陈学义 方国华 吴承君

摘 要：针对水电站多目标联合优化调度问题，提出双层改进粒子群算法（TIPSO）。该算法通过动态廊道约束，提高粒子群算法中粒子初始解的质量;通过改进动态权重系数，增强粒子群算法在前期的全局寻优能力和后期的局部寻优能力，提高粒子群算法的收敛性。将该算法应用于求解河南省陆浑水电站多目标优化调度问题，计算结果表明双层改进粒子群算法具有较好的收敛性能;通过与动态规划算法计算结果对比，表明该算法求解高维、复杂、多约束问题的可靠性和有效性。

关键词：水电站;优化调度;多目标优化;粒子群算法

中图分类号：TV213.4文献标志码：A

doi：10.3969/j.issn.1000-1379.2020.06.012

Study on Hydropower Station Optimal Operation Under Electricity

Market Environment Based on Two-Layer Improved Particle Swarm Optimization

CHEN Xueyi， FANG Guohua， WU Chengjun

（College of Water Conservancy and Hydropower，Hohai University，Nanjing 210098，China）

Abstract：Aiming at the multi-objective optimal operation of hydropower station， a two-layer improved particle swarm optimization algorithm （TIPSO） was proposed. The TIPSO increased the quality of initial solution in particle swarm optimization through dynamic corridor constraint. By improving the dynamic weight coefficient， TIPSO could improve the global optimization ability in the early stage and the local optimization ability in the late stage. It improved the convergence of particle swarm optimization. The TIPSO was applied to the multi-objective optimization operation of Luhun Hydropower Station in Henan Province. The results show that TIPSO has better convergence performance. Compared with the results of the dynamic programming algorithm， it shows that the algorithm is reliable and effective in solving high-dimensional， complex and multi-constrained issues.

Key words： hydropower station; optimize operation; multi-objective optimization; Particle Swarm Optimization

1 引 言

為满足可持续发展要求，水电作为一种可再生能源，越来越受到重视。伴随着经济全球化的发展，在市场环境下，以发电量最大为目标的水电站运行已不能满足当前需求，而以发电效益作为经济衡量的手段，越来越被提上日程[1-2]。电力市场环境下，水电站运行一方面需满足合约电量的发电要求，另一方面要提高现货竞争电量的发电效益[3]，因此水库优化调度逐渐从单目标调度转化为多目标优化调度[4-5]，水库优化调度求解算法也经历了由传统优化算法到群智能优化算法的转变[6-7]。

1995年Eberhart和Kennedy[8]提出了粒子群算法（PSO）的概念，PSO是一种群智能优化算法，通过更新粒子飞行速度和粒子当前位置，寻求最优解，具有一定的并行性和鲁棒性[9]。由于PSO计算的快速性及编程易实现性，自提出以来，在不同领域均得到了广泛的研究和应用[10-12]。……