Strategic Regimes and Equilibrium Behavior in EU Electricity Markets. A Game-Theoretic Analysis

Abstract

The restructuring of Europe's electricity sector following Directive 2009/72/EC introduced strategic interdependence among national energy systems through liberalized market dynamics. This study applies a repeated game-theoretic framework to examine how five major EU member states - France, Germany, Italy, the Netherlands, and Spain - adjusted their price-output strategies over 2010–2023 in response to competitive pressures and institutional evolution. Each country is modelled as a strategic player choosing among four discrete strategy types based on relative price and output positions, with payoffs derived from observed electricity prices, generation volumes, and LCOE-based cost proxies. A multinomial logit model with theory-driven variable selection estimates the probability of strategic transitions conditional on current strategy, economic performance, and the post-reform institutional context. The analysis identifies High Price–High Output as the unique Nash equilibrium and Pareto-efficient outcome in every year of the study period - a result confirmed as robust to classification threshold variation through a systematic sensitivity analysis. Despite this clear payoff dominance, most countries exhibit persistent deviation from equilibrium, driven by path-dependent institutional inertia, grid constraints, and regulatory design. Germany demonstrates full and stable Nash alignment throughout the period, while France, Italy, Spain, and the Netherlands remain in behavioral clusters that diverge from the payoff-maximising configuration. These findings highlight the structural limits of market liberalization in delivering strategic convergence and carry direct implications for the 2024 EU Electricity Market Design reform and the REPowerEU renewable energy targets.

