Location-Based ISONE Pollutant Intensity Analysis

Transcription

1 Location-Based ISONE Pollutant Intensity Analysis For Harvard University s Office for Sustainability Final Report S. Hoedl, Ph.D., J.D. August 30, 2016

2 Introduction This white paper summarizes a research project that investigated whether the CO2 intensity of the ISONE grid varies enough with time that an electricity consumer could lower greenhouse gas emissions associated with its electricity consumption, the so-called Scope 2 CO2 emissions, by shifting consumption from high-polluting hour to low polluting hours. 1 The project answered this question by analyzing ISONE s generation fuel mix for every hour between Dec 4, 2014 and April 30, As part of this analysis, the project calculated four quantities for CO2, NOX and SO2, all in units of lbs/mwh, for the analyzed time period: (1) the hourly pollutant intensity,, which represents how much of pollutant is emitted per MWh of electricity consumed in each hour,, of the analyzed time period (See Figures 1 through 5); (2) the average pollutant intensity,, which represents the average amount of pollutant that is emitted per MWh of electricity consumed over the analyzed time period (See Table 3 and 4); (3) the average daily pollutant intensity profile,, which represents how, on average, the pollutant intensity varies with the clock hour,, of a day (See Figures 6 through 9); and (4) the average daily pollutant intensity difference,, which represents the difference in the pollutant intensity of pollutant between the most polluting and least polluting hours of the day (See Table 6). As discussed below, the analysis discovered that biomass has a big impact on these quantities. Thus, these four quantities were calculated under two scenarios: biomass is either completely carbon neutral or completely not carbon neutral. In addition, a detailed uncertainty analysis was undertaken for and under both biomass scenarios. The CO2 pollutant intensities were used to test two case studies: (1) an electric boiler, either powered directly using resistive heating or powered by a heat pump, that could displace the combustion of natural gas during those hours that the electric grid is less pollutant intense than natural gas consumption on site; and (2) a cold-storage device that can produce and store cold water in the early morning hours for distribution in later warmer hours when airconditioning would ordinarily be needed. In addition, in order to investigate whether using the hourly CO2 pollutant intensity instead of average annual pollutant intensity would make a difference in calculating an entity s annual Scope 2 CO2 emissions, Harvard s ISONE grid consumption data was used to calculate total CO2 emissions using both the hourly and annual average CO2 pollutant intensity. Note that because this analysis was focused on Scope 2 emissions, from an accounting perspective, it did not estimate how much CO2 emissions were avoided on a grid level by time- 1 Note that the Greenhous Gas Protocol s Scope 2 guidance explicitly allows entities that use the Protocol to calculate their Scope 2 emissions using detailed grid pollution data, so-called Advanced Grid Studies. GHG Protocol, GHG Protocol Scope 2 Guidance, (March 2015) available at 2 The start date for the analysis was determined by the availability of detailed generator type mix data from ISONE. Page 2 of 28

3 shifting electricity consumption. 3 In order to calculate avoided emissions, a sophisticated dispatch model, such as EPA s AVERT, 4 would have to be used. These models attempt to forecast how individual power plants within an electrical grid respond to changes in electrical demand and thereby estimate how pollution emissions from individual power plants change with electrical demand. Although more sophisticated, these models are not necessarily superior to the accounting approach presented here. For example, unlike the analysis presented here, AVERT does not include emissions due to imported electricity or biomass combustion, such as landfill gas, municipal solid waste, or wood. AVERT only focuses on fossil-fuel fired power plants within the United States. 5 As demonstrated below, these sources can have a significant impact on the ISONE CO2 pollutant intensity. Key conclusions of the analysis are presented in the executive summary. Fourteen figures are included at the end of this report that illustrate the time variations in the pollutant intensities and show correlations between the pollutant intensities and other variables, including the Boston Zone Locational Marginal Price, the total ISONE system load, and the minimum and maximum daily temperature. The remainder of this white paper is divided into four parts. Part 1 describes the data sources and collection method. Part 2 describes the analysis procedure. Part 3 describes how uncertainties in the pollutant intensities were calculated. Finally, Part 4 describes the two case studies. Two appendices provide additional detail on the underlying data and assumptions used for the analysis. Part 1: Data Collection ISONE Generation Fuel Mix ISONE does not report the CO2 or other pollutant intensity on an hourly basis. ISONE does, however, provide the generation fuel mix at intervals typically ranging between 5 minutes and 1 hour through ISONE webservices. 6 The generation fuel mix describes how much electricity is being generated within ISONE in units of MW at the reported time for ten different 3 Likewise, the GHG Protocol does not support the use of an avoided emissions analysis to calculate an entity s Scope 2 emission reductions. GHG Protocol, supra note 1 at EPA, AVoided Emissions and generation Tool (AVERT), last accessed August 16, EPA, AVERT User Manual 45 (July 7, 2016), available at 6 ISONE operates a webservices website that provides a wide range of data about the ISONE electrical grid. This data is available to anyone with a webservices account, which can be found at mn-3&p_p_col_count=1&savelastpath=false&_58_struts_action=%2flogin%2fcreate_account. The data can be accessed using a Representational State Transfer Interface, and is available in either XML or JSON formats. See ISONE, Data Feeds, last accessed August 16, Page 3 of 28

