Oil Sands Community Alliance
2016 Oil Sands Co-generation and Connection Report
Issued December 2017
Prepared for the OSCA and
Co-generation Task Group By
Power System Solutions International Inc.
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1 Executive Summary The Oil Sands Community Alliance (OSCA), formerly the Oil Sands Developers Group (OSDG), has been
tracking on-site electricity demand and co-generation capacity associated with oil sands developments
since 1999, with the objective of providing information to operators, the Alberta Electric System
Operator (AESO), and Alberta and Federal government policy makers on issues related to electricity
demand, supply, and transmission development. Co-generation has been employed by the oil sands
industry to assist with the production of bitumen since the mid 1970’s. The 2016 Oil Sands Co-
Generation & Connection Report summarizes the results of 2016 survey.
The survey requested feedback on the major influential factors impacting the decision to build on site
co-generation, actual and forecast values for co-generation operating capacity, on-site demand,
requirements for stand-by power from the grid, and potential power sales or net exports. The core of
the questionnaire was kept the same with previous years to allow comparison. However, many
questions were updated with the input from the OSCA members and the questionnaire was extended
especially with respect to identifying the potential for future co-generation.
The report also includes the duration curve analysis of actual metered net power injections into the
system and metered demands. The methodology was kept identical to that used in previous years to
allow for comparison and identification of trends.
For the first time the report includes the results of probabilistic reliability analysis of the supply and
demand in the Fort McMurray area.
The survey was divided into two parts:
1) General co-generation questions which were company based and which focused on electricity
markets and,
2) Specific project based questions that focused on the company’s plans for individual projects.
In the first part, respondents were asked to provide feedback on factors influencing the decision to
develop co-generation projects.
GHG Compliance cost or benefit was ranked as the most important factor influencing the decision to
build co-generation.
The price of grid power versus the cost of self-generation ranked very high or high as the factor
influencing the decision to build.
Reliability of power from the grid, while listed as the most important factor in the 2014 survey,
continued to be significant but dropped below the above factors. It is reasonable to assume that the
relative drop is related to improved reliability of supply in Fort McMurray area. This is a result of the
local installed generation exceeding the local load, hence reducing the reliance on the rest of the AIES
and improving the reliability as shown later in the report.
Transmission costs continued to be an important factor in decision making. Combustion of produced
gas, distribution costs/forecast, an internal policy of self-sufficiency and public perception all had a
relatively low impact on decision making.
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Just as importantly, the questionnaire asked what factors would result in respondents no longer
considering co-gen projects. The responses included uncertainty of GHG compliance cost in particular
with respect to co-generation projects, depressed power prices and lack of a level playing field caused
by excessive subsidies to renewables.
Other factors noted by survey respondents included the consideration of OTSG (Once Through Steam
Generator) versus Cogen as well as maximizing efficiency and technology and participation in the open
power market. These factors were noted by more than one respondent as the most significant factors in
the decision making process.
The prevalence of transmission related influential factors amongst those ranked with “high importance” should provide an indication of how important reliable and cost effective access to the provincial transmission grid is for oil sands developers. While some factors influencing decision to develop co-generation are outside the direct control of the policy maker, many can be influenced to the great extent. The factors that are within the policy makers control and that are important to development of co-generation include:
Reduction/elimination of Fort McMurray area interconnection transfer capacity constraints (e.g. by including development of 500kV tie)
GHG compliance cost and certainty about the policy and rules
Grid supply reliability
Encourage the use of Industrial System Designations (ISDs) and the development of efficient and economic industrial systems for oil sands operations.
The environmental and economic benefits associated with on-site co-generation are significant and quantifiable. Government policies should be formulated to support development of co-generation with oil sands projects. Similar to previous survey results, the majority of respondents plan to use both on-site co-generation
and purchased power from the grid to meet their power needs. Figure 1 shows the medium forecast of
on-site demand and on-site co-generation from the surveys. While representative of the expected
growth, the response to the survey does not include all installed co-generation in Alberta. Furthermore,
at number of sites, the sum of generator nameplate installed capacity exceeds significantly the reported
average available generating capacity presumably reflecting the existing transmission constraints.
The cumulative forecasted growth between 2015 and 2023 is 71% and 72% for demand and supply
respectively. While the forecast of the growth is lower when compared to the survey response from
previous years, it still significantly exceeds other segments of economy and the magnitude of generation
is a large part of the AIES installed capacity.
