distribution line fault [26]

• 3.5 hrs average time to reconfigure network [26]

125

• 3 hrs average time to isolate fault [26]

• 1 hr isolation, load transfer, and switching [21]

• 0.25 hrs to restore microgrid after a complete shutdown [26]

• 4 - 8 hrs to restore power after upstream outage [25]

• 4 hrs to restore power after outage [21]

• 90 - 160 minutes per interruption (middle two quartiles SAIDI for US utilities)

[52]

• 55 - 630 minutes per interruption (outer two quartiles SAIDI for US utilities)

[52]

D.1.2 Demand Growth Rate

• 3% [39]

• 2% [28]

• 1.7% [2]

• 1.5% peak demand growth, 1.3% energy consuption growth [33, p. 2-3]

D.2 Component Operating Data

• Microturbine

◦ 26% efficiency [45] (subref)

◦ 29% efficiency [105]

◦ 25 - 26% efficiency [65]

◦ 20 - 30% efficiency [9]

◦ over 80% efficiency with CHP [11]

◦ 80% efficiency with CHP [9]

◦ 2.1 max heat-to-power ratio [105]

126

◦ 20% min loading [45] (subref)

◦ 90% min loading with CHP [104]

◦ 50 - 100% loading [6]

◦ 724.6 gCO2/kWh 0.2 gNOx/kWh 0.004 gSO2/kWh [45]

◦ 56 kgCO2/GJ for NG (⇒ 202 gCO2/kWh ⇒ 672 gCO2/kWh at 30% effi-

ciency) [72]

• NG Fuel Cell

◦ 40% efficiency [45]

◦ 60 - 65% efficiency [16]

◦ 10% min loading [45]

◦ 489.4 gCO2/kWh 0.014 gNOx/kWh 0.003 gSO2/kWh [45]

D.3 Investment costs

D.3.1 Distributed Generation, etc.

• $1826 - $1576 /kW for a microturbine [65] (2008)

• $2082 - $1769 /kW for microturbine with heat recovery [65] (2008)

• $2377 - $1936 /kW for microturbine with heat recovery [64] (2009)

• $1200 - $1700 /kW for a microturbine [9] (2002)

• $1400 - $1600 /kW for a GE reciprocating engine around 1 - 2 MW in size, or

$1800/kW for a 330kW reciprocating engine.1

1 This includes engine, heat exchanger and controls, but does not include the oftenvery costly demolition, duct work, etc. required for installation, which is highly vari-able and often makes projects infeasible. This was based on personal communicationswith GE sales personnel on 16 August, 2011.

127

• 450 purchase + 985 installation $/kW for “DG” [2] (2010)

• 500,000 JPY/kW = 6250$/kW PV [4] (2010)

• 50,000 JPY/kW = 625$/kW Battery inverter [4] (2010)

• 50,000 JPY/kWh = 625$/kWh Battery capacity [4] (2010)

• $190/kW for CHP heat exchanger [11] (2009)

• 2732 $/kW for a CH4 fuel cell $7.65 M for 2.8 MW CH4 fuel cell [95] (2009)

• DG costs and efficiencies can be found in [16] (2000)

• CHP Incremental Investment cost $230/kW [16, p.6] (2000)

• Microturbine lifetime 10 years [65]

• 15 year DG investment [4]

• 20 year investment [11]

D.3.2 Microgrid Infrastructure and Controller Costs

• e300 for each wind and PV MC [96] (2009)

• e500 for each MGCC [96] (2009)

• e100 for each LV LC [96] (2009)

• e1000 for each piece of equipment to be controlled [104] (2011)

• Residential AMI Meter $40-$80 per unit + 7-10$ installation [32] (2011)

• Residential AMI Meter + Disconnect $70-$130 per unit + 7-10$ installation [32]

(2011)

• Commercial & Industrial AMI Meter + Disconnect $120-$500 per unit + 20-65$

installation [32] (2011)

• Direct Load Control $728 per customer [32] (2011)

• AMI with DR $940 per customer [32] (2011)