4 types of generators including (1) coal; (2) nuclear; (3) hydropower; 7 (4) natural gas; 8 (5) oil; (6) landfill gas; (7) refuse; (8) wind; (9) wood; and (10) solar. The generation fuel mix can be used to estimate pollutant emissions from ISONE generators on an hourly basis by multiplying each generator type output by a pollutant emission factor in units of lbs/mwh. As described in Appendix 1, the pollutant emission factors for each generator type were determined by analyzing four databases, including (1) the EPA s Clean Air Markets Database ( CAMD ); 9 (2) egrid2012; 10 (3) NEPOOL GIS; 11 and (4) the Clean Power Plan s technical documents. 12 Nuclear, hydropower and solar pollutant intensities were set to zero as carbon-free sources of electricity. Uncertainties in the CO2 emission factors were also estimated using the variance of the emission factors of the generators selected from each database. Uncertainties in the other pollutants were not estimated to simplify the analysis. Table 1 lists the assigned emission factors and their uncertainties. Note that each database treats biomass combustion differently. For example, while egrid2012 assumes that all biomass combustion is carbon neutral, NEPOOL GIS assumes that most biomass combustion is not carbon neutral. Resolving the extent to which biomass combustion is carbon neutral is beyond the Scope of this report. Although the EPA assumes that biomass is carbon neutral, many studies of biomass combustion, especially that of wood, have determined that far more CO2 is removed from the atmosphere by properly managing forests than by harvesting forests to displace fossil fuel. 13 Instead of taking a position with regard to the carbon neutrality of biomass combustion, this paper calculated the CO2 pollutant intensity under a biomass-is-carbon-neutral ( CN ) scenario and a biomass-is-not-carbon-neutral ( NCN ) scenario. Under the NCN scenario, no correction was made to account for biomass combustion all CO2 emissions were included as pollutants. Under the CN scenario, wood and landfill gas CO2 emission factors were set to zero on the assumption that wood is a fully renewable resource and landfill gas emissions would have occurred anyway, even if the landfill gas was not used for 7 Note that pumped storage is classified as a hydropower generation source when it is used to provide electricity to the grid. ISONE, Explanation of Generation by Fuel Type (June 24, 2015), available at 8 Note that as of December 4, 2014, the reported generation mix accounts for the fuel burned by dual fuel units: dual-fuel generation units report to ISONE the fuel that they intend to burn at the reporting time. Id. 9 EPA, Air Markets Program Data, last accessed August 16, EPA, Emissions & Generation Resource Integrated Database (egrid), last accessed August 16, NEPOOL-GIS, NEPOOL-GIS System Mix report (2016), last accessed August 16, EPA, Clean Power Plan Final Rule Technical Documents, Technical Support Document: Emission Performance Rate and Goal Computation, Data File: Goal Computation Appendix 1-5 (August 2015), available at xlsx. 13 See, for example, Stephen R. Mitchell, et al., Carbon Debt and Carbon Sequestration Parity in Forest Bioenergy Production, 4 GLOBAL CHANGE BIOLOGY BIOENERGY (2012). Page 4 of 28

5 producing electricity. Refuse emissions under the CN scenario were set according to the biomass adjustment factors in the egrid2012 database, 14 which reduces the CO2 emissions from refuse generators by about 50% to reflect the fact that, on a national basis, about half of the heat content of municipal solid waste is discarded food waste. 15 Table 1. Assigned ISONE generator emission factors. Emission Factor (lbs/mwh) Generation Type CO2 (CN) CO2 (NCN) NOX SO2 Coal Natural Gas Oil Refuse Landfill Gas Wood Nuclear Hydro Wind Solar Imports into ISONE ISONE imports about 18% of electricity consumed in ISONE from neighboring grids. 16 Because the imported electricity is often generated with hydropower, it tends to have a lower CO2 pollutant intensity than that of ISONE. Accounting for this difference lowers ISONE s CO2 pollutant intensity. ISONE webservices reports the power entering or leaving ISONE, in units of MW, over six grid ties in five minute increments. These ties connect ISONE with the electrical grids of New Brunswick, Quebec, and New York. Unfortunately, at the present time, these jurisdictions do not provide CO2 pollutant intensity or generation mix data on an hourly basis. 17 As an 14 EPA, egrid2012 Technical Support Document 12 (October 2015), available at 15 See Energy Information Agency, More recycling raises average energy content of waste used to generate electricity (September 18, 2012) available at See also Energy Information Agency, Waste-to-energy electricity generation concentrated in Florida and Northeast (April 8, 2016) available at 16 On the basis of the ISONE data analyzed here, between December 4, 2014 and April 30, 2016, 3.5%, 4.8%, 9.6% and 0.16% of electricity consumed in or exported out of New England was imported from New Brunswick, New York Upstate, Quebec, and Long Island, respectively. 17 New York does provide generation fuel mix data in the form of daily CSV files. See last accessed August 16, However, this data does not distinguish Page 5 of 28

