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Traditional bulk load flexibility options, such as load shifting and loadcurtailment, for managing uncertainty in power markets limit the diversity ofoptions and ignore the preferences of the individual loads, thus reducingefficiency and welfare. This paper proposes an alternative to bulk loadflexibility options for managing uncertainty in power markets: a reinforcementlearning based dynamic matching market. We propose a novel hybridlearning-based model for maximizing social welfare in the dynamic matchingmarket. The key features of our model is a fixed rule-based function and alearnable component that can be trained by data gathered online with no priorknowledge or expert supervision. The output of the learnable component is aprobability distribution over the matching decisions for the individualcustomers. The proposed hybrid model enables the learning algorithm to find aneffective matching policy that simultaneously satisfies the customers'servicing preferences. The simulations show that the learning algorithm learnsan effective matching policy for different generation-consumption profiles andexhibits better performance compared to standard online matching heuristicssuch as Match on Arrival, Match to the Highest, and Match to the EarliestDeadline policies.

Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning