Greening the Saskatchewan Grid:
Comparative Analysis of the Costs and Effectiveness of Three Policy Approaches to
Lowering Greenhouse Gas Emissions in the Saskatchewan Electricity Sector
Brett Dolter
1 Greening the Saskatchewan Grid
1. Introduction
In recent years, the Saskatchewan electricity sector has registered the highest per capita
electricity sector greenhouse gas emissions (GHGs) in Canada (See Figure 1).
20.0
Electricity GHG Emissions per capita (tonnes CO2e/person)
18.0
16.0
14.0
NL
PEI
12.0
NS
NB
PQ
10.0
ON
MB
8.0
SK
AB
6.0
BC
YT
4.0
NWT & NT
2.0
10
20
11
09
20
08
20
07
20
06
20
05
20
04
20
03
20
02
20
01
20
00
20
99
20
98
19
97
19
96
19
95
19
94
19
93
19
92
19
91
19
19
19
90
0.0
Year
(Data source: Environment Canada NIR, 2013 for electricity GHG data; Statistics Canada, 2014 CANSIM
table 051-0001 for population data; author’s calculations)
Figure 1 – Canadian Per Capita GHG Emissions in the Electricity Sector
Saskatchewan’s electricity is generated primarily by coal-fired power plants, natural gas
turbines (including natural gas fired cogeneration facilities), hydroelectric dams and a
small but growing number of wind farms (See Figure 2). Crown utility SaskPower owns
2 much of the generation fleet, but also purchases power from wind farms and natural gas
fired cogeneration facilities operated by the potash and oil and gas pipeline industries.
Electricity Generation (GWh '000s)
25
20
15
Other
Imports
Wind
Hydro
10
Gas
Coal
5
0
2004
2005
2006
2007
2008
2009
2010
2011
2012
Year
(Data source: SaskPower, 2009 & SaskPower, 2013))
Figure 2 – Saskatchewan Electricity Generation by Fuel Type
SaskPower has an opportunity to transform its electricity generation mix substantially as
aging generation stations reach the end of their useful lives (SaskPower, 2011). Figure 3
displays Saskatchewan’s electricity generation capacity and the scheduled retirements
that will lead that capacity to decrease in the coming years.
3 4500
4000
Installed Net Capacity (MW)
3500
3000
2500
Cogeneration
Wind
2000
Hydro
Natural Gas
1500
Coal
1000
500
48
46
44
42
40
38
36
34
32
30
28
26
50
20
20
20
20
20
20
20
20
20
20
20
20
22
20
18
16
24
20
20
20
20
20
20
20
14
0
Year
(Data source: SaskPower, 2011)
Figure 3 –Electricity Capacity in Saskatchewan and Scheduled Retirements
Saskatchewan must also meet the increasing electricity needs of a growing population
and a growing economy. Forecasts indicate that electricity demand in Saskatchewan will
increase by approximately 15,000 Gigawatt-hours (GWh) by 2050 (SaskPower, 2013;
SaskPower, 2011; author’s calculations). Peak demand will also increase by
approximately 1400 Megawatts (MW) in the same period (SaskPower, 2013; SaskPower,
2011; author’s calculations).
4 In this paper I seek to identify the least cost path of meeting Saskatchewan’s future
electricity generation needs in the short, medium, and long-term.1 I also work to
understand the cost and effectiveness of three greenhouse gas emission reduction policy
scenarios for Saskatchewan’s electricity sector. The scenarios are as follows:
1. Carbon Tax – A carbon tax is set at $15 per tonne carbon dioxide equivalent
(CO2e) in the short-term, rising to $25/tonne CO2e in the medium term, and
$35/tonne CO2e in the long-term.
2. Renewable Portfolio Standard - An increasing renewable portfolio standard is
imposed on Saskatchewan’s electricity sector requiring 30% renewable electricity
generation in the short-term, 40% renewable generation in the medium-term, and
50% renewable generation in the long-term.
3. Regulation – The Saskatchewan electricity sector must meet increasingly strict
GHG regulations, achieving a 20% reduction below the short-term baseline
business-as-usual (BAU) emissions in the short-term, a 50% reduction below
BAU in the medium-term, and an 80% reduction below the short-term BAU in the
long-term.
To conduct this analysis I use a non-linear programming approach over three time steps.
In each time step the objective is to minimize the cost of meeting Saskatchewan’s
electricity needs. The three time steps allow for the growth of electricity demand and the
phased retirement of existing electricity generation infrastructure.
1
The planning periods are approximations of SaskPower’s planning process (SaskPower, 2011).
Short-term means approximately 3-5 years; medium-term 10-15 years; and long-term 25-30
years.
5 This paper proceeds as follows; in section two I discuss the linear programming and
nonlinear programming approach to energy policy modeling and key examples of its use
in electricity policy analysis. In section three I describe the model created for this
exercise. Results and a discussion of my analysis are presented in section four. Section
five concludes by clarifying the limitations of the current study and outlining next steps
for the research.
2. Linear and Nonlinear Programming
The linear and nonlinear programming approaches to energy policy analysis are both
examples of constrained optimization (Chinneck, 2001). In the constrained optimization
approach an objective function is specified that mathematically represents a desired goal.
