AUTHOR=Mu Runzhi , Shu Hongchun , Zhang Yuming , Wan Xiongbiao , Luo Shunji , Zhou Zichao , Wang Guangxue , Lei Shunguang TITLE=Frequency coordinated control and parameter optimization for photovoltaic–energy storage systems based on a GA-BP hybrid algorithm JOURNAL=Frontiers in Energy Research VOLUME=Volume 13 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2025.1640949 DOI=10.3389/fenrg.2025.1640949 ISSN=2296-598X ABSTRACT=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.