In the context of high-penetration renewable energy integration, the increasing adoption of photovoltaic (PV) systems gives rise to significant operational challenges to power grids owing to the inherent intermittency and volatility of solar energy resources. Energy storage (ES) systems, with their flexible regulation capabilities, have been widely deployed to mitigate the impact of renewable generation fluctuations. However, existing methodologies often fail to adequately balance PV integration levels with system economic viability, which hinders the large-scale deployment of ES. To address this issue, we establish a PV integration cost metric to coordinate the integration effectiveness and economic benefits. The ES systems can therefore be leveraged by enhancing the PV hosting capacity in distribution networks. PV sensors are also employed to collect irradiance data, which, combined with load profiles, enable optimized power supply–demand matching through demand response strategies. Furthermore, the mirage search optimization algorithm is applied to improve computational efficiency, ultimately deriving the optimal ES configuration. The superiority of the algorithm is verified through a comparative analysis of the convergence performance on benchmark test functions. Simulation results demonstrate that the proposed model effectively increases the PV integration rate while reducing operational costs by approximately 8.19%, confirming its dual advantages in both economic efficiency and technical benefits.