Energy Storage Management via Deep Q-Networks
This addresses energy management for privately owned storage systems, but it is incremental as it applies an existing reinforcement learning method to a specific domain.
The paper tackles the problem of real-time control for energy storage units co-located with renewable energy and inelastic loads, without distributional assumptions, and achieves near-optimal performance in simulations.
Energy storage devices represent environmentally friendly candidates to cope with volatile renewable energy generation. Motivated by the increase in privately owned storage systems, this paper studies the problem of real-time control of a storage unit co-located with a renewable energy generator and an inelastic load. Unlike many approaches in the literature, no distributional assumptions are being made on the renewable energy generation or the real-time prices. Building on the deep Q-networks algorithm, a reinforcement learning approach utilizing a neural network is devised where the storage unit operational constraints are respected. The neural network approximates the action-value function which dictates what action (charging, discharging, etc.) to take. Simulations indicate that near-optimal performance can be attained with the proposed learning-based control policy for the storage units.