Abstract:As a cost-effective and reliable alternative to supply remote areas, standalone hybrid energy systems (HESs) are recently under investigation to address various concerns associated with technical, financial, and environmental issues. This paper presents a comprehensive algorithm that can simultaneously optimize the component size, operation strategy, and slope of the photovoltaic panels of a standalone HES using an improved variant of particle swarm optimization (PSO), designated as the passive congregation PSO. A new operation strategy is proposed based on the set points of the control system. The optimization algorithm determines the optimal values of the set points to efficiently optimize the HES operation. The applicability and effectiveness of the proposed method are investigated through some numerical analyses performed on a practical remote area in Iran. In doing so, the proposed method is applied to various HES configurations and the results are compared with those obtained using the existing methods. Several load growth and wind speed scenarios are considered, and their impacts on the optimization results are examined.

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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.