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Modelling electricity demand with representative load curves

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  • Balachandra, P.
  • Chandru, Vijay

Abstract

Models for electricity planning require inclusion of demand. Depending on the type of planning, the demand is usually represented as an annual demand for electricity (GWh), a peak demand (MW) or in the form of annual load–duration curves. The demand for electricity varies with the seasons, economic activities, etc. Existing schemes do not capture the dynamics of demand variations that are important for planning. For this purpose, we introduce the concept of representative load curves (RLCs). Advantages of RLCs are demonstrated in a case study for the state of Karnataka in India. Multiple discriminant analysis is used to cluster the 365 daily load curves for 1993–94 into nine RLCs. Further analyses of these RLCs help to identify important factors, namely, seasonal, industrial, agricultural, and residential (water heating and air-cooling) demand variations besides rationing by the utility.

Suggested Citation

  • Balachandra, P. & Chandru, Vijay, 1999. "Modelling electricity demand with representative load curves," Energy, Elsevier, vol. 24(3), pages 219-230.
    • Handle: RePEc:eee:energy:v:24:y:1999:i:3:p:219-230
    DOI: 10.1016/S0360-5442(98)00096-6
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    Cited by:

    1. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    2. Falah Alhameli & Ali Ahmadian & Ali Elkamel, 2021. "Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub," Energies, MDPI, vol. 14(20), pages 1-17, October.
    3. Balachandra, P., 2006. "Implications of private sector participation in power generation--a case study from India," Energy Policy, Elsevier, vol. 34(16), pages 2466-2479, November.
    4. Morgan Bazilian & Patrick Nussbaumer & Hans-Holger Rogner & Abeeku Brew-Hammond & Vivien Foster & Shonali Pachauri & Eric Williams & Mark Howells & Philippe Niyongabo & Lawrence Musaba & Brian Ó Galla, 2011. "Energy Access Scenarios to 2030 for the Power Sector in Sub-Saharan Africa," Working Papers 2011.68, Fondazione Eni Enrico Mattei.
    5. van Ruijven, Bas J. & Schers, Jules & van Vuuren, Detlef P., 2012. "Model-based scenarios for rural electrification in developing countries," Energy, Elsevier, vol. 38(1), pages 386-397.
    6. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    7. Sayegh, Hasan & Leconte, Antoine & Fraisse, Gilles & Wurtz, Etienne & Rouchier, Simon, 2022. "Computational time reduction using detailed building models with Typical Short Sequences," Energy, Elsevier, vol. 244(PB).
    8. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    9. Saab, Samer & Badr, Elie & Nasr, George, 2001. "Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon," Energy, Elsevier, vol. 26(1), pages 1-14.
    10. Ravindra, Kumudhini & Iyer, Parameshwar P., 2014. "Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis," Energy, Elsevier, vol. 76(C), pages 32-41.
    11. Orosz, Matthew & Altes-Buch, Queralt & Mueller, Amy & Lemort, Vincent, 2018. "Experimental validation of an electrical and thermal energy demand model for rapid assessment of rural health centers in sub-Saharan Africa," Applied Energy, Elsevier, vol. 218(C), pages 382-390.
    12. Balasubramanian, S. & Balachandra, P., 2021. "Effectiveness of demand response in achieving supply-demand matching in a renewables dominated electricity system: A modelling approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).

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