Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
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Abstract
Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL's performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL's effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability.Keywords
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This publication has 14 references indexed in Scilit:
- Deep reinforcement learning based voltage control revisitedIET Generation, Transmission & Distribution, 2023
- A Hierarchical Deep Reinforcement Learning-Based Community Energy Trading Scheme for a Neighborhood of Smart HouseholdsIEEE Transactions on Smart Grid, 2022
- Target-Value-Competition-Based Multi-Agent Deep Reinforcement Learning Algorithm for Distributed Nonconvex Economic DispatchIEEE Transactions on Power Systems, 2022
- Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement LearningIEEE Transactions on Power Systems, 2022
- Multi-Agent Deep Reinforcement Learning for Coordinated Energy Trading and Flexibility Services Provision in Local Electricity MarketsIEEE Transactions on Smart Grid, 2022
- Deep Reinforcement Learning for Cyber SecurityIEEE Transactions on Neural Networks and Learning Systems, 2021
- Peer-to-Peer Energy Trading and Energy Conversion in Interconnected Multi-Energy Microgrids Using Multi-Agent Deep Reinforcement LearningIEEE Transactions on Smart Grid, 2021
- Resilient Operation of Distribution Grids Using Deep Reinforcement LearningIEEE Transactions on Industrial Informatics, 2021
- A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement LearningIEEE Transactions on Power Systems, 2020
- Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid OperationsIEEE Transactions on Power Systems, 2019