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An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system

Wang, Rui, Li, Guozheng, Ming, Mengjun, Wu, Guohua, Wang, Ling
Energy 2017 v.141 pp. 2288-2299
algorithms, batteries, case studies, decision making, generators (equipment), greenhouse gas emissions, models, probability, renewable energy sources, solar collectors, wind, wind turbines
Hybrid renewable energy system (HRES) has continuously been demonstrated effective in making use of renewable energies, e.g., solar, wind. This study proposes a novel multi-objective model and algorithm for optimizing the size of a typical stand-alone HRES that is composed of photovoltaic (PV) panels, wind turbines, battery banks and diesels. Notably, the proposed model considers minimization of annualized system cost (economy), loss of power supply probability (reliability) and greenhouse gas emission (environment), and enables a decision maker to optimize both the number and the type of PV panel, wind turbine, battery and diesel generator as well as the PV panel installation angle, the wind turbine installation height. To effectively solve the model, in particular, dealing with mixed types of decision variables including integer, real and categorical values, the non-dominated sorting algorithm II (NSGA-II) embedded with a re-ranking based genetic operators is proposed. Lastly, a case study is presented to demonstrate the effectiveness and efficiency of the proposed model and algorithm.