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

G.H. Philipo, J.N. Kakande, S. Krauter, Energies 15 (2022).

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Journal Article | Published | English
Author
Philipo, Godiana Hagile; Kakande, Josephine Nakato; Krauter, StefanLibreCat
Abstract
<jats:p>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.</jats:p>
Publishing Year
Journal Title
Energies
Volume
15
Issue
14
Article Number
5215
ISSN
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Philipo GH, Kakande JN, Krauter S. Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping. Energies. 2022;15(14). doi:10.3390/en15145215
Philipo, G. H., Kakande, J. N., & Krauter, S. (2022). Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping. Energies, 15(14), Article 5215. https://doi.org/10.3390/en15145215
@article{Philipo_Kakande_Krauter_2022, title={Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping}, volume={15}, DOI={10.3390/en15145215}, number={145215}, journal={Energies}, publisher={MDPI AG}, author={Philipo, Godiana Hagile and Kakande, Josephine Nakato and Krauter, Stefan}, year={2022} }
Philipo, Godiana Hagile, Josephine Nakato Kakande, and Stefan Krauter. “Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping.” Energies 15, no. 14 (2022). https://doi.org/10.3390/en15145215.
G. H. Philipo, J. N. Kakande, and S. Krauter, “Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping,” Energies, vol. 15, no. 14, Art. no. 5215, 2022, doi: 10.3390/en15145215.
Philipo, Godiana Hagile, et al. “Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping.” Energies, vol. 15, no. 14, 5215, MDPI AG, 2022, doi:10.3390/en15145215.

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