d13.5.kpi (levels 1 2) assessment and conclusion v1

BEST PATHS BEyond State-of-the-art Technologies for rePowering Ac corridors and multi-

Terminal Hvdc Systems

Contract number 612748 Instrument Collaborative project

Start date 01-10-2014 Duration 48 months

D13.5

KPI (Levels 1&2) assessment and

conclusion

BEST PATHS deliverable fact sheet

Deliverable number: 13.5

Deliverable title: KPI (Levels 1&2) assessment and conclusion

Responsible partner: CIRCE

Work Package no.: 13

Work Package title: Integrated global assessment for future replication in EU27

Task: 13.7 and 13.8

Due date of deliverable:

M48, September 2018

Actual submission date:

Authors:

David Rivas (CIRCE), Samuel Borroy (CIRCE), Laura Giménez (CIRCE), Noemi Galan (CIRCE), Adrian Alonso (CIRCE), H.G. Svendsen (SINTEF) and T.K. Vrana (SINTEF)

Version: 1.3

Version date: 09/11/2018

Approvals

Name Organisation

Author (s) (See above) CIRCE and SINTEF

Task leader Samuel Borroy CIRCE

WP leader Samuel Borroy CIRCE

Document history

Version Date Main modification Author

1.1 16/10/2018 Initial draft CIRCE and SINTEF

1.2 07/11/2018 Including feedback from Demo 1, Demo 2, Demo 3 and Demo 4

CIRCE

1.3 09/11/2018 Minor corrections CIRCE

Dissemination level (please X one)

X PU = Public

PP = Restricted to other programme participants (including the EC)

RE = Restricted to a group specified by the consortium (including the EC)

CO = Confidential, only for members of the consortium (including the EC)

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TABLE OF CONTENT

Disclaimer ............................................................................................................................................................. 5

Acronyms .............................................................................................................................................................. 6

Executive summary ............................................................................................................................................... 8

1. Introduction: KPIs definition ..................................................................................................................... 10

1.1. Level 1 KPIs definition ................................................................................................................................. 10

1.2. Level 2 KPIs definition ................................................................................................................................. 11

2. KPIs application and evaluation................................................................................................................. 13

2.1. Step 1. Reference case (Business as Usual). .................................................................................................. 14

2.2. Step 2. Best Paths scenario definition and modelling based on level 3 KPI assessments for each Demo project. ..... 15

2.3. Step 3. Definition of the proposed methodology for the evaluation of level 1 and level 2 KPIs .............................. 16

3. KPIs results ............................................................................................................................................... 21

3.1 Level 2 KPI ................................................................................................................................................ 21

3.2 Level 1 KPI ................................................................................................................................................ 24

4. Qualitative assessment of Demo research benefits .................................................................................... 25

4.1 Demo 1 ..................................................................................................................................................... 25

4.2 Demo 2 ..................................................................................................................................................... 26

4.3 Demo 3 ..................................................................................................................................................... 29

4.4 Demo 4 ..................................................................................................................................................... 30

4.5 Demo 5 ..................................................................................................................................................... 32

5. Conclusions................................................................................................................................................ 35

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Disclaimer

This document has been prepared by Best Paths project partners as an account of work carried out within the framework of the EC-GA contract nº 612748.

Neither Project Coordinator, nor any signatory party of Best Paths Project Consortium Agreement, nor any person acting on behalf of any of them:

(a) makes any warranty or representation whatsoever, express or implied,

1. with respect to the use of any information, apparatus, method, process, or similar item disclosed in this document, including merchantability and fitness for a particular purpose, or

2. that such use does not infringe on or interfere with privately owned rights, including any party's intellectual property, or

3. that this document is suitable to any particular user's circumstance; or

(b) assumes responsibility for any damages or other liability whatsoever (including any consequential damages, even if Project Coordinator or any representative of a signatory party of the Best Paths Project Consortium Agreement, has been advised of the possibility of such damages) resulting from your selection or use of this document or any information, apparatus, method, process, or similar item disclosed in this document.

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Acronyms

AC Alternating Current

AIT Average Interruption Time

AWM Aircraft Warning Markers

BAU Business as Usual

BP Best Paths

CAPEX Capital Expenditures

CBA Cost Benefit Analysis

DC Direct Current

DER Distributed Energy Resources

DLR Dynamic Line Rating

EEGI European Electricity Grids Initiative

ENS Energy Not Served

GRP Glass fiber Reinforced Plastics

HC Hosting Capacity

HSE Health and Safety

HTLS High Temperature Low Sag

HV High Voltage

HVDC High Voltage Direct Current

ILA Improved Life-Time of Assets

IOP Interoperability

ISF Increase in System Flexibility

KPI Key Performance Indicator

LCA Life Cycle Assessment

LLW Live-Line Working

MMC Modular multilevel converter

MT Multiterminal

NC Network Capacity

NCAC Network Capacity at Affordable Cost

NF Network Flexibility

NSOG North Sea Offshore Grid

OHL Overhead line

OPEX Operational Expenditures

OPF Optimal Power Flow

PCI Project of Common Interest

PV Photovoltaics

RC Replacement Cost

RES Renewable Energy Source

RPI Reserve Price Index

SF System Flexibility

SFAC System Flexibility at Affordable Cost

TEP Transmission Expansion Planning

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TSO Transmission System Operator

TYNDP Ten Year Network Development Plan

VSC Voltage Source Converter

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Executive summary

The purpose of this document is to define the methodology for calculating the KPIs of level 1 and level 2 and to applicate it to obtain the aforementioned indicators. The assessment methodology to be developed (section 2) is based on calculating and comparing the KPIs of the BAU (Business as Usual) and Best Paths (Deliverable 13.2) scenarios respectively. The evaluation of the individual KPIs is based on the definition of the EEGI R&I Roadmap 2013-2022 but it has been adapted to a Pan-European trasmission power system scope to capture the benefits yielded by the Best Paths technologies. The outcome of this evaluation shows the benefits that the Best Paths technologies bring to the power transmission system.

The defined and calculated level 1 and level 2 KPIs (section 3) are as follows:

- Increased network capacity at affordable cost (Level 1)

- Increased system flexibility at affordable cost (Level 1)

- Increased RES and DER hosting capacity (Level 2)

- Reduced energy curtailment of RES and DER (Level 2)

- Increased flexibility from energy players (Level 2)

- Extended asset life time (Level 2)

- Power quality and quality of supply (Level 2)

Table 1 shows a summary of the results obtained for level 1 and level 2 KPIs, the section 3 shows more in detail these results and the explanation of the calculations. As these results are obtained along the CBA performed in D13.4 it is worthy to mention that the same uncertainty discussed in that deliverable also applies to the obtained KPIs.

