A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading

Paolo Falbo, Juri Hinz, Piyachat Leelasilapasart and Cristian Pelizzari
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Paolo Falbo: Department of Economics and Management, University of Brescia, 25122 Brescia, Italy
Juri Hinz: School of Mathematical and Physical Sciences, University of Technology Sydney, P.O. Box 123, Ultimo, NSW 2007, Australia
Piyachat Leelasilapasart: School of Mathematical and Physical Sciences, University of Technology Sydney, P.O. Box 123, Ultimo, NSW 2007, Australia
Cristian Pelizzari: Department of Economics and Management, University of Brescia, 25122 Brescia, Italy

JRFM, 2021, vol. 14, issue 6, 1-22

Abstract: Due to recent technical progress, battery energy storages are becoming a viable option in the power sector. Their optimal operational management focuses on load shift and shaving of price spikes. However, this requires optimally responding to electricity demand, intermittent generation, and volatile electricity prices. More importantly, such optimization must take into account the so-called deep discharge costs, which have a significant impact on battery lifespan. We present a solution to a class of stochastic optimal control problems associated with these applications. Our numerical techniques are based on efficient algorithms which deliver a guaranteed accuracy.

Keywords: approximate dynamic programming; energy trading; optimal control; power sector (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
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