一种求解热电联供经济调度问题的双种群混合智能优化算法

A Dual Population Hybrid Intelligent Optimization Algorithm for Combined Heat and Power Economic Dispatch

随着电力系统中热电联供所占比重的越来越多,热电联供经济调度问题的解决迫在眉睫。本文针对热电联供经济调度问题的特点,结合粒子群算法(PSO)和差分进化算法(DE)的各自优势,设计了一种双种群混合智能优化算法,该算法在一个种群中采用PSO算法产生新个体并进行更新迭代操作,在另一个种群中采用DE算法产生新个体并进行更新迭代操作,通过对每次迭代过程中两个种群产生的最优个体进行信息交流,协调维持了整个种群的多样性,使得算法在最优解寻找过程中的性能得到提升。对两个热电联供测试系统的仿真实验表明,相比于其他进化算法,本文提出的混合差分进化与粒子群优化算法(DEPSO)在热电联供经济调度问题中可以得到更好的结果。

With the increasing proportion of the combined heat and power in power system,it is urgent to solve the problem of combined heat and power economic dispatch.A dual-population swarm intelligent optimization algorithm -hybrid difference evolution and particle swarm optimization algorithm (DEPSO)is proposed to solve economic dispatch of combined heat and power system.In the algorithm,the population is divided into two sub-populations.One subpopulation adopts PSO algorithm to generate new individuals and update iteration operation.The other population produces new individuals and iterative update the operation through difference evolution (DE)algorithm.These two sub-populations share their information through exchanging the best individual on each iteration process.The two combined heat and power testing systems are simulated to verify the effectiveness of the proposed algorithm in the cogeneration economic dispatch system.The results show that compared to other evolutionary algorithms,the proposed hybrid differential evolution algorithm and particle swarm optimization (DEPSO)algorithm can get better solutions to the cogeneration economic dispatch problem.