2017 default emission factors

Supporting Data for the proposed default emission factors for Ontario’s cap & trade program (supporting tables)

1. Introduction

In April 2015, the province of Ontario announced its decision to establish a cap and trade program to reduce greenhouse gas (GHG) emissions. Importers of electricity into Ontario will be required to achieve compliance for the electricity they import under the proposed program. Requiring imports to comply achieves two objectives, it:

• Levels the playing field between imported and domestically produced electricity; and
• Mitigates emissions leakage, which occurs when there is an increase in emissions in one jurisdiction as a result of a decrease in emissions in another jurisdiction, in this case Ontario.

Ontario proposes to establish default emission factors annually for imported electricity from select jurisdictions in Canada and the US.

Navigant was retained by the Ministry of Energy to develop and recommend a methodology to establish the default emission factors based on the emissions intensity of marginal generation resources in the following jurisdictions:

• ISO-NE;
• NYISO;
• PJM;
• MISO; and
• Manitoba.

Navigant was also retained to develop and recommend a methodology to establish a generic default emission factor for imports originating outside of the above mentioned jurisdictions.

The default emissions factors for 2017 for various balancing authorities can be found in Table 1. These factors are based on a report prepared by Navigant Consulting on behalf of the Ministry of Energy. The full report is available below.

This report contains four sections and five appendices. The first section, provides an introduction and describes the scope of Navigant’s work. The second section, describes Navigant’s proposed methodology to calculate the marginal default emission factors. The third section, discusses the sensitivity analysis that Navigant conducted on three aspects of the methodology. The fourth and final section presents the results of the methodology and the proposed default emission factors. The appendices provide an overview of Navigant’s approach to electricity and natural gas market modelling for North America and the underlying assumptions for each jurisdiction.

2. Proposed Methodology

Through the use of a marginal default emission factors, Ontario aims to minimize emission leakage and to create an efficient price signal for imports into Ontario relative to domestic production. The general concept of a marginal resource is well-defined – the next available unit of production required to meet the next unit of demand. However, in the context of an electricity system managed through a security-constrained least cost dispatch, there are different ways to interpret, and determine, the marginal resource(s). Navigant also recognizes that there are different approaches that Ontario could take to estimate the emissions associated with imported electricity into Ontario.

Navigant’s proposed approach relies on a mapping of a forecast of hourly locational marginal prices, averaged across nodes and zones to a single price for each jurisdiction, and the firing costs (i.e. the fuel and variable operating cost) of individual generating units within a jurisdiction to determine the marginal resource(s) in each hour. Once the marginal resource(s) are identified, a single emission intensity is then calculated for each hour and averaged across time periods to determine the default emission factors. This methodology is described in detail below.

2.1 Overview

The following six steps summarise Navigant’s proposed methodology. Each step is described in further detail in the sections that follow.

1. Using PROMOD, Navigant forecast unit dispatch and locational marginal prices within the entire Eastern Interconnection for 2017.
2. Calculated a single hourly marginal price for each jurisdiction by calculating a load-weighted average of the hourly locational marginal prices for each node within each jurisdiction.
3. Calculated the hourly firing cost (i.e. fuel and variable operating costs) for each generation unit within a jurisdiction.
4. Grouped the generation units within each jurisdiction into deciles, based on firing cost, and calculated a capacity-weighted average emission intensity of the generation units within each decile.
5. For each jurisdiction, mapped the hourly marginal price to the closest decile and assigned the emission intensity of that decile to the hour.
6. Calculate the default emission factor for each jurisdiction by time period by averaging the assigned emission intensities for each hour by season and time of day, as appropriate.

2.2 Detailed Methodology

2.2.1 Forecast dispatch and price

Navigant used PROMOD to simulate the dispatch of the Eastern Interconnection and to forecast locational marginal prices. Appendix A contains more detail on Navigant’s approach to electricity market modelling in North America.

