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Home / Renewable Grid Power – Index / Renewable Grid Power – Baseline

Renewable Grid Power – Baseline

Case Study on Renewable Grid-Power Electricity

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4. The Electrical Sector Baseline

4.1 Introduction

This section presents the electrical sector baseline used in the model and case study. To ensure consistency with other case studies being completed by the NRTEE and to ensure the results are comparable with other modelling efforts (such as the AMG), the Canada Energy Outlook – Update, 1999 (CEOU99) energy baseline is adopted.22 The discussion is organized to match the parameters that are used in the RFF model, namely:
  • Geographical coverage
  • Time period
  • Discount rate and dollar year
  • Marginal cost of fossil fuel generation
  • Baseline emissions intensity of fossil fuel electricity generation
  • Carbon abatement cost curve for fossil fuel electricity generation
  • Price elasticity of demand for electricity
  • Required return on R&D investment
  • Current expenditures on renewables R&D in Canada
  • Baseline demand for electricity (kWh) from fossil fuel in two periods
  • Baseline of annual renewable energy supplied (kWh) in two periods
  • Carbon price.

4.2 Geographical Coverage

The analysis is conducted from the perspective of Canada. While it may be analytically preferable to assess the management instruments for each region or province and then roll up the number to an aggregated national total, it was agreed during the initial project meeting that budget constraints necessarily limited the analysis to the national level.23 Although the case study uses aggregated national parameters for the analysis, it is important to note that all national data were developed from disaggregated regional and sectoral values. Consequently, in developing the aggregated national parameters, weighting schemes were used to ensure that they reflect the overall circumstances that prevail in each region.

4.3 Time Period

Based on discussions with the NRTEE, the starting year for applying the EFR instruments is 2010. A second period is also modelled in 2030 to capture the longer-run impacts of the EFR instruments and to account for R&D effects and learning effects on renewable costs and supply.

4.4 Discount Rate and Dollar Year

To remain consistent with analysis produced by the National Climate Change Process (and Treasury Board), a discount rate of 10% is used to express all results in 2000 Canadian dollars.

4.5 Marginal Cost of Fossil Fuel Generation

Consistent with the agreed overall case study approach, the CEOU99 was used to estimate the marginal cost of fossil fuel generation. In this case, the CEOU99 forecast price of electricity in 2010 was used as the marginal cost of electricity in 2010. As the RFF model requires a single electricity price, the electricity price estimates for 2010 and 2030 were weighted to account for end-user shares of electricity demand. This was done by using the price of electricity and the share of electricity by end users (residential, industrial and commercial) to develop a weighted electricity price for 2010. The resulting weighted electricity price was $25.70/GJ or $0.092/kWh.

