Developing a Greenhouse Gas Tool for Buildings in California: Methodology and User s Manual v.2

Developing a Greenhouse Gas Tool for Buildings in California: Methodology and User s Manual v.2 April 2009 Prepared by: Amber Mahone, Snuller Price, William Morrow Energy and Environmental Economics, Inc. 101 Montgomery, Suite 1600 San Francisco, California, 94104 Phone: 415-391-5100 Fax: 415-391-6500 Web: http://www.ethree.com i

TABLE OF CONTENTS 1 OVERVIEW...5 1.1 PURPOSE OF THE GREENHOUSE GAS TOOL FOR BUILDINGS IN CALIFORNIA...5 1.2 CHANGES BETWEEN VERSION 1 AND VERSION 2 OF THE TOOL AND REPORT...6 2 METHODOLOGY...8 2.1 DEFINITION OF AVERAGE AND MARGINAL EMISSION RATES...10 2.2 COMPARISON OF SEASONAL ON- AND OFF-PEAK EMISSIONS RATES...18 2.3 OVERVIEW OF PLEXOS SOLUTIONS MODEL...23 2.4 SOURCES AND ASSUMPTIONS FOR 2008 AND 2020 MODEL RUNS...24 2.5 LIMITATIONS OF THE GHG TOOL FOR BUILDINGS AND AREAS FOR FUTURE RESEARCH...25 3 USER S MANUAL...29 3.1 REQUIRED INPUTS...29 3.2 STEP-BY-STEP INSTRUCTIONS TO INPUT DATA INTO THE TOOL...30 3.3 EMISSIONS DATA AND TIME-OF-USE (TOU) DEFINITION TABS...39 ii

TABLE OF FIGURES FIGURE 1. HOURLY DISTRIBUTION OF CALIFORNIA S 2008 AVERAGE EMISSIONS INTENSITY COMPARED TO THE MARGINAL EMISSIONS INTENSITY...11 FIGURE 2. SAMPLE HOURLY CO2 EMISSIONS OF A BUILDING, APPLYING AN AVERAGE HOURLY EMISSIONS RATE TO THE BUILDING S ELECTRICITY CONSUMPTION...12 FIGURE 3. SAMPLE HOURLY CO2 EMISSIONS OF THE SAME BUILDING, APPLYING A MARGINAL HOURLY EMISSIONS RATE TO THE BUILDING S ELECTRICITY CONSUMPTION.13 FIGURE 4. HISTORICAL AND FORECAST CALIFORNIA ANNUAL AVERAGE ELECTRICITY EMISSIONS RATES: COMPARISON BY YEAR AND DATA SOURCE...15 FIGURE 5. NON-BASELOAD OUTPUT EMISSION RATES FROM THE US EPA EGRID DATABASE AND ESTIMATES OF MARGINAL EMISSIONS RATES FROM THE GHG TOOL FOR BUILDINGS...18 FIGURE 6. CALIFORNIA 2008 AVERAGE EMISSIONS RATE BY SEASON AND TIME PERIOD..19 FIGURE 7. CALIFORNIA 2008 MARGINAL EMISSIONS RATE BY SEASON AND TIME PERIOD20 FIGURE 8. HOURLY DISTRIBUTION OF THE CALIFORNIA 2008 MARGINAL EMISSIONS RATES BY SEASON AND TIME PERIOD...22 FIGURE 9. INPUTTING BUILDING DATA...32 FIGURE 10. SPECIFICATION OF BUILDING DESIGN OUTPUTS...33 FIGURE 12. CHARTING A BUILDING S HOURLY EMISSIONS PROFILE...35 FIGURE 13. CHARTING MARGINAL OR AVERAGE GHG EMISSIONS...36 FIGURE 14. THREE-DIMENSIONAL REPRESENTATION OF A BUILDING S HOURLY GHG EMISSIONS...37 FIGURE 15. COMPARING BUILDINGS LIFETIME GHG EMISSIONS...38 iii

1 Overview 1.1 Purpose of the Greenhouse Gas Tool for Buildings in California In May 2008, the California Public Interest Energy Research (PIER) commissioned Energy and Environmental Economics, Inc. (E3) to develop a simple tool to help building designers, architects and engineers better understand the greenhouse gas emissions associated with California buildings electricity and on-site gas consumption. The purpose of the tool is to provide a forecast of greenhouse gas (GHG) emissions associated with the operation of a specific building design, or several building designs, located in California. The GHG emissions calculation is based on either the hourly electricity load profile and hourly gas consumption of the building, or the timeof-use electric and gas consumption of the building. 1 The tool is also able to simultaneously compare the GHG emissions associated with two design options of the same building on a yearly, and on a seasonal on- and off-peak basis. 2 By using hourly estimates of California s GHG emission rates from electricity, the tool allows building designers to estimate the implications of changing a building s electricity consumption pattern across time periods. For example, in California, reducing electricity demand during on-peak hours generally saves more carbon than the 1 For most applications of the tool, we assume that a building s hourly load profile and gas consumption estimates will be derived from a building simulation tool such as EnergyPlus, DOE-2, BLAST or other commercially available tools. However, if actual building performance data is available on a time of use basis, this data may be used in the tool as well. 2 Eight time periods are modeled in the tool including summer, fall, spring and winter for both on-peak and off-peak demand hours. Peak demand hours are defined here as Monday through Friday, 6am to 10pm. Offpeak demand hours are weekdays 11pm to 5am, weekends and federal holidays. 5