6 approximation, for each grid tie, an hourly CO2 pollutant intensity was assigned to imported electricity equivalent to the average CO2 pollutant intensity of the grid from which the electricity is imported for both biomass scenarios. For New York, the average pollutant intensities were set using the egrid2012 database. For Quebec and New Brunswick, the average pollutant intensities were set using recent annual generation fuel mix data and ISONE pollutant emission factors. The full method by which the pollutant intensities and their uncertainties is estimated are described in Appendix 2. The assigned pollutant intensities are listed in Table 2. Table 2. Assigned pollutant intensities of imported electricity. Pollutant Intensity (lbs/mwh) Grid Tie Name Connected Grid CO2 (CN) CO2 (NCN) NOX SO2 SALBRYNB New Brunswick ROSETON NY Upstate HQ_P1_P2345 Quebec HQHIGATE120 Quebec SHOREHAM138 NY Long Island NRTHPORT138 NY Long Island ISONE Locational Marginal Price (LMP) ISONE webservices provides the LMP in all of ISONE s load zones on an hourly basis. The LMP was downloaded in order to look for correlations between CO2 pollutant intensity and LMP and as an input for the cold storage and electric boiler dispatch algorithms. ISONE System Load ISONE webservices provides the system load, defined as the total electrical power, including generation and imports, that must be provided to meet ISONE demand, including pumped storage, in five minute increments. The system load data was downloaded in order to provide an error check against the generation fuel mix and import data. Temperature in Boston Daily maximum and minimum temperatures as recorded at Logan Airport were downloaded from the National Climate Data Center. 18 This data was used to look for correlations between daily CO2 pollutant intensities and daily temperatures. dual fuel units that burn natural gas from dual fuel units that burn oil. In addition, the data, in the form of individual CSV files, is very cumbersome to analyze at this time. 18 National Oceanic and Atmospheric Administration, Global Historical Climatology Network Daily (GHCN- Daily), Version 3, last accessed August 16, For an overview of the database, see M. J. Menne et al., An Overview of the Global Historical Climatology Network-Daily Database 29 J. ATMOS. OCEANIC TECHNOL (2012). Page 6 of 28

7 Harvard Consumption Harvard s ISONE consumption was provided by Harvard University s Campus Services. Part 2: Calculation Methods Fixing gaps in ISONE generation mix, import data and system load data There are many instances in the ISONE generation mix data between December 4, 2014 and April 30, 2016 when the time between generation mix data points is greater than 60 minutes. These gaps were filled in order to have a continuous hourly generation mix record by adding data points in between ISONE data points if the time between the ISONE data points was greater than 20 minutes. These additional data points were assigned generation mix values equal to the average of the generation mix data before and after the gap. 19 Both the system load and the import data also have gaps. These gaps come in two varieties: first, there are several instances when the data is reported at minutes that are not on even 5 minute clock intervals, such as when the data is reported at 12:17 PM instead of 12:15 PM. Second, there are several instances when the time between data points is greater than 5 minutes. Each data series was fixed by first adjusting data points that did not fall onto five minute clock intervals so that they were on five minute clock intervals. Next, the gaps were filled by assigning to each missing five minute increment data point a value equal to the average of the value before and after the gap. Putting generation mix data into an hourly basis in units of energy (MWh) ISONE provides the generation mix data in units of power (MW) at variable time points. In order to calculate a pollutant intensity, the generation mix data must be in units of energy (MWh) on an hourly basis. The data was converted to this form through the following method. First, a time interval, t, was assigned to each generation mix data point. The time interval was calculated according to the following diagram and formulas, which depend on whether the data point is the first data point within an hour, the last data point within an hour, or neither. (* represents a data point, while represents a boundary between clock hours). 19 More sophisticated methods, such as a linear interpolation within the gap, did not improve, and sometimes worsened, the correspondence between the generation mix and system load data. For that reason, this simple averaging method was adopted. Page 7 of 28

8 * * * t0 t1 t2 t3 t Eq. 1. Second, for each clock hour, the generation mix data was converted into units of energy according to the following formula: Δ Eq. 2. where represents the generation mix data, in units of MWh, for generation type j of hour h, and represents the generation mix data, in units of MW, for generation type j of hour h and data point i within hour h. Putting system load and import data into an hourly basis in units of energy (MWh) ISONE provides the import and system load data in units of power instead of units of energy in five minute increments. These data sets were converted to an hourly basis in units of MWh according to the following formula: 24 12, Eq where represents the import or system load data, in units of MWh, for import tie j of hour h, and represents the import or system load data, in units of MW, for import tie j of hour h and data point i within hour h. Note that i = 0 and i = 12 corresponds to the data points at the top of hours h-1 and h, respectively. Calculating pollutant intensities Hourly Pollutant Intensity For each hour, the hourly pollutant intensity,, in units of lbs/mwh, was calculated for both biomass scenarios according to the following formula:, Eq. 4. where Ei(t) represents the electrical energy either generated or imported during hour by ISONE generator type or import grid tie i, and represents the pollutant intensity assigned to Page 8 of 28

9 generation type or import grid tie for pollutant. Plots of the hourly pollutant intensities are presented in Figures 1 through 5. Pollution emission that could be attributed to exports are purposefully ignored because exports do not change the pollutant intensity of the ISONE grid. As an analogy, consider pouring food dye into a glass of water. The final color of the water depends on the types and quantity of dyes that are poured into the water; the color does not depend on how much of the water is poured out of the glass after the dye is added. Similarly, the pollutant intensity of the ISONE grid only depends on the pollutant intensity and quantity of electricity that is injected into the ISONE grid, the pollutant intensity does not depend on how much electricity is consumed from the grid, either as ISONE load or as exports to neighboring grids. The Average Pollutant Intensity The average pollutant intensity,, was calculated for each pollutant and for each biomass scenario according to the following formula:, Eq. 5. where all the variables have the same meaning as those in Equation 4. Tables 3 and 4 list the values of for each pollutant and biomass scenario for the full date range and for the 2015 calendar year, respectively. The method by which the uncertainties in the CO2 pollutant intensities were calculated are described below. A separate calculation, referred to in Tables 3 and 4 as ISONE generation only, was performed in which the sums over in both the numerator and the denominator only included ISONE generators, i.e., imports were ignored. Transmission loss in the ISONE distribution system was taken into account in Tables 3 and 4 by dividing by the transmission loss factor, assumed to be Table 3. Average pollutant intensities for the full date range of the analysis. Pollutant Intensity (lbs/mwh) Calculation Method CO2 (CN) CO2 (NCN) NOX SO2 ISONE Generation Only Including Imports At the point of consumption ISONE assumes an 8% loss in the distribution and transmission system for the purposes of system planning. See ISONE, CELT Report: Forecast Report of Capacity, Energy, Loads, and Transmission (May 2016), available at Page 9 of 28

