PA-DDS算法在水库多目标优化调度中的应用

PA-DDS algorithm for multi-objective reservoir operation

Pareto存档动态维度搜索(Pareto-Archived Dynamically Dimensioned Search,PA-DDS)算法是一种求解多目标问题的随机搜索启发式算法。本文将PA-DDS算法引入考虑供水和发电的多目标优化模型优化水库调度图,与非支配排序遗传算法(Non-dominaled Sorting Genetic AlgorithmⅡ,NSGA-II)和多目标粒子群算法(Multi-Objectives Particlce Swarm Optimization,MOPSO)对比了收敛性,并在求得非劣解分布的均匀性和与理论Pareto前端的相似性方面与NSGA-II进行比较,分析该算法求解水库多目标调度问题的优化性能,对比分析不同目标下的优化调度图。结果表明:PA-DDS算法能够得到更高质量的非劣解集,优化调度图与原设计调度图相比,能更有效协调供水和发电的矛盾,在小幅降低(0.96%)发电量的前提下显著提高(8.07%)水库供水量,平均每年增加经济效益0.55亿元。

PA-DDS（Pareto-Archived Dynamically Dimensioned Search） is one of the multi-objective evolu-tionary algorithms designed to obtain multiple solutions,which offer different trade-off of the problem objec-tives. The algorithm was introduced into reservoir operating rule curves optimization for the targets of watersupply and power generation,and compared with NSGA-Ⅱ（Non-dominated Sorting Genetic Algorithm Ⅱ）and MOPSO（Multi-Objective Particle Swarm Optimization） in convergence. Furthermore,the performanceof PA-DDS and NSGA-Ⅱ are also analyzed in distribution and approximation between whole Pareto frontsof non-dominated solutions. The capacity of PA-DDS solving multi-objective reservoir operating problemsand optimal rule curves were analyzed. It is shown that PA-DDS algorithm performs better than NSGA-Ⅱalgorithm in getting the non-dominated solutions. The optimized rule curves can effectively mitigate the con-flict between water supply and power generation by dramatically increasing（8.07 %） the water supply yieldwith slight decrease（0.96 %） in power generation,and can improve the economic benefit by 55 million yu-an annually.