基于概率潮流的光伏电站中储能系统的优化配置方法

doi:10.19799/j.cnki.2095-4239.2021.0151

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (6): 2244-2251.doi: 10.19799/j.cnki.2095-4239.2021.0151

基于概率潮流的光伏电站中储能系统的优化配置方法

张德隆(), MUBAARAK Saif, 蒋思宇, 王龙泽, 刘金鑫, 陈永聪, 李美成()

华北电力大学新能源学院，北京 102206

收稿日期:2021-04-12 修回日期:2021-04-16 出版日期:2021-11-05 发布日期:2021-11-03
作者简介:张德隆（1988—），男，博士，博士后，研究方向为储能系统、综合能源系统的优化运行与规划，E-mail：zhangdelong@ncepu.edu.cn|李美成，教授，博士生导师，研究方向为太阳能电池、锂离子电池等新能源材料与器件，E-mail：mcli@ncepu.edu.cn。
基金资助:
国家自然科学基金项目(71974055);国家电网公司科技项目(SGJX0000KXJS1900321);北京市科技计划项目(Z18110000511802);中央高校基本科研课题(2020FR002)

Optimal allocation method of energy storage in PV station based on probabilistic power flow

Delong ZHANG(), Saif MUBAARAK, Siyu JIANG, Longze WANG, Jinxin LIU, Yongcong CHEN, Meicheng LI()

School of New Energy, North China Electric Power University, Beijing 102206, China

Received:2021-04-12 Revised:2021-04-16 Online:2021-11-05 Published:2021-11-03

摘要/Abstract

摘要：

高比例光伏发电接入会对电力系统的稳定性等造成不利影响，而储能则被认为是消除这些影响的有效手段之一。本文从电力系统潮流的角度，分析了光伏发电对电力系统的影响，进而分析了储能对抑制这种影响的作用。首先，介绍了电力系统中元件的概率分布模型和储能模型，以及拉丁超立方采样法及Gram-Schmidt序列正交化方法；其次，建立了多目标优化模型，模型考虑了储能系统的成本、支路潮流的越限概率和电网的网络损耗，并用遗传算法求取目标函数的最优解；最后，在IEEE24节点测试系统中进行了仿真，分析了光伏不同接入容量和接入位置对电力系统的影响，储能对电力系统的作用，得出了对应不同光伏容量的储能最优配置。

关键词: 光伏发电, 储能系统, 优化配置, 概率潮流, 遗传算法

Abstract:

Large-scale photovoltaic (PV) stations will adversely affect the stability of the power system, while energy storage is considered to be one of the effective means to eliminate these effects. In this paper, the influence of PV plants on the power system and the function of the energy storage system (ESS) are analyzed from the perspective of power flow. First, the probability distribution model of power system components, storage model, Latin hypercube sampling (LHS) method, and Gram-Schmidt orthogonal sequence method are introduced in this paper. Second, this paper establishes the multiple objective optimization models that consider the energy storage system costs, the probability of a branch remaining active beyond the limit, and network loss. The genetic algorithm is used to find the best solution to the objective function (GA). Finally, the simulation is run on the IEEE 24 bus test system, including the effects of different PV capacities and access positions, as well as the effect of ESS. In this simulation, the optimal energy storage configuration for various PV capacities is obtained.

Key words: PV power generation, energy storage system, optimal configuration, probabilistic power flow, genetic algorithm

中图分类号:

TM 910.1

张德隆, MUBAARAK Saif, 蒋思宇, 王龙泽, 刘金鑫, 陈永聪, 李美成. 基于概率潮流的光伏电站中储能系统的优化配置方法[J]. 储能科学与技术, 2021, 10(6): 2244-2251.

Delong ZHANG, Saif MUBAARAK, Siyu JIANG, Longze WANG, Jinxin LIU, Yongcong CHEN, Meicheng LI. Optimal allocation method of energy storage in PV station based on probabilistic power flow[J]. Energy Storage Science and Technology, 2021, 10(6): 2244-2251.

