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

TL;DR

This paper introduces a reinforcement learning-based dynamic matching market for power grids that improves efficiency and customer satisfaction by learning optimal matching policies online without prior knowledge.

Contribution

It presents a novel hybrid learning model combining fixed rules and data-driven training to optimize power load matching in real-time power markets.

Findings

The model effectively learns matching policies for various generation-consumption profiles.

It outperforms standard online matching heuristics in simulations.

The approach enhances social welfare and customer satisfaction.

Abstract

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.