A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use

Abstract

Energy Policy, 39, (10), pp. 6376-6389, DOI: 10.1016/j.enpol.2011 http://www.nrc-cnrc.gc.ca/ircThe material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. For more information visit http://laws.justice.gc.ca/en/showtdm/cs/C-42Les renseignements dans ce document sont protégés par la Loi sur le droit d'auteur, par les lois, les politiques et les règlements du Canada et des accords internationaux. Ces dispositions permettent d'identifier la source de l'information et, dans certains cas, d'interdire la copie de documents sans permission écrite. Pour obtenir de plus amples renseignements : http://lois.justice.gc.ca/fr/showtdm/cs/C-42 AbstractWe analysed the peak load reductions due to a residential direct load control program for air-conditioners in one jurisdiction in southern Ontario in 2008. In this program, participant thermostats were increased by 2°C for four hours on five event days (when systemwide capacity was expected to be strained). We used hourly, whole-house data for 195 load control participant households and 268 non-participant households, and four different methods of analysis ranging from simple spreadsheet-based comparisons of average loads on event days, to complex time-series regression. Average peak load reductions were 0.2-0.9 kW per household, or 10-35%. However, there were large differences (up to a factor of four) between event days and across event hours, and in results for the same event day/hour with different analysis methods. There was also a wide range of load reductions between individual households. Policy makers would be 1 '" wise to consider multiple analysis methods when making decisions regarding which demand-side management programs to support, and how they might be incentivized .Further investigation of what type of households contribute most to aggregate load reductions would also help policy makers better target programs.

The challenge of adapting centralised electricity systems: peak demand and maladaptation in South East Queensland, Australia

“…The literature cites a number of adaptation options for managing peak demand (Vine 2012), including building new electricity generation and network infrastructure, direct control of air-conditioners and other electricity 'hungry' devices during peak periods (Newsham 2011;Reddy 1991), introducing new time-of-use electricity tariffs (Newsham and Bowker 2010), educating consumers to shift demand and improving housing/household energy efficiency (Vine 2012).…”

The mitigating effect of strategic behavior on the net benefits of a direct load control program

“…While there are several methods for estimating baseline consumption patterns and load comparisons, we use a regression-based baseline modeling approach. The wide scale adoption of smart grid meters and availability of high-resolution, hourly or 15-minute energy consumption data has contributed a great deal to improvements in regression-based baseline models (Carrie Armel et al 2013, Newsham et al 2011, Santin and Itard 2010, Santin et al 2009). Mathieu et al (2011) find that the regression-based baseline model performs better than most models used in evaluating DR programs.…”

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

“…A key aspect for assessing the potential of DR is the user responsiveness, which has been recognized as being influenced by several variables, such as the pricing scheme and incentive mechanism [10], the type of loads [11], the presence of generation from renewable sources [12], the occupancy of the household [13], and the weather [14]. All these factors result in a variability of performances; something that is confirmed by the quantitative results reported in the context of successful pilot projects running in Germany [15], Belgium [16,17], and the Netherlands [18].…”