best paths

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

D13.2

Definition and Building of Best Paths

Scenario

BEST PATHS deliverable fact sheet

Deliverable number: 13.2

Deliverable title: Definition and Building of Best Paths Scenario

Responsible partner: CIRCE

Work Package no.: 13

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

Task: 13.1 and 13.4

Due date of

deliverable:

Actual submission

date:

Authors:

David Rivas (CIRCE), Samuel Borroy (CIRCE), Laura Giménez (CIRCE), Noemi Galan (CIRCE), Adrian Alonso (CIRCE), Javier Garcia (Comillas), Quanyu Zhao (Comillas), Lejla Halilbasic (DTU), Florian Thams (DTU), Spyros Chatzivasileiadis (DTU), Pierre Pinson (DTU).

Version: 1.6

Version date: 09/11/2018

Approvals

Name Organisation

Author (s) (See above) CIRCE, COMILLAS, DTU

Task leader Samuel Borroy CIRCE

WP leader Samuel Borroy CIRCE

Document history

Version Date Main modification Author

1.0 26/06/2018 BAU Scenario CIRCE

1.1 17/09/2018 BAU and Best Paths Scenarios

CIRCE, DTU and COMILLAS

1.2 26/09/2018 Take into account feedback from RTE

CIRCE, DTU and COMILLAS

1.3 15/10/2018 Take into account feedback from Demo 5

CIRCE, DTU and Comillas

1.4 24/10/2018 Including feedback from Demo 3 members

CIRCE

1.5 05/11/2018 Minor corrections CIRCE

1.6 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)

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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TABLE OF CONTENT

Disclaimer ............................................................................................................................................................. 4

Executive Summary ............................................................................................................................................... 7

1. Business as Usual scenario (BAU) ................................................................................................................ 9

1.1. ENTSO-E NETWORK MODEL .......................................................................................................................... 9

1.2. Load profiles for network nodes .................................................................................................................... 13

1.3. RES .......................................................................................................................................................... 14

1.4. Adjusting Load and Generation Data to ENTSO-E Data for 2016 ....................................................................... 16

1.5. Grid topology for 2030 ................................................................................................................................ 19

1.6. UK and Scandinavia network ........................................................................................................................ 28

1.7. Including Load and Generation Projections for 2030 ........................................................................................ 35

2. Reduction of the Business as Usual scenario ............................................................................................. 37

2.1. Available methods in commercial software ..................................................................................................... 37

2.2. Proposed methodology for grid reduction ....................................................................................................... 40

2.3. Results from the reduction process ............................................................................................................... 41

2.4. Verification of reduction process ................................................................................................................... 43

3. Development of Best Path scenario ........................................................................................................... 44

3.1. Introduction ............................................................................................................................................... 44

3.2. North Sea offshore grid development ............................................................................................................ 45

3.3. Continental AC corridors repowering ............................................................................................................. 55

3.4. Superconductivity scenario .......................................................................................................................... 58

4. Conclusions................................................................................................................................................ 59

5. Annex: mathematical formulation of the OFEM model ............................................................................... 61

5.1. Sets and indices ......................................................................................................................................... 61

5.2. Parameters ................................................................................................................................................ 62

5.3. Variables ................................................................................................................................................... 63

5.4. Equations .................................................................................................................................................. 64

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6. References ................................................................................................................................................. 65

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Executive Summary

The aim of this deliverable is to describe the general and specific methodologies that have been used throughout Work Package 13 "Integrated global assessment for future replication in EU27" to model European scenarios that will allow us to assess the benefits of the Best Paths technologies developed in the framework of the project.

The advantages and disadvantages of these Best Paths technologies have to be compared with the current technologies offered by the market in order to solve the future challenges of the network. Therefore, the focus of this work package is to define the expected development of the European network by 2030, as representative as possible, in which two different scenarios are considered, one with current technologies, which is called Business as Usual Scenario, BAU, and another that considers Best Paths technologies, which is called Best Paths Scenario.

BAU scenario (Section 1¡Error! No se encuentra el origen de la referencia.) will take as its starting point the ENTSO-E network, a detailed analysis of this will indicate the shortcomings to be resolved (section 1.1). Also, in order to complete the scenario, load profiles for every node in the network have been included (section 1.2) and the most significant offshore wind farms, onshore wind farms and photovoltaic plants in each country have been installed (section 1.3). Load and generation data to ENTSO-E Data for 2016 (section 1.4) have been also adjusted. In order to reflect the expected progress of the grid topology for 2030 different projects have been added (section 1.5) following the recommendations of the Ten Year Network Development Plan (TYNDP) 2016, the recommendations of the e-HighWay2050 project and the Projects of Common Interest (PCI). In addition, UK and Scandinavia networks had to be added (section 1.6) to include and allocate the high offshore wind potential located in the North Sea offshore area extending across UK, Belgium and Netherlands. Finally, the last steps to build the BAU included: load and Generation projections for 2030 (section 1.7) and some adjustments in the initial Generation Mix considered for some countries (ES,FR and DE). The process of building BAU Scenario comprised three stages as can be seen in Figure 1.

Figure 1.BAU Stages

A reduction (section 2) that allows decreasing computational times of the procedures and calculation that have been done during the scalability, replicability and CBA, has been carried out. In this reduction the DigSilent network reduction methods (Network reduction for load flow

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and Network reduction for short circuit, namely Ward equivalent) have been studied and discarded due to the fact that the reduction obtained with these methods was not enough. A method explained in section 2.2 will be used to carry out the reduction. This method will reduce nodes by 55.58% and lines by 45.86%.

Finally, in order to obtain the Best Paths scenario (section 3), it is necessary to study how the technologies being developed (tasks 13.1 “Reference use case definition and reference KPI measurements” and 13.4 “Gathering field and simulation data related to level 3 KPIs) in our project will scale (13.2 “Review of the scaling potential of the network innovative solutions by demo leaders”) and then replicate (13.3 “Analysis of the replication potential of the network innovative solutions and identification of the main barriers”), so that both their performance conditions and location are determined and allow us to build the Best Paths scenario.

Figure 2. Description of BAU and Best Paths Scenario.

The inclusion of Best Paths technologies in this scenario can be divided into three parts. 1) North Sea offshore grid (NSOG) expansion which mainly utilize HVDC technologies to host more offshore wind resources (section 3.2), 2) continental AC corridors repowering which is meant to increase the transmission capacity (section 3.3), and 3) the possibility to build DC superconducting links have also been taken into account.

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1. Business as Usual scenario (BAU)

The objective of this study is to obtain a scenario, as representative as possible, of the European electrical model of 2030 (BAU scenario). Best Paths scenario is based on BAU Scenario and best paths technologies are included. The comparison between BAU Scenario and Best Paths Scenario will indicate the benefits and disadvantages of these technologies through the calculation of the KPIs and the Cost-Benefit Analysis (CBA).

The BAU scenario is based on ENTSO-E network model. In order to obtain the BAU scenario, different projects have been added following the recommendations of the Ten Year Network Development Plan (TYNDP) 2016, the recommendations of the e-HighWay2050 project and the Projects of Common Interest (PCI). To complete the scenario, the most significant offshore wind farms, onshore wind farms and photovoltaic plants in each country have been installed. UK and Scandinavia networks had to be added because the high offshore wind potential located in the North Sea offshore area extending across UK, Belgium and Netherlands. Finally, it has been necessary to implement hourly profiles throughout the year to observe the behaviour of the network and evaluate the best path technologies through the calculation of KPIs and CBA. BAU developed within this deliverable is a 2030 network scenario but not complete enough to represent a 2030 generation and load. Therefore, it will be completed in the Scalability analysis (D13.1 Technical and economical scaling rules for the implementation of demo results) where generation and loads will be upscaled to 2030 values. In Figure 3 the components of BAU Scenario is presented.

Figure 3. BAU Scenario

1.1. ENTSO-E NETWORK MODEL

The network model used as the foundation for the building of the BAU scenario was obtained through a formal request to ENTSO-E.

Two different files were obtained, one in PSS-E format and the other in DigSilent format. After comparing the network models included in those files, the network in DigSilent format was

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selected as the base network, because of its completeness. DigSilent network is made of different national networks (ES, FR, IT…). Table 1 below summarizes the main characteristics of the considered networks.

Table 1. Characteristics of Networks

Characteristics of Networks

Name Number of Nodes

Number of Lines

Number of 2

windings transformers

Number of 3

windings transformers

Number of Generators

Number of Loads

1 BA 265 309 72 1 5 164

10 RS 527 613 128 0 15 303

11 SK 48 52 10 1 10 18

2 BG 797 786 227 0 4 419

20 AT 104 123 52 0 22 40

21 CH 193 259 48 0 20 82

22 IT 1213 817 602 0 144 341

23 SI 132 111 23 0 3 65

30 ES 1388 1339 201 167 71 647

31 FR 2497 2599 613 0 105 996

32 PT 408 365 273 0 5 87

4 GR 1113 1131 53 72 22 367

40 NL 994 905 167 128 62 244

41 PL 611 986 310 12 36 199

42 DE 3588 3378 847 567 292 859

43 DK 242 247 40 4 8 66

44 BE 139 176 48 0 20 36

45 LU 38 41 12 0 1 11

46 CZ 288 106 55 80 25 76

47 AL 330 191 110 58 0 110

5 HR 299 329 92 0 3 171

6 HU 119 92 28 23 8 37

7 ME 94 79 13 17 0 35

8 MK 148 146 21 11 0 85

9 RO 1089 1193 230 0 5 654

99 TR 4517 1943 3604 12 127 1245

999 EU 291 6 0 0 0 20

As it can be seen in Table 1, the name of each network (47 AL, 8 MK ….) represents each modelled country, except the name “999 EU”, which represents the European interconnections between countries.

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In a detailed assessment of the provided ENTSO-E network data, different difficulties were found. The main problems and the corresponding solutions that were adopted are summarised as follows:

- Issue 1: ENTSO-E network model is anonymous

ENTSO-E network is anonymous, which means that no information about the location of nodes, lines, generators...was available i.e., latitude or longitude data that geolocate the elements was missing.

In addition, all nodes, lines, loads, generators... have their name encrypted, so the name does not provide any information.

Each network component (line, transformer or generator) and the network buses are only classified by the country (6 HU, 44BE …) they belong to. In order to deal with the network dataset, it is necessary to have at least two criteria of classification to better locate those elements.

The chosen solution is to use the classification of “nodes by zones” that is available in the PSSE model and extrapolate it to those nodes in the DigSilent model that do not display this classification. Figure 4 shows a comparison of the classification by network (left) and the classification by zone (right).

Figure 4. ENTSO-E network classification by network and by zone.

- Issue 2: Node analysis

After analysing the network, it was observed that there were nodes with no voltage values. Therefore, it has been decided to make a more detailed assessment of those nodes with missing data. The analysis yields that those nodes are of two different kinds:

A. Isolated nodes that have neither generation nor load.

B. Lines connecting nodes that have neither generation nor load.

The reached solution was to eliminate these islanded network areas, as they do not have any impact on the behaviour of the network.

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- Issue 3: Synchronous generators

ENTSO-E network generators are all modelled as synchronous machines with the same Automatic Voltage Regulator (AVR) and governor. Therefore, it is not possible to identify the energy source of the generation (nuclear, thermal...). In addition, the economic data of these generators that are necessary to perform the CBA and Optimal Power Flow (OPF) analysis are not available.

