Abstract: The study involves the integration of stochastic modeling and optimization of the on-grid photovoltaic (PV) system with Homer Pro and Python tools. Probabilistic functions were applied, along with 3 metrics (coefficient of determination R², Akaike AIC information criterion and Kolmogorov-Smirnov KS test) on consumption data from the Cotopaxi Technical University (UTC). Under the UTC, there was a variation in daily radiation between 3.98 - 4.55 kWh/m²/day, while the monthly demand varied from a minimum equal to 521 kWh to a maximum of 4,529.00 kWh. The results of the optimal photovoltaic system involved several technical aspects, with which it must have a capacity of 6.20 kW, annual production of 6,764.00 and a capacity factor of 9.62%. In relation to the economic aspects, the economic viability of the system was demonstrated by having a levelized cost of energy (LCOE) of 0.085 USD/kWh and a net present cost (NPC) of $47,711.87. This study is essential for modelling the charging behaviour, which improves the robustness of the system which provides solid support for sustainable energy planning.

Keywords: solar energy, stochastic modeling, gamma distribution, energy optimization, HOMER Pro, Python

Pages: 15-24