Along with the widespread adoption of solar energy, it is fundamental to develop methods and tools that help practitioners during the design phase of photovoltaic (PV) systems. Currently, multiple commercial software can quantify a particular location's annual energy yield while including the horizon's shading effect (e.g., mountains, buildings, and trees). To do so, precise information about the PV system's surroundings is necessary. This information is gathered by specialized equipment or by having access to satellite imagery. Therefore, to offer a more practical approach, we propose a method that requires only a cellphone camera, a fixed point for taking a panoramic photograph, and a compass. Once the panoramic image is taken, the obstacles’ width, height, and altitude are calculated, and the skyline is built. With this information, the method correlates the position of the sun with meteorological data to include the effect of shading on direct irradiation. The method was tested using one–year meteorological data to determine the best orientation of a PV system. The image processing method and the general method were validated by getting PV power generation data and aerial images and comparing them to the method's predictions. Therefore, we introduce a method that, with low computational complexity, facilitates the study of shading on the performance of PV systems.

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Energy storage is vital for a future where energy generation transitions from a fossil fuels-based one to an energy system that relies heavily on clean energy sources such as photovoltaic (PV) solar energy. To foster this transition, engineers and practitioners must have open-access models of PV systems coupled with battery storage systems (BESS). These models are fundamental to quantifying their economic and technical merits during the design phase. This paper contributes in this direction by carefully describing a model that accurately represents the power directions and energy dealings between the PV modules, the battery pack, and the loads. Moreover, the general model can be implemented using two different PV generation methods, the Gaussian model and the meteorological data-based model (MDB). We found that the MDB model is more appropriate for short-term analysis compared to the Gaussian model, while for long-term studies, the Gaussian model is closer to measured data. Moreover, the proposed model can reproduce two different energy management strategies: peak-shaving and maximizing self-consumption, allowing them to be used during PV–BESS sizing stages. Furthermore, the results obtained by the simulation are closed when compared to a real grid-tied PV–BESS, demonstrating the model’s validity.@en