For the purpose of establishing the default emission factors, Navigant used PROMOD inputs from its Fall/Winter 2015 Reference Case, an integrated forecast of the North American electricity system. Navigant updates its reference case assumptions semi-annually to account for changes in market dynamics. The inputs to the Fall/Winter 2015 Reference Case were updated for this engagement to account for recent natural gas forward prices and major changes in supply and demand (e.g., the recent announcements in Ontario about the timing of the nuclear refurbishment).^{footnote 1[1]}

2.2.2 Single price for each jurisdiction

PROMOD is a detailed hourly chronological market model that simulates the dispatch and operation of wholesale electricity markets. PROMOD can be run as a zonal or nodal model. Navigant runs it in the full nodal model with full transmission representation, as opposed to a zonal model that aggregates transmission constraints. The nodal model establishes individual locational marginal prices for each generation node and a load-weighted locational marginal price for each zone. To establish a single price for each jurisdiction, Navigant calculated the load-weighted average of the individual zonal prices.

2.2.3 Calculation of firing costs and emission factor

Navigant calculated the firing cost per megawatt-hour for each generation unit using the following formula:

Hourly Firing Cost = (Fuel Cost x Heat Rate) + Variable O&M + Emissions Cost

The fuel costs vary by month, meaning that each generating unit has twelve firing costs over the year. An emissions cost was added for units that are subject to emission costs in their region. The emission cost was calculated using the following formula:

Emissions Cost = Emission Price * (Heat Rate x GHG Content of the Fuel)

For generation units subject to the Regional Greenhouse Gas Initiative (RGGI), Navigant assumed a price of USD 2014 $7.88 per short ton or USD 2014 $8.69 per tonne. GHG Content of the Fuel refers to the amount of carbon dioxide that is emitted when a million British Thermal Units (MMBtu) of fuel is combusted. The assumed carbon content of coal, oil and gas are 220, 170 and 119 short tons per MMBtu respectively.

2.2.4 Emission factor by decile

Navigant grouped generation units within each jurisdiction into deciles by cumulative available capacity, such that each decile contains roughly the same amount of available generation. The first decile comprised the least expensive units and the tenth decile comprised the most expensive units. Navigant then calculated a single firing cost and emission intensity for each decile based on the capacity-weighted average firing cost and emission intensity of the generation units within each decile. An example of the decile groupings for NYISO is presented in Figure 1.

Figure 1. NYISO Decile Grouping

2.2.5 Map emission intensities to hourly prices

Navigant mapped an emission intensity to each hour of the year based on the emission intensity of the decile with the firing cost closest to the hourly price.

2.2.6 Default emissions factors

Navigant calculated two annual default emission factors for each jurisdiction by averaging the hourly emission intensities across the peak and off-peak periods. Navigant used a non-standard definition of peak, 7:00 am eastern standard time to 10:59:59 pm eastern standard time seven days a week including holidays.

The choice of a single set of peak and off-peak factors, rather than multiple factors for each month or season, and the non-standard peak definition are discussed in more detail in Section 3.

2.2.7 Manitoba

The methodology outlined above was applied to all of the US jurisdictions. Manitoba, however, has a significantly different electricity system. Manitoba’s electricity comes almost entirely from renewable sources (hydroelectric and wind), and while there are some natural gas and one coal-fired generation unit, they are primarily dispatched for local reliability reasons and not for export.

The renewable generation facilities in Manitoba have a marginal emission intensity of zero. Hence Navigant recommends that the default emission factors for Manitoba be set at zero.

2.3 Generic Factor

In addition to the default emission factors for specifically named jurisdictions, Navigant was retained to develop a methodology and propose default emission factors for imports emanating from other unspecified regions.

The vast majority of Ontario’s electricity imports originate in the specifically named jurisdictions. However, a very small number of imports in the past have originated in other parts of the Eastern Interconnection. It is extremely rare for Ontario to import electricity from the Western Electricity Coordinating Council (WECC) or the Electricity Reliability Council of Texas (ERCOT).

Hence, to establish a generic factor for imports from the unspecified regions, Navigant compared the generation resource mix in the remainder of the Eastern Interconnection (e.g., South East Reliability Council, Southwest Power Pool, and the Florida Regional Coordinating Council) to the specified jurisdictions.

