Optimal Design of an On-Grid MicroGrid Considering Long-Term Load Demand Forecasting: A Case Study

Abstract

In this article, an optimal on-grid MicroGrid (MG) is designed consideringlong-term load demand prediction. Multilayer Perceptron (MLP) ArtificialNeural Network (ANN) is used for time-series load prediction. Yearlydemand growth has also been considered in the optimization process based onthe forecasted load profile. Two different case studies are performed with theforecasted and historical load profiles, respectively. According to the results,by considering the predicted load profile, realistic results of net present cost(NPC), cost of energy (COE), and MG configuration would be achieved. TheNPC and COE are obtained as 566,008$ and 0.0240 $/kWh, respectively.It is also demonstrated that utilizing battery storage systems (BSSs) is not economic in the proposed approach. The introduced MG also produces loweremissions compared to the system with the historical load profile. In thisregard, 563,909 kg of CO2 is produced over the optimization year, whichis 35,623 kg lower than the case with no load growth rate. According to thesensitivity analysis results, when the inflation rate increases from 18.16 % to32.36 %, the COE’s value rises to 0.021 USD/kWh accordingly. In contrast,the NPC of the system decreases significantly from above 400 × 103 USD toabout 200 × 103 USD as the inflation rate increases from 18.16 to 32.36.