On the Performance of Reinforcement Learning Algorithms for Dynamic Matching of Renewable Energy with Flexible Loads

This paper analyses the performance and efficiency of reinforcement learning algorithms for matching the availability of uncertain renewable energy sources (RES) with flexible loads. More specifically, this paper proposes a novel scalable (with the number of customers) and efficient learning-based energy matching solution for maximizing social welfare in dynamic matching markets. The key features of the proposed solution is combining a simple rule-based function and a learnable component to achieve the aforementioned properties. The output of the learnable component is a probability distribution over the matching decisions for the individual customers. The proposed hybrid model enables the learning algorithm to find an effective matching policy that simultaneously satisfies the customers’ servicing preferences. Extensive simulations are presented to show that the learning algorithm learns an effective matching policy for different generation-consumption profiles despite of the complexity reduction. The proposed solution exhibits significantly better performance compared to standard online matching heuristics such as Match on Arrival, Match to the Highest, and Match to the Earliest Deadline policies.