To overcome this barrier, it was decided to use The RE-Europe data set [1] provided and developed by DTU, which contains economic data of all the generation units installed in each country, classified by their energy source (nuclear, coal, hydro, natural gas...). This data set is easily exportable to the DigSilent network model. For this purpose, the following methodology was devised.

A. Using RE-Europe dataset, generation classification by energy source for each country is obtained. The share (%) for each energy source, i.e., generation mix, was calculated for each country.

B. Generators in DigSilent network were matched with the generation presented in the RE-Europe data on a per country basis. For each one of the generators in ENTSO-E dataset, the closest one in RE-Europe dataset was selected in terms of installed capacity. Since some small differences could be expected, the following rule to find the corresponding generator in RE-Europe dataset was used:

Pgen,ENTSO-E - 5 MVA<=Pgen,RE-Europe<=Pgen,ENTSOE + 5 MVA

C. Remaining generators in the ENTSO-E data which had not been classified were assigned to specific energy sources trying to comply with the shares for every energy source as calculated in A. This classification criterion provides a rough estimation regarding the sources share (%), as generator installed capacities are determined by ENTSO-E network dataset and cannot be modified to achieve a precise adjustment. During the analysis it has been observed that the information about generators contained in ENTSO-E dataset is not complete, therefore some energy sources are not properly represented.

D. In later stages of WP13 development a final double check to correct generation mix will be carried out for some countries based on the expertise and available information of national TSOs.

- Issue 4: There are no generation or load profiles

The load and generation models do not include a yearly load profile. These profiles are necessary because they allow evaluating the network behaviour through the whole year on an hourly basis, in order to compute the required KPIs.

The solution to this problem is explained in section 1.2

- Issue 5: Incomplete ENTSO-E network

ENTSO-E network model is incomplete as there are countries such as UK, IE, Norway, part of Denmark, Sweden and Finland that are not included in the ENTSO-E network model.

Although the main focus of WP13 is the continental European transmission system it is necessary to include these countries to consider the injection of the foreseeable large wind production of that area into continental Europe

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The solution to this problem is explained in section 1.6.

- Issue 6: HVDC links not available

ENTSO-E network model does not include any HVDC links that are already connected, commissioned or supposed to be installed in the near term. For example, the HVDC SACOI link between Italy, Corsica and Italy.

The solution to this problem is explained in section 1.5.5.

- Issue 7: Turkey network

In ENTSO-E network model, the Turkish network is included. Nevertheless, there is no information available about the generators, generation profiles and load profiles of the aforementioned grid. Therefore, Turkish network was removed from the model and has not been taken into account.

1.2. Load profiles for network nodes

The ENTSO-E network model provides snapshot for power values (active and reactive) for every single node in the continental European network that are deemed to be maximum ones. Nevertheless, for the purpose of our studies the hourly load profile for the whole year is needed.

In order to create this input data synthetically, following assumptions are made:

• Each node demand should have an hourly evolution identical to load profile of the country.

• The power factor was kept constant through the year for every single node.

The hourly load profiles for the whole year and every European country are obtained from the ENTSO-E open database [2]. Once the hourly load profile is obtained, it is scaled according to the nodal demands provided by ENTSO-E dataset. These values are obtained for 2016, in the Scalability analysis (D13.1 Technical and economical scaling rules for the implementation of demo results) the load profile will be upscaled to 2030 expected values.

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Figure 5. Hourly demand profile (whole year) for Germany, France and Spain 2016

1.3. RES

As it was mentioned earlier, RES generation was not included in the ENTSO-E network model. In order to have a scenario for 2030 as complete as possible it is paramount to include European RES plants.

It is worthy to mention that the derived values for RES in this first stage have certain limitations. The main of these limitations is the same that ENTSO-E dataset presents for conventional generation: not all the existing generators are included in the ENTSO-E dataset. In this first stage the total installed capacity for RES is limited by the amount of conventional generation capacity included in ENTSO-E dataset and respects the proportion between RES and conventional sources. RES values will be upscaled to reflect the expected 2030 installed capacity in the Scalability analysis (D13.1 Technical and economical scaling rules for the implementation of demo results).

In order to decide which onshore wind farms, offshore wind farms and PV are eligible to be plugged into the model, the following methodology has been developed.

- Step 1: Share (%) for each type of generation

According to [3] installed generation capacity was obtained from which the share (%) of each type of generation was derived by country.

- Step 2: Total power in ENTSO-E network

Analysing the ENTSO-E network model (DigSilent), the total amount of conventional generation capacity in each network (country) can be derived. Table 2 summarizes the total conventional generation capacity which is included in ENTSO-E network dataset for each country.

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

DE

MA

ND

(M

W)

DE ES FR

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Table 2 Total power in ENTSO-E network

Total power in ENTSO-E network

Country Power (MW)

PT 1105,386

ES 2619,86

FR 65894,69

IT 20241,88

CH 7287,479

DE 53014,08

BE 9795,51

NL 15383,15

AT 4430,59

CZ 6251,516

SI 1943,382

SK 3122,027

HU 2607,369

HR 780

BA 1154,002

PL 11251,63

RS 4170,169

RO 3180,2600

BG 5348,9

GR 0

MK 0

AL 0

ME 0

DK 53014,08

LU 110,614

- Step 3: Calculation of RES

For each country, the total power of the ENTSO-E network was selected (step 2) and multiplied by the share (step 1) of the selected RES type (wind farm onshore, wind farm offshore or PV). Below, an example of how the number of MW of wind farms in Italy has been calculated.

=% ∗ = 20.241,88!"2# ∗ 21,461%!"1# = 4344,11&

The same process was carried out for all types of renewables and for all countries.

The determination of the location for the wind farms and the PVs was carried out separately as different sources of information had been used for that purpose.

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- Step 4: Wind farms

WindPro website [4] provides a list of all wind farms classified by country. This list also includes the geolocation (latitude and longitude) of each wind farm.

Wind farms from the obtained list were selected until the total power calculated in step 3 is reached.

Once the selection was done, the wind farms were added to the ENTSO-E network (DigSilent) according to the following criteria:

1. Eligible nodes must be able to withstand the installed power

2. Approximate geolocation.

WindPro software will provide the annual hourly wind profile for the different wind farms according to their coordinates.

- Step 5: PV

A list of the most representative PV plants from Europe was obtained, as the number of PV is not excessive (31 PV), all PV were modelled.

In order to integrate the PV into the ENTSO-E network, the same process was performed as with the wind farms (selection of nodes capable of withstanding the PV power filtered by approximate geolocation).

Ninja renewables database [5] provided the annual hourly photovoltaic production profile for the different PV plants according to their coordinates.

1.4. Adjusting Load and Generation Data to ENTSO-E Data for 2016

As it is explained in D13.1 “Technical and economical scaling rules for the implementation of demo results” in order to fully represent a 2030 scenario, load and generation data needed to be adjusted to consider the nuclear phase-out in Germany [6] as well as the projected increase in electricity consumption and installed capacity of renewable energy sources (RES) [7].

The connected loads represent net demand at the different nodes and already consider (RES) generation at lower voltage levels in the form of lower or negative demand. In Germany for example, 96% of the wind generation [8] and almost 100% of the PV generation [9] is connected to the distribution grid, which means that this share of RES generation is not visible in the data set and only represented through lower demand. This also entails that the installed generation capacities in the data set do not correspond to the official values provided by ENTSO-E [10].

However, in order to be able to upscale the load to projection levels for the year 2030, a more detailed differentiation was required. Otherwise, the upscaling of RES and load could not consider the generation on the distribution level.

To this end, this process adjusted and updated the previously values obtained for load and generation and considers the RES sources in the data set as the share of RES connected to the transmission system. Then, using the power statistics database [11] and the ENTSO-E statistical factsheet [10], the share of installed generation capacity on the transmission level has been derived by comparing the currently installed generation capacity in the original data set (for each type of generation separately) with the actual total installed capacity (given in [10]) on a per country basis. This gave us an estimate of how much energy was generated by the different generation types on the transmission and distribution level, respectively, which in turn allowed us to determine the share of RES production in the net demand. Note that we did not assume any conventional generation on the distribution level but adjusted the installed

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capacities of conventional generators on the transmission level (and thus, in the data set) to fully reflect the total installed capacity of conventional generation for each country individually. The data on electric energy generation and consumption per country were obtained from ENTSO-E's monthly domestic values reports [11]. Combined with the RES profiles as given in section 1.3, the energy ' generated by the different RES types ( on the distribution level ')*+, which is 'hidden' in the net demand, can be computed per country:

E-,./0123456789: = ; ; P=>,?,./01234@A:B !h#DEFGHIJK

LM:

NOBA

PM:,

E-,./01234QR = E-,./012349D65S9 − E-,./0123456789: , x ∈ Ψ = wind,solar,geo-thermal,hydro,biofuel,otherRES,

where Ncountry represents the number of nodes in the corresponding country. The net demand of the entire country is calculated by fitting the wind EDL

wind,country and solar energy EDL PV,country

on the distribution level to wind and solar generation profiles taken, if available, from the BAU scenario Stage 1 or from [12], [13], [14], otherwise. If the data set already contains more than one profile for a specific country, these profiles are interpolated and then fitted to the required energy level:

P=?,./01234!h# = P=?,./01234!h# ⋅E-,./01234QR

∑ P=?,./01234@A:B !h#NOBAPM:

Generated energy from other RES sources in the distribution level (geothermal, hydro, bio-fuel, renewable waste, others [11]) is uniformly distributed over the whole year due to missing generation profiles. The net demand curves of the different countries Enetdemand,country can be computed by subtracting the different distribution level profiles from the electricity consumption profile obtained from ENTSO-E's power statistic data base:

E1p2qprs1q,./01234 = E./1t0rpq,./01234 −;EL,./01234QR|v|

LM:

The total load of Germany as well as the net demand and the power produced by different RES sources on the distribution level over the course of a whole year is visualized in Figure 6.

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Figure 6 : Visualization of the disaggregation of the total load in net demand and RES production on distribution level for the

example of Germany

The computed net demand was distributed among the different nodes in the countries according to the load values Pl,Node|k,country given in ENTSO-E's grid model.

E1p2qprs1q,D/qpw,./01234 = E1p2qprs1q,./01234 ⋅Px,D/qpw,./01234

∑ Px,D/qpL,./01234DEFGHIJKLM:

Pumped hydro storage power plants were assumed to be able to operate annually for a limited number of hours. We determined the number of hours during which pumped hydro storage power plants were allowed to operate based on the actual generated energy provided in the ENTSO-E statistical factsheet for 2016 [10] and the installed pumped hydro capacity per country. The hours were allocated to the periods with high demand and low availability of other renewable energy (i.e., wind and solar).

The electricity production of run-of-the-river and other hydro power plants depends on seasonal river flows and reservoir/pondage limitations, which often prevent the hydroelectric power plant from operating (at higher output levels). Hence, most hydroelectric power plants are not always dispatchable to their maximum possible level. In order to consider these limitations, we reduced their installed capacities, such that even when operating during each hour of the year at maximum capacity, their production would not exceed the observed hydroelectric energy production in 2016 [10] on the transmission level. The alternative approach to limit the number of operating hours as applied to pumped hydro storages leaded to a lot of infeasible hours due to the lack of sufficient base generation. Not changing the installed capacity of run-of-the-river and other hydroelectric power plants (except for pumped storages) while considering them dispatchable throughout the entire year leads to unrealistically high hydro production levels and would not reflect the actual 2016 production levels as listed by ENTSO-E. Therefore, we chose to adjust their installed capacity levels instead while assuming them dispatchable during each hour of the year.

