Stochastic Optimal Sizing of Micro-Grids Using the Moth-Flame Optimization Algorithm

Optimal sizing of renewable and sustainable energy systems should consider the uncertainties associated with various input data to ensure the financial sustainability of developing such systems, especially in the case of stand-alone systems. This paper proposes a novel stochastic modelling framework for the optimal sizing of micro-grids subject to satisfying a reliability index for supplying the loads. The proposed framework incorporates a model reduction technique, a state-of-the-art meta-heuristic optimization algorithm (i.e. moth-flame optimization algorithm), as well as an uncertainty analysis technique using Monte Carlo simulations based on a new scenario reduction process. It also preserves the computational tractability. A micro-grid test system incorporating photovoltaic panels, wind turbines, battery packs, a DC/AC inverter., and an electric vehicle fast charging station is used to assess the validity and effectiveness of the proposed stochastic framework. Accordingly, the impact of uncertainties associated with renewable power generation and load demand on the sizes of the considered micro-grid components are evaluated. The numerical simulation results for the considered micro-grid test system are presented and compared with those generated by a deterministic model, which have demonstrated the effectiveness of the proposed stochastic modelling framework.