Table 1. Results of Level 1 and Level 2 KPIs.

Definition BAU Scenario Best Paths Scenario

Level 1

Increased network capacity at affordable cost (KPI 1.1)

--- 36.4 W/EUR

Increased system flexibility at affordable cost (KPI 1.2)

--- 8.3 W/EUR

Level 2

Renewable energy hosting capacity (KPI 2.1)

840553 MW 864326 MW (increase of 2.83%)

Renewable energy curtailment (KPI 2.2)

0.83 TWh/year 0.13 TWh/year (reduction of

84.34%)

Increased flexibility from energy players (KPI 2.3)

472898 MW 482969 MW (increase of 2.13%)

Extended asset lifetime (KPI 2.4) explained in the section 3.1.4

Power quality (KPI 2.5) explained in the section 3.1.5

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All the potential benefits that Best Paths technologies can bring to the network are not reflected in the KPIs therefore a section (section 4) in which these demos and their benefits are explained in more detail is included in the present document.

Table 2 summarizes the benefits of demos that cannot be quantified using level 1 and level 2 KPIs.

Table 2. Summary of demos benefits

Benefit Demo 1 Demo 2 Demo 3 Demo 4 Demo 5

Improve security of supply X X X

Increase grid availability X X X X

Increase public acceptance X X X X

Improve maintenance quality X X X

Improve competition and cost

reduction on equipment

X X

Improve standardization at the EU

level

X X

Suppress risks associated to

provider

disappearance/unavailability

X

Facilitate harnessing of offshore

wind

X

Enable faster construction X X X X

Support Health and safety (HSE) X X X

Increase basic knowledge X X X X X

Installation in “protected” areas X X

Increase number of options for new

grid deployment

X X

Enable deployment with reduced

engineering and qualification

duration

X X X

Improve product quality and

security

X X

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1. Introduction: KPIs definition

Figure 1. Level 1 and Level 2 KPIs

1.1. Level 1 KPIs definition

The overarching goal of the EEGI R&I Roadmap 2013-2022 is “to allow European electricity networks continuously deliver effective flexible capacities to integrate actions of grid users at affordable costs”. The fulfilment of this overarching goal involves as well the capacity of electrical networks to connect renewable energy generation, ensuring enough flexibility for the system operation and serving costumers according to affordable electricity pricing, while keeping the system reliability at levels compatible with societal needs, which is closely linked to the compliance with EU energy policy goals, in aspects as sustainability, market competitiveness and security of supply.

The improvement of the achievement of these goals is measured through the following level 1 KPIs:

1. Increased network capacity at affordable cost

“Network Capacity” (NC) is the additional electrical power that can be transmitted or distributed in the selected framework (to connect new RES generation, to enhance an interconnection, to solve a congestion, or even all the transmission capacity of a TSO).

“Network Capacity at Affordable Cost” (NCAC) is the additional network capacity gained per unit of cost, considering the cost (C) as the OPEX and/or CAPEX of the installations included in order to gain network capacity.

Thus, the proposed KPI is calculated as follows:

∆��

A positive ∆NCAC result indicates that the solutions proposed in Best Paths contribute to the increasing of network capacity in an affordable way, while a negative one suggests that the innovative solutions are unlikely to be beneficial in the considered circumstances.

2. Increased system flexibility at affordable cost

“System Flexibility” (SF) is the amount of electrical power (generation and load) that can be modulated to the needs of the system operation within a specified unit of time.

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“System Flexibility at Affordable Cost” (SFAC) can be indicated as total load and generation (including RES and DER) connected to the transmission and distribution system, that can be modulated in response to market signals or system needs, considering the cost (C) as the OPEX and/or CAPEX needed in order to gain system flexibility.

Thus, the proposed KPI is calculated as follows:

∆��

A positive ∆SFAC result indicates that the solutions proposed in Best Paths contribute to the increasing of system flexibility in an affordable way, while a negative one suggests that the innovative solutions are unlikely to be beneficial in the considered circumstances.

1.2. Level 2 KPIs definition

Increasing network capacity and/or system flexibility can be further monitored through different specific KPIs, which are the level 2 KPIs. The following definitions of KPIs are based on Deliverable D3.4 “Define EEGI Project and Programme KPIs” of GRID+ Project “Supporting the Development of the European Electricity Grids Initiative (EEGI) which have been already defined and gathered in Deliverable D2.1 “Data set, KPIs, tools & methodologies for impact assessment” of Best Paths project.

1. Increased RES and DER hosting capacity

The RES/DER hosting capacity (HC) is the total installed capacity of RES/DER that can be connected without endangering system stability and reducing system reliability.

The proposed KPI is calculated as follows:

��% �!��"!��!�� #100

Where EHC means enhanced hosting capacity.

It has to be considered that though a RES/DER project is accepted for connection, there is no guarantee of curtailment or tripping under given circumstances in order to maintain a secure network operation. This curtailment part is covered in other KPI.

2. Reduced energy curtailment of RES and DER

Due to network technical problems such as overvoltage, overfrequency, local congestion, etc., RES/DER production can be curtailed partially or totally, i.e. tripped. An objective of the proposed innovations is to provide solutions to reduce and minimize shedding of RES/DER, while still maintaining system security and reliability.


�&'(�)*+�,% �-./0��123�4��"-./0��123�4��-5/0��123�4�� #100

The energy curtailment considered is due to network problems. Other problems that are out of control of network operators are not included such as storm, breakdown of generators or plant maintenance.

3. Increased flexibility from energy players

Flexibility is an indication of the ability of the electricity system to respond to (and balance) supply and demand in real time. Flexibility already exists and is reliably used, but increasing the presence of variable renewables involves greater need for and different management of

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electricity flows. Therefore, along with an increase of renewable energies penetration, increase of flexibility is one of the objectives of the future network.

As the need for flexibility increases, dispatchable generation technologies are not enough to guarantee it and additional factors are needed, such as increased interconnection capability, storage and demand response.