10 Table 4. Average pollutant intensities for the 2015 calendar year only. Pollutant Intensity (lbs/mwh) Calculation Method CO2 (CN) CO2 (NCN) NOX SO2 ISONE Generation Only Including Imports At the point of consumption Table 5. A comparison of the average pollutant intensities for ISONE generators only as calculated by five different methods including: this analysis under both biomass scenarios, egrid2012, ISONE, and NEPOOL-GIS. Pollutant Intensity (lbs/mwh) Pollutant Intensity Calculation Method CO2 NOX SO2 This analysis 2015 (CN) This analysis 2015 (NCN) egrid ISONE Report (2014) NEPOOL-GIS (2015) Note that ISONE s CO2 pollutant intensity in the NCN scenario is about 23% greater than the CO2 pollutant intensity in the CN scenario for the 2015 calendar year. To determine the contribution of each biomass generator type to this difference between the scenarios, the ISONE generation only mix was repeatedly calculated with different biomass generators assumed to be not carbon neutral. These calculations demonstrated that 60% of the increase between the scenarios was due to wood, 39% was due to refuse, and 1% was due to landfill gas. The average pollutant intensity, as calculated here for ISONE generators only, is consistent with calculations by other organizations. Table 5 compares the average CO2, NOX and SO2 pollutant intensities as calculated under both biomass scenarios and as reported by egrid2012, ISONE for the 2014 calendar year and NEPOOL-GIS for the 2015 calendar year. The CO2 pollutant intensity under the CN scenario is consistent with egrid2012. This consistency makes sense as egrid2012 explicitly treats all biomass combustion as carbon neutral. 24 The CO2 pollutant intensity under the NCN scenario is in between ISONE and NEPOOL-GIS. This also makes sense as ISONE uses emission data from a mixture of databases 21 EPA, egrid2012, egrid2012 Summary Tables (October 8, 2015), available at 22 ISONE, 2014 Air Emission Report 31 (2016), available at 23 NEPOOL-GIS, NEPOOL-GIS System Mix report (2016), supra note EPA, egrid2012 Technical Support Document, supra note 14 at Page 10 of 28

11 that treat biomass combustion differently, 25 so that some generators in ISONE s dataset may have been treated as carbon neutral. In addition, NEPOOL-GIS treats most biomass combustion as not carbon neutral. 26 However, NEPOOL-GIS may not make a correction for CHP generators, which would also tend to increase NEPOOL s CO2 pollutant intensities. Inconsistencies between the different methods for NOX and SO2 are likely due to the fact that controls for these pollutants are rapidly improving so that the emission factors used for some of the calculation methods may be out-of-date. The Average Daily Pollutant Intensity Profile The average daily pollutant intensity profile,, was calculated for each pollutant according to the following formula:, Eq. 6. number of days where the sum in the numerator includes all hours in the analysis period when equals clock hour, and the numerator represents the total number of days in the analysis period. The average daily pollutant intensity profiles for each pollutant and under both biomass scenarios are plotted in Figures 6 though 9. For CO2, the early morning hours are consistently less CO2 intense than the afternoon hours because baseload generators in ISONE are mostly nuclear and hydropower. For NOx and SO2, the daily pollutant intensity is reversed: the early morning hours are consistently more polluting than the afternoon hours. This difference is likely due to the fact that coal power plants, which are mostly responsible for NOx and SO2 emissions, operate continuously, but the total system load increases in the afternoon, and thereby, dilutes the NOx and SO2 pollutant intensity in the afternoon. The Average Daily Pollutant Intensity Difference The average daily pollutant intensity difference,, was calculated for each pollutant according to the following formula: hph number of peak hours hnph, Eq. 7. number of non peak hours 25 ISONE uses emission data from the EPA s Clean Air Markets Division database, NEPOOL GIS, and EPA s egrid2012. ISONE, 2014 Air Emission Report, supra note 22 at 7. As discussed in Appendix 1, EPA s Clean Air Markets Division database does not cover most biomass combustion generators. NEPOOL GIS assumes that most biomass generators are not carbon neutral, while EPA s egrid2012 assumes that all biomass combustion is carbon neutral. 26 See Appendix 1. Page 11 of 28

