电力系统无功优化的多智能体粒子群优化算法

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

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——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 IEEE 30 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 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.