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Demand-side management for off-grid solar-powered microgrids: A case study of rural electrification in Tanzania

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  • Wang, Xinlin
  • Wang, Hao
  • Ahn, Sung-Hoon

Abstract

This work proposes a novel and sustainable energy development strategy for addressing the energy shortages in rural areas and the low energy efficiency of off-grid solar power systems. This study combines the analysis of power consumption type with consumption anomaly detection to characterize households’ power consumption habits and ensure the safety of a system. Specifically, the proposed anomaly detection method is a hybrid nonintrusive model. The home power usage data are collected and processed by auto-data-binning without manual labeling, and thus, the training cost is reduced to enable the application of machine learning technologies in underdeveloped areas with limited computational resources. With the premise of limited energy sources in off-grid areas, the proposed power consumption analysis method divides home power usage habits into four different types. Different feedback mechanisms are adopted to extend the microgrid’s supply time according to the analysis results. The proposed method significantly increases the utilization of local renewable energy and improves residents’ experience. The proposed method is implemented in a rural village in Tanzania; after long-term monitoring, the validity of the proposed method is demonstrated.

Suggested Citation

  • Wang, Xinlin & Wang, Hao & Ahn, Sung-Hoon, 2021. "Demand-side management for off-grid solar-powered microgrids: A case study of rural electrification in Tanzania," Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221004783
    DOI: 10.1016/j.energy.2021.120229
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    1. Srinivasan, Dipti & Rajgarhia, Sanjana & Radhakrishnan, Bharat Menon & Sharma, Anurag & Khincha, H.P., 2017. "Game-Theory based dynamic pricing strategies for demand side management in smart grids," Energy, Elsevier, vol. 126(C), pages 132-143.
    2. Jamal, Taskin & Urmee, Tania & Shafiullah, G.M., 2020. "Planning of off-grid power supply systems in remote areas using multi-criteria decision analysis," Energy, Elsevier, vol. 201(C).
    3. Asma Mohamad Aris & Bahman Shabani, 2015. "Sustainable Power Supply Solutions for Off-Grid Base Stations," Energies, MDPI, vol. 8(10), pages 1-38, September.
    4. Bhandari, Binayak & Lee, Kyung-Tae & Lee, Caroline Sunyong & Song, Chul-Ki & Maskey, Ramesh K. & Ahn, Sung-Hoon, 2014. "A novel off-grid hybrid power system comprised of solar photovoltaic, wind, and hydro energy sources," Applied Energy, Elsevier, vol. 133(C), pages 236-242.
    5. Moussa P. Blimpo & Malcolm Cosgrove-Davies, 2019. "Electricity Access in Sub-Saharan Africa [Accès à l’électricité en Afrique subsaharienne]," World Bank Publications - Books, The World Bank Group, number 31333.
    6. Roy, Anthony & Auger, François & Dupriez-Robin, Florian & Bourguet, Salvy & Tran, Quoc Tuan, 2020. "A multi-level Demand-Side Management algorithm for offgrid multi-source systems," Energy, Elsevier, vol. 191(C).
    7. Centolella, Paul, 2010. "The integration of Price Responsive Demand into Regional Transmission Organization (RTO) wholesale power markets and system operations," Energy, Elsevier, vol. 35(4), pages 1568-1574.
    8. Campillo, Javier & Dahlquist, Erik & Wallin, Fredrik & Vassileva, Iana, 2016. "Is real-time electricity pricing suitable for residential users without demand-side management?," Energy, Elsevier, vol. 109(C), pages 310-325.
    9. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    10. Pelzer, R. & Mathews, E.H. & le Roux, D.F. & Kleingeld, M., 2008. "A new approach to ensure successful implementation of sustainable demand side management (DSM) in South African mines," Energy, Elsevier, vol. 33(8), pages 1254-1263.
    11. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    12. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    13. Wang, Xinlin & Ahn, Sung-Hoon, 2020. "Real-time prediction and anomaly detection of electrical load in a residential community," Applied Energy, Elsevier, vol. 259(C).
    14. Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
    15. Fankhauser, Samuel & Tepic, Sladjana, 2007. "Can poor consumers pay for energy and water? An affordability analysis for transition countries," Energy Policy, Elsevier, vol. 35(2), pages 1038-1049, February.
    16. da Silva, Patrícia Pereira & Cerqueira, Pedro André & Ogbe, Wojolomi, 2018. "Determinants of renewable energy growth in Sub-Saharan Africa: Evidence from panel ARDL," Energy, Elsevier, vol. 156(C), pages 45-54.
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    3. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    4. Wang, Xinlin & Flores, Robert & Brouwer, Jack & Papaefthymiou, Marios, 2022. "Real-time detection of electrical load anomalies through hyperdimensional computing," Energy, Elsevier, vol. 261(PA).
    5. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    6. Hirwa, Jusse & Zolan, Alexander & Becker, William & Flamand, Tülay & Newman, Alexandra, 2023. "Optimizing design and dispatch of a resilient renewable energy microgrid for a South African hospital," Applied Energy, Elsevier, vol. 348(C).
    7. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).

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