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Following this approach, the RES penetration level could be evaluated accurately considering not only the 'visible' RES generation on the transmission level (see section 1.3) but also the 'hidden' generation on the distribution level. Furthermore, this approach allowed us to (a) adjust the load and generation data as accurately as possible to the ones given in the 2016 monthly domestic values reports from ENTSO-E [11] and (b) better include 2030 load and generation projections, as shown in section 1.7.

1.5. Grid topology for 2030

As it was mentioned earlier, the BAU scenario is based on the expected scenario for 2030. Having that in mind, a selection of eligible projects based on Projects of Common Interest (PCI) [15], the recommendations of the TYNDP 2016 [16] and the recommendations of the e-Highway2050 [17] must be added.

1.5.1. Project of Common Interest (PCI)

The Projects of Common Interest are defined as key infrastructure projects in [15]. They intended to help the EU achieve its energy policy and climate objectives: affordable, secure and sustainable energy for all citizens, and the long-term decarbonisation of the economy. Projects are selected as PCIs based on the following criteria:

A. Have a significant impact on at least two EU countries.

B. Enhance market integration and contribute to the integration of EU countries´ networks.

C. Increase competition on energy markets by offering alternatives to consumers.

D. Enhance security of supply.

E. Contribute to the EU´s energy and climate goals facilitating the integration of an increasing share of energy from variable renewable energy sources.

Figure 7 shows the projects of common interest in Europe.

Figure 7. Projects of Common Interest (PCI) [15].

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The majority of PCIs have been included in the BAU network model. Those belonging to areas that are not modelled in our scenario have been discarded, namely those belonging or connecting to Turkey and those which area located inside the networks of the countries not included in the provided ENTSO-E network dataset such as UK, IE, Norway, part of Denmark, Sweden and Finland.

1.5.2. Ten Year Network Development Plan (TYNDP) 2016

TYNDP 2016 [16] is the most comprehensive and up-to-date planning reference for the pan-European transmission electric network. It presents and assesses all relevant pan-European projects at a specific time horizon as defined by a set of scenarios.

ENTSO-E is structured into six regional groups for grid planning and other system development tasks. The countries belonging to each regional group are shown in Figure 8.

Figure 8. ENTSO-E. Six regional groups. [16]

As shown in the Figure 8, each region contains different countries, for example, “continental south west” region contains Spain, France and Portugal. Therefore, each region will make recommendations for the countries it contains.

1.5.3. E-Highway2050

As it is stated in [18], e-Highway2050 project develops energy scenarios and identifies the required electrical transmission grid needs in the year 2050. Once the target situation in 2050 is defined, a back-casting approach is used to suggest a possible pan-European modular development plan from the year 2020 to 2050.

The recommendations from e-Highway2050 [19] [18] are as follows:

- Spain-Portugal and Italy do not have enough connections with the rest of Europe because they are peninsulas.

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- Germany presents a great demand that is not satisfied with its generation. It would be good to facilitate its connection with North Sea region and Scandinavian countries.

- France has the largest nuclear generation but needs more interconnections to exploit them. In addition, in winter there are occasional peaks of demand that cannot be covered.

- Norway and Sweden are the countries in energy surplus. That is to say, they have a very limited demand and a very large generation, especially in hydroelectric and some wind power.

- North Sea. Offshore wind farms are in “surplus”. As indicated in [19]”[…] the capacities

of the radial links are only around half of the installed North Sea off-shore wind capacities”.

1.5.4. Selected projects to include in the model

From the recommendations of e-Highway2050, TYNDP 2016 and PCI, the following projects were selected (see Table 3) to be included in the ENTSO-E networks. Table 3 also indicates the countries and the location of the project, the reference to the PCI and TYNDP 2016 that corresponds to the project, and finally the explanation of why the project has been decided to be implemented.

Table 3. Summary of selected projects

Projects

Countries Location PCI

TYNDP 2016

Explanation for why to decided implement the project

Spain-

France

Marsillon (Aragon)/ Cantegrit (Navarra)

2.27 276 E-Highway2050: Spain belongs to a peninsula that needs more connections.

Spain- France

Marsillon (Aragon)

2.27.B 270 E-Highway2050: Spain belongs to a peninsula that needs more connections.

Spain- France

Santa LLogaia/Bescano

Bescano/Baixas

2.6 Already built and in operation

E-Highway2050: Spain belongs to a peninsula that needs more connections.

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Projects


TYNDP 2016


Spain - Portugal

Beariz-Fontefria (ES)

Fontefira_Ponte de Lima

Ponte de Lima/Vila de Famalicao (PT)

2.17 4 E-Highway2050: Spain belongs to a peninsula that needs more connections.

Denmark-

Netherlands Endrup (DK)-/Eemshaven (NL)

1.5 71 PCI

Germany-

Netherlands Niederrhein (DE) /Doetinchem (NL)

2.12 113 E-highway2050: Germany has a high demand not satisfied with its own local generation. It would be good to facilitate its connection with the North Sea region and Scandinavia.

Germany-

Denmark Kasso (DK) -

Audorf (DE)

1.4.1 39 E-highway2050: Germany has a high demand not satisfied with its own local generation. It would be good to facilitate its connection with the North Sea region and Scandinavia.

Germany Audorf (DE) /

Hamburg (DE)


Germany Hamburg (DE) /

Dollern (DE)


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Projects


TYNDP 2016


Germany-

Denmark Endrup (DK) /

Niebüll (DE)


Germany-

Denmark Endrup (DK) /

Brunsbüttel (DE)


Germany -

Norway Wilster (DE) / Tonstad (NO)

1.8 37 E-highway2050: Germany has a high demand not satisfied with its own local generation. It would be good to facilitate its connection with the North Sea region and Scandinavia.

Croatia-

Bosnia and

Herzegovina

Dakovo (HR) / Tuzla/Gradac

- 241 TYNDP 2016: The project 241 objectives, in line with the basic goals of EU energy policy, are to:

Improve functioning and reliability of the electricity markets in Croatia and Bosnia and Herzegovina;

Facilitate further integration and expansion of the 400 kV network in the region;

Increase value of GTC on the border HR-BH which will facilitate higher level of market exchanges;

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Projects


TYNDP 2016


Croatia -

Serbia Ernestinovo (HR) - Sombor 3 (HU)

- 243 TYNDP 2016: The project 243 objectives, which in line with the basic goals of EU energy policy, are to:

Improve functioning and reliability of the electricity markets in Croatia and Serbia;

Facilitate further integration and expansion of the 400 kV network in the region.

Increase value of GTC on the border HR-RS which will facilitate higher level of market exchanges;

Bulgaria -

Greece Maritsa East 1 (BG) - Nea Santa (GR)

- 279 TYNDP 2016: Project will increase transmission capacity in the long term by 40MMW for dominant direction from north (RO+BG) to south (GR) that corresponds to an approximately 30% increase of the total capacity in the BG-GR borders.

Serbia -

Romania Djerdap (RS) - Portile de Fier (RO)

- 144 TYNDP 2016: Project 144 aims to enhance the transmission capacity along the East-West corridor in the South-Eastern and Central Europe. Grid Transfer Capacity (GTC) was calculated for a common boundary in South East region, between the West borders of Romania and Bulgaria, which are main exporters of the area on one hand and Serbia and Hungary on the other hand.

Germany -

Denmark

(kontek-1)

Offshore wind power at Kriegers Flak)

E-highway2050:Currently already built and in operation

Germany -

Denmark

(kontek-2)

Bjaeverskov (DK) - Bentwisch (DE)

179 E-highway2050: Germany has a great demand that does not satisfy with its generation. It would be good to facilitate its connection with the North Sea region and Scandinavia.

Denmark -

Poland Avedore (DK) - Dunowo (PL)

234 TYNDP2016: This project is deemed as an improvement for market integration as well as for additional RES connection.

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Projects


TYNDP 2016


Germany -

Sweden Güstrow (DE) -

SE4 (SE)

176 E-highway2050: Germany has a great demand that does not satisfy with its generation. It would be good to facilitate its connection with the North Sea region and Scandinavia.

Germany-

Poland Eisenhűttenstadt (DE) - Plewiska (PL)

3.14.1

230 TYNDP2016: Reinforcements in the Polish transmission network in western part of the country near Polish/German border

Hungary-

Romania Debrecen-Jozsa (HU) - Oradea (RO)

259 TYNDP 2016: This connection contributes to not only market integration but also the security of supply in both countries.

France -

Switzerland Genissiat (FR) - Verbois (CH)

22 TYNDP 2016: The border in the western part of the CCS Region (FR-CH, FR-DE, and CH-DE) appears as the most congested.

Germany-

Switzerland Beznau (CH) - Tiengen (DE)


France-

Germany Vigy (FR)- Uchtelfangen (DE)

244 TYNDP 2016 The border in the western part of the CCS Region (FR-CH, FR-DE, and CH-DE) appears as the most congested.

France-

Germany Eichstetten (DE) - Muhlbach (FR)


1.5.5. HVDC link modelling

Once the projects were selected, they were included in the ENTSO-E network. For this purpose, it was necessary to have information on DC links, converters and transformers. Therefore, a small study was carried out to provide the necessary information to model the projects, since the available information in TYNDP 2016, PCI and e-Highway2050 does not include the level of detail required for modelling purposes.

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The parts to be studied are the DC conductors and the converters.

- DC links

When modelling the selected projects, it is paramount to obtain information about the subsea/underground DC cables and overhead DC conductors, since in the new projects there are different types of links.

Subsea cables are often used to connect offshore wind farms, islands (such as the links with UK or IE) or different countries (such as the Nordlink project between Norway and Germany). Onshore underground cables or HVDC overhead lines (OHL) are commonly used to connect different countries (such as the HVDC underground interconnection between France and Spain) or asynchronous zones (as the Great Belt Power Link in Denmark).

Underground DC cable model is based on INELFE link between Spain and France [20] which covers all the elements used to model the whole HVDC link: converters, transformers, cables... The XLPE cable model [21] was used because it has a capacity suitable for the requirements of this kind of links, and, therefore, it was taken as the reference cable model. Prysmian catalogue [21] has provided information regarding the different DC cable models that can be used in all DC projects. Figure 9 shows the XLPE cable used in the INELFE project. In Figure 9 on the left, the layers of the cable are viewed and table on the right contains the summary of the parameters.

Figure 9. XLPE conductor from Prysmian Cables [20]

Moreover, ABB [22] and Nexans [23] models will be used to model subsea DC cables. Both manufacturers use cables with insulated XLPE. Figure 10 shows the XLPE cable from ABB.

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Figure 10. ABB Cable [22].

- Converters

As mentioned above, an important part of modelling HVDC projects is the converter modelling, since converter losses highly depend on the type, converter topology and configuration of HVDC link must be taken into account.

HVDC links above 1000 MW have been modelled as 2 symmetrical monopole links [20] (HVDC INELFE link has a total capacity of 2000MW) and links below 1000 MW have been modelled as symmetrical monopole. Figure 11 shows the summary of converter configurations.

Figure 11. Summary of HVDC link configurations

Losses in the converters depend on the modulation and the type of converter. In this case, the focus was made on MMC-VSC (Modular Multilevel – Voltage Source Converter) converters and sinusoidal PWM modulation. Therefore, the losses for those converters are:

Short circuit impedance of the transformer in the AC side of the converter: It is a fixed value of 15% [24].