4.6 Baseline Emissions Intensity of Fossil Fuel Generation

The baseline fossil fuel emission intensity estimates the emission reductions associated with three possible situations stimulated by the EFR instruments:
  • Reduction in the fossil fuel intensity
  • Increased renewables uptake that displaces fossil fuels
  • Decreased end-use demand due to price change in electricity.
In each of the above situations, the challenge is to determine what type of electricity is backed out or displaced by the RET grid power. This is a very complex area that has been the source of extensive analysis and debate in recent years. Fortunately, this case study was able to rely on the results of a recent (August 2003) study conducted for Environment Canada’s Pilot Emissions Removals, Reductions and Learnings (PERRL) initiative. PERRL is confronted by exactly the same emissions displacement issues as in this case study, namely: when renewable electricity projects displace conventional electricity sources, what type of generation (and associated emissions) are displaced? The Environment Canada study was conducted using ICF’s proprietary modelling tool, IPM (Integrated Planning Model),24 to project the type of capacity and related emissions likely to be displaced with the integration of renewable energy products. The IPM distinguishes between two categories of electricity generation: “baseload” and “on-margin.” The PERRL analysis assumes that renewable sources will primarily displace the fuels used for on-margin generation. Excerpts from the PERRL report, which identify the major assumptions related to on-margin generation in each province, are provided below.25
  • Alberta, Saskatchewan, Nova Scotia and New Brunswick are predominantly powered by coal on the margin with some instances of natural gas. The situation is slightly different in New Brunswick, where Orimulsion plays a large part in total generation and is dispatched regularly on the margin. Historically, Alberta is a winter-peaking province with a coal-based grid. Therefore, less gas would be used during the summer, low-demand months as shown by the coal capacity on-margin in those months. In Saskatchewan, ICF assumes that in shoulder months coal units are less available due to outages for maintenance, etc. This lower availability, combined with import and gas prices, creates the situation where U.S. imports are on-margin during shoulder months.26  
  • Ontario is a more diversified province in its electricity generation, and a mix of fuels is seen on the margin. Ontario is typically nuclear and hydro baseloaded, with mainly coal and imports on the margin. Unlike current conditions in Ontario, the added nuclear power post-2004 reduces reliance on coal and U.S. imports. Therefore, coal and imports are often the on-margin units, not baseload. The changes in Ontario’s on-margin capacity type – that is, bituminous, lignite and U.S. imports – reflect the forecast changes in demand (increasing over the years), environmental constraints (NOx and SOx tighter caps, and pending carbon constraints) and fuel and power prices. The combination of these factors shifts the on-margin capacity type over time.  
  • British Columbia, Manitoba and Quebec are supplied power mainly through their baseloaded hydro operations. In British Columbia, biomass rather than gas is dispatched on-margin as a result of relatively low provincial demand and little fossil capacity. Natural gas does, however, cover the highest-demand periods in the winter months. Manitoba’s marginal power generation is dominated by coal, except in the peak months of December and January when demand becomes high enough to acquire U.S. imports. Quebec has very low fossil fuel use and therefore has very low emission factors. The province relies mainly on biomass and landfill gas for marginal generation in the high- and low-demand months, respectively; however, during higher-demand periods, U.S. imports are on-margin.
Manitoba and Quebec are similar in their heavily baseloaded hydro systems; however, Manitoba’s on-margin unit is almost always coal, with the exception of one month of importing. Manitoba is a winter-peaking province. Therefore, December is the highest-demand month and it is reasonable that they may have to import during this peak period. This gives Manitoba an emission factor of 1.02. Quebec’s on-margin units are most often biomass and to a lesser extent landfill gas. Quebec does have approximately 1.5 GW of oil and combustion turbine capacity, but they are rarely used. A Statistics Canada publication, Electric Power Generation, Transmission and Distribution, 1999, indicates that they have a less than 3% capacity factor. This translates to approximately 250 hours per year. It is expected that the most conservative influence that gas would have on the emission intensities would be a gas value of less than 0.40 kg/kWh in the peak month of December. The Environment Canada study identified the monthly on-margin type of generation that would be displaced by increased renewables uptake. The results were developed for each province on a monthly basis for the 2004 to 2007 period; the study then developed emission intensity coefficients that corresponded with each of these situations. As the RFF model works on the basis of a national emissions intensity factor, the detailed monthly and provincial on-margin generation and emission intensities in the Environment Canada study were used to develop a weighted national emission intensity coefficient. The results are shown in Exhibit 4.1.
Exhibit 4.1
Weighted Emission Intensity for Canada  

Year
Emission Intensity
[tonnes CO2/MWh]

2004

0.47

2005

0.50

2006

0.61

2007

0.53
Since the base year is 2010 for this case study, and the Environment Canada report only provides data for 2004 to 2007, there were three choices for estimating the emission intensity in 2010:
  • Use the average of 2004 to 2007, which is 0.53 tonnes CO2/MWh.
  • Use the 2007 estimate as the 2010 value, which is 0.53 tonnes CO2/MWh.
  • Forecast an estimate for 2010 based on a trend in the 2004 to 2007 data, which is 0.66 tonnes CO2/MWh.
To be conservative, this case study selected 0.53 tonnes CO2/MWh.

4.7 Carbon Abatement Cost Curve for Fossil Fuel Generation

This parameter is used to identify the amount of carbon abatement for a given carbon price that will come from the electric power sector. It is a benchmark, where firms in the electrical sector that are faced with an emission constraint compare the internal cost of emission reductions with the cost of reductions from the renewables sector. The carbon abatement curve for the electrical sector is based on existing plant displacement, where emission reduction constraints force GHG-intensive generation to be substituted with less GHG intensive generation. Abatement costs are then estimated by comparing the full costs of the new generation (i.e., capital and variable) versus the variable costs for the existing plant. When estimating the cost of carbon reductions in Canada, the marginal cost of carbon reductions for the electrical sector includes the full cost of a combined-cycle gas plant (the typical gas plant in future periods) minus the variable cost of coal (i.e., current coal fuel costs must be accounted for in the carbon reduction cost). Similarly, the incremental emission reduction from a gas plant is simply the emission intensity for coal minus the emission intensity for gas. The marginal abatement cost curve is then a combination of displaced emissions and incremental costs, where emission reductions are supplied up to the maximum reductions available from coal (i.e., the point at which coal is totally displaced by gas). In reality this point would not be reached, and indeed in this analysis the emission reduction target is not expected to approach the total displacement of coal. It is perhaps surprising that a reliable estimate of the carbon abatement curve is not available for Canada. That said, the incremental emission reductions and costs that comprise the elements of the carbon abatement cost curve can be readily estimated. First, incremental emission reductions are estimated by province when coal plants are displaced by gas plants under a binding EFR instrument. The provincial differences in the incremental emission reductions when coal is displaced for gas are provided in Exhibit 4.2. The cumulative (or total potential) carbon removed in tonnes per CO2/MWh is also provided.
Exhibit 4.2
Incremental and Cumulative Emission Reduction  
 

