A quantile-based simulation optimization model for sizing hybrid renewable energy systems

As the public has gradually realized the adverse impacts brought by global warming, hybrid renewable energy system (HRES) has become increasingly popular because it can reduce dependence on fossil fuels, while maintaining the stability of power supply. While the HRES is an attractive option in many aspects, the fundamentally uncertain nature of renewable energy sources makes the determination of the proper sizing of the HRES a very challenging task. Contrasting with the existing models that are largely focused on expectation-based system performance, this paper provides a quantile-based simulation optimization model, followed by the development of an efficient solution methodology, to enable the control of the upside risk and, as a result, to enhance the decision quality regarding the sizing of HRES. One advantage of the proposed model is that they can be based on any existing deterministic model that carries a cost structure regarding the sizing of the HRES. Moreover, the proposed solution methodology, consisting of a Monte Carlo simulation method, quantile estimation techniques, and an efficient stochastic optimizer, allows for not only accurate estimation of the objective function value, but also quick identification of the optimal solution due to a uniquely-defined neighborhood structure. An extensive numerical experiment is conducted to verify the efficacy and efficiency of the proposed solution methodology. Finally, in collaboration with a partner in industry, the proposed model and the solution methodology are integrated into a decision support system to provide visualized results for sizing HRES in practice.