Multi-objective particle swarm optimization of component size and long-term operation of hybrid energy systems under multiple uncertainties

Abstract

In real-world operation conditions, Hybrid Energy Systems (HESs) are exposed to a wide variety of uncertainties, which cause an unexpected operation and performance in the case of neglecting the effects of uncertainties in design and operation processes. This paper presents a multi-objective optimization of the component size and long-term operation of the HES in the presence of multiple uncertainties, considering the Net Present Cost and Energy Not Served as the objective functions. Uncertainties related to load forecasting, wind speed, and components' outage are probabilistically modeled and incorporated into the Multi-Objective Particle Swarm Optimization approach using the Monte Carlo Simulation (MCS) method. MCS generates samples (future scenarios) of uncertain variables based on the probabilistic models of the uncertainties. The optimization algorithm determines the optimal values of component's size as well as the operation parameters to efficiently optimize the HES operation. The applicability and effectiveness of the proposed method are investigated through some numerical analyses. The proposed method can be a useful stochastic optimization tool to consider important uncertainties in the practical design and operation of the HES.