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Figure 1 Forecast of On-Site Demand and On-Site Generating Capability from Survey Responses
The analysis included deterministic analysis of Fort McMurray area demand, supply and area export
duration curves using the same methodology used previously. The results are shown in Figure 2.
The actual exports are, by definition, below the system normal maximum transfer limits but exceed the
N-1 (transmission element on outage) transfer limits. The forecast of the export duration curves for
2020 and 2023 significantly exceeds the available transfer capability of the existing 240 kV circuits for
more than 1000 hours per year.
Probabilistic analysis of Fort McMurray supply and demand balance was prepared for the first time. The
analysis allows to assess probabilistically the adequacy of the area supply to meet the area demand and
allows to assess the reliance on the rest of AIES (imports) and surplus capacity available for export. The
probabilistic analysis reflected each generating unit MCR capacity, average failure rate, average repair
times (so called forced outages) and annual preventative maintenance. Canadian Electrical Association
(CEA) data were used as the input. The analysis reflects the transmission unconstrained system and the
export potential is determined from the results together with the probabilistic assessment of impact of
congestion.
The results indicate that the area has presently more than adequate generation supply and its reliance
on the rest of AIES is minimal. However, as the area generation reserve margin decreases, so does the
reliability of supply as shown in Table 1.
0.0
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2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
MW
Year
Available Installed Capacity On-site Power Demand
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Figure 2: Observed and Forecast FMM Export Duration Curves
Table 1: Typical Reliability Indices for Fort McMurray Area
2015 2020 2023
Reserve Margin 51% 29% 19%
LOLE [h/y] 0.00006 0.0084 0.2072
EENS [MWh/year] 0.00167 0.3048 8.9836
Table 2 and Figure 3 shows the present and forecasted export potential given the Fort McMurray area
installed generating capacity and the area load. The results indicate that the present transmission
constraint results in significant congestion which will, in the absence of major transmission development
continue into the future.
Table 2: Export Potential from Fort McMurray Area
2015 2020 2023
Export Potential [MWh] 8,244,714 8,023,189 7,167,267 Export Constrained [MWh] 2,025,114 1,803,589 947,667
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0 1000 2000 3000 4000 5000 6000 7000 8000 9000
FMM
Exp
ort
[M
W]
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2020 2023 2015
Present Export Limit
Import Limit
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Figure 3: Probabilistic Surplus Capability Model
0.0
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0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Exp
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abili
ty [
MW
]
Probability
Export Capability Probabilistic Model
2015 2020 2023 Limit
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Table of Contents 1 Executive Summary ................................................................................................................................ i
2 Introduction .......................................................................................................................................... 1
2.1 Background ................................................................................................................................... 1
3 Methodology ......................................................................................................................................... 2
3.1 The Survey ..................................................................................................................................... 2
3.2 Analysis ......................................................................................................................................... 3
4 Results – Survey Basic Analysis ............................................................................................................. 4
4.1 Market Related Questions ............................................................................................................ 4
4.1.1 Factors Influencing the Decision to Develop Co-Generation ................................................ 4
4.1.2 Primary constraints to Installing New or Additional Co-Generation .................................... 7
4.1.3 Qualitative Question: What factors or changes could be made to existing policy or
processes that would change your decision to install new or additional co-generation in the future?
9
4.1.4 Qualitative Question: What factors or changes to existing policy or processes would cause
you to not (or no longer) consider co-generation or additional co-generation at site? ...................... 9
4.1.5 Qualitative Question: Have the provincial policy announcements related to climate
change and electricity, including the proposed coal retirement schedule impacted your decision to
install new or additional co-generation at a future date? .................................................................... 9
4.1.6 Qualitative Question: Given the announcement of focus on renewable energy additions
and their dispatchability issues, would you be able to provide grid support by varying generation
exports and/or demand or by installing storage capacity? .................................................................. 9
4.2 Project Related Questions .......................................................................................................... 10
4.2.1 Background and Analysis Notes .......................................................................................... 10
4.2.2 General Project Related Questions ..................................................................................... 11
4.2.3 Project Expected In Service Date ........................................................................................ 13
4.2.4 Large Electrical Load ........................................................................................................... 14
4.3 Power Co-Generation Focused Questions .................................................................................. 15
4.3.1 On site Power Demand ....................................................................................................... 15
4.3.2 Options Considered to Supply Load .................................................................................... 16
4.3.3 Installed Capacity ................................................................................................................ 17
4.3.4 Exports to the grid............................................................................................................... 19
4.3.5 Pricing of Exports ................................................................................................................ 20
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4.3.6 Standby Power Requirements ............................................................................................ 21
4.3.7 Heat Recovery ..................................................................................................................... 23
4.3.8 Status of Co-Generation Projects ........................................................................................ 24
4.3.9 Regulatory Status ................................................................................................................ 25
4.3.10 Reliability ............................................................................................................................. 25
4.3.11 Other On-site Power Generation ........................................................................................ 25
5 Duration Curve Analysis ...................................................................................................................... 26
5.1 Load Duration Curves .................................................................................................................. 26
5.1.1 Alberta Interconnected Load .............................................................................................. 26
5.1.2 Fort McMurray .................................................................................................................... 27
5.2 Generation Duration Curves ....................................................................................................... 31
5.3 Fort McMurray Area Export ........................................................................................................ 32
5.4 Fort McMurray Area Forecast ..................................................................................................... 35
6 Probabilistic Analysis ........................................................................................................................... 37
6.1 Probabilistic Reliability Analysis Basics ....................................................................................... 37
6.1.1 Probabilistic Approach Used on This Study ........................................................................ 38
6.1.2 Fort McMurray area Reliability and Export Potential ......................................................... 38
6.2 Conclusions ................................................................................................................................. 42
References .................................................................................................................................................. 43
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List of Tables
Table 1: Typical Reliability Indices for Fort McMurray Area ........................................................................ iv
Table 2: Export Potential from Fort McMurray Area ................................................................................... iv
Table 3: Development Stage Discounts ........................................................................................................ 3
Table 4: Factors Influencing the Decision to Develop Co-Generation .......................................................... 5
Table 5: Factors Significant as Primary Constraints to Installing New or Additional Co-Generation ........... 7
Table 6: Number of Projects with Co-Generation and the Generation Capacity by Location .................... 12
Table 7: Project Type .................................................................................................................................. 12
Table 8: Geographical Breakdown of the Project Type .............................................................................. 13
Table 9: Project Status ................................................................................................................................ 13
Table 10: Large Electric Loads ..................................................................................................................... 14
Table 11: Large Electric Load Broken Down by Type of Project ................................................................. 14
Table 12: Generating Capacity by Stage of Development .......................................................................... 24
Table 13: Other Generation ........................................................................................................................ 25
Table 14: AIL Peak Load and Energy Growth ............................................................................................. 26
Table 15: Fort McMurray Municipal Demand Peak Load and Energy Growth ........................................... 28
Table 16: Fort McMurray Oil Sands Demand Peak Load and Energy Growth ............................................ 29
Table 17: Fort McMurray Oil Sands Load Factor for the First 500 Hours and for the Middle Section of the
Load Duration Curve ................................................................................................................................... 30
Table 18: Fort McMurray Area Peak Load and Energy Growth .................................................................. 30
Table 19: Fort McMurray Area Generation Growth ................................................................................... 31
Table 20: Fort McMurray Area Export Statistics ......................................................................................... 34
Table 21: Fort McMurray Generation and Load Forecast Assumed in the Probabilistic Analysis .............. 39
