# Energy Storage Management via Deep Q-Networks

## TL;DR

This paper proposes a deep reinforcement learning method using deep Q-networks for real-time energy storage management, effectively handling renewable variability and market prices without distributional assumptions.

## Contribution

It introduces a novel RL-based control policy for energy storage that respects operational constraints and adapts to real-time conditions, outperforming traditional methods.

## Key findings

- Near-optimal performance achieved in simulations
- No assumptions on renewable or price distributions
- Effective real-time control of storage units

## Abstract

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.