Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm
Abstract Distributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model contai...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.1902 |
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| _version_ | 1841557234621874176 |
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| author | Hongli Liu Li Hao Li Ji Shao Lei |
| author_facet | Hongli Liu Li Hao Li Ji Shao Lei |
| author_sort | Hongli Liu |
| collection | DOAJ |
| description | Abstract Distributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate. |
| format | Article |
| id | doaj-art-5d7168e3a0874a4aa8a50a784510857d |
| institution | Kabale University |
| issn | 2050-0505 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-5d7168e3a0874a4aa8a50a784510857d2025-01-06T14:45:33ZengWileyEnergy Science & Engineering2050-05052024-11-0112114904491710.1002/ese3.1902Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithmHongli Liu0Li Hao1Li Ji2Shao Lei3School of Electrical Engineering and Automation Tianjin University of Technology Tianjin ChinaSchool of Electrical Engineering and Automation Tianjin University of Technology Tianjin ChinaSchool of Electrical Engineering and Automation Tianjin University of Technology Tianjin ChinaSchool of Electrical Engineering and Automation Tianjin University of Technology Tianjin ChinaAbstract Distributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate.https://doi.org/10.1002/ese3.1902distributed photovoltaicsimproved particle swarm optimizationpower qualityreactive power optimization |
| spellingShingle | Hongli Liu Li Hao Li Ji Shao Lei Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm Energy Science & Engineering distributed photovoltaics improved particle swarm optimization power quality reactive power optimization |
| title | Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm |
| title_full | Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm |
| title_fullStr | Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm |
| title_full_unstemmed | Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm |
| title_short | Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm |
| title_sort | distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm |
| topic | distributed photovoltaics improved particle swarm optimization power quality reactive power optimization |
| url | https://doi.org/10.1002/ese3.1902 |
| work_keys_str_mv | AT hongliliu distributedphotovoltaicreactivepowercontrolstrategybasedonimprovedmultiobjectiveparticleswarmalgorithm AT lihao distributedphotovoltaicreactivepowercontrolstrategybasedonimprovedmultiobjectiveparticleswarmalgorithm AT liji distributedphotovoltaicreactivepowercontrolstrategybasedonimprovedmultiobjectiveparticleswarmalgorithm AT shaolei distributedphotovoltaicreactivepowercontrolstrategybasedonimprovedmultiobjectiveparticleswarmalgorithm |