Resistive loss factor: This loss value is modelled with a series resistance [24]. The values or Rtfos are as follows.

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Table 4. Summary of Resistive loss factor

Resistive Loss Factor

Rating of the converter

Rtfos (Ω)

100 MVA 2,9

200 MVA 1,45

300 MVA 0,966

400 MVA 0,726

500 MVA 0,58

600 MVA 0,483

700 MVA 0,415

800 MVA 0,362

900 MVA 0,323

1000 MVA 0,29

1200 MVA 0,241

1400 MVA 0,207

1500 MVA 0,193

1600 MVA 0,181

1800 MVA 0,162

2000 MVA 0,145

2500 MVA 0,116

Cooper losses, these losses represent 0.3% of total losses [24].

Switching factors, these losses are the losses produced in the switching of the IGBTs or MOSFETs, which represent 0.1% of the total losses [24].

No load-losses 0.6% of the total [24].

1.6. UK and Scandinavia network

In the first stage, in order to complement the continental network model (based on ENTSO-E dataset) with the missing UK and Scandinavian networks, it was intended to use the datasets from National Grid website [25] (UK) and Twenties Project (Scandinavian).

Nevertheless, at the time of modelling the UK network, several problems were encountered making the available datasets not usable for our purposes, namely:

• Generator id location was not defined

• Node names were not unique

• No reactive power for demand and generators was available

Due to the aforementioned problems, an alternative method was devised for the inclusion of the grid pertaining to UK and Scandinavian countries.

A simplified grid model was built based on the assumptions taken in the aggregated grid representation developed by e-Highway2050 project. Since the focus of WP13 is on continental Europe, the use of this aggregated model for these areas is aligned with the objectives of this work package. There will be no assessment of the internal congestions of the countries under

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this simplification but the use of this model allows plugging the affected countries into the more detailed continental model and consider the injection of the foreseeable large wind production of that area into continental Europe. The data provided by e-Highway2050 is already updated for 2030 but later upscaling is needed to reflect properly the considered EUCO2030 scenario as explained in section 1.7.

Therefore, the simplified network model should cover, at least, the following points:

• Network nodes

• Line capacity between nodes

• Node generation

• Node demand

E-Highway2050 results in several scenarios for 2040 and 2050 [17]:

• Fossil & nuclear: In this scenario, decarbonisation is achieved mainly through nuclear and Carbon Capture Storage (CCS). RES plays a less significant role and centralised projects are preferred. GDP growth is high. Electrification of transport and heating is significant and energy efficiency is low.

• Big & market: In this scenario, the electricity sector is assumed to be market-driven. A preference is thus given to centralised projects (renewable and non-renewable) and no source of energy is excluded. CCS is assumed to be mature. GDP growth is high. Electrification of transport and heating is significant but energy efficiency is limited.

• Large-scale RES: The scenario focuses on the deployment of large-scale RES such as projects in the North Sea and North Africa. GDP growth is high and electrification of transport and heating is very significant. The public attitude is passive resulting in low energy efficiency and limited demand-side management. Thus, the electricity demand is very high.

• Small & local: The Small & local scenario focuses on local solutions dealing with de-centralised generation. GDP and population growth are low. Electrification of transport and heating is limited but energy efficiency is significant, resulting in a low electricity demand.

• 100 % RES: This scenario relies only on RES, thus nuclear and fossil energy generation are excluded. High GDP, high electrification and high energy efficiency are assumed. Storage technologies and demand side management are widespread.

The 2040/2050 scenarios proposed by e-Highway2050 are derived from visions for 2030 in the TYNDP 2016 [16] which provide a good insight of the expected evolution of the electrical sector for 2030 term. The alignment between visions and scenarios is presented in Figure 12.

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Figure 12. Matching between visions (TYNDP2016) and Scenarios (e-Highway2050) [18]

Table 5. TYNDP 2016 Visions [16]

Slowest progress

Constrained

progress

National green

transition

European green

revolution

V1 V2 V3 V4

Economic and financial conditions

Least favourable Less favourable More favourable Most favourable

Focus of energy policies National European National European

Focus of R&D National European National European

CO2 and primary

fuel prices

low CO2 price, high fuel price

low CO2 price, high fuel price

high CO2 price, low fuel price

high CO2 price, low fuel price

RES Low national RES (>= 2020 target) Between V1 and V3 High national RES On track to 2050

Electricity demand

Increase (stagnation to small growth)

Decrease compared to 2020 (small growth but higher energy efficiency)

stagnation compared to 2020

Increase (growth demand)

Demand

response (and smart grids)

As today Partially used Partially used Fully used

0% 5% 5% 20%

Electric vehicles

No commercial break through of electric plug-in vehicles

Electric plug-in vehicles (flexible charging)

Electric plug-in vehicles (flexible

charging)

Electric plug-in vehicles (flexible charging and

generating)

0% 5% 5% 10%

Heat pumps Minimum level Intermediate level Intermediate level Maximum level

1% 5% 5% 9%

Adequacy National - not

autonomous limited back-up capacity

European - less back-up capacity than V1

National - autonomous high back-up capacity

European - less back-up capacity than V3

Merit order Coal before gas Coal before gas Gas before coal Gas before coal

Storage As planned today As planned today Decentralized Centralized

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The main characteristics of 2030 visions that are defined in TYNDP 2016 are summarized in Table 5.

For the purposes of the including generation and demand for the simplified grid model along with the capacity of some specific links, Vision 3 and Large Scale RES Scenario were selected. Vison 1 and Vision 4 are either too aggressive (assuming too much RES) or too conservative (small progress on RES) in terms of development. Comparing V2 and V3, V3 aligns with the EU long-term strategic development (target 20-20-20) or the scope of BestPaths project the most, i.e., to promote RES in large-scale using innovative technology and to enhance pan-European network capacity and market integration.

Figure 13. Simplified grid representation

Previous figure (Figure 13) and next table (Table 6) show the structure of the grid and the interconnection between the different nodes comprising this simplified network model. According to what is mentioned earlier in this section, it is needed to take into account the transfer capacity between the nodes, which are interconnected in order to develop a grid model later on. This information will be based on the starting grid for Large Scale RES Scenario in 2030 as stated in e-Highway2050 project [17].

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Table 6. Node breakdown of the simplified grid

Country Number of nodes

UK (Great Britain and North Ireland) 6

IE (Ireland) 1

SE (Sweden) 4

NO (Norway) 7

FI (Finland) 2

Table 7. Capacity link and length between nodes of the simplified grid

Link Length

km

Capacity

MW Link

length

km

Capacity

MW

21_fr - 96_ie 682 700 80_no - 81_no 143 1500

22_fr - 90_uk 313 1000 80_no - 82_no 172 5300

26_fr - 90_uk 275 2000 81_no - 83_no 283 800

28_be - 90_uk 353 1000 82_no - 83_no 188 400

30_nl - 79_no 739 700 82_no - 88_se 323 2148

30_nl - 90_uk 390 1000 83_no - 84_no 488 200

31_de - 79_no 652 1400 83_no - 87_se 316 1000

31_de - 89_se 474 1200 84_no - 85_no 497 700

38_dk - 79_no 335 1700 84_no - 86_se 234 700

38_dk - 88_se 465 740 84_no - 87_se 363 250

45_pl - 89_se 423 600 86_se - 87_se 407 4200

72_dk - 89_se 168 1700 87_se - 88_se 499 7300

73_ee - 75_fi 404 1000 88_se - 89_se 307 6500

74_fi - 75_fi 498 3500 90_uk - 91_uk 232 7600

74_fi - 85_no 315 50 90_uk - 92_uk 196 8000

74_fi - 86_se 286 1800 91_uk - 92_uk 204 5000

75_fi - 88_se 690 1350 92_uk - 93_uk 239 7900

77_lt - 88_se 698 700 92_uk - 96_ie 385 500

79_no - 80_no 152 1500 93_uk - 94_uk 273 4500

79_no - 81_no 216 1700 93_uk - 95_uk 287 500

79_no - 93_uk 719 1400 95_uk - 96_ie 188 1100

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Table 8 shows the distribution of demand (GWh) of Vision 3 values according to the distribution factor for simplified grid nodes provided by e-Highway2050 in Large Scale RES Scenario. The hypothesis is that the same distribution factors would apply for 2030. It is worth mentioning that the values obtained following this approach are the total yearly demand. Therefore, in order to obtain the maximum demand value it is needed to upscale the hourly load profiles for the whole year and for the affected countries, obtained from the ENTSO-E open database, until the yearly demand value is reached according to Table 8.

yz_|~ ="|~

∑ |_|~!#NOBAMA

"|~,) = yz_|~&(!|_|~!## Where pcountry_node is the per unit hourly load profile of the country where the node is located, obtained from [2].

Table 8. Demand distribution according e-Highway 2050 distribution factors

Node/Country Demand distribution factor Demand 2030 GWh

FI

91.551

74_fi 10.1% 9.266

75_fi 89.9% 82.285

NO

145.805

79_no 14.9% 21.725

80_no 13.4% 19.504

81_no 11.8% 17.241

82_no 37.2% 54.254

83_no 11.1% 16.13

84_no 10.2% 14.909

85_no 1.4% 2.042

SE

147.296

86_se 3.0% 4.419

87_se 9.0% 13.257

88_se 68.0% 100.161

89_se 20.0% 29.459

UK

378.825

90_uk 37.0% 140.403

91_uk 9.0% 34.152

92_uk 36.0% 136.608

93_uk 10.0% 37.947

94_uk 5.0% 18.973

95_uk 3.0% 10.742

IE

32.567

96_ie 100.0% 32.567

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A similar approach was followed for nodal installed capacity. For each country, total installed capacities for the different technologies (as stated in TYNDP 2016 Vision 3) are distributed among the corresponding aggregated nodes using the same distribution factors defined in Large Scale RES Scenario (e-Highway2050). A similar node breakdown is displayed in Table 9.

Table 9. Nodal installed capacity in the simplified grid

Node Biofuels

MW

Gas

MW

Hydro

MW

Nuclear

MW

Oil

MW

Others

non-RES

MW

Others

RES

MW

Solar

MW

Wind

MW

74_fi 145 194.00 1626.89 0.00 1082.50 278.00 1167.50 1243.78 909.80

75_fi 435 776.00 2723.11 3350.00 1082.50 1112.00 3502.50 1256.22 4090.20

79_no

0.00 9954.15

365.94

80_no

855.00 6786.31

434.91

81_no

0.00 10200.01

270.97

82_no

0.00 4846.62

762.88

83_no

0.00 3084.72

408.69

84_no

0.00 5536.26

451.78

85_no

0.00 391.93

214.84

86_se

0.00 4560.79 0.00 165.00

445.00 441.14 1923.11

87_se

0.00 7931.21 0.00 165.00

3115.00 324.35 2215.65

88_se

950.00 2704.60 5356.50 165.00

1335.00 188.48 5682.53

89_se

0.00 1006.39 1785.50 165.00

445.00 46.03 1578.71

90_uk

12674.77 41.79 1691.63 0.00 1380.22 3274.44 3276.52 11896.47

91_uk

5633.23 1.23 2819.38 0.00 613.43 0.00 4012.64 7610.38

92_uk

11266.46 4118.64 1127.75 0.00 1226.87 2338.89 4201.94 17419.45

93_uk

2816.62 232.98 2819.38 75.00 429.40 1403.33 2786.19 6805.64

94_uk

4224.92 3287.35 563.88 0.00 460.07 1403.33 1282.71 7358.06

95_uk

1590.00 50.00 0.00 150.00 180.00 320.00 300.00 1730.00

96_ie

4270.00 558.00 0.00 260.00 710.00 1200.00 500.00 5500.00

The equivalent network from the e-Highway2050 project was added to the ENTSO-E network. Due to the lack of link information, they were modelled as conductors in DC as the resistance

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value is known. In addition, the HVDC links were modelled as point-to-point. The converters were modelled as ideal converters, i.e. the converters have no loss values. Figure 14 shows the HVDC link between 84-NO Network and 85-NO Network.

