A Risk-Aware Supply Function Nash Equilibrium Framework for Strategic Bidding in Day-Ahead Electricity Markets

Strategic bidding in day-ahead electricity markets requires generation companies to maximize expected profits while managing financial risks arising from market uncertainty and competitors’ strategic behavior. This paper proposes a game-theoretic risk-aware strategic bidding framework based on a Supply Function Nash Equilibrium (SFNE) for dominant market operators in a uniform-pricing day-ahead market. Each operator strategically determines cluster-level bidding markups for its heterogeneous generation portfolio while anticipating competitors’ strategies. Demand uncertainty is represented by a finite scenario set, and Conditional Value-at-Risk (CVaR) of profit shortfall is incorporated into each operator’s expected-profit objective. The resulting non-cooperative equilibrium is solved using a relaxed Nikaido–Isoda (NI) best-response algorithm with convergence criteria based on the relative NI gap, strategy variation, and utility variation. The framework is validated using publicly available Italian day-ahead market offer data, where technology-oriented clustering reduces the strategic decision dimension while preserving portfolio heterogeneity. The proposed algorithm satisfies all convergence criteria within approximately 54 iterations. Numerical results show that the proposed risk-aware SFNE reduces aggregate downside-profit risk by approximately 7% compared with the risk-neutral SFNE while maintaining comparable expected profitability and slightly lowering market-clearing prices and procurement costs. A realistic 24 h market simulation further confirms the robustness and applicability of the proposed framework under time-varying market conditions. Overall, the proposed framework provides an economically interpretable and computationally tractable benchmark for risk-aware strategic bidding in day-ahead electricity markets.