In this paper we investigate a robust optimization framework for controlling energy storage devices in power networks with high share of fluctuating renewable energy sources. Our approach relies on the industry-standard DC power flow approximation, together with a multi-stage model that incorporates renewable uncertainty and an approximation of battery dynamics. More precisely, we consider storage device operation under linear control and taking into account power limits, energy conversion efficiencies, and energy limits for the state of charge. The aim of the robust optimization is to minimize costs for generating energy from conventional power generators while relying on storage to compensate for renewable output forecast errors. In order to obtain a solution we propose a cutting-plane procedure which can be used for investigating practical case studies.
CITATION STYLE
Matke, C., Bienstock, D., Muñoz, G., Yang, S., Kleinhans, D., & Sager, S. (2017). Robust optimization of power network operation: Storage devices and the role of forecast errors in renewable energies. Studies in Computational Intelligence, 693, 809–820. https://doi.org/10.1007/978-3-319-50901-3_64
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