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Residential End-Use Electricity Demand: Results from a Designed Experiment

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Author Info

  • Robert Bartels
  • Denzil G. Fiebig

Abstract

Being able to disaggregate total energy demand into components attributable to specific end uses provides useful information and represents a primary input into any attempt to simulate the impact of policies aimed at encouraging households to use less energy or shift load. Conceptually the estimation problem can be solved by directly metering individual appliances. Not surprisingly, this has not been widely practised and by far the most common estimation procedure has been the indirect statistical approach known as conditional demand analysis. More recently, with access to limited direct metering, both approaches have been used in combination. This paper reports on a substantial modelling exercise that represents a unique example of combining data of this type. The distinctive aspects are the extent and richness of the metering data and the fact that optimal design techniques were used to decide on the pattern of metering. As such, the empirical results are able to provide a very detailed and accurate picture of how total residential load is disaggregated by end uses. Significantly, the consumption of high penetration end uses such as lighting, which cannot be estimated by conventional conditional demand analysis, has been successfully estimated. Also, by matching our estimates of end-use load curves with some recent prices paid by distributors to purchase electricity from an electricity market pool, we have been able to determine the costs to distributors associated with servicing individual end uses.

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Bibliographic Info

Article provided by International Association for Energy Economics in its journal The Energy Journal.

Volume (Year): Volume21 (2000)
Issue (Month): Number 2 ()
Pages: 51-81

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Handle: RePEc:aen:journl:2000v21-02-a03

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Cited by:
  1. Claudio A Agostini, 2011. "La Demanda Residencial por Energía Eléctrica en Chile," Working Papers wp_013, Adolfo Ibáñez University, School of Government.
  2. Newsham, Guy R. & Donnelly, Cara L., 2013. "A model of residential energy end-use in Canada: Using conditional demand analysis to suggest policy options for community energy planners," Energy Policy, Elsevier, vol. 59(C), pages 133-142.
  3. Hanne Marit Dalen & Bodil M. Larsen, 2013. "Residential end-use electricity demand. Development over time," Discussion Papers 736, Research Department of Statistics Norway.
  4. Muhammad Akmal & David I. Stern, 2001. "The structure of Australian residential energy demand," Working Papers in Ecological Economics 0101, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.
  5. Muhammad Akmal & David I. Stern, 2001. "Residential energy demand in Australia: an application of dynamic OLS," Working Papers in Ecological Economics 0104, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.
  6. Frondel, Manuel & Schmidt, Christoph M., 2001. "Evaluating environmental programs: the perspective of modern evaluation research," ZEW Discussion Papers 01-59, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  7. Claudio Agostini & M. Cecilia Plottier & Eduardo Saavedra, 2012. "Residential Demand for Electric Energy in Chile," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(3), pages 64-83, December.
  8. Bodil M. Larsen & Runa Nesbakken, 2003. "How to quantify household electricity end-use consumption," Discussion Papers 346, Research Department of Statistics Norway.
  9. Muhammad, Akmal, 2002. "The structure of consumer energy demand in Australia: an application of a dynamic almost ideal demand system," 2002 Conference (46th), February 13-15, 2002, Canberra 125050, Australian Agricultural and Resource Economics Society.
  10. Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, vol. 26(2), pages 179-200, March.

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