改进粒子群优化算法用于电网优化购入电量

为实现从多个发电厂商购入电量总成本最小的目标函数,建立了应用改进粒子群优化算法综合考虑供求约束和发电厂发送能力约束的电网优化购入电量模型,它还同时考虑了竞争原则下电厂发送能力可取0的情况是一个含有非连续变量的优化问题。在按价格最低原则预先确定购买电量的厂商,再用改进粒子群优化算法解决非连续变量问题从而提供了一种解决电网优化购入电量问题的新思路,节约了购电成本。

Application of Improved Particle Swarm Optimization in Power Purchase Model Optimization Problem

Optimizing power purchase model is an effective way to reduce cost of power purchase to the minimum,which can meet the purchaser' profits to the greatest degree.Therefore research of power purchase model and algorithm optimization is of vital practical significance for power purchaser.This paper first takes total expense from many power suppliers as an objective function,the model of power purchase for power companies is established,which synthetically considers supply and demand,and transfer capability constraints.It is very complex to deal with these problems by classical optimization method such as the simplex method and the Lagrangian relaxation method.Their basic shortcomings are long searching time and hard finding globe optimal solution.The particle swarm optimization(PSO) algorithm is applied to solve the model which makes sure that the solution is global optimal,then introduces a novel PSO algorithm which is modified by means of restricting the search tactics,simultaneously considering the non-continual variables,and through ordering the power suppliers in advance.The improved particle swarm optimization algorithm makes the search more direct and faster.Numerical simulation results show that the improved PSO algorithm has advantages both in effectiveness and efficiency.It is capable of obtaining higher quality solution efficiently and also saves considerable costs,furthermore,the algorithm is versatile and robust,and has merits of higher reliability and speed of convergence than other methods.Therefore,it is concluded that the algorithm is supposed to be an effective way to deal with the optimized issue in the power market,and has a wide potential application in power market.