The impact of national interests and European coordination on securing investment in trans-national electricity networks

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The impact of national interests and European coordination on securing investment in

trans-national electricity networks

van Blijswijk, Martti DOI

10.4233/uuid:78bba76e-2a0c-4b3b-baeb-c7033e86702c

Publication date 2017

Document Version Final published version

Citation (APA)

van Blijswijk, M. (2017). The impact of national interests and European coordination on securing investment in trans-national electricity networks. https://doi.org/10.4233/uuid:78bba76e-2a0c-4b3b-baeb-c7033e86702c Important note

To cite this publication, please use the final published version (if applicable). Please check the document version above.

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The impact of national interests and

European coordination on securing

investment in trans-national electricity

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The impact of national interests and

European coordination on securing

investment in trans-national electricity

networks

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties,

in het openbaar te verdedigen op woensdag 20 december 2017 om 12:30 uur

door

Martti Juhani VAN BLIJSWIJK

Bestuurskundig ingenieur,

Technische Universiteit Delft, geboren te Leidschendam.

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copromotor: dr. ir. L.J. de Vries Samenstelling promotiecommissie:

Rector Magnificus, voorzitter

Prof. dr. ir. M.P.C. Weijnen, Technische Universiteit Delft

Dr. ir. L.J. de Vries, Technische Universiteit Delft

Dr. G. van der Lee, TenneT TSO B.V.

Onafhankelijke leden:

Prof. dr. J.-M. Glachant, European University Institute

Prof. dr. B.F. Hobbs, Johns Hopkins University

Prof. dr. P. Palensky, Technische Universiteit Delft

Dr. M. Supponen, Europese Commissie

Prof. dr. ir. P.M. Herder, Technische Universiteit Delft, reservelid

Dit onderzoek is tot stand gekomen dankzij financiering door TenneT TSO B.V.

Keywords: Electricity, transmission, grid, TSO, cross-border, trans-national, network investment, agent-based, national interests, investment decision

Printed by: Ridderprint BV, Ridderkerk

Cover image: Enlargement of the ENTSO-E grid map of the interconnected Eu-ropean electricity transmission network (version 31-12-2015),

cour-tesy of ENTSO-E (https://www.entsoe.eu)

Copyright c 2017 by M.J. van Blijswijk

ISBN 978-94-6186-871-8

An electronic version of this dissertation is available at:

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Preface

This thesis is the result of a joint research project between Delft University of Tech-nology and TenneT TSO B.V., which I performed primarily in the period between 2011 and 2015. In spring 2011, when I was writing my Master’s thesis on the topic of congestion management in the electricity sector at TenneT, dr. ir. Laurens de Vries, who was supervising my Master thesis project at the time, suggested the possibility to continue my research after graduation by writing a PhD thesis. This idea was also well received at TenneT by dr. Gert van der Lee, who was my team leader and supervisor of my Master thesis project on behalf of TenneT.

After graduating in May 2011 and enjoying a long summer vacation in the three months thereafter, I started my PhD thesis project in September 2011 with financing from TenneT. The cooperation between TenneT and TU Delft allowed me to expe-rience both the academic world as well as the industry from within while writing this thesis. Working in both places at the same time was very interesting and being part of the long-term grid planning team was a valuable experience that helped me develop the simulation model and write parts of the thesis itself, not in the least because it got me involved with the TYNDP work that TenneT participates in at ENTSO-E, which is closely related to the topic of this thesis. The main part of the work during the project was the construction of a simulation model, which is discussed in Chapter 3. The scope of work sort of exploded during model con-struction. This will be extensively discussed in the thesis, but put shortly: trying to include too many aspects of the electricity system in too much detail causes a great degree of model complexity, a lot of work to be done on the input side, and difficulty fine-tuning the outputs, leading to unpredictable results... Well, in the end, this meant countless hours of bug-fixing, calibrating, and validating – all of which are performed in an iterative and repetitive manner, of course, because the theory of linear model construction (conceptualization—specification—verification & validation—use) never applies to model building in reality. Of course!

I want to express my deepest gratitude to prof. dr. ir. Margot Weijnen, dr. ir. Laurens de Vries, and dr. Gert van der Lee for their support in the past six years. In particular, I am grateful for their everlasting confidence that I would one day be able to overcome all the modeling chaos and produce useful simulation runs that would form the basis for finishing this thesis. I also want to thank all my former and present colleagues at Delft University of Technology and at TenneT, in particular those at the Energy & Industry section and the Long-term Grid Planning team, for their neverending (content-wise and mental) support as well as for always doing their best to turn business hours into fun hours whenever possible. I will remember the Friday-afternoon beers at Beestenmarkt, driving go-karts in the pouring rain, all the Christmas, Sinterklaas, Thanksgiving, birthday and PhD parties, motorbike tours, section dinners, and probably many other things that were a great deal of

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unforgettable fun and which I should’ve remembered to mention. Last but not least, I want to thank my friends and family for their unabated (may I say – relentless) interest and inquiry into my planned date of defense. In particular I want to thank my parents, who have always supported me and whose general help during the final stages of this work has contributed to finishing this thesis more than they would take credit for, and Angelina, who was under the impression that I already held a doc-torate when we met (and was shocked to learn that I did not), for always believing in me and for putting up with me during the last steps of my research. Thank you all!

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2.5.3 Regulatory gap . . . 52 2.5.4 Compensation mechanisms. . . 54 2.5.5 Knowledge gap . . . 55 3 Model specification 57 3.1 Modeling objective . . . 57 3.2 Modeling method. . . 57 3.3 System conceptualization. . . 58 3.3.1 Input data. . . 59 3.3.2 Planning cases . . . 61 3.3.3 Bidding behavior . . . 62 3.3.4 System boundaries . . . 62

