Optimal Design of an On-Grid MicroGrid Considering Long-Term Load Demand Forecasting: A Case Study
DOI:
https://doi.org/10.13052/dgaej2156-3306.3546Keywords:
Artificial neural networks (ANNs), load forecasting, microgrids (MGs), renewable energy sources (RESs), multilayer perceptron (MLP).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.
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