12 where the sums in the numerator includes hours in a day that are classified as either peak, ph, or non-peak, nph, and the denominators represent the number of peak or non-peak hours in each day. Table 6 lists for each pollutant and the hours that were selected as peak or nonpeak. Uncertainties in are explained below. Table 6. The average daily pollutant intensity difference, calculated for the full date range. Pollutant (lbs/mwh) (lbs/mwh) Peak Hours Non-Peak Hours (CN) (NCN) CO AM 9 PM 3 AM 4 AM NOX AM 4 AM 12 PM 9 PM SO AM 4 AM 12 PM 9 PM Calculating Harvard s Emissions Harvard s pollutant emissions due to consumption of ISONE electricity over the analyzed time period and the 2015 calendar year were calculated using two methods. First, Harvard s emissions were calculated on an annual basis for each pollutant,, by simply multiplying Harvard s total consumption by the average emission rate:, Eq. 8. where represents Harvard s hourly electricity consumption in units of MWh at hour. Second, Harvard s emissions were calculated on an hourly basis according to the following formula:, Eq. 9. where PIx(t) represents the hourly pollutant intensity for pollutant x. The results calculated using both these methods are listed in Table 7 for the 2015 calendar year. The two methods differ for CO2 by less than 0.7%, suggesting that there is, at present, little advantage to either accounting method. However, if Harvard or any other organization within ISONE were to adopt an hourly accounting method, it could readily account for any emission reductions achieved by time shifting electricity consumption. Page 12 of 28

13 Table 7. A comparison of Harvard s pollutant emissions due to electricity consumption for the 2015 calendar year as calculated on an average or hourly basis. Pollutant Emissions Calculation Method CO2 (CN) CO2 (NCN) NOX SO2 Hourly Basis 135,800, ,200,000 73,200 41,000 Annual Basis 136,800, ,200,000 75,400 44,700 Calculating Correlations Four different correlations between the CO2 pollutant intensity and other quantities under the CN scenario were investigated in order to look for easy proxies that could be readily used for electricity consumption dispatch. However, none of the correlations were sufficiently strong to justify a dispatch algorithm. The correlations are likely even weaker under the NCN scenario, and thus, were not investigated. The following correlations were investigated: (1) and Boston load zone LMP on an hourly basis. This correlation, plotted in Figure10, asked whether hourly LMP was a proxy for hourly CO2 pollutant intensity. Although there is a correlation so that the most expensive hours tend to be the most CO2 intense hours, there are also many hours when the price is high, and CO2 pollutant intensity is low, and vice-a-versa. (2) and system load on an hourly basis. This correlation, plotted in Figure 11, asked whether hourly system load was a proxy for hourly CO2 pollutant intensity. Although there is a correlation, so that the high system load hours tend to be high CO2 pollutant intensity hours, there are many hours with low system load that have as high a CO2 pollutant intensity as the hours with the highest system load. (3) and daily minimum temperature. This correlation, plotted in Figure 12, asked whether a day s minimum temperature was a proxy for a day s average CO2 pollutant intensity. Although there is a correlation, so that both the high and low minimum temperature days tend to have average high CO2 pollutant intensities, there are many days with moderate daily minimum temperatures that have a high CO2 pollutant intensity. (4) and daily maximum temperature. This correlation, plotted in Figure 13, asked whether a day s maximum temperature was a proxy for a day s average CO2 pollutant intensity. Although there is a correlation, so that both the high and low maximum temperature days tend to have average high CO2 pollutant intensities, there are many days with moderate daily maximum temperatures that have a high CO2 pollutant intensity. Part 3: Uncertainties Because pollutant emissions were inferred from the hour-by-hour ISONE generation mix and import data, rather than reported by individual generators, the actual emissions during the analyzed date range could have been different than those analyzed here. The difference between Page 13 of 28

14 the inferred CO2 emissions and the actual CO2 emissions was estimated on the basis of three sources of uncertainty: 27 (1) the generation reporting method and data gaps; (2) the use of average generator emission factors; and (3) the assumption that imported electricity has a constant pollutant intensity. This section describes the estimation method for each source of uncertainty and how that source contributes to uncertainty in and. Note that uncertainties in and are not, strictly speaking, statistical confidence intervals. Rather, they provide only a rough estimate of how confident one should be in these quantities. The quadrature sums of the uncertainties in for both biomass scenarios and different date ranges are listed in Tables 3 and 4. The specific uncertainties in are listed in Table 8. Generation Reporting Method and Gaps in Data To estimate the accuracy of the generation fuel mix data and gap filling procedure, the generation mix and import data were compared to the system load data from ISONE for each hour according to the following formula:, Eq. 10. where represents the energy generated by or imports from ISONE generator type or import grid tie i, and and represent the exports and system load per hour as downloaded and corrected from ISONE, respectively. Exports are subtracted from generation and imports because the system load data explicitly excludes exports. This formula is plotted in Figure 14 and demonstrates that, except for a few outlying data points, the generation mix, import and system load data generally match to within about 2%. The few outlying data points are generally due to gaps in the data sets that are not completely fixed by the gap filling procedure described above. To estimate how the slight mismatches between the generation mix, import and system load data creates an uncertainty in, the following calculation was performed over all hours:, Eq Uncertainties in other pollutant intensities were not estimated because the underlying uncertainty in generator emission factors is very high due to improving pollution control technology. For example, the SO 2 emission factor for coal-based generators varies by a factor of four between the CAMD and the egrid2012 databases, while the SO2 emission factor for wood-based generators varies by a factor of 35 between egrid2012 and NEPOOL-GIS. See Appendix 1. Page 14 of 28

