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A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets

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  • Silva, Ana R.
  • Pousinho, H.M.I.
  • Estanqueiro, Ana

Abstract

Market agents with renewable resources face amplified uncertainty when forecasting energy production to securely place bids in electricity markets. To deal with uncertainties, stochastic modelling has been applied to optimize the bidding strategy of these market agents. However, studies found in the literature usually focus on day-ahead and balancing markets, leaving aside intraday markets that could be used to correct bidding positions as uncertainty gets resolved. This paper proposes a multistage stochastic decision-aid algorithm based on linear programming to optimize the bidding strategy of market agents in three different electricity markets - day-ahead, intraday, and balance markets. The market agent represents a Virtual Power Plant with wind, solar PV, and storage technologies, and its participation in three electricity markets was compared to the participation in DA and BM markets only. Results show that participating in all three markets increased the profit achieved by the VPP agent by 10.1% while also decreasing the incurred imbalances by 63.8%. The results demonstrate that having accurate tools to deal with the multi-settlement framework of electricity markets while considering the uncertainties of daily operations is key to a successful integration of renewable energy resources into electricity markets and power systems.

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  • Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222017595
    DOI: 10.1016/j.energy.2022.124856
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    3. Silva-Rodriguez, Lina & Sanjab, Anibal & Fumagalli, Elena & Gibescu, Madeleine, 2024. "Light robust co-optimization of energy and reserves in the day-ahead electricity market," Applied Energy, Elsevier, vol. 353(PA).
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    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. Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
    7. Xie, Rui & Wei, Wei & Li, Mingxuan & Dong, ZhaoYang & Mei, Shengwei, 2023. "Sizing capacities of renewable generation, transmission, and energy storage for low-carbon power systems: A distributionally robust optimization approach," Energy, Elsevier, vol. 263(PA).
    8. Li, Xiao & Liu, Pan & Cheng, Lei & Cheng, Qian & Zhang, Wei & Xu, Shitian & Zheng, Yalian, 2023. "Strategic bidding for a hydro-wind-photovoltaic hybrid system considering the profit beyond forecast time," Renewable Energy, Elsevier, vol. 204(C), pages 277-289.
    9. Visser, L.R. & AlSkaif, T.A. & Khurram, A. & Kleissl, J. & van Sark, W.G.H.J.M., 2024. "Probabilistic solar power forecasting: An economic and technical evaluation of an optimal market bidding strategy," Applied Energy, Elsevier, vol. 370(C).

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