Figure 14. HVDC link between 84-NO Network and 85-NO-Network.

When HVDC link is built between the e-Highway2050 network and the ENTSO-E network, the selection of a node capable of supporting the power that will circulate through the HVDC link will be the key to select the connection bus with the ENTSO-E network along with the rough location of the nodes in the ENTSO-E DigSilent file.

1.7. Including Load and Generation Projections for 2030

The scenario for 2030 is based on the EUCO 2030 scenario developed by the European Commission [26]. The scenario reflects the achievements of the 2030 climate and energy targets as agreed by the European Council in 2014 and includes an energy efficiency target of 30% [7].

The following section describes the required upscaling of the load and the different RES generation sources.

ENTSO-E provides a detailed list of installed capacity projections per generation technology on a per country basis for the EUCO 2030 scenario [7]. We adjusted the installed generation capacities in our data set c2016 to the projected levels for 2030 c2030 by determining an

upscaling factor per generation type on a per country basis, up2030 x,country:

y),|@AA = ),|@AA

),|@A:B

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,|~,|@AA !ℎ# = y),|@AA ⋅ ,|~,|@A:B !ℎ#. Furthermore, the actual load within the net demand was also upscaled according to the given values using the same approach. Finally, the installed capacity Pmax all nuclear power plants in Germany was set to zero considering the planned nuclear phase-out.

Thus, this approach allowed to fit the model as accurately as possible to the EUCO 2030 scenario developed by the European Commission [26]. The data for 2016 and 2030 is given on a per country basis in the appendix of deliverable D13.1 “Technical and economical scaling rules for the implementation of demo results”.

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2. Reduction of the Business as Usual scenario

As it has been previously explained, the Business as Usual scenario is a very complex and detailed model of the whole European Transmission Network. The model is based on the anonymous continental grid model provided by ENTSO-E along with the simplified grid model provided by e-Highway2050 covering UK and Scandinavian countries.

This initial model has been enhanced with the information obtained from TYNDP 2016 and PCIs. The foreseen grid expansions in the aforementioned sources have been compiled and included in the grid model in order to represent the expected progress of the European grid for 2030.

Since the complete model ranges from lines and nodes with a voltage of 400 kV down to lines and nodes with a voltage of 6.3 kV, a reduction of the complexity and detail of the grid in order to produce a simplified grid model with less number of nodes is paramount. The reduced grid will be more usable for the purpose and the scope of the Replicability and Scalability studies. Those studies will provide the required feedback to build the Best Paths Scenario and hence perform the CBA analysis of the technological advances of Best Paths Project. The software tools intended to be used in those studies have a limitation on the number of nodes they can deal with, around 7000 nodes, due to the long running times involved.

Table 10. Breakdown of Nodes according to Voltage Level for BAU Model

Breakdown of Nodes

Voltage Number of Nodes

<=20 kV 2491

>20 kV & <=30 kV 782

>30 kV & <=45 kV 221

>45 kV & <=66 kV 225

>66 kV & <=110 kV 4231

>110 kV & <=132 kV 40

>132 kV & <=220 kV 4040

>220 kV & <=400 kV 5096

>400 kV 12

2.1. Available methods in commercial software

DigSilent tool provides two different methods to perform the reduction of the grid:

1. Network reduction for load flow: Network reduction for load flow is an algorithm based on sensitivity matrices. The basic idea is that the sensitivities of the equivalent grid, measured at the connection points in the original grid, must be equal to the sensitivities of the grid that has been reduced. This means that for a given (virtual) set of P and Q injections

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in the branches, from the original grid to the grid to be reduced, the resulting u and φ (voltage magnitude and voltage phase angle variations) in the boundary nodes must be the same for the equivalent grid as those that would have been obtained for the original grid (within a user defined tolerance).

2. Network reduction for short-circuit (Ward Equivalent): Network reduction for short-circuit is an algorithm based on nodal impedance/nodal admittance matrices. The basic idea is that the impedance matrix of the equivalent grid, measured at the connection points in the original grid, must be equal to the impedance matrix of the reduced grid (for the rows and columns that correspond to the boundary nodes). This means that for a given (virtual) additional I injection (variation of current phasor) in the boundary branches, from the original grid to the reduced grid, the resulting u (variations of voltage phasor) in the boundary nodes must be the same for the equivalent grid, as those that would have been obtained for the original grid (within a user defined tolerance). This must be valid for positive sequence, negative sequence, and zero sequence cases, if these are to be considered in the calculation (unbalanced short-circuit equivalent).

In order to check the performance of the aforementioned methods provided by DigSilent the electrical networks from three different countries have been reduced using both methods. The selected countries are Spain (Table 12), France (Table 13) and Portugal (Table 11).

The following tables show the comparison between both methods for the test networks.

Table 11. Reduction for Portugal network using available methods

Portugal

Complete Network

Reduced Network

Load Equivalent Ward Equivalent

Nodes 408 280 280

Static Generators 153 8 8

General loads 87 126 7

Shunt/Filter 28 9 9

Synchronous machines 5 1 1

Lines 365 222 222

2 Winding Transformers 273 211 211

AC voltage sources 0 119 119

Common impedances 0 527 527

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Table 12. Reduction for Spain network using available methods

Spain

Complete Network

Reduced Network


Nodes 1388 1388 1388



Shunt/Filter 71 66 66


Lines 1339 1339 1339





Table 13. Reduction for France network using available methods

France

Complete Network

Reduced Network


Nodes 2497 2465 2465



Shunt/Filter 109 65 65


Lines 2599 2566 2566




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Due to these results and the slight reduction provided (with no reduction at all in some cases), an alternative method is needed to simplify the grid model and make it more usable.

2.2. Proposed methodology for grid reduction

This section explains the steps to take in order to reduce a complex and detailed transmission and distribution systems model into a simplified one.

The objective of this methodology is to reduce the granularity of the grid, limiting the node voltage to be considered. This way, only nodes and lines with rated voltage higher or equal to 220 kV are going to be considered. Grids below that value are going to be reduced to their connection points with upper voltage grids through the corresponding power transformer, only considering the node connected to the higher voltage side of the transformer, hereafter named parent nodes.

The problem lies in how to allocate the loads and generation profiles of the grid to be reduced on those parent nodes when there are more than one for a single grid. Since some of the grids to be reduced are connected to other grids that are going to be reduced as well, this is an iterative bottom-to-up process that will be run until no grid that is subject to reduction remains.

Our approach consists of analysing the grids to be reduced and computing the electrical distance between the node where the load and/or generation to be allocated is connected and the parent nodes of the grid. The electrical distance between a pair of nodes is defined as the equivalent impedance between them, i.e., the voltage drops between the nodes when a current of 1 A is injected in one and withdrawn from the other. Once the electrical distances have been calculated, the load and/or generation will be distributed inversely proportional to the electrical distance: the less the electrical distance to a certain parent node, the higher the amount of power allocated in that parent node. In the case of dispatchable generators, they will not be split among the different parent nodes but re-allocated in the closest one (regarding electrical distance).

A breakdown of the different steps involved in the methodology is presented next:

1. Analysis of the whole data set of the detailed grid to select the nodes and lines, which are part of grids with voltages below 220 kV.

2. Sorting the selected networks from lowest to highest voltage. This way, the bottom-to-up process is warranted.

3. For each selected grid, according to the order established in the previous step, the following process is performed.

a. Parent nodes are detected

b. For the selected grid to be reduced, the nodal admittance matrix (Ybus) has to be calculated. The admittance matrix is a NxN matrix which describes a power system with N buses indicating their nodal admittance.

c. Once the admittance matrix has been obtained, the impedance matrix (Zbus), which is the inverse of the admittance matrix, is calculated as follows: y = y:

d. The electrical distance between two nodes is calculated according to the following formula: = y + y − 2y

e. For each node of the grid containing load/generation profiles and dispatchable generator(s), the distance to the different parent nodes is calculated according to the previous formula.

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f. Power from load and generation profiles is distributed among the parent nodes inversely proportional to the electrical distance. Dispatchable generators are allocated in the closest parent node.

After the reduction process has taken place, a final step was carried out. The internal connectivity of the grid was checked in order to avoid the existence of isolated areas inside the European transmission system.

A first check yielded that, after removing some portions of the grid with nominal voltage below 220 kV, some portions of the grid remained isolated and some countries were not connected to their neighbours. In order to correct this issue, a manual checking process has been devised.

The methodology is based on the comparison of both grid models: complete and reduced one and it is as follows:

1. Select a portion of the grid that has been reduced by comparing both grid model: reduced and complete

2. Manually remove this part in the complete grid model

3. Check connectivity with DigSilent tool.

4. If the remaining grid in the complete model does not present isolated areas, the portion of the grid selected in 1 is removed definitively. If not, the portion of the grid is added to the reduced model. Only the minimum selection of nodes to grant connectivity in the grid model are added.

The four steps are repeated until the consistency of the model has been checked and there are no connectivity issues.

2.3. Results from the reduction process

The network model obtained according to the aforementioned methodology has undergone a reduction around the 50% of the size compared to the original one. The following table provides a comparison between both models: BAU Scenario and Reduced BAU Scenario.

Table 14. Size comparison between complete and reduced scenarios

Size comparison between grid models

BAU Scenario Reduced BAU Scenario Reduction

Nodes 17137 7613 55.58%

Lines 16551 8961 45.86%

The results shed by the reduction imply that the Scalability and Replicability Assessment tools will be able to deal with the Reduce BAU Scenario since the number of nodes is on the safe side of the performance of the aforementioned tools.

The following pictures show the comparison between the original network model and the reduced one for some areas of the European Transmission Grid.

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Figure 15. Complete grid model

Figure 16. Reduced grid model

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2.4. Verification of reduction process

Once the reduced scenario has been obtained, a small check will be made that the BAU scenario and the reduced BAU scenario behave in a similar way. In order to carry out the analysis, it was decided to use the load flow data (loads and generators) of the ENTSO-E network.

Due to the large size of the network, it is difficult to analyze the behavior of the network. A portion of the network corresponding country will be analyzed that should fulfill the following conditions:

• The country network has suffered a considerable reduction.

• The country network has several interconnections with other countries in such a way that it is influenced by the reduction of the neighboring countries.

The country network selected is AL (Albania), since its network is reduced considerably as can be seen in the Table 15 and is also connected to several countries: Serbia, Montenegro, Macedonia

and Greece.

Table 15. Size comparison between complete and reduced scenarios in Albania

Size comparison between grid models in AL

BAU Scenario Reduced BAU Scenario

Nodes 330 36

Lines 192 50

The results of the analysis, will focus on loading of the lines since this value is the main indicator for selecting the lines to repower and corridors to reinforce with the addition of best paths technologies, these results show that:

• Lines in the BAU scenario perform the same way in the reduced BAU scenario, presenting the same loading levels in both scenarios.

• The average error regarding the loading of the lines when comparing the original BAU with the reduce one is 3.64%.