The linear or nonlinear program then works to optimize the objective function by
changing the values of decision variables.
In a linear program the decision variables affect the objective function in a linear fashion;
decision variables cannot affect each other in a multiplicative way. In a nonlinear
program the decision variables can affect each other multiplicatively. This introduces a
degree of uncertainty into the analysis; the nonlinear program solver finds a local
optimum, but there is no guarantee that this is a global optimum. The nonlinear program
solver can be sensitive to the initial starting values of decision variables and finding a
globally optimum solution may require changing these initial values.
6 In both linear and nonlinear programming the values of the decision variables (or other
functions influenced by the values of the decision values) are constrained to meet certain
conditions. (Chinneck, 2001)
In this paper the objective function is a calculation of the annual operating costs of the
Saskatchewan electricity sector (See Section 3). The goal of this exercise is to minimize
these annual operating costs by changing the values of two decision variables; investment
in electricity generation capital, and the hours that available electricity generation
facilities operate within a year. These two decision variables affect each other in a
multiplicative way so a nonlinear programming approach is required.
Without constraints, the nonlinear program could minimize annual operating costs simply
by setting the value of investment and hours to zero. Constraints are required to create a
non-trivial solution. In this model there are constraints in each time step requiring the
electricity system to supply a minimum level of electricity; constraints to ensure that
adequate capacity is available to meet peak electricity demand; constraints to limit the
feasible annual run-time of electricity generation facilities; and specialized constraints
relevant to specific electricity generation options such as wind and hydro-electricity.
Constraints are also used to specify the renewable portfolio standard policy and the GHG
regulation policy. A full presentation of the constraints is included in Section 3.
Constrained optimization is commonly used in energy policy analysis. The MARKALTIMES family of linear and nonlinear programming energy models have been developed
7 and refined over the past thirty years by the International Energy Agency (IEA) and
associated researchers (Loulou, Goldstein and Noble, 2004). The MARKAL models offer
a prescriptive method of optimizing the energy technologies that will meet the demand
for energy using services (Jaccard, Loulou, et al., 2003; Loulou et al., 2004). MARKAL
is “technologically explicit” (Jaccard, Loulou, et al., 2003: 149) in that it provides
detailed cost and operating information about a range of technologies, including
electricity generation technologies (Loulou et al., 2004).
MARKAL has inspired other optimization modeling efforts. Henning & Trygg (2008)
use the MODEST linear programming model, which they state is “similar to MARKAL”
(p. 2338), to analyze demand side management potential in the Swedish electricity sector.
Howell et al. (2011) have introduced an open-source optimization model for energy
policy called OSeMOSYS. With OSeMOSYS Howell et al. (2011) endeavor to reduce
the financial barriers to optimization modeling. In this essay I construct a nonlinear
programming model in Microsoft Excel and use the built-in ‘Generalized Reduced
Gradient’ algorithm in Solver to solve the model (Solver, 2014).
This paper builds on other optimization studies of Canadian electricity and sustainability
policy. Benitez et al. (2008) use a nonlinear mathematical optimization program to
analyze the impact of increasing wind electricity penetration on the Alberta electricity
grid. They find that a high penetration of wind electricity can lower GHGs in Alberta at a
cost of between $41 – $56/tonne CO2e. The higher wind penetration will also require
additional natural gas peaking plant capacity for times when wind energy is not available.
8 Lin, Huang et al., (2010) developed a MARKAL-based energy systems planning
optimization model to analyze GHG reduction policy in Saskatchewan. The
Saskatchewan model includes the electricity sector and other energy sectors such as oil
and gas extraction and coal mining. Lin, Huang et al. (2010) determine that the electricity
sector would be the least-cost sector in which to reduce Saskatchewan’s GHGs.
3.0 Model Description
The model used in this essay is much simpler than other models in the literature. Figure 4
presents the MARKAL model structure. The electricity sector is a small part of this
model. It depends upon the resource and processes sectors for inputs, and meets demands
for end-use energy services. In MARKAL, these demands respond to changes in energy
prices (Loulou et al., 2004).
(Seebregts et al., 2001; modified by the author)
Figure 4 – MARKAL Model Structure
9 The model used in this paper simplifies the MARKAL structure substantially by
assuming that demand for electricity is constant and that inputs such as coal and natural
gas are available in unlimited quantities and at constant prices. Figure 5 presents the
simplified model structure used in this analysis.
Electricity
Generation
Fuel
Electricity
Demand
Figure 5 – Model Structure for this Analysis
3.1 Scenarios and Time Steps
The simplified model is used to analyze four scenarios: business-as-usual (BAU); as well
as the three sustainability policy scenarios outlined in section 1: carbon taxation,
renewable portfolio standard, and GHG regulation (See Table 1 below).
For each scenario the least cost investment and operations strategies are calculated for
three time periods, which coincide with SaskPower’s (2011) short-, medium-, and longterm planning horizons. Electricity demand and peak electricity generation requirements
grow throughout the three time steps as summarized in Table 1 below.