For the TSO, increased network flexibility would be a measure of additional RES/DER participation in flexibility services. It is also an indication of the introduction of cost effective balancing technologies for these services. The KPIs can thus be defined as:

( ) ( )P

PPNF

Total

RES BAURES BestPaths−

=∆

( )( )P

PRPI

tTotal BAU

tTotal BestPaths

cos_

cos_

=

Where,

NF∆ is the increased network flexibility

PRES is the amount of renewable capacity participating in balancing

PTotal is the total reserve capacity required to balance the area under evaluation

RPI is the Reserve Price Index

PTotal_cost is the average cost of the reserve capacity

4. Extended asset life time (Improved Life-Time of Assets)

This indicator deals with the increase of the life-time of assets. Some of the innovations to be demonstrated will avoid congestion situations, which will contribute to this idea.

The proposed KPI (ILA, Improved Life-Time of Assets) can be calculated by looking at the total cost of exploiting a given group of assets, both the capital expenditure as well as the operational expenditure:

67� � 8��9�:�� + <9�:��= 8��9�:�� + <9�:��=8��9�:�� + <9�:��= Replacement costs (RC), which are included in the CAPEX, can be reduced as a consequence of the improvements given by the innovations. New elements derived from the project may increase the expense in OPEX (more monitoring, supervision, predictive or reliability-centred maintenance, etc.). However, this extra expense in OPEX should justify or compensate the reduction in RC.

This indicator may also be evaluated by looking only at the replacement costs (RC) in a business as usual situation and in a situation where new asset management policies (from R&I projects) have been applied:

67� � >�� >��9�:��

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5. Power quality and quality of supply

Although it is not included in Figure 1, this indicator is also interesting to be taken into account. One of the most commonly used indexes for this purpose, from the TSO point of view, is the Average Interruption Time (AIT). The AIT is interrupted minutes per year.


[ ]% 100% xAIT

AITAITAIT

BAU

BestPathsBAU −=

2. KPIs application and evaluation

In order to assess the effect of the new technologies and solutions developed and tested in the five demonstrations of Best Paths under a Pan-European perspective, it is necessary to simulate the optimal transmission-constrained annual operation of the whole Pan-European system, and to compare the obtained results (generation costs, wind curtailments, etc.) under the different alternatives under study. This approach would allow assessing the cross-effects that the five demonstrations could have among themselves, as they could be taken into account individually or jointly.

Level 1 and level 2 KPIs evaluation will assess the impact of the innovations presented in the five demonstration projects over the Pan-European system. Figure 2 shows the methodology has been carried out to perform such assessment.

Figure 2. Methodology to calculate the Level 1 and Level 2 KPIs.

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2.1. Step 1. Reference case (Business as Usual).

It is necessary to define a common reference transmission system, at European level, to be used as the scenario to perform the simulations needed for the impact assessment.

To build this BAU scenario the following roadmap has been followed (Deliverable 13.2):

1) The foundation of the BAU scenario has been the DigSilent formatted dataset for the continental European transmission system (thus excluding UK and Scandinavian countries) which has been provided by ENTSO-E. This dataset lacks of some needed data in order to properly build a reference case scenario, therefore, some estimations, additions and adjustments have been carried out in later steps.

2) In order to represent the expected European transmission system for 2030 the following transmission upgrades have been taken into account and modelled:

a) All the recommendations from Ten Year Network Development Plan (TYNDP) 2016

b) Project of Common Interest (PCI), many of them overlap with the TYNDP 2016 recommendations.

c) Recommendations from e-Highways2050 project.

3) In order to include the missing parts from Europe, namely UK and Scandinavian countries, it has been developed a simplified grid model based on the clustered approach of the e-Highways2050 project. This solution has been selected since the datasets available for these regions was very incomplete and non-usable.

4) To include the European renewable energy plants (since this information was not provided in the ENTSOE dataset) the following procedure has been followed:

a) Calculate the total power of each country in the DigSilent network model

b) “EU Commission, DG Ener, Unit A4; Energy statistics” provides Power Installed per country and technology (% of total)

c) Calculate the corresponding RES power according to the values in the model

d) List of existing Onshore & Offshore Wind Farms and PV plants are obtained from different sources available online

e) The most significant PV plants, Offshore and Onshore parks have been installed and located (according their coordinates) in each country until reaching the full PV and wind power in the country as calculated in point c).

5) RES Profiles were obtained from online databases (Ninja Renewables) and different proprietary SW Tools according to the coordinates of the plants.

6) The ENTSO-E network model provides the maximum power values (active a reactive) for every single node in the continental European network. Nevertheless, for the purpose of our studies the hourly load profile for the whole year is needed.

In order to create synthetically these input data, the following assumptions were made:

a) Each country node has a load profile with the same shape as the load profile of the country.

b) The power factor is kept constant through the year for every single node.

c) The hourly load profiles for the whole year and every European country are obtained from the ENTSO-E open database. Once the hourly load profile is obtained, it is scaled

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according to the maximum value of active power in each node as provided by ENTSO-E dataset.

7) Generators available and present in the ENTSO-E dataset have been identified matching technical and economical characteristics and technology type with those available in “The RE-Europe dataset”.

8) Load and Generation Data has been adjusted to match ENTSO-E Data for 2016 taking to account not only the generation present in the transmission network but also the effect of distributed generation in the net load during the Scalability assessment.

9) Scalability assessment has also provided valuable feedback to the BAU scenario (Deliverable D13.1), adjusting and scaling load and renewable energy installed to expected 2030 values according to EUCO2030 scenario

10) Additionally, Replicability Assessment (see Deliverables D13.2 and D13.3) has updated the BAU scenario correcting the classification if some of the synchronous generators available in the dataset to reflect in a more realistic way the generation mix of the following countries: Spain, France and Germany

11) In the last stage of the project (CBA assessment, Deliverable D13.4) some issues regarding the ENTSO-E dataset were corrected which affect to the final BAU scenario

2.2. Step 2. Best Paths scenario definition and modelling based on level 3 KPI assessments for each Demo project.

A pan-European scenario which considers the implementation of the innovations and improvements achieved by each Demo project will be defined for the impact assessment. Such scenario is referred to in this section as “Best Paths scenario” (Deliverable 13.2).

Finding the right balance between the level of accuracy of the model, the data availability, and the computational burden is crucial.