Navigant found that the resource mix across the unspecified regions is similar to that of PJM. Hence, Navigant expects that if the marginal analysis was conducted for the reminder of the Eastern Interconnection, the results would be similar to PJM. As a result, Navigant recommends using the PJM default factors as the default factors for imports from any other jurisdiction.

3. Sensitivities And Other Considerations

Through the course of the analysis, Navigant identified a number of methodological assumptions that could have a material impact on the results. This section discusses the results of a sensitivity analysis around three such assumptions:

• The choice of a single factor for peak and off peak regardless of month or season;
• The choice of a non-standard peak definition; and
• The decision to include all zones and generation units within a jurisdiction, regardless of the historical pattern of power flows.

3.1 Seasons

The results presented in Section 4 are based on a single peak and off peak definition across the entire year. In other words, they do not vary by month or season. In order to understand the impact and validity of this assumption, Navigant analysed the pattern of average daily emissions by month and grouped them by season. The seasonal definitions are summarised in Table 2.

Navigant plotted the average daily emission factors for each jurisdiction by month, grouped by season, in order to identify similar patterns. Ultimately, Navigant concluded that, while some seasonal patterns exist, they were not strong enough within jurisdictions or consistent enough across jurisdictions to warrant seasonal emission factors.

For ISO-NE (Figure 4):

• There is little discernable pattern across the winter months;
• Across the summer and shoulder months the pattern is relatively consistent;
• Within the summer and shoulder periods there is considerable variability in the magnitude of the emission intensity; and
• The lowest average emission factor occurs in October (0.32), whereas the highest occurs in July (0.55).

Figure 4. ISO-NE Daily Emission Intensity Profile by Season

For NYISO (Figure 5):

• There is consistency across weekdays in the winter months but not weekend days;
• Across the summer and shoulder months the pattern is relatively consistent;
• Within the summer and shoulder periods there is considerable variability in the magnitude of the emission intensity;
• October and May are outliers; and
• The lowest average emission factor occurs in October (0.30), whereas the highest occurs in July (0.56).

Figure 5. NYISO Daily Emission Intensity Profile by Season

For PJM (Figure 6):

• Within and across the winter and shoulder seasons the profiles are generally consistent;
• The pattern in the summer months is distinctly different than the winter and shoulder;
• Within the summer, there is little consistency; and
• The lowest average emission factor occurs in December (0.62), whereas the highest occurs in July (0.75).

Figure 6. PJM Daily Emission Intensity Profile by Season

For MISO (Figure 7):

• Within and across the winter and shoulder seasons the profiles are generally consistent;
• The pattern in the summer months is different than the winter and shoulder;
• Within the summer, May and June are distinctly different from July and August; and
• The lowest average emission factor occurs in July (0.78), whereas the highest occurs in October (0.95).

Figure 7. MISO Daily Emission Intensity Profile by Season

3.2 Peak Period

The results presented in the Section 4 are based on a non-standard definition of the peak period. The decision to use a non-standard definition was based on an analysis of hourly emission factors within each jurisdiction.

Navigant analysed the pattern of hourly emission intensity for an average week in each jurisdiction. The graphs below show the profile of hourly emission intensities starting on a Monday at 12:00 am eastern standard time through to Sunday evening at 11:59:59 pm.

As evident from the charts below, the emission intensity during the daytime on weekends more closely resembles the emission intensity during the daytime on weekdays. As a result, Navigant recommended using the non-standard definition which is based on a 16 hour-per day peak period seven days a week, rather than the standard five days a week definition typically used by the Independent System Operators. Navigant believes that this definition results in a more uniform emission factor within each period.

3.3 Transmission Constraints

Another methodological assumption that Navigant tested was the exclusion of highly congested zones from the calculation of the default emission factors.

From a practical standpoint, there are a number of zones within the specified jurisdictions that are transmission constrained and as a result are generally not the source of imports into Ontario (e.g.,New York City). As such, an argument could be made that these zones should be excluded from the analysis.

To test this hypothesis and the impact on the results, Navigant ran the analysis for NYISO by excluding New York City and Long Island (zones J and K respectively) and for PJM by excluding zones in the transmission constrained east (JCPL, METED, OVEC, PECO, PENELEC, PEPCO, PPL, PSEG, RECO, UGI). In both cases Navigant did not find materially differing results.

The specific zones selected for exclusion could be a matter of debate. Hence, based on the limited impact observed through the sensitivity analysis, Navigant recommends including all zones and generation units within a jurisdiction in the calculation of the emission factor for the jurisdiction.

3.4 Comparison With California And Quebec

Ontario’s cap and trade program will be linked to the programs in California and Quebec. Hence, it is important to understand how the proposed emission factors for the Ontario program align with the emission factors used in California and Quebec.

For the most part, California uses specific emission factors tied to the specific generating resource from which the electricity is being imported. For the rare unspecified imports, California applies a default emission factor of 0.428 MTCO₂e/MWh.^{footnote 2[2]}

Similarly, where possible, Quebec applies an emission factor tied to the specific generating resource from with the electricity is being imported. For imports into Quebec that are sourced from an identifiable facility for which the information needed to calculate specific greenhouse gas emissions is not available, and for imports from unidentifiable facilities, Quebec relies on the following calculation and default regional factors.^{footnote 3[3]}

GHG = MWh_imp * EF_D

Where:

• GHG = Annual greenhouse gas emissions attributable to the production of electricity acquired outside Québec and produced by the identifiable facility, in tonnes CO2 equivalent;
• MWh_imp = Total quantity of electricity acquired from the identifiable facility and consumed or sold annually in Québec, including an estimate of transmission losses, from the facility’s busbar, in megawatt-hours;
• EF_D = Greenhouse gas emission factor for the province or North American market from which the electricity comes, in tonnes of CO2 per megawatt-hour:
  • As indicated in Table 3;
  • Zero when the electricity comes from an identifiable nuclear, hydroelectric, sea current, wind, solar or tidal power facility; and
  • 0.999 if the electricity comes from a non-identifiable facility.

4. Results

This section presents the results of the methodology outlined in Section 2 and Navigant’s proposed default emission factors.

4.1 ISO Specific Factors

The calculated default emission factors for each jurisdiction are shown in the table below:

In MISO and PJM, the marginal resources during the off-peak period are almost entirely coal, whereas during the peak periods the marginal resources are a mix of natural gas and coal. For these two regions, this results in a higher emission factor during the off peak than during the peak.

In ISO-NE and NYISO, the base load resources that are marginal during the off peak are a combination of renewables, nuclear and efficient natural gas plants. Whereas, the during the peak period the marginal resources are a mix of natural gas plants of varying efficiencies and some oil-fired generation units.

For Manitoba, a factor of zero is appropriate as discussed in Section 2.2.7.

The forecast supply stacks for ISO-NE, NYISO, PJM and MISO are provided in the appendices.

4.2 Generic Factor

As outlined in Section 2.3 Navigant recommends using the PJM results as the default factors for other, non-specified jurisdictions. The emission factors presented in Table 5 are rounded for simplicity.

Appendix A. Electricity market modelling methodology

Navigant employs a variety of commercial and proprietary energy market modeling tools to project generating capacity retirements and additions, generating unit dispatch, fuel consumption, gas pipeline flows, and commodity prices in organized (e.g., ISO-NE, NYISO, PJM, ERCOT, MISO, SPP, CA-ISO) and traditional markets (e.g., Southeast, Pacific Northwest). A schematic of these tools is shown below, followed by a brief description of each tool.

A.1 PROMOD Electric System Simulation Model

PROMOD IV is a detailed hourly chronological market model that simulates the dispatch and operation of the wholesale electricity market. This model replicates the least-cost optimization decision criteria used by system operators and utilities in the market while observing generating operational limitations and transmission constraints. PROMOD can be run as a zonal or nodal model; although Navigant normally runs it in the full nodal model with full transmission representation.

A.2 Transmission Planning - PSS/E, PSLF, and MUST

Both PSS/E and PSLF are transmission planning software licensed from Siemens PTI and GE, respectively. Both programs include power flow, optimal power flow, balanced and unbalanced fault analysis, dynamic simulations, extended term dynamic simulations, open access and pricing, transfer limit analysis, and network reduction. Siemens PTI's MUST is used to determine transmission transfer capability (FCITC, ATC, TTC) by simulating network conditions with equipment outages under different loading conditions.

A.3 Gas Price Competition Model (GPCM)

GPCM is a commercial linear-programming model of the North American gas marketplace and infrastructure. Navigant applies its own analysis to provide macroeconomic outlook and natural gas supply and demand data for the model, including infrastructure additions and configurations, and its own supply and demand elasticity assumptions. Forecasts are based upon the breadth of Navigant’s view, insight, and detailed knowledge of the U.S. and Canadian natural gas markets. Adjustments are made to the model to reflect accurate infrastructure operating capability as well as the rapidly changing market environment regarding economic growth rates, energy prices, gas production growth levels, sectoral demand and natural gas pipeline, storage and LNG terminal system additions and expansions. To capture current expectations for the gas market, this long term monthly forecast is combined with near term NYMEX average forward prices for the first two years of the forecast.

A.4 EVA's Coal Price Forecast

Navigant currently obtains the delivered coal price forecast from Energy Ventures Analysis, Inc. (EVA).

A.5 Navigant’s Portfolio Optimization Model (POM)

Navigant’s proprietary Portfolio Optimization Model (POM) is a capacity expansion model that emphasizes impacts of environmental policies and focus on renewable generation, while being suitable for risk analysis. It simultaneously performs least-cost optimization of the electric power system expansion and dispatch in multi-decade time horizons. Optionally POM can perform multivariate optimization, which considers other value propositions than just cost minimization, such as sustainability, technological innovation, or spurring economic development. This makes it especially suitable for modeling future renewable generation expansion.

A.6 Coal Plant Retirement Model

Navigant’s proprietary Coal Retirement Forecast model rapidly estimates the total coal fired capacity in danger of retirement due to EPA regulations, determines which states require the greatest emissions reductions to be compliant with the Cross-State Air Pollution Rule (CSAPR), and identifies the specific units and plants most at risk of retirement. The tool reviews the historical emissions of all existing coal units, the existing emissions equipment, and unit allocations for NOx and SOx emissions in order to determine which units are economic to retrofit with pollution control technology and which should be retired. The retirement or retrofit decision is based on the opportunity cost of replacing the coal units with natural gas generation. The Coal Retirement Forecast model summarizes the coal retirements and retrofits by state, ISO, and NERC region, and reports the retirements and retrofits as announced or economically driven. The tool will also estimate how far in or out of the money each unit is to retrofit and the emissions equipment required to be compliant with EPA regulations.

A.7 Navigant’s REC Price Forecast (RECPET)

RECPET© a linear optimization forecasting model used to estimate future prices for RECs and SRECs. RECPET© integrates a diverse set of NCI proprietary models and datasets as well as public data sources to estimate the variables affecting REC/SREC values either within a state (e.g., New Jersey) or across a regional trading area (e.g., PJM RTO). What sets RECPET© apart from traditional REC/SREC forecasting models is its macro-level forecasting approach: starts with a notional value of the incremental revenue required by renewable resources to provide targeted returns over the life of the project then adjusts the notional value based on projections of supply and demand characteristics in the market as they are traded and contracted for by various entities. Using this approach, we help our clients understand the market dynamics that can cause such fluctuations in the prices of RECs/SRECs.

Appendix B. ISO-NE modelling assumptions

The following figures and tables summarise Navigant’s key assumptions for the abovementioned jurisdiction.

Figure 12. ISO-NE 2015 Supply Stack

Appendix C. NYISO modelling assumptions

The following figures and tables summarise Navigant’s key assumptions for the abovementioned jurisdiction.

Figure 15. NYISO 2015 Supply Stack

Appendix D. PJM modelling assumptions

The following figures and tables summarise Navigant’s key assumptions for the abovementioned jurisdiction.

Figure 18. PJM 2015 Supply Stack

Appendix E. MISO modelling assumptions

The following figures and tables summarise Navigant’s key assumptions for the abovementioned jurisdiction. Figure 21. MISO 2015 Supply Stack