10 Short
Medium
Long
System Requirements
Policy Scenarios
Electricity
Peak
Carbon Tax Renewable GHG Regulation
Demand
Demand
($/tonne
Portfolio
(% below Short(GWh)
(MW)
CO2e)
Standard
term BAU)
25,000
4000
$15
30%
20%
30,000
4600
$25
40%
50%
35,000
5400
$35
50%
80%
Table 1 – Model System Requirements and Scenarios
The detailed retirement schedule presented in Figure 3 is simplified to align with the
three time steps in this analysis (See Figure 4). Retirements within each planning period
are subtracted from capital capacity at the beginning of each time step.
3500
Generation Capacity (MW)
3000
2500
2000
Wind
Hydro
1500
Gas
Coal
1000
500
0
Now
Short
Medium
Long
Planning Horizon Time Steps
(Data source: SaskPower, 2011; author’s calculations)
Figure 6 – Simplified Schedule of Retirements
11 Investment decisions made in a previous period lead to carry-over electricity generation
capacity in future periods. The stock of electricity generation capacity at the beginning of
each period is thus equal to:
𝐸𝑞. 1 𝑆𝑡𝑜𝑐𝑘!" = 𝑆𝑡𝑜𝑐𝑘!(!!!) + 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡!(!!!) − 𝑅𝑒𝑡𝑖𝑟𝑒𝑚𝑒𝑛𝑡!(!) Where subscript k refers to the type of electricity generation technology, t refers to the
current time period, t-1 refers to the previous time period, and all units are in megawatts
(MW). The capital stock thus provides a memory of previous investments in each
electricity generation technology.
A further simplification involves optimizing the investment and operation strategy within
each time step. This approach does not seek an inter-temporal optimization where
decisions made in earlier stages are optimal for later stages. Instead the model optimizes
the objective function within each time step, which can lead to sub-optimal capital
residuals in future time steps. This approach bears some resemblance to the ‘bounded
rationality’ approach of simulation models like CIMS (Jaccard, Loulou, et al., 2003).
3.2 Objective Function
Drawing from the MARKAL model described by Loulou (2004: 42) the objective
function in the Saskatchewan Electricity Model (SEM) is presented in Equation 2.
12 𝐸𝑞. 2 𝐴𝑛𝑛𝑢𝑎𝑙𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝐶𝑜𝑠𝑡𝑠
=
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡! ∗ 𝐴𝑛𝑛𝑢𝑎𝑙𝑖𝑧𝑒𝑑𝐶𝑎𝑝𝐶𝑜𝑠𝑡! + 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑆𝑡𝑜𝑐𝑘!
!
∗ 𝐹𝑖𝑥𝑒𝑑𝑂&𝑀! + 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦! ∗ (𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑂&𝑀! + 𝐹𝑢𝑒𝑙𝐶𝑜𝑠𝑡! )
+ 𝐺𝐻𝐺𝑠! ∗ 𝐶𝑎𝑟𝑏𝑜𝑛 𝑇𝑎𝑥
Where,
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡! = new investment in a generation technology k ($),
𝐴𝑛𝑛𝑢𝑎𝑙𝑖𝑧𝑒𝑑𝐶𝑎𝑝𝐶𝑜𝑠𝑡! = the annualized cost of capital for technology k ($),
𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑆𝑡𝑜𝑐𝑘! = total capital stock including existing and new investment for each
technology k (Megawatts - MW),
𝐹𝑖𝑥𝑒𝑑𝑂&𝑀! = annual fixed operation and maintenance (O&M) costs of capital for each
technology k ($/MW),
𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦! = annual electricity generated in the present time-step by technology k
(Megawatt-hours – MWh),
𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑂&𝑀! = annual variable operating and maintenance costs for each technology k
($/MWh),
𝐹𝑢𝑒𝑙𝐶𝑜𝑠𝑡! = fuel cost for technology k ($/MWh),
𝐺𝐻𝐺𝑠! = total GHGs in time period t (tonnes CO2e),
𝐶𝑎𝑟𝑏𝑜𝑛 𝑇𝑎𝑥 = a tax placed on GHG emissions ($/tonne); this was set to zero in all but
the escalating carbon tax policy scenarios where it escalated from $15/tonne CO2e in the
short-term, to $25/tonne CO2e (medium-term), and $35/tonne CO2e (long-term).
Splitting the annual costs of the electricity system into four different cost types allows
flexibility in the model. It is possible, for example, for an optimum solution to involve
13 purchasing low-cost natural gas single-cycle peaking turbines to meet the peak demand
requirement constraint (see below) but for these turbines to also run for zero hours and
provide zero electricity due to their high variable O&M and fuel costs.
3.3 Available Technologies
Table 2 summarizes the technologies available in this model and their associated costs.
All values are in 2013 Canadian dollars ($CDN). Coal CCS stands for coal with carbon
capture and storage. Natural gas CC refers to combined-cycle natural gas turbines.
Natural gas SC refers to single-cycle natural gas turbines that are most often used in
peaking applications. Solar refers to solar photovoltaic technology. The electricity costs
in $/kilowatt-hour (kwh) are calculated by assuming that a 1 MW installation operates at
the capacity factor indicated in Table 2 for an entire year; the total annualized cost of
operating the system is then divided by the total amount of electricity generated. GHG
intensity figures are derived from SaskPower (2013) with modifications based on
Delucchi & Jacobson (2011). Please see Appendix A for further details on how the costs
in Table 2 were derived.
