Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm
IntroductionFrequency oscillations induced by stochastic disturbances pose significant challenges to grid-connected photovoltaic (PV) systems. This study proposes an adaptive optimization strategy for photovoltaic-energy storage systems (PV-ESS) based on a GA-BP neural network to address this issue....
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1640949/full |
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| author | Runzhi Mu Hongchun Shu Hongchun Shu Hongchun Shu Hongchun Shu Yuming Zhang Xiongbiao Wan Shunji Luo Shunji Luo Shunji Luo Shunji Luo Zichao Zhou Guangxue Wang Guangxue Wang Guangxue Wang Guangxue Wang Shunguang Lei Shunguang Lei Shunguang Lei Shunguang Lei |
| author_facet | Runzhi Mu Hongchun Shu Hongchun Shu Hongchun Shu Hongchun Shu Yuming Zhang Xiongbiao Wan Shunji Luo Shunji Luo Shunji Luo Shunji Luo Zichao Zhou Guangxue Wang Guangxue Wang Guangxue Wang Guangxue Wang Shunguang Lei Shunguang Lei Shunguang Lei Shunguang Lei |
| author_sort | Runzhi Mu |
| collection | DOAJ |
| description | IntroductionFrequency oscillations induced by stochastic disturbances pose significant challenges to grid-connected photovoltaic (PV) systems. This study proposes an adaptive optimization strategy for photovoltaic-energy storage systems (PV-ESS) based on a GA-BP neural network to address this issue.MethodsFirst, the working principles and characteristics of virtual synchronous generator (VSG) technology are elaborated. Second, the power control point positioning under deloading operation of PV systems and the virtual inertia control of energy storage systems are analyzed. Subsequently, a GA-BP neural network is introduced and applied to the adaptive parameter design of the PV-ESS system, enabling real-time dynamic adjustment of the moment of inertia J, damping coefficient D, and virtual inertia coefficient K, thereby enhancing the dynamic response performance of active power.ResultsThe experimental results demonstrate that under active power command mutation scenarios, compared with fixed-parameter control strategies, the proposed strategy reduces the frequency nadir deviation by 14.81%, overshoot by 62.5%, and steady-state recovery time by 44.44%.DiscussionThe adaptive parameter adjustment mechanism effectively mitigates frequency oscillations, offering a robust solution for grid stability in PV scenarios. |
| format | Article |
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| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-b3eee7c4a3fb4da3b410312b5f488c602025-08-20T03:57:32ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-08-011310.3389/fenrg.2025.16409491640949Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithmRunzhi Mu0Hongchun Shu1Hongchun Shu2Hongchun Shu3Hongchun Shu4Yuming Zhang5Xiongbiao Wan6Shunji Luo7Shunji Luo8Shunji Luo9Shunji Luo10Zichao Zhou11Guangxue Wang12Guangxue Wang13Guangxue Wang14Guangxue Wang15Shunguang Lei16Shunguang Lei17Shunguang Lei18Shunguang Lei19Yunnan Electric Power Test and Research Institute (Group) Co., Ltd., Kunming, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming University of Science and Technology, Kunming, ChinaState Key Laboratory Collaborative Innovation Center for Smart Grid Fault Detection, Protection and Control Jointly, Kunming University of Science and Technology, Kunming, ChinaYunnan Provincial Key Laboratory of Green Energy, Kunming, ChinaDigital Electric Power Measurement and Protection Control, Kunming University of Science and Technology, Kunming, ChinaYunnan Electric Power Test and Research Institute (Group) Co., Ltd., Kunming, ChinaYunnan Electric Power Test and Research Institute (Group) Co., Ltd., Kunming, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming University of Science and Technology, Kunming, ChinaState Key Laboratory Collaborative Innovation Center for Smart Grid Fault Detection, Protection and Control Jointly, Kunming University of Science and Technology, Kunming, ChinaYunnan Provincial Key Laboratory of Green Energy, Kunming, ChinaDigital Electric Power Measurement and Protection Control, Kunming University of Science and Technology, Kunming, ChinaYunnan Electric Power Test and Research Institute (Group) Co., Ltd., Kunming, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming University of Science and Technology, Kunming, ChinaState Key Laboratory Collaborative Innovation Center for Smart Grid Fault Detection, Protection and Control Jointly, Kunming University of Science and Technology, Kunming, ChinaYunnan Provincial Key Laboratory of Green Energy, Kunming, ChinaDigital Electric Power Measurement and Protection Control, Kunming University of Science and Technology, Kunming, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming University of Science and Technology, Kunming, ChinaState Key Laboratory Collaborative Innovation Center for Smart Grid Fault Detection, Protection and Control Jointly, Kunming University of Science and Technology, Kunming, ChinaYunnan Provincial Key Laboratory of Green Energy, Kunming, ChinaDigital Electric Power Measurement and Protection Control, Kunming University of Science and Technology, Kunming, ChinaIntroductionFrequency oscillations induced by stochastic disturbances pose significant challenges to grid-connected photovoltaic (PV) systems. This study proposes an adaptive optimization strategy for photovoltaic-energy storage systems (PV-ESS) based on a GA-BP neural network to address this issue.MethodsFirst, the working principles and characteristics of virtual synchronous generator (VSG) technology are elaborated. Second, the power control point positioning under deloading operation of PV systems and the virtual inertia control of energy storage systems are analyzed. Subsequently, a GA-BP neural network is introduced and applied to the adaptive parameter design of the PV-ESS system, enabling real-time dynamic adjustment of the moment of inertia J, damping coefficient D, and virtual inertia coefficient K, thereby enhancing the dynamic response performance of active power.ResultsThe experimental results demonstrate that under active power command mutation scenarios, compared with fixed-parameter control strategies, the proposed strategy reduces the frequency nadir deviation by 14.81%, overshoot by 62.5%, and steady-state recovery time by 44.44%.DiscussionThe adaptive parameter adjustment mechanism effectively mitigates frequency oscillations, offering a robust solution for grid stability in PV scenarios.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1640949/fullcoordinated PV-ESS controlfrequency regulationGA-BP neural networkdeloading controldynamic parameters |
| spellingShingle | Runzhi Mu Hongchun Shu Hongchun Shu Hongchun Shu Hongchun Shu Yuming Zhang Xiongbiao Wan Shunji Luo Shunji Luo Shunji Luo Shunji Luo Zichao Zhou Guangxue Wang Guangxue Wang Guangxue Wang Guangxue Wang Shunguang Lei Shunguang Lei Shunguang Lei Shunguang Lei Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm Frontiers in Energy Research coordinated PV-ESS control frequency regulation GA-BP neural network deloading control dynamic parameters |
| title | Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm |
| title_full | Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm |
| title_fullStr | Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm |
| title_full_unstemmed | Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm |
| title_short | Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm |
| title_sort | frequency coordinated control and parameter optimization for photovoltaic energy storage systems based on a ga bp hybrid algorithm |
| topic | coordinated PV-ESS control frequency regulation GA-BP neural network deloading control dynamic parameters |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1640949/full |
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