基于NSGA-Ⅱ风蓄协调的多目标优化调度研究

Research on multi-objective optimal dispatch for the coordination between wind energy and pumped storage based on NSGA-Ⅱ

针对风电并网带来弃风与常规机组污染排放问题,在考虑经济效益、提高风电利用率的同时,建立一种多目标风电-抽水蓄能联合优化日运行模型,采用带精英策略的快速非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)进行机组特性分层对比,确定抽蓄机组与常规机组工况,以最大化抽蓄机组削峰填谷的效益和最小化常规机组耗煤与排放量为目标,寻求最优运行方式。通过算例对比3种不同的运行方式,证明该模型能够有效地减少弃风功率,煤耗量和排放量,同时与多目标粒子群算法(multi-objectiveparticle swarm optimization,MOPSO)对比显示出NSGA-Ⅱ求解的优越性。

Aiming at the problems of wind power curtailment and pollutant emission of conventional units, and considering the economic benefits and improvement of wind energy utilization, a multi-objective combined operation model of wind power and pumped storage is established. Non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)is used to compare unit characteristics and determine the working conditions of the storage unit and the conventional unit. In order to maximize the benefit of peak shaving and valley filling of pumped storage units and minimize coal consumption and emission of conventional units, the optimum running mode is studied. Comparing three different running modes, the example proves that the model can effectively reduce the wind power curtailment, coal consumption and emissions. Meanwhile, comparing with multiobjective particle swarm optimization, the superiority of NSGA-Ⅱ is shown.