3.3.5 Nodes and zones . . . 63

3.3.6 Neighboring areas. . . 63

3.3.7 Generators, consumers, and the market. . . 64

3.3.8 Network. . . 65

3.3.9 Regulation . . . 65

3.4 Evaluating network investment options . . . 66

3.4.1 Cross-continental project clusters . . . 66

3.4.2 Investment evaluation perspectives . . . 67

3.4.3 Agents’ objective function for network investment . . . 69

3.4.4 Calculating project benefits . . . 71

3.4.5 Calculating project cost . . . 72

3.4.6 Forecasting . . . 73

3.5 Representation of operational processes. . . 74

3.5.1 NTC calculation . . . 76

3.5.2 Market clearing. . . 76

3.5.3 Network calculations . . . 79

3.5.4 Redispatch . . . 80

3.6 Representation of network development. . . 80

3.6.1 Project identification. . . 82

3.6.2 Project assessment . . . 85

3.6.3 Project re-evaluation. . . 88

3.7 Calibration of parameters . . . 88

4 Verification and validation 91 4.1 Objective of verification and validation . . . 91

4.2 Verification . . . 92

4.2.1 Verification of model components . . . 92

4.2.2 Expert verification: general model behavior. . . 94

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Contents ix

4.3 Validation. . . 95

4.3.1 Literature validation . . . 97

4.3.2 Extreme conditions and sensitivity analysis. . . 97

4.3.3 Expert validation. . . 103

4.3.4 The model as a solver . . . 103

4.3.5 Model replication. . . 104

4.3.6 Conclusion . . . 104

5 Model experiments 105 5.1 Experiment design and setup . . . 105

5.1.1 Investment identification algorithms. . . 106

5.1.2 Investment evaluation approaches. . . 106

5.1.3 Scenarios . . . 107

5.1.4 Hardware . . . 110

5.1.5 Gigawatt-kilometers . . . 111

5.2 Model results . . . 111

5.2.1 Renewable transition everywhere . . . 113

5.2.2 Limited investment in renewables . . . 116

5.2.3 Realistic renewables expansion. . . 118

5.2.4 Renewable transition somewhere. . . 120

5.2.5 Renewables expansion somewhere. . . 126

5.2.6 Renewable transition with storage and demand-side response . 129 5.2.7 Gas disruption . . . 132

5.2.8 High carbon prices in a (s)low-RES system. . . 134

5.2.9 Energy Roadmap 2050. . . 136

5.2.10 Differing storage potentials. . . 138

5.3 Discussion. . . 141

5.3.1 Investment volumes are mostly scenario-driven. . . 142

5.3.2 Investment identification perspectives. . . 145

5.3.3 Investment evaluation perspectives . . . 145

5.3.4 Overall system cost differences are small . . . 146

5.3.5 Internal network reinforcements in Germany . . . 149

5.3.6 Storage and demand-side response. . . 149

5.4 Robustness of results. . . 150

6 Simulating network investment decisions over time 151 6.1 Results of this study . . . 151

6.1.1 Modeling investment decisions. . . 151

6.1.2 Agent-based modeling framework . . . 152

6.1.3 Reading guide. . . 152

6.2 Long-term evolution of the grid . . . 153

6.3 Inducing adequate network investment . . . 154

6.3.1 Identification of power transfer corridors . . . 154

6.3.2 Evaluation perspective has a minor effect on SEW. . . 156

6.3.3 Financial compensation mechanisms. . . 158

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6.4 Reflection on the modeling approach . . . 161

6.4.1 Agent-based simulation. . . 162

6.4.2 Representation of the electricity system. . . 163

6.4.3 Identifying transmission investment projects . . . 165

6.4.4 Network investment decisions . . . 167

6.4.5 Project evaluation: interactions in project benefits. . . 169

6.4.6 Geographic delineation. . . 171

6.4.7 Interaction with generation investment . . . 172

6.4.8 Lost load and generation investment . . . 173

6.4.9 Comparison with TYNDP . . . 174

6.4.10 Conclusion on the modeling approach. . . 175

7 Conclusion and recommendations 177 7.1 Conclusion . . . 177

7.1.1 Geographic scope of the cost-benefit assessment for network investments. . . 177

7.1.2 Regulatory gap . . . 178

7.1.3 Hypotheses . . . 179

7.2 Policy recommendations . . . 180

7.2.1 Uncertainty. . . 181

7.2.2 Network capacity is presently lagging behind. . . 181

7.2.3 Iterative TOOT. . . 182

7.2.4 Beyond redistributing economic welfare. . . 182

7.3 Reflection and future work. . . 183

7.3.1 Reflection on the modeling approach . . . 184

7.3.2 Reflection on the analysis . . . 187

7.3.3 Future work. . . 188 Bibliography 195 Glossary 209 Acronyms 213 Summary 215 Samenvatting 221 Curriculum Vitæ 227 Appendices 229 A Model specification 231 A.1 Physical network . . . 231

A.2 Renewables availability factors. . . 233

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Contents xi

B Verification test cases 239

B.1 Load agent sets. . . 239

B.2 Load scenario data . . . 240

B.3 Verification of the load-flow module. . . 240

B.4 Verification of the redispatch module . . . 246

B.5 Verification of the investment module. . . 247

B.5.1 2 nodes, single link. . . 248

B.5.2 2 nodes, single link: import capacity to lower dispatch costs. . 251

B.5.3 3 nodes: cross-continental capacity . . . 254

B.5.4 4 nodes: cross-continental capacity . . . 256

B.5.5 4 nodes: internal line and loop flows across. . . 261

B.5.6 Two planning cases, 2 nodes. . . 265

B.5.7 Prediction by extrapolation . . . 267

B.5.8 Competing investments. . . 269

C Validation tests 275 C.1 Extreme conditions and sensitivity analysis. . . 275

C.1.1 Invest for present need, no time lag in investment . . . 275

C.1.2 Value of lost load (VOLL) . . . 277

C.1.3 Economic lifetime. . . 278

C.1.4 ITC fund size. . . 283

C.1.5 Construction costs . . . 290

C.1.6 Project realization time . . . 297

C.1.7 Opportunity cost of hydro generators . . . 297

C.1.8 NTC. . . 302

C.1.9 NTC iterations . . . 304

C.2 The model as a solver . . . 304

D Scenarios 313 D.1 Renewable transition everywhere . . . 314

D.2 Limited investment in renewables . . . 316

D.3 Realistic renewables expansion. . . 318

D.4 Renewable transition somewhere. . . 320

D.5 Renewables expansion somewhere. . . 322

D.6 Renewable transition with storage and extreme demand-side response 324 D.7 Gas disruption . . . 326

D.8 High carbon prices in a (s)low-RES system. . . 328

D.9 Energy Roadmap 2050. . . 330

D.10Differing storage potentials. . . 332

D.11Base case 2015 (all scenarios) . . . 334

E Model results – graphs 335 E.1 Renewable transition everywhere . . . 335

E.2 Limited investment in renewables . . . 337

E.3 Realistic Renewables Expansion. . . 338

E.4 Renewable transition somewhere. . . 340

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E.6 Renewable transition with storage and extreme demand-side response 343

E.7 Gas disruption . . . 344

E.8 High carbon prices in a (s)low-RES system. . . 346

E.9 Energy Roadmap 2050. . . 347

E.10 Differing storage potentials. . . 349

F Model results – maps 351 F.1 Renewable transition everywhere . . . 351

F.2 Limited investment in renewables . . . 354

F.3 Realistic Renewables Expansion. . . 357

F.4 Renewable transition somewhere. . . 359

F.5 Renewables expansion somewhere. . . 362

F.6 Renewable transition with storage and extreme demand-side response 365 F.7 Gas disruption . . . 367

