Optimizing storage capacity in 100 % renewable electricity supply: A GIS-based approach for Italy

The sustainability of energy systems relies on the integration of renewable local sources. This study aimed to optimize Italy's electricity supply by leveraging a hybrid PV-wind energy system, employing advanced optimization techniques. The primary goal was pinpointing the minimum storage capacity necessary for Italy's power grid in a scenario completely reliant on PV and wind energy. To achieve this, the potential of both PV and wind energy was evaluated through a GIS-based analysis, while dynamic simulation was used to estimate power generation across regions. The Mixed-integer linear programming algorithm underwent a three-step process: computing the hourly residual load for diverse PV and wind capacity combinations, determining the hourly storage requirements and ultimately identifying the mix with the least storage capacity. Applying Mixed-integer linear programming to Italy's complete PV and wind energy potential revealed a necessity for 33 TWh of storage capacity. To decrease the required storage capacity, two new scenarios were proposed: the island scenario, in which the total annual electricity production from solar and wind energy is equal to the annual electricity demand, and the peak hour scenario, where generation from PV and wind is matched to the consumption in peak hour electric demand. The economic analysis of the proposed scenarios shows that although hydrogen can be used to store enormous amounts of energy, the inefficiencies in the conversion processes make it less cost-effective compared to other technologies. Pumped-hydro storage is the most cost-effective option for energy storage. The results show that the most economically viable scenario is the island scenario with an optimal mix of 16.9 % PV and 83.1 % wind, requiring a storage capacity of 7.04 TWh and a 3.34 trillion euro investment for pump-hydro storage. • Spatial analysis of PV and wind generation. • Hourly renewable potential. • MILP optimization of HRES. • Storage costs comparison.