Photovoltaic panel size algorithm
In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays’ layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window.
In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays’ layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window.
The proposed methodology is evaluated on two test sites that differ in urban morphology, building size, and spatial resolution. The results show that the plane segmentation method can accurately extract planar segments, achieving 88.7% and 99.5% precision in the test sites.
The algorithms include two user-identified targets for optimal PV placement: (a) maximizing PV panel efficiency, where users aim to maximize the total energy generation, and (b) minimizing operational energy cost, where best panels are selected considering different utility rates for operational energy cost.
The developed GA algorithm is divided into two parts, the first part aims to find the optimum configuration of the system (PV panel type and number of PV panels in parallel and battery type and number of batteries in parallel) that is needed to meet the load demand.
Based on the candidate sites identified for PV panel placement, the maximal PV panel coverage problem (MPPCP) is introduced to determine the optimal spatial layout of solar PV panels. The problem identifies the optimal spatial configuration for multiple solar PV panel placement with a consideration of panel orientation and alignment scenarios.
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