same reductions made during off-peak hours. 3 The ability of the Tool to quantify this distinction is an improvement over using a single annual average electricity emissions rate. For example, time-differentiated emissions rates will improve the estimate of CO2 savings of a more efficient air conditioning system or of thermal energy storage. The tool is a standalone spreadsheet (Microsoft Excel 2003), allowing the calculations and assumptions to be transparent and user-accessible. Users manually input to the tool one or more building s hourly or time of use load profile data and the tool estimates the building s marginal and average greenhouse gas emissions. Several tool options allow users to configure results which are presented in graphical and tabular formats. 1.2 Changes between Version 1 and Version 2 of the Tool and Report In January 2009, Version 1 of the Greenhouse Gas Tool for Buildings in California was posted on the E3 website, freely available to download, along with an earlier version of this report. 4 E3 requested feedback on the tool, though an on-line survey, from users who had registered to download the tool. The electricity emissions numbers contained in the tool are calibrated only to California, however, interest in the tool extended well outside of the state. Between January and March 2009, the tool was downloaded from the website by over 150 individuals from non-profit foundations, universities, private sector firms and research institutions located across the world, from over 20 countries including Australia, Brazil, Canada, India, Italy, Korea and Taiwan. 3 In other regions of the country, the opposite is true: saving electricity during off-peak hours saves more carbon emissions than the same electricity saving would during on-peak hours. This is because the mix of power plants, and the operational patterns of those power plants, varies by region of the country. See: Erickson, J. B. Ward and J. Mapp (2004) Peak Demand Reduction vs. Emissions Savings: When Does it Pay to Chase Emissions? ACEEE Summer Study Paper. 4 This report and the GHG Tool for Buildings is available at: http://www.ethree.com/e3_public_docs.html 6

Some of the feedback from users downloading the tool included a request to expand the coverage of the emissions data, allowing users from other regions of the country or the world to apply regionally appropriate emissions rate data to buildings in their locality. Such an undertaking is beyond the scope of this project, but does show that there is demand for time-differentiated emissions data for regions outside of California. Other users who had downloaded the tool suggested that we explain in more detail the difference between the marginal and average emissions rates, explain why time-differentiated emissions rates are better than annual average emissions rates, and discuss the differences between the emissions rates contained in this tool compared to the annual emissions rates contained in the EPA s egrid data. This updated version of the GHG Tool for Buildings report addresses each of these issues. Finally, in response to another suggestion, Version 2 of the GHG Tool for Buildings in California now contains the ability to input a building s load profile by time period, rather than only on an hourly basis. This enhancement makes it easier to analyze the emissions profile of existing buildings when annual hourly consumption data is not available. 7

2 Methodology The GHG Tool for Buildings provides estimates of hourly emissions rates for the California grid based on assumptions about how the electrical grid is likely to operate in 2008 and in 2020. The greenhouse gas (GHG) emissions of a building s electricity consumption are calculated by multiplying the hourly, or time of use, load profile of the building with an estimated hourly GHG emissions profile of California s electricity generation. In addition, the model calculates hourly emissions from on-site fuel combustion (natural gas or propane) based on the carbon dioxide (CO2) content of the fuel combusted. Unlike electricity, the emissions intensities of propane and natural gas are constant, and so are much more straightforward to apply. The emissions rate of electricity is based on the summarized output of a production simulation model. The production simulation model, PLEXOS, contains detailed information about nearly every generator in the Western Electricity Coordinating Council (WECC), as well as the transmission paths in the WECC. PLEXOS uses estimates of hourly load, fuel prices, and transmission constraints to estimate the least cost, constrained dispatch of generation in the West, and to estimate hourly CO2 emissions for each generator (see Section 2.3 for more information about PLEXOS). E3 decided to use a production simulation dispatch model of the West, rather than historical emissions data, for a number of reasons. First, the production simulation model approach makes it possible to estimate marginal emissions rates relatively easily. In contrast, while historical emissions data like the EPA s Emissions Tracking System/Continuous Emissions Monitoring (ETS/CEM) and the EPA s Emissions and Generation Resource Integrated Database (egrid), are valuable sources of emissions information, it is a non-trivial task to estimate marginal GHG emissions rates from these 8

datasets. 5 As an approximation of a marginal emissions rate, the egrid data provides a regionally specific annual non-baseload output emissions rate. However, this number does not vary by season or time period. Second, without access to many years of historical data it difficult to know whether the year, or years, in question represent a typical or normal year, and whether a particular historical year is reflective of current or future emissions rates. The production simulation modeling approach allowed E3 to model a typical year, in terms of temperature and hydroelectric availability, and allowed us to forecast future emissions rates as far into the future as 2020 based on planned new generation and transmission projects, including those necessary to achieve California s 20% Renewables Portfolio Standards (RPS) and other states RPS requirements in 2020. In forecasting future generation and transmission development, we have largely assumed only those policies that have been codified in law. For example, we do not attempt to forecast the effect on electricity emissions of meeting the state s more aggressive RPS goal (33% RPS by 2020) laid out in Governor Schwarzenegger s Executive Order S-14-08. The 33% RPS may become law in 2009, but when this project was underway, the 33% RPS target had not been codified into law. Likewise, we do not attempt to forecast the emissions effect of meeting California s statewide 2020 emissions cap (Assembly Bill 32, Statutes 2006). The impact on the electricity sector of reducing statewide emissions to 1990 levels, by 2020, remains uncertain. Finally, the GHG Tool assumes a linear relationship between the emissions rates of 2008 and 2020. For years beyond 2020, the emissions intensity of the grid is set equal to the 2020 emissions rate. 5 egrid contains greenhouse gas (GHG) emissions rates from electricity production in 2005 on an annual average basis (egrid2007 version 1.1). This data is based on, and comes from a variety of federal data sources, including the EPA s ETS/CEM. See U.S. EPA egrid: http://www.epa.gov/cleanenergy/energyresources/egrid/index.html. 9

