Abstract
The concept of bounded rationality has garnered substantial attention and interest from scholars since its inception. It is widely recognized that in complex systems, decision-making by its members is bounded by cognitive limitations. In this context, multi-agent simulation has emerged as a popular tool to model complex systems. One important question is how to incorporate the bounded rationality of market participants in such simulations. This paper introduces a novel multi-agent simulation model that incorporates the bounded rationality of generation companies (GENCOs) in electricity markets. We also propose evaluation metrics to quantify the differences in simulation outcomes between the proposed model and agent-based models that overlook bounded rationality, assessing the performance of market mechanisms when facing the bounded rationality of GENCOs. Using the inability of power generators to accurately predict future load curves as an illustration of bounded rationality, we conduct numerical simulation experiments on various electricity market compensation fee mechanisms. The simulation results demonstrate the effectiveness of the proposed simulation model and evaluation metrics.
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References
PJM - Who We Are. https://www.pjm.com/about-pjm/who-we-are
Aliabadi, D.E., Kaya, M., Sahin, G.: Competition, risk and learning in electricity markets: an agent-based simulation study. Appl. Energy 195, 1000–1011 (2017). https://doi.org/10.1016/j.apenergy.2017.03.121
Andrianesis, P., Liberopoulos, G.: On the design of electricity auctions with non-convexities and make-whole payments. In: 2013 10th International Conference on the European Energy Market (EEM), pp. 1–8 (2013). https://doi.org/10.1109/EEM.2013.6607386
Chai, X., Su, Z., Li, J., Zhang, N., Lv, Q.: Bounded rational agent bidding model of generators for spot market simulation. Power Syst. Technol. 46(12), 4800–4810 (2022). https://doi.org/10.13335/j.1000-3673.pst.2022.0632
Daniel, K.: Prospect theory: an analysis of decisions under risk. Econometrica 47, 278 (1979)
Du, Y., Li, F., Zandi, H., Xue, Y.: Approximating nash equilibrium in day-ahead electricity market bidding with multi-agent deep reinforcement learning. J. Mod. Power Syst. Clean Energy 9(3) (2021). https://doi.org/10.35833/MPCE.2020.000502
Gigerenzer, G., Selten, R.: Bounded Rationality: The Adaptive Toolbox. MIT Press, Cambridge (2002)
Guo, H., Chen, Q., Shahidehpour, M., Xia, Q., Kang, C.: Bidding behaviors of GENCOs under bounded rationality with renewable energy. Energy 250, 123793 (2022). https://doi.org/10.1016/j.energy.2022.123793
Guo, H., Gu, Y., Xia, Q.: A data-driven pattern extraction method for analyzing bidding behaviors in power markets. IEEE Trans. Smart Grid 11(4), 13 (2020)
Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: Proceedings of the 35th International Conference on Machine Learning, pp. 1861–1870. PMLR, July 2018
Heiner, R.A.: The origin of predictable behavior. Am. Econ. Rev. 73(4), 560–595 (1983)
Hu, Z., Zhang, J.: Toward general robustness evaluation of incentive mechanism against bounded rationality. IEEE Trans. Comput. Soc. Syst. 5(3), 698–712 (2018). https://doi.org/10.1109/TCSS.2018.2858754
Liang, Y., Guo, C., Ding, Z., Hua, H.: Agent-based modeling in electricity market using deep deterministic policy gradient algorithm. IEEE Trans. Power Syst. 35(6), 4180–4192 (2020). https://doi.org/10.1109/TPWRS.2020.2999536
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv e-prints p. arXiv:1509.02971, September 2015
Liu, Y., Sun, M.: Application of duopoly multi-periodical game with bounded rationality in power supply market based on information asymmetry. Appl. Math. Model. 87, 300–316 (2020). https://doi.org/10.1016/j.apm.2020.06.007
Mallard, G.: Modelling cognitively bounded rationality: an evaluative taxonomy. J. Econ. Surv. 26(4), 674–704 (2012). https://doi.org/10.1111/j.1467-6419.2010.00673.x
Omorogiuwa, D., Onyendi, A.: Comprehensive review on artificial intelligent techniques on bidding strategies in competitive electricity markets 4, 20–31 (2020)
Patrick Evans, B., Prokopenko, M.: Bounded strategic reasoning explains crisis emergence in multi-agent market games. Royal Soc. Open Sci. 10(2), 221164 (2023). https://doi.org/10.1098/rsos.221164
Ringler, P., Keles, D., Fichtner, W.: Agent-based modelling and simulation of smart electricity grids and markets – a literature review. Renew. Sustain. Energy Rev. 57, 205–215 (2016). https://doi.org/10.1016/j.rser.2015.12.169
Saxena, A., Kumar, R., Bansal, R.C., Mahmud, M.A.: Chapter 18 - Bidding strategies of a power producer in power market: measurement indices and evaluation. In: Zobaa, A.F., Abdel Aleem, S.H.E. (eds.) Uncertainties in Modern Power Systems, pp. 635–652. Academic Press, January 2021. https://doi.org/10.1016/B978-0-12-820491-7.00018-9
Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69(1), 99 (1955). https://doi.org/10.2307/1884852
Thaler, R.H.: Mental accounting matters. J. Behav. Decis. Making 12(3), 183–206 (1999). https://doi.org/10.1002/(SICI)1099-0771(199909)12:3<183::AID-BDM318>3.0.CO;2-F
Vahid-Pakdel, M.J., Ghaemi, S., Mohammadi-ivatloo, B., Salehi, J., Siano, P.: Modeling noncooperative game of GENCOs’ participation in electricity markets with prospect theory. Appl. Math. Model. 15(10), 5489–5496 (2019). https://doi.org/10.1109/TII.2019.2902172
Watkins, C., Dayan, P.: Q-learning. Mach. Learn. (1992). https://doi.org/10.1007/BF00992698
Zhu, Z., Hu, Z., Chan, K.W., Bu, S., Zhou, B., Xia, S.: Reinforcement learning in deregulated energy market: a comprehensive review. Appl. Energy 329, 120212 (2023). https://doi.org/10.1016/j.apenergy.2022.120212
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Pan, Z., Jing, Z., Ji, T., Song, Y. (2024). A Multi-agent Simulation Model Considering the Bounded Rationality of Market Participants: An Example of GENCOs Participation in the Electricity Spot Market. In: Nardin, L.G., Mehryar, S. (eds) Multi-Agent-Based Simulation XXIV. MABS 2023. Lecture Notes in Computer Science(), vol 14558. Springer, Cham. https://doi.org/10.1007/978-3-031-61034-9_9
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