Stochastic optimization of a hybrid photovoltaic/fuel cell/parking lot system incorporating cloud model

The interaction and optimization of electric parking lots (EPLs) with hybrid renewable energy to exchange power and improve the system load reliability has been welcomed systems in energy industry, but it is accompanied by challenges such as uncertain parameters, and also implementing the optimal energy management. This paper proposes a novel stochastic optimization framework for a hybrid energy system including photovoltaic resources, hydrogen storage based-fuel cell integrated with an EPL (PV/FC/EPL) aiming for minimization of the total net present cost (TNPC) while ensuring the maximum probability of power shortage (PPSHmax) to meet a commercial load in Tianjin, China using the real meteorological data. A new improved kepler optimization algorithm (IKOA) utilizing the golden sine strategy is applied to optimize system component sizes and EPL charge/discharge. In a deterministic scenario, the optimal system configuration ensures cost-efficiency and reliability for load supply. Also, a new stochastic modeling framework is proposed using the Cloud Model (CM) to evaluate the effect of uncertainties in irradiance, temperature, and system demand on TNPC and PPSH. Deterministic findings reveal enhanced reliability and reduced TNPC through FC and EPL overlap during power shortages, compared to systems lacking EPL. Specifically, TNPC and PPSH are $8,632,643 and 1.45 %, respectively, for PPSHmax = 2 % over 20 years of operation. However, stochastic optimization demonstrates a 10.82 % increase in TNPC and an 8.67 % decrease in PPSH compared to the deterministic model under PPSHmax = 2 %, indicating heightened cost and reduced reliability when uncertainties are considered. This underscores the efficacy of the cloud model-based stochastic approach in addressing uncertainty in hybrid system optimization. Also, it reveals fluctuations in optimal cost and reliability values with changes in cloud droplet distribution around the expected value, emphasizing the significance of stochastic modeling for robust decision-making in hybrid energy systems. Moreover, evaluating the incorporating the penalty for the system's inability to supply the load, changing the storage investment cost, and interest rate variations on the system optimization cleared considerable effect of the system cost and reliability.