Technology
Coal
Coal CCS
Natural gas CC
Natural gas SC
Hydro
Wind
Solar
Annualized
Fixed O&M
Capital ($/MW) ($/kW/year)
$
202,950 $
33.69
$
344,758 $
56.44
$
93,487 $
14.32
$
59,650 $
8.10
$
221,095 $
16.68
$
189,637 $
37.08
$
595,437 $
14.29
Variable
O&M
($/MWh)
$
5.63
$
5.38
$
2.45
$
11.93
$
2.94
$
$
-
Fuel Cost Capacity
($/MWh)
factor
15.61
74%
18.04
74%
26.76
42%
45.50
38%
$
65%
$
38%
$
21%
GHG Intensity
$/kwh
(g CO2e/kwh)
0.058
1.35
0.085
0.2025
0.059
0.45
0.078
0.735
0.045
0
0.068
0
0.331
0
Table 2 - Electricity Generation Options
14 3.4 Constraints on the Objective Function
To motivate a non-trivial solution to the model constraints must be introduced into the
nonlinear program. Constraints common to each scenario are as follows:
1. 𝑇𝑜𝑡𝑎𝑙 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦! 𝐺𝑊ℎ ≥ 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦! (𝐺𝑊ℎ)
2. 𝑃𝑒𝑎𝑘𝑖𝑛𝑔 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦! (𝑀𝑊) ≥ 𝑃𝑒𝑎𝑘 𝐷𝑒𝑚𝑎𝑛𝑑 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡! (𝑀𝑊)
3. 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐻𝑜𝑢𝑟𝑠! ≤ 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝐹𝑎𝑐𝑡𝑜𝑟! % ∗ 8760 (ℎ𝑟𝑠) ∗ 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝑀𝑊)
4. 𝐻𝑦𝑑𝑟𝑜 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝑀𝑊) ≤ 1000 𝑀𝑊
5. 𝑊𝑖𝑛𝑑 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐺𝑊ℎ ≤ 20% ∗ 𝑇𝑜𝑡𝑎𝑙 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 (𝐺𝑊ℎ)
The first constraint requires the total electricity generated in the program to equal or
exceed the required electricity in terms of GWh. The required electricity increases with
each time step, beginning at 25,000 GWh in the short-term, then increasing to 30,000
GWh in the medium-term and 35,000 GWh in the long-term.
In the second constraint peaking capacity refers to the total installed capacity of
generating technologies that can be made available on demand. This includes coal, coal
with carbon capture and storage (CCS), natural gas combined cycle, natural gas single
cycle (peaking) plants, and hydroelectricity. It does not include intermittent generation
technologies such as wind and solar. Peaking capacity must be large enough to meet the
required peak demand. This peak demand begins at 4000 MW in the short-term,
increasing to 4600 MW in the medium-term and 5400 MW in the long-term. Often this
15 constraint was not binding in the policy scenarios as historic assets with low run times
provided peak capacity.
The third constraint is a means of converting the stock of electricity generation capital
measured in MW into a flow of electricity generation measured in Megawatt-hours
(MWh). The number 8760 represents the number of hours in a year. Technologies are not
able to run for all 8760 hours in a year and are limited by technology specific capacity
factors.
The third constraint is also the reason that a nonlinear approach to optimization is
required. As outlined in Equation 1 above, available electricity generation capacity is a
function of inherited capital and new investments. New investment in capacity is one of
the decision variables for which the program must solve. The number of hours that
available electricity generation capacity will run is also a decision variable. This means
that two decision variables are affecting the program in a multiplicative fashion.
The fourth constraint reflects limited hydroelectric potential in Saskatchewan. SaskPower
(2011) notes that hydroelectric potential is located in northern Saskatchewan, far from
large markets, and is limited in quantity. I allow an additional 350 MW of hydroelectric
capacity to be built above and beyond the existing 650 MW for a total of 1000 MW.
The fifth constraint imposes a restriction on the amount of wind-generated electricity that
can be used on the grid. Wind is low-cost and the resource is plentiful in Saskatchewan,
16 but this analysis assumes that the intermittency of the wind electricity resource leads
system planners to allow a maximum of 20% of generated electricity to come from wind.
To encourage realism in the model I constrained new investment in coal to zero. This is
because Saskatchewan is not contemplating new coal facilities unless they are equipped
with carbon capture and storage.
6. 𝐶𝑜𝑎𝑙 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑀𝑊 ≤ 0
Constraints specific to the policy scenarios were also added when applicable:
7. 𝑅𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐺𝑊ℎ ≥ 𝑋% ∗ 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 (𝐺𝑊ℎ)
8. 𝐺𝐻𝐺𝑠 𝑀𝑡 ≤ 𝑋% ∗ 𝑆ℎ𝑜𝑟𝑡𝑡𝑒𝑟𝑚 𝐵𝐴𝑈 𝐺𝐻𝐺𝑠 (𝑀𝑡)
The seventh constraint was used in the renewable portfolio standard analysis. It required
that the sum of electricity generated by hydroelectric, wind, and solar equaled or
exceeded a certain percentage of the total electricity generated. This percentage was set at
30% in the short-term, 40% in the medium-term, and 50% in the long-run.
