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• W3210045385 abstract "In this paper, we explore the optimization of virtual power plants (VPP), consisting of a portfolio of biogas power plants and a battery whose goal is to balance a wind park while maximizing their revenues. We operate under price and wind production uncertainty and in order to handle it, methods of machine learning are employed. For price modeling, we take into account the latest trends in the field and the most up-to-date events affecting the day-ahead and intra-day prices. The performance of our price models is demonstrated by both statistical methods and improvements in the profits of the virtual power plant. Optimization methods will take price and imbalance forecasts as input and conduct parallelization, decomposition, and splitting methods in order to handle sufficiently large numbers of assets in a VPP. The main focus is on the speed of computing optimal solutions of large-scale mixed-integer linear programming problems, and the best speed-up is in two orders of magnitude enabled by our method which we called Gradual Increase." @default.
• W3210045385 created "2021-11-08" @default.
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• W3210045385 date "2021-11-03" @default.
• W3210045385 modified "2023-09-25" @default.
• W3210045385 title "Optimal Balancing of Wind Parks with Virtual Power Plants" @default.
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• W3210045385 doi "https://doi.org/10.3389/fenrg.2021.665295" @default.
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