Abstract: With the phasing out of the fixed time-of-use (TOU) pricing mechanism for industrial and commercial power consumers across numerous regions in China, the electricity purchasing price for end-users has transformed from government-regulated time-of-use pricing to dynamic pricing determined by electricity spot market. Such policy shift forces the profit framework of conventional integrated photovoltaic-energy storage (PV-ES) power plants relying on the original pricing mechanism to be restructured urgently. Against such research background, the optimal capacity allocation of PV-ES hybrid stations has turned into a prevailing research hotspot in the power industry. In this paper, an adaptive charge and discharge optimization strategy for energy storage systems is established, where multiple technical constraints covering charge-discharge efficiency of energy storage equipment and battery degradation characteristics are fully considered, and the time-sequential operational schedules of energy storage charging and discharging are solved accordingly. On this basis, the internal rate of return (IRR) maximization of the whole integrated PV-ES station is set as the optimization objective function; the particle swarm optimization (PSO) algorithm is adopted to implement global optimal searching for matching capacity combinations of photovoltaic and energy storage units, and the final optimal installed capacity configuration scheme for PV-ES integrated power station is determined consequently.