Improving Solar Power PV Plants Using Multivariate Design Optimization

Abstract

The proliferation of photovoltaic (PV) installations across the globe has accelerated dramatically in the past decade covering home, rural, mobile, industrial, and utility-scale applications. In all these cases, improving payback time and energy production for PV installations is a very complex design tradeoff that involves multiple variables such as irradiance fluctuations, inverter efficiency, operating temperature variation, and PV panel type. In this paper, a detailed multivariate study of PV plant design is presented, resulting in an improved technique to increase the potential benefits of solar plants with lower capital costs. This new approach includes detailed consideration of the probabilistic hourly temperature and solar irradiation profile of the installation site, the efficiencies and operating areas of different grid-tie inverters, and detailed models of different PV modules in the optimal design process. The harvested energy, total costs, and payback time are the objective functions in this approach, while the number of series and parallel panels, the tilt angle, and inverter topology and PV module type are determined from a list of possible candidates. The optimization process is implemented for a sample system, and the results are compared to both a traditional and design software approach. It is seen that by applying the proposed approach with lower capital costs, the harvested energy, financial benefits, and the payback time can be improved by 9.3%, 1%, and 6.95%, respectively. Several case studies are then presented to investigate the sensitivity and robustness of the design with regard to the ambient temperature variation, solar irradiation fluctuation, and available surface area for PV module installation.