F.8 High carbon prices in a (s)low-RES system. . . 370

F.9 Energy Roadmap 2050. . . 373

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List of Figures

2.1 Synchronous AC grids in Europe . . . 31

2.2 Cut out from ENTSO-E grid map, 2013 . . . 32

2.3 ENTSO-E Regional Groups (System Development) . . . 35

3.1 Representation of the electricity system . . . 60

3.2 Flat supply curve assumed for neighboring areas . . . 64

3.3 Model structure. . . 75

3.4 Schematic overview of the iterative-TOOT process . . . 87

5.1 Two representations of a 500 MW network connecting A and C . . . 111

5.2 Development of total network size (Scenario 1: Renewable transition everywhere) . . . 115

5.3 Development of total cross-border capacity (Scenario 1: Renewable transition everywhere) . . . 115

5.4 Cumulative system costs (Scenario 1: Renewable transition everywhere)116 5.5 Development of total network size (Scenario 2: Limited investment in renewables). . . 117

5.6 Development of total cross-border capacity (Scenario 2: Limited in-vestment in renewables) . . . 118

5.7 Cumulative system costs (Scenario 2: Limited investment in renew-ables) . . . 118

5.8 Development of total network size (Scenario 3: Realistic renewables expansion). . . 119

5.9 Development of total cross-border capacity (Scenario 3: Realistic re-newables expansion) . . . 120

5.10 Cumulative system costs (Scenario 3: Realistic renewables expansion) 120 5.11 Development of total network size (Scenario 4: Renewable transition somewhere) . . . 121

5.12 Development of total cross-border capacity (Scenario 4: Renewable transition somewhere) . . . 122

5.13 Cumulative system costs (Scenario 4: Renewable transition somewhere)123 5.14 Network development (Scenario 4: RTS, cross-continental, system wide)123 5.15 Development of total network size (Scenario 5: Renewables expansion somewhere) . . . 127

5.16 Development of total cross-border capacity (Scenario 5: Renewables expansion somewhere) . . . 128

5.17 Cumulative system costs (Scenario 5: Renewables expansion some-where) . . . 128

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5.18 Development of total network size (Scenario 6: Renewable transition

with storage and demand-side response) . . . 130

5.19 Development of total cross-border capacity (Scenario 6: Renewable transition with storage and demand-side response) . . . 130

5.20 Cumulative system costs (Scenario 6: Renewable transition with stor-age and demand-side response) . . . 131

5.21 Annual dispatch and lost load costs (Scenario 6: Renewable transition with storage and demand-side response) . . . 131

5.22 Development of total network size (Scenario 7: Gas disruption) . . . 133

5.23 Development of total cross-border capacity (Scenario 7: Gas disruption)133 5.24 Cumulative system costs (Scenario 7: Gas disruption) . . . 134

5.25 Development of total network size (Scenario 8) . . . 135

5.26 Development of total cross-border capacity (Scenario 8: High carbon prices in a (s)low-RES system) . . . 135

5.27 Cumulative system costs (Scenario 8: High carbon prices in a (s)low-RES system) . . . 136

5.28 Development of total network size (Scenario 9: Energy Roadmap 2050)137 5.29 Development of total cross-border capacity (Scenario 9: Energy Roadmap 2050). . . 137

5.30 Cumulative system costs (Scenario 9: Energy Roadmap 2050) . . . . 138

5.31 Development of total network size (Scenario 10: Differing storage potentials). . . 139

5.32 Development of total cross-border capacity (Scenario 10: Differing storage potentials) . . . 140

5.33 Cumulative system costs (Scenario 10: Differing storage potentials) . 140 5.34 Annual dispatch and lost load costs (Scenario 10: Differing storage potentials). . . 141

5.35 Network development (Renewable transition everywhere, cross-border, system wide) . . . 143

5.36 Network development (Renewable transition somewhere, cross-border, system wide) . . . 143

5.37 Average market price development under Gas disruption . . . 148

5.38 Network development (Gas disruption, cross-continental, national ver-sus system-wide) . . . 148

B.1 Linear 2-node: Test case setup . . . 241

B.2 Triangular 3-node: Test case setup . . . 241

B.3 Meshed 4-node: Test case setup . . . 242

B.4 Linear 2-node: Observed flows. . . 243

B.5 Triangular 3-node: Observed flows . . . 244

B.6 Meshed 4-node: Observed flows . . . 245

B.7 2 nodes, single link: System costs and capacity development . . . 250

B.8 2 nodes, single link: Lost load and congestion rents . . . 251

B.9 2 nodes, single link: import capacity to lower dispatch costs: Network development. . . 253

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List of Figures xv

B.10 2 nodes, single link: import capacity to lower dispatch costs: Total

system costs . . . 253

B.11 3 nodes: cross-continental capacity: System costs and capacity de-velopment . . . 255

B.12 4 nodes: cross-continental capacity: System costs and capacity de-velopment . . . 258

B.13 4 nodes: cross-continental capacity: Net ITC revenues . . . 260

B.14 4 nodes: internal line and loop flows across: Market prices . . . 262

B.15 4 nodes: internal line and loop flows across: Network capacity devel-opment . . . 263

B.16 4 nodes: internal line and loop flows across: System costs . . . 264

B.17 Two planning cases, two nodes: Network capacity development . . . 267

B.18 Two planning cases two nodes: Market prices . . . 268

B.19 Prediction by extrapolation: Network development and market prices 269 B.20 Competing investments: Network development and market price de-velopment . . . 271

C.1 Invest for present need, no time lag in investment: Network development276 C.2 Invest for present need, no time lag in investment: System costs. . . 276

C.3 Invest for present need, no time lag in investment: System costs. . . 277

C.4 Extreme conditions analysis, VOLL: Network development. . . 279

C.5 Extreme conditions analysis, VOLL: System costs. . . 280

C.6 Sensitivity analysis, VOLL: Network development. . . 281

C.7 Sensitivity analysis, VOLL: System costs. . . 282

C.8 Extreme conditions analysis, economic lifetime: Network development284 C.9 Extreme conditions analysis, economic lifetime: Network development285 C.10 Sensitivity analysis, ITC: (Net) ITC payments per price zone . . . . 286

