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A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations

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  • Srivastava, Mahima
  • Tiwari, Prashant Kumar

Abstract

The operation of generating resources connected to VPPs during peak demand is crucial for determining the economic sustainability of participants in a competitive electricity market and minimizing the need for expensive peaking power plants. This paper proposes a profit driven strategy for scheduling of resources associated with VPP during peak load demand in competitive electricity market followed by different machine learning approaches for predicting the pattern of generation in DAM. The MILP has been developed by using GAMS integrated with MATLAB. The Intra-VPPs consisting TGs and DERs have been designed by using IEEE 33 bus distribution system. The performance of this system aligns with cases which investigate the interplay of EM dynamics, DERs contribution and scheduling of TGs, in order to optimize the operation of VPPs and assured the profits along with generating power amount for given time and load demand. Additionally, the generation pattern for future load in DAM operations has been forecasted using different neural network fitting models for enhancing the system's efficiency. The proposed work reduced the operating cost, nodal prices of the system followed by considerable improved profit for designed VPPs, in which more than 50 % profit driven by DERs itself of the whole system.

Suggested Citation

  • Srivastava, Mahima & Tiwari, Prashant Kumar, 2024. "A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations," Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:energy:v:310:y:2024:i:c:s0360544224028524
    DOI: 10.1016/j.energy.2024.133077
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    1. Tabassum, Fariya & Azim, M.Imran & Islam, Md.Rashidul & Rahman, M.A. & Ali, Liaqat & Rahman, Md.Mahfuzur & Hossain, M.J., 2025. "Energy data security and pricing model in local energy markets using artificial intelligence," Applied Energy, Elsevier, vol. 401(PB).
    2. Zhang, Mingze & Li, Weidong & Yu, Samson S. & Li, Haomin & Lv, Yanling & Shen, Jiakai, 2025. "A day-ahead self-dispatch optimization framework for load-side virtual control units participating in active power regulation of power grids," Energy, Elsevier, vol. 318(C).
    3. Zhao, Chen & Ye, Jinchi & He, Ping & Zhang, Shaohua & Fan, Jiale, 2026. "Two-stage data-driven adaptive robust bidding model for a virtual power plant in multi-market based on nonparametric method of LSSVM-AKDE under uncertainties," Renewable Energy, Elsevier, vol. 256(PA).

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