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

12 Apr 2021  ·  Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar ·

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 framework... More specifically, a novel hybrid learning model is proposed for determining the matching policy that is a composition of a fixed rule-based function and a trainable component that can be trained by matching data with no prior knowledge or expert supervision. The output of the trainable component is a probability distribution over the matching decisions for the individual customers. The proposed hybrid learning model enables the learning algorithm to find an effective matching policy and simultaneously satisfy the load servicing constraints. The simulations show that the proposed 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. read more

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