C.11 Sensitivity analysis, ITC: Network development . . . 287

C.12 Sensitivity analysis, ITC: Network investment . . . 288

C.13 Sensitivity analysis, ITC: System costs . . . 289

C.14 Extreme conditions analysis, cost: Network development . . . 291

C.15 Extreme conditions analysis, cost: System costs . . . 292

C.16 Sensitivity analysis, cost: Network development . . . 293

C.17 Sensitivity analysis, cost: System costs . . . 294

C.18 Sensitivity analysis, discount rate: Network development. . . 295

C.19 Sensitivity analysis, discount rate: System costs. . . 296

C.20 Extreme conditions analysis, construction time: Network development298 C.21 Extreme conditions analysis, construction time: System costs . . . . 299

C.22 Sensitivity analysis, hydro variable cost: Network development . . . 300

C.23 Sensitivity analysis, hydro variable cost: System costs . . . 301

C.24 Sensitivity analysis, ATC horizon: Network development . . . 302

C.25 Sensitivity analysis, ATC horizon: Average market prices . . . 303

C.26 Sensitivity analysis, ATC horizon: System costs . . . 303

C.27 Sensitivity analysis, NTC iterations: Network development . . . 305

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C.29 Sensitivity analysis, NTC iterations: Average market price . . . 307

C.30 Sensitivity analysis, NTC iterations: NTC as proportion of physical

capacity . . . 308

C.31 Solver: Network development . . . 310

C.32 Solver: System costs . . . 311

E.1 Annual total dispatch cost (Renewable transition everywhere) . . . . 335

E.2 Annual network investment costs (cumulative) (Renewable transition

everywhere) . . . 336

E.3 Network development (Renewable transition everywhere) . . . 336

E.4 Annual total dispatch cost (Limited investment in renewables) . . . 337

E.5 Annual network investment costs (cumulative) (Limited investment

in renewables). . . 337

E.6 Network development (Limited investment in renewables) . . . 338

E.7 Annual total dispatch cost (Realistic renewables expansion) . . . 338

E.8 Annual network investment costs (cumulative) (Realistic renewables

expansion). . . 339

E.9 Network development (Realistic renewables expansion) . . . 339

E.10 Annual total dispatch cost (Renewable transition somewhere) . . . . 340

somewhere) . . . 340

E.12 Network development (Renewable transition somewhere) . . . 341

E.13 Annual total dispatch cost (Renewables expansion somewhere) . . . 341

E.14 Annual network investment costs (cumulative) (Renewables

expan-sion somewhere) . . . 342

E.15 Network development (Renewables expansion somewhere) . . . 342

E.16 Annual total dispatch cost (Renewable transition with storage and

demand-side response) . . . 343

with storage and demand-side response) . . . 343

E.18 Network development (Renewable transition with storage and

ex-treme demand-side response) . . . 344

E.19 Annual total dispatch cost (Gas disruption) . . . 344

E.20 Annual network investment costs (cumulative) (Gas disruption) . . . 345

E.21 Network development (Gas disruption) . . . 345

E.22 Annual total dispatch cost (High carbon prices in a (s)low-RES system)346

E.23 Annual network investment costs (cumulative) (High carbon prices

in a (s)low-RES system) . . . 346

E.24 Network development (High carbon prices in a (s)low-RES system) . 347

E.25 Annual total dispatch cost (Energy Roadmap 2050) . . . 347

E.26 Annual network investment costs (cumulative) (Energy Roadmap 2050)348

E.27 Network development (Energy Roadmap 2050) . . . 348

E.28 Annual total dispatch cost (Differing storage potentials) . . . 349

E.29 Annual network investment costs (cumulative) (Differing storage

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List of Figures xvii

E.30 Network development (Differing storage potentials) . . . 350

F.1 Network development (Renewable transition everywhere, cross-border,

national). . . 351

national with ITC) . . . 352

promoters) . . . 352

system wide) . . . 352

F.5 Network development (Renewable transition everywhere, cross-continental,

national). . . 353

F.9 Network development (Limited investment in renewables, cross-border,

national). . . 354

F.13 Network development (Limited investment in renewables, cross-continental,

national). . . 355

F.17 Network development (Realistic Renewables Expansion, cross-border,

national). . . 357

F.21 Network development (Realistic Renewables Expansion, cross-continental,

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F.25 Network development (Renewable transition somewhere, cross-border,

