Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning
The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/24/8270 |
| _version_ | 1797505155980066816 |
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| author | Nikita Tomin Nikolai Voropai Victor Kurbatsky Christian Rehtanz |
| author_facet | Nikita Tomin Nikolai Voropai Victor Kurbatsky Christian Rehtanz |
| author_sort | Nikita Tomin |
| collection | DOAJ |
| description | The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction. |
| first_indexed | 2024-03-10T04:14:35Z |
| format | Article |
| id | doaj.art-f2d45a9198b044e1a27271f994742919 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:14:35Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f2d45a9198b044e1a27271f9947429192023-11-23T08:04:54ZengMDPI AGEnergies1996-10732021-12-011424827010.3390/en14248270Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement LearningNikita Tomin0Nikolai Voropai1Victor Kurbatsky2Christian Rehtanz3Melentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, RussiaMelentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, RussiaMelentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, RussiaInstitute of Energy Systems, Energy Efficiency and Energy Economics (ie3), TU Dortmund University, 44227 Dortmund, GermanyThe increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.https://www.mdpi.com/1996-1073/14/24/8270voltage flexibilitydroop controlmultiagent reinforcement learninghosting capacityactive distribution systemmicrogrid |
| spellingShingle | Nikita Tomin Nikolai Voropai Victor Kurbatsky Christian Rehtanz Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning Energies voltage flexibility droop control multiagent reinforcement learning hosting capacity active distribution system microgrid |
| title | Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning |
| title_full | Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning |
| title_fullStr | Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning |
| title_full_unstemmed | Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning |
| title_short | Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning |
| title_sort | management of voltage flexibility from inverter based distributed generation using multi agent reinforcement learning |
| topic | voltage flexibility droop control multiagent reinforcement learning hosting capacity active distribution system microgrid |
| url | https://www.mdpi.com/1996-1073/14/24/8270 |
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