128

• Upgrading a network switch to an “intelligent” recloser or relay $50,000 [32]

(2011)

• Installation costs of 9-25$/endpoint [32] (2011)

D.3.3 Transformers and Substations

• $43,063 /MWavg. in 1999 → $55,521 /MWavg. in 2011 (assuming a 2.14%

inflation rate) [88]

• $400 /kV A for generic distribution equipment in 2010 [46] (2010)

D.3.4 Capacitor Banks

• $25 - $250 /kVAR depending on size. 2 See Fig. D–1 (2009)

D.3.5 Distribution Feeders

• $200k - $500k /MW avg. in 1999→ $258k - $645k /MWavg. in 2011 (assuming

a 2.14% inflation rate) [88]

D.3.6 Interconnection Cost

• $23000 /MW generally [57] (2006)

D.3.6.1 Operations and Maintenance Costs

• 1% of investment cost [25] (2006)

• 2.0 ¢/kW (microturbine) [63] (2009)

• 2.5 JPY/kWh (microturbine) [6] (2009)

• 0.6 - 1.5 ¢/kWh (microturbine) [9] (2002)

• 1.5 ¢/kWh (microturbine) [65] (2008)

• 2.0 ¢/kWh (fuel cell) [16, p.66] (2000)

2 Taken from values given by NEPSI [75].

129

Figure D–1: Costs of capacitor banks by reactive power rating. Based on valuesgiven by NEPSI.

• 0.5 ¢/kWh (unspecified DG) [2] (2010)

• 0.3 ¢/kWh (substation and feeder) [2] (2010)

• $3-11 /year/endpoint (AMI meter) [32] (2011)

D.4 Commodity Prices

D.4.1 Electricity prices

• e8.24 ¢/kWh [28] (2009)

• mean wholesale: 7.5e¢/kWh [87] (2009)

• mean retail: 13.5e¢/kWh [87] (2009)

• e90 /MWh for import and 50 e/MWh for export [87] (2009)

• $100 /MWh [33] (2010)

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Table D–1: Ancillary Services Market Clearing Price (average hourly $/MW, 2004)[57]

ISO Regulation Synchronous Spin-ning Reserves

Non-Spinning Op-erating Reserves

NYISO $ 22.60 $ 2.40 $ 0.30PJM $ 32.60 $ 7.40 $ 0.23ERCOT $ 10.30 $ 7.60 $ 2.40

• $120 /MWh [2] (2010)

• $110 /MWh [11] (2009)

• $50 /MWh heating [11] (2009)

• $109 /MWh for DG energy [2] (2010)

D.4.2 Natural Gas prices

• e10 ¢/m3, 8.8 kWh/m3 (⇒ 1.1 ¢/kWh) [45] (2009)

• e29.82 ¢/m3 (⇒ 3.4 ¢/kWh) [28] (2009)

• .89 ¢/kWh (approximately 1.14 ¢/kWh in 2012 US dollars) [105] (2001)

• 3.0 ¢/kWh [64] (2009)

D.4.3 Ancillary Service Prices

Ancillary Services Prices shown in Table D–1.

• e3.0 ¢/kWh for primary frequency reserves [104] (2011)

• $1000 - $4000 /MVAR-year for reactive power reserves [62] (2006)

D.5 Emission Costs

D.5.1 Carbon Emissions

• e12-15 /ton CO2 [87] (2009)

• using the 0.45 ton/MWh emission data for natural gas-fired units, emission

cost in range of e6 /MWh (+/- 20%) [87] (2009)

131

• $10 - 30 /t in Canada [14,19] (2008)

• $1.02 /ton (low), 16.85$/ton (med), $102.14 /ton (high) [33, p. 4-48] (2005)

• $123 /ton [64] (2009)

D.5.2 Non Carbon Gaseous Emissions

• $639 /ton (low), $1483 /ton (med), $3589 /ton (high) NOx coal and gas

weighted avg. [33, p. 4-48] (2005)

• $1878 /ton (low), $5987 /ton (med), $21980 /ton (high) SO2 coal and gas

weighted avg. [33, p. 4-48] (2005)

