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Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping

Author

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  • Godiana Hagile Philipo

    (Electrical Energy Technology—Sustainable Energy Concepts, Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Pohlweg 55, D-33098 Paderborn, Germany
    Department of Material, Energy, Water and Environmental Sciences, The Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania)

  • Josephine Nakato Kakande

    (Electrical Energy Technology—Sustainable Energy Concepts, Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Pohlweg 55, D-33098 Paderborn, Germany
    Department of Electrical and Computer Engineering, CEDAT, Makerere University, Kampala P.O. Box 7062, Uganda)

  • Stefan Krauter

    (Electrical Energy Technology—Sustainable Energy Concepts, Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Pohlweg 55, D-33098 Paderborn, Germany)

Abstract

Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers.

Suggested Citation

  • Godiana Hagile Philipo & Josephine Nakato Kakande & Stefan Krauter, 2022. "Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping," Energies, MDPI, vol. 15(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5215-:d:865931
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