Table 22: Typical Reliability Indices for Fort McMurray Area ..................................................................... 41
Table 23: Export Potential from Fort McMurray Area ................................................................................ 41
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List of Figures
Figure 1 Forecast of On-Site Demand and On-Site Generating Capability from Survey Responses ............ iii
Figure 2: Observed and Forecast FMM Export Duration Curves ................................................................. iv
Figure 3: Probabilistic Surplus Capability Model .......................................................................................... v
Figure 4: Factors influencing the decision to build Co-generation ............................................................... 6
Figure 5: Primary Constraints to Installing New or Additional Co-Generation ............................................. 8
Figure 6: Alberta Oil Send Regions .............................................................................................................. 11
Figure 7: Expected Project ISD .................................................................................................................... 14
Figure 8 On-Site Power Demand:................................................................................................................ 15
Figure 9: Annual Forecasted Load Growth ................................................................................................. 16
Figure 10: Options Considered to Supply on Site Demand ......................................................................... 17
Figure 11: Forecast of Installed Capacity .................................................................................................... 18
Figure 12: Annual installed capacity increase ............................................................................................. 18
Figure 13: Capacity Available for Export ..................................................................................................... 19
Figure 14: Growth of Capacity Available for Export ................................................................................... 20
Figure 15: Pricing of Exports ....................................................................................................................... 21
Figure 16: Standby Power Requirement ..................................................................................................... 22
Figure 17: Stand-by Requirement Compared with Electric Power Demand .............................................. 22
Figure 18: Cumulative Growth of Grid Standby Requirement compared to Growth in Demand and Co-
Generation .................................................................................................................................................. 23
Figure 19: Percentage of Project Heat Requirement that is related to Electricity Production. ................. 24
Figure 20: Load Duration Curves - Alberta Interconnected Load Demand ................................................. 27
Figure 21: Load Duration Curves – Fort McMurray Municipal Load ........................................................... 28
Figure 22: Load Duration Curves – Fort McMurray Oil Sands Load ............................................................ 29
Figure 23: Load Duration Curves – Fort McMurray Area ............................................................................ 31
Figure 24: Generation Duration Curves – Fort McMurray Area ................................................................. 32
Figure 25: Fort McMurray Area Export Variation Curve - 2014 .................................................................. 33
Figure 26: Fort McMurray Area Export Variation Curve - 2015 .................................................................. 33
Figure 27: Fort McMurray Area Export Duration Curve for 2010 to 2015 ................................................. 34
Figure 28: Forecast of Load and Generation Based on Survey Results....................................................... 35
Figure 29: Comparison of Load Growth Forecast from Survey with AESO Forecast from Recent Long Term
Outlook ....................................................................................................................................................... 36
Figure 30: Forecast of Fort McMurray Area Export Duration Curve for 2020 and 2023 ............................ 36
Figure 31: Probabilistic Model .................................................................................................................... 39
Figure 32: Generation Probabilistic Model ................................................................................................. 40
Figure 33: Load Model ................................................................................................................................ 40
Figure 34: Probabilistic Surplus Capability Model ...................................................................................... 42
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2 Introduction
2.1 Background Extraction methods in the oil sands have varying on-site electricity and heat demands. Co-generation is the simultaneous generation of electricity and useful heat, either steam or hot water, and is by definition more efficient at producing electricity and steam or hot water when compared with other technologies (e.g. coal or natural gas fired facilities) and standalone boilers. Despite the efficiencies and other benefits associated with co-generation, not all oil sands operators elect to install co-generation as part of their oil sands facilitates.
The Oil Sands Community Alliance (OSCA), formerly the Oil Sands Developers Group (OSDG), began
tracking and forecasting the growth in co-generation in 1999. Information gathered will assist operators,
the AESO, and Alberta and Federal government policy makers to highlight issues related to co-generation
and transmission development. The OSCA Power and Co-Generation Task Group manages the survey and
issues this report. The focus of the task group is to:
Ensure reliability and availability of transmission in and out of the Athabasca Oil Sands Area
Identify alternate sources of generation
Ensure reasonable cost of transmission service
Prioritize transmission infrastructure initiatives
Conduct and issue the Oil Sands Co-generation and Connection survey and report
Until 2014, the survey was completed annually, however in 2015, the decision was made to conduct it
bi-annually. The 2016 survey was conducted during the Fort McMurray wildfire and in the midst of a
period of continued uncertainty over the direction of oil prices. The study’s goal is to determine the
current and potential electrical capacity of cogeneration plants located within oil sands projects.
If you have any comments on this report please contact:
Shafak Sajid
Policy Analyst
Oil Sands Community Alliance (OSCA)
617 – 8600 Franklin Avenue
Fort McMurray, Alberta Canada T9H 4G8
Phone: (403)267 1118
e-mail @oscaalberta.ca
www.oscaalberta.ca
This report was prepared for the OSCA by Power System Solutions International Inc.
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3 Methodology The source of data for the 2016 Oil Sands Co-generation Report was a survey of oil sands companies
conducted during the spring, summer and fall of 2016.
3.1 The Survey The process consisted of establishing initial contact, followed by e-mailing an electronic questionnaire.
Out of 56 companies initially targeted, only 30 responded. Out of these 30, several declined to
participate due to low staffing and other issues associated with the economic difficulties characterizing
the industry at the time. The survey response deadlines were also extended several times to
accommodate the change in priorities following the Fort McMurray fire. In the end, the final number of
usable responses was 14.