Each Demo project will provide the needed assumptions that will allow defining the Best Paths scenario and quantifying the impacts envisaged in Step 2. Accordingly, specifications related to each demo shall be decided, to characterize the Best Paths scenario that will be modelled as a variation of the reference case defined in Step 1. Specifically, to assess the replicability of innovative solutions in the entire pan-European system (i.e., the coexistence of large AC transmission corridors and Multi-Terminal HVDC grids), it will be necessary to identify a number of candidate MT-HVDC interconnections, and AC transmission lines.

Using BaU scenario as starting point the Scalability Assessment (Deliverable D13.1) will provide three possible grid development scenarios within the scalability assessment:

1. AC upgrade scenario

2. DC upgrade scenario

3. Combined upgrade scenario.

Each of the first two upgrade scenarios focuses on grid developments related to the corresponding transmission technology only and considers the part of the grid pertaining to the other transmission technology unchanged. Therefore these scenarios provide the boundaries where the optimal Best Paths scenario will lie. In the other hand, the Combined Upgrade Scenario builds on the DC upgrade scenario and determines for each stage of DC grid development the additional gains to be made under a concurrent AC grid development. This Combined scenario will be an approximation to the optimal Best Paths scenario.

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The next stage to follow is the Replicability analysis which, based on the findings of the Scalability assessment, will provide the optimal Best Paths Scenario. The Replicability analysis will also provide the optimal configuration for the North Sea Offshore Grid (NSOG) using Transmission Expansion Plannning (TEP) tools (Deliverable D13.2) using as inputs the information provided by the different Demos (mainly Demo 2) about the performance, costs and technical characteristics of HVDC technologies.

During CBA assessment some issues regarding the dataset provided by ENTSO-E were found, therefore Best Paths Scenario has been updated and rebuilt (Deliverable D13.4).

Figure 3. Creation of the Best Paths Scenario taking as a starting point the BAU Scenario..

2.3. Step 3. Definition of the proposed methodology for the evaluation of level 1 and level 2 KPIs

According to the expected impact derived from the implementation of the results obtained by each demo project an identification of the affected level 1 and level 2 KPIs affected by the whole project has been done in previous work packages (WP2, Deliverable D2.1). Table 3 summarizes this estimated identification.

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Table 3. Identification of the affected level 1 and level 2 KPIs.

Affected Level 1 KPIs Affected Level 2 KPIs

Increased

network

capacity at

affordable

cost

Increased

system

flexibility

at

affordable

cost

Increased

RES and

DER

hosting

capacity

Reduced

energy

curtailment

of RES and

DER

Increased

flexibility

from

energy

players

Extended

asset life

time

Power

quality

and

quality of

supply

D1

Proposed multi-

terminal HVDC

configurations

x x x x x

D2

Proposed HVDC

configurations

with multi-vendor

interoperability

x x x x x

D3

HVDC VSC x

HTLS DC

conductors x x x x x

DC extruded

submarine cables x x x x x

DC fault location x

D4

HTLS AC lines &

insulated cross-

arms

x x x x x

Dynamic line

rating x x x x x x

Live line working x x

D5 HVDC

superconductor x x x x x

To obtain the optimal transmission-constrained operation of the whole pan-European system for a whole year it is necessary to perform detailed power flow studies. By comparing the results obtained both with (Best Paths scenario) and without (BAU scenario), the impact assessment of the innovative solutions can be carried out.

It is proposed to use a linearized (or quadratized) Optimal Power Flow model. A very interesting result of this approach will be the nodal-marginal costs that can be obtained as an outcome of the optimization from the dual information of the nodal demand-balance equations. Thus, apart from the technical operation of the network and the generators, it would be possible to obtain economic information about the locational marginal costs, and how they could vary due to the enhanced network flexibility tested in Best Paths.

Using the aforementioned model, the following methodologies can be implemented to assess the different KPIs indicated in Section 0 of the present deliverable.

Since Level 1 KPIs will need some Level 2 KPIs input (as devised in the following methodology), the latter are first presented:

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2.3.1 Increased network capacity at affordable cost (Level 1)

From simulation results, the total power exchange volume (import+export) for all countries was collected for each timestep. The time average exchange volume was used to measure the network capacity in the system (The factor ½ is because the sum over all countries double-counts overall exchange volume)

�� 12@AB�&)+CD(8E= + �&�FCD(8E=G&, � 1@A�&)+CD(8E=&, The Increased Network Capacity at affordable cost (NCAC) is then defined as the change in network capacity divided by the difference in investment costs:

�� ∆��∆� � ��

2.3.2 Increased system flexibility at affordable cost (Level 1)

This KPI is calculated as follows:

∆�� , Where the numerator is Level 2 KPI Increased flexibility from energy players as calculated previously.

2.3.3 Increased RES and DER hosting capacity (Level 2)

1) Generate scenarios of additional RES resources by multiplying the nominal values by a factor kept constant for the whole year. Do it in the form of a loop: Factor =Factor + stepsize

2) For each value of Factor:

a) run the OPF for the BAU scenario for each hour of the year.

b) run the OPF for the Best Paths scenario (s) for each hour of the year.

c) Compute the RES curtailments for BAU. In case a certain threshold of curtailments has been reached (not zero, as this might be too conservative), record this factor as FactorBAU

d) Compute the RES curtailments for Best Paths. In case a certain threshold of curtailments has been reached (not zero, as this might be too conservative), record this factor as FactorBEST PATHS

3) Assuming that are the nominal values of installed RES, the hosting capacity would be:

∑∈

=Nn

CAP

nBAUBAU PFactorHC

∑∈

=Nn

CAP

nBESTPATHSBESTPATHS PFactorHC

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2.3.4 Reduced energy curtailment of RES and DER (Level 2)

The “energy curtailment of RES” can be evaluated as the difference between available RES

energy (ERES,available), given by the nodal generation profile I,,, and deployed RES energy

(ERES,deployed):

��JKELMNOPQER�S,, � �,T-U,�V�)*�W*� �,T-U,X�C*DY�X � Z[I,,9,T-U,��\\\\\\\\\\\ 9,,T-U]^E_

��JKELMNOPQER�S � A ��'(�)*+�,��,,,∈�

��JKELMNOPQER��@9�@��,, � �,T-U,�V�)*�W*� �,T-U,X�C*DY�X � Z[I,,9,T-U,��\\\\\\\\\\\ 9,,T-U] ^E_

��JKELMNOPQER��@9�@�� A ��'(�)*+�,�-U_�_!U,,,∈�

This index should be a direct result from an Optimal Power Flow simulation.