D.5.3 Particulate Emissions

• $2712 /ton (low), $8966 /ton (med), $69780 /ton (high) PM2.5 coal and gas

weighted avg. [33, p. 4-48] (2005)

• $156 /ton (low), $447 /ton (med), $3425 /ton (high) PM10 coal and gas

weighted avg. [33, p. 4-48] (2005)

D.6 Reliability Value

D.6.1 General

• 12.5 AUD/kWh market price cap [8] (2010)

D.6.2 Residential

• Value of service $2.50 /kWh [15] (2008)

• e1.50 /kWh [25] (2006)

• $16.8 /kWh for momentary duration [90] (2009)

• $3.5 /kWh for 30 min duration [90] (2009)

• $2.2 /kWh for 1 hour duration [90] (2009)

• $1.2 /kWh for 4 hours duration [90] (2009)

132


D.6.3 Commercial

• $10.00 /kWh [15, p. 26] (2008)

• e35.00 /kWh [25] (2006)

D.6.4 Industrial

• $25.00 /kWh [15, p. 26] (2008)

• e20.00 /kWh [25] (2006)

D.6.5 Small Commercial and Industrial

(2.2kWavg.)






D.6.6 Medium and Large Commercial and Industrial

(815kWavg.)






Restoration cost per 200 customers $1000 for a non-major outage, $1500 for a

major outage. [15] (2008)

133

References

[1] C. Abbey and G. Joos. A stochastic optimization approach to rating of energystorage systems in wind-diesel isolated grids. IEEE Transactions on PowerSystems, 24(1):418–426, 2009.

[2] M. F. Akorede, H. Hizam, I. Aris, M. Z. A. Ab. Kadir, and E. Pouresmaeil.Economic viability of distributed energy resources relative to substation andfeeder facilities expansion. IEEE International Conference on Power and En-ergy, pages 238 – 242, 2010.

[3] Alberta Electric System Operator (AESO). Blackstart services procurementplan. Published on the AESO website. Available at http://www.aeso.ca/

files/Nov05_BlackstartPP_v1.pdf, November 2005.

[4] H. Asano, W. Ariki, and S. Bando. Value of investment in a microgrid underuncertainty in the fuel price. IEEE Power and Energy Society General Meeting,pages 1 – 5, 2010.

[5] H. Asano and S. Bando. Economic evaluation of microgrids. In IEEE Powerand Energy Society 2008 General Meeting: Conversion and Delivery of Elec-trical Energy in the 21st Century, PES, 2008.

[6] H. Asano and S. Bando. Optimization of a microgrid investment and oper-ation: Energy saving effects and feasibility of ancillary service provision. InTransmission and Distribution Conference and Exposition: Asia and Pacific,T and D Asia, 2009.

[7] ATCO Electric. Standard for the interconnection of generators to ATCOelectric’s distribution system. Available on the ATCO website, www.

atcoelectric.com, July 2002.

[8] Australia Energy Market Operator. An introduction to Australia’s nationalelectricity market. Available at AEMO website http://www.aemo.com.au/,July 2010.

134

[9] O. Bailey, B. Ouaglal, E. Bartholomew, C. Marnay, and N. Bourassa. Anengineering-economic analysis of chp technologies in a micro-grid application.Berkeley Lab Report #LBNL-50023, prepared for the U.S. Environmental Pro-tection Agency. Lawrence Berkeley National Laboratory. Berkeley, CA, 2002.

[10] Bank of Canada. Inflation and price stability. Available on theinternet at http://www.bankofcanada.ca/wp-content/uploads/2010/11/

inflation_price_stability.pdf, 2010.

[11] A.K. Basu, S. Chowdhury, and S.P. Chowdhury. Strategic deployment of chp-based distributed energy resources in microgrids. In 2009 IEEE Power andEnergy Society General Meeting, PES ’09, 2009.

[12] BC Hydro. Distribution power generator islanding guidelines. Available on theBC Hydro website, www.bchydro.com, June 2006.

