基于改进PSO的水火电短期发电优化调度作者：方娜万畅余俊杰
来源：《现代电子技术》2020年第06期
摘; 要：針对水火电力系统短期发电调度决策变量多、维数高、规模大、非凸、非线性及求解困难等特点，提出改进的粒子群算法并进行求解。该算法采用反向学习策略提高初始解的质量，建立参数自适应动态调整机制控制群体进化过程，引入混沌局部搜索增强算法局部寻优能力。同时，根据不同类型的约束条件，采用能有效处理多重复杂约束的方法。仿真结果验证了算法和约束处理方法的可行性和有效性，为水火电力系统联合调度问题的求解提供了高效、实用的新方法。
关键词：水火电力系统; 发电调度; 粒子群优化算法; 反向学习策略; 约束处理; 仿真分析
中图分类号： TN915⁃34; TP391.4; ; ; ; ; ; ; ; 文献标识码： A; ; ; ; ; ; ; ; ; ; ; ;文章编号：1004⁃373X（2020）06⁃0119⁃05
Short⁃term hydrothermal power generation optimal scheduling based on improved PSO
FANG Na1，2， WAN Chang1，2， YU Junjie1，2
（1. Hubei Key Laboratory for High⁃efficiency Utilization of Solar Energy and Operation Control of Energy Storage System， Hubei University of Technology， Wuhan 430068， China; 2. Hubei Power Grid Intelligent Control and Equipment Engineering Technology Research Center，Hubei University of Technology， Wuhan 430068， China）
Abstract： An improved particle swarm optimization （PSO） is proposed to overcome the difficulties of short⁃term hydrothermal power generation scheduling， such as more decision variables， high dimension， large scale， no convex， nonlinearity and difficult solution. In this algorithm， the backward learning strategy is used to improve quality of initial solutions， the parameter adaptive dynamic adjustment mechanism is established to control the swarm evolution process， and the chaotic local search method is introduced to enhance the algorithm's local optimization capability. At the same time， according to different types of constraint conditions， a method that can effectively deal with multiple complex constraints is adopted. The simulation results verify the feasibility and effectiveness of the proposed algorithm and the constraint handling method， which provides an efficient and practical new method for solving the joint scheduling problem of hydrothermal power system.
Keywords： hydrothermal power system; power generation scheduling; particle swarm optimization; reverse learning strategy; constraints handling; simulation analysis
