A Bi-Level Optimization Model for Bidding Strategies in Inter-Provincial Electricity Spot Markets Based on an Improved Quantum Genetic Algorithm

Participants in inter-provincial electricity spot markets optimize their bidding strategies to minimize purchasing costs or maximize sales revenue, directly influencing market-clearing prices and resource allocation efficiency. This paper proposes a bi-level optimization model based on an improved Quantum Genetic Algorithm (IQGA) to address the nonlinear bidding strategy optimization problem. The upper-level model targets cost minimization or revenue maximization, while the lower-level model simulates market clearing. By introducing a dynamic rotation angle strategy and second-order differential gradient estimation, IQGA enhances convergence speed and global search capability. Case studies demonstrate that IQGA outperforms traditional algorithms (PSO, GA, QGA) in convergence speed, stability, and optimization performance. Key results include a social welfare improvement of 1.6407 million CNY and increased utilization rates of cross-regional DC and AC transmission lines by 7.82% and 57.1%, respectively. This research provides theoretical and practical tools for enhancing the efficiency and coordination of inter-regional electricity markets.