national). . . 359

F.29 Network development (Renewable transition somewhere, cross-continental,

national). . . 361

F.33 Network development (Renewables expansion somewhere, cross-border,

national). . . 362

F.37 Network development (Renewables expansion somewhere, cross-continental,

national). . . 363

F.41 Network development (Renewable transition with storage and

ex-treme demand-side response, cross-border, national) . . . 365

ex-treme demand-side response, cross-border, national with ITC). . . . 365

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List of Figures xix

ex-treme demand-side response, cross-border, system wide) . . . 366

ex-treme demand-side response, cross-continental, national) . . . 366

ex-treme demand-side response, cross-continental, national with ITC) . 366

ex-treme demand-side response, cross-continental, promoters) . . . 367

ex-treme demand-side response, cross-continental, system wide) . . . . 367

F.49 Network development (Gas disruption, cross-border, national) . . . . 367

F.50 Network development (Gas disruption, cross-border, national with ITC)368

F.51 Network development (Gas disruption, cross-border, promoters). . . 368

F.52 Network development (Gas disruption, cross-border, system wide) . 368

F.53 Network development (Gas disruption, cross-continental, national) . 369

F.54 Network development (Gas disruption, cross-continental, national with

ITC) . . . 369

F.55 Network development (Gas disruption, cross-continental, promoters) 369

F.56 Network development (Gas disruption, cross-continental, system wide)370

F.57 Network development (High carbon prices in a (s)low-RES system,

cross-border, national) . . . 370

cross-border, national with ITC) . . . 370

cross-border, promoters) . . . 371

cross-border, system wide) . . . 371

cross-continental, national) . . . 371

cross-continental, national with ITC) . . . 372

cross-continental, promoters) . . . 372

cross-continental, system wide) . . . 372

F.65 Network development (Energy Roadmap 2050, cross-border, national)373

F.66 Network development (Energy Roadmap 2050, cross-border, national

with ITC) . . . 373

F.67 Network development (Energy Roadmap 2050, cross-border, promoters)373

F.68 Network development (Energy Roadmap 2050, cross-border, system

wide). . . 374

F.69 Network development (Energy Roadmap 2050, cross-continental,

na-tional) . . . 374

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pro-moters) . . . 375

sys-tem wide) . . . 375

F.73 Network development (Differing storage potentials, cross-border,

na-tional) . . . 375

na-tional with ITC) . . . 376

pro-moters) . . . 376

sys-tem wide) . . . 376

F.77 Network development (Differing storage potentials, cross-continental,

national). . . 377

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List of Tables

2.1 Submarine high-voltage direct current (HVDC) links in Europe . . . 33

3.1 Required model input data . . . 60

5.1 Overview of scenarios and their scenario space parameters . . . 109

5.2 Dispatch and network investment costs in 2030, by scenario . . . 112

5.3 Dispatch and network investment costs in 2050, by scenario . . . 113

5.4 Development of cross-border capacities under Renewable transition

somewhere (in GW) . . . 123

5.5 Development of specific internal north-south routes under Renewable

transition somewhere (in GW) . . . 124

5.6 Differences in annual dispatch cost and cumulative investment cost,

by scenario . . . 147

6.1 Relevant extensions in model parameters vs practical simplifications 163

6.2 Interconnection capacities in 2030, model versus TYNDP . . . 175

A.1 Nodes represented in the model . . . 231

A.2 Initial link properties . . . 232

A.3 Solar availability factors . . . 233

A.4 Offshore wind availability factors . . . 234

A.5 Onshore wind availability factors . . . 235

B.1 2 nodes, single link: import capacity to lower dispatch costs: Initial

characteristics . . . 252

B.2 4 nodes: cross-continental capacity: Power flows, MCP, traded

vol-ume (national and national with ITC ) . . . 259

B.3 4 nodes: cross-continental capacity: Power flows, MCP, traded

vol-ume (promoters and system wide). . . 260

D.1 Installed generation capacities | Scenario 1: Renewable transition

ev-erywhere, 2030 (in MW) . . . 314

ev-erywhere, 2050 (in MW) . . . 315

D.3 Installed generation capacities | Scenario 2: Limited investment in

renewables, 2030 (in MW) . . . 316

D.4 Installed generation capacities | Scenario 2: Limited investment in

renewables, 2050 (in MW) . . . 317

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D.5 Installed generation capacities | Scenario 3: Realistic renewables

ex-pansion, 2030 (in MW) . . . 318

D.6 Installed generation capacities | Scenario 3: Realistic renewables

ex-pansion, 2050 (in MW) . . . 319

somewhere, 2030 (in MW) . . . 320

somewhere, 2050 (in MW) . . . 321

D.9 Installed generation capacities | Scenario 5: Renewables expansion

somewhere, 2030 (in MW) . . . 322

D.10 Installed generation capacities | Scenario 5: Renewables expansion

somewhere, 2050 (in MW) . . . 323

with storage and extreme demand-side response, 2030 (in MW) . . 324

with storage and extreme demand-side response, 2050 (in MW) . . 325

D.13 Installed generation capacities | Scenario 7: Gas disruption, 2030 (in

MW) . . . 326

D.14 Installed generation capacities | Scenario 7: Gas disruption, 2050 (in

MW) . . . 327

D.15 Installed generation capacities | Scenario 8: High carbon prices in a

(s)low-RES system, 2030 (in MW) . . . 328

D.16 Installed generation capacities | Scenario 8: High carbon prices in a

(s)low-RES system, 2050 (in MW) . . . 329

D.17 Installed generation capacities | Scenario 9: Energy Roadmap 2050,

2030 (in MW) . . . 330

D.18 Installed generation capacities | Scenario 9: Energy Roadmap 2050,

2050 (in MW) . . . 331

D.19 Installed generation capacities | Scenario 10: Differing storage

poten-tials, 2030 (in MW) . . . 332

D.20 Installed generation capacities | Scenario 10: Differing storage

poten-tials, 2050 (in MW) . . . 333

D.21 Installed generation capacities | Base case (all scenarios), 2015 (in

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1

Introduction

1.1. _{Transmission planning in a pan-European}

con-text

After decades of relatively straightforward development, the European electricity sector is currently undergoing a transition that fundamentally changes its structure and functioning. The efforts to switch from conventional energy sources (such as coal, gas, and nuclear) to renewable alternatives encompasses changes in many el-ements of the electricity system. Particularly, wind and solar power are beginning

to play a major role in the European energy system (Hirth, 2013; Monforti et al.,

2014).1 Wind and solar are non-controllable resources, which means that their

avail-ability fluctuates over time and across regions. Contrary to conventional sources, their power output does not vary according to demand patterns. This means that mismatches between generation and load will frequently occur, in different regions

and at different times, in a future, renewable electricity system (Rodriguez et al.,

2014).

This chapter will argue that the development of transmission grid infrastructure is a key driver for the success of a transition to renewable energy sources.

Integrat-ing large volumes ofvariable renewable energy sources (VRES)in a system that was

originally designed for predictable loads and dispatchable generation is not

straight-forward (Schleicher-Tappeser,2012;Rodriguez et al.,2014). It affects the

function-ing of electricity markets, the requirements and provision of ancillary services, and the flows in, as well as the use of, transmission and distribution grids. Security of supply increasingly becomes a trans-national matter if electricity is available in different locations at different times. However, transmission grid development is

still predominantly a national matter, with nationaltransmission system operators

(TSOs)andnational regulatory authorities (NRAs)focusing primarily on domestic

1 _{According to}_Hirth₍₂₀₁₃_{), hydro power and biomass are already used to their fullest potential,}

andSteinke et al.(2013) argue that biomass can contribute to about 10% of the annual European

electricity demand at most.

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needs and interests. The remainder of this section will introduce the (recent) history of the European electricity system, roughly from the 1990s onwards, and elaborate on the challenges that transmission planners observe in the context of the transition to a renewable energy supply.

1.1.1. _{Liberalization of the European electricity sector}

Historically, the electricity industry in Europe was mostly organized according to national and regional administrative boundaries, with vertically integrated under-takings (VIUs) that were responsible for electricity generation and transmission in a designated area. With the advent of liberalization, which took place in most

Eu-ropean countries around the turn of the century2 _{(see, e.g.,} _Serrallés_, ₂₀₀₆_{), the}

transmission and distribution tasks of these VIUs were unbundled from

genera-tion, trade, and supply in the Member States of the European Union (EU)in line

with the requirements posed by Directive 96/92/EC (later repealed by Directives 2003/54/EC and 2009/72/EC). (In the Netherlands, for instance, the publication of this Directive triggered the implementation of the Electricity Law 1998.) Rather than planning generation capacity and transmission networks in an integrated

man-ner, the newly formed independentTSOswere now presented with the task to plan

their networks ahead for the future, but they were no longer able to determine the dispatch of power plants or decide on the type and location of newly installed units. European legislation has gradually opened up European electricity markets for competition, driven by a series of Regulations and Directives that were passed in 1996, 2003, and 2009, which required deregulation and stood at the basis of

cre-ating a single market for energy in the EU (Makkonen et al.,2012). The concept

of the liberalized European electricity system is based on the notion that the com-petitive elements of the electricity supply system (generation, trade, retail) must be separated from the monopolistic elements (grids). Market parties (generators

and consumers) should be able to freely trade electricity as a commodity (Karova,

2011) and operate within the realm of three freedoms: the freedom of connection,

transaction, and dispatch. Network operators are responsible for facilitating market parties to exercise these freedoms. The fact that electricity is not an average com-modity but rather a peculiar combination of a good and a service is reflected in the responsibilities of these network operators. Network operators are responsible for maintaining three primary control targets in the operation of the electricity system (capacity, frequency, and voltage) for the electricity system to function in a safe and reliable manner. Market parties pay for the system services that network operators deliver through network tariffs, but they also provide supporting ancillary services (e.g., balancing power) that are purchased by network operators to perform these system services that enable them to achieve their control targets.

