Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning
On the map: Deep reinforcement learning methods → Quantum reinforcement learning
Abstract (arXiv)
In response to the increasing complexity of electricity markets due to low-carbon requirements and the integration of sustainable energy sources, this paper proposes a dynamic quantum computing enhanced bilevel optimization model for electricity market operations. The upper level focuses on market mechanism optimization using Reinforcement Learning (RL), specifically Proximal Policy Optimization (PPO), while the lower level models the bidding strategies of Generating Companies (GENCOs) using a Multi-Agent Deep Q-Network (MADQN) enhanced with quantum computing through a Variational Quantum Circuit (VQC). The three main contributions of this work are: (1) establishing a dynamic bilevel model with timely feedback between the upper and lower levels; (2) parameterizing and optimizing market mechanisms to derive the most effective designs; and (3) introducing quantum computing into the context of electricity markets to more realistically simulate market operations. The proposed model is tested on the IEEE 30-bus system with six GENCOs, demonstrating its effectiveness in capturing the complexities of modern electricity markets.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 6 references that are in the map. First posted on arXiv 28 Oct 2024.
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References · 1
- 2017Proximal Policy Optimization Algorithmscited by 15,215
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