2.1 Definition of Average and Marginal Emission Rates The GHG Tool for Buildings displays greenhouse gas information based on two methods of calculating emissions from the electric grid: marginal emissions and average emissions. It is worth briefly discussing the relative merits and limitations of each of these metrics. The average emissions metric is the simple sum of all CO2 associated with California s electricity consumption, divided by the total generation required to serve California s load. It is most appropriate to use the average emissions rates to calculate a building s carbon footprint or the estimated CO2 associated with a building s total electricity consumption over a season, year or multi-year period. It is less straightforward to define and measure marginal emissions from an electric system. In the strictest definition of the term, slight changes to the load profile of a building, through energy efficiency for example, might change the emissions intensity of the grid by reducing the need for some generation. However, estimating this type of granularity of responsiveness between a building s load profile and grid operations would be extremely difficult, and is not attempted here. Instead, the marginal emissions metric applied in the tool is a measure of the emissions intensity of the generator operating at the margin. Marginal generation is defined as the output of generators whose dispatch patterns change with a small (in this case 500 MW) change in demand, as simulated in the PLEXOS model. It is most appropriate to use the marginal emissions rate in the context of determining how small changes in a building s load profile would change the greenhouse gas emissions associated with the building s electricity consumption. In California, the hourly marginal emissions rates are more variable between high a low values, but as a whole are higher than the hourly average emissions rates. This difference is due to the way each metric is calculated. The marginal emissions rate captures the fact that, in California, less efficient natural gas power plants, often 10

combustion turbines, are usually operating at the margin, dispatched last according to their economic order. However, in a few hours of the year, other types of generation including coal and hydropower are dispatched last, at the margin. The average emissions rate is lower than the marginal rate because it includes all generation, including low emissions generation, like hydropower, nuclear and renewables, as well as higher emissions generation like coal and natural gas. Figure 1 below shows the hourly distribution of California s 2008 average emissions intensity compared to the marginal emissions intensity, as produced by the PLEXOS model. The annual mean of the average emissions intensities (0.41 tonnes/mwh) is about 20 percent lower than the annual mean of the marginal emissions intensities (0.51 tonnes/mwh). The hourly distribution of the marginal emissions rate shows two distinct modes: the lower mode is at the emissions intensity of a fairly efficient natural gas generator (0.46 tonnes/mwh), and the higher mode is at the emissions intensity of a less efficient natural gas generator (0.54 tonnes/mwh). Figure 1. Hourly Distribution of California s 2008 Average Emissions Intensity Compared to the Marginal Emissions Intensity Number of hours in the year _ 1000 900 800 700 600 500 400 300 200 100 0 Marginal Average 0 0.2 0.4 0.6 0.8 1 Emissions intensity (tonnes/mwh) Emissions intensity (tons CO2/MWh) _ 11

The marginal and average emissions rates should not be applied interchangeably, since they are the result of different definitions and calculations of electricity grid carbon emissions. Figures 2 and 3 below demonstrate the difference between using the hourly average versus the hourly marginal emissions rate to calculate a building s GHG emissions. The figures show hourly greenhouse gas emissions from the same building, over the same four day time period in January. Figure 2 applies the average emissions rate; Figure 3 applies the marginal emissions rate. The dark red color represents emissions from electricity consumption; the lighter blue color represents additional emissions from natural gas consumption. Figure 2. Sample Hourly CO2 Emissions of a Building, Applying an Average Hourly Emissions Rate to the Building s Electricity Consumption Reference Design Tonnes CO2 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0 24 12 24 12 24 12 24 12 1/3 1/4 1/5 1/6 Time Ref. Avg. Electric Ref. Gas CO2 12

Figure 3. Sample Hourly CO2 Emissions of the Same Building, Applying a Marginal Hourly Emissions Rate to the Building s Electricity Consumption Reference Design Tonnes CO2 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0 24 12 24 12 24 12 24 12 1/3 1/4 1/5 1/6 Time Ref. Marg. Electric Ref. Gas CO2 2.1.1 Calculating the Average Emissions Rate The simulated California average electricity emissions rates are derived from a base load run of the production simulation dispatch model (PLEXOS) for 2008 and 2020. In each base load run, the total electricity demand used in the model is assumed to be average demand for 2008 and 2020 respectively. The production simulation dispatch model produces an estimate of the emissions and generation for every power plant in the Western Electricity Coordinating Council (WECC), for each hour of the year. This generator-by-generator emissions information is summarized in the GHG Tool for Buildings by hour into the average hourly emissions rate of California s electricity production. California s average emissions rate is defined as the sum of the hourly average emissions rate of: 1. All generators operating within California s territorial boundary ( CA gens. in the formula below), 2. All out-of-state generators operating with known long-term, specified contracts with California retail providers ( CA specified gens. in the formula below) and, 13

3. Electricity imports to California. Electricity imports are tagged with the average emissions intensity of all generators operating in the WECC in a given hour, excluding generators tagged for California ( CA imports in the formula below). More formally, the hourly (h) average emissions rate of the California electric grid is defined as: Average emissions rateh = ( CO2 CA gensh + CO2 specified gensh + CO2 CA importsh ) ( MWh CA gensh + MWh specified gensh + MWh CA importsh) This method is consistent with the California Air Resources Board statewide greenhouse gas emissions inventory from 1990 2004, which accounts for greenhouse gas emissions associated with electricity imports as well as certain power contracts between California utilities. 6 In contrast, the US EPA s egrid statewide electricity emissions rate does not explicitly account for electricity imports or exports, and is based on the physical location or the ownership shares of power plants. 7 Since, in California, the CO2 content of electricity imports and out-of-state power contracts is typically higher than the average CO2 content of in-state electricity production, this method tends to result in a higher estimate of average electricity emissions than the U.S. EPA egrid method. Figure 4 below presents a comparison of the EPA egrid and the E3 GHG Tool for Buildings annual average electricity emissions rates in different years. 6 These include, for example, the Intermountain coal-fired power plant in Utah, the Four Corners and San Juan coal-fired powers plant in New Mexico, among others. See the California Air Resources Board, Greenhouse Gas Emissions Inventory 1990 2004, available at: http://www.arb.ca.gov/cc/inventory/data/data.htm 7 US Environmental Protection Agency, The Emissions & Generation Resource Integrated Database for 2007 (egrid2007) Technical Support Document, September 2008. 14

Figure 4. Historical and Forecast California Annual Average Electricity Emissions Rates: Comparison by Year and Data Source tonnes CO2/MWh 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 egrid2006 version 2.1, WECC California (CAMX), 2004 data egrid2007 version 1.1, WECC California (CAMX), 2005 data 2008, GHG Tool for Buildings in California 2020, GHG Tool for Buildings in California 2.1.2 Treatment of Electricity Imports to California Grid To calculate California s average emissions intensity, it is not only necessary to understand the carbon emissions associated with in-state generation, but also to include the correct emissions intensity for imported electricity. Determining the appropriate emissions intensity for imported electricity is not a trivial task, given that electricity generation is currently not tagged with a greenhouse gas emissions profile. The basic methodology for determining the emissions content of imported electricity is summarized as follows. The PLEXOS model estimates hourly imports and exports to California in 2008 and 2020. From this estimate of hourly net imports (GWh), E3 subtracted the hourly output of any out-of-state generation that has specified longterm contracts with California retail service providers. These out-of-state generators with long-term specified contracts with California retail service providers are mostly coal-fired generators but also include California s portion of the Hoover hydroelectric dam output. This generation is referred to as specified generation, and is assigned its 15

exact PLEXOS simulated emissions intensity. The remaining unspecified electricity imports for each hour receive the WECC-wide average (excluding California) emissions intensity for that hour. 8 2.1.3 Calculating the Marginal Emissions Rate To calculate the California marginal electricity grid emissions rates in each hour, we ran two additional runs of the production simulation model PLEXOS for 2008 and 2020. In addition to the base load model runs, PLEXOS was also run after decrementing California s load by 500 MW in each hour. The difference between the generation dispatch patterns of the base load year and the decremented year reveals which generators are operating at the margin in any given hour in the model, enabling the calculation of the state s electricity grid marginal emissions intensity rate. Here, the designation of in-state versus out-of-state generation is not important. The data of relevance are which generators in the WECC increase or decrease their output in response to a small change in California s load. More formally, the hourly (h) marginal emissions rate of the California electric grid is defined as the following, where summations are across all generators in the WECC: Marginal emissions rateh = ( CO2 base loadh CO2 decremented loadh) ( MWh base loadh MWh decremented loadh) 8 This method differs somewhat from the CPUC and CEC Decision (D.)07-09-017 which recommended that all unspecified, or non-contracted imported electricity should receive a regional deemed emissions intensity of 1,100 pounds of carbon dioxide equivalent (CO2e) per megawatt hour (MWh). However, given that the emissions data for the rest-of-wecc is available through the PLEXOS output, it seemed appropriate to rely on this generator-specific data, rather than a single deemed emissions intensity, which was developed for different policy needs. 16

