改进粒子群算法应用于热电联产负荷优化分配

Application of Improved Particle Swarm Optimization to Combined Heat and Power Optimal Load Dispatch

随着热电联产机组应用规模的扩大,优化各机组间热电比例以降低成本显得越来越重要。为了寻求热电联产机组负荷分配的最优方法,在对现存负荷分配方法进行分析和比较的基础上,将3种不同的粒子群算法用于求解热电联产负荷优化分配的问题。根据热电联产负荷分配的可行域,给出了热电联产机组负荷分配的算法步骤和数学模型。通过算例将不同的算法应用到matlab仿真,结果表明,XPSO方法能够提高算法的收敛速度,并且具有较好的目标优化值;EPSO算法虽然收敛速度较慢,但是具有较好的寻优能力;SPSO算法虽然具有较快的收敛速度,但是成本函数值较大。综上所述,XPSO算法是求解热电联产机组负荷优化分配的一种有效方法,具有很大的理论和工程实用价值。

With the increasing application scale of cogeneration unit, it has become increasingly important to optimize the heat-electricity proportion between the various units for reducing the costs. In order to seek the optimal eogeneration load dispatch method, this paper firstly analysed and compared the existing load dispatch methods, on this basis, three different particle swarm optimizations are proposed to solve the combined heat and power load dispatch problem. According to the feasible region of combined heat and power load dispatch, the detailed algorithm steps and mathematical model for combined heat and power load dispatch problem are concluded in this paper. Different algorithms are applied to the simulation of matlab through a case, the results show that XPSO algorithm can improve the convergence speed, and has good objective optimization value; The convergence speed of EPSO algorithm is slow, but has better optimization ability; SPSO algorithm has faster convergence speed, but the cost function value is larger, therefore, XPSO algorithm is an effective method for solving combined heat and power optimal load dispatch, which has greater theoretical and practical value.