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- W4313191614 abstract "This paper proposes the use of Artificial Neural Networks (ANN) for the efficient bidding of a Photovoltaic power plant with Energy Storage System (PV-ESS) participating in Day-Ahead (DA) and Real-Time (RT) energy and reserve markets under uncertainty. The Energy Management System (EMS) is based on Multi-Agent Deep Reinforcement Learning (MADRL). The MADRL scheme aims to maximize the profit of the hybrid PV-ESS plant through an efficient bidding in both markets. Results show that the MADRL framework can fulfill both the financial and physical constraints faced by the PV-ESS plant while providing energy and ancillary services. Daily market incomes have comparable mean values regarding traditional optimization approaches (average value of 1839 USD), but with a 45.3% smaller variance. Furthermore, it maintains a reference-tracking performance of 86.63% for one-year-round participation, against a 73.05% and 79.13% performance obtained with scenario-based robust and stochastic programming implementations, respectively." @default.
- W4313191614 created "2023-01-06" @default.
- W4313191614 creator A5031677319 @default.
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- W4313191614 date "2022-07-17" @default.
- W4313191614 modified "2023-09-27" @default.
- W4313191614 title "Efficient Bidding of a PV Power Plant with Energy Storage Participating in Day-Ahead and Real-Time Markets Using Artificial Neural Networks" @default.
- W4313191614 doi "https://doi.org/10.1109/pesgm48719.2022.9916732" @default.
- W4313191614 hasPublicationYear "2022" @default.
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