The survey was divided into two parts:
3) General co-generation questions which were company based and which focused on electricity
markets and,
4) Specific project based questions that focused on the company’s plans for individual projects.
The former part was more qualitative in nature and was addressed by ranking and open-ended
questions.
The latter part was focused on numerical responses and ranking was used to further elaborate on the
answers. It included questions on forecast of production, electrical load, generation capacity, exports
etc. The forecast data was requested for three scenarios:
1) Low Range Scenario where the project would be built to the minimum anticipated scope. This
may reflect a minimum capital spend, lower oil prices, higher operating costs, and / or poor
economic conditions.
2) Medium Range Scenario where the project would be built to the most probable or planned
scope in a business-as-usual environment.
3) High Range Scenario where the project would be built to the maximum anticipated scope. This
may relate to a larger capital spend, higher oil prices, lower operating costs, and / or more
robust economic conditions.
It is important to remember that this survey was executed at the time of a significant downturn in oil
prices and hence the “Low”, “Medium” and “High” scenarios were adjusted to the economic conditions
prevalent at the time. The new low prices thus reflect the Medium Range Scenario (as opposed to Low
Range Scenario that would be applicable before the oil price drop). This issue was discussed with
respondents and the meaning of each scenario was interpreted as follows:
1) Low Range Scenario reflected the situation where the then present low oil prices would prevail
and not recover.
2) Medium Range Scenario reflected the situation where the then present low oil prices would
recover but not reach the pre-oil price drop levels.
3) High Range Scenario reflected the situation where the oil prices would recover to or beyond the
pre-oil price drop levels.
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While the survey was significantly updated and extended in 2016, an effort was made to include either
the same or similar questions as were used in the previous years to allow for comparison and show
trends.
The overall survey participation was lower in 2016 than in previous years. The major reason was staffing
levels that have decreased drastically following the oil price drop and coincidence with the Fort
McMurray fires. While the deadline for the survey was extended well into the fall of 2016 and reminders
were sent in September and October, the secondary response rate was not high.
Extending the response deadline over a longer period also presented an increased risk of comparing the
responses of those who had more time to adjust to the new economic reality of low oil prices with those
answered at the earlier times when it was more prevalent to see the price dip as temporary. Given the
circumstances of 2016, the overall response rate is considered acceptable.
The practice of establishing the contact prior to mailing the questionnaire to ascertain who in the
company is in the best position to respond proved very useful.
Lastly, it is important to acknowledge that the sum of forecasts of production, load and generation does
not represent provincial totals since not all oilfield producers responded.
3.2 Analysis The analysis performed included the following:
1) basic analysis of the responses to individual questions
2) duration curves analysis for power flows in and out of Athabasca region
3) probabilistic analysis of the power flows in and out of Athabasca region
In agreement with the analysis performed in the previous years, questionnaire capacities were not
presented only as reported but discounting was also used to present adjusted quantities for
comparison. The discounting is a form of probabilistic analysis recognizing that the projects have
different probabilities of successful completion. One measure of a project’s successful completion is its
stage. The more advanced the project, the more likely to be brought to successful completion.
The discounting factors were the same as the previous analysis and are shown in Table 3.
Table 3: Development Stage Discounts
Status Discount
Cancelled 0%
Conceptual 10%
Announced 25%
Approved 60%
Regulatory Approval of the Scheme 80%
Regulatory Approval 90%
Construction 100%
Operating 100%
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4 Results – Survey Basic Analysis This section presents the basic analysis of the responses to the individual questions. The Market related
questions are presented first, followed by the project related questions.
4.1 Market Related Questions
4.1.1 Factors Influencing the Decision to Develop Co-Generation Fourteen factors were ranked according to their importance in the decision to install co-generation.
These factors were changed slightly from prior surveys in response to questionnaire review and re-
design. OSCA members have actively participated in this process and provided many of the suggested
changes.
Table 4 provides a brief description of each factor.
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Table 4: Factors Influencing the Decision to Develop Co-Generation
Factor Description
Proximity of power needs to grid supply Distance of the project from the existing Alberta power grid (AIES).
Timing of grid connection Certainty (or uncertainty) to when transmission capacity will be available for oil sands projects.