15 where all variables have the same meaning as in Eqn. 8. According to this formula, the sum of the generation mix, import, and export data over the entire data set match the sum of the system load data within 0.009%, suggesting that the generation mix reporting method and gap fixing procedure only create an uncertainty of about 0.06 lbs/mwh in. To estimate how the slight mismatches between the generation mix, import and system load data creates an uncertainty in, the following calculations were performed. First, the uncertainty in, for both peak and non-peak hours was estimated: tph tnph hph number of peak hours ph hnph number of non peak hours tnph Eq. 12. where peak and non-peak hours are defined in Table 6. Second, these uncertainties were added in quadrature: Eq. 13. This formula suggests that the generation mix reporting method and gap fixing procedure only create an uncertainty of about 0.4 lbs/mwh in. Generator Emission Factors and Import Grid s Pollutant Intensities The emission factor of a generator is not constant in time. The emission factor depends on both the generator s duty factor and weather conditions. For example, a coal plant is likely to have a higher efficiency in the winter than in the summer due to the increased thermodynamic efficiency of coal-fueled plants when the ambient air temperature is lower. In addition, emission factors of a given generator type vary dramatically between different generators. For example, according to the EPA s egrid2012 database, the most polluting coal plant in ISONE has a CO2 emission factor 60% greater than the least polluting coal plant. Because ISONE does not report the exact generators used to produce electricity on an hourly basis, using an average pollutant emission factor for a generator type can introduce an error into the analysis. Similarly, the pollutant intensity of an import grid will not be constant in time. In addition, because this analysis assumed that generators in New Brunswick and Quebec have the same emission factors as generators in ISONE, the actual pollutant intensities of the Quebec and New Brunswick grid s may be different than those used here. The uncertainty in due to these sources of error was estimated according to the formula: Page 15 of 28

16 , Eq. 14. where represents the CO2 emission factor or pollutant intensity uncertainty assigned to generation type or import grid tie, and represents the electrical energy either generated by or imported from ISONE generator type or import grid tie during hour. The uncertainty in depends, in part, on whether biomass generators are considered carbon neutral and whether imports are included. The uncertainty ranges from 27 lbs/mwh in the CN scenario that includes imports to 51 lbs/mwh in the NCN scenario that does not include imports. The uncertainty in due to CO2 emission factors and pollutant intensity uncertainties was estimated by recalculating for different values of. For each generator type and import tie, was recalculated with that generator type or import tie s CO2 pollutant intensity increased by the assigned. This uncertainty was then calculated according to the formula:, Eq. 15. where represents the recalculated value of for ISONE generator or import tie. This uncertainty was 7.3 and 11.2 lbs/mwh under the CN and NCN scenarios, respectively. Lack of Hourly Import CO 2 Pollutant Intensity Data In the analysis, import data was assumed to have a constant CO2 pollutant intensity. Because 20% of ISONE s electricity is imported, this simplification could introduce an error into the analysis. The uncertainty created by neglecting import s hourly CO2 pollutant intensity variation was investigated through the following method. First, was recalculated using ISONE generators only, i.e., imports were ignored. Ignoring imports, is equal to 67.8 and 18.5 lbs/mwh, equivalent to a variation of 11% and 2.3%, under the CN and NCN scenarios, respectively. Second, the import CO2 pollutant intensities were adjusted to mimic a 11% or 2.3% daily variation. For example, under the biomass-is-carbon-neutral scenario, the import CO2 pollutant intensities were lowered by 5.5% during the hours between midnight and noon and raised by 5.5% during the hours between noon and midnight. This change simulated how one would expect the neighboring grid s CO2 pollutant intensity to vary over the course of the day if the neighboring grids behave like the ISONE grid. Third, and were re-calculated using the modified import CO2 pollutant intensities. This third step did not change Page 16 of 28

17 but did increase by 5.4 and 5.8 lbs/mwh under the CN and NCN scenarios, respectively. These changes were taken as the uncertainty in due to the assumption that imports have a constant pollutant intensity. Although this approach is very simple, it provided a good estimate of the scale of changes one would expect if hourly import CO2 pollutant intensities were available. If the import grid s CO2 pollutant intensity varied in the opposite fashion of ISONE so that the imports were more CO2 intense during the morning hours and less CO2 intense during the evening hours, import hourly variations would decrease, instead of increase,. However, because the average CO2 pollutant intensity of all import grids is lower than the CO2 pollutant intensity of natural gas generators, which are the typical marginal generators, this opposite case is unlikely. Added generation during the evening hours will almost certainly increase the CO2 pollutant intensity because this generation type will have a CO2 pollutant intensity higher than the grid s average CO2 pollutant intensity. For this reason, only the upper limit on the uncertainty in was changed due to uncertainty created by the assumption that imports have a constant pollutant intensity. Table 8. Summary of uncertainties in. Uncertainty (lbs/mwh) Source of uncertainty (CN) (NCN) Generator reporting method and data gaps Use of historical CO2 pollutant intensity Constant import intensity assumption +5.4 / / -0.0 Total +9.1 / / Part 4: Case Studies Two types of equipment were modeled to investigate whether Harvard or another entity within ISONE could take advantage of the daily variations in the ISONE CO2 pollutant intensity. Electric Boiler The ISONE grid has a sufficiently low CO2 pollutant intensity that there are hours when grid-supplied electricity can provide heat, either directly or via a heat-pump, with lower CO2 emissions than natural gas can provide when burned directly. A 100% efficient direct heating electric boiler will be responsible for less CO2 emissions than a 90% efficient natural gas boiler whenever the transmission-level CO2 pollutant intensity is less than 396 lbs/mwh. 28 A heat- 28 According to the U.S. Energy Information Administration, the average heat content of natural gas sold in the U.S. is 1,032 BTU per cubic foot, EIA, Heat Content of Natural Gas Consumed (July 29, 2016), available at and combustion of such gas emits lbs of CO 2 per cubic foot, EIA, Carbon dioxide Emission Coefficients by Fuel (February 2, 2016), available at Page 17 of 28