Tonnes CO2/MWh

      

Province

Coal

Gas

A. Reduction: Coal minus Gas

B. Coal Production 2010 MWh
 
A*B =
Tonnes CO2/MWh Removed

Cumulative CO2MWh Removed

SK

1.4

0.45

1.09

13,331,000

14,497,463

14,497,463

ON

1.15

0.45

0.70

44,301,000

30,899,948

45,397,410

MB

1.02

0.45

0.57

310,000

175,925

45,573,335

AB

1.02

0.45

0.57

39,678,000

22,517,265

68,090,600

Source: ICF, 2003.   Second, Exhibit 4.3 provides the data and calculations used to estimate the incremental and cumulative cost of CO2 reduced.
Exhibit 4.3
Incremental and Cumulative Costs27  
 
A. Incremental Gas Plant $/kWh
B. Coal Production 2010 MWh
A*B =
Incremental Cost* $/MWh
Cumulative Cost $/MWh
 

SK

0.0189

13,331,000

$251,428,000

$251,428,000

ON

0.0173

44,301,000

$765,568,000

$1,016,996,000

MB

0.0177

310,000

$5,497,000

$1,022,493,000

AB

0.0194

39,678,000

$769,446,000

$1,791,939,000

* Numbers may not add due to rounding.   Source: ICF, 2003. The carbon abatement cost curve is estimated by regressing cumulative tonnes removed in Exhibit 4.2 against cumulative cost in Exhibit 4.3. The values in the tables are expressed as average costs but are easily converted to total costs:
where
  From our data, we estimate by ordering the abatement options by region according to cost effectiveness, and then regressing cumulative abatement costs on . The resulting coefficient has a very good fit and needs only be scaled by total production to produce the parameter to reflect the increase in marginal generation costs from a unit decrease in emissions intensity.

4.8 Price Elasticity of Demand for Electricity

Elasticities are useful to determine the change in end-use electricity demand associated with electricity price increases. In the model, the elasticity is required when the EFR instruments affect end use by increasing the price of electricity. If the electricity price increases, it can be expected that there will be some demand response to the increased price. The model is designed to capture emission changes that stem from the changes in end use that are attributable to the EFR instrument. Thus, a nationally usable price elasticity of demand is required that can be applied across the end users. The aggregate elasticity estimate must reflect regional as well as end-use differences; however, such elasticities are not readily available in Canada. Short-run elasticities for end users found in the literature range between -0.03 and -0.70. Based on both the literature review and our own analysis, the short-run price elasticity for electricity is expected to be low, indicating that end users are not that responsive to price changes. Over the longer term, however, price responsiveness is much greater. Based on these considerations, a price demand elasticity for electricity of -0.3 was selected. This implies that a 10% price increase in electricity will result in a 3% drop in demand.

4.9 Return on R&D Investment (ROI)

The ROI is used in the model to capture investments by the renewables firms in renewable technologies. Firms will continue to invest in R&D as long as the ROI objective is satisfied. ROI objectives of firms vary, with no clear guidance on the level firms require. A range of possible ROI estimates, including 30%, 40% and 50%, was considered. To be conservative, and to reflect the comments made during the Scoping Session, 30% was used in the model.

