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

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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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    1. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.
    2. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    3. Yadoo, Annabel & Cruickshank, Heather, 2012. "The role for low carbon electrification technologies in poverty reduction and climate change strategies: A focus on renewable energy mini-grids with case studies in Nepal, Peru and Kenya," Energy Policy, Elsevier, vol. 42(C), pages 591-602.
    4. Awais Manzoor & Nadeem Javaid & Ibrar Ullah & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes," Energies, MDPI, vol. 10(9), pages 1-28, August.
    5. Independent Evaluation Group, 2008. "The Welfare Impact of Rural Electrification : A Reassessment of the Costs and Benefits," World Bank Publications - Books, The World Bank Group, number 6519, December.
    6. Mohammad Rasoul Narimani & Maigha & Jhi-Young Joo & Mariesa Crow, 2017. "Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles," Energies, MDPI, vol. 10(5), pages 1-18, May.
    7. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    8. Valeria Gambino & Andrea Micangeli & Vincenzo Naso & Emanuele Michelangeli & Luca Di Mario, 2014. "A Sustainable and Resilient Housing Model for Indigenous Populations of the Mosquitia Region (Honduras)," Sustainability, MDPI, vol. 6(8), pages 1-18, August.
    9. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    10. Heffner, G. & Maurer, L. & Sarkar, A. & Wang, X., 2010. "Minding the gap: World Bank's assistance to power shortage mitigation in the developing world," Energy, Elsevier, vol. 35(4), pages 1584-1591.
    11. Shahidur R. Khandker & Douglas F. Barnes & Hussain A. Samad, 2013. "Welfare Impacts of Rural Electrification: A Panel Data Analysis from Vietnam," Economic Development and Cultural Change, University of Chicago Press, vol. 61(3), pages 659-692.
    12. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
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