Sizing a grid-connected photovoltaic system under economic and environmental uncertainties

Several looming challenges confront the implementation of PV systems. The decision-maker must consider technical, economic, and environmental uncertainties to maximize the PV systems' efficiency under specific operating constraints. In this research, we have developed a two-stage stochastic programming model using a multiobjective optimization technique, extending the approach originally introduced by Attia et al. (2021), to optimize the performance of a grid-coupled PV system considering economic and environmental uncertainties. The capacity of a PV system is determined by deciding the number of PV arrays, the amount of energy imported from the grid, and the amount exported to external utilities. The inflation rate, hourly irradiation, ambient temperature, and energy demand are considered uncertain parameters associated with economic and environmental factors. Uncertain parameters are characterized by possible scenarios associated with the corresponding probability of occurrence. A case study of sizing a grid-connected PV system to provide power to the housing area is presented to demonstrate the usefulness of the proposed technique. A sensitivity analysis is conducted by changing the levels of the primary parameters to grasp the tradeoffs that govern the research framework. It is found that the system can meet the entire demand using 1,566 PV arrays at an annual cost of M$1.47 and a reduction of CO2 emissions by 96.32%.