[13] BC Hydro. 35 kV and Below Interconnection Requirements for Power Gener-ators. Available on the BC Hydro website, www.bchydro.com, May 2010.

[14] BC Ministry of Small Business and Revenue. British Columbia carbontax. available on the Internet at http://www.rev.gov.bc.ca/documents_

library/notices/British_Columbia_Carbon_Tax.pdf, February 2008.

[15] Stan Blazewicz, Gene Shlatz, Forrest Small, Steven Tobias, Jacquelyn Bean,and (Navigant Consulting, Inc.). The value of distribution automation. Cali-fornia Energy Commission, PIER Energy Systems Integration Program. CEC-500-2007-103, 2008.

[16] California Air Resources Board. Air pollution emission impacts associated witheconomic market potential of distributed generation in california, June 2000.

[17] Bullfrog Power. Bullfrog Power Products, January 2012. http://www.

bullfrogpower.com/products/electricity.cfm.

[18] N. Cai, X. Xu, and J. Mitra. A hierarchical multi-agent control scheme fora black start-capable microgrid. In 2011 IEEE Power and Energy SocietyGeneral Meeting, pages 1–7, 2011.

[19] CBC News. Carbon tax proposal a non-starter in Alberta. Available on theInternet at http://www.cbc.ca/money/story/2008/01/08/renner-carbon.

html, January 2008.

135

[20] A. Chaurey and T.C. Kandpal. A techno-economic comparison of rural elec-trification based on solar home systems and pv microgrids. Energy Policy,38(6):3118–3129, 2010.

[21] A.A. Chowdhury, S.K. Agarwal, and D.O. Koval. Reliability modeling of dis-tributed generation in conventional distribution systems planning and analysis.IEEE Transactions on Industry Applications, 39(5):1493–1498, 2003.

[22] Ali Chowdhury and Don Koval. Power Distribution System Reliability. JohnWiley & Sons Inc., Hoboken, NJ, USA, 2009.

[23] L.A. Cifuentes, E. Sauma, H. Jorquera, and F. Soto. Preliminary estimationof the potential ancillary benefits for chile: Ancillary benefits and costs ofgreenhouse gas mitigation. Proc. Organisation for Economic Co-operation andDevelopment, Paris, pages 237–261, 2000.

[24] S. Conti, R. Nicolosi, and S.A. Rizzo. An analytical formulation to assess distri-bution system reliability in presence of conventional and renewable distributedgenerators. In Cigre 2011 Bologna Symposium, 2011.

[25] P.M. Costa and M.A. Matos. Economic analysis of microgrids including relia-bility aspects. In 2006 9th International Conference on Probabilistic MethodsApplied to Power Systems, PMAPS, 2006.

[26] P.M. Costa and M.A. Matos. Assessing the contribution of Microgrids to the re-liability of distribution networks. Electric Power Systems Research, 79(2):382–389, 2009.

[27] P.M. Costa and M.A. Matos. Avoided losses on lv networks as a result ofmicrogeneration. Electric Power Systems Research, 79(4):629–634, 2009.

[28] M. Dicorato, G. Forte, and M. Trovato. A procedure for evaluating microgridstechnical and economic feasibility issues. In 2009 IEEE PowerTech, 2009.

[29] A.L. Dimeas and N.D. Hatziargyriou. Operation of a multiagent system formicrogrid control. IEEE Transactions on Power Systems, 20(3):1447–1455,2005.

[30] Environment Canada. Electricity Intensity Tables. Available atEC website: http://www.ec.gc.ca/ges-ghg/default.asp?lang=En&n=

EAF0E96A-1, April 2010.

136

[31] Environment Canada. Water Survey of Canada. Available at WSC website:www.ec.gc.ca/rhc-wsc, August 2011.

[32] EPRI. Estimating the costs and benefits of the smart grid. Palo Alto, CA:2011. 1022519.

[33] EPRI. Methodological approach for estimating the benefits and costs of smartgrid demonstration projects. Palo Alto, CA: 2010. 1020342.

