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Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy

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  • Wang, Sen
  • Li, Fengting
  • Zhang, Gaohang
  • Yin, Chunya

Abstract

Energy storage (ES) can mitigate the pressure of peak shaving and frequency regulation in power systems with high penetration of renewable energy (RE) caused by uncertainty and inflexibility. However, the demand for ES capacity to enhance the peak shaving and frequency regulation capability of power systems with high penetration of RE has not been clarified at present. In this context, this study provides an approach to analyzing the ES demand capacity for peak shaving and frequency regulation. Firstly, to portray the uncertainty of the net load, a scenario set generation method is proposed based on the quantile regression analysis and Gaussian mixture model clustering. Then, a multi-scenario and multi-time scale optimal operation model is established to handle the uncertainty of net load, and the power correction model for ES operations is established to accommodate the balance of ES charging/discharging and optimization of system operation cost. Finally, based on the solution results of the above models, the method for determining the system's demand for ES capacity is proposed, and the relationship between the penetration of RE, ES power and capacity, and the confidence level of meeting demand is obtained. Numerical studies show that with a confidence level of 90% for satisfying demand, the 49.5% RE penetration system (the maximum load is 9896.42 MW) needs ES power and capacity of 1358 MW and 4122 MWh for peaking and ES power and capacity of 478 MW and 47 MWh for frequency regulation. Further, as the penetration of RE increases, the proportion of ES demand power to the system's power supply capacity and duration demand of ES also increase.

Suggested Citation

  • Wang, Sen & Li, Fengting & Zhang, Gaohang & Yin, Chunya, 2023. "Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy," Energy, Elsevier, vol. 267(C).
  • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222034739
    DOI: 10.1016/j.energy.2022.126586
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    as
    1. Zhang, S. & Mishra, Y. & Shahidehpour, M., 2017. "Utilizing distributed energy resources to support frequency regulation services," Applied Energy, Elsevier, vol. 206(C), pages 1484-1494.
    2. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    3. Pilpola, Sannamari & Lund, Peter D., 2020. "Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways," Energy, Elsevier, vol. 201(C).
    4. Cárdenas, Bruno & Swinfen-Styles, Lawrie & Rouse, James & Hoskin, Adam & Xu, Weiqing & Garvey, S.D., 2021. "Energy storage capacity vs. renewable penetration: A study for the UK," Renewable Energy, Elsevier, vol. 171(C), pages 849-867.
    5. Niu, Jide & Tian, Zhe & Yue, Lu, 2020. "Robust optimal design of building cooling sources considering the uncertainty and cross-correlation of demand and source," Applied Energy, Elsevier, vol. 265(C).
    6. Lu, Mengke & Guan, Jun & Wu, Huahua & Chen, Huizhe & Gu, Wei & Wu, Ye & Ling, ChengXiang & Zhang, Linqiang, 2022. "Day-ahead optimal dispatching of multi-source power system," Renewable Energy, Elsevier, vol. 183(C), pages 435-446.
    7. Buonomano, Annamaria & Calise, Francesco & d'Accadia, Massimo Dentice & Vicidomini, Maria, 2018. "A hybrid renewable system based on wind and solar energy coupled with an electrical storage: Dynamic simulation and economic assessment," Energy, Elsevier, vol. 155(C), pages 174-189.
    8. Chen, Xiaojiao & Huang, Liansheng & Liu, Junbo & Song, Dongran & Yang, Sheng, 2022. "Peak shaving benefit assessment considering the joint operation of nuclear and battery energy storage power stations: Hainan case study," Energy, Elsevier, vol. 239(PA).
    9. Yuan, Wenlin & Xin, Wenpeng & Su, Chengguo & Cheng, Chuntian & Yan, Denghua & Wu, Zening, 2022. "Cross-regional integrated transmission of wind power and pumped-storage hydropower considering the peak shaving demands of multiple power grids," Renewable Energy, Elsevier, vol. 190(C), pages 1112-1126.
    10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    11. Schill, Wolf-Peter & Zerrahn, Alexander, 2018. "Long-run power storage requirements for high shares of renewables: Results and sensitivities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 156-171.
    12. Guo, Li & Hou, Ruosong & Liu, Yixin & Wang, Chengshan & Lu, Hai, 2020. "A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid," Applied Energy, Elsevier, vol. 263(C).
    13. Zhao, Pan & Dai, Yiping & Wang, Jiangfeng, 2014. "Design and thermodynamic analysis of a hybrid energy storage system based on A-CAES (adiabatic compressed air energy storage) and FESS (flywheel energy storage system) for wind power application," Energy, Elsevier, vol. 70(C), pages 674-684.
    14. Verástegui, Felipe & Lorca, Álvaro & Olivares, Daniel & Negrete-Pincetic, Matias, 2021. "Optimization-based analysis of decarbonization pathways and flexibility requirements in highly renewable power systems," Energy, Elsevier, vol. 234(C).
    15. You, Wei & Geng, Yong & Dong, Huijuan & Wilson, Jeffrey & Pan, Hengyu & Wu, Rui & Sun, Lu & Zhang, Xi & Liu, Zhiqing, 2018. "Technical and economic assessment of RES penetration by modelling China's existing energy system," Energy, Elsevier, vol. 165(PB), pages 900-910.
    16. Liu, Ye & Wu, Xiaogang & Du, Jiuyu & Song, Ziyou & Wu, Guoliang, 2020. "Optimal sizing of a wind-energy storage system considering battery life," Renewable Energy, Elsevier, vol. 147(P1), pages 2470-2483.
    17. Ghahramani, Mehrdad & Nazari-Heris, Morteza & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2019. "Energy and reserve management of a smart distribution system by incorporating responsive-loads /battery/wind turbines considering uncertain parameters," Energy, Elsevier, vol. 183(C), pages 205-219.
    18. Zhang, Xuehan & Son, Yongju & Cheong, Taesu & Choi, Sungyun, 2022. "Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation," Energy, Elsevier, vol. 242(C).
    19. McPherson, Madeleine & Tahseen, Samiha, 2018. "Deploying storage assets to facilitate variable renewable energy integration: The impacts of grid flexibility, renewable penetration, and market structure," Energy, Elsevier, vol. 145(C), pages 856-870.
    20. Frazier, A. Will & Cole, Wesley & Denholm, Paul & Greer, Daniel & Gagnon, Pieter, 2020. "Assessing the potential of battery storage as a peaking capacity resource in the United States," Applied Energy, Elsevier, vol. 275(C).
    21. Solomon, A.A. & Bogdanov, Dmitrii & Breyer, Christian, 2019. "Curtailment-storage-penetration nexus in the energy transition," Applied Energy, Elsevier, vol. 235(C), pages 1351-1368.
    22. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2016. "Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory," Energy, Elsevier, vol. 114(C), pages 846-861.
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