For several decades, the production of electricity was the domain of large-scale power plants. The technologies used were rather stable, with gradual increases in efficiency being the main change in the sector. Consumption also followed a largely stable pattern, with predictable variations occurring on a seasonal and a daily

ba-2_{Restructuring of the Dutch electricity sector began in 1998, with the liberalization of the wholesale}

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1.1. Transmission planning in a pan-European context 3

sis. As a result of this, power flows could be anticipated on beforehand with relative ease. Generation and network expansion planning was a rather straightforward task, because electricity consumption grew by a few percent annually, roughly in line with economic growth. Network operators could rely on deterministic planning models

(van der Weijde and Hobbs,2012). This relative predictability has become a relic

of the past, however, as market parties now make individual, uncoordinated deci-sions with regard to investment and dispatch. These developments are accelerated particularly by the transition to a sustainable energy supply, which imposes further complexity on the system.

1.1.2. Transition to a renewable energy supply

Shortly after restructuring the electricity sector –roughly after a decade– the fun-damentals of the energy landscape started to change again. The societal ambition to become (more) sustainable, and the start of what might potentially lead to a

complete overhaul of the energy supply by a transition torenewable energy source

(RES), are radically changing the way in which electricity is generated and

con-sumed. In the past years, Europe has seen a true paradigm change in its electricity system. Between 2005 and 2015, the volume of installed wind and solar capacity

in the European Union increased from 41 to 142 GW and from 2 GW to 95 GW,

respectively. All over the continent, wind and solar capacities are increasing. Sev-eral countries, such as Germany with its famous Energiewende, decided to phase out nuclear power entirely and replace it with renewable energy sources (mainly wind and solar). Decentralized sources are likely to account for a substantial portion of total generation, an effect that can already be seen with, for instance, the pop-ularity of small-scale PV systems with individual households in several European countries. Generation no longer takes place solely in large-scale conventional power plants, but also by small-scale local generation, such as rooftop solar PV systems, and large-scale, centralized intermittent power generation, such as offshore wind farms.

Various different storage technologies, such as batteries, hydrogen, and of course ‘conventional’ pumped hydro, are likely play a role in our future energy system too, as may alternative forms of ‘storage’, such as shifting demand in time or to other forms of energy (e.g., having the ability to switch from natural gas to electricity, depending on system conditions). In which combinations and to what extent is unknown and may affect the timing of demand or generation in a positive or neg-ative way. Especially solar PV poses a fundamental challenge for electricity grids as a result of the installation of a large volume of decentralized generation capacity

(Schleicher-Tappeser, 2012). On sunny days, especially in times of low

(domes-tic) demand, network users that were traditionally consumers (such as households) now start feeding power into the grid. Although the individual capacities of such

prosumers are small, their aggregated contribution is potentially large.

Demand itself is also up for radical changes, both in terms of total consumption and the patterns of demand during a day and throughout the year. Several tech-nologies that allow for a (more) sustainable use of energy use electricity as an energy carrier, thus substituting electricity for fossil fuels. Examples of such technologies

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include heat pumps (replacing natural gas) and electric vehicles (replacing oil). De-mand levels and deDe-mand patterns in the future will likely not only be determined by the gradual change in penetration and efficiency of household appliances and industrial output, but may change completely in their nature because electricity is used at different moments in time for different purposes than today. Electrifi-cation of the energy supply could cause the absolute level of annual consumption to rise substantially. Furthermore, ‘smart’ technologies may make it possible (and more attractive) to shift demand in time. Not only are these technologies likely to cause an upward shift of total electricity consumption, they also affect the pat-terns that demand follows throughout the day or the year, for instance when they enable consumers to respond to current (supply) conditions, such as fluctuations

in generation from variable renewable energy sources. They affect the occurrence

of peak loads and, consequently, the corresponding transportation requirements for electricity grids. While changes in the demand pattern can be beneficial from the perspective of system efficiency (electricity consumption is shifted (partly) to mo-ments with abundant supply) they can also have adverse effects, e.g. for distribution grids which experience sudden peaks in flows when many consumers respond to low wholesale prices during periods of high wind and increase their demand.

One distinctive characteristic of the renewable energy sources that are dominant in Europe (wind and solar) that generally sets them apart from their conventional counterparts is the spatio-temporal intermittency of their power output, as a result of which mismatches between generation and load will occur in different regions

at different times (Rodriguez et al., 2014; Widén et al., 2015). Variable renewable

generation capacity must be realized at locations that are suitable from the perspec-tive of resource availability, which may not necessarily correspond to the location

of load, causing a need for increased power transmission in general (Knieps,2013;

ECF,2013). While alternative options exist, such as storage and shifting demand in

time, transmission capacity is a good option to deal with the geographic variation

in generation from renewable energy sources (Mills and Wiser, 2010; Roques et al.,

2010). Battaglini et al. (2009) argue that producing renewable energy at locations

with abundant supply (as opposed to producing it close to loads) has the potential to lower the cost of electricity, even when the cost of transporting power across large distances is included. Also, transmission capacity can reduce the curtailment of re-newable generation sources that takes place when surpluses cannot be used or stored

at a given moment. Bove et al.(2012) andSpiecker et al. (2013) showed that grid

extensions can indeed reduce the need for (conventional) backup generation in areas with a large share of stochastic wind and water penetration. A similar conclusion

is reached byBrancucci Martínez-Anido et al.(2012), who argue that the need for

back-up capacity can be reduced by investing in cross-border transmission capac-ity, although it cannot be completely eliminated in scenarios with large volumes of renewable generation capacity.