[34] Lester H. Fink, Kan-Lee Liou, and Chen-Ching Liu. From generic restora-tion actions to specific restoration strategies. IEEE Trans. on Power Systems,10(2):745–751, 1995.

[35] M. Fotuhi-Firuzabad and A. Rajabi-Ghahnavie. An analytical method to con-sider DG impacts on distribution system reliability. In 2005 IEEE/PES Trans-mission and Distribution Conference & Exhibition, 2005.

[36] N. Fraser, E. Jewkes, I. Bernhardt, and M. Tajima. Global Engineering Eco-nomics: Financial Decision Making for Engineers, 4th ed. Pearson PrenticeHall, Toronto, Ontario, Canada, 2009.

[37] R. Fulton and C. Abbey. Planned islanding of 8.6 MVA IPP for BC Hydrosystem reliability. In First International Conference on the Integration of REand DER, December 2004.

[38] H. A. Gil and G. Joos. Models for quantifying the economic benefits of dis-tributed generation. IEEE Transactions on Power Systems, 23(2):327–335,2008.

[39] H.A. Gil and G. Joos. On the quantification of the network capacity defer-ral value of distributed generation. IEEE Transactions on Power Systems,21(4):1592–1599, 2006.

[40] M.H. Gomes and J.T. Saraiva. Allocation of reactive power support, active lossbalancing and demand interruption ancillary services in microgrids. ElectricPower Systems Research, 80(10):1267–1276, 2010.

[41] Antonio Gomez-Exposito, Antonio J. Conejo, and Claudio Canizares. ElectricEnergy Systems: Analysis and Operation. CRC Press Taylor & Francis Group,6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL, 33487-2742,2009. Chapter 5 writen by Galiana, F. and Conejo, A. J.

137

[42] C. Grigg, P. Wong, P. Albrecht, R. Allan, M. Bhavaraju, R. Billinton, Q. Chen,C. Fong, S. Haddad, S. Kuruganty, W. Li, R. Mukerji, D. Patton, N. Rau,D. Reppen, A. Schneider, M. Shahidehpour, and C. Singh. The IEEE Reli-ability Test System-1996. IEEE Trans. on Power Systems, 14(3):1010–1020,1999.

[43] L. D. Danny Harvey. Energy and the New Reality 1: Energy Efficiency and theDemand for Energy Services. Earthscan Ltd., 2010.

[44] N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay. Microgrids. IEEEPower and Energy Magazine, 5(4):78–94, 2007.

[45] N.D. Hatziargyriou, A.G. Anastasiadis, J. Vasiljevska, and A.G. Tsikalakis.Quantification of economic, environmental and operational benefits of micro-grids. In 2009 IEEE Bucharest PowerTech: Innovative Ideas Toward the Elec-trical Grid of the Future, 2009.

[46] Rodrigo Hidalgo Anfossi. An assessment of the technical and economic benefitsof distributed generation. Master’s thesis, McGill University, Montreal, QC,Canada, 2010.

[47] M. Hlatshwayo, S. Chowdhury, S. P. Chowdhury, and K. O. Awodele. Reli-ability enhancement of radial distribution systems with DG penetration. InUPEC2010, 2010.

[48] HOMER Energy LLC. HOMER Energy Website, August 2011. http:

//homerenergy.com/.

[49] Hydro One Networks Inc. Distributed generation technical interconnectionrequirements interconnections at voltages 50kv and below. Available on theHONI website, www.hydroone.com, June 2011.

[50] Hydro One Networks Inc. Feed-in tariff price schedule. Available on the HONIwebsite, www.hydroone.com, June 2011.

[51] Hydro Quebec. Requirements for the interconnection of distributed generationto the Hydro-Quebec medium-voltage distribution system. Available on theHydro Quebec website, www.hydroquebec.com, February 2009.

[52] IEEE. 1366-2012 IEEE Guide for Electric Power Distribution Reliability In-dices, May 2012.

138

[53] IEEE Standards Coordinating Committee 21. 1547-2003 IEEE Standard forInterconnecting Distributed Resources with Electric Power Systems, July 2003.

