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Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities

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  • Cai, Qiran
  • Xu, Qingyang
  • Qing, Jing
  • Shi, Gang
  • Liang, Qiao-Mei

Abstract

A power system dominated by renewable energy is one of the key measures for achieving carbon neutrality. Demand response (DR) is a promising flexible resource for alleviating the supply-demand matching of high-proportion renewable energy systems. With the application of modern technologies, the potential for residential DR is growing. Electricity price is the key to improving residential DR capacity. However, existing dynamic pricing programs may fail to motivate end-users to adjust demand based on fluctuations in wind and photovoltaic (PV) output. This study proposes a dynamic pricing model that combines the fluctuation characteristics of residential electricity demand and wind and PV output, and utilizes bi-level optimization to coordinately dispatch the flexible loads. A case study of smart residential community consisting of 200 households shows that dynamic pricing incentivizes residential consumers to shift flexible loads from morning and evening to noon or early morning, which effectively improves the degree of matching between wind and PV output and residential electricity demand. Moreover, bi-level optimization effectively alleviates the potential rebound peak caused by large-scale residential participation in DR and achieves a relatively flat net grid demand profile. Furthermore, the dynamic pricing can incentivize residential consumers to participate in DR by reducing electricity bills.

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  • Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222021776
    DOI: 10.1016/j.energy.2022.125293
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    4. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    5. Shu, Lei & Mo, Yunjeong & Zhao, Dong, 2024. "Energy retrofits for smart and connected communities: Scopes and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    6. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
    7. Nirbheram, Joshi Sukhdev & Mahesh, Aeidapu & Bhimaraju, Ambati, 2023. "Techno-economic analysis of grid-connected hybrid renewable energy system adapting hybrid demand response program and novel energy management strategy," Renewable Energy, Elsevier, vol. 212(C), pages 1-16.
    8. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging," Energy, Elsevier, vol. 293(C).
    9. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
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