mlsanityData to 14 Oct 2026

Quantum Reinforcement Learning-Based Two-Stage Unit Commitment Framework for Enhanced Power Systems Robustness

arXiv 2410.21240 ↗First posted 28 Oct 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

Unit commitment (UC) optimizes the start-up and shutdown schedules of generating units to meet load demand while minimizing costs. However, the increasing integration of renewable energy introduces uncertainties for real-time scheduling. Existing solutions face limitations both in modeling and algorithmic design. At the modeling level, they fail to incorporate widely adopted virtual power plants (VPPs) as flexibility resources, missing the opportunity to proactively mitigate potential real-time imbalances or ramping constraints through foresight-seeing decision-making. At the algorithmic level, existing probabilistic optimization, multi-stage approaches, and machine learning, face challenges in computational complexity and adaptability. To address these challenges, this study proposes a novel two-stage UC framework that incorporates foresight-seeing sequential decision-making in both day-ahead and real-time scheduling, leveraging VPPs as flexibility resources to proactively reserve capacity and ramping flexibility for upcoming renewable energy uncertainties over several hours. In particular, we develop quantum reinforcement learning (QRL) algorithms that integrate the foresight-seeing sequential decision-making and scalable computation advantages of deep reinforcement learning (DRL) with the parallel and high-efficiency search capabilities of quantum computing. Experimental results demonstrate that the proposed QRL-based approach outperforms in computational efficiency, real-time responsiveness, and solution quality.

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 6 of its 31 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

6 of 31 references.

References · 6

  1. 2017Deep Reinforcement Learning that Matterscited by 754
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2017Deep Reinforcement Learning: An Overviewcited by 333
  4. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  5. 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
  6. 2020Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Timecited by 7

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