[54] IEEE Standards Coordinating Committee 21. IEEE Guide for Design, Oper-ation, and Integration of Distributed Resource Island Systems with ElectricPower Systems, July 2011.

[55] Independent Electricity System Operator (IESO) (Ontario). Ancillary ServicesContracts Page, IESO website, August 2011. http://www.ieso.ca/imoweb/

marketdata/ancilSrvContracts.asp.

[56] ISO New England (ISONE). ISO New England Openaccess Transmission Tar-iff. Published on the ISONE website, http://www.iso-ne.com, August 2011.

[57] Douglas E. King. Electric Power Micro-grids: Opportunities and Challengesfor an Emerging Distributed Energy Architecture. PhD thesis, Carnegie MellonUniversity, Pittsburgh, PA, USA, May 2006.

[58] Daniel Kirschen and Goran Strbac. Fundamentals of Power System Economics.John Wiley & Sons Ltd., 2004.

[59] J.D. Kueck, R.M. Staunton, and B.J. Kirby. Microgrids and demand response.Public Utilities Fortnightly, pages 55–58, May 15 2003.

[60] Robert Lasseter, Abbas Akhil, Chris Marnay, John Stevens, Jeff Dagle, RossGuttromson, A. Sakis Meliopoulos, Robert Yinger, and Joe Eto. The CERTSMicrogrid concept. CEC Consultant Report P500-03-089F. Sacramento, CA:California Energy Commission, October, 2002.

[61] Lawrence Berkeley National Laboratory. DER-CAM Website, July 2011.http://der.lbl.gov/der-cam.

[62] Fangxing Li, Wenjuan Zhang, Leon M. Tolbert, John D. Kueck, and D. TomRizy. Assessment of the economic benefits from reactive power compensation.In Power Systems Conference and Exposition, 2006. PSCE ’06. 2006 IEEEPES, pages 1767–1773, 2006.

[63] C. Marnay, J. Lai, M. Stadler, and A. Siddiqui. Added value of reliability toa microgrid: Simulations of three california buildings. In 2009 CIGRE/IEEEPES Joint Symposium Integration of Wide-Scale Renewable Resources into thePower Delivery System, 2009.

139

[64] C. Marnay, M. Stadler, G. Cardoso, and O. Megel. The added economic andenvironmental value of solar thermal systems in microgrids with combined heatand power. Berkeley Lab Report #LBNL-2629E. Berkeley, CA, August 2009.

[65] C. Marnay, G. Venkataramanan, M. Stadler, A.S. Siddiqui, R. Firestone, andB. Chandran. Optimal technology selection and operation of commercial-building microgrids. IEEE Transactions on Power Systems, 23(3):975–982,2008.

[66] Chris Marnay and Owen Bailey. The certs microgrid and the future of themacrogrid. Berkeley Lab Report #LBNL-55281. Berkeley, CA, 2003.

[67] Chris Marnay, Bruce Nordman, and Judy Lai. Future roles of milli-, micro-,and nanogrids. In Cigre 2011 Bologna Symposium, 2011.

[68] S. Martel and D. Turcotte. Review of distributed generation product andinterconnection standards for canada. In IEEE Electrical Power Conference,October 25-26 2007.

[69] J.A. Momoh, Y. Xia, and G.D. Boswell. An approach to determine distributedgeneration (dg) benefits in power networks. In 40th North American PowerSymposium, NAPS2008, 2008.

[70] C. L. Moreira, F. O. Resende, and J. A. Pecas Lopes. Using low voltagemicrogrids for service restoration. IEEE Trans. on Power Systems, 22(1):395–403, 2007.

[71] National Energy Board. Reporting of Electric Reliability Information byCanadian Entities. Available at NEB website: http://www.neb-one.gc.

ca/clf-nsi/rnrgynfmtn/nrgyrprt/lctrcty/lctrcrlbltycndnntt2007/

lctrcrlbltycndnntt2007-eng.html, August 2007.

