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Optimal energy storage planning for stacked benefits in power distribution network

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  • Gu, Chenjia
  • Wang, Jianxue
  • Zhang, Yao
  • Li, Qingtao
  • Chen, Yang

Abstract

Energy storage system (ESS) is regarded as an effective tool to promote energy utilization efficiency and deal with the operational risk of the power distribution network (PDN), which is caused by the inherent uncertainties of distributed energy resources and the surging of new loads from emerging energy sectors. Multiple benefits could be accrued by ESSs when providing various services to the PDN. However, investing in ESSs without a comprehensive cost-benefit analysis will underestimate the ESSs’ profitability and result in a suboptimal planning scheme. To this end, this paper proposes a bi-level ESS planning method considering the stacked benefits of ESSs. First, to facilitate the comprehensive cost-benefit analysis in the investment decision making process, systematic definitions and quantitative methods of ESS benefits are proposed to evaluate both the investment and operational benefits of ESSs properly. Novel ESS reserve model and load hosting capacity evaluation model are also developed to improve the accuracy of the benefit assessment. Second, a game theory based bi-level programming method is proposed to precisely capture the two-layer dynamic interaction structure of ESS planning using an easy-to-implement mathematical optimization-based formulation with global optimum guarantee. Specifically, the upper level optimizes the sites and sizes of ESSs for maximizing the stacked benefits, while the lower level economic dispatch model determines the optimal scheduling of the installed ESSs. Finally, to make the bi-level ESS planning problem tractable, it is reformulated as a single-level mathematical programming with equilibrium constraints by the duality-based reformulation. The linearization method is applied to further convert it into a mixed-integer programming problem that could be directly solved by commercial solvers. Numerical results verify that the proposed planning strategy could yield cost-effective results under different settings. Comprehensive cost-benefit analysis also indicates that transmission and distribution system upgrade deferral, reserve/regulation provision and energy arbitrage are the top-three applications for maximizing ESS benefits, and it is not always reasonable to keep investing in ESSs as their marginal benefits will decrease to zero with the increasing ESS investment.

Suggested Citation

  • Gu, Chenjia & Wang, Jianxue & Zhang, Yao & Li, Qingtao & Chen, Yang, 2022. "Optimal energy storage planning for stacked benefits in power distribution network," Renewable Energy, Elsevier, vol. 195(C), pages 366-380.
    • Handle: RePEc:eee:renene:v:195:y:2022:i:c:p:366-380
    DOI: 10.1016/j.renene.2022.06.029
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    References listed on IDEAS

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    1. Grimm, Veronika & Grübel, Julia & Rückel, Bastian & Sölch, Christian & Zöttl, Gregor, 2020. "Storage investment and network expansion in distribution networks: The impact of regulatory frameworks," Applied Energy, Elsevier, vol. 262(C).
    2. Sardi, Junainah & Mithulananthan, N. & Gallagher, M. & Hung, Duong Quoc, 2017. "Multiple community energy storage planning in distribution networks using a cost-benefit analysis," Applied Energy, Elsevier, vol. 190(C), pages 453-463.
    3. Bai, Linquan & Jiang, Tao & Li, Fangxing & Chen, Houhe & Li, Xue, 2018. "Distributed energy storage planning in soft open point based active distribution networks incorporating network reconfiguration and DG reactive power capability," Applied Energy, Elsevier, vol. 210(C), pages 1082-1091.
    4. Bozorgavari, Seyed Aboozar & Aghaei, Jamshid & Pirouzi, Sasan & Nikoobakht, Ahmad & Farahmand, Hossein & Korpås, Magnus, 2020. "Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    5. Kumar, Abhishek & Meena, Nand K. & Singh, Arvind R. & Deng, Yan & He, Xiangning & Bansal, R.C. & Kumar, Praveen, 2019. "Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1205-1230.
    7. He, Hongjie & Du, Ershun & Zhang, Ning & Kang, Chongqing & Wang, Xuebin, 2021. "Enhancing the power grid flexibility with battery energy storage transportation and transmission switching," Applied Energy, Elsevier, vol. 290(C).
    8. He, Yi & Guo, Su & Zhou, Jianxu & Ye, Jilei & Huang, Jing & Zheng, Kun & Du, Xinru, 2022. "Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages," Renewable Energy, Elsevier, vol. 184(C), pages 776-790.
    9. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    10. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
    11. Gu, Chenjia & Zhang, Yao & Wang, Jianxue & Li, Qingtao, 2021. "Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle," Applied Energy, Elsevier, vol. 301(C).
    12. Canizes, Bruno & Soares, João & Lezama, Fernando & Silva, Cátia & Vale, Zita & Corchado, Juan M., 2019. "Optimal expansion planning considering storage investment and seasonal effect of demand and renewable generation," Renewable Energy, Elsevier, vol. 138(C), pages 937-954.
    13. Xia, Shiwei & Chan, K.W. & Luo, Xiao & Bu, Siqi & Ding, Zhaohao & Zhou, Bin, 2018. "Optimal sizing of energy storage system and its cost-benefit analysis for power grid planning with intermittent wind generation," Renewable Energy, Elsevier, vol. 122(C), pages 472-486.
    14. Zhao, Bo & Ren, Junzhi & Chen, Jian & Lin, Da & Qin, Ruwen, 2020. "Tri-level robust planning-operation co-optimization of distributed energy storage in distribution networks with high PV penetration," Applied Energy, Elsevier, vol. 279(C).
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