电力系统无功优化的多智能体粒子群优化算法

无功优化是电力系统实现电压和无功功率最优控制和调度的基础,提出了一种全新的优化算法一多智能体粒子群优化算法来求解此类优化问题。该算法结合multi-agent系统和粒子群优化技术,构造了一个格子环境,所有Agent都固定在格子环境中。每一个Agent相当于粒子群优化算法中的一个粒子,它们通过与其邻居的竞争、合作和自学习操作,并且吸收了粒子群优化算法的进化机理,能够更快地、更精确地收敛到全局最优解。在IEEE30节点系统上进行校验,并与其它方法比较,结果表明,提出的算法具有质量高的解、收敛特性好、运行速度快的突出优点。

A MULTI-AGENT PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REACTIVE POWER OPTIMIZATION

A novel multi-agent particle swarm optimization algorithm (MAPSO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in MAPSO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value quickly, agents compete and cooperate with their neighbors, and they can also use knowledge. Making use of these agent-agent interactions and evolution mechanism of PSO, MAPSO realizes the purpose of minimizing the value of objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches.