This report describes analytic work completed by EPRI to estimate avoided greenhouse gas (GHG) emissions associated with industrial end-use energy efficiency (EE) projects. In addition, EPRI evaluated two approaches that could be used by electric companies to increase deployment of industrial end-use EE projects.
The Tennessee Valley Authority (TVA) assisted EPRI in conducting this study by providing EPRI with the results from regional dispatch modeling efforts, and by providing access to key staff with expertise in industrial end-use EE projects, generation planning and electricity dispatch modeling.
Background
The U.S. Environmental Protection Agency (EPA) recently adopted final regulations to reduce carbon dioxide (CO2) emissions from existing fossil-fired electric power plants under section 111(d) of the 1990 Clean Air Act (CAA). One way States and electric companies may seek to comply with these new Clean Power Plan (CPP) standards is by implementing end-use EE projects that may reduce the regional dispatch of fossil-fired power plants that emit GHGs.
Separately, some large industrial electricity customers have been seeking ways to reduce their GHG emissions “footprint” to achieve corporate sustainability goals. One way companies are seeking to achieve these goals is to reduce their so-called “Scope 2” GHG emissions (i.e., the GHG emissions “embedded” in the electricity they purchase and use in their operations).
In response to these developments, there is a growing interest among electric companies and other stakeholders to better understand how end-use EE projects can reduce GHG emissions, and how these EE-based GHG reductions can be calculated in an analytically robust manner.
Objectives
The objectives of this study were to explore and compare different analytic approaches that could be used by electric companies and others to calculate avoided carbon dioxide (CO2) emissions associated with implementation of large-scale end-use EE projects. This study also evaluated two approaches electric companies potentially could use to increase deployment of end-use EE projects and reduce future GHG emissions: (i) creating a low carbon industrial tariff roughly analogous to existing “green power” pricing programs; and, (ii) third-party development of industrial EE-based GHG emission reduction projects.
Analytic Approach
First, the EPRI project team described and evaluated two approaches that could be used to deploy EE-based GHG emissions reductions: (i) development of a low carbon industrial tariff; and,
(ii) third-party development of industrial EE-based GHG reduction projects.
Second, the EPRI project team developed two illustrative large-scale industrial EE projects and corresponding “load shapes” to analyze quantitatively: (i) an assumed 100 MW load reduction project that operates five days per week for 16 hours each day (i.e., 100 MW 5x16); and, (ii) a larger Aggregate EE load reduction program. These illustrative EE projects were developed solely for the analytic purposes described in this report, and in no way are indicative of TVA’s future plans, policies or considerations regarding future development or deployment of specific EE projects.
Third, we used five different analytic approaches to estimate the annual expected quantity of avoided CO2 emissions associated with the two example EE projects for 2015 and 2020, and cumulatively across the six-year period 2015-2020. These five analytic approaches included: (i) EPA’s eGRID database; (ii) EPA’s AVERT computer model; (iii) EPRI’s US-REGEN model; (iv) TVA regional dispatch modeling; and, (v) EPRI’s EE-CO2 Calculator.
Results
Using these five analytic approaches, the EPRI project team compared the projected annual and cumulative marginal avoided CO2 emissions for two example industrial EE projects that are assumed to be implemented in the Southeastern region of the United States.
We estimated the example 100 MW 5x16 EE project would avoid 417,600 MWh of electric load annually. This reduced electric load can be expected to reduce the need to generate electric power in the region, and so avoid GHG emissions that otherwise would have been emitted to the atmosphere. These estimates range between 283,400 and 336,703 “short” tons (2,000 lbs) tCO2 avoided in 2015 with an average value of 303,888 tCO2, and 229,240 to 281,749 tCO2 avoided in 2020 with an average value of 254,469 tCO2.
We estimated that the larger Aggregate EE load reduction can be expected to avoid 1,078,708 MWh of load annually. This load reduction is estimated to avoid CO2 emissions between 730,100 and 869,741 tCO2 in 2015 with an average of 794,005 tCO2, and 609,961 to 747,440 tCO2 avoided in 2020 with an average of 684,029 tCO2.
We also estimated cumulative avoided electric load and CO2 emissions for the two example EE projects implemented in the Southeastern region for the six-year period 2015-2020. The 100 MW 5x16 project can be expected to avoid 2.51 million MWhs over this period, and avoid cumulative CO2 emissions ranging from 1.57 to 2.02 million tCO2 with a mean of 1,741,769 tCO2. The Aggregate EE load reduction can be expected to avoid 6.47 million MWhs, and avoid between 4.16 and 5.22 million tCO2 with a mean of 4,573,174 tCO2.
The annual and cumulative estimates of avoided CO2 emissions differ among the five analytic methods for several reasons: (i) each analysis approach begins with a different assumed regional power generation mix in the base year; (ii) each approach incorporates different ways to estimate how the regional generation mix may change between 2015 and 2020; (iii) several of the approaches incorporate different base years; and, (iv) each approach defines the southeast region differently on a geographic basis.
Applications, Value, and Use
The results of this analysis can help electric companies and others to better understand the strengths and weaknesses of different analytic approaches that may be used to estimate avoided CO2 emissions associated with end-use EE projects, and help electric companies to select an appropriate approach to use in the future if they choose to implement end-use EE projects to reduce CO2 emissions.
We also evaluated two approaches that could be used by electric companies to implement large-scale end-use EE projects in their service territories: (i) creating a low carbon industrial tariff; and,
(ii) third-party development of industrial EE-based GHG reduction projects. The exploration and evaluation of the analytic strengths and weaknesses of these two approaches can help electric companies, electric customers and the public to better understand different approaches that may be used to develop, finance, and implement large-scale EE projects to reduce GHG emissions.
EPRI also reviewed and used the EPA’s AVERT computer simulation tool, and this report includes a summary of the strengths and weaknesses of using AVERT to estimate avoided CO2 emissions associated with end-use EE projects.
It is important for potential AVERT users to be aware that AVERT is not designed to be a “projection tool,” and EPA has specifically warned that AVERT is not intended to be used for analysis that extrapolates more than five years from the baseline year. This is a significant limitation because it means AVERT cannot be used to project longer-term impacts of EE or renewable energy (RE) activities on energy use or emissions. Given this limitation, AVERT is not an appropriate tool to use to analyze the potential for EE activities to be used to achieve compliance with the CPP, since the CPP is expected to impact electric generation from 2020 through 2030 and beyond, far beyond EPA’s five-year projection limitation for using AVERT.
The intended audience for this report includes electric company staff engaged in strategy development, sustainability, generation planning, capacity analysis and production-cost modeling. This report is designed to help electric company staff, stakeholders and the general public to better understand the challenges and opportunities associated with trying to use large-scale industrial EE projects to reduce CO2 emissions and quantitative methods that may be used to quantify EE-based CO2 emissions reductions.