In order to cope with the geographical and temporal variations in intermittent wind and solar generation, there will thus be a need to transport large volumes of electricity over large distances across the continent depending on their availability at

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1.2. Research problem 5

transmission grid must accommodate larger power flows than today (ENTSO-E,

2014a). The current transmission grid is not capable of transporting these much

larger volumes across the continent, so additional investment is required (Battaglini

et al.,2012;Buijs et al.,2011;Schaber et al.,2012;Teusch et al.,2012;Steinke et al.,

2013;Scholz et al.,2014).

1.1.3. _{Functions of cross-border transmission capacity}

The role of cross-border electricity transmission in Europe continues to change. Originally, cross-border connections were realized as a means to improve security of

supply (Glachant and Ruester,2014). By connecting electricity systems, it became

possible to rely on neighboring systems in case of exceptional contingencies (function 1). Later, cross-border capacity also became a means to facilitate competition in a common European electricity market, by enabling power transports from lower price

to higher price areas (Teusch et al.,2012;Lynch et al.,2012). Further connecting the

European electricity markets can improve economic efficiency of power generation

as well as enhance competition, in addition to improving security of supply (Hobbs

et al.,2005;Creti et al.,2010;Zerrahn and Huppmann,2017) (function 2).

In recent years a third role of transmission capacity has become increasingly important, namely its capability to balance fluctuations in renewable energy pro-duction that occur across the continent. Transmission capacity makes it possible to transport surpluses of renewable generation that exist in one area at a certain time, and make use of these in other areas. This allows for a more efficient utilization of

variable renewable energy sources (function 3). However, it requires that electricity

transports across larger distances and across national borders are facilitated. Realizing the required cross-continental transmission capacities is not straight-forward. While European transmission system operators have increasingly worked together for many years, investment in electricity transmission grids is a

compli-cated matter (as Chapter 2 of this thesis will show). Borders are often congested

(e.g. Buijs et al., 2011; van Koten, 2012; Zerrahn and Huppmann, 2017) and

var-ious authors argue that more interconnection capacity should be built in Europe

(e.g.Meeus et al.,2006;Schaber et al.,2012;Brancucci Martínez-Anido et al.,2013;

Fürsch et al.,2013; Steinke et al., 2013; Huppmann and Egerer, 2015). Rodriguez

et al.(2014) show that in a system with 100% renewables penetration in all

coun-tries, transmission capacity in Europe should be between two and five times as large as today in order to make efficient use of the available renewable energy capacity

across the continent. Brancucci Martínez-Anido et al. (2012) argue that with the

currently planned expansions included, the European transmission grid is capable of dealing with required power transports under the best-estimate scenario of the

European Network of Transmission System Operators for Electricity (ENTSO-E)

(see Chapter2.3) (as used in TYNDP 2012) until 2025.

1.2. _{Research problem}

Various authors argue that the current national scope of network regulation poses

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2013; Zachmann, 2013). As a result of the historical structure of the electricity sector, electricity networks in Europe are still mainly organized along national boundaries, which now results in a need for more cross-border transmission

ca-pacity (Huppmann and Egerer, 2015). Most countries appointed a single

trans-mission sytem operator, which is responsible for the development and operation of the electricity grid at higher voltages, while multiple distribution grid operators are responsible for the lower-voltage distribution grids in different areas of a country. National governments and regulators are responsible for ensuring that investments by these grid companies are aligned with the (national) societal interest through proper legislative and regulatory measures. Hence, a discrepancy exists between the institutional setup of the transmission sector (which is national) and the tech-nical nature of the challenge with respect to integrating large volumes of renewable energy sources (which is supranational). This ‘fragmented’ institutional structure may prove problematic in the context of an electricity system that requires (and is) technically integrated in order to function (i.e., cope with the spatio-temporal

fluctuations in generation fromVRES).

Various authors argue that the current regulatory structure may hamper the

realization of the required transmission expansions (e.g., Supponen, 2011; Boltz,

2013; Zachmann, 2013). A risk exists that the development of the infrastructure

will not be able to accommodate (all) the large power flows that are created by intermittent sources throughout the continent, which could lead to curtailment of these intermittent sources and lead to economically sub-optimal dispatch of gen-eration. There appears to be a tension between the increasing volume of cross-continental power flows, which need to facilitated by transmission infrastructure, and the predominantly national organization of regulators, which have a large

in-fluence on investment incentives for TSOs. Supponen (2011) argued that national

and company interests hamper the realization of certain transmission investments that are in the pan-European interest. The message from literature seems to be that instead of deciding on network expansions from a purely national perspective, it is necessary to strengthen the European perspective in such decisions, in order to avoid national interests and objectives from hampering grid expansions that are in the overall interest albeit not in the interest of a certain stakeholder.

This thesis seeks to answer the following research question:

“Can the incentives for TSOs to develop the transmission grid according to soci-etal needs be improved by a regulatory perspective that remunerates investment on the basis of pan-European, as opposed to national, benefits?”

In this context, this thesis defines “regulatory perspective” as the geographic scope

that is considered during the process of identifying or assessing transmission expan-sion projects, that may or may not be beneficial to pursue by the perspective of a country (or otherwise delineated area). “Societal needs” are defined in the context of this thesis as the objective of maximizing social welfare by striking the right balance between the costs and benefits of transmission expansions. The concept of ‘social welfare’ is broad and not necessarily limited to monetary effects. For example, in a

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1.3. Research approach 7

broad sense, it also includes the “cost” related to the (perceived) effects of landscape changes due to building a transmission line. In the context of this thesis, however, the concept of social welfare is treated more narrowly. Only changes to economic

surpluses of generators, consumers, and TSOs are considered in the modeling

ap-proach that is developed and these are assumed to represent ‘social welfare’ for the sake of simplicity. Hence, this thesis considers changes to socio-economic welfare as a result of transmission projects, defined as changes in, on the benefit side, the sum of total variable generation costs and, if applicable, a monetized cost of lost load, and, on the cost side, the monetary cost of transmission expansions (investments in infrastructure). If the net effect of these cost and benefit factors is positive, an in-vestment is considered to increase social welfare, and if it is negative, an inin-vestment is considered to decrease social welfare.

In Chapter2.5three hypotheses are defined that further elaborate on the main

research question and set forth a case that can be tested using a simulation model.

1.3. _{Research approach}

The research problem at hand concerns the matter of how the geographic scope of cost-benefit evaluation that lie at the basis of zonal network planners’ investment decisions, and measures to provide compensation in order to adjust the outcomes of their decisions, affect investments in the European transmission grid, and, in turn, how this affects (pan-European and national) social welfare in the long term. The objective is to find out how these investment decisions affect investment decisions, and how these decisions can be influenced, in order to maximize pan-European socio-economic welfare by developing transmission grids in a manner that is consistent with societal needs. It is not straightforward to envision what kind of network will develop over time, as a diversity of actors make individual decisions that, over

time, affect the electricity infrastructure (Bollinger, 2014). Their decisions are,

furthermore, influenced by decisions in previous time steps.

