基于PSO-GA算法的电力系统机组组合研究

机组组合优化问题是一个大规模、多约束、非线性的混合整数规划问题,因此求解非常困难。粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域。PSO算法的优势在于操作简单,可调参数少易于实现而又功能强大。该文采用二进制粒子群优化方法解决机组状态组合问题,用遗传算法结合启发式技术解决经济分配问题,并对最小开停机时间及启停费用进行了处理,使得运算速度大大加快。方法的可行性在10台机组系统中检验。模拟结果表明文章所提出的算法具有收敛速度快及解的质量高等优点。

PSO-GA algorithms for unit commitment of power system

Unit commitment(UC) is a large scale,multi-constraints and non-linear hybrid integer programming problems.Particle swarm optimization is a stochastic global optimization technique.It finds optimal regions of complex search spaces through the interaction of individuals in a population of particles.Particle swarm optimization has become the hotspot of evolutionary computation because of its simpleness for implementation,excellent performance and few parameters need to be tuned.The start-up of unit can be solved by the BPSO method,and the economic dispatch(ED) problem can be solved by the GA algorithm uniting heuristic method for the minimization of the production cost.At the same time,the paper proposes a new method on transition and minimum up/down time.The feasibility of the proposed method is demonstrated for 10 unit systems.The simulation results show that the proposed method possesses high convergence speed and high quality solutions.