References

Awerkin, A., & Vargiolu, T. (2021). Optimal installation of renewable electricity sources: The case of Italy. Decisions in Economics and Finance, 44(2), 1179–1209. https://doi.org/10.1007/s10203-021-00365-4
Backe, S., Kara, G., & Tomasgard, A. (2020). Comparing individual and coordinated demand response with dynamic and static power grid tariffs. Energy, 201, 117619. https://doi.org/10.1016/j.energy.2020.117619
Bastianon, E. (n.d.). Optimizations according Nash and Pareto for the equilibria in ECB’s monetary policy assessment of model. https://www.academia.edu/24977848/
Cadre, H. L. (2019). On the efficiency of local electricity markets under decentralized and centralized designs: A multi-leader Stackelberg game analysis. Central European Journal of Operations Research, 27(4), 953–984. https://doi.org/10.1007/s10100-018-0521-3
Cheng, L., Huang, P., Zhang, M., Ru, Y., & Wang, Y. (2025). Optimizing electricity markets through game-theoretical methods: Strategic and policy implications for power purchasing and generation enterprises. Mathematics, 13(3), 373. https://doi.org/10.3390/math13030373
Dimitriadis, C. N., Tsimopoulos, E. G., & Georgiadis, M. C. (2021). A review on the complementarity modelling in competitive electricity markets. Energies, 14(21), 7133. https://doi.org/10.3390/en14217133
Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., & Costantino, N. (2014). A Nash equilibrium simulation model for the competitiveness evaluation of the auction-based day-ahead electricity market. Computers in Industry, 65(4), 774–785. https://doi.org/10.1016/j.compind.2014.02.014
Egerer, J., Weibezahn, J., & Hermann, H. (2016). Two price zones for the German electricity market – Market implications and distributional effects. Energy Economics, 59, 365–381. https://doi.org/10.1016/j.eneco.2016.08.002
Eurostat. (2023). Electricity market indicators. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Electricity_market_indicators
Grimm, V., Martin, A., Schmidt, M., Weibelzahl, M., & Zöttl, G. (2016). Transmission and generation investment in electricity markets: The effects of market splitting and network fee regimes. European Journal of Operational Research, 254(2), 493–509. https://doi.org/10.1016/j.ejor.2016.03.044
Gugler, K., Liebensteiner, M., & Schmitt, S. (2017). Vertical disintegration in the European electricity sector: Empirical evidence on lost synergies. International Journal of Industrial Organization, 52, 450–478. https://doi.org/10.1016/j.ijindorg.2017.04.002
Hu, Z., Zhu, Z., Wei, X., Chan, K. W., & Bu, S. (2025). Mixed strategy Nash equilibrium analysis in real-time pricing and demand response for future smart retail market. Applied Energy, 391, 125815. https://doi.org/10.1016/j.apenergy.2025.125815
Huppmann, D., & Egerer, J. (2015). National-strategic investment in European power transmission capacity. European Journal of Operational Research, 247(1), 191–203. https://doi.org/10.1016/j.ejor.2015.05.056
Kang, K., Su, Y., Yang, P., Wang, Z., Zhang, Y., Qi, N., & Liu, F. (2025). Understanding cross-market strategic behaviors of prosumers: An equilibrium-driven evolutionary game approach. Journal of Cleaner Production, 513, 145345. https://doi.org/10.1016/j.jclepro.2025.145345
Kim, H. J., Chung, Y. S., Kim, S. J., Kim, H. T., Jin, Y. G., & Yoon, Y. T. (2023). Pricing mechanisms for peer-to-peer energy trading: Towards an integrated understanding of energy and network service pricing mechanisms. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2023.113435
Kunz, F., & Zerrahn, A. (2016). Coordinating cross-country congestion management: Evidence from Central Europe. The Energy Journal, 37, 81–100. https://doi.org/10.5547/01956574.37.SI3.fkun
Mezghani, I., Papavasiliou, A., & Le Cadre, H. (2018). A generalized Nash equilibrium analysis of electric power transmission-distribution coordination. In E-Energy 2018 Proceedings of the 9th ACM International Conference on Future Energy Systems (pp. 526–531). https://doi.org/10.1145/3208903.3214346
Navon, A., Ben Yosef, G., Machlev, R., Shapira, S., Chowdhury, N. R., Belikov, J., Orda, A., & Levron, Y. (2020). Applications of game theory to design and operation of modern power systems: A comprehensive review. Energies, 13(15), 3982. https://doi.org/10.3390/en13153982
Oggioni, G., & Smeers, Y. (2012). Degrees of coordination in market coupling and counter-trading. The Energy Journal, 33(3), 39–90. https://doi.org/10.5547/01956574.33.3.3
Oggioni, G., Allevi, E., & Schaible, S. (2012). A generalized Nash equilibrium model of market coupling in the European power system. Networks and Spatial Economics, 12(4), 503–560. https://doi.org/10.1007/s11067-011-9166-7
Oggioni, G., Murphy, F. H., & Smeers, Y. (2014). Evaluating the impacts of priority dispatch in the European electricity market. Energy Economics, 42, 183–200. https://doi.org/10.1016/j.eneco.2013.12.009
Oggioni, G., & Smeers, Y. (2013). Market failures of market coupling and counter-trading in Europe: An illustrative model-based discussion. Energy Economics, 35, 74–87. https://doi.org/10.1016/j.eneco.2011.11.018
Paola, A. D., Angeli, D., & Strbac, G. (2018). On distributed scheduling of flexible demand and Nash equilibria in the electricity market. Dynamic Games and Applications, 8(4), 761–798. https://doi.org/10.1007/s13235-017-0237-3
Pfeifer, A., Feijoo, F., & Duić, N. (2023). Fast energy transition as a best strategy for all? The Nash equilibrium of long-term energy planning strategies in coupled power markets. Energy, 284, 129109. https://doi.org/10.1016/j.energy.2023.129109
Soriano, L. A., Avila, M., Ponce, P., Rubio, J. d. J., & Molina, A. (2021). Peer-to-peer energy trades based on multi-objective optimization. International Journal of Electrical Power & Energy Systems, 131, 107017. https://doi.org/10.1016/j.ijepes.2021.107017
Tang, J., Qian, B., Luo, Y., Lin, X., Zhou, M., Zhang, F., & Wang, H. (2025). Evolutionary game theory-based analysis of power producers’ carbon emission reduction strategies and multi-group bidding dynamics in the low-carbon electricity market. Processes, 13(4), 952. https://doi.org/10.3390/pr13040952
Tutz, G., Pößnecker, W., & Uhlmann, L. (2015). Variable selection in general multinomial logit models. Computational Statistics & Data Analysis, 82, 207–222. https://doi.org/10.1016/j.csda.2014.09.009
Wang, H., Xie, Z., Pu, L., Ren, Z., Zhang, Y., & Tan, Z. (2022). Energy management strategy of hybrid energy storage based on Pareto optimality. Applied Energy, 327, 120095. https://doi.org/10.1016/j.apenergy.2022.120095
Yarar, N., Yoldas, Y., Bahceci, S., Onen, A., & Jung, J. (2024). A comprehensive review based on the game theory with energy management and trading. Energies, 17(15), 3749. https://doi.org/10.3390/en17153749
Zahid, F. M., & Tutz, G. (2013). Multinomial logit models with implicit variable selection. Advances in Data Analysis and Classification, 7, 393–416. https://doi.org/10.1007/s11634-013-0136-4
Published
2026-06-30
How to Cite
GKATSIKOS, Alexandros. Strategic Regimes and Equilibrium Behavior in EU Electricity Markets. A Game-Theoretic Analysis. Theoretical and Practical Research in Economic Fields, [S.l.], v. 17, n. 2, p. 446 - 469, june 2026. ISSN 2068-7710. Available at: <https://journals.aserspublishing.eu/tpref/article/view/9518>. Date accessed: 02 july 2026. doi: https://doi.org/10.14505/tpref.v17.2(38).11.