1.3.1. _{Game-theoretic approaches to network investment}

Transmission expansions shift socio-economic welfare between generators, consumers,

and TSOs. Finding the optimal set of network expansions and sharing the gains

of, or efficiently allocating costs to parties that benefit from, these network

expan-sions, has been a topic of research for various decades. Gately (1974) proposes a

game-theoretic framework for sharing the gains from regional cooperation in net-work investment planning, using the Shapley value to find an acceptable set of core

imputations for all cooperating regions. Contreras et al.(1998) developed a

multi-agent system to simulate the formation of coalitions and distribution of costs and pay-offs in a six-bus transmission grid, subject to the constraints that agents’ (gener-ators, consumers, or independent system operators) pay-off or cost always improves

with the next step in the algorithm. Liu and Hobbs(2013) simulate how

transmis-sion constraints can be exploited by tacit collusive strategic behavior of generation firms in a deregulated electricity market. Their model, which is based on work by

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repeated interactions between firms in order to analyze the collusive game problem, differs from other equilibrium models used in game theory by not merely considering the static interactions between firms in a one-stage game, which, as they argue, is the case with most existing models. They acknowledge the dynamic interactions that take place when market participants interact with each other repeatedly, as is the case in reality where market participants interact on the electricity market on

a daily basis. Zhou et al.(2013) model negotiation between aVRESgenerator and

a transmission company, to find agreement on how to allocate costs for a new line that connects a wind farm to the grid. They propose a negotiation methodology based on Nash bargaining to share the uncertainties and market risks between these parties to analyse the effectiveness of renewable energy subsidies in the context of uncertainty and market risks.

Yen et al. (2000) and Mepokee et al. (2004) discuss that the deregulation of

the electricity system has made it necessary to develop multi-agent approaches to properly model the formation of coalitions and the allocation of costs or benefits,

because the system of central planning no longer represents reality. Yen et al.(2000)

apply a multi-agent approach to analyze cooperation between different agents, with their own interests, with regard to coalition-forming and cost allocation that takes

place in such cooperation. Mepokee et al.(2004) consider the transmission

expan-sion problem as a cooperation game, and use an agent-based modeling approach to allocate costs of transmission expansion to cooperating generators and loads in a five-node system. Their method allows for cost allocation on the basis of benefits

from a transmission expansion. Tohidi and Hesamzadeh (2014) develop a model

for non-cooperative transmission planning in which various regional transmission planners minimize their cost, which they compare to a cooperative solution. They conclude that the lack of proper compensation under a non-cooperative planning environment leads to an inefficient solution. Their model neglects cost allocation issues on inter-zonal lines, but it is unclear whether both planners must agree to an expansion between their zones or that a single zonal planner can extend the capacity to a neighboring area.

1.3.2. _{Agent-based modeling}

The electricity system in Europe has undergone rapid changes in the past two decades (going from fossil, national electricity monopolies, to a single competitive,

European energy market with a large penetration of VRES) and is structurally

different from any moment in history. We cannot do experiments with different regulatory parameters on the actual North West European electricity system, and as a result we have no real-world measurements available. For this reason, in

ad-dition to the more traad-ditional methods of induction and deduction,Axelrod(2006)

considers simulation as “a third way of doing science”. While similar to induction, simulation differs from it in the sense that it is not based on data from real-world measurements, but instead from the observations that are the result of interactions that take place according to a set of specified rules.

This thesis seeks to investigate the long-term effects on the North West European electricity system if zonal transmission planners seek to maximize zonal (in this case:

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1.3. Research approach 9

national) welfare by investing in (internal and cross-border) transmission capacity

that is expected to increase this national welfare. Macal(2005) argues that humans

are constrained by linear thinking and cannot understand all the interactions that take place simultaneously. Simulation provides a means to deal with this complexity that cannot be grasped by human thinking. With the help of a simulation model it becomes possible to determine what the effect of different approaches with regard to

the valuation of network investments (termed regulatory perspectives by this thesis

and further discussed in section3.4.2) is on network development and, consequently,

the effect on socio-economic welfare – both for the system as a whole as well as the distributional effects on individual countries.

In order to analyze the effects of possible choices regarding these regulatory pa-rameters and answer the research question an agent-based model was developed that is capable of simulating network investment in the European transmission grid under different of such mechanisms. The model endogenously simulates investment in the transmission grid over time as a result of repetitive investment decisions of

non-cooperative zonal planners (represented throughTSOsagents), which are based

on cost-benefit evaluations that consider only the benefits of a geographically delin-eated area, in order to represent national-strategic interests. Various model runs are simulated to constrast the effects of national interests to a hypothetical situation in which transmission planners act purely in the system-wide (pan-European) interst.

These different approaches are referred to asregulatory perspectives.

The choice foragent-based modeling (ABM)is related to the presence of

invid-ual welfare-maximizing TSOsin Europe, each of which has jurisdiction only for a

geographically delineated area (country), but which affects and is affected by

deci-sions of otherTSOsdue to the interconnected, meshed European transmission grid.

These TSOsdecide on network investments which affect socio-economic welfare in

their own and other areas. These decisions are repeatedly made in a series of time steps, and decisions (both the agent’s own as well as the decisions of other agents) affect the environment of the agent in a next time step. Agents’ decisions drive the

change in the system, which fits well with an agent-based approach (Chappin,2011).

ABMprovides the ability to address the effects which interactions on the micro-level

have for complexity at the macro-level (Ma and Nakamori, 2005). Nikolić (2009)

describes thatABMcan be applied to explore patterns of future states that a system

may develop into, based on its current state and rules. Furthermore, ABM allows

us to explore the emergent consequences of adjusting the decisions of autonomous

TSOsat individual time steps over a repeated series of interactions (Chappin,2011).

It is not understood how the consequences of different parameters underlying agents’

(TSOs’) decisions (network reinforcements) at given moments in time result in

sys-tem outcomes over a repeated series of interactions in which agents continuously respond to the outcomes of other agents’ decisions. This makes it a suitable choice for capturing the property of institutional fragmentation, with decentralized actor decisions on a micro-level at different time steps affecting a technically integrated system at the macro-level in the long term. With an agent-based simulation model it is possible to explore the effect of different institutional parameters, which provide incentives or set barriers to the way (potential) expansion projects are evaluated by