水电作为灵活的可调节性能源,与流域周边的光伏电站打捆运行,形成水光互补系统,可有效发挥多能源互补优势。然而,随着电源种类的增加,调度主体的目标与约束条件也随之改变,水光互补系统优化调度问题的求解变得愈发复杂。现有水库调度研究以纯水电调度为主,较少考虑新能源消纳,传统水光互补系统优化调度,一般多以发电效益目标为主,无法满足多目标综合运用的需求。为了 避免飞蛾扑火优化算法(MFO)陷入局部最优,改进后的多目标飞蛾扑火算法从更新公式、飞蛾直线飞行路径的启发和火焰种群更新策略3个方面对MFO算法进行改进,为了区分这些在Pareto支配下不受彼此支配的个体,结合参考点提出了R支配,两者结合形成了一种新的性能良好的多目标进化算法R-IMOMFO。综合考虑水光互补系统发电效益和容量效益指标,构建了水光互补系统多目标优化调度模型,并采用R-IMOMFO算法对模型进行求解,针对丰、平、枯3种典型年提出了优化调度方案,结果表明建立的多目标优化模型可以较好协调水光互补系统发电效益、容量效益间的关系,可为水光互补系统多目标优化调度方案编制提供参考。

[Objectives] Existing reservoir scheduling studies mainly focus on pure hydropower scheduling, with limited consideration of renewable energy integration. Traditional optimal scheduling of hydro-photovoltaic complementary systems typically prioritizes power generation benefits, which fails to meet the requirements of multi-objective comprehensive utilization. Moreover, compared with pure hydropower scheduling, the optimal scheduling of hydro-photovoltaic complementary systems is more complex to solve. This study aims to establish a multi-objective optimal scheduling model for hydro-photovoltaic complementary systems with the objectives of maximizing annual power generation benefits and maximizing the minimum output during specific periods. [Methods] To overcome the local optimum issue in the Moth-Flame Optimization (MFO) algorithm, improvements were made to the multi-objective MFO from three aspects: update formula, inspiration from moths’ linear flight paths, and flame population update strategy. To distinguish individuals that are mutually non-dominated under Pareto dominance, R-domination incorporating reference points was introduced. The combination of these two led to the development of a new high-performance multi-objective evolutionary algorithm: R-IMOMFO. A multi-objective optimization scheduling model for hydro-photovoltaic complementary systems was established, considering both power generation benefits and capacity benefits, and the model was solved using the R-IMOMFO algorithm. [Results] The R-IMOMFO algorithm demonstrated fast convergence, strong resistance to premature convergence, and high accuracy, proving to be an effective method for solving complex multi-objective optimization problems. Using the R-IMOMFO algorithm, non-dominated scheduling solution sets were obtained under three runoff scenarios—wet year, normal year, and dry year—for both power generation and capacity benefits. For each typical year, two extreme schemes and one intermediate scheme were selected for comparative analysis. This enabled scheduling operators to select more appropriate solutions based on their prioritization of different objectives. [Conclusions] The proposed multi-objective optimization model effectively coordinates the relationship between power generation benefits and capacity benefits in hydro-photovoltaic complementary systems, providing data support for decision-making in multi-objective optimal scheduling.