The selection of 500 MW for the change case between the base load and the decremented load is a practical one, although other increments of demand could be selected to estimate marginal emissions. The project team selected 500 MW because this is approximately the size of a small to average-sized power plant. Thus, changing California s demand by 500 MW results in large enough changes to the generator dispatch in the production simulation model to produce meaningful results, but the changes are still small enough that the effect may be considered marginal. It is important to note that this definition of marginal emissions rates does not imply that the building s simulation data applied in the GHG tool would have electricity demand as high as 500 MW. Rather, this definition of marginal emissions rates is designed to answer the question: if a building s load changed by a small amount (due to energy efficiency improvements, for example), what would be an appropriate approximation of the avoided carbon emissions from electricity savings on an hourly or time-period basis? The US EPA egrid database does not explicitly provide an estimate of a marginal emissions rate. However, egrid does provide a regionally specific, annual emissions rate for fuel combustion, non-baseload generation, which may be used as a rough estimate to determine how much emissions could be avoided if energy efficiency and/or renewable generation displaces fossil fuel generation. 9 The egrid technical support document describes the methodology for calculating the non-baseload emissions rate. In general, non-baseload generation is defined as generation which combusts fuels and has a capacity factor lower than 80 percent. Figure 5 below compares the egrid non-baseload generation emissions rates for the WECC California (CAMX) region to the GHG Calculator for Buildings estimate of the state s annual 9 US Environmental Protection Agency, The Emissions & Generation Resource Integrated Database for 2007 (egrid2007) Technical Support Document, September 2008. 17

marginal emission rate. The methods for deriving each metric are different, and so are not strictly comparable. However, it is a useful benchmark to note that the estimates of the marginal emissions rates in the GHG Tool for Buildings falls within the range of non-baseload output emissions rates generated in egrid2006 version 2.1 and egrid2007 version 1.1. Figure 5. Non-Baseload Output Emission Rates from the US EPA egrid Database and Estimates of Marginal Emissions Rates from the GHG Tool for Buildings 0.60 0.58 tonnes CO2/MWh 0.56 0.54 0.52 0.50 0.48 0.46 0.44 egrid2006 version 2.1, WECC California (CAMX), 2004 data egrid2007 version 1.1, WECC California (CAMX), 2005 data 2008, GHG Tool for Buildings in California 2020, GHG Tool for Buildings in California 2.2 Comparison of Seasonal On- and Off-Peak Emissions Rates It is useful to distinguish between the average and marginal emissions rates of electricity by season and time period as well. For the purposes of our analysis we define eight time periods using an on- and off-peak period for each of the four seasons. Winter is defined as December through February, Spring is March through May, Summer is June through August, and Fall is September through November. Peak demand hours are 18

defined here as Monday through Friday, 6am to 10pm. Off-peak demand hours are weekdays 11pm to 5am, weekends and federal holidays. Average emissions rates vary by season, but do not vary substantially between on- and off-peak hours. In contrast, marginal emissions rates vary between on- and offpeak hours, but do not vary substantially by season. Figure 6 below shows that average emissions rates are highest during the fall when more coal and natural gas are dispatched to meet demand, in both on- and off-peak hours. Average emissions rates are lowest in the spring, when more low-carbon hydropower is available to meet demand. Figure 6. California 2008 Average Emissions Rate by Season and Time Period 0.50 Average Emissions Rate (tonnes CO2/MWh) 0.45 0.40 0.35 0.30 Summer On-peak Fall Onpeak Winter On-peak Spring On-peak Summer Off-peak Fall Offpeak Winter Off-peak Spring Offpeak 19

Figure 7. California 2008 Marginal Emissions Rate by Season and Time Period 0.60 Marginal Emissions Rate (tonnes CO2/MWh) 0.55 0.50 0.45 0.40 0.35 0.30 Summer On-peak Fall Onpeak Winter Onpeak Spring Onpeak Summer Off-peak Fall Offpeak Winter Offpeak Spring Offpeak In contrast to the average emissions rates, the marginal emissions rates do not show much variation between seasons. Rather, the marginal emissions rates show the largest differences between on- and off-peak hours. On the whole, marginal emissions rates are higher during on-peak hours and lower during off-peak hours. Figure 7 shows that the marginal emissions intensity of on-peak hours is close to 0.55 tonnes CO2/MWh during all seasons, while the off-peak hours are closer to 0.52 tonnes/mwh during all seasons. Our analysis of the range of emissions rates within each time period shows that there is significant variation, especially of the hourly marginal emissions rates, but on the whole the time period relationships exhibit the expected emissions patterns. However, the tool also allows the user to easily define different time periods. For example, if data is available only in time periods defined by the local utility in its retail tariffs, the GHG Tool TOU definitions may be changed to reflect the local utility TOU definitions. With the seasonal on- and off-peak definitions used here to evaluate the general trends of the system, there is a distinct bi-modal distribution of hourly marginal 20

emissions rates in many of the time period hourly distributions, as shown in Figure 8 below. This implies that the eight time-periods applied in the GHG Tool for Buildings might be usefully re-defined to better match emissions rates, if this was their primary purpose. However, this effort is not undertaken here. 21

Figure 8. Hourly Distribution of the California 2008 Marginal Emissions Rates by Season and Time Period 2008 Summer On-peak 2008 Summer Off-peak Number of hours in the year 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 2008 Fall On-peak 2008 Fall Off-peak Number of hours in the year 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 2008 Winter On-peak 2008 Winter Off-peak Number of hours in the year 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 2008 Spring On-peak 2008 Spring Off-peak Number of hours in the year 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 350 300 250 200 150 100 50 0 0 0.2 0.4 0.6 0.8 1 Marginal emissions rate (tonnes CO2/MWh) Marginal emissions rate (tonnes CO2/MWh) 22