图/表 8

图1

图2

图3

图4

图5

图6

图7

图8

参考文献 21

1	WAN Can, ZHAO Jian, SONG Yonghua, et al. Photovoltaic and solar power forecasting for smart grid energy management[J]. CSEE Journal of Power and Energy Systems, 2015, 1(4): 38-46.
2	国家能源局. 国家能源局2021年一季度网上新闻发布会文字实录[EB/OL]. http://www.nea.gov.cn/2021-01/30/c_139708580.htm. 2021-01-30.
	National Energy Administration. Transcript of the National Energy Administration's online press conference for the first quarter of 2021[EB/OL]. http://www.nea.gov.cn/2021-01/30/c_139708580.htm. 2021-01-30.
3	CHEN J C, SONG X D. Economics of energy storage technology in active distribution networks[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(4): 583-588.
4	YUAN Y, CAO Y, ZHANG X S, et al. Optimal proportion of wind and PV capacity in provincial power systems based on bilevel optimization algorithm under low-carbon economy[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(1): 33-40.
5	ALAM M J E, YAN R, SAHA T K. Desirable control features of battery energy storage systems for commercial scale solar PV plants[C]//2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2015.
6	ZHENG Y, DONG Z Y, HUANG S L, et al. Optimal integration of mobile battery energy storage in distribution system with renewables[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(4): 589-596.
7	LI Jianing, Wu Zhi, ZHOU Suyang, et al., Aggregator service for PV and battery energy storage systems of residential building[J]. CSEE Journal of Power and Energy Systems, 2015, 1(4): 3-11.
8	DOUKAS D I, BAKAS P, MARINOPOULOS A, et al. Energy storage integration into grid connected utility-scale photovoltaic (PV) systems[J]. 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013, 2013.
9	DOUKAS D I, PAPASTERGIOU K, BAKAS P, et al. Energy storage sizing for large scale PV power plants base-load operation-comparative study & results[C]// 2012 38th IEEE Photovoltaic Specialists Conference, 2012.
10	BOSE S, GAYME D F, TOPCU U, et al. Optimal placement of energy storage in the grid[C]// 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012.
11	DVIJOTHAM K, BACKHAUS S, CHERTKOV M. Operations-based planning for placement and sizing of energy storage in a grid with a high penetration of renewables[EB/OL]. 2011: arXiv: 1107.1382[math.OC]. https://arxiv.org/abs/1107.1382
12	陈璨, 吴文传, 张伯明, 等. 考虑光伏出力相关性的配电网概率潮流[J]. 电力系统自动化, 2015, 39(9): 41-47.
	CHEN C, WU W C, ZHANG B M, et al. Probabilistic load flow of distribution network considering correlated photovoltaic power output[J]. Automation of Electric Power Systems, 2015, 39(9): 41-47.
13	任洲洋, 颜伟, 项波, 等. 考虑光伏和负荷相关性的概率潮流计算[J]. 电工技术学报, 2015, 30(24): 181-187.
	REN Z Y, YAN W, XIANG B, et al. Probabilistic power flow analysis incorporating the correlations between PV power outputs and loads[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 181-187.
14	XU Y, JEWELL W T, PANG C. Optimal location of electrical energy storage unit in a power system with wind energy[C]//2014: IEEE.
15	刘宇, 高山, 杨胜春, 等. 电力系统概率潮流算法综述[J]. 电力系统自动化, 2014, 38(23): 127-135.
	LIUYU, GAO S, YANG S C, et al. Review on algorithms for probabilistic load flow in power system[J]. Automation of Electric Power Systems, 2014, 38(23): 127-135.
16	GHOFRANI M, ARABALI A, ETEZADI-AMOLI M, et al. A framework for optimal placement of energy storage units within a power system with high wind penetration[J]. IEEE Transactions on Sustainable Energy, 2013, 4(2): 434-442.
17	YU H, ZHONG Z Y, HUANG J B, et al. Power system probabilistic power flow calculation method using Latin hypercube sampling[J]. Automation of Electric Power System, 2009,33(21): 32-35.
18	GHOFRANI M, ARABALI A. A stochastic framework for power system operation with wind generation and energy storage integration[C]//ISGT 2014.
19	李俊芳, 张步涵. 基于进化算法改进拉丁超立方抽样的概率潮流计算[J]. 中国电机工程学报, 2011, 31(25): 90-96.
	LI J F, ZHANG B H. Probabilistic load flow based on improved Latin hypercube sampling with evolutionary algorithm[J]. Proceedings of the CSEE, 2011, 31(25): 90-96.
20	HOLLAND J H. Adaptation in natural and artificial systems[M].Cambridge: MIT Press, 1975
21	于晗, 钟志勇, 黄杰波, 等. 采用拉丁超立方采样的电力系统概率潮流计算方法[J]. 电力系统自动化, 2009, 33(21): 32-35,81.YU H, CHUNG C Y, WONG K, et al. A probabilistic load flow calculation method with Latin hypercube sampling[J]. Automation of Electric Power Systems, 2009, 33(21): 32-35, 81.