4.10 Current Expenditures on Renewables R&D in Canada

This parameter is used to account for increased production levels (renewables uptake) that may occur when R&D expenditures are increased. For the model, current and projected R&D expenditures on renewable technologies are required to forecast the value of R&D expenditures by the renewables sector, including government expenditures, in 2010. According to the latest industrial R&D survey by Statistics Canada, R&D expenditures on renewable resources energy technologies were $91 million in 2001.28 This includes $66 million in company self-funding, $11 million in government funding and $13 million from “other sources.” Growth rates and projections for the R&D expenditures of the renewable resources energy sector are not readily available, but growth can be inferred from a number of indicators, including company self-funded R&D, government renewables funding, and other sources. Further discussion of each indicator is provided below:
  • Company self-funded R&D expenditures in the light manufacturing and electric utility sectors ranged between 0.6% and 1% of revenues between 1999 and 2001. This range can be used in conjunction with a projection of revenue for renewable energy from the CEOU99 to estimate a proxy for RET R&D in 2010. Revenue to RETs from the CEOU99 was estimated by taking the weighted price of electricity (weighted for industrial, commercial and residential prices by their share of end-use sales) and the share of renewables in the base-case CEOU forecast. By assuming a ratio of R&D expenditures to renewables revenue of 0.8%, the base level of private sector renewables R&D investment was estimated to be $61 million in 2000 and $84 million in 2010. This implies a growth rate in expenditures in the order of 3.6% per year in RET R&D between 2000 and 2010.  
  • Government funding on renewable energy technologies increased 15% between 1990 and 1999, at an annual growth rate of 1.5%.29 However, this number doubled between 1999 and 2002.30 When this is considered, the annual growth rate between 1990 and 2002 was in the order of 6%. This estimate is used as a proxy for future growth in the government portion of the 2001 industrial R&D survey estimate (of $11 million), indicating government expenditures in renewable R&D to be in the order of $20 million in 2010. This is likely a conservative estimate given recent R&D funding programs under the National Climate Change Action Plan.  
  • The “other sources” portion of renewable resources technology R&D in 2001 is grown at the company self-funded rate of 4% per year between 2001 and 2010. The $13 million in other sources R&D would then grow to $14.9 million in 2010.
Based on the above indicators, total R&D expenditures in 2010 for RETs were forecast to be in the range of $129 million. This implies a growth of 42% in overall RET R&D spending between 2001 and 2010, or 4% per year.

4.11 Baseline Demand for Electricity from Fossil Fuel

The demand for electricity generated by fossil fuels is the reference point from which changes in emissions, demand and renewables share of generation are estimated. In addition, the quantity of electricity generated from fossil fuels is a key determinant that is used to estimate the costs attributable to the EFR instruments. The forecast quantity of electrical generation from fossil fuel is taken directly from the CEOU99 and is 197,728 GWh in 2010.31 The CEOU99 does not forecast a quantity for 2030, and therefore the case study prepared an estimate of electrical generation from fossil fuel for 2030. Using the fossil fuel generation data in the CEOU99, a value for 2030 was estimated using a linear regression (see Exhibit 4.4). Using the equation shown in Exhibit 4.4, which was estimated from the share of fossil fuel generation from 1990 to 2020, forecast fossil fuel generation was estimated to be 316,628 GWh in 2030.
Exhibit 4.4
Estimated Baseline Fossil Fuel Electricity Generation in 2030   Estimated Baseline Fossil Fuel Electricity Generation in 2030 Exhibit 4.5 provides a summary of the electricity output in Canada that is covered under this case study. This study covers 37% of electrical generation in Canada in 2010.
Exhibit 4.5
Projected Share of Grid-Power RETs and Fossil Fuel Generation in 2010  

Electricity-Generating Technology

Projected Electricity Generation in 2010 [GWh]

Percent of Total Generation
Grid-Power RETs
(as included in this study)

31,000*

5%

Fossil Fuels (coal, gas, oil as included in this study)

198,000**

32%

Other (nuclear and renewables excluded from this study)

394,000

63%

TOTAL

623,000**

100%
* Canada’s Energy Future: Scenarios for Supply and Demand to 2025 (Techno-Vert Scenario), National Energy Board, 2003 http://www.neb-one.gc.ca/energy/SupplyDemand/2003/index_e.htm. ** Canada’s Emissions Outlook: An Update, Natural Resources Canada, 1999 http://www.nrcan.gc.ca/es/ceo/update.htm.

4.12 Baseline of Annual Renewable Energy Supplied

As with fossil fuel generation, the baseline quantity of generation from renewables is an important reference point for the model, as it is from this starting point that changes in the share of renewables in the generation mix are measured. Unfortunately, the CEOU99 is flawed with respect to the forecast of renewables and an alternative source was required. Consequently, we used the growth rate in grid-power RETs that is used by the National Energy Board, 200332 and applied this value to the CEOU99. In the NEB, 2003, renewables grow by 512% between 2000 and 2025. This implies that renewables growth will be in the order of 600% between 2000 and 2030. Applying this estimate to the actual 1997 value reported in the CEOU99 produced a baseline grid-power RET generation of 31,000 GWh in 2010 and 97,000 GWh in 2030, which were assumed in the model.
Exhibit 4.6
Estimated Baseline Grid-RET Electricity Generation in 2030  

4.13 Carbon Price

The carbon price is set in the model to establish an emission reduction (or environmental) target. The carbon price is linked to the carbon abatement cost curve so that an emission reduction target is identified at a given carbon price. Setting the carbon price is important for the analysis since it identifies the emission reduction target that all of the EFR instruments must achieve. That is, the model ensures that the instruments are compared on a consistent basis through the identification of a carbon reduction target. The carbon price is expressed in dollars per tonne reduced and is set at $10/tonne. The following section presents the case study methods and results.