Optimal Allocation of Energy Storage Capacity of Power Generation System Based on Probabilistic Production Simulation

LUO Ding

Distributed Energy ›› 2021, Vol. 6 ›› Issue (1) : 27-34.

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Distributed Energy ›› 2021, Vol. 6 ›› Issue (1) : 27-34. DOI: 10.16513/j.2096-2185.DE.2106003
Basic Research

Optimal Allocation of Energy Storage Capacity of Power Generation System Based on Probabilistic Production Simulation

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Abstract

Configuring energy storage in the power generation system can improve the flexibility of the system and relieve the great pressure brought by the grid connection of high proportion renewable energy to peak shaving. Among many energy storage technologies, battery energy storage is favored, and many demonstration projects of battery energy storage have been built around the world to promote its development. Reasonable allocation of energy storage capacity at the power supply end is helpful to improve the economy of power generation system. Therefore, an optimal allocation model of storage battery capacity in power generation system based on probabilistic production simulation was proposed. The model aims at minimizing the comprehensive cost of power generation system, taking into account the operation constraints of system, generator set and energy storage; The probabilistic production simulation algorithm with stored energy was used to optimize the charging and discharging scheduling strategy and optimal allocation capacity of stored energy. The effectiveness of the proposed method and model is verified by IEEE-RTS79 test system, which can provide reference for energy storage power market operation and power supply planning.

Key words

battery energy storage / peak shaving / capacity configuration / probabilistic production simulation / comprehensive cost

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Ding LUO. Optimal Allocation of Energy Storage Capacity of Power Generation System Based on Probabilistic Production Simulation[J]. Distributed Energy Resources. 2021, 6(1): 27-34 https://doi.org/10.16513/j.2096-2185.DE.2106003

