<p>Existing studies on the bidding dilemma in electricity markets mostly adopt mandatory participation and rarely integrate reputation effects. To fill this gap, this paper proposes reputation-driven Q-learning as a solution to the bidding dilemma between heterogeneous generation groups, developing a coupled evolutionary model of reputation, strategy, and voluntary participation. The authors examine 256 social norms under low, medium, and high demand scenarios. Results show that the synergy of reputation and voluntary participation significantly outperforms mandatory participation: It supports more social norms and promotes cooperative high-bidding by rewarding good-reputation generation companies (GENCOs) and punishing bad-reputation ones, whereas mandatory participation only works under limited norms and often relies on unfair reward-punishment rules. The authors further reveal the phase-transition patterns of cooperative high-bidding under different social norms, which provides clear guidance for designing low-cost market mechanisms. The proposed reputation-driven Q-learning mechanism can achieve spontaneous market cooperation without heavy administrative intervention, thus reducing regulatory costs and improving market efficiency.</p>