Optimal Balancing of Wind Parks with Virtual Power Plants in the Market Environment

Vadim Omelčenko () and Valery Manokhin
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Vadim Omelčenko: Academy of Sciences of the Czech Republic
Valery Manokhin: Royal Holloway, University of London

A chapter in Handbook of Smart Energy Systems, 2023, pp 3047-3093 from Springer

Abstract: Abstract In this chapter, we explore the optimization of virtual power plants (VPP), consisting of a portfolio of biogas power plants, hydro storage facilities, and batteries whose goal is to balance a wind park while maximizing their revenues. We operate under price and wind production uncertainty, and to handle it, methods of machine learning are employed. Three methods of price forecasting were implemented, and their performance is demonstrated by both statistical methods and improvements in the profits of the virtual power plant. Apart from our forecasting methods, two more commercial price forecasts were employed. Optimization methods will take price and imbalance forecasts as input and conduct parallelization, decomposition, and splitting methods to handle sufficiently large numbers of assets in a VPP. The 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 the method Gradual Increase. Further progress in this methodology and its new facets are demonstrated. A piece of code implementing Gradual Increase is presented.

Keywords: Virtual power plant; Gradual increase; Modified gradual increase; Proximal Jacobian ADMM; Cold warmstart; Warm warmstart; MIPgap (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_179

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DOI: 10.1007/978-3-030-97940-9_179

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