考虑风电并网系统的储能优化配置

Economic Allocation for Energy Storage System Considering Wind Power

随着风电的大规模并网,风电的随机性、波动性给系统的运行带来巨大挑战.而储能在保证电力系统的安全稳定经济运行中扮演着重要角色,尤其是在降低系统的运行成本和提高电压质量方面具有重要意义.如何实现储能容量的配置以及储能位置的选取显得尤为重要.为此,提出一种混合多目标粒子群优化算法.该算法将引入精英策略的非支配排序遗传算法(Nondominated Sorting Genetic Algorithm,NSGA-II)与概率潮流相结合,用威布尔分布描述风速的概率分布,通过五点估计法(FivePoint Estimation Method,5PEM)对风力分布进行离散化处理.考虑风力发电的不确定性,通过寻找储能单元的最优位置和容量以实现系统运行成本最小化,并且改善系统的电压分布.通过IEEE-30节点系统进行案例分析,验证所提算法的有效性以及储能优化配置的必要性.

With the large-scale wind power integrating into power grid,wind power randomness,volatility to the operation of the system to bring great challenges.The energy storage plays an important role in ensuring the safe and stable economic operation of the power system,especially for reducing the operating cost of the system and improving the voltage distribution of the system. How to achieve the storage capacity of the configuration and storage location selection is particularly important. This paper presents a hybrid multi-objective particle swarm optimization（ HMOPSO） algorithm.The algorithm combines the nondominated sorting genetic algorithm（ NSGA-II） with the probabilistic load flow technique.The probability distribution of wind speed is described by the Weibull distribution.In this paper,by looking for the location and capacity of the energy storage unit to achieve the system operating costs to minimize,and improve the system voltage distribution,improve voltage quality.The case study of IEEE-30 node system is carried out to verify the validity of the proposed algorithm and the necessity of optimal storage of energy storage.