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Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants

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  • Kong, Xiangyu
  • Wang, Zhengtao
  • Liu, Chao
  • Zhang, Delong
  • Gao, Hongchao

Abstract

There is a consensus regarding the need to realize the transformation of renewable energy by enhancing demand-side regulating ability. This paper proposes a peak shaving potential assessment model based on the price elasticity mechanism and consumer psychology, focusing on the adjustable user load in virtual power plants. The values of deterministic parameters and the distribution of the uncertain parameter of the model are obtained through the long short-term memory network (LSTM) and mixture density network (MDN). Then, the refined distribution of peak shaving potential considering external conditions, incentive inputs, and spatial and temporal scales is obtained. Based on the evaluation results, a peak shaving decision-making model for virtual power plants is constructed using a scenario scheme. Differentiated schemes for traditional, risk-averse, and risk-seeking virtual power plant decision-makers are considered. Case studies using the data of a virtual power plant pilot area show that the proposed model can better characterize the features of virtual power plant users, and a refined control strategy with better economic benefits can be obtained.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261922018669
    DOI: 10.1016/j.apenergy.2022.120609
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    as
    1. El Geneidy, Rami & Howard, Bianca, 2020. "Contracted energy flexibility characteristics of communities: Analysis of a control strategy for demand response," Applied Energy, Elsevier, vol. 263(C).
    2. Yang, Qing & Wang, Hao & Wang, Taotao & Zhang, Shengli & Wu, Xiaoxiao & Wang, Hui, 2021. "Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant," Applied Energy, Elsevier, vol. 294(C).
    3. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    4. Bhuiyan, Erphan A. & Hossain, Md. Zahid & Muyeen, S.M. & Fahim, Shahriar Rahman & Sarker, Subrata K. & Das, Sajal K., 2021. "Towards next generation virtual power plant: Technology review and frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    5. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    6. Bartusch, Cajsa & Alvehag, Karin, 2014. "Further exploring the potential of residential demand response programs in electricity distribution," Applied Energy, Elsevier, vol. 125(C), pages 39-59.
    7. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    8. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    9. Ming, Hao & Meng, Jing & Gao, Ciwei & Song, Meng & Chen, Tao & Choi, Dae-Hyun, 2023. "Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design," Applied Energy, Elsevier, vol. 331(C).
    10. Dranka, Géremi Gilson & Ferreira, Paula, 2019. "Review and assessment of the different categories of demand response potentials," Energy, Elsevier, vol. 179(C), pages 280-294.
    11. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
    12. Ibrahim, Charles & Mougharbel, Imad & Kanaan, Hadi Y. & Daher, Nivine Abou & Georges, Semaan & Saad, Maarouf, 2022. "A review on the deployment of demand response programs with multiple aspects coexistence over smart grid platform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    13. Parrish, Bryony & Heptonstall, Phil & Gross, Rob & Sovacool, Benjamin K., 2020. "A systematic review of motivations, enablers and barriers for consumer engagement with residential demand response," Energy Policy, Elsevier, vol. 138(C).
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    Cited by:

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    3. Wenhui Zhao & Zilin Wu & Bo Zhou & Jiaoqian Gao, 2024. "Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives," Sustainability, MDPI, vol. 16(8), pages 1-23, April.
    4. Wang, Zhenyi & Zhang, Hongcai, 2024. "Customer baseline load estimation for virtual power plants in demand response: An attention mechanism-based generative adversarial networks approach," Applied Energy, Elsevier, vol. 357(C).
    5. Gao, Hongchao & Jin, Tai & Feng, Cheng & Li, Chuyi & Chen, Qixin & Kang, Chongqing, 2024. "Review of virtual power plant operations: Resource coordination and multidimensional interaction," Applied Energy, Elsevier, vol. 357(C).
    6. Bao, Peng & Xu, Qingshan & Yang, Yongbiao & Nan, Yu & Wang, Yucui, 2024. "Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations," Applied Energy, Elsevier, vol. 358(C).
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