The stochastic nature of energy demand and renewable energy (RE) resources make the design of hybrid renewable energy systems as a complex problem. In this paper, an innovative stochastic optimization approach is proposed for optimal sizing of hybrid renewable energy systems (HRES) incorporating existing uncertainties in RE resources and energy load. The design problem is formulated based on multiobjective optimization framework with three objective functions including minimize total net present cost (NPC), maximize renewable energy ratio (RER), and minimize fuel emission. The reliability index named loss of load probability (LLP) is considered as a constraint with a desirable level. The Pareto front (PF) of developed multi-objective optimization problem is approximated with the help of the integration of dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, simulation module, and sampling average method. Synthetic data generation approaches are applied to tackle the randomness in wind speed, solar irradiation, ambient temperature, and energy load. A building located in Canada is used as the case study to assess the performance of the developed model. Finally, the obtained PF by the stochastic optimization approach is examined against the deterministic PF using the most famous performance metrics.

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