Elsevier

Energy

Volume 85, 1 June 2015, Pages 10-22
Energy

Demand response: A carbon-neutral resource?

https://doi.org/10.1016/j.energy.2015.02.067Get rights and content

Highlights

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    Demand response reduces electricity system expenditures.

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    Demand response accelerates retirement of operationally expensive power plants.

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    Demand response found to have little impact upon CO2 emissions.

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    Coupled with renewables, demand response reduces regional CO2 emissions.

Abstract

Recent literature on demand response raises questions about the long-term capacity and carbon emissions impacts of expanding its deployment. To provide economy-wide insights into how demand response, capacity planning, and carbon emissions might interact in the future, we perform economic forecasts using a computational general equilibrium model based on the Energy Information Administration's National Energy Modeling System. We develop multiple scenarios of assumptions about the load-shifting and load reduction potential of demand response based on prior literature. The results of these scenarios suggest that demand response can defer large amounts of peak capacity construction. Contrary to expectations of increased carbon intensity, the results of our scenarios also suggest that demand response will have little impact on overall carbon emissions from electric power generation. This suggests that demand response can serve as a long-term, low-cost alternative for peak-hour load balancing without increasing carbon emissions.

Introduction

DR (demand response) refers to programs that incentivize electric power customers to change their patterns of consumption [14]. DR has been used extensively in industrial and commercial sectors since the 1970s, but today's DR is being transformed by technology and market innovations. Wholesale markets are incentivizing DR to participate in markets, smart grid technologies and dynamic pricing are enabling faster and better control of DR resources, and increasingly system aggregators are enabling smaller entities to participate. Many agree that DR can, on the one hand, reduce daily peak loads and contribute to system reliability, and on the other hand, reduce the cost of electricity supply. DR's impact on carbon emissions, by contrast, is less well understood.

The limited understanding of the emissions impacts of DR is especially noteworthy considering the attention given to the long-term impacts of DR and related energy policies. Recent work focuses on the potential for DR to enable renewable electricity resources (see Ref. [1] for a review). But the broader issue of the role of DR in a carbon-constrained future has received little attention.

This paper's goal is to illuminate the role of DR in a carbon-constrained future. To achieve this goal, we use a scenario modeling approach that covers the entire US energy economy and multiple possibilities for DR program implementation. We also use regression analysis to elucidate further insights from the scenario analysis. Specifically, this paper characterizes the capacity and emissions consequences of DR at highly aggregated levels, at large scales of DR deployment, and over an extended planning horizon. Given trends of increasing DR utilization in the U.S. (and globally), what should we expect the capacity and emissions impacts of DR programs to be? Based on the findings from the literature reviewed below, we anticipate that in the short run, increasing DR deployment will increase carbon dioxide emissions by shifting demand from relatively less-carbon-intensive peak capacity to relatively more-carbon-intensive non-peak capacity. The likely effects on CO2 emissions in the long term are more difficult to forecast.

The paper is organized as follows. Section 2 provides a review of literature surrounding the environmental impacts of DR. Section 3 outlines our research design and the scenario modeling approach used. The results of the scenario modeling approach are presented in Section 4. In turn, Section 5 presents the methodology and results from our regression analysis, applied to the results of the scenario modeling approach. Section 6 provides conclusions.

Section snippets

The environmental attributes of DR: a review of the literature

While DR has been demonstrated to induce energy savings through load shaving [9], [12] and enable low-carbon renewable capacity [1], [15], [17], shifting load from peak to off-peak periods could increase carbon emissions from base load [11] and distributed back up generation. In contrast, the ability of DR to defer construction of peak capacity [4], [13], [18] may constitute a strategic option for reducing emissions. If near-term peak capacity options are more carbon-intensive, utility planners

Research design

GT-NEMS is the tool used to generate the scenarios analyzed in this study. GT-NEMS is a CGE (computational general equilibrium) model based on the 2013 distribution of the EIA (Energy Information Administration)'s National Energy Modeling System, which generated EIA's 2013 Annual Energy Outlook [6]. The Annual Energy Outlook forecasts energy supply and demand for the U.S. through 2040. Other than modifications necessary to operate the National Energy Modeling System model on networked servers

Results of demand response analysis in GT-NEMS

Before examining the emissions and capacity impacts of DR, we first describe the effect of our scenarios on the forecasted deployments of DR itself. Fig. 5 displays the trajectories of DR capacity and dispatch across the four scenarios. From these graphs, it is clear that raising the STLIM value – allowing DR to meet a larger share of peak demand – increases the amount of DR actually used by GT-NEMS. By contrast, reducing DR load-shifting diminishes DR deployment only slightly (by about 10% in

Results of regression analysis of DR scenarios

While the basic outputs of GT-NEMS forecast aggregate-level outcomes, the relationship between outcome variables requires greater clarification. For example, we see that combustion turbine capacity builds are far less in some of the DR scenarios than in the reference case, but what's driving this difference cannot be established by looking at these results alone. Moreover, while the GT-NEMS documentation explains how variables are related in the model's optimization formulae, it remains unclear

Conclusions

Overall, we find that DR is likely to have little impact on CO2 emissions from the U.S. electricity sector under a variety of scenarios and ways that DR might operate. Neither large nor small levels of load-shifting between peak and off-peak periods appear to significantly change CO2 emissions. Further, large increases in peak load that can be reduced by DR also do not produce much change in electric power CO2 emissions. Finally, we find that even the CO2 emissions from distributed generation

Acknowledgments

The authors thank the U.S. Department of Energy and Oak Ridge National Laboratory (Melissa Lapsa and Roderick Jackson) contract number: 4000105765 for sponsoring this project and providing useful insights. The authors thank the anonymous reviewers for thorough readings and insightful comments during the review process. The authors also thank Stan Hadley (ORNL) and Joy Wang and Daniel Matisoff at the Georgia Institute of Technology for preliminary reviews and feedback. Any remaining errors are

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