Photovoltaic (PV) reconfiguration is an effective solution for reducing the hot spot effect caused by partial shadows on PV arrays. This paper proposed an efficient atom search optimization- (ASO-) based PV reconfiguration method. The analysis and comparison with the other four reconfiguration methods were performed by three evaluation criteria, which are mismatch loss, fill factor, and standard deviation, respectively. The conclusion can be drawn that the efficiency, rapidity, and reliability of ASO are superior to those of other methods. Besides, a moving cloud shadow mode of 9 × 9-scaled PV array is designed in this paper, which can be widely recommended to thoroughly study a PV reconfiguration approach.

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In hybrid energy systems, the intermittent and fluctuating nature of new energy sources poses major challenges for the regulation and control of power systems. To mitigate these challenges, energy storage devices have gained attention for their ability to rapidly charge and discharge. Collaborating with wind power (WP), energy storage (ES) can participate in the frequency control of regional power grids. This approach has garnered extensive interest from scholars worldwide. This paper proposes a two-region load frequency control model that accounts for thermal power, hydropower, ES, and WP. To address complex, nonlinear optimization problems, the dingo optimization algorithm (DOA) is employed to quickly obtain optimal power dispatching commands under different power disturbances. The DOA algorithm’s effectiveness is verified through the simulation of the two-region model. Furthermore, to further validate the proposed method’s optimization effect, the DOA algorithm’s optimization results are compared with those of the genetic algorithm (GA) and proportion method (PROP). Simulation results show that the optimization effect of DOA is more significant than the other methods.