Optimal battery operation for revenue maximization of wind-storage hybrid power plant

• Published in: Electric power systems research Vol. 189; p. 106631
• Main Authors: Das, Kaushik, Philippe Grapperon, Anatole Louis Theodore, Sørensen, Poul E., Hansen, Anca D.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2020; Elsevier Science Ltd
• Subjects: Algorithms; Alternative energy; Balancing market; Battery storage; Day-ahead optimisation; Economic forecasting; Electric power generation; Electric power plants; Forecasts; Hybrid power plant; Maximization; Optimization; Optimization algorithms; Renewable energy sources; Revenue; Revenue maximization; Spot market; Uncertainty; Wind power; Spot market; Day-ahead optimisation; Balancing market; Revenue maximization; Wind power; Battery storage; Hybrid power plant
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2020.106631

In order to participate in energy market, variable renewable energy sources need to reduce the uncertainty of forecast errors. Inclusion of storage can be a viable option not only to minimize the penalties due to forecast uncertainties but also to maximize the revenue generation. This paper presents a decision framework for respecting the market constraints and maximise the revenues of a wind-storage based hybrid power plant. Wind power and price forecast are used in convex optimisation algorithm for making day ahead decisions on battery operation. This day ahead optimisation results feed to an algorithm for operating in the balancing market. Several scenarios and case studies have been simulated to assess the value of storage for revenue maximization of a wind power plant. The results show that proposed algorithms can increase the revenue by more than 10% compared to the operation of wind power plant without battery.