With the results obtained in the analysis, it can be stated that the behavior of the reduced BAU scenario and the BAU scenario is very similar and therefore the results obtained in the reduced BAU scenario can be extrapolated to the BAU scenario.

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3. Development of Best Path scenario

3.1. Introduction

The final goal of the work-package WP13 is to assess up to what extent the new technologies and solutions tested within Best Paths can be beneficial for the whole system. This assessment needs to be carried out under a global perspective in order to capture the potential cross effects and synergies derived from the joint implementation of the demos. In order to carry out this assessment, it has been decided to build two main scenarios that will be subject to a deep comparison. These scenarios must foresee different levels of development of the European electricity system in the year 2030. The first scenario is the so called Business as Usual (BAU) scenario that has been previously presented and which does not consider Best Paths technologies. The second one is the Best Paths (BP) scenario which is built taking the BAU as starting point but includes additional changes to take into account the novel capabilities of the tested Best Paths technologies, such as multi-terminal HVDC grids, and AC corridors repowering techniques (HTLS conductor, DLR, etc.).

As shown in Figure 17, the entire upgrade from BAU to BP scenario can be divided into three parts: 1) North Sea offshore grid (NSOG) expansion which mainly utilize HVDC technologies to host more offshore wind resources, 2) continental AC corridors repowering which is meant to increase the transmission capacity, and 3) the possibility to build DC superconducting links have also been taken into account. The outcome of this analysis will be a set of files that will be taken as input data in order to carry out the Cost Benefit Analysis.

Figure 17 General picture to build the Best Paths scenario

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3.2. North Sea offshore grid development

3.2.1. Background

Building a credible scenario on an HVDC network in the North Sea that takes advantage of the possibility of having multi-terminal systems offers several difficulties. The first one is that apart from the network developments that are taken into account in the TYNDP 2016, there is no clear consensus on the HVDC Offshore Grid architectures that could be implemented in the coming years. In addition, governance constraints among involved countries can affect notably the final adopted design. The reviewed research works can be summarized in the next table:

Table 16 North Sea HVDC architectures

Reviewed HVDC Offshore Grid architectures

Initiative Characteristics

OffshoreGrid 1 : Split Design

Proposed in 2011 considering a potential capacity of 126 GW offshore wind, amount to 13.330 TWh in 25 years [27].

Development is planned in three steps which requires a long time, “realization within a reasonable time frame raises challenges”

Ambitious 3rd step with a central node connecting 4 nodes, “a variety of new technologies needed for safe and secure operation, particularly with respect to power flow control mechanisms, security issues, increased capacity and reduction of energy losses”

Built nodes in areas where the OWF projects have been cancelled

EWEA: 2030 timeframe

Proposed in 2009 [28]. It considers 150 GW installed capacity producing 563 TWh annually in 2030 which according to the evolution of the development of the off-shore industry in Europe seems too optimistic.

The designed topology connects DC nodes with 4 other nodes

Friends of the Supergrid (FOSG): Phase 1

Proposed in 2010 [29]. It considers 23 GW of offshore wind installed capacity (from the Firth-of-Forth, Dogger-Hornsea, Norfolk Bank, German and Belgian Offshore clusters) which does not capture the current known potential wind resources

The design is based on the concept of a superNode that does not consider the individual converters representation

Extendable to onshore: beneficial from the overall European perspective

Central node located in the North sea portion belonging to the UK has 5 links.

1 http://www.offshoregrid.eu/

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3.2.2. Methodology

Given the variety of possible North Sea grid topologies available from previous studies, and in order to avoid being conditioned by any of them, it has been decided to use an expansion planning model called Offshore Grid Expansion Model (OGEM) [30]. This model is focused on the specificities of the North Sea and it is able to find the optimal integrated2 HVDC network to host the optimal amount of available wind resource potential keeping a right balance between investment costs and operational saving. This model takes as input the clustered European grid model used in the European project eHighway 2050 which has 103 onshore and 11 offshore nodes. The model considers 25 GW of offshore wind power installed capacity as starting point in 2030, and assess the profitability of adding extra capacity to reach a maximum of 115 GW total capacity in 2050. In order to build such network, the model considers as available alternatives HVAC lines, HVDC point-to-point links, and Multiterminal (MT) HVDC, which are characterized by their corresponding techno-economic parameters.

It is important to highligh that the offshore grid involves the participation of several actors that can belong to multiple levels, ranging from the sub-national entities in charge of network planning studies, until the European institutions responsible for defining the energy policy. The fact that the new corridors allow not only to connect wind farms developed by each country with its own AC system, but also serve as additional interconnection ties between different countries, represents a quite difficult challenge from the planning point of view. In this sense, the model OGEM allows to include or not a set of constraints derived from governance issues that may condition the development of the network. In order to achieve the most efficent design, the model has been run removing the aforementioned issues hindering the deployment of multiterminal HVDC links. This approach is aligned with the recommendation provided in Deliverable D.13.3 “Identified barriers for replicability” regarding the need to foster a coordinated planning of both AC and DC future networks.

One thing worth to notice that to find an optimal development for a transmission system is an extremely challenging task. Not only because the size and complexity of the resulting optimization problem is very high, but also because some aspects such environmental, social, and governance issues, are difficult to be modeled in a quantitatively manner. In this task, the complexity of the problem is brought to another level as both transmission and generation need to be expanded simultaneously given that the development of the offshore grid involves making decisions not only regarding the construction of new lines, but also on the installation of additional wind farms. The purpose of this section is to provide general description of the model used in this analysis [30], in order to shed some light on the methodology for North Sea offshore grid development used to build the BP scenario.

3.2.3. Model Description

The overall planning problem consists of determining the optimal transmission capacities that should (or should not) be installed among the nodes of the grid, as well as the generation capacities, i.e., Offshore Wind Farms (OWF) that should be installed at the pre-selected offshore nodes. In summary, OGEM optimizes investment decisions (transmission and generation) and the operation of the European power system for sequential expansion planning periods (from year 2030 to 2050). It follows a deterministic sequential-static (or myopic) Mixed-Integer Linear Programming (MILP) approach which mimics the real-world decision-making process.

The sequential periods, each are ten years apart, i.e., year 2030, 2040 and 2050. Every expansion period only considers the current period. For example, the optimization for year 2030 only considers the subsection of the time horizon (one decade from year 2030 to 2040) of the complete problem. Consequently, investment decisions resulted from OGEM are path-

2 Integrated lines are the ones that allow to connect different countries.

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dependent, i.e., investment decisions taken in previous periods have a strong effect on the decisions in subsequent periods.

OGEM represents an step forward with respect to existing expansion models as it implicitely considers the offshore grid connected to the AC system (in an aggregated reprepresentation. It is built based on the Python for Power System Analysis (PyPSA) toolbox. The selected candidate line in each optimization period will be considered as existing lines in the next period. The initial system for 2030 is based on the e-Highway 2050 project, both initial OWFs and links are shown below as in Figure 18.

Figure 18 2030 Initial System [31]

For each expansion period (i.e., one decade), one representative year is considered. Thus, OGEM runs through all steps three times (for 2030, 2040 and 2050), as shown in Figure 19. First, a full-year (8760 hours) system operation is simulated without consideration of any candidate lines (step 1). Each hour (or system state) is represented by one snapshot. In this way, the system base state is established. One hundred representative snapshots are then selected in step 2 using the k-medoids algorithm with marginal prices of all system nodes as input data [32]. This entails that snapshots are grouped to minimize the within-cluster nodal price differences. In step 3, the investment and operation cost minimization problem is solved with the one hundred selected representative snapshots. The investment decisions on generation and transmission are obtained. They are added back to the base network in step 4. A full-year system operation is then again simulated in step 5 including those new built transmission lines and OWFs. This allows to compare the operation of the expanded system

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against the base state of the network. If there were next expansion period, the model iterates automatically and continues with step 1.

Figure 19 Expansion Planning Flowchart

The model considers three possible offshore transmission technologies, i.e., HVAC, PTP HVDC and MTDC. HVAC are connected directly to AC nodes, while PTP HVDC and MTDC are connected to AC nodes through converters. DC CBs are considered when evaluating the profitability of MTDC networks. The starting installed capacity in 2030 is 25.34GW [33], with additional potential of 25.75GW. The maximum possible installed capacities are aligned with e-Highway2050 project [31].

Table 17 North Sea Offshore Wind Installed Capacity and Potential

Installed capacity

Component 2030 2040 2050

Starting installed capacity 25.34 25.34 25.34

Additional wind potential 25.75 57.65 89.56

Maximum possible installed capacity 51.09 82.99 114.90

Step 1

Base Case:

•Full year system operation optimization (8760 hours)

•To assess the performance of the base state

Step 2

Clustering:

•Cluster Snapshots (100)

•Scale time series

•Fixed storage units

Step 3

Solve GTEP

•Solve investment and operation optimization problem

•To obtain both generation (new wind farms) and transmission investment decisions (off-shore grid)

Step 4

Expanded Case

•Add invested lines and generators (decision made in previous step) to the base case

•Unfixed storage units

Step 5

Final Simulation

•Full year system operation optimization (8760 hours)

• Assess the performance of the expanded network

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Table 18 presents the conceptual formulation for the expansion problem for a single period. Decision variables are shown in Table 19. Assumed component cost and lifespan are presented in Table 20. The detailed mathematical formulation is included in the Annex and more detailed information, regarding time-series scaling, etc., can be found in [30].

Table 18 OGEM formulation

Formulation

Minimize Generation costs

+ Penalty cost on load curtailment

+ Offshore investment

+ Offshore transmission lines investment

+ AC/DC Converter costs

+ DC Circuit breaker costs

Subject to Nodal balance constraints

Transmission flow constraints

Transmission thermal limit

AC/DC converter thermal limit

Minimum transmission investment

Generation limit

Storage dispatch limit

Energy-constraints limit

Hydropower dispatch limit

Storage state-of-charge constraints

Table 19 Decision variables of OGEM

Decision variables

Operational Investment

Generation dispatch OWF investment

Storage dispatch Transmission line investment

Storage state-of-charge AC/DC converter investment

Transmission line flows DC circuit breaker investment

AC/DC converters injection

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Table 20 Input data [30]

Component cost and lifetime data

Component CAPEX Lifetime (years)

Offshore wind farm

Nearshore 1800 k€/MW

25

Far shore 2200 k€/MW

MTDC cable 1765.7 €/MW∙km

40

AC/DC converter 123000 €/MW

HVAC cable 2895.6 €/MW∙km

DC CB 16666.7 €/MW

3.2.4. Obtained North Sea Offshore Grid Topology

This section presents the results of the application of the methodology described in the previous section. The results of the model allow to obtain the North Sea Offshore Grid (NSOG) expansion plan from year 2030 until 2050. In particular, the BP scenario is referred to the network topology corresponding to 2030 which is presented in Table 21.

