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 considering long-term load demand prediction. Multilayer Perceptron (MLP) Artificial Neural Network (ANN) is used for time-series load prediction. Yearly demand growth has also been considered in the optimization process based on the forecasted load profile. Two different case studies are performed with the forecasted 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. The NPC 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 lower emissions compared to the system with the historical load profile. In this regard, 563,909 kg of CO2 is produced over the optimization year, which is 35,623 kg lower than the case with no load growth rate. According to the sensitivity analysis results, when the inflation rate increases from 18.16 % to 32.36 %, 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 to about 200 × 103 USD as the inflation rate increases from 18.16 to 32.36.