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Uncertain renewable energy supplies, load demands and the non-linear characteristics of some components of photovoltaic (PV) systems make the design problem not easy to solve by classical optimization methods, especially when relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing PV systems. However, simple methods like worst month method are still largely used in sizing simple PV systems. In the present study, a method for sizing remote PV systems based on genetic algorithms has been compared with two classical methods, worst month method and loss of power supply probability (LPSP) method. The three methods have been applied to a PV lighting system with orientation due south and inclination angles between 0°and 90°in Adrar city (south Algeria). Because measured data for the chosen location were not available, a year of synthetic hourly meteorological data of this location, generated by PVSYST software, have been used in the simulation. Genetic algorithms and worst month methods give results close to each other between 0°and 60°but the system is largely oversized by the worst month method when the tilted angle is over 60°. The results obtained by LPSP method show that the system is very undersized. Hence, a proposition has been made to improve results obtained by this method.

-Conventional methodologies (empirical, analytical, numerical, hybrid, etc.) for sizing photovoltaic (PV) systems cannot be used when the relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing the PV systems. In the present study, the optimum PV/inverter sizing ratio for grid-connected PV systems with orientation due south and inclination angles of 45° and 60° in selected Algerian locations was determined in terms of total system output using type-2 fuzzy logic. Because measured data for the locations chosen were not available, a year of synthetic hourly meteorological data for each location generated by the PVSYST software was used in the simulation.

This paper presents a technical and economic simulation of a solar photovoltaic system with three different storage types. Battery lead‐acid, battery lithium‐ion, and hydrogen storage have been used to cover the consumption of houses in an isolated village in southwest Algeria. The main objective of this study is trying to use the roof of the houses to cover consumption by installing solar panels and build a room for electric equipment like inverter, battery, power system management, and so forth. The proposed hybrid system size is 50 photovoltaic panels with 7 kW electrolyzer and a tank of 6 kg for hydrogen storage. The hourly consumption of this houses was estimated during two seasons; season of low consumption (winter) and season of high consumption (summer). The results obtained from Hybrid Optimization Model for Electric Renewable PRO software have been validated using MATLAB/Simulink software. The results show that the best storage system is the hydrogen storage due to low excess energy with no unmet load, the results show also that the system that uses hydrogen storage is the most economic system compared to the other storage types (lead‐acid and lithium‐ion) due to low investment cost and long lifetime. This system costs 51 282€.