基于改进PSO算法的水库群防洪优化调度

An enhanced PSO-based approach for optimizing the flood control operation of reservoir networks

摘要

【目的】水库群防洪优化调度在暴雨洪涝灾情中发挥着重要作用，但现有研究在改进PSO算法中缺乏迭代过程中对粒子与最优解距离的约束与调节以及综合考虑优化调度期间下游防洪对象与水库自身安全。【方法】为更好地解决水库群防洪优化调度问题，建立以最大削峰和最高水位最小为目标函数的优化调度模型，以山东费县祊河流域的龙王口、上冶、许家崖和石岚四个水库为研究对象，利用三角函数和贝塔分布对PSO算法的惯性权重和学习因子进行动态调整优化迭代过程，同时引入中心极值定理对迭代过程进行实时约束与调控，对PSO算法进行改进，以百年一遇和千年一遇设计洪水的入库流量作为输入条件，结合防洪调度约束和洪水演进对山东费县水库群优化调度模型进行评估。【结果】结果显示：库容越大，削峰效果越明显，在百年一遇的输入条件下，许家崖水库最大下泄流量相比于常规调度减少了559.62 m³/s,相比于标准PSO优化调度减少了279.81 m³/s,削峰率为10.4%,库容相比于常规调度降低了6.4%,相比于标准PSO优化调度降低了5.3%,在千年一遇的输入条件下，许家崖水库最大下泄流量比常规调度减少了701.79 m³/s,相比于PSO优化调度减少了350.90 m³/s,削峰率为12.1%,库容相比于常规调度降低了9.2%,相比于PSO优化调度降低了4.8%。【结论】结果表明：该优化调度模型在实现最大削峰和最低水位控制方面表现出显著效果。所提出的算法在寻优过程中的精度和稳定性得到了有效保障，显示出良好的优化性能和较强的实际应用价值。

Abstract

[Objective] The flood control optimization scheduling of reservoir clusters plays a crucial role in flood management during heavy rain and flooding events. However, existing studies on improving the PSO algorithm often lack constraints and adjustments on the distance between particles and the optimal solution during the iteration process. Additionally, they fail to comprehensively consider both the downstream flood control targets and the safety of the reservoirs themselves during the optimization scheduling. [Methods] To better address the flood control optimization scheduling problem of reservoir clusters, an optimization model is established with the objective of maximizing peak shaving and minimizing the highest water level. The model focuses on four reservoirs in the Fei River Basin of Feixian County, Shandong: Longwangkou, Shangye, Xujiaya, and Shilan. The inertia weight and learning factors of the PSO algorithm are dynamically adjusted during the optimization process using trigonometric functions and Beta distributions. Additionally, the Central Limit Theorem is introduced to impose real-time constraints and regulation on the iterative process, further improving the PSO algorithm. The input conditions are the inflow of design floods with a recurrence interval of 100 years and 1000 years, and the optimization scheduling model is evaluated by considering flood control constraints and flood evolution. [Results] The result demonstrate that the larger the reservoir capacity, the more significant the peak shaving effect. Under the input conditions of a 100-year flood, the maximum discharge flow from the Xujiaya Reservoir was reduced by 559.62 m³/s compared to conventional scheduling, and by 279.81 m³/s compared to the standard PSO-optimized scheduling, achieving a peak shaving rate of 10.4%. The reservoir capacity was reduced by 6.4% compared to conventional scheduling and by 5.3% compared to the standard PSO optimization. Under the input conditions of a 1000-year flood, the maximum discharge flow from the Xujiaya Reservoir was reduced by 701.79 m³/s compared to conventional scheduling, and by 350.90 m³/s compared to PSO-optimized scheduling, achieving a peak shaving rate of 12.1%. The reservoir capacity was reduced by 9.2% compared to conventional scheduling and by 4.8% compared to PSO-optimized scheduling. [Conclusion] The result indicate that the proposed optimization scheduling model shows significant effects in achieving maximum peak shaving and minimizing water levels. The algorithm ensures effective precision and stability during the optimization process, demonstrating strong optimization performance and substantial practical application value.

关键词

水库群 / 削峰准则 / 改进PSO算法 / 优化调度 / 影响因素

Key words

reservoir group / peak shaving criterion / improved PSO algorithm / optimization scheduling / influencing factors

基金资助

国家自然科学基金项目(52179012)