  Traditional bulk load flexibility options, such as load shifting and load
curtailment, for managing uncertainty in power markets limit the diversity of
options and ignore the preferences of the individual loads, thus reducing
efficiency and welfare. This paper proposes an alternative to bulk load
flexibility options for managing uncertainty in power markets: a reinforcement
learning based dynamic matching market. We propose a novel hybrid
learning-based model for maximizing social welfare in the dynamic matching
market. The key features of our model is a fixed rule-based function and a
learnable component that can be trained by data gathered online with no prior
knowledge or expert supervision. 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. The simulations show that the learning algorithm learns
an effective matching policy for different generation-consumption profiles and
exhibits better performance compared to standard online matching heuristics
such as Match on Arrival, Match to the Highest, and Match to the Earliest
Deadline policies.
