Optimal Size and Orientation of Solar Tree with Hybrid Crow Search and Particle Swarm Algorithm

Abstract

Due to increasing demand in urban areas, solar PV installation has become popular for harvesting renewable energy. PV installation has a few drawbacks in a particular location, including a limited installation area and difficulty tracking irradiance due to its variable nature throughout the day. To overcome the challenges in an urban area, a solar tree (ST) can be installed because it has several advantages over a solar panel. Therefore, ST can be installed at a fixed orientation with consideration of the clearness index (K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf> ), which tracks the diffused radiations as well. This study describes the ideal ST size and orientation for a specific location in India in order to deliver the maximum amount of energy in the smallest amount of PV area possible. Also, the maximum energy is extracted by optimal orientation. It is achieved by the annual variation of the solar azimuth angle (ß) and latitude tilt angle (ϒ). The GHI and DNI are validated in the MATENORM software for a location of 24.7577° N and 92.7923° E at their optimal angles. A recent metaheuristic algorithm, hybrid crow search and particle swarm optimization (HCSA-PSO), has been used to find optimal values for ST design parameters. A comparative study shows the proposed meta-heuristics approach gives the maximum output power for the tree configuration as compared to other techniques, such as GA, PSO, and CSA. Further, the two 2 kW ST model has been compared with the horizontal tilt surface PV panels that result in a 4.72 % improvement in output energy with the tree configuration.