机组组合是电站经济运行问题中典型的复杂非线性优化问题,其求解难度随系统规模增大呈非线性增长,如何对其进行高效求解一直是电力系统研究领域的热点和难点问题。为此,提出一种适用于电站经济运行中机组组合问题的二进制和声粒子群算法(BHSPSO):首先将粒子群算法的信息共享机制纳入到和声搜索算法的和声记忆库考虑操作中,并利用全局极值实现音调微调;然后采用启发式智能调整策略处理时段关联型约束条件,即根据机组优先顺序修复旋转备用约束,在此基础上,设计了一种“开-停-开”的修复策略处理最小开停机时间约束,有效改善了优化计算结果质量。将该方法分别应用于电站10台机组(简称10机)至电站100台机组(简称100机)系统标准算例,仿真结果表明:所提算法具有简单高效、收敛速度快、鲁棒性强等优点,为水、火电机组组合优化运行问题的高效求解提供一种新的途径。
Abstract
Unit commitment is a typical issue involving large-scale complicated nonlinear optimization. The difficulty of solving unit commitment increases nonlinearly with the increase of system scale. Effectively solving this problem has always been a hotspot and difficulty in power system research. In this paper, a Binary Harmony Search Particle Swarm Optimization (BHSPSO) algorithm is proposed for unit commitment problem. Firstly, the information sharing mechanism of particle swarm optimization is incorporated into the process of learning the harmony memory of the harmony search algorithm. And then the heuristic intelligent strategy is used to deal with the complex constraints of the time series. The spinning reserve constraints are repaired according to the priority of the unit, and an "on-off-on" repair strategy is designed to deal with the constraints of minimum power-off time and power-on time, effectively improving the quality of the results obtained. The BHSPSO algorithm is applied to standard calculation examples of six systems with 10, 20, 40, 60, 80 and 100 units. The simulation results show that the proposed algorithm has advantages of simplicity, fast convergence and strong robustness. This research offers a new approach for efficient solution of unit commitment optimization problem.
关键词
电站经济运行 /
机组组合 /
二进制和声搜索算法 /
粒子群算法 /
修复策略
Key words
economic running of power house /
unit commitment /
binary harmony search /
particle swarm optimization /
repair strategy
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基金
国家重点研发计划课题(2016YFC0402205); 国家自然科学基金重大研究计划重点支持项目(91547208); 国家电网公司华中分部科技项目(52140015000Y)
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