Frontiers in Energy Research (Mar 2024)

Modelling and optimizing microgrid systems with the utilization of real-time residential data: a case study for Palapye, Botswana

  • T. B. Seane,
  • Ravi Samikannu,
  • Ravi Samikannu,
  • Moses Tunde Oladiran,
  • Abid Yahya,
  • Patricia Makepe,
  • Gladys Gamariel,
  • Maruliya Begam Kadarmydeen,
  • Nyagong Santino David Ladu,
  • Heeravathi Senthamarai

DOI
https://doi.org/10.3389/fenrg.2023.1237108
Journal volume & issue
Vol. 11

Abstract

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Microgrids are becoming a realistic choice for residential buildings due to the increasing need for affordable and sustainable energy solutions in developing nations. Through modeling and simulation, the main goal is to evaluate the viability and performance of a solar microgrid system. Residential load modeling is used, which is vital to developing an effective Energy Management System (EMS) for the microgrid. A residential household’s load metering data is examined using statistical methods, including time series and regression analysis. For the residential community load in this research, Proportional-Integral-Derivative (PID) controllers and Fuzzy Logic Controllers (FLC) are used to generate the necessary Direct Current (DC) microgrid voltage. The simulation research shows that FLC have benefits over PID controllers. The FLC technique performs better at reducing total harmonic distortion, which improves the microgrid system’s overall power quality. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was found to be the most appropriate and reliable model for the dataset after the performance of the models was evaluated using the metrics. The optimization results also showed that FLC optimization improves the microgrid system’s stability. The exponential Gaussian process regression (GPR) produced the highest R-squared measure of 0.49 and RSME measure of 7.9646, making it the best goodness fit for modeling the total daily energy usage and the peak daily usage.

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