The eighth constraint was used in the GHG regulation scenarios. It required total GHGs
to be restricted to a certain percentage of business-as-usual GHG emissions in the shortrun, which totaled 14.2 Mt CO2e. The percentage was 80% of BAU emissions in the
short-term, 50% of BAU emissions in the medium-term, and 20% of BAU emissions in
the long-term.
17 4.0 Results
The four scenarios result in different least-cost investment pathways. In the business-asusual (BAU) scenario, hydroelectric electricity production is maximized at 1000 MW
capacity throughout all three time steps. Combined cycle natural gas turbines fill in the
gap as coal plants are retired and demand increases (See Figure 7).
Business as Usual
40,000
Generation Capacity (MW)
35,000
30,000
Imports
25,000
Solar
Wind
20,000
Hydro
Natural gas SC
15,000
Natural gas CC
10,000
Coal CCS
Coal
5,000
2014
Short
Medium
Long
Planning Horizon Time Steps
Figure 7 – Business-As-Usual Electricity Generation
18 Escalating Carbon Tax
40,000
Generation Capacity (MW)
35,000
30,000
Imports
25,000
Solar
Wind
20,000
Hydro
Natural gas SC
15,000
Natural gas CC
10,000
Coal CCS
Coal
5,000
2014
Short
Medium
Long
Planning Horizon Time Steps
Figure 8 – Escalating Carbon Tax
In the carbon tax scenario, the $15/tonne CO2e tax is not high enough in the short-term to
encourage investment to deviate from the BAU scenario. In the medium-term, when the
tax reaches $25/tonne CO2e, the tax does motivate a change in investment strategy and
investments are made in wind turbines (See Figure 8). Investments in wind are expanded
in the long-term to maintain wind electricity production at the maximum allowable level
(i.e. 20% of total generation).
19 Renewable Portfolio Standard
40,000
Generation Capacity (MW)
35,000
30,000
Imports
25,000
Solar
Wind
20,000
Hydro
Natural gas SC
15,000
Natural gas CC
10,000
Coal CCS
Coal
5,000
2014
Short
Medium
Long
Planning Horizon Time Steps
Figure 9 – Renewable Portfolio Standard
The renewable portfolio standard offers another distinct investment pathway (Figure 9).
Some investments are made in wind even in the short-term to meet the 30% renewable
generation requirement. In the long-term the 1000 MW capacity constraint on
hydroelectric electricity and the limitation on wind comprising more than 20% of
electricity generation means that substantial investment is required in solar
photovoltaics.2 The high cost of solar photovoltaics substantially increases operating
costs in the final time step of the renewables scenario. Large cost reductions could be
obtained were the binding constraints on hydroelectric capacity and wind generation
loosened.
2
Whether this should be allowed in the model is debatable; the intermittent nature of solar may
also lead to maximum penetration rates being established. An additional constraint may be
necessary in future iterations of the model.
20 GHG Regulation
40,000
Generation Capacity (MW)
35,000
30,000
Imports
25,000
Solar
Wind
20,000
Hydro
Natural gas SC
15,000
Natural gas CC
10,000
Coal CCS
Coal
5,000
2014
Short
Medium
Long
Planning Horizon Time Steps
Figure 10 – GHG Regulation
GHG regulation is the only scenario in which coal with carbon capture and storage (Coal
CCS) plays a role. Coal CCS is an expensive technology, but is allowed to contribute to
peak demand capacity in the SEM; something that wind and solar cannot do. Coal CCS
also has the lowest GHG-intensity of all of the peak-demand-eligible generation options.
The hazard of choosing an optimal investment strategy within each time period, rather
than across time periods, is illustrated in this scenario. Despite there being 3538 MW of
natural gas combined cycle capacity available in the long-term time step, this capital is
made to sit idle in order to meet the restrictive 80% below BAU GHG regulation. An
inter-temporal optimization would have allowed for greater foresight and a different
investment strategy in the short-term and medium-term. This also points to the
importance of long-term and consistent policy signals; the bounded rationality present in
21 SEM may be a useful approximation of the current climate policy context in
Saskatchewan and Canada where long-term policy guidance on GHG reductions is
absent.
Short
Medium
Long
$
$
$
Net Operating Costs (2013 $Millions)
BAU
Carbon Tax
Renewables
868 $
868 $
879
1,015 $
1,066 $
1,105
1,181 $
1,040 $
2,259
Regulation
$
947
$
1,111
$
3,809
Table 3 – Net Operating Costs by Scenario and Time Step
Table 3 presents the net operating costs of each scenario and time step. Regulation is
clearly the most expensive scenario, followed by the renewable portfolio standard.
Interestingly, the carbon tax scenario has a lower net cost in the long-term than BAU.