2.3 Overview of Plexos Solutions Model The electricity grid emissions intensity data for 2008 and 2020, calculated using production simulation, is summarized in the GHG Tool for Buildings, in the Emissions Data tab of the GHG Tool. The PLEXOS for Power Systems TM model, developed by Plexos Solutions, LLC. is a comprehensive, chronological optimization model that employs an advanced linear, quadratic, or mixed integer (LP, QP, or MIP) solution. The PLEXOS model represents the entire Western Electricity Coordinating Council (WECC) based on the objective function of minimizing production costs subject to generation, transmission, and system constraints. Electricity imports and exports from California to outside states are calculated and constrained by appropriate transmission constraints that include individual transmission line limits, interface limits and nomogram constraints. PLEXOS co-optimizes the following system elements: Ancillary service requirements and markets; Hydro and thermal commitment and dispatch; Pumped storage pumping, generation, and provision of ancillary services; and Energy and emission-limited facilities. The model provides a detailed assessment of greenhouse gas emissions from generating units, once appropriate resource plans have been developed and input. The PLEXOS software is commonly used in California for many applications. PLEXOS was licensed by the CAISO for modeling their Market Redesign and Technology Upgrade (MRTU) effort, performing competitive path assessment, and economic transmission feasibility analysis. Other California government agencies or utilities that have used the PLEXOS model include the CPUC, SMUD, NCPA, City of Roseville, and the City of Palo Alto. PLEXOS and selected databases have been calibrated against historical prices, reviewed by transmission experts at the CAISO, and 23

vetted in public hearings such as the CAISO s Transmission Economic Assessment Methodology and Palo Verde Devers II hearings. 10 2.4 Sources and Assumptions for 2008 and 2020 Model Runs Creating the appropriate set of inputs for the 2008 and 2020 PLEXOS model runs requires making a number of assumptions about what generation resources will be available in 2008 and 2020, and what electricity demand will be in various Western zones in each year. To conserve resources, and to avoid replicating previous work, E3 relied on the same set of generation and load assumptions about the WECC for the PLEXOS runs as were applied in E3 s modeling work of the California electricity system undertaken for the California Public Utilities Commission, California Energy Commission and the California Air Resources Board. This work was done as part of the state s greenhouse gas proceedings for the implementation of Assembly Bill 32, California s Global Warming Solutions Act (CPUC R.06-04-009 / CEC docket 07-OIIP- 01). 11 For the 2008 model runs, the project team relied on the generator, transmission and related data from the WECC Seams-Steering Group of the Western Interconnect (SSG-WI) 2008 case. For the 2020 model runs, a 2020 case was developed by E3 and PLEXOS starting from the WECC Transmission Expansion Planning Policy Committee (TEPPC) 2017 data, and extrapolating this to 2020. 12 Developing the 2020 case from the 2017 TEPPC case required adjusting the 2017 resource plans to ensure that all states 10 For more information about PLEXOS Solutions see: http://www.plexossolutions.com 11 Additional documentation of the assumptions applied in the development of the 2008 and 2020 PLEXOS runs is available through the CPUC/CEC GHG dockets R.06-04-009 and 07-OIIP-01, or on the E3 website, at: http://www.ethree.com/cpuc_ghg_model.html 12 For more information about the WECC SSG-WI and TEPPC databases see: http://www.wecc.biz/ 24

would meet expected load growth and mandated renewable energy portfolio requirements by 2020. The 2020 case is developed to reflect a fairly conservative business-as-usual emissions profile of California and the WECC. For example, 20% of California s retail sales are projected to come from qualifying renewable energy by 2020. Currently, the state s Renewable Portfolio Standard (RPS) law requires that the investor owned utilities achieve 20% of retail sales from renewable energy by 2010, and that the publicly owned utilities set similar targets. The California Air Resources Board has recommended increasing the state s RPS to 33% by 2020, however, this target is not yet law, and so is not reflected in the 2020 business-as-usual case applied in the GHG Tool for Buildings. In addition, the California Energy Commission s November 2007 load growth forecast developed for the 2007 Integrated Energy Resource Plan is used in the modeling. This load forecast for California does not reflect the higher levels of energy efficiency and combined heat and power currently under consideration by the California Air Resources Board and other policy makers as measures to reduce statewide GHG emissions. 2.5 Limitations of the GHG Tool for Buildings and Areas for Future Research The GHG Tool for Buildings provides an estimate of greenhouse gas emissions of a building based on simulated, forecast electricity emissions data. The results of the calculator should be interpreted as a guide for a building s potential greenhouse gas emissions, and not as a building s actual emissions profile. Forecasting greenhouse gas emissions from electricity production requires a number of assumptions, and will never produce results that are entirely consistent with actual grid performance. It is important to note that while the tool produces hourly marginal greenhouse gas emissions, this level of detail and disaggregation results in highly variable data which are sensitive to the specifications of the production simulation dispatch model used to produce the emission estimates. Therefore, the marginal emissions rates in a single hour, in particular, should only be relied on with caution and a high degree of 25

