Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

作     者：Yun, Pingping Ren, Yongfeng Xue, Yu 

作者机构：Inner Mongolia Univ Technol Coll Energy & Power Engn Hohhot 010051 Inner Mongolia Peoples R China Inner Mongolia Engn Res Ctr Wind Power Technol & Hohhot 010051 Inner Mongolia Peoples R China 

出 版 物：《ENERGIES》 (能源)

年 卷 期：2018年第11卷第12期

页      面：3393-3393页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：National Natural Science Foundation of China [51567020, 51367012] Inner Mongolia Science & Technology Plan (2018) Natural Science Foundation of Inner Mongolia of China [2015MS0532, 2011BS0903] 

主　　题：wind-power fluctuation smoothing energy storage system Markov prediction model particle swarm optimization algorithm multi-objective optimization energy-storage battery output level 

摘      要：Wind power penetration ratios of power grids have increased in recent years;thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.