Stochastic optimization of hybrid renewable energy systems using sampling average method

Abstract

The stochastic attribute of renewable energy sources and the variability of energy load is a preeminent barrier to design hybrid renewable energy systems. In this paper, a new methodology is advanced to incorporate the uncertainties associated with RE resources and load in sizing an HRES in the application of buildings with low to high renewable energy ratio (RER). Dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, simulation module, and sampling average technique are used to approximate a Pareto front (PF) for an HRES design through a multi-objective optimization framework. The main aim of design is to simultaneously minimize total net present cost (NPC), maximize renewable energy ratio, and minimize fuel emission while satisfy a desirable level of loss of load probability (LLP). The existing randomness in wind speed, solar irradiation, ambient temperature, and energy load is considered using synthetically data generation and sampling average method. The performance of the model has been examined in a building located in Canada as the case study, in which RER of the building is increased by using renewable energy technologies. The generated PF by the stochastic approach is compared to a deterministic PF using well-known performance metrics. Finally, a sensitivity analysis is carried out where the economic characteristics of the model are varied.