uncertainty. The marginal emissions rates averaged by season and on- and off-peak hours are likely to be more robust than the estimate of the marginal emissions rate for any particular hour in the year. The goal of developing this tool is to better understand the greenhouse gas emissions implications of different building design options within the state of California, and to lay the methodological foundation for creating a more sophisticated, expanded tool in the future. Through this research, three priority areas for future research have been identified. The first is to estimate the variability and uncertainty surrounding hourly and time-period average and marginal emissions rate estimates, perhaps through a validation process with measured emissions data from the grid. A related approach would be to investigate the relationship between key predictors of grid operation, such as hydroelectric availability, with emissions intensity. Secondly, more research is needed to better understand the relationship between local weather, as it affects building operation, and regional weather, as it affects electricity demand and generation. The third is how best to correlate the weather data used for building simulation with the load profile assumptions implicit in the production simulation modeling. A complete description of this issue is included below. Other improvements could be made as well. At this point, the tool does not incorporate analysis of greenhouse gasses associated with district heating or cooling, for example. Likewise, the tool does not consider greenhouse gas emissions that are not directly associated with electricity use or on-site fuel combustion, such as from transportation to or from the building, or the upstream or life-cycle greenhouse gas emissions of fuel consumption, electricity consumption or building materials usage. In addition, the GHG Tool for Buildings only reports carbon dioxide (CO2) emissions and does not track other greenhouse gases such as un-combusted methane or SF6, which are associated with the natural gas and electricity sectors. Finally, we note that the greenhouse gas emissions profile generated for use in the tool is for the state of California as a whole, not for individual utilities. This approach results in a representation of the emissions associated with operation of the electric grid 26

as a single, pooled resource. Individual utilities do own and enter into power contracts with specific generators that may have lower or higher emissions rates than the average of the state as a whole. However, this tool does not attempt to estimate utility-level emissions profiles based on contracts or generator ownership. Producing utility-specific hourly or time-period electricity emissions rates may be an area of interest for future research as well. 2.5.1 Weather versus Load Profile Assumptions The weather file assumptions applied in building simulation tools are not currently normalized to be consistent with the weather assumptions applied in the development of the electricity grid GHG emission rates used in the GHG Tool. To do so would not be straightforward and is not attempted in this modeling work. Most production simulation models do not input weather assumptions the same way that building simulation tools rely on weather data. Rather, hourly load shapes for different bubbles (i.e. regions) are applied, often based on historic load profiles of an average year. These historic load profiles are influenced by weather, (i.e. hotter afternoons result in higher electricity demand during those hours) but regional load shapes are also determined by other independent factors such as consumer demand and economic activity. For this modeling effort, the 2008 and 2020 production simulation model runs performed by PLEXOS rely on the load shape assumptions developed by the WECC for their SSG-WI and TEPPC datasets. In general, this means that historical 2002 normalized load shapes were applied, adjusted for expected energy and peak demand in each year. For example, the documentation of the SSG-WI database states: Load shapes are determined for each bubble [i.e. region]...with two exceptions, hourly shapes for each bubble are normalized using 2002 actual loads as the sample year. Exceptions: (1) hourly shapes developed in RMATS [Rocky Mountain Area Transmission Study] are used for the Rocky Mountain States; (2) 27

hourly shapes produced by the Council/BPA s HELMS model are used for the NW states. The load shapes produced by HELMS use 2002 temperatures (consistent with medium hydro generation assumptions). With some exceptions, historical 2002 load shapes are also used in the TEPPC database. Documentation of the TEPPC and SSG-WI load profile assumptions are available on the WECC website. Determining the extent that using different weather assumptions in a building simulation tool and the production simulation dispatch model biases the final emissions results would be an interesting area for future research. Likewise, normalizing the weather assumptions applied in the building simulation tools to the average 2002 load profiles developed for the WECC could be another important area for future work. 28

3 User s Manual 3.1 Required inputs The GHG Tool for Buildings is designed as a Microsoft Excel-based spreadsheet tool, allowing the calculations and assumptions to be transparent and user-accessible. The calculator has been populated with estimates of hourly average and hourly marginal emissions rates for California electricity use for 2008 and 2020, with an assumption of linear change of emissions intensities between these years, and an assumption of constant 2020 emissions intensities beyond 2020. The GHG Tool could be modified to reflect different emissions intensity assumptions, or emissions data from other regions, provided that hourly data were available. However, for the purposes of this methodology description, we assume that users will not seek to change the emissions intensities figures used in the model. The key inputs which the user of the GHG Tool for Buildings needs to provide are: One or more net hourly or time of use electricity load profiles for a building or set of buildings in kwh; One or more hourly or time of use profiles of on-site gas use, including the type of fuel used in the building (propane or natural gas) 13 in therms; and The expected occupancy year of the building (the user may choose a year between 2008 and 2020), and the expected lifetime of the building as constructed. 13 Currently, the tool is set up to account for the carbon associated with on-site building combustion of only propane or natural gas. Other fuel types could be added at a future time, if needed. 29

