Stochastic modeling and optimization of photovoltaic systems using probabilistic profiles of demand and solar radiation with HOMER Pro

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original 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.

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