Table 21 BP Scenario of the NSOG

BP NSOG (2030)

Names Capacity (GW) Length (km)

New Lines

90_uk - 26_fr_AC 5,953 92,7

89_se - 38_dk_DC 2,776 223,1

106_ns - 26_fr_DC 0,726 214

106_ns - 30_nl_DC 2,447 206,1

111_ns - 30_nl_DC 3,815 120,2

112_ns - 38_dk_DC 2,777 241,9

112_ns - 31_de_AC 8,104 150,5

114_ns - 89_se_AC 0,515 78,7

Existing Links

117_ns - 90_uk_ptp 6,299 216,9

118_ns - 92_uk_ptp 3,149 295

119_ns - 93_uk_ptp 0,525 230,9

120_ns - 94_uk_ptp 0,525 161,9

121_ns - 28_be_ptp 2,172 187,9

122_ns - 30_nl_ptp 2,534 140,4

123_ns - 31_de_ptp 7,964 210,2

124_ns - 38_dk_ptp 1,629 128,5

125_ns - 72_dk_ptp 0,543 170,9

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The resulting network consists of MTDC grids, PTP HVDC links and AC links. It allows to capture an annual energy 173 TWh/year with a total installed capacity 45,5 GW. In order to visualize them, they are represented by solid lines in different colors in Figure 20 where New OWF refer to candidate areas where new wind farms could be installed.

Figure 20 NSOG Expansion Plan for 2030. The legend on the top-left corner and the background map is from ECOFYS [34].

The initial system originated from e-Highway2050 project has 103 onshore (represented by gray circles) and 9 offshore nodes (represented by dark orange circles). Existing connections are represented by black solid lines. Please note that each node is an aggregated node that represents wind installed capacity within a certain area. In order to differentiate between existing OWFs and candidate offshore wind clusters (that will be decided by the OGEM model), the new offshore wind clusters or OWFs are depicted in light-orange circles (in total, there are 11 nodes). The model assumes that adding new wind farms in the nodes where there is already existing intalled generation will entail the development of new lines. In addition, it considers the possibility to install new wind farms in the candidate nodes characterized by a maximum wind resource that could be captured. For illustrative purpose, the aggregated nodes that represents expansion of existing ones are placed close to the existing ones.

One thing worth to mention is that OGEM model oriented to provide a regulatory insight to assess the impact of governance constraints for future expansion and therefore it has some limitations. For example, security constraints are not considered, and a highly aggregated network is used to reduce the complexity of the optimization problem and to enhance the tractability of the problem. Despite this fact, the model takes into account the impact of the investment cost required to build the new infrastructures according to the total capacity installed and the type of line built (AC, HVDC point-to-point, or HVDC multiterminal). Meanwhile, since UK and Scandinavia regions are already represented with DC links (Figure 18), profitability is the only considered factor when building connections between this region with the continental Europe. It can be observed that between UK and France, the model has decided to build an AC link. However, as this might be unrealistic given the experts opinion, it

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has been decided to substitute it by a DC linkc. More details can be seen in deliverable D13.4. “Cost Benefit Analysis”

Figure 21 NSOG Expansion Plan for 2040

Figure 22 NSOG Expansion Plan for 2050

3.2.5. Analysis of the NSOG without consideration of DC CB cost

As discussed in the barriers identification of demos 1 and 2 of of D13.3 “Identified barriers for replicability”, DC CB is an indispensable component for MTDC system protection. In the previous section, OGEM has assumed the commercial availability of DC CBs. However, the related investment costs are very high. In order to assess the impact of DC CBs cost on the

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entire NSOG topology, the expansion problem was solved again assuming null DC CB cost. It is important to notice that this assessment has been carried out just to assess the impact of this cost parameter which is subject to uncertainty. However, the final BP scenario provided to the cost-benefit analysis will assume the nominal DC CB costs shown in Table 20.

The results are shown in the following figures.

Figure 23 NSOG Expansion Plan for 2030 (without DC CB cost).


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In the previous figures, it is difficult to compare visually the obtained grids with and without DCCB costs, since the total investment level depends on two factors: length of the lines and related capacity. In order to compare the two cases, the total products of lengh (km) and capacity (GW) for each transmission technology are listed respectively (Table 22). It is possible to check that the non consideration of DCCB cost increases the installed capacity of MTDC grids.

Table 22 Transmission investment level comparison

Investment costs

Type MTDC

[km.GW]

AC

[km.GW]

PTP DC

[km.GW]

With DC CB cost 9312.87 4855.529 5375.035

Without DC CB cost 11815.27 1991.032 4913.79

On one hand, MTDC investment costs (including DC CB cost) could be lower than an equivalent PTP HVDC grid, since each node would only need one converter to absorb and inject power. On the other hand, it is not necessary for the OGEM to favor a MTDC structure since in that case, power flows through lines are constrained by power flow equations, while for PTP links, the capacity is only limited by the thermal capacities. Therefore, there is a trade-off between savings from converter investment for MTDC network and additional power flow constraints.

In this case study, it is indeed observed that without consideration of DC CB cost, OGEM shifts the investment and intends to invest more in MTDC network rather PTP DC links or AC lines, i.e., from 9312.87 km.GW to 11815.27 km.GW.

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3.3. Continental AC corridors repowering

In order to build the AC part of the BP scenario, the outcome of the scalability analysis presented in deliverable D13.1” Technical and economical scaling rules for the implementation of demo results” has been taken as input data. During the BaU scenario development, some adjustments have been introduced to the data set. For that reason, in order to validate the set of candidate lines identified in D13.1 “Technical and economical scaling rules for the implementation of demo results”, the hybrid AC-DC optimal power flow model developed by Comillas-IIT has been run for a set of selected representative days. The main features of this model are the following ones:

• The model simulates a transmission-constrained operation of the whole pan-European system for a set of sequential snap-shots.

• Centralized point of view. The objective function is to maximize the total social welfare (which is equivalent to minimize the operational cost when the demand is inelastic) while satisfying the nodal demand. These results try to mimic the behaviour of a competitive integrated market.

• The model includes a detailed representation of the network (AC and DC, including the converter stations of the HVDC grid and links).

• Both conventional and RES generators are considered: cost functions & technical parameters.

• A linear version of the model is available which can deal with large-scale systems: ~7.000 nodes ~ 10.000 lines

Some previous research has shown that the OPF solution highly depends on how the converter stations are modeled [35]. For that reason, the Comillas-IIT model considers a detailed representation of both HVDC and AC systems losses (both at the lines and at the converters). In addition, the reported different behaviour of the converter when acting as an inverter or as a rectifier is also taken into account.

To compare and validate the results, several days with extreme conditions were chosen for running the simulations instead of the whole year due to heavy computational burden. Detailed analysis was carried out on the simulation results afterwards. Table 23 shows the results of the two studies where the column “DTU” refers to the scalability analysis results (D13.1) , and the “Comillas” column indicates with a cross whenever such line has been identified as a candidate one to be repowered due to have a high marginal value.

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Table 23 Congested Lines Comparison and validation

Congested lines comparison

Line From Bus To Bus From Bus To Bus DTU Comillas

No. No. No. ID ID

6 6994 7003 'RO926880' 'RO926963' X

9 217 5475 'BA910535' 'HR922472' X X

10 7106 5464 'RS927759' 'HR922327' X X

11 381 5399 'BG911235' 'GR921301' X X

12 1804 2055 'DE915466' 'DK916219' X X

244 142 7336 'AT910383' 'XMN_BR21_OV' X

245 1226 7336 'DE913834' 'XMN_BR21_OV' X

324 142 7378 'AT910383' 'XRU_MN21_OV' X X

325 491 7378 'CH911768' 'XRU_MN21_OV' X

971 491 468 'CH911768' 'CH911745' X

1154 658 623 'CZ912129' 'CZ911986' X

1175 658 657 'CZ912129' 'CZ912128' X

2950 2583 2169 'ES917103' 'ES916537' X

6067 5139 4243 'FR920572' 'FR919539' X

7380 6065 5814 'IT923502' 'IT923113' X

7675 6207 6162 'IT923731' 'IT923656' X

7709 6300 6296 'LU923879' 'LU923874' X X

7745 6370 6340 'NL924374' 'NL924183' X

9932 7291 4498 'XHE_AR11_OV' 'FR919839' X

9933 3190 7218 'FR918327' 'XAR_AR21_OV' X

9935 7427 3617 'XVI_BA11_OV' 'FR918815' X X

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Although, the identified congested lines do not fully match, one has to keep in mind that the new simulations have only been carried out for some days instead of a whole year. Consequently, 21 lines are chosen with the proposed upgraded capacity (as shown in Table 24) which constitute the proposed BP scenario.

Table 24 Selected AC lines to be repowered

Congested lines selected

From Bus To Bus From Bus To Bus Previous

Rating

Upgraded

Rating

No. No. ID ID MW MW

6994 7003 RO926880 RO926963 1052,63 1100,85

217 5475 BA910535 HR922472 526,32 1052,63

7106 5464 RS927759 HR922327 500,00 1000,00

381 5399 BG911235 GR921301 500,00 1000,00

1804 2055 DE915466 DK916219 500,00 1000,00

142 7336 AT910383 XMN_BR21_OV 1500,00 1867,61

1226 7336 DE913834 XMN_BR21_OV 1500,00 1867,61

142 7378 AT910383 XRU_MN21_OV 1500,00 2449,55

491 7378 CH911768 XRU_MN21_OV 1500,00 2449,55

491 468 CH911768 CH911745 1500,00 1815,30

658 623 CZ912129 CZ911986 1500,00 1516,83

658 657 CZ912129 CZ912128 1500,00 1934,73

2583 2169 ES917103 ES916537 5000,00 5456,00

5139 4243 FR920572 FR919539 5000,00 6450,72

6065 5814 IT923502 IT923113 5000,00 6236,10

6207 6162 IT923731 IT923656 5000,00 6609,60

6300 6296 LU923879 LU923874 1500,00 2039,26

6370 6340 NL924374 NL924183 1500,00 1650,23

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Congested lines selected

From Bus To Bus From Bus To Bus Previous

Rating

Upgraded

Rating

7291 4498 XHE_AR11_OV FR919839 600,00 1200,00

3190 7218 FR918327 XAR_AR21_OV 200,00 210,41

7427 3617 XVI_BA11_OV FR918815 300,00 600,00

3.4. Superconductivity scenario

Demo 5 has demonstrated an underground DC superconducting cable system that is able to transmit up to 3.2 GW operating at a voltage level of up to 320 kV and 10 kA. Thanks to the modular design developped in DEMO 5, the rated power level can be easily adjusted to meet the local grid requirements. Given that a pumping and cooling station is needed ideally every 50-70 km along the cable route, the DC links where this technology could be more easily applied correspond to short distance links. However, more distant nodes could also be connected in the future according to the experts’ opinion drawn from the survey that was conducted for the replicability study D13.3 “Identified barriers for replicability”and the last results obtained in Demo 5.

With the objective to include in the BP scenario the possibility of having superconducting DC links, the following DC links shown in Table 25 have been identified as candidate ones to transfer bulk power taking advantage of this novel technology. According to involved partners and experts’ opinion, from a practical point of view, TSOs are likely to start experimenting on DC superconducting links with short distances where only two pumping and cooling stations are needed. Therefore, the criteria used for its selection has been that they have to be inland DC links with distances lower than 100 km.

Table 25 Selected DC lines for DC Superconducting application

Superconducting Links

From Bus To Bus From Bus To Bus Rating Length

No. No. ID ID MW km

153 152 DE913166-DC(2) DE913166-DC(-) 200 100

193 199 DK916411-DC(-)(2) Kontek1_DC(-)(1) 150 86

191 197 DK916411-DC(+)(2) Kontek1_DC(+)(1) 150 85

194 248 HU922519-DC(+) RO926935-DC(+) 250 80

195 249 HU922519-DC(-) RO926935-DC(-) 250 80

236 238 ProjectINELFE(1)_ES_DC+ ProjectINELFE(1)_FR_DC+ 350 64,5

239 237 ProjectINELFE(1)_FR_DC- ProjectINELFE(1)_ES_DC- 350 64

240 242 ProjectINELFE(2)_ES_DC+ ProjectINELFE(2)_FR_DC+ 350 64

243 241 ProjectINELFE(2)_FR_DC- ProjectINELFE(2)_ES_DC- 350 64

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4. Conclusions

The main outcome of this deliverable is the development of network models and scenarios of the expected pan European transmission system by 2030:

• Business as usual: Considering the current state of the art technologies and the already planned reinforcements according to different initiatives.