[72] Natural Resources Canada. CO2 Emission Factors. Available atNRCan website: http://oee.nrcan.gc.ca/industrial/technical-info/

benchmarking/canadian-steel-industry/6602, November 2008.

[73] Natural Resources Canada. RETScreen International Website, August 2011.http://www.retscreen.net/.

[74] New Brunswick System Operator (NBSO). New brunswick systemoperator open access transmission tariff. Published on the NBSO

140

website. Available at http://www.nbso.ca/Public/en/docs-EN/Tariff/

TARIFF\%20\%28April\%201\%202010\%29.pdf, April 2010.

[75] Northeast Power Systems, Inc. Mv-acb - budgetary cost and preliminary di-mensions and weights. Available on the NEPSI website, http://www.nepsi.com/acbcost.htm, 2009.

[76] Commonwealth of Australia. Handbook of cost-benefit analysis, January 2006.

[77] Ontario Hydro. Ontario time-of-use electricity rates. Available at OntarioHydro website www.ontario-hydro.com/index.php?page=current_rates,November 2011.

[78] J. A. Pecas Lopes, N. Gil, F. Resende, E. Voumvoulakis, and N. Hatziargyriou.Dd3 - strategies for emergency functions: Islanding with several microgrids &blackstart. Available at More Microgrids website http://www.microgrids.

eu/documents/649.pdf, December 2008.

[79] A. Piccolo and P. Siano. Evaluating the impact of network investment deferralon distributed generation expansion. IEEE Transactions on Power Systems,24(3):1559–1567, 2009.

[80] PJM Interconnection. Pjm open access transmission tariff. Published on thePJM website, http://www.pjm.com, August 2010.

[81] Y. Rebours, D. Kirschen, M. Trotignon, and S. Rossignol. A survey of fre-quency and voltage control ancillary services Part I: Technical features. IEEETransactions on Power Systems, 22(1):350–357, 2007.

[82] Y. Rebours, D. Kirschen, M. Trotignon, and S. Rossignol. A survey of fre-quency and voltage control ancillary services Part II: Economic features. IEEETransactions on Power Systems, 22(1):358–366, 2007.

[83] K. Remoundou and P. Koundouri. Environmental effects on public health: Aneconomic perspective. International Journal of Environmental Research andPublic Health, 6(8):2160–2178, 2009.

[84] J.T. Saraiva and M.H. Gomes. Provision of some ancillary services by microgridagents. In 7th International Conference on the European Energy Market, 2010.

[85] C. Schwaegerl, J. Pecas Lopes, J. Vasiljevska, R. Ferreira, C. Moreira, andA. Madureira. Report on the technical, social, economic, and environmental

141

benefits provided by microgrids on power system operation - Annex 5. Availableat More Microgrids website http://www.microgrids.eu/documents, Decem-ber 2009.

[86] C. Schwaegerl, L. Tao, P. Mancarella, and G. Strbac. Can microgrids pro-vide a new paradigm for network operation? an evaluation of their technical,commercial and environmental benefits. In CIRED 2009. 20th InternationalConference and Exhibition on Electricity Distribution, 2009.

[87] C. Schwaegerl, L. Tao, J. Pecas Lopes, A. Madureira, P. Mancarella, A. Anas-tasiadis, N. Hatziargyriou, and A. Krkoleva. Report on the technical, social,economic, and environmental benefits provided by Microgrids on power systemoperation. Available at More Microgrids website http://www.microgrids.eu/documents/668.pdf, December 2009.

[88] W. Shirley, R. Cowart, R. Sedano, F. Weston, C. Harrington, and D. Moskovitz.State electricity regulatory policy and distributed resources: Distribution sys-tem cost methodologies for distributed generation. The Regulatory AssistanceProject, NREL, Oct. 2001.

[89] Goran Strbac, Pierluigi Mancarella, Danny Pudjianto, Christine Schwaegerl,Liang Tao, Julija Vasiljevska, Ricardo Bessa, Manuel Matos, Anestis Anas-tasiadis, Nikos Hatziargyriou, and Antonis Tsikalakis. More Microgrids DH3:Business cases for Microgrids. Available at More Microgrids website http:

//www.microgrids.eu/documents/682.pdf, April 2009.

