Coordinated Control Strategy of Energy Storage Systems in Distribution Network with the Integration of New Energy
-
摘要: 以提高电网对新能源接纳能力为目的,本文研究了含新能源的配电网中电池储能系统的协调控制策略,针对配电网有功功率波动、新能源接入点电压波动建立优化模型,使用NSGA-II算法(快速非支配排序遗传算法)求解其Pareto非支配解集,实现多个储能系统的协调控制。使用该控制方法,可以将分布式储能系统联合调度,较好地平抑配电网整体的有功功率波动,同时改善新能源接入节点电压波动,提高电力系统稳定性。最后,本文对IEEE14节点配电网进行算例分析,在该配网中接入了接近负荷一半的光储和风储系统,将原本只和本节点光伏或风电配合的储能系统联合考虑,协调控制其出力,最后通过对比一个完整调度周期内储能系统单独控制和协调控制的效果,表明本文协调控制策略的有效性。Abstract: With the purpose of improving the ability of power grid to incorporate new energy, the coordinated control strategy of the battery energy storage systems in the distribution network with the integration of new energy is studied in this paper. An optimization model is built by considering of the active power fluctuation of power grid and voltage fluctuation on the access points of new energy, and the NSGA-II algorithm is used to solve the Pareto non-dominated solution set to achieve coordinated control of multiple energy storage systems. By using this control method, the distributed energy storage systems can be jointly dispatched to stabilize the active power fluctuation of distribution network, to improve the voltage fluctuation on access point of new energy, and to enhance the stability of the power system. In this paper, the analysis case of IEEE14-node distribution network is studied, which incorporates PV-storage and wind-storage systems with capacity close to half load. The joint dispatching of energy storage systems with PV or wind power generations are taken to control power output of the system. In the end, by comparing the effects of individual control and coordinated control of energy storage systems in a complete scheduling cycle, the effectiveness of the proposed strategy in this paper is demonstrated.
-
Key words:
- new energy /
- coordinated control /
- battery energy storage /
- Pareto optimal /
- NSGA-II
-
[1] 艾欣,董春发. 储能技术在新能源电力系统中的研究综述[J]. 现代电力,2015,32(5):19.[2] 李建林,靳文涛,惠东,等. 大规模储能在可再生能源发电中典型应用及技术走向[J]. 电器与能效管理技术,2016,14:914.[3] 胡荣,钟勇,符杨,等. 混合储能在配电网中的优化配置[J]. 现代电力,2015,32(3):4953.[4] 陶琼,桑丙玉,叶季蕾,等. 高光伏渗透率配电网中分布式储能系统的优化配置方法[J]. 高电压技术,2016(7):21582165.[5] 吴小刚,刘宗歧,田立亭,等. 基于改进多目标粒子群算法的配电网储能选址定容[J]. 电网技术,2014,38(12):34053411.[6] 尤毅,刘东,钟清,等. 主动配电网储能系统的多目标优化配置[J]. 电力系统自动化,2014,38(18):4652.[7] 赵金利,于莹莹,李鹏,等. 基于锥优化的储能系统参与配电网运行调节快速计算方法[J]. 电力系统自动化,2016,39(2):3035,48.[8] 章美丹,宋晓喆,辛焕海,等. 计及网损的配电网电池储能站优化运行策略[J]. 电网技术,2013,37(8):21232128.[9] Antunes C H, Pires D F, Barrico C, et al. A multi-objective evolutionary algorithm for reactive power compensation in distribution networks[J]. Applied Energy, 2009, 86(78):977984.[10]Coello C A C, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3):256279.[11]Ramirez-Rosado I J, Dominguez-Navarro J A. New multiobjective tabu search algorithm for fuzzy optimal planning of power distribution systems[J]. IEEE Transactions on Power Systems, 2006,21(1):224233.[12]熊超,马瑞. 考虑降损和平抑峰谷的配电网储能电池Pareto优化模型[J]. 电力建设,2015,36(8):3440.[13]孙伟卿,王承民,张焰,等. 基于Pareto最优的电力系统有功—无功综合优化[J]. 电力系统自动化,2009,33(10):3842.[14]阴皓. 基于云计算和改进NSGA-II的无功优化算法研究[D].北京:华北电力大学,2015.[15]陈婕,熊盛武,林婉如. NSGA-II算法的改进策略研究[J]. 计算机工程与应用,2011(19):4245. -
点击查看大图
计量
- 文章访问数: 513
- HTML全文浏览量: 18
- PDF下载量: 5
- 被引次数: 0
下载:
