基于超效率DEA效益评估的水火电力系统优化调度方式研究

Study of Hydro-thermal Electrical Power System Optimization Scheduling Based onSuper Efficiency DEA Efficiency Evaluation

粒子群优化算法在求解含有多维约束的多目标优化问题时,存在易陷入局部最优解或者全局收敛不唯一,从而造成优化结果的多样性。针对该问题,提出运用数据包络分析算法对采用改进粒子群求解的水火电多目标问题的优化结果进行效益评估,选取有效的决策单元,同时将多个有效单元进行排序,为决策者提供利用优化目标和超效率DEA值双重准则来选取决策方案。实例仿真证明该方法可有效减少多个目标追求下的决策盲目性,为决策者提供了决策选择。

In solving multi-objective optimization problems with multi-dimensional constraints,the particle swarm optimization(PSO) is easy to fall into local optimal solution or global convergence being not unique,that makes the optimization resultbeing diverse. Aiming at this problem,the data envelopment analysis( DEA) algorithm is proposed to evaluate the optimizationresult of improved PSO for hydro-thermal power system scheduling,then the effective scheduling schemes are selected andsorted,and finally the scheduling scheme is selected by decision makers based on the dual criteria of optimizing objective andsuper-efficiency DEA value. The example simulation proves that the method can effectively reduce the blindness of decision-making under multiple targets and provide final choice for decision-makers.