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Solar Photovoltaics (PV) is seen as one of the renewable energy technologies that could help reduce the world’s dependence on fossil fuels. However, since it is dependent on the sun, it can only generate electricity in the daytime, and this restriction is exacerbated in electricity grids with high PV penetration, where solar energy must be curtailed due to the mismatch between supply and demand. This study conducts a techno-economic analysis to present the cost-optimal storage growth trajectory that could support the dynamic integration of solar PV within a planning horizon. A methodology for cost-optimal assessment that incorporates hourly simulation, Monte Carlo random sampling, and a proposed financial assessment is presented. This approach was tested in Japan’s southernmost region since it is continuously increasing its solar capacity and is at the precipice of high PV curtailment scenario. The results show the existence of a cost-optimal storage capacity growth trajectory that balances the cost penalty from curtailment and the additional investment cost from storage. This optimal trajectory reduces the impact of curtailment on the energy generation cost to manageable levels and utilizes more solar energy potential that further reduces CO2 emissions. The results also show that the solar capacity growth rate and storage cost significantly impact the optimal trajectory. The incorporation of the Monte Carlo method significantly reduced the computational requirement of the analysis enabling the exploration of several growth trajectories, and the proposed financial assessment enabled the time-bound optimization of these trajectories. The approach could be used to calculate the optimal growth trajectories in other nations or regions, provided that historical hourly temperature, irradiance, and demand data are available.

Dynamic Cost-Optimal Assessment of Complementary Diurnal Electricity Storage Capacity in High PV Penetration Grid