基于混合粒子群算法的梯级泵站优化调度

Optimum dispatching of multistage pumping station based on mixed particle swarm optimization

针对不同时段电价差异,以流量平衡为基础,建立以梯级泵站耗电电费最小为目标的优化调度模型,并采用粒子群算法求解.为克服粒子群优化算法易早熟、迭代后期收敛速度慢的缺点,引入免疫思想,以粒子适应度为标准,通过克隆变异算子、疫苗接种算子和优胜劣汰算子,构建双粒子群,增强了粒子群搜索精度和搜索范围,并将其应用于广东某供水工程.优化调度仿真对比分析表明:免疫粒子群算法(IAPSO)能够有效地解决梯级泵站优化调度问题,降低了泵站运行成本,与基本粒子群算法(PSO)和自适应惯性权重粒子群算法(APSO)相比,收敛速度更快,搜索精度更高.

The optimal operation model of cascade pumping stations was built considering the effect of electricity price in different times,which aiming for the minimum total operation costs.In order to overcome the low convergence speed of particle swarm optimization,which is easy to be trapped in local optimization,the clone operator,vaccination operator and survival of the fittest operator in immune algorithm were used,therefore the double particle swarms were constructed.The new approach is verified with an application to water supply project.The results show that the mixed particle swarm optimization is more effective and superior to the basic particle swarm optimization（PSO） and adaptive inertia weight particle swarm optimization（APSO）.The results of optimum dispatching of multistage pumping station are satisfied.