Renewable generation will produce as much as possible unless curtailment is necessary due to grid bottlenecks or low consumption (no other curtailment situations will be taken into account).

2.3.5 Increased flexibility from energy players (Level 2)

Assuming that demand is uncertain and can deviate from the scheduled scenario demand 9,,a,U�-��Tbc by some amount ∆9,,a , another optimization can be run to determine the maximum

deployable flexibility for each time step at the given operating point as determined through the original DC-OPF. The change in generator output is a decision variable, which is chosen by the optimization in such a way that remedial actions, which can be accommodated by the grid, are maximized. The change in demand ∆9,,a is an input parameter, generated from random number

to give deviations up to 10%. This change is compensated for by a change in conventional

generation ∆9d,, a change in renewable generation ∆9,,>�� and associated changes in line flows ∆�*,. Note that renewable generators can only provide up-reserve (∆9,,T-U > 0), if curtailment

already occurs in the initial operating point determined in DC-OPF. 9d,, 9,,>��and �*, are

determined as a direct output of the original DC-OPF and considered as parameters in the present optimization.

The objective function maximizes the deviation in scheduled production (i.e., the deployment of flexibility actions) for each time-step E,

max iAj∆9d,jd +Aj∆9,,T-Uj, k The constraint set includes a balance equation for ensuring balance between supply and demand and inequalities determining the upper and lower bounds of the generation and line flow deviations.

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∆9,,a � ∆9,,T-U +AM,,d ∙ ∆9d, +d AM,,* ∙ ∆�*,*

9,,a,U�-��Tbc ≤ ∆9,,a

9d, ≤ ∆9d, ≤ 9d�c�n,��\\\\\\\\\\\\\ 9d, 9,,T-U ≤ ∆9,,T-U ≤ I,, ∙ 9,T-U,��\\\\\\\\\\\ 9,,T-U

�*+�F\\\\\\\ �*, ≤ ∆�*, ≤ �*+�F\\\\\\\ �*,

This optimization (maximization) yields the total system flexibility PFlexibility, including flexibility from conventional and renewable generation alike.

The presence of the absolute value in the objective function above makes the problem non-linear and harder to solve. However, it can be linearised in standard fashion by introducing integer variables and so-called big-M constraints.

2.3.6 Extended asset life time/Improved Life-Time of assets (Level 2)

This KPI is hard to assess with the tools at our disposal and the devised methodology. Additionally, information provided by Demos is not sufficient to evaluate the expected lifetime and replacement cost for Best Paths technologies. Moreover, the initial envisioned methodology to assess this KPI was reviewed by Demo leaders and its use was discouraged since it would not yield a consistent output.

Alternatively, it is proposed to assess this KPI in a qualitative way.

2.3.7 Power quality and quality of supply (Level 2)

This index should be also a direct result from an Optimal Power Flow simulation.

For each scenario, BAU and Best Paths, compute ��U�-��Tbc � ∑ p 9,,��X^E_, .

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Table 4. Notation of the equations.

Notation RES

tnP,

Scheduled/Deployed power output of RES generator at node n for

time t [MW]. I,, Expected generation profile of RES generation at node n for time t [-].

N Set of all nodes. 9,,��X Load shed at node n during time t [MW].

T Considered time horizon (i.e., 1 year). �*, Scheduled line flow on line l for time t [MW].

9d, Scheduled/Deployed power output of conventional generator g for

time t [MW].

9,,a,U�-��Tbc Scheduled demand at node n for time t as given by scenario case

[MW]. SCENARIOD

nP , Nominal demand at node n as given by scenario case (MW)

∆9d, Deviation of conventional generation g from scheduled generation 9d, at time t [MW].

∆9,,T-U Deviation of renewable generation from scheduled generation 9,,T-Uat node n and time t [MW].

∆9,,a Deviation of demand from scheduled demand (as given by BaU case)

at node n and time t [MW].

∆�*, Deviation of line flow from scheduled flow (due to deployment of

flexibility actions) on line l during time t [MW]. �*+�F\\\\\\\ Capacity of line l [MW]. 9d�c�n,��\\\\\\\\\\\\\ Installed capacity of conventional generator g [MW]. 9,T-U,��\\\\\\\\\\\

Installed RES capacity at node n [MW].

M,,* Incidence parameter (+1 if n is sending node of line l, -1 if n is

receiving node of line l, 0 otherwise). M,,d Incidence parameter indicating location node of generator g.

3. KPIs results

3.1 Level 2 KPI

3.1.1 Renewable energy hosting capacity (KPI 2.1)

The renewable energy hosting capacity of the network is is the total installed capacity of RES/DER that can be connected without endangering system stability and reducing system reliability. This hosting capacity has been identified through simulations by scaling up all renewable generator capacity until futher increase just gives more curtailment.

A comparison of renewable energy hosting capacity in the BAU and Best Paths scenarios is provided in Table 5. The increased capacity is 23773 MW. The share of capacity between PV and onshore wind is not straightforward to determine, but the total amount is considered robust.

If there is an oversupply, wind and/or solar have to be curtailed. It is not relevant which of these two is curtailed, or if both are curtailed, at which exact shares.

The total curtailement of sustainable energy matters. There has not been any effort to design a mechanism that distributes the curtailment in a specific way, so the calculated curtailment

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shares amongst the different renewable technologies are to some extent random. The hosting capacity shares are therefore also somewhat random.

Table 5: Renewable energy hosting capacity (MW)

type \ case: BAU Best Paths Increment (%)

PV 177164 168288

wind onshore 580114 603394

wind offshore 83274 92644

SUM 840553 864326 2.83%

3.1.2 Renewable energy curtailment (KPI 2.2)

Since renewable energy curtailment represents spilled energy at very low cost and carbon emissions, a key benefit of a strenghtened grid is that such curtailment is reduced. A comparison of curtailment in the BAU and Best Paths scenarios is provided in Table 6, showing annual reduction in curtailment of PV and onshore wind in the Best Paths scenario.

Table 6: Renewable energy curtailment (TWh/year)

type \ case: BAU Best Paths Reduction (%)

PV 0.37 0.02

wind onshore 0.46 0.11

wind offshore 0.00 0.00

hydro 0.00 0.00

SUM 0.83 0.13 84.34%

The system cost reduction in the Best Paths scenario is significantly larger than the monetary value of the reduction of curtailment. This is in line with results in D13.4, showing that the benefits are not to mainly related to curtailment reduction. The main reason for this is that there is little congestion and curtailment already in the BAU scenario which may not be realistic.

