The present paper introduced a collaboration of techno-economic optimization of a PV-Battery system based on a hybrid iterative evolutionary algorithm. The major aim was to conceive the most effective design of the PV-battery system components based on an on-line Power Management Strategy (PMS) and considering the desired required energy reliability LPSPd\documentclass[12pt]{minimal}
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\begin{document}$$\left({LPSP}_{d}\right)$$\end{document} with the lowest installation cost. The PMS control algorithm enables the proposed management control to highlight the optimal sizing methodology. In fact, the data base collected from the PMS strategy affects the convergence of the proposed sizing optimization design by considering them, for each day of the month, as a new physical constraint to the next economic optimization process. Indeed, the deployed optimization approach is considered as dynamic taking into account at each step, the previous states of optimization and management algorithms. To this end, an original modeling tool dedicated to techno-economic co-optimization approach was investigated and implemented to characterize the PV/Battery system performance. To highlight the effectiveness of the proposed economic optimization approach, a comparative study between Particle Swarm Optimization (PSO) algorithm and the Accelerated Particle Swarm Optimization (APSO) algorithm was performed. The obtained simulation results showed the robustness of the APSO algorithm in terms of speed of convergence and optimum system configuration.
