Demand Response for Flexibility Carriers using Reinforcement Learning
Hoeij Schilthouwer Pompe, L. van
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In recent work concerning demand response for flexibility carriers, specialized methods have achieved considerable progress for specialized instances of carriers. These methods often rely on complicated design decisions in feature engineer- ing or utility function design. Furthermore, flexibility carriers are often stochastic in their behaviour. In this paper we propose a general model-free reinforcement learning approach using limited feature engineering and a straightforward utility function. We validate our approach on a simulation of a flexibility carrying cold storage cell. Our results indicate significant cost savings can be achieved through our approach, at the cost of a long exploration period. Our approach requires ap- proximately 69 simulated days before offering an improvement in cost over stan- dard carrier behaviour.