Abstract
Solar energy is considered one of the most important renewable energy resources, and can be used to power a stand-alone photovoltaic (SAPV) system for supplying electricity in a remote area. However, inconstancy and unpredictable amounts of solar radiation are considered major obstacles in designing SAPV systems. Therefore, an accurate sizing method is necessary to apply in order to find an optimal configuration and fulfil the required load demand. In this study, a novel hybrid sizing approach was developed on the basis of techno-economic objectives to optimally size the SAPV system. The proposed hybrid method consisted of an intuitive method to estimate initial numbers of PV modules and storage battery, an iterative approach to accurately generate a set of wide ranges of optimal configurations, and a Pareto envelope-based selection algorithm (PESA-II) to reduce large configuration by efficacy obtaining a set of Pareto front (PF) solutions. Subsequently, the optimal configurations were ranked by using an integrated analytic hierarchy process (AHP) and vlsekeriterijumskaoptimizacija i kompromisonoresenje (VIKOR). The techno-economic objectives were loss of load probability, life cycle cost, and levelized cost of energy. The performance analysis results demonstrated that the lead–acid battery was reliable and more cost-effective than the other types of storage battery.
Highlights
The electricity demand is rapidly increasing due to growth in population and the risk of increasing electricity bills and tariffs
Where Eload represents the daily energy load demand, PSH is the peak sunshine hours, ηinv and ηbat are the efficiencies of the inverter and system, A is the area of the PV array, and SF is the design safety factor
After determining the bounders of the PV modules and storage batteries numbers, the iterative method is employed by increasing the numbers of PV modules and storage battery from minimum to maximum based on predefined ranges
Summary
The electricity demand is rapidly increasing due to growth in population and the risk of increasing electricity bills and tariffs. These issues lead to encourage energy system designers to a transformation of technology in terms of “leaving the grid” or “living in off-grid” [1,2]. SAPV systems need to be optimally designed in order to maximize their reliability and minimize the total cost of the system [3,4,5,6]. The intuitive method utilizes simplified calculations without considering the fluctuation of meteorological data and the nonlinearity relationship between subsystems [10]. The average daily meteorological data were used to minimize the annualized life cycle cost (ALCC), and the technical criteria was not revealed.
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