18 pump-based electric boiler, with a coefficient-of-performance of 3, will be responsible for less CO2 emissions than a 90% efficient natural gas boiler whenever the transmission-level CO2 pollutant intensity is less than 1188 lbs/mwh. 29 Within the date range analyzed, 2.7% of hours have a CO2 pollutant intensity below 396 lbs/mwh in the CN scenario, while 100% of hours have a pollutant intensity below 1188 lbs/mwh under both scenarios. There are no hours under the NCN scenario that had a CO2 pollutant intensity below 396 lbs/mwh. A variety of dispatch strategies were investigated for energizing a 1 MW electric boiler. Strategies included dispatch according to (1) time of day; (2) system load; (3) LMP; (4) CO2 pollutant intensity; and (5) a combination of these variables. Of these strategies, only those that include a CO2 pollutant intensity criteria were able to consistently lower CO2 emissions. Three different dispatch strategies for a 1 MW electric boiler, both direct powered and heat-pump powered were modeled for both biomass scenarios, as described below. For each dispatch strategy, electric boiler technology, and biomass scenario, the CO2 emissions compared to those of a natural gas boiler, the average wholesale electric price for operating the electric boiler, fraction of operating hours, and fraction of operating days, were estimated. Confidence limits in the CO2 emission savings were estimated by recalculating the CO2 emissions with the CO2 pollutant intensity increased and decreased by 4.9% or 6%, for the two biomass scenarios, respectively. This percent change is equal to the percent uncertainty created by uncertainty in CO2 emission factors and import grid pollutant intensities, as listed in Table 3. Tables 9 and 10 summarize the results of these calculations for each dispatch strategy. Dispatch Strategy 1: CO 2 emissions minimization alone. In this strategy, an electric boiler was dispatched during those hours that was lower than 396 or 1188 lbs/mwh for the direct heating and heat-pump-driven boiler, respectively. No other criteria was applied. so that natural gas combustion emits lbs/btu. 1 BTU is equivalent to MWh so that natural gas combustion releases 387 lbs per MWh of energy. Assuming that a natural gas hot water boiler is 90% efficient, Department of Energy, Purchasing Energy-Efficient commercial Boilers, last accessed August 10, 2016, a 100% efficient direct heating electric boiler will be responsible for less CO 2 emissions than a natural gas boiler whenever the electric grid has a CO 2 pollutant intensity at the point of consumption of less than 430 lbs/mwh, which, accounting for an 8% loss in the transmission and distribution system, is equivalent to a transmission-level CO 2 pollutant intensity of 396 lbs/mwh. 29 Residential ground-sourced heat pump systems installed in the U.S. must have a coefficient-of-performance greater than between 3.1 and 4.1, depending on the technology used for the heat exchange. See EPA and DOE, Geothermal Heat Pumps Key Product Criteria (January 1, 2012), available at Commercial scale systems are likely to have similar coefficients-of-performance. A coefficient-of-performance of 3 means that the heat pump can provide three times more heat than a direct heating electric boiler. Thus, a coefficient-ofperformance of 3 heat pump is responsible for three times less CO 2 emissions than a direct heating electric boiler and will emit less CO 2 emissions than a natural gas boiler when the transmission-level CO 2 pollutant intensity is less than = 1188 lbs/mwh. Page 18 of 28

19 Dispatch Strategy 2: CO 2 minimization when the price is low. In this strategy, an electric boiler was dispatched when was lower than the CO2 pollutant intensity criteria in Strategy 1, and the LMP was less than $20/MWh. Dispatch Strategy 3: CO 2 minimization when the price is low and when the time is not peakconsumption. In this strategy, an electric boiler was dispatched when was lower than the CO2 pollutant intensity criteria in Strategy 1, the LMP was less than $20/MWh, and the hour was before 8 AM or after 8 PM. The time window was chosen to minimize the risk that the electric boiler would increase a consuming entity s demand charge. Table 9. Summary of direct heating electric boiler dispatch strategies under the CN scenario. Dispatch Strategy CO2 Savings (thousand lbs) Average Wholesale Electricity Cost ($/MWh) Percent of Operating Hours Percent of Operating Days Table 10. Summary of heat-pump based electric boiler dispatch strategies under both biomass scenarios. Dispatch Strategy CO2 Savings (million lbs) CN Scenario NCN Scenario Average Wholesale Electricity Cost ($/MWh) Percent of Operating Hours Percent of Operating Days Cold Water Storage A cold water storage device was modeled to estimate CO2 and financial savings that could be achieved through operation of the device. The device was modeled as a 2 MW load, operating for three hours a day between 2 AM and 5 AM on those days that the maximum temperature exceeded 75 F. The emissions and wholesale electricity costs to operate the device were compared to the emissions and wholesale electricity costs of drawing a 1 MW load for 6 hours between 1 PM and 6 PM on the same day. According to this dispatch schedule, the cold storage device would have operated for 97 days between Dec 4, 2014 and April 30, 2016 and Page 19 of 28

