Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning
The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variatio...
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/3/653 |
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| author | Fumiya Matsushima Mutsumi Aoki Yuta Nakamura Suresh Chand Verma Katsuhisa Ueda Yusuke Imanishi |
| author_facet | Fumiya Matsushima Mutsumi Aoki Yuta Nakamura Suresh Chand Verma Katsuhisa Ueda Yusuke Imanishi |
| author_sort | Fumiya Matsushima |
| collection | DOAJ |
| description | The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids. |
| format | Article |
| id | doaj-art-3f57cbb417d6447f91577a31cb4b29b1 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-3f57cbb417d6447f91577a31cb4b29b12025-08-20T03:12:35ZengMDPI AGEnergies1996-10732025-01-0118365310.3390/en18030653Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement LearningFumiya Matsushima0Mutsumi Aoki1Yuta Nakamura2Suresh Chand Verma3Katsuhisa Ueda4Yusuke Imanishi5Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electric Power Research and Development Center, Chubu Electric Power Co., Inc., Nagoya 459-8522, JapanDepartment of Electric Power Research and Development Center, Chubu Electric Power Co., Inc., Nagoya 459-8522, JapanThe integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids.https://www.mdpi.com/1996-1073/18/3/653distributed energy resourcesmulti-timescale voltage controldeep reinforcement learningShapley additive explanationvoltage estimationdeep neural network |
| spellingShingle | Fumiya Matsushima Mutsumi Aoki Yuta Nakamura Suresh Chand Verma Katsuhisa Ueda Yusuke Imanishi Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning Energies distributed energy resources multi-timescale voltage control deep reinforcement learning Shapley additive explanation voltage estimation deep neural network |
| title | Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning |
| title_full | Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning |
| title_fullStr | Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning |
| title_full_unstemmed | Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning |
| title_short | Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning |
| title_sort | multi timescale voltage control method using limited measurable information with explainable deep reinforcement learning |
| topic | distributed energy resources multi-timescale voltage control deep reinforcement learning Shapley additive explanation voltage estimation deep neural network |
| url | https://www.mdpi.com/1996-1073/18/3/653 |
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