Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control extremely difficult and requires storage units as an additional concern. The goal of this research is to design and develop a flexible tool, which will allow us to obtain (1) an optimal capacity of an integrated photovoltaic (PV) system and storage units and (2) an optimal operational decision policy considering the current and future market prices of the electricity. The proposed tool is based on hybrid (system dynamics model and agent-based model) simulation and meta-heuristic optimization. In particular, this tool has been developed for three different scenarios (involving different geographical scales), where PV-based solar generators, storage units (compressed-air-energy-storage (CAES) and super-capacitors), and grid are used in an integrated manner to supply energy demands. Required data has been gathered from various sources, including NASA and TEP (utility company), US Energy Information Administration, National Renewable Energy Laboratory, commercial PV panel manufacturers, and publicly available reports. The constructed tool has been demonstrated to (1) test impacts of several factors (e.g. demand growth, efficiencies in PV panel and CAES system) on the total cost of the integrated generation and storage system and an optimal mixture of PV generation and storage capacity, and to (2) demonstrate an optimal operational policy.