References

[1]
王冰,王楠,田政,等. 美国电化学储能产业政策分析及对我国储能产业发展的启示与建议[J]. 分布式能源2020, 5(3): 23-28.
WANG Bing, WANG Nan, TIAN Zheng, et al. Policy analysis of electrochemical energy storage industry in United States and its enlightenment and suggestions for development of China's energy storage industry[J]. Distributed Energy, 2020, 5(3): 23-28.
[2]
黎静华,汪赛. 兼顾技术性和经济性的储能辅助调峰组合方案优化[J]. 电力系统自动化2017, 41(9): 44-50, 150.
LI Jinghua, WANG Sai. Control strategy for battery energy storage system based on modular multilevel convers[J]. Automation of Electric Power Systems, 2017, 41(9): 44-50,150.
[3]
孙伟卿,宋赫,秦艳辉,等. 考虑灵活性供需不确定性的储能优化配置[J]. 电网技术2020, 44(12): 4486-4497.
SUN Weiqing, SONG He, QIN Yanhui, et al. Optimal allocation of energy storage considering the uncertainty of flexible supply and demand[J]. Power System Technology, 2020, 44(12): 4486-4497.
[4]
徐国栋,程浩忠,马紫峰,等. 用于缓解电网调峰压力的储能系统规划方法综述[J]. 电力自动化设备2017, 37(8): 3-11.
XU Guodong, CHENG Haozhong, MA Zifeng, et al. Overview of ESS planning methods for alleviating peak-shaving pressure of grid[J]. Electric Power Automation Equipment, 2017, 37(8): 3-11.
[5]
张智勤,刘艳. 低碳背景下基于展开型表述的随机生产模拟[J]. 电力系统自动化2017, 41(3): 54-60.
ZHANG Zhiqin, LIU Yan. Power system probabilistic production simulation based on extensive-form representation in low-carbon context[J]. Automation of Electric Power Systems, 2017, 41(3): 54-60.
[6]
朱睿,胡博,谢开贵,等. 含风电-光伏-光热-水电-火电-储能的多能源电力系统时序随机生产模拟[J]. 电网技术2020, 44(9): 3246-3253.
ZHU Rui, HU Bo, XIE Kaigui, et al. Time series probabilistic production simulation of multi-energy power system including wind power, photovoltaic, photothermal, hydropower, thermal power and energy storage[J]. Power System Technology, 2020, 44(9): 3246-3253.
[7]
廖庆龙,谢开贵,胡博. 含风电和储能电力系统的时序随机生产模拟[J]. 电网技术2017, 41(9): 2769-2776.
LIAO Qinglong, XIE Kaigui, HU Bo. Sequential probabilistic production simulation of power systems with wind power and energy storage[J]. Power System Technology, 2017, 41(9): 2769-2776.
[8]
肖云鹏,王锡凡,王秀丽. 基于随机生产模拟的直购电交易成本效益分析[J]. 电网技术2016, 40(11): 3287-3292.
XIAO Yunpeng, WANG Xifan, WANG Xiuli. Cost and benefit analysis on direct electricity purchase transaction based on probabilistic production simulation[J]. Power System Technology, 2016, 40(11): 3287-3292.
[9]
徐昊亮,靳攀润,姜继恒,等. 基于随机生产模拟的火电灵活性改造容量规划[J]. 全球能源互联网2020, 3(4): 393-403.
XU Haoliang, JIN Panrun, JIANG Jiheng, et al. Capacity optimal plan of thermal power flexibility transformation based on probabilistic production simulation[J]. JournaI of GIobaI Energy Interconnection, 2020, 3(4): 393-403.
[10]
周明,李琰,李庚银. 基于随机生产模拟的日前发电-备用双层决策模型[J]. 电网技术2019, 43(5): 1606-1613.
ZHOU Ming, LI Yan, LI Gengyin. A day-ahead power generation-reserve bi-level decision-making model for power system based on probabilistic production simulation[J]. Power System Technology, 2019, 43(5): 1606-1613.
[11]
刘纯,屈姬贤,石文辉. 基于随机生产模拟的新能源消纳能力评估方法[J]. 中国电机工程学报2020, 40(10): 3134-3144.
LIU Chun, QU Jixian, SHI Wenhui. Evaluating method of ability of accommodating renewable energy based on probabilistic production simulation[J]. Proceedings of the CSEE, 2020, 40(10): 3134-3144.
[12]
罗定,刘艳. 基于随机生产模拟的光电消纳能力评估[J]. 分布式能源2018, 3(2): 9-15.
LUO Ding, LIU Yan. Evaluation of PV absorptive capacity based on probabilistic production simulation[J]. Distributed Energy, 2018, 3(2): 9-15.
[13]
Probabilistic Methods Subcommittee. IEEE reliability test system[J]. IEEE Transactions on power Apparatus and Systems, 1979, PAS-98(6): 2047-2054.
[14]
ALLAN R N, BILLINTON R, ABDEL-GAWAD N M K. The IEEE reliability test system—extensions to and evaluation of the generating system[J]. IEEE Transactions on Power Systems, 1986, 1(4): 1-7.
[15]
MALIK A S, CORY B J. An application of frequency and duration approach in generation planning[J]. IEEE Transactions on Power Systems, 1997, 12(3): 1076-1084.
[16]
许汝文,田雁冰. 发电厂的环境成本分析[J]. 内蒙古环境保护2004, 16(4): 24-27.
XU Ruwen, TIAN Yanbing. The environmental cost analysis of power generation plant[J]. Inner Mongolia Environmental Protection, 2004, 16(4): 24-27.
[17]
ELIA. Wind-power generation data[EB/OL]. [2020-05-18].
[18]
胡阳春. 基于改进k均值聚类算法的电力负荷模式识别方法研究[D]. 成都:电子科技大学,2018.
HU Yangchun. Research on power load pattern recognition method based on improved k-means clustering algorithm[D]. Chengdu: University of Electronic Science and Technology of China, 2018.
[19]
吴玮坪,胡泽春,宋永华. 结合随机规划和序贯蒙特卡洛模拟的风电场储能优化配置方法[J]. 电网技术2018, 42(4): 1055-1062.
WU Weiping, HU Zechun, SONG Yonghua. Optimal sizing of energy storage system for wind farms combining stochastic programming and sequential Monte Carlo simulation[J]. Power System Technology, 2018, 42(4): 1055-1062.
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