The overall renewable energy curtailment achieved in the Best Paths scenario accounts for the 15.6% of the BAU scenario. In the specific case of wind energy the curtailment is seven times lower and in the PV energy the curtailment is only 5.4% of the BAU curtailment.

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3.1.3 Increased flexibility from energy players (KPI 2.3)

For both the BAU and the Best Paths scenarios, an additional simulation was run with the demand profiles replaced by new profiles deviating from the original ones by some amount, and with the objective function and constraints modified such that the balancing actions were maximised. Normally in the market, balancing actions are as small as necessery, and the maximisation of balancing actions does not represent a realistic balancing market, but was used merely as a means to quantify the maximum system flexibility (SF).

Table 7 indicates average (per timestep) maximum flexibility in the system.

The relative increse in system flexibility (ISF) is

ISF = (482969– 472898) MW/ 472898 MW = 2.1 %

Table 7: Parameters for computation of system flexibility (average maximum flexibility per time-step)

param \ case: BAU Best Paths Increment

SF 472898 MW 482969 MW 2.13%

3.1.4 Extended asset lifetime (KPI 2.4)

From Table 3 only Demo 4 technologies, specifically Life-line Working and Dynamic Line Rating, impact this KPI.

As it is explained in deliverable D13.4, Life-line working can decrease the need to cease operation of lines for repowering works and can therefore assist to reduce installation and maintenance cost and reduce the downtime. Life-line working technologies investigated in this project, new conductor car and new Life-line working equipment, increase the quality of the maintenance operations leading to an improved lifetime of the grid assets being subject of those operations.

Dynamic Line Rating is based on low cost sensors allowing higher currents on standard overhead line technologies under most weather conditions. The cost of upgrading an existing line with sensor is significantly cheaper than constructing new lines or changing the conductors on existing lines according to the estimations (6-20kEUR/km). The achieved gain in transmission capacity is not a fixed number but is stochastic by nature and correlated to the weather making it difficult to assess with most regular line models and simulation tools. DLR has therefore not been included in the simulation study developed in D13.4 which feeds the calculation of KPIs in this present deliverable. This increased capacity of the line can delay the need of the reinforcement and repowering of a specific corridor for a foreseen loading growth thus extending the lifetime of the installed lines.

3.1.5 Power quality (KPI 2.5)

There was no load shedding in the datasets used with the simulations in Deliverable 13.4. Hence, quantification of differences in of power quality based on load shedding was not possible.

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3.2 Level 1 KPI

Level 1 KPIs take into account the costs of improvements.

The costs of grid upgrades in the Best Paths scenario vs the Business as Usual (BAU) scenario have been estimated considering the sum of upgrades, and best judgment of the costs for each of them. This is explained in Deliverable 13.4 (section 5.2)

C(Best Paths) – C(BAU) = 1319 MEUR/year

3.2.1 Increased network capacity at affordable cost (KPI 1.1)

Average import+export between countries, summed for all countries

Table 8: Parameters for computation of KPI 1.2

param \ case: BAU Best Paths

Avg exchange volume

235 GW 283 GW

Delta Cost 0 1319 MEUR

SFAC = Delta exchange/Delta cost = (283-235) GW/ 1319 MEUR = 36.4 W/EUR

3.2.2 Increased system flexibility at affordable cost (KPI 1.2)

The increased system flexibility at affordable cost is quantified using the following formula as explained in section 2.

�� ,This number gives the average system flexibility per time-step.

Table 9: Parameters for computation of KPI 1.2

param \ case: BAU Best Paths

SF 546374 MW 557184 MW

Delta Cost 0 1319 MEUR

SFAC = Delta SF/Delta cost = 8.3 W/EUR

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4. Qualitative assessment of Demo research benefits

In order to assess the technologies and research results investigated in Best Paths Demos, a qualitative assessment of the benefits and advantages of the investigations is carried out for each Demo.

The aim of this assessment is to complement the economical benefits of the research (as stated in the “Exploitation Plan” and the “Cost-Benefit-Analysis”) by other benefitial characteristics of the different Demos research results that cannot be captured properly with the methodologies developed for CBA (D13.4) and KPIs assessment.

4.1 Demo 1

Many of the technologies investigated in Demo 1 provide positive impact to one or more of these (non-economical) benefits categories:

• Increase the reliability of offshore grids

• Increase penetration of renewables

• Increase competition by opening research to new entrants

• Reduce the risk of HVDC technology

• Improve security of supply

Table 10. Complementary benefits of Demo 1 technologies.

Technology Demo 1 activities on this

technology

Complementary benefits of Demo 1 activities on

this technology

Interactions

between

offshore

wind farms

and HVDC

grid

converters

DEMO #1 studied how future

offshore wind farms that will use

HVDC grids to export the energy

they generate may be affected by

undesired interactions between

the wind turbine converters and

the HVDC converters. A set of

simulation models were developed

and three 60 kW MMC converters

were built to validate the model

results. A series of tests were

carried out to find possible

interactions, resulting in

recommendations for the

avoidance of interactions that

could potentially stop the

transmission of offshore wind

energy to the onshore AC grid.

Increase the reliability of offshore grids

Understanding when interactions between wind

farms and HVDC grids could appear and

avoiding them increases the reliability of

offshore transmission grids.

Increase penetration of renewables

Offshore HVDC grids provide additional routes

for the energy generated by offshore wind

farms in case a single link fails

Increase competition by opening research

to new entrants

The models and laboratory developed in Demo 1

are open to any stakeholder. This can allow

small companies and research institutions to

have acces to a state of the art Power

Hardware In the Loop system, which will

ultimately increase competition.

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Reduce the risk of HVDC technology

The models and publicly available demonstrator

will help foster research on the topic, thus

reducing technology risks

Improve security of supply

The recommendations set forth in Demo #1 will

help reduce unavailabilities in the transmission of

energy from offshore wind powers to the

European mainland

4.2 Demo 2



• Increase grid availability

• Increase public acceptance

• Improve maintenance quality

• Improve competition and cost reduction on equipment

• Decrease procurement delays

• Improve standardization at the EU level

• Enable competition and cost reduction on refurbishment

• Suppress risks associated to provider disappearance / unavailability

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Table 11. Complementary benefits of Demo 2 technologies.