[90] M. Sullivan, M. Mercurio, and J. Schellenberg. Estimated value of service reli-ability for electric utility customers in the united states. Berkeley Lab Report#LBNL-2132E, prepared for Office of Electricity Delivery and Energy Reli-ability U.S. Department of Energy. Lawrence Berkeley National Laboratory.Berkeley, CA, USA, 2009.

[91] P. Tenti, D. Trombetti, A. Costabeber, and P. Mattavelli. Distribution lossminimization by token ring control of power electronic interfaces in residentialmicro-grids. In IEEE International Symposium on Industrial Electronics, pages2377–2381, 2010.

[92] The National Energy Technology Laboratory. Building a smart grid businesscase, August 2009. Developed for the U.S. Department of Energy Office ofElectricity Delivery and Energy Reliability.

142

[93] R. Thomas, T. Mount, R. Schuler, W. Schulze, R. Zimmerman, D. Shawhan,and D. Toomey. Markets for reactive power and reliability: Awhite paper. Available on the CERTS website, http://certs.lbl.gov/

certs-randm-pubs.html, December 2006.

[94] P. Torcellini, S. Pless, M. Deru, and D. Crawley. Zero energy buildings: A criti-cal look at the definition. In ACEEE Summer Study, Pacific Grove, California,2006.

[95] UC San Diego. UC San Diego receives $11 million in incentives for renewableenergy. Available on the UC San Diego website, http://ucsdnews.ucsd.edu/newsrel/science/07-09CCSE.asp, 2009.

[96] J. Vasiljevska, J.A. Pecas Lopes, and M.A. Matos. Multi-microgrid impactassessment using multi criteria decision aid methods. In 2009 IEEE BucharestPowerTech: Innovative Ideas Toward the Electrical Grid of the Future, 2009.

[97] Julija Vasiljevska and J. A. Pecas Lopes. On the micro-grid and multi micro-grid impact assessment: Cost and benefits evaluation. In Cigre 2011 BolognaSymposium, 2011.

[98] G. Venkataramanan and C. Marnay. A larger role for microgrids. IEEE Powerand Energy Magazine, 6(3):78–82, 2008.

[99] D.T.-C. Wang, L. Ochoa, and G. Harrison. DG impact on investment deferral:Network planning and security of supply. IEEE Trans. on Power Systems,25(2):1134 – 1141, 2010.

[100] D.T.-C. Wang, L. Ochoa, G. Harrison, C. J. Dent, and A. R. Wallace. Evaluat-ing investment deferral by incorporating distributed generation in distributionnetwork planning. In Power Systems Computation Conference (PSCC), Glas-gow, Scotland, 2008.

[101] M. Wei, S. Patadia, and D.M. Kammen. Putting renewables and energy effi-ciency to work: How many jobs can the clean energy industry generate in theus? Energy Policy, 38(2):919–931, 2010.

[102] G. Young Morris, C. Abbey, G. Joos, and C. Marnay. A framework for theevaluation of the cost and benefits of Microgrids. In Cigre 2011 Bologna Sym-posium, 2011.

143

[103] C. Yuen and A. Oudalov. The feasibility and profitability of ancillary servicesprovision from multi-microgrids. In 2007 IEEE Lausanne POWERTECH, Pro-ceedings, pages 598–603, 2007.

[104] C. Yuen, A. Oudalov, and A. Timbus. The provision of frequency control re-serves from multiple microgrids. IEEE Transactions on Industrial Electronics,58(1):173–183, 2011.

[105] Hisham Zerriffi. Electric power systems under stress: An evaluation of central-ized versus distributed system architectures. Master’s thesis, Carnegie MellonUniversity, Pittsburgh, PA, USA, 2004.

[106] Y. Zoka, A. Sugimoto, N. Yorino, K. Kawahara, and J. Kubokawa. An eco-nomic evaluation for an autonomous independent network of distributed energyresources. Electric Power Systems Research, 77(7):831–838, 2007.

144

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