Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing

TL;DR

This paper introduces Q-MADQN, a quantum-enhanced multi-agent reinforcement learning method that improves the simulation of electricity market bidding strategies by better handling uncertainties, demonstrated on a standard test network.

Contribution

The paper proposes the Q-MADQN algorithm integrating quantum circuits into MARL to better model market uncertainties and simulate diverse bidding strategies.

Findings

Q-MADQN captures more potential bidding strategies.

It offers more accurate market dynamic simulations.

Maintains computational efficiency with quantum integration.

Abstract

The analysis of decision-making process in electricity markets is crucial for understanding and resolving issues related to market manipulation and reduced social welfare. Traditional Multi-Agent Reinforcement Learning (MARL) method can model decision-making of generation companies (GENCOs), but faces challenges due to uncertainties in policy functions, reward functions, and inter-agent interactions. Quantum computing offers a promising solution to resolve these uncertainties, and this paper introduces the Quantum Multi-Agent Deep Q-Network (Q-MADQN) method, which integrates variational quantum circuits into the traditional MARL framework. The main contributions of the paper are: identifying the correspondence between market uncertainties and quantum properties, proposing the Q-MADQN algorithm for simulating electricity market bidding, and demonstrating that Q-MADQN allows for a more thorough exploration and simulates more potential bidding strategies of profit-oriented GENCOs, compared to conventional methods, without compromising computational efficiency. The proposed method is illustrated on IEEE 30-bus test network, confirming that it offers a more accurate model for simulating complex market dynamics.