Evolutionary game-theoretical approaches for long-term strategic bidding among diverse stakeholders in large-scale and local power markets: Basic concept, modelling review, and future vision

• This paper reviews the application of EGT to long-term strategic bidding in power generation-side markets (PGM). • It compares classical game theory (CGT) and EGT, highlighting EGT's ability to account for bounded rationality and dynamic adaptation. • A detailed analysis of the evolutionarily stable equilibrium (ESE) of GENCOs in PGMs under MCP and PAB clearing mechanisms is provided. • The paper discusses how EGT better reflects real-world complexities, offering adaptive bidding outcomes for GENCOs. • A comparative case study demonstrates that MCP leads to more effective, low-price outcomes than PAB in guiding PGMs to long-term stability. Evolutionary game theory (EGT) has unique advantages in analyzing the spontaneous formation of social habits, norms, institutions or systems and their influencing factors. In the electricity bidding market, power generation companies and grid enterprises encounter increasingly complex multi-subject optimization decision-making challenges that cannot be comprehensively handled by conventional optimization methods due to their reliance on centralized objectives, perfect information, and fully rational participants. This survey focuses on long-term strategic bidding strategies, which involve sustained decision-making processes over extended periods to optimize cumulative profits and market positions. Moreover, classical game models assume complete rationality, thus failing to capture the iterative and adaptive decision-making behaviors prevalent in modern power markets. However, the long-term market bidding process involving groups of generators in the power generation-side market (PGM) under asymmetric information conditions is a complex process of long-term dynamic evolution. To contextualize these complexities, we incorporate a comparative survey illustrating the main methods, assumptions, and knowledge gaps in existing research, ensuring a clear understanding of why evolutionary game-theoretic approaches can more thoroughly capture the dynamic, bounded-rational nature of bidding. This paper reviews in detail the research on the application of EGT to multi-group bidding games in PGMs. First, the basic structure and development history of EGT are briefly introduced, and the essential differences between EGT and classical game theory (CGT) in terms of modeling are compared from several aspects, based on which several core concepts of EGT are further elaborated. Then, the relevant theories of electricity market (EM) are described, especially for the PGM, the definition and characteristics of EM are described, and the typical PGM transaction model and market bidding mechanism are summarized. Following that, this paper reviews and analyzes the current status of research on bidding strategies in PGMs from four aspects, including cost analysis of generators, electricity price forecasting, bidding behavior, and bidding decision support systems. On this basis, this paper reviews the research on the application of game theory, especially EGT, to long-term strategic bidding in PGM. In this paper, we also present a comparative case study between CGT and EGT to demonstrate how EGT better accounts for bounded rationality and dynamic strategy adaptation. Through our comparative case study, we show that EGT more accurately reflects real-world complexities, producing more robust and adaptive bidding outcomes than CGT. Finally, the paper concludes with a summary and outlook, aiming to provide new insights and practical guidance for power producers to formulate effective long-term bidding strategies in actual electricity market scenarios. Overall, our work is of pivotal importance because it provides a more realistic and robust framework—evolutionary game theory—that captures the dynamic, distributed, and uncertain nature of real-world bidding. This approach not only fills a gap in existing theories but also offers actionable insights for grid operators and policymakers seeking more efficient and equitable market outcomes.