Optimal design of hybrid renewable energy systems using simulation optimization

•A new model is proposed for the design of hybrid renewable energy systems in uncertain environments.•A RSM-based method, coupled with a Monte Carlo approach, is proposed to solve the model.•Numerical experiments show that the solution method can solve the proposed model of realistic size efficiently.•The proposed method is shown to be more efficient than the existing heuristic methods.

A hybrid renewable energy system (HRES) provides a viable solution for electrification of remote areas when grid extension is impossible or uneconomical. Such a system has several power stations and each of which includes photovoltaics (PV) and wind power generators, along with a diesel power generator as backup when renewable energy supply is insufficient. While the HRES is attractive due to the minimal environmental and health impact compared to fossil fuels, the design of HRES, specifically the determination of the size of PV, wind, and diesel power generators and the size of energy storage system in each power station, is very challenging. This is mainly due to a large number of factors involved in the problem, the profound uncertainty arising from the renewable resources and the demand load, and the complex interaction among factors. In this paper, we investigate the use of Monte Carlo simulation, along with simulation optimization techniques, for obtaining the optimal design of HRES in uncertain environments. The proposed model considers not only the equipment installation, including PV, wind and diesel power generators and the energy storage systems in each power station, but also the power generation, allocation and transmission within the HRES so as to achieve minimum expected total cost, while satisfying the power demand. An extensive computational study shows that the proposed model of realistic size can be solved efficiently, enabling quality decisions to be generated in practice.