The data for the hourly or time of use electricity load profile and on-site gas use must reflect weekdays and weekends which are normalized to the calendar year for 1991. 14 If the electricity and fuel combustion data are not normalized to the calendar year 1991, building data for weekdays, weekends and holidays will not match the emissions intensity profiles of the weekdays, weekends and holidays used to generate the electricity grid emissions estimates. 3.2 Step-by-Step Instructions to Input Data into the Tool To input the required information in the Calculator, first ensure that macros are enabled in Microsoft Excel when the spreadsheet is opened. The following steps describe how to input information into the tool, and are illustrated and numbered in the figures below: On the Enter Building Data Here tab: 1. Select the fuel type for on-site gas combustion from the drop-down menu in column D. 2. Input a year between 2008 and 2020 for the expected building occupancy date in cell D4. 3. Enter a name for the first building design which will be analyzed in the tool in cell C6. 4. Select whether you will input the building data as eight time of use (TOU) consumption blocks. If you will enter TOU data, enter TRUE in 14 The 1991 normalized calendar year is the standard for California building simulation results used in the state s Title 24 Standards for the Alternative Calculation Method (ACM). Most building simulation tools will allow the user to define the calendar year output for the results, even if 1991 is not the default selection. 30

cell D7. If you will enter hourly consumption data, enter FALSE in cell D7. This selection will grey out the blocks of data you have not selected. 5. Depending on whether you have selected to input hourly or TOU data, paste the building s consumption data in the appropriate sections of Columns C and D. TOU or annual hourly (8760 hours) net electricity consumption (in kwh) should be pasted in column C and the building s TOU or annual hourly net gas consumption (in therms) should be pasted in column D. Repeat this process for other building designs in the other yellow columns. The tool is pre-loaded with sample building load profiles which may be directly overwritten by your own building load profiles. These steps are numbered in Figure 9 below, shown with a snapshot from the GHG Tool for Buildings. 31

Figure 9. Inputting Building Data 5. 3. 4. 2. 1. On the Results tab: 1. Select which year of emissions data for the building you would like to display in the charts on the Results tab. To do this, input a year between 2008 and 2020 in cell E4. Note that the year selected to display emissions results of the building(s) must not be earlier than the building s selected occupancy date, selected in cell D4 on the Enter Building Data Here tab. 2. Select, from the drop-down menu, which building design (as you named previously on the Enter Building Data Here tab) you wish to display as the reference design, and which building design you wish to display as the 32

alternative design. Note that this function will only work if you have entered two or more building design load profiles on the Energy Building Data Here tab. Be sure to select from among these tabs each time you add new data, so that the model knows to update the outputs with the new data. 3. Click the Chart Update button to ensure that the charts are correctly updated. Figure 10. Specification of Building Design Outputs 1. 2. 3. The GHG Tool for Buildings will now display the carbon footprint of the selected reference and alternative building designs in the tables found in this section of the tool. Sample tables, using mock building design information, are shown below. In this example, the reference case building, Design 1, emits 18 metric tonnes of CO2 over the course of the year. The building s GHG footprint is calculated using the simulated average hourly emissions rate of electricity. 15 Building Design 1 emits the highest GHG emissions during on-peak hours. The alternative building Design 2 emits fewer 15 Carbon dioxide emissions levels in the Tool are displayed in metric tonnes throughout. 33

emissions over the course of the year at 11 tonnes of CO2, and shows a flatter emissions profile between on-peak and off-peak hours. 16 Both building designs show similar trends in terms of on- and off-peak hours when comparing marginal CO2 emissions as compared to average emissions, however, the marginal emissions calculation results in higher overall CO2 emissions than the average emissions represented by the GHG footprint calculation. This is to be expected since the average emissions calculation for electricity generation includes lowcarbon generation such as renewable and nuclear energy, whereas, in California, highercarbon natural gas generation will mostly determine the marginal emissions rate. Figure 11. Comparing Buildings Annual and TOU Average and Marginal GHG Emissions 4. Next, select which day of the year to display as the first date in the emissions profile charts using the First Day to Display sliding bar in row 24. Cell J24 will display the date you have selected. To change the number of days displayed in 16 These sample building designs do not reflect actual buildings. These sample building designs represent 300 square meter buildings located in a mild climate. Larger buildings, and buildings located in a climate zone with more extreme temperatures would be expected to show higher GHG emissions. 34

the chart, use the Number of Days to Display sliding bar in Row 25 which allows you to select between 1 and 365 days. Or, manually input the number of days to display in cell J25 to override the sliding bar selection. This step is shown in Figure 12. Figure 12. Charting a Building s Hourly Emissions Profile 4. The dark red portion of the charts represents CO2 emissions associated with electricity consumption (either using the marginal or average emissions rate, as selected by the user of the tool.) On top of the electricity emissions is layered the emissions associated with on-site gas combustion in blue. So, in the example shown above, the reference design building emits, at its highest, 0.007 tonnes of CO2 during a few select hours on December 11th, including emissions from both electricity and on-site natural gas consumption. 35

5. In the drop-down menu in cell O24, select whether you wish to display the emissions profile of your building based on an estimate of electricity s marginal emissions rate or average emissions rate. See Section 2.1 for a discussion of the difference between applying a marginal versus an average emissions rate for buildings. This step is shown in Figure 13. Figure 13. Charting Marginal or Average GHG Emissions 5. 6. Scrolling down, you may also select whether the yearly topographical charts of greenhouse gas emissions should display carbon from both electricity and gas use combined, only electricity use, or only gas use. To display emissions from both sources, cells H103 and M103 should both say TRUE. To exclude the emissions from either electricity or gas use, input FALSE into the appropriate cell. This step is shown in Figure 14. 36

Figure 14. Three-Dimensional Representation of a Building s Hourly GHG Emissions 6. The two charts in this section are 3-dimensional representations of your building s CO2 emissions over a one year time period. In the top chart, across the x-axis are the hours in a day (1 24), the y-axis portrays the months in the year (January December) while the z-axis shows the sum of the emissions across each hour of each month. The 37