This is because the net cost of the carbon tax scenario involves the total operating cost
including carbon tax payments minus the carbon tax payments. It is assumed that these
carbon tax payments are collected without cost from the utility and redistributed as
spending for public priorities or as lump-sum payment to Saskatchewan citizens. The
carbon tax provides an incentive for GHG reduction but, due to the redistribution of the
tax revenue, it is the gentlest scenario in terms of net operating cost.
4.1 Greenhouse Gas Emission Scenarios
The effectiveness of the sustainability policies is evaluated using greenhouse gas
emissions totals. Though this is a narrow conception of sustainability, and misses
important impacts on land, water, air, and ecosystems, it does offer an important test of
the sustainability policy scenarios.
22 Greenhouse Gas Emissions Scenarios
Greenhouse Gas Emissions (Mt CO2e)
18
16
14
12
10
BAU
Carbon Tax
8
Renewables
6
Regulation
4
2
0
Present
Short
Medium
Planning Horizon Time Steps
Long
Figure 11 – Greenhouse Gas Emissions
Figure 11 presents the GHG emissions resulting from all four scenarios. Even without
policy action, GHGs are set to fall as coal plants are retired and replaced by natural gas
facilities. The carbon tax scenario leads to reductions in GHGs relative to BAU, but has
the least impact of the three sustainability scenarios. Higher carbon taxes would be
required to make additional GHG reductions.
The renewable portfolio standard policy leads to reductions similar to the carbon tax
scenario in the short- and medium-term. A substantial decrease is then made in the longterm when 50% of electricity must be generated by renewable sources. Most surprising
might be that the renewable scenario only manages to reduce GHGs by 50% from the
2014 starting point. This is because no restrictions were placed on the other 50% of
23 electricity generation. This result highlights the importance of a well-rounded GHG
reduction policy; renewable portfolio standards alone risk the continuation of GHGintensive electricity generation outside of the renewable portfolio.
GHG regulation offers the greatest reductions in GHG emissions. This is of course a fait
accompli of the nonlinear optimization program; GHG emissions were constrained in the
optimization routine. It highlights, however, that substantial GHG emissions reductions
are possible when GHG reduction is made to be an important (and binding) objective.
The cost of GHG reduction in each scenario can be calculated by dividing the
incremental net operating cost relative to BAU by the GHG reductions relative to BAU.
Table 4 presents the results. Note again that the cost of GHG mitigation in the final
period of the carbon tax scenario is negative reflecting the lower net operating cost of that
scenario relative to BAU. Also note the influence of solar photovoltaics in the final step
of the Renewables and Regulation scenarios. GHG mitigation costs take a substantial
jump when solar is used to replace natural gas generation.
Short
Medium
Long
GHG Reduction Cost ($/tonne CO2e)
BAU
Carbon Tax
Renewables
$
21
$
21 $
36
$
(46) $
206
Regulation
$
28
$
15
$
256
Table 4 – GHG Mitigation Costs by Scenario and Time Step
24 5.0 Limitations and Next Steps
Optimization models have inherent limitations. One of these is that they will always
choose the lowest cost generation option, but this can lead to “penny-switching” where
large swings in investment and operation occur (Jaccard, Loulou et al., 2003: 153). In a
future SEM model I can work to mitigate penny-switching by imposing market share
constraints on each technology (Jaccard, Loulou et al., 2003).
Future versions of SEM can also explore the possibility of inter-temporal optimization.
This would model decision-making as if SaskPower had perfect foresight and would
avoid the type of idle capital seen in the final time step of the GHG Regulation scenario.
In this model, the technology costs were fixed across time. This is unrealistic. It is very
likely that costs will change over time. Solar in particular is expected to decrease in cost
over the coming decades (Delucchi & Jacobson, 2011). Future models can incorporate
changing costs to generation technologies, perhaps in a ‘learning-by-doing’ format. It
would also be interesting to explore the addition of technologies such as renewable
energy storage that could allow wind to play a larger, unconstrained role in the electricity
system.
This analysis could be enhanced by a sensitivity analysis. Factors that should be
considered in this sensitivity analysis include: the discount rate, the cost of natural gas,
and cost decreases for renewables.
25 Future work will involve adding macroeconomic feedback into the model so that
forecasts of electricity demand are not exogenous and instead interact with electricity
prices to reach an equilibrium between supply and demand.
26 References
Bank of Canada. (2014). Monthly Exchange Rates. Available on-line at:
http://www.bankofcanada.ca/rates/exchange/monthly-average-lookup/. Last accessed
April 15, 2014.
Benitez, Liliana, Pablo Benitez, G. Cornelis van Kooten. (2008). “The economics of wind
power with energy storage.” Energy Economics. 30, pp. 1973-1989.
Chinneck, John. (2001). Practical Optimization: a Gentle Introduction. Available on-line
at: http://www.sce.carleton.ca/faculty/chinneck/po.html.
Delucchi, Mark and Mark Jacobson. (2011). “Providing all global energy with wind,
water, and solar power, Part II: Reliability, system and transmission costs, and policies.”
Energy Policy. 39, pp. 1170-1190.
Energy Information Administration (EIA). (2013a). Assumptions to the Annual Energy
Outlook 2013. U.S. Department of Energy: Washington, DC.
