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Contribution to the analysis of urban residential electrical energy demand in developing countries

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  • Tatiétsé, Thomas Tamo
  • Villeneuve, Paul
  • Ngundam, John
  • Kenfack, François

Abstract

Access of large segments of the population to electrical energy continues to be limited in the cities of developing countries where the standards pertaining to installed power and consumption are ill suited to the level of appliances owned by households. In this paper, a new evaluation method of residential users’ actual needs in electricity is proposed for Cameroon. The approach is based on the determination of the nature, number and power of the appliances, as well as their operation periods. The method used is a survey administered to a representative sample of households. A detailed questionnaire appraises specific consumption and installed power and is also used to construct the households’ load curve. The need for electrical energy varies according to the size of the city and, inside the city, to household income. The average power used per dwelling accounts for less than half the value prescribed by current norms. If implemented, the method would help optimise network investments and operation. The load curve obtained in the experiment shows that the peak output power is recorded in the evening and that household demand makes up most of this charge. The proposed strategy would reduce electrical energy costs, thus being of great potential interest for poor urban populations.

Suggested Citation

  • Tatiétsé, Thomas Tamo & Villeneuve, Paul & Ngundam, John & Kenfack, François, 2002. "Contribution to the analysis of urban residential electrical energy demand in developing countries," Energy, Elsevier, vol. 27(6), pages 591-606.
    • Handle: RePEc:eee:energy:v:27:y:2002:i:6:p:591-606
    DOI: 10.1016/S0360-5442(02)00006-3
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    Cited by:

    1. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    2. Nfah, E.M. & Ngundam, J.M. & Tchinda, R., 2007. "Modelling of solar/diesel/battery hybrid power systems for far-north Cameroon," Renewable Energy, Elsevier, vol. 32(5), pages 832-844.
    3. Olabisi, Michael & Tschirley, David L. & Nyange, David & Awokuse, Titus, 2019. "Energy demand substitution from biomass to imported kerosene: Evidence from Tanzania," Energy Policy, Elsevier, vol. 130(C), pages 243-252.
    4. Nfah, E.M. & Ngundam, J.M., 2009. "Feasibility of pico-hydro and photovoltaic hybrid power systems for remote villages in Cameroon," Renewable Energy, Elsevier, vol. 34(6), pages 1445-1450.
    5. Trotter, Philipp A. & McManus, Marcelle C. & Maconachie, Roy, 2017. "Electricity planning and implementation in sub-Saharan Africa: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1189-1209.
    6. Nfah, E.M. & Ngundam, J.M. & Vandenbergh, M. & Schmid, J., 2008. "Simulation of off-grid generation options for remote villages in Cameroon," Renewable Energy, Elsevier, vol. 33(5), pages 1064-1072.
    7. Ghaem Sigarchian, Sara & Paleta, Rita & Malmquist, Anders & Pina, André, 2015. "Feasibility study of using a biogas engine as backup in a decentralized hybrid (PV/wind/battery) power generation system – Case study Kenya," Energy, Elsevier, vol. 90(P2), pages 1830-1841.

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