Multi-Objective Optimal Sizing of HRES under Multiple Scenarios with Undetermined Probability

Abstract

In recent years, technologies for renewable energy utilization have been booming. Hybrid renewable energy systems (HRESs), integrating multiple energy sources to mitigate the unstable, unpredictable, and intermittent characteristics of a single renewable energy source, have become increasingly popular. However, due to the inherent intermittency and uncertainty of renewable energies, the impact of uncertain factors on the capacity optimization of HRESs needs to be considered. In the traditional scenario-based planning method, when dealing with uncertain factors, the probability corresponding to the scenario is difficult to determine. Furthermore, when applying the robust optimization method, it is difficult to fully use existing data to describe uncertain parameters in the form of intervals. To tackle these difficulties, this study proposes a probability undetermined scenario-based sizing model (PUSS model) for stand-alone HRES configuration optimization and a multi-objective evolutionary algorithm as the problem solver. The solution set obtained by the method covers multiple possible values of scenario probability combinations and can provide decision-makers with an overview of alternatives for HRES sizing under different power supply pressures. Based on the real environment data and load data of a certain place, the proposed model and algorithm are applied to sizing a typical HRES comprising wind generators, solar photovoltaic panels, energy-storage devices, and diesel generators. The experimental results show that the proposed PUSS method is both effective and efficient.