Technology Demo 2 activities on this

technology

Complementary benefits of Demo 2 activities on

this technology

Interoperab

ility for

multi-

vendor

HVDC

converter

systems

DEMO #2 assessed the level of

interoperability (IOP) of available

HVDC technology for converters

on a variety of situations,

illustrated some cases where IOP

issues were effectively

suppressed, and provided a

general methodology to study and

fix virtually and IOP issue in a

multi-vendor HVDC system (based

on VSC technology).

Improve security of supply:

IOP is key to the development of DC grids which

will improve the security of supply of the mixed

AC/DC power system.

Increase grid availability

Controlled power flows (thanks to HVDC and

power electronic based equipment) make it

possible to enhance the overall availability for

the AC/DC system

Increase public acceptance

HVDC makes it possible to use long underground

cables, or reuse OHL conductors to transmis

more power than the AC counterpart.

Maximizing IOP facilitates the development of

HVDC systems (whether point-to-tpoint of DC

grids) which can be used for network

development with better public acceptance.

Decrease procurement delays

IOP makes it possible to have different converter

providers for a single HVDC link/grid project.

This means that converter implementation can

be largely reduced as it can be paraxllelized by

various suppliers.

Improve maintenance quality

Thanks to common standards needed to enable

IOP ( see ‘standardization’ item hereunder),

some key aspects such as converter interfaces

will be similar for various suppliers, which in turn

will ease maintenance.

Furthermore, the use a Master Control (as

demonstrated in DEMO 2) is an efficient way to

have a common coordination equipement,

whereas this control layer is provided in various

flavours by each individual vendor as of today.

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Improve competition and cost reduction on

equipment

Having ‘interoperable’ or ‘plug’n’play’ converters

results in a more competitive market. And

more competition is likely to result in reduced

equipement prices.

Improve standardization at the EU level

DEMO #2 is committed to deliver

recommendations for various stakeholders and

standardization bodies. As an example, a joint

workshop is organized by DEMO #2 with

CENELEC to provide feedback on the the

guidelines which are proposed by this

standardization body, and suggest some

improvements.

Enable competition and cost reduction on

refurbishment

So far, converter refurbishment is provided by

the original supplier. Thanks to improved IOP,

it is possible to consider new suppliers for

converter refurbishment. The resulting

competition for the refurbishment market is

likely to result in global costs reduction.

Suppress risks associated to provider

disappearance / unavailability

HVDC converters are designed to last for 40-50

years (some existing links are even older.

During such a long period, the risk to have a

defaulting supplier is rather important. This

put the availability of spare equipement (and

refurbishment ) at risk. IOP reduces this risk.

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4.3 Demo 3


• Support Health and safety (HSE)




• Enable faster construction


• Facilitate harnessing of offshore wind

Table 12. Complementary benefits of Demo 3 technologies

Technology Demo 3 activities on this technology Complementary benefits of

Demo 3 actictivities on this

technology

HVDC

converter

- Development of MMC VSC converter

with smaller footpirint

HVDC

submarine

cables

- Development of HVDC XLPE cable for

higher depths

Facilitate harnessing of

offshore wind

Increase grid availability


HVDC land

cables

- Development of new technology for

HVDC XLPE land cable.


HVDC HTLS

conductors

- Development two new technologies of

HVDC HTLS conductors


HVDC

insulation

- Characterisation of HVDC insulators Increase grid availability


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4.4 Demo 4








Table 13 Complementary benefits of Demo 4 technologies

Technology Demo 4 activities on this technology Complementary benefits of Demo 4 actictivities on this technology

HTLS conductor systems

Scientific approach to assess reliability of conductors

Test results to compare the long-term reliability of different HTLS technology alternatives

Demonstration of the installation of two HTLS conductor technologies

- Increase grid availability

- Increase public acceptance

- Enable faster construction

Insulated cross-arms

Demonstration of installation of two insulated cross-arms technologies on the Stevin project


Enable faster construction

New conductor car (Life line working)

Light-weight and cost-efficient non-conductive conductor car made of novel materials and technology

Non-conductive material allows to move between the phase conductors even in case of so-called “compact geometries” where conventional live working techniques cannot been performed

The ability of passing through suspension insulators allows the conductor car to be used much more efficiently along a whole span

- Improve security of supply


- Improve maintenance quality

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New Life-Line working equipment

Insulator replacement technology allows to change damaged composite insulators even in case of “compact geometries”

Non-conductive, novel tools and equipment

Revision of the classification methods of conductive clothing guarantees maximal safety of live-line workers during the whole process

- Support Health and safety (HSE)



- Improve maintenance quality

Robot for air warning markers

Designed, built and tested a prototype robot for the installation of Aicraft Warning Markers.

Based on this prototype several additional AWM installation robots were built and to install over 400 AWMs in total.

Support Health and Safety (HSE)

i.e. Less working in height


i.e. 5 minutes total on average per AWM was achieved during testing (helicopter required)


i.e. Installation (and eventually replacement) of AWM can be performed while line is in operation (not yet tested)

420kV composite tower

Full scale test of a 25m long composite pipe both show the strength of the material and the accuracy in the calculations

Glue tests show that there is possible to glue together two coposite pipes with a sleeve quite efficiently

The design shows that it is possible to build a 30 meter composite tower weighing less than 5 tonnes compared to 12 tonnes on a comparable steel lattice tower

Support Health and Safety (HSE)

i.e. Reducing the number of helicopter lifts to a third of a comparable steel lattice tower

i.e. No workers in the tower when a new section is assembled

i.e. Less work hours at risky heights


i.e. A more "elegant" design with less members


i.e. Larger section assembled on the ground and less helicopter lifts enable faster construction

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Rock foundation

Investigate potential of a new foundation concept using light-weight GRP materials for towers placed on solid rock/bedrock.

Performed structural analysis and design.

The investigated concept was found to not have sufficent structural strength for use with 420kV towers.

Remarks :

Materials' inherent weakness for fire/heat became a complicating factor in the design considerations. The team found that fire/heat considerations for transmisison lines are poorly documented on a global scale.

Support Health and safety (HSE)


Dynamic Line Rating

New algorithm for calculation allows to predict ampacity more efficiently than existing, conventional methods

New concept (“line sections”) makes calculation more accurate

Taking precipitation’s cooling effects into account allows higher ampacity in critical cases

Novel soft-computing methods increase the efficiency of calculations

New-low-cost sensor developed and tested




4.5 Demo 5

Meany of the technologies investigated in Demo 5 provide positive impact to one or more of these (non-economic) benefits categories:

• Support health and safety (HSE)

• Increase grid availability and/or reliability


• Increase basic knowledge


• Increase number of options for new grid deployment

• Improve product quality and security

• Installation in “protected” areas

• Enable deployment with reduced engineering and qualification duration

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Table 14. Complementary benefits of Demo 5 technologies

Technology Demo 5 activities on this technology Complementary benefits of Demo 5 activities on this technology

MgB2 wires Design new wires with increased transmission currents

Improve the reproducibility and quality of the wires. On line control and measurement methods implemented

Demonstration of conductor assembly on classical industrial cable manufacturing lines

Provide guidance on the test method to standardization bodies

Increase basic knowledge

Increase public and users’ acceptance

MgB2 cable conductor

Scientific modeling to access the operation during normal operation (current & magnetic field) and transient phenomena (fault, ripples, power reversals)

Implementation into commercial software, while developing new codes (AC ripples losses in MgB2 wires)


Increase grid reliability

Support health and safety requirements

HV insulation Test methods and equipment to measure at cryogenic temperature the space charge distribution in the HV insulation

Provide guidance on the test method to Cigré and standardization bodies


Increase grid reliability


Cryogenic technology

New calculation to estimate the temperature rise and pressure drop along a cryogenic envelope with different cryogenic fluids (nitrogen, helium, hydrogen, etc.). It shows that lengths longer than 50 km can be manageable with today’s cryogenic technologies

Provide guidance on cryogenic envelope design for long-length systems


Increase number of options for new grid deployment

Support health and safety requirements

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MgB2 HVDC cable system

Improvement in the LCA on the overall cable system. Beside low transmission losses it shows lower raw material consumption than resistive cables and no use of rare resources.

Validation of environmental impact on the direct surroundings of the cable system (small foot print, no thermal impact, etc.)

Major reduction of civil work, construction materials and engine traffic. Installation management as for a single classical underground cable circuit.

Independence from thermal effects (ambient temperature, soil characteristics) even with very high power ratings



Installation in “protected” areas

Increase number of options for new grid deployment

MgB2 HVDC cable system

320 kV modular termination design separating the current and the voltage management. It has been designed as a toolbox that is adaptable to the grid specifications. It includes the design and the manufacturing of efficient current leads for 10 kA to enable an electrical connection from room temperature to cryogenic temperature.

320 kV modular cable design to manage independently the high voltage and the high current


Enable deployment with reduced engineering and qualification duration

MgB2 HVDC cable testing

HV testing on a full-scale 30 m long superconducting loop

Provide guidance on the test methods to Cigré and standardization bodies


Improve product quality and security

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5. Conclusions

A consistent methodology for assessing the impact of the adoption of new transmission technologies in a Pan-European scope has been developed during the activities carried out in WP13. This methodology has been applied for the technologies developed in the Best Paths project. As a summary the methodology consists on the following steps:

1. Building of a Business as Usual (BAU) scenario: This scenario uses as input the dataset provided by ENTSO-E but it has been heavily modified and completed to reflect the expected progress of the transmission system for 2030, including projected load and generation mix for countries. The scenario could be easily extended for other time horizons provided available information exists to properly characterize them and account for all the expected grid reinforcements.

2. Performing replicability and scalability analysis to detect which areas have to be reinforced and to what extent. This analysis is tuned for the electrical parameters that Best Paths technologies provide but could also be modified and extended to include other potential technologies.

3. According to the previous step the new scenario containing the new reinforced areas with the deployment of Best Paths technologies is built: Best Paths (BP) scenario.

4. The methodology for assessing the Level 1 and Level 2 KPIs is defined. This methodology has to take into account the scope of the analysis to be performed and define an adequate approach to properly capture the impact of the deployed technologies.

5. Once the methodology is defined, KPIs are obtained for BAU and BP scenarios.

The results of the assessment of the KPIs shows the benefits that Best Paths technologies yield to the transmission system. There are still some considerations regarding some KPIs:

1. Renewable curtailment: As it is explained in section 3.1.2 the system cost reduction in the Best Paths scenario is significantly larger than the monetary value of the reduction of curtailment (in line with results in D13.4). The benefits are not to mainly related to curtailment reduction. The main reason for this is that there is little congestion and curtailment already in the base case (BAU scenario) which is an issue related to the modelling process and a limitation of the methodology presented in D13.4 due to the input dataset quality. Benefits of having a reinforced NSOG (North Sea Offshore Grid) could be related to the balancing (of wind power) that can be done more economically, due to the additional transmission infrastructure, with Nordic hydropower and other cheap resources instead of local thermal generation.

2. Energy Not Served: The methodology and the scope of the analysis carried out only takes account for Energy Not Served if this is due to local congestions that could lead to load shedding. It was not realistic considering that load shedding could happen even in the BAU scenario. It is worthy to mention that the methodology does not capture the effect of the faults in the transmission grid that could lead to Energy Not Served: The scope, timescale and tools used for the KPI assessment cannot include this effect to the analysis. Moreover, technologies developed in the Best Paths framework are not focused on the improvement of detection and location of faults in the transmission system.

3. Extended lifetime of the assets (Improved Life-Time of assets): Originally a methodology was devised to assess quantitatively this KPI, it established a direct relationship between this KPI and the congestion level of the lines, but after receiving the feedback from Demo 4 experts, this methodology was deemed not consistent and not realistic. Therefore, it was decided to assess this KPI in a qualitative way indicating which technologies could impact positively in this KPI and the rationale behind.

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In addition, a qualitative evaluation of the benefits that Best Paths technologies has been added for each one of the technologies developed in the demos to complement the analysis presented in this deliverable and deliverable D13.4 (Cost and Benefit Analysis). The benefits BP technologies yield are as follows:




• Decrease procurement delays


• Improve competition and cost reduction on equipment

• Improve standardization at the EU level

• Suppress risks associated to provider disappearance/unavailability

• Facilitate harnessing of offshore wind



• Increase basic knowledge

• Installation in “protected” areas

• Increase number of options for new grid deployment

• Enable deployment with reduced engineering and qualification duration

• Improve product quality and security

d13.5.kpi (levels 1 2) assessment and conclusion v1

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