Optimal Design of an On-Grid MicroGrid Considering Long-Term Load Demand Forecasting: A Case Study
In this article, an optimal on-grid MicroGrid (MG) is designed considering long-term load demand prediction. Multilayer Perceptron (MLP) Artificial Neural Network (ANN) has been used for time-series load prediction. Yearly demand growth has also been considered in the optimization process based on the forecasted load profile. Two case studies have been performed with the forecasted and historical load profiles, respectively. It has been shown that by applying the forecasted load profile, realistic results of net present cost (NPC), cost of energy (COE) and MG configuration would be achieved. Moreover, it has been demonstrated that utilizing battery storage systems (BSSs) are not economic in the proposed system. The introduced MG also produces lower emission compared to the system with the historical load profile.
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