Research on the Optimization of Intra-Month Trading Strategies for Hydropower Stations Considering Deviation Revenue Recovery

To promote the optimal allocation and utilization of hydropower resources while fully leveraging the regulatory mechanism of the electricity market, this paper aims to maximize the monthly settlement revenue of hydropower stations considering the medium- and long-term deviation revenue recovery costs. On the one hand, the Conditional Value at Risk (CVaR) and an improved golden section search method are introduced to identify the optimal confidence level, quantifying the risk of monthly settlement revenue loss under extreme real-time market electricity price scenarios. On the other hand, based on the joint optimization of intra-month contract electricity and declared electricity in the real-time market, an optimization model for intra-month trading strategies of hydropower stations considering the medium- and long-term deviation revenue recovery mechanism is established. Case study results show that, under the same medium- and long-term contract signing conditions, the monthly settlement revenue is increased by 14.4% compared to the traditional intra-month trading strategy aimed at minimizing the medium- and long-term deviation revenue recovery. Furthermore, after introducing CVaR to quantify the risk of monthly settlement revenue loss under extreme real-time market price scenarios, the average medium- and long-term deviation revenue recovery cost is reduced by 44.98%, the expected monthly settlement revenue increases by CNY 382.7 thousand, and the minimum monthly settlement revenue increases by CNY 2.2641 million. The results verify the superiority of the proposed model in fully realizing the benefits of optimal hydropower resource allocation under market regulatory mechanisms, providing a valuable reference for hydropower stations participating in medium- and long-term intra-month trading decisions.