Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China

Demand response to time-varying pricing of electricity is critical to a smart grid's efficient management of electrical resources. This paper presents a new approach to quantify residential demand responsiveness to (time-of-use) TOU rates, which does not entail an econometric estimation of TOU demand equations. Based on one of the four smart grid pilots in China, our approach uses the survey data collected in 2011 from 236 residents in Yinchuan to implement a Monte Carlo simulation to obtain the minimum, expected and maximum demand responsiveness to four TOU rate designs. We find that residents do not respond to TOU pricing when the TOU rate design only causes a 10% increase in their existing electricity bills under non-TOU rates. However, their estimated peak demand responsiveness is 8.41% (21.26%) when the peak-time price increases by 20% (40%). Based on these findings, we conclude that suitably designed TOU rates are useful to the efficient operation of a smart grid.

[1]  R. Parks,et al.  Measuring the consumer welfare effects of time-differentiated electricity prices , 1984 .

[2]  A. Faruqui,et al.  Household response to dynamic pricing of electricity: a survey of 15 experiments , 2010 .

[3]  I. Pearson.  Smart grid cyber security for Europe , 2011 .

[4]  Chi-Keung Woo,et al.  Estimating the option value of a non-firm electricity tariff , 2007 .

[5]  Snuller Price,et al.  Smart meter, customer choice and profitable time-of-use rate option , 1999 .

[6]  M. P. Moghaddam,et al.  Demand response modeling considering Interruptible/Curtailable loads and capacity market programs , 2010 .

[7]  C. Woo,et al.  Now that California has AMI, what can the state do with it? , 2008 .

[8]  Yolande Strengers,et al.  Air-conditioning Australian households: The impact of dynamic peak pricing , 2010 .

[9]  Enzo Sauma,et al.  Do distribution companies loose money with an electricity flexible tariff?: A review of the Chilean case , 2013 .

[10]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

[11]  R. Rajagopal,et al.  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior , 2013 .

[12]  D. Aigner,et al.  The Residential Electricity Time-of-Use Pricing Experiments: What Have We Learned? , 1985 .

[13]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[14]  H. Allcott,et al.  Rethinking Real Time Electricity Pricing , 2011 .

[15]  R. Walawalkar,et al.  Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO , 2010 .

[16]  Karen Herter,et al.  Residential response to critical-peak pricing of electricity: California evidence , 2010 .

[17]  J. Torriti,et al.  Demand response experience in Europe: Policies, programmes and implementation , 2010 .

[18]  A. Rosenfeld,et al.  An exploratory analysis of California residential customer response to critical peak pricing of electricity , 2007 .

[19]  J. Torriti,et al.  Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy , 2012 .

[20]  Chi-Keung Woo,et al.  Designing Pareto-superior demand-response rate options , 2006 .

[21]  C. Goldman,et al.  Option value of electricity demand response , 2005 .

[22]  P. Cappers,et al.  Demand Response in U.S. Electricity Markets: Empirical Evidence , 2010 .

[23]  Dean C. Mountain,et al.  Some initial evidence of Canadian responsiveness to time-of-use electricity rates: Detailed daily and monthly analysis , 1995 .

[24]  J. Robert Malko,et al.  The residential demand for electricity by time-of-use: A survey of twelve experiments with peak load pricing☆ , 1983 .

[25]  I. Vassileva,et al.  Understanding energy consumption behavior for future demand response strategy development , 2012 .

[26]  Karen Herter.  Residential implementation of critical-peak pricing of electricity , 2007 .

[27]  C. Clastres.  Smart grids: Another step towards competition, energy security and climate change objectives , 2011 .

[28]  Douglas W. Caves,et al.  Consistency of residential customer response in time-of-use electricity pricing experiments , 1984 .

[29]  Hamid Lesani,et al.  Assessing the performance and benefits of customer distributed generation developers under uncertain , 2011 .

[30]  Douglas W. Caves,et al.  Econometric analysis of residential time-of-use electricity pricing experiments , 1980 .