Energy Information Administration (EIA). (2013b). Updated Capital Cost Estimates for
Utility Scale Electricity. U.S. Department of Energy: Washington, DC.
Energy Information Administration (EIA). (2012). Annual Energy Review 2012. U.S.
Department of Energy: Washington, DC.
27 Environment Canada. (2013). National Inventory Report 1990-2011: Greenhouse Gas
Sources and Sinks in Canada. The Canadian Government’s Submission to the UN
Framework Convention on Climate Change. Ottawa, ON: Minister of the Environment.
Farret, Felix A., M. Godoy Simoes. (2006). Integration of Alternative Sources of Energy.
Wiley Publishing: Hoboken, New Jersey.
Henning, Dag and Louise Trygg. (2008). “Reduction of electricity use in Swedish
industry and its impact on national power supply and European CO2 emissions.” Energy
Policy. 36, pp. 2330-2350.
Howells, Mark, Holger Rogner, Neil Strachan, Charles Heaps, Hillard Huntington,
Socrates Kypreos, Alison Hughes, Semida Silveira, Joe DeCarolis, Morgan Bazillian, and
Alexandar Roehrl. (2011). “OSeMOSYS: The Open Source Energy Modeling System An
Introduction to its ethos, structure and development.” Energy Policy. 39, pp. 5850-5870.
Jaccard, Mark, Richard Loulou, Amit Kanudia, John Nyboer, Alison Bailie, Maryse
Labriet. (2003). “Methodological contrasts in costing greenhouse gas abatement policies:
Optimization and simulation modeling of micro-economic effects in Canada.” European
Journal of Operational Research. 145, pp. 148-164.
28 Lin, Q.G., G.H. Huang, B. Bass, and Y.F. Huang. (2010). “The Optimization of Energy
Systems under Changing Policies of Greenhouse-gas Emission Control – A Study for the
Province of Saskatchewan.” Energy Sources. 32, pp. 1587-1602.
Loulou, Richard, Gary Goldstein, and Ken Noble. (2004). Documentation for the
MARKAL Family of Models. Energy Technology Systems Analysis Program.
National Energy Board (NEB). (2013). Canada’s Energy Future 2013: Energy Supply
and Demand Projections to 2035. Ottawa, ON: National Energy Board.
SaskPower. (2013). SaskPower Annual Report 2012. Regina, SK: SaskPower. Available
on-line at: http://www.saskpower.com/wpcontent/uploads/2012_saskpower_annual_report.pdf, Last accessed February 11, 2014.
SaskPower. (2011). SaskPower’s Electricity and Conservation Strategy for Meeting
Saskatchewan’s Needs. Regina, SK: SaskPower.
SaskPower. (2009). SaskPower Annual Report 2008. Regina, SK: SaskPower.
Seebregts Ad J., Gary A. Goldstein, Koen Smekens. (2002). “Energy/Environmental
Modeling with the MARKAL Family of Models.” Eds. P. Chamoni, R. Leisten, A.
Martin, J. Minnemann, H. Stadtler. Operations research proceedings 2001: Selected
29 papers of the International Conference on Operations, Duisburg, September 3-5, 2001.
pp. 75-82.
Solver. (2014). Frontline Solvers website for the Excel Solver. Available on-line at:
http://www.solver.com. Last accessed April 17, 2014.
Statistics Canada. (2014). Statistics Canada CANSIM Database. Ottawa, ON:
Government of Canada. Available on-line at: http://www5.statcan.gc.ca/cansim/homeaccueil?lang=eng&p2=50&HPA.
Statistics Canada. (2014). All-Items Consumer Price Index (CPI) – CANSIM Table 3260021. Available online at: http://www5.statcan.gc.ca/cansim/a05. Last accessed April 15,
2014.
30 Appendix A – Calculating the Cost of Electricity Generation
There are four categories of cost associated with electricity generation: capital, fixed
operations and maintenance (O&M), variable O&M, and fuel. Capital refers to the cost of
building a generation facility, including feasibility and engineering studies, site
preparation, building structures, boilers, turbines, and electrical connection to the grid
(EIA, 2013b). Fixed O&M are annual costs incurred at a facility independent of the level
of electricity generation. Fixed O&M costs include expenditures such as staffing,
administrative expenses, grounds maintenance, pump maintenance, and equipment such
as tools and safety supplies (EIA, 2013b). Variable O&M costs are annual expenditures
that depend on the amount of electricity generated at a facility. They include expenditures
on water, wastewater disposal, catalysts, lubricants, and other “consumable materials and
supplies” (EIA, 2013b: 2-9). Fuel costs are annual expenditures on coal and natural gas
for fossil-fuel plants.
The cost of electricity generation for each technology is derived largely from Delucchi &
Jacobson (2011) with some information from Benitez et al. (2008) and EIA (2013b) (See
Table A1).
