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Growth in the penetration of renewable energy sources makes supply moreuncertain and leads to an increase in the system imbalance. This trend,together with the single imbalance pricing, opens an opportunity for balanceresponsible parties (BRPs) to perform energy arbitrage in the imbalancesettlement mechanism. To this end, we propose a battery control framework basedon distributional reinforcement learning (DRL). Our proposed control frameworktakes a risk-sensitive perspective, allowing BRPs to adjust their riskpreferences: we aim to optimize a weighted sum of the arbitrage profit and arisk measure while constraining the daily number of cycles for the battery. Weassess the performance of our proposed control framework using the Belgianimbalance prices of 2022 and compare two state-of-the-art RL methods, deep Qlearning and soft actor-critic. Results reveal that the distributional softactor-critic method can outperform other methods. Moreover, we note that ourfully risk-averse agent appropriately learns to hedge against the risk relatedto the unknown imbalance price by (dis)charging the battery only when the agentis more certain about the price.

Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism