基于自适应PSO算法的机组优化组合研究

An Adaptive Particle Swarm Optimization Algorithm for Solving Unit Commitment

提出了解决电力系统机组优化组合问题的一种新的方法——自适应粒子群优化算法(APSO).PSO算法能解决许多遗传算法能解决的优化问题,但却只需要一些简单的参数就可稳定收敛得到高质量的解.将该算法应用到IEEE10机系统中,结果表明该算法用于求解机组优化组合是有效可行的.

This paper proposes a new approach to solve the optimization of unit commitment using an adaptive particle swarm optimization algorithm .In practice, the unit commitment problem is quit difficult to obtain the best globe solution in theory due to its inherent higher-dimensional, multi-phases, nonconlex, a large set of operating constraints and nonlinear mixed integer programming problem in mathematics. The PSO method can be used to solve many optimization problems of the same kind as GA and SA methods; and it can generate high-quality solutions with stable convergence, requiring only some concise parameters. The feasibility of the proposed method is demonstrated for 10 unit systems, and the test results show that it is indeed efficient and reliable.