Abstract
We analyze the behavior of the Italian electricity market with an agent-based model. In particular, we are interested in testing the assumption that the market participants are fully rational in the economical sense. To this end, we suppose that while constructing its strategy the agent takes into account all the possible strategies the other (competitors) agents might adopt in the future, not only their last strategies, as it is done in the literature. This motivates us to propose a co-evolutionary approach to strategy optimization, which better reflects the way actual decision makers behave in reality. The experiments carried out corroborate our hypothesis and show an improvement in the results compared to the literature.
C. da Costa Pereira—Acknowledges support of the PEPS AIRINFO project funded by the CNRS.
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Notes
- 1.
In the following we will use the terms generator and power plant interchangeably.
- 2.
The details about the function can be found in [8].
- 3.
Notice that bid data are publicly available on the power exchange website with a one-week delay, therefore, information about what plants were actually present and the like is supposed to be common knowledge.
References
Betrò, B., Cugiani, M., Schoen, F.: Monte Carlo Methods in Numerical Integration and Optimization. Applied Mathematics Monographs, CNR. Giardini, Pisa (1990)
Bevilacqua, S., da Costa Pereira, C., Guerci, E., Precioso, F., Sartori, C.: Analysing the impact of rationality on the italian electricity market. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds.) MDAI 2019. LNCS (LNAI), vol. 11676, pp. 236–247. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26773-5_21
Ela, E., et al.: Electricity markets and renewables. IEEE Power Energ. Mag. 15(27), 1540–7977 (2015)
Faia, R., Pinto, T., Vale, Z.A.: GA optimization technique for portfolio optimization of electricity market participation. In: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6–9 December 2016, pp. 1–7 (2016)
Giulioni, G., Hernández, C., Posada, M., López-Paredes, A.: Artificial Economics: The Generative Method in Economics. Lecture Notes in Economics and Mathematical Systems, vol. 631, 1st edn. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02956-1
GME - Gestore Mercati Energetici. Data access through. http://www.mercatoelettrico.org/it/download/DatiStorici.aspx
Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Boston (1989)
Guerci, E., Rastegar, M.A., Cincotti, S.: Agent-based modeling and simulation of competitive wholesale electricity markets. In: Rebennack, S., Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A. (eds.) Handbook of Power Systems II, pp. 241–286. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12686-4_9
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning and Data Mining, pp. 967–972. Springer, Boston (2017). https://doi.org/10.1007/978-0-387-30164-8
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Part IV, pp. 1942–1948 (1995
Santos, G., Pinto, T., Praça, I., Vale, Z.: A new approach for multi-agent coalition formation and management in the scope of electricity markets. Energy 36(8), 5004–5015 (2011)
Santos, G., Pinto, T., Praça, I., Vale, Z.: MASCEM: optimizing the performance of a multi-agent system. Energy 111(Supplement C), 513–524 (2016)
Silva, F., Teixeira, B., Pinto, T., Santos, G., Praça, I., Vale, Z.: Demonstration of realistic multi-agent scenario generator for electricity markets simulation. In: Demazeau, Y., Decker, K.S., Bajo Pérez, J., de la Prieta, F. (eds.) PAAMS 2015. LNCS (LNAI), vol. 9086, pp. 316–319. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18944-4_36
Sioshansi, F.P.: Evolution of Global Electricity Markets. Academic Press, Boston (2013)
Tribbia, C.: Solving the Italian electricity power exchange (2015)
Urieli, D.: Autonomous trading in modern electricity markets. AI Matters 2(4), 18–19 (2016)
Urieli, D., Stone, P.: Autonomous electricity trading using time-of-use tariffs in a competitive market. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, 12–17 February 2016, pp. 345–352 (2016)
Vytelingum, P., Ramchurn, S.D., Voice, T., Rogers, A., Jennings, N.R.: Trading agents for the smart electricity grid. In: 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, 10–14 May 2010, vol. 1–3, pp. 897–904 (2010)
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da Costa Pereira, C., Bevilacqua, S., Guerci, E., Precioso, F., Sartori, C. (2019). A Co-evolutionary Approach to Analyzing the Impact of Rationality on the Italian Electricity Market. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_15
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