:: Volume 11, Issue 1 (Spring 2008) ::
IJE 2008, 11(1): 45-66 Back to browse issues page
Modeling and Analyzing Daylight Saving Time Impact on Electricity Consumption: A Simulation Approach Based on Seemingly Unrelated Regressions
SM Sajjadi * , SM Asadzadeh
, sm_sajjadi@aut.ac.ir
Abstract:   (13460 Views)
The main purpose of this paper is to study the effect of daylight saving time (DST) policy on electricity consumption in Tehran province. A simulation approach based on econometric models is developed. The econometric model used is a set of 24 regression equations called seemingly unrelated regressions (SUR). A simulation approach based on SUR equations is employed to analyze different scenarios. Three DST scenarios are introduced and analyzed: DST, Extended DST and Double DST. Result of simulation approach based on the SUR model using data in years 2004-2006 indicates that the following electricity decreasing and peak-cut-off potentials exist under different scenarios. Under DST scenario there is 162886 MWh decreasing potential in electricity consumption (equal to 0.87 % of first six months consumption). The peak-cut-off potential in this scenario is 102 MW or 1.9 %. Under Extended DST scenario these amounts are a bit more. There is 176732 MWh decreasing potential in electricity consumption (0.82 % of 8 months –from Esfand to Mehrconsumption). In extended DST, the peak can be cut by 97 MW or 1.9 %. In double DST scenario a potential of 68474 MWh (equal to 0.36 % of first six months consumption) exists but under this scenario the peak increases by 482 MW (9.3%). The analysis of DST impact on electricity consumption in Tehran province can be extended to the other provinces in the country using similar data. The simulation tool developed and used in this paper is a useful tool to explore the precise impact of DST on electricity nation wide.
Keywords: Daylight saving time, electricity consumption, Tehran province, seemingly unrelated regressions
Full-Text [PDF 355 kb]   (2096 Downloads)    
Type of Study: Research | Subject: Energy Planning Models
Received: 2011/11/24 | Published: 2008/04/15


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