2PnS-EG: A general two-population n-strategy evolutionary game for strategic long-term bidding in a deregulated market under different market clearing mechanisms

Based on assumptions of bounded rationality and limited information, the evolutionary game theory (EGT) can well describe evolution rule of population behaviors and predict individuals' decision behaviors. Therefore, this paper focuses on a general two-population n-strategy evolutionary game (2PnS-EG), especially for the general two-population three-strategy evolutionary game (2P3S-EG), and applies it to investigate the evolutionary equilibrium properties of long-term strategic bidding issues in deregulated homogeneous and heterogeneous power generation-side markets (PGMs) under different market clearing mechanisms. First, relative net payoff (RNP) parameters are defined for two types of PGMs. Then, based on RNP parameters, the long-term equilibrium characteristics of 2P3S-EG-based homogeneous PGM bidding are investigated in 4 cases based on theoretical analysis and dynamic simulation. Moreover, long-term evolutionarily stable equilibrium characteristics of 2P3SEG based heterogeneous PGM bidding are investigated via qualitative analysis and dynamic simulation in the conditions of with and without government supervision, which are then further studied via quantitative analysis and dynamic simulation in two clearing mechanisms of MCP and PAB. Results reveal that MCP is more effective than PAB in guiding PGMs to evolve toward an expected and reasonable long-term evolutionarily stable bidding equilibrium state via adjusting the markets' RNP parameters appropriately. Finally, the future development of EGT-based strategic bidding issues in PGM is discussed. Overall, the models, methods and conclusions of this paper are practicable and scalable to some extent, aiming to enrich research work on long-term bid equilibrium of PGM, and provide some ideas and theoretical reference for relevant departments and PGMs in competitive ongrid bidding with bounded rationality and limited information.