Research on energy storage configuration for power stabilization of photovoltaic power stations

Yulong Zhao

doi:10.54097/tzrff614

Authors

Yulong Zhao

DOI:

https://doi.org/10.54097/tzrff614

Keywords:

Photovoltaic power stations, fluctuation suppression, uncertainty, capacity configuration.

Abstract

To reduce the negative impact of power fluctuation of photovoltaic power stations (PPS) on power grid, energy storage system (ESS) is often introduced to smooth out their output power fluctuations. Considering that integrating ESS increases the investment costs of PPS, an ESS capacity configuration method is proposed to stabilize the power fluctuation of PPS. The method employs a photovoltaic random output model based on Latin hypercube sampling and a scenario reduction approach based on probabilistic distance to handle the uncertainty of PPS outputs. Comprehensively consider the various costs of ESS, an optimized operation model for PPS energy storage systems is established. This model aims to minimize the total lifecycle cost of the ESS while ensuring that the PPS meets grid connection requirements. Taking the measured data of a domestic PPS as an example, the simulation is carried out to complete the optimal capacity configuration of ESS.

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References

[1] Tan Jingyang, Jiang Xiaoyan, Jiao Qianzhi, et al. Overview of research on energy storage technology to suppress photovoltaic output fluctuation [J]. Journal of Electric Power, 2023,38(05):389-396.

[2] Yang W W, Liu S J, Li P, et al. Research on optimal allocation of energy storage system in large-scale new energy transmission bases[C]. 3rd Asian Conference on Frontiers of Power and Energy, 2024: 555-561.

[3] Pang Xiulan, Xu Ximeng, Ma Chao, et al. Research on the operation mode and capacity configuration of high-capacity ratio photovoltaic power station [J]. Renewable Energy Resources, 2024, 42(11): 1511-1518.

[4] Pei S, Han C, Zhang P, et al. Energy storage system configuration considering battery characteristics for photovoltaic power stations [C]. 2021 4th International Conference on Energy, Electrical and Power Engineering (CEEPE), 2021: 120-124.

[5] Yan Y, Huang C, Guan J, et al. Stochastic optimization of solar-based distributed energy system: An error-based scenario with a day-ahead and real-time dynamic scheduling approach[J]. Applied Energy, 2024, 363: 123099-123121.

[6] Ma Wei, Xie Lirong, Xie Lirong, Ma Lan, et al. Short-term wind power prediction error and probability modeling of output fluctuation [J]. 2023, 44(11): 361-366.

[7] Yuanyu Y, Siqiao Z, Bin O, et al. Research on generation and reduction of typical operation scenarios in virtual power plants[C]. 2023 IEEE 6th International Electrical and Energy Conference (CIEEC), 2023: 2667-2671.

[8] Dong Wenlue, Wang Qun, Yang Li. A coordination and scheduling model for virtual power plants incorporating wind, solar and hydroelectric resources with distribution companies [J], Automation of Electeic Power Systems, 2015, 39(09): 75-81+207.

[9] China Electricity Council. Technical requirements for connecting photovoltaic power station to power system:GB/T 19964-2024[S]. China Standards Press, 2024.

[10] Chen Chongde, Guo Qiang, Song Ziqiu, et al. Optimal configuration of hybrid energy storage capacity for wind farms considering carbon trading revenue [J]. Electric Power, 2022,55(12):22-33.

[11] Zhang Qing, Li Xinran, Yang Ming, et al, Capacity determination of hybrid energy storage system for smoothing wind power fluctuations with maximum net benefit [J], Transactions of China Electortechnical Society, 2016, 31(14): 40-48.

[12] Li G, Yuan J, Gan D. Research on capacity optimization of new energy hybrid ESS system of high-speed railway[J]. Journal of Physics: Conference Series, 2024, 2728(1): 012012-012020.