The uncertain analysis of fixed solar compound parabolic concentrator (CPC) collector system is investigated for use in combination with solar PV cells. Within solar CPC PV collector systems, any radiation within the collector acceptance angle enters through the aperture and finds its way to the absorber surface by multiple internal reflections. It is essential that the design of any solar collector aims to maximize PV performance since this will elicit a higher collection of solar radiation. In order to analyze uncertainty of the solar CPC collector system in the optimization problem formulation, three objectives are outlined. Seasonal demands are considered for maximizing two of these objectives, the annual average incident solar energy and the lowest month incident solar energy during winter; the lowest cost of the CPC collector system is approached as a third objective. This study investigates uncertain analysis of a solar CPC PV collector system using fuzzy set theory. The fuzzy analysis methodology is suitable for ambiguous problems to predict variations. Uncertain parameters are treated as random variables or uncertain inputs to predict performance. The fuzzy membership functions are used for modeling uncertain or imprecise design parameters of a solar PV collector system. Triangular membership functions are used to represent the uncertain parameters as fuzzy quantities. A fuzzy set analysis methodology is used for analyzing the three objective constrained optimization problems.
This study proposes a method, grounded in a multilevel decision-making approach, for a stationary fixed-plate photovoltaic (PV) collector system. The system is comprised of three different subsystems: cell, panel, and array. We consider photovoltaic effects for output performance and an inverter system for distribution from the PV collector, including multiple conflicting objectives in individual subsystems in terms of cell conversion efficiency, power output, incident solar energy, seasonal characteristics, and costs. In terms of the performance in individual subsystems, the problem is reformulated into several smaller subproblems at each subsystem, and a coordination problem at the system level is compromised for optimization purposes. Multilevel optimization for the stationary fixed-plate PV collector system is achieved through the results of single-objective optimization that uses Genetic Algorithm programming (GA) to find global optimum solutions with decision-making under modified game theory. Thus, this work contributes to the optimal design of a stationary fixed-plate PV collector system for the best compromise solution based on specified requirements.
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