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Optimal Energy Storage Configuration of Prosumers with Uncertain Photovoltaic in the Presence of Customized Pricing-Based Demand Response

Author

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  • Luwen Pan

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Jiajia Chen

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

Setting an acceptable pricing strategy to attract prosumers to participate in demand response and orderly configure energy storage is a critical topic for virtual power plants (VPPs) in improving sustainable development. Based on this, this paper proposes a two-layer iterative optimization to develop a customized pricing-based demand response for energy storage with uncertain photovoltaic (PV) for prosumers. In the upper layer, the VPP formulates a customized price consisting of a two-part electricity price, on-grid electricity price and auxiliary service price according to the load characteristics of prosumers, so as to make the power supply and demand of prosumers more controllable. In the lower layer, prosumers adjust their energy storage configurations and energy consumption behavior according to the price signal, considering the uncertainty of PV. The research shows that the proposed optimization approach can encourages prosumers to configure energy storage, and explore user-side flexibility resources. The full utilization of energy storage has increased the PV output of the prosumers by 10%, and its benefits have also increased by 7%.

Suggested Citation

  • Luwen Pan & Jiajia Chen, 2024. "Optimal Energy Storage Configuration of Prosumers with Uncertain Photovoltaic in the Presence of Customized Pricing-Based Demand Response," Sustainability, MDPI, vol. 16(6), pages 1-18, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2230-:d:1352684
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    References listed on IDEAS

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    1. Zakeri, Behnam & Cross, Samuel & Dodds, Paul.E. & Gissey, Giorgio Castagneto, 2021. "Policy options for enhancing economic profitability of residential solar photovoltaic with battery energy storage," Applied Energy, Elsevier, vol. 290(C).
    2. Kong, Xiangyu & Wang, Zhengtao & Liu, Chao & Zhang, Delong & Gao, Hongchao, 2023. "Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants," Applied Energy, Elsevier, vol. 334(C).
    3. Kong, Xiangyu & Lu, Wenqi & Wu, Jianzhong & Wang, Chengshan & Zhao, Xv & Hu, Wei & Shen, Yu, 2023. "Real-time pricing method for VPP demand response based on PER-DDPG algorithm," Energy, Elsevier, vol. 271(C).
    4. Wang, Ligang & Yang, Yongping & Dong, Changqing & Morosuk, Tatiana & Tsatsaronis, George, 2014. "Multi-objective optimization of coal-fired power plants using differential evolution," Applied Energy, Elsevier, vol. 115(C), pages 254-264.
    5. Wang, Ziyang & Sun, Mei & Gao, Cuixia & Wang, Xin & Ampimah, Benjamin Chris, 2021. "A new interactive real-time pricing mechanism of demand response based on an evaluation model," Applied Energy, Elsevier, vol. 295(C).
    6. Zhang, Tianhan & Qiu, Weiqiang & Zhang, Zhi & Lin, Zhenzhi & Ding, Yi & Wang, Yiting & Wang, Lianfang & Yang, Li, 2023. "Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets," Applied Energy, Elsevier, vol. 329(C).
    7. Mashlakov, Aleksei & Pournaras, Evangelos & Nardelli, Pedro H.J. & Honkapuro, Samuli, 2021. "Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties," Applied Energy, Elsevier, vol. 290(C).
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