[1]	黄鹏, 聂枝根, 陈峥, 舒星, 沈世全, 杨继鹏, 申江卫. 基于优化Elman神经网络的锂电池容量预测[J]. 储能科学与技术, 2022, 11(7): 2282-2294.
[2]	郭雨涵, 郁丹, 杨鹏, 王子绩, 王金涛. 基于贪婪算法的分布式储能系统容量优化配置方法[J]. 储能科学与技术, 2022, 11(7): 2295-2304.
[3]	吴田, 林闽城, 海浩, 孙海渔, 温兆银, 马福元. 面向一次调频的镍氢电池系统开发[J]. 储能科学与技术, 2022, 11(7): 2213-2221.
[4]	张平, 康利斌, 王明菊, 赵广, 罗振华, 唐堃, 陆雅翔, 胡勇胜. 钠离子电池储能技术及经济性分析[J]. 储能科学与技术, 2022, 11(6): 1892-1901.
[5]	陶飞跃, 王焕然, 李瑞雄, 赵静, 葛刚强, 贺新, 陈昊. 利用环境再冷的二氧化碳储能热电联产系统及其热力学分析[J]. 储能科学与技术, 2022, 11(5): 1492-1501.
[6]	李昊, 刘畅, 苗博, 张静. 考虑冷热电互补及储能系统的多园区综合能源系统协调优化调度[J]. 储能科学与技术, 2022, 11(5): 1482-1491.
[7]	田峰, 程志江, 杨涵棣, 杨天翔. 模块化多电平复合变换器电池储能系统容错控制策略[J]. 储能科学与技术, 2022, 11(5): 1583-1591.
[8]	李建林, 张则栋, 李雅欣, 周毅, 岳云力. 碳中和目标下移动式储能系统关键技术[J]. 储能科学与技术, 2022, 11(5): 1523-1536.
[9]	韩帅, 孙乐平, 卢健斌, 郭小璇. 含电动汽车的气电互联虚拟电厂区间多目标优化调度策略[J]. 储能科学与技术, 2022, 11(5): 1428-1436.
[10]	范馥麟, 李军徽, 严干贵, 李翠萍. 储能友好型频率响应服务市场：英国视角[J]. 储能科学与技术, 2022, 11(4): 1278-1288.
[11]	刘迪, 张甜甜, 彭宇维, 唐晓梅, 王丹, 毛承雄. 压缩空气储能系统释能环节轴系建模与振荡分析[J]. 储能科学与技术, 2022, 11(2): 563-572.
[12]	郭斌, 邢洁, 姚飞, 景小敏. 基于双层规划模型的用户侧混合储能优化配置[J]. 储能科学与技术, 2022, 11(2): 615-622.
[13]	朱信龙, 王均毅, 潘加爽, 康传智, 邹燚涛, 杨凯杰, 施红. 集装箱储能系统热管理系统的现状及发展[J]. 储能科学与技术, 2022, 11(1): 107-118.
[14]	高晓芝, 王磊, 田晋, 刘佳璐, 刘庆华. 基于参数优化变分模态分解的混合储能功率分配策略[J]. 储能科学与技术, 2022, 11(1): 147-155.
[15]	沈渭程, 甄文喜, 邵冲, 谢琦. 基于超导磁储能系统的双馈风机协同故障穿越策略[J]. 储能科学与技术, 2022, 11(1): 136-146.

基于概率潮流的光伏电站中储能系统的优化配置方法

Optimal allocation method of energy storage in PV station based on probabilistic power flow

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价