Reliability of power supply from the grid versus self-generation
Transmission system is inadequate to provide the level of "up time" required for oil sands projects.
Price of grid power versus cost of self-generation
Cost of electricity from co-generation plus stand-by transmission charges compared to purchasing from third party suppliers plus transmission charges.
Transmission costs/forecast AESO wires charges for delivery of electricity and/or stand-by capacity from the grid.
Distribution costs/forecast DISCO wires charges for delivery of electricity and/or stand-by capacity from the power system.
Natural gas price/forecast Risks associated with the correlation between natural gas and electricity prices, or system heat rate.
GHG compliance cost or benefit Consideration of GHG cost and regulation compliance (uncertainty and potential positive/negative impacts).
Combustion of produced gas A convenient way of addressing the issue of onsite produced gas.
Regulatory processes, timeline and associated costs
Difficulties, delays and costs associated with following required regulatory approval processes.
Securing of Industrial Systems Designation
Potential AESO tariff savings associated with ISD (e.g. net metering).
Technical expertise to self-generate
Self-generation, while not being a part of core business, increases significantly the complexity of overall operation.
Internal policy of self sufficiency Corporate policy to control/ manage/ generate electric energy supply.
Public perception Public and/or environmental implications of co-generation or other electric energy supplies.
The individual factor ranking was extended to five categories as a part of the questionnaire review.
For the first time ever, the GHG Compliance cost or benefit was ranked as the most important factor
influencing the decision to build co-generation with ten respondents listing it as very high or high. The
price of grid power versus the cost of self-generation, as well as the proximity of power needs to the
grid supply each had nine respondents indicating very high or high influence on their decision to build.
Overall, the price of grid power versus the cost of self-generation was ranked as very high by five
respondents.
Reliability of power from the grid was ranked as the most important factor in 2014. While this factor continued to be significant in 2016, three respondents ranked it as not important.
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Figure 4: Factors influencing the decision to build Co-generation
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Factors Influencing the Decision to Build Co-Generation
Very High High Medium Low Not Important
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Transmission costs continued to be an important factor in decision making. Combustion of produced
gas, distribution costs/forecast, an internal policy of self-sufficiency and public perception all had a
relatively low impact on decision making.
Other factors noted by survey respondents included the consideration of OTSG versus Cogen as well as
maximizing efficiency and technology and participation in the open power market. These factors were
noted by more than one respondent as the most significant factors in the decision making process.
4.1.2 Primary constraints to Installing New or Additional Co-Generation Respondents were also asked to rank the factors listed in Table 5Error! Reference source not found.
based on their significance as the primary constraints to installing new or additional co-generation:
Table 5: Factors Significant as Primary Constraints to Installing New or Additional Co-Generation
Factor
Capital requirements/cogeneration economics
Impact to project schedules or construction timelines
Additional regulatory process, timeline and associated cost
Reliability of power supply from the grid versus self-generation
Price of grid power versus cost of self-generation
Adequate transmission capacity to export power
Uncertainty of GHG regulations
Natural gas price/forecast
Technical expertise to self-generate/non-core business
Figure 5 illustrates the results, indicating that capital requirements are the primary constraint for
installing co-generation; not surprising in the current economic environment. Uncertainty of GHG
regulations and adequate transmission capacity to export power were also ranked very high or high by
at least 50% of the respondents.
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Figure 5: Primary Constraints to Installing New or Additional Co-Generation
Factors which do not significantly constrain installation include the reliability of the power supply from
the grid and technical expertise to self-generate.
0
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Primary Contraints to Installing New or Additional Co-Generation
Very High High Medium Low Not Important
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4.1.3 Qualitative Question: What factors or changes could be made to existing policy or
processes that would change your decision to install new or additional co-generation in
the future? Many respondents cited greater clarity on GHG regulations and other regulatory processes. Clarity of
carbon pricing was also a concern.
4.1.4 Qualitative Question: What factors or changes to existing policy or processes would cause
you to not (or no longer) consider co-generation or additional co-generation at site? Many respondents indicated that if new GHG or climate change policies or regulations are released
which unfavorably treat co-generation facilities the plans to install co-generation may be abandoned.
Number of respondents also indicated that power prices remaining near current low levels may alter
their decision to install co-generation.
Many respondents also identified significant subsidies for renewables and the resulting lack of level
playing field as the potential impediment to proceed