31 Source
Delucchi & Jacobson
Coal
(2011) A.1a
Delucchi & Jacobson
Coal CCS
(2011) A.1a
Natural gas
Delucchi & Jacobson
combined cycle
(2011) A.1a
Delucchi & Jacobson
(2011) A.1a & Benitez
Natural gas single et al. (2008) & EIA
cycle (peaking)
(2013)
Delucchi & Jacobson
Hydropower
(2011) A.1a
Delucchi & Jacobson
Wind onshore
(2011) A.1a
Delucchi & Jacobson
(2011) A.1a
Solar PV
Dollars
Capital
cost
($/kW)
Size (mW)
Fixed O&M
($/kW/year)
Variable
O&M
($/mWh)
Capacity
factor
Fuel
efficiency
(%)
2007 $USD
1
2058
27.53
4.6
74%
37%
2007 $USD
1
3496
46.12
4.4
74%
32%
2007 $USD
1
948
11.7
2
42%
51%
2011 $USD
1
632
6.92
10.19
38%
30%
2007 $USD
1
2242
13.63
2.4
65%
2007 $USD
1
1923
30.3
0
38%
2007 $USD
1
6038
11.68
0
21%
Table A1 – Source Data for Electricity Generation Costs
Costs are inflated to 2013 dollars using the All-Item Consumer Price Index (CPI)
(Statistics Canada, 2014). Costs are converted from $USD to $CDN using the average
US-CDN exchange rate for March, 2014, which was .90 $USD for 1 $CDN (Bank of
Canada, 2014). The results in 2013 $CDN are presented in Table A2 – Electricity
Generation Costs in Canada.
Source
Delucchi & Jacobson
Coal
(2011) A.1a
Delucchi & Jacobson
Coal CCS
(2011) A.1a
Natural gas
Delucchi & Jacobson
combined cycle
(2011) A.1a
Delucchi & Jacobson
Natural gas single (2011) A.1a & Benitez et
cycle (peaking)
al. (2008) & EIA (2013)
Delucchi & Jacobson
Hydropower
(2011) A.1a
Delucchi & Jacobson
Wind onshore
(2011) A.1a
Delucchi & Jacobson
(2011) A.1a
Solar PV
Dollars
Size (mW)
Capital
cost
($/kW)
Fixed O&M
($/kW/year)
Variable
O&M
($/mWh)
Capacity
factor
Fuel
efficiency
(%)
2013 $CDN
1
2518
33.69
5.6
74%
37%
2013 $CDN
1
4278
56.44
5.4
74%
32%
2013 $CDN
1
1160
14.32
2.4
42%
51%
2013 $CDN
1
740
8.10
11.9
38%
30%
2013 $CDN
1
2744
16.68
2.9
65%
2013 $CDN
1
2353
37.08
0.0
38%
2013 $CDN
1
7389
14.29
0.0
21%
Table A2 – Electricity Generation Costs in 2013 $CDN
32 In this paper the linear program is set to minimize the annual operating cost of the
Saskatchewan electricity system; the key word in this sentence is annual. While fixed
O&M, variable O&M, and fuel costs are annual, capital is a one-time expenditure that
occurs at the start of a facility’s life. Capital costs are annualized using the following
formula from Loulou (2004: footnote 21 on page 42):
!"#$
(1 + 𝑟)!!
𝐴𝑛𝑛𝑢𝑎𝑙𝑖𝑧𝑒𝑑 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑠𝑡 = 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑠𝑡/
!!!
Where j equals year, and r equals the discount rate. For this paper I have used Delucchi &
Jacobson’s (2011) approach of calculating annual capital cost using a lifetime of 30 years
for each technology and a discount rate of 7%.
To calculate the annual cost of fuel I assume that fossil fuel energy is being converted to
electricity at the fuel efficiency factor indicated in Tables A1 and A2. I also assume that
fossil fuel prices are $22.00/short ton for lignite coal3 and $4/MMBTU for natural gas4.
Table A3 summarizes the fuel costs for coal and natural gas generation facilities.
3
SaskPower predominantly uses locally mined lignite coal in their coal generation stations. The
price of lignite coal from the EIA is $18.76/short ton in 2010 $USD (EIA, 2012: 215). Converting
to 2013 $CDN the price is approximately $22.00/short ton.
4
The price of natural gas is taken from the low-price scenario in NEB (2013).
33 Fuel
Required
Efficiency
BTU/kWh
Required BTU Content
fuel
(%)
conversion BTU/kWh
of Fuel
Unit
(Q/kWh)
37%
3412
9,222
13,000,000 /short ton
0.00071
32%
3412
10,663
13,000,000 /short ton
0.00082
Coal
Coal CCS
Natural gas
combined cycle
51%
3412
6,690
1,000,000 /MMBTU
Natural gas
single cycle
(peaking)
30%
3412
11,374
1,000,000 /MMBTU
BTU$content$of$lignite$coal$is$6500$btu/lb$(Farret$&$Simoes,$2006)
MMBTU$=$million$British$thermal$units
Fuel Price
Fuel Cost
($)
Unit
($/MWh)
$22.00 /short ton
15.61
$22.00 /short ton
18.04
0.00669
$4.00 /MMBTU
26.76
0.01137
$4.00 /MMBTU
45.50
Table A3 – Fuel Cost Calculation and Assumptions
Table A3 presents the annualized capital costs along with the variable costs that occur
when each facility-type is operating at the capacity factor indicated in Tables A1 and A2.
34