20 saved $35.4/MWh for a total dollar savings in the wholesale cost of electricity of $20,908. The.. cold storage device would have also reduced CO2 emissions by and lbs/mwh, for a total CO2 emission savings of 41,000,, and 15,000,, lbs of CO2 under the CN and NCN scenarios, respectively. The uncertainty in the CO2 emission savings were estimated by increasing and decreasing the CO2 pollutant intensity during the cold storage charging hours by the uncertainties in, as listed in Table 8. Page 20 of 28

21 Appendix 1: Emission Factors for ISONE Generators This appendix describes how the emission factors were selected for the different generator types within ISONE. Four different databases were used to select emission factors for coal, natural gas, oil, refuse, landfill gas, and wood generators including (1) the EPA s Clean Air Markets Database ( CAMD ); 30 (2) egrid2012; 31 (3) NEPOOL GIS; 32 and (4) the Clean Power Plan s technical documents. 33 Unfortunately, the emission factors within these databases are not the same, particularly for NOx and SO2 emissions, likely for at least three reasons. First, many generators burn multiple types of fuel, thus the emission factors for a given generator depend on how much of each fuel that generator burned within the year covered by the database. Further, the emission factor of generator fuel type within a database depends on how the generators are classified. For example, a generator that burns both oil and natural gas may be classified as an oil combustion generator in one database and a natural gas generator in another. Second, the fact that SO2 and NOx pollution controls have become increasingly stringent means that emission factors in older databases are out-of-date. Third, all databases make different assumptions regarding the underlying electricity produced and air pollutant emitted. Critical differences include: (1) CAMD only specifies a generator s gross electricity production instead of a generator s net production that is injected into the ISONE grid; (2) egrid2012 adjusts the emission factors for both biomass combustion and combined-heat-and-power ( CHP ) generators it assumes that biomass combustion is carbon neutral, and it reduces air pollutant emissions from a CHP generator by the fraction of a generator s electricity production that is injected into the ISONE grid; and (3) NEPOOL GIS reports total emissions; it does not make an explicit CHP or biomass correction. Given these inconsistencies, an attempt was made to select the emission factors from each database that are likely most representative of present day emissions and that are most consistent with the generator fuel mix classifications of the ISONE generation fuel mix data. For each database, an average emission factor for each generation type was calculated. Tables 11 through 13 list the emission factors calculated for each database. An emission factor for each generator type was then selected from these four databases. CAMD CO2, SO2 and NOx emission factors were used for coal, natural gas, and oil generators as they are the most up-to-date emission factors among the four databases. egrid2012 CO2 emission factors were used for landfill gas, refuse, and wood for both CN and NCN scenarios because egrid2012 provides a means to explicitly apply CHP and biomass correction factors for these generator types. NEPOOL GIS SO2 and NOx emission factors were used for landfill gas, refuse, and wood as egrid2012 is out-of-date for those pollutants. 30 EPA, Air Markets Program Data, supra note EPA, Emissions & Generation Resource Integrated Database (egrid), supra note NEPOOL-GIS, NEPOOL-GIS System Mix report, supra note EPA, Clean Power Plan Final Rule Technical Documents, supra note 12. Page 21 of 28

22 The emission factors were calculated according to the following methods for each database: EPA s Clean Air Market Database The EPA s Clean Air Market Database lists total pollutant emissions and gross electricity generated for every electricity generating facility since 1980 that is required to report emissions by (1) the Acid Rain Program; (2) the Transport Rule SO2 Annual Group 1 and 2 Programs; (3) the Transport Rule NOx Annual Program; (4) the Mercury and Air Toxics Standards; and (5) the Regional Greenhouse Gas Initiative. 34 In ISONE, these programs cover coal, natural gas, and oil-burning generators. Although CAMD is the most up-to-date database available, there are at least three problems with using CAMD alone. First, because CAMD lists gross electricity generation instead of net electricity generation, emission factors calculated using CAMD alone generally underestimate the actual emission factor of electricity injected into the ISONE grid. Second, for several facilities that have multiple generating units, CAMD neglects to include electricity generated by some units, thereby artificially increasing, in some cases dramatically, the emission factors for that facility. Lastly, CAMD s identification of a primary fuel type is not consistent with its reported emissions: some generators that have natural gas generators listed as the primary fuel have emission factors that are more consistent with oil-combusting generators. Given these three challenges with CAMD, an average emission factor for coal, natural gas, and oil generators was calculated in the following manner. First, generators of each type were included in the average only if (1) CAMD listed the same primary and secondary fuel; and (2) the CAMD gross electricity generation in 2012 was greater than the egrid2012 net generation for that facility. Second, a net electricity generation efficiency was calculated for each of these generators by dividing the net electricity generation in egrid2012 by the gross electricity generation as listed in CAMD s 2012 reporting year. Third, the emission factor for each selected generator in CAMD s 2015 reporting year was then calculated by dividing the total pollutant emitted by the gross electricity generated and then multiplying the result by the net electricity generation efficiency. Lastly, an average emission factor was calculated by averaging these emission factors, weighted by each generator s net 2015 electricity generation. An uncertainty in the CO2 emission factor for each generation type that could be used for this analysis was estimated by calculating the average variance of the emission factors for the selected generators, weighted by the net generation according to the following formula: Δ,,, 1, Eq Note that not all programs have operated since 1980, and the database has varying levels of comprehensiveness since that date. Page 22 of 28