• Best Paths: considering and deploying the technologies developed in the project in the areas to be reinforced and improved to maximize the exploitation of the renewable energy potential (as expected according to EUCO2030 scenario).

In detail the main features of each of the scenarios are the following:

Business as Usual Scenario:

1. Corrections of the ENTSO-E dataset

a. Node identification. All the available data was anonymous, therefore a time consuming a thorough process was followed to properly identify the existing nodes in the input dataset provided by ENTSO-E.

b. Removal of isolated areas present in the ENTSO-E dataset.

c. Classification of synchronous generation. Al the generators have been classified according to the fuel source and completed with economic data needed for further analysis to be made in the scope of WP13.

2. Inclusion of loads profiles for network nodes. These loads have been corrected and adjusted to represent load projections as indicated in the EUSO2030 scenario.

3. Inclusion of European RES plants: RES penetration level has been evaluated accurately considering not only the 'visible' RES generation on the transmission level but also the 'hidden' generation on the distribution level

4. Grid topology updated to 2030: The grid topology of the pan European transmission system has been updated to the expected progress for 2030, taking into account the already planned transmission projects and recommendations coming from the following sources:

a. Projects of Common Interest

b. Ten Year Network Development Plan 2016

c. e-Highway2050 recommendations

5. Inclusion of a simplified representation of UK and Scandinavia network: Since there was no information regarding these areas in ENTSO-E dataset, a simplified representation of the portion of the transmission system has been developed based on e-Highway2050 clustered model. This simplified model has been plugged into the more detailed continental one.

6. Network reduction to only consider lines and nodes with voltage equal or superior to 220 kV. This reduction will allow us to run the simulation with acceptable computation times.

Best Paths scenario:

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1. This scenario is built upon the Business as Usual scenario; therefore, it includes all the above listed features.

2. Projected North Sea Offshore Grid according to detailed Transmission Expansion Tools.

3. AC corridors to be reinforced.

4. Candidates links to be reinforced using superconducting DC technology.

5. All the electrical and technical parameters for the elements to be included correspond to elements belonging to the cutting edge technologies developed in the framework of Best Paths project.

As a summary, models developed in the project reflect the expected progress of European transmission system by 2030, considering all the already planned TYNDP 2016, PCIs and e-Highway2050 (both BAU and Best Paths scenario) along with the needed reinforcements in the AC and DC corridors using Best Paths technologies (only Best Paths scenario). Therefore, they are far more complete models to reflect the foreseen transmission system considering the cutting-edge transmission technologies in the case of Best Paths Scenario.

Consequently, the models and the methodologies detailed in this deliverable can be used as a cornerstone to build scenarios for longer terms (2040, 2050…) and to assess the impact of the inclusion and deployment of future transmission technologies.

Although the scenarios are valuable as themselves, it needs to be highlighted that their sole purpose was to serve as benchmark tool to assess the benefit and impact of the deployed Best Paths technologies.

It is worthy to mention, that the process to develop the scenarios has been an iterative procedure which has been fed by most of the activities of WP13. This process has entitled us to develop a very realistic model of the expected pan European transmission system in 2030.

One of the main barriers to develop these models was the access to the ENTSO-E dataset, which resulted in a very long process hindering the development of the task. Moreover, the available dataset was quite incomplete with missing, outdated or defective information, all the information has to be corrected, adjusted and completed to be usable in research projects.

Therefore, European Commission or ENTSO-E should maintain an open study-model of the present European power system with sufficient detail to facilitate in-depth and accurate analysis in research projects, including the analysis of future scenarios. The study-model should include electrical data and geographical layout of the transmission grid; information about generators, including capacity, type and location in the transmission grid; and distribution of power demand. Insofar as possible, planned future changes should be included. In the same vein, grid information regarding not continental Europe and Scandinavian countries should be included to fully and accurately represent pan European transmission system. This open model could replace the ENTSO-E study-model provided under confidentiality agreements today. Granting access to this study-model would be a strong asset for future EU-funded research projects as it would avoid overlap and effort in this recurring task.

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5. Annex: mathematical formulation of the OFEM model

5.1. Sets and indices

n ∈ N System nodes

n ∈ Ν| ∀nsc, nodes of country nsc, Ν| ⊂ N

g ∈ G Generators

g ∈ G Energy-constrained generators, G ∈ G

g ∈ G Load shedding generators, G ∈ G

g ∈ G Hydro generators, G ∈ G

g ∈ G| ∀nsc, generators of country nsc, G| ⊂ G

g ∈ G Offshore wind clusters

l ∈ L Transmission lines

l ∈ LA Existing transmission lines, LA ∈ L

l ∈ L+ Candidate transmission lines, L+ ∈ L

l ∈ L+ Candidate integrated transmission lines, L+ ∈ L+

l ∈ L|+ ∀nsc, candidate integrated transmission lines of country nsc, L|+ ∈ L+

l ∈ L¢ HVAC transmission lines, L¢ ∈ L

l ∈ L * MTDC transmission lines, L * ∈ L

l ∈ L££ PTP HVDC transmission lines, L££ ∈ L

l ∈ L AC/DC converters, L ∈ L

nsc ∈ NSC North Sea countries NSC = BE, DE, DK, FR, GB, IE, NO, NL, SE

sn ∈ SN Snapshots

s ∈ S Storage units

s ∈ S| ∀nsc, storage units of country nsc, S| ∈ S

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5.2. Parameters

«|. Nodal marginal price for node n, in current iteration it

A,|® Availability factor in [0, 1] for generator in snapshot sn

B¯.|+ Incidence matrix value for transmission line l and node n

C|,¯+ Cost distribution matrix of candidate lines to nodes

C|,¯ Cost distribution matrix of offshore wind clusters to nodes

D|,| Node demand at snapshot sn

° Penalty to minimize transmission flows

E Annual available energy for energy-constrained generator g

±,| Inflow for storage unit s at snapshot sn

F²¯ Maximum transmission capacity for transmission line l

¯ Starting node for transmission line l

³¯ End node for transmission line l

K+ Annuitized, hour-equivalent investment cost of transmission line l

K Annuitized, hour-equivalent investment cost of offshore wind cluster g

K® Operational marginal cost of generator g ∈ G − G

K Storage cost of storage unit s

Mµ Disjunctive (big M) parameter for flow constraints

M| Disjunctive (big M) parameter for welfare constraints for country nsc

M¶ Disjunctive (big M) parameter for the minimum investment ratio

P Generation and storage capacity for storage unit s

P® Generation capacity for generator g ∈ G − G

PA Starting generation capacity for offshore wind cluster g ∈ G

P% Maximum generation % per snapshot for hydro generator g

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P% Minimum generation % per snapshot for hydro generator g

R¯ Minimum investment ratio for candidate line l

SH Maximum storage hours for storage unit s

VOLL Value of lost load for load shedding generators

W| Probability of snapshot sn, ∑W| = 1

W|A Base welfare for country nsc

X¯ Reactance for transmission line l ∈ L¢

º¯ Resistance for transmission line l ∈ L*

5.3. Variables

,| Generation of generator g at snapshot sn

,| Generation of storage unit s at snapshot sn

,| Storage of storage unit s at snapshot sn

,| State of charge of storage unit s at snapshot sn

,| Flow of transmission line l at snapshot sn

» ,|» Absolute flow of transmission line l at snapshot sn

Maximum transmission capacity of candidate converter l

Generation capacity of offshore wind cluster g ∈ G

½|,| Node n voltage angle at snapshot sn

¾|,| Node n voltage magnitude at snapshot sn

¯ Binary investment decision for candidate line l

Investment ration for candidate line l

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5.4. Equations

Equation (1-20) represents the expansion problem in one period. Equation (1) is the objective function that minimizes together both investment and operation cost. Nodal balance constraints are established in Equation (2). Power flow equations are enforced with Equation (3-6). Equation (3-4) are for existing transmission lines, while (5a-b) and (6a-b) are for selected candidate lines. Equation (7-9) limits the maximum transmission capacity for existing lines, candidate lines and candidate converters respectively. Equation (10-11) set the minimum transmission investment level. Maximum capacity of onshore generation, offshore wind generators and storage units are constrained by Equation (12-14) taking into account availability factors. The annual total generation of energy-constrained generators are limited by Equation (15). Equation (16) sets the upper and lower bound of generation percentage of hydro generators. Equation (17) limits the maximum capacity for storage units and snapshots. The state-of-charge constraint of storage units between two consecutive snapshots are established by Equation (18) considering inflows and the net dispatch of the unit. While Equation (19) limits the state-of-charge accordingly to the energy reservoir size.

min ; ; W|=∈88¿À∗ ÁK® ∗ ,|Â|

+; ; W|=∈8ÃÄÅ∗ ÁÆÇÈÈ ∗ ,|Â|

+; ; W|x∈RRÉ∗ ° ∗

|» ,|» +; K ∗ ! − PA#=∈8¿À

+; K+ ∗ F²¯ ∗ x∈RÉÊRÉ+; K+ ∗

x∈RÉÊ∩RÉ

(1)

s. t. ,| + ,| − ,| +; B¯.|+ ∗ ,|¯= D|,| ∀, : «Í, (2)

!½Î,| − ½Î,|# Ï¯⁄ = ,| ∀l ∈ LA ∩ L¢ , (3)

!¾Î,| − ¾Î,|# º¯⁄ = ,| ∀l ∈ LA ∩ L * , (4)

,| ≤ Á½Î,| − ½Î,|Â Ï¯⁄ +& ∗ !¯ − 1# ∀l ∈ L+ ∩ L¢ , (5a)

,| ≤ Á½Î,| − ½Î,|Â Ï¯⁄ −& ∗ !¯ − 1# ∀l ∈ L+ ∩ L¢ , (5b)

,| ≤ Á¾Î,| − ¾Î,|Â º¯⁄ + & ∗ !¯ − 1# ∀l ∈ L+ ∩ L * , (6a)

,| ≤ Á¾Î,| − ¾Î,|Â º¯⁄ − & ∗ !¯ − 1# ∀l ∈ L+ ∩ L * , (6b)

−F²¯ ≤ ,| ≤ F²¯ ∀l ∈ LA, (7)

−F²¯ ∗ ≤ ,| ≤ F²¯ ∗ ∀l ∈ !L+ − L#, (8)

− ≤ ,| ≤ ∀l ∈ L+ ∩ L , (9)

+ &¶ ∗ !1 − ¯# ≥ R¯ ∀l ∈ L+, (10)

≤ ¯ ∀l ∈ L+, (11)

,| ≤ P® ∗ A,|® ∀g ∈ G − G , (12)

,| ≤ ∗ A,|® ∀g ∈ G , (13)

,| ≤ ² ∀, (14)

; ,| ≤ E| ∀g ∈ G , (15)

P% ∗ P® ≤ ,| ≤ P% ∗ P® ∀g ∈ G , (16)

,| ≤ P ∀, (17)

,| − ,|: − ,| + Ó| ∗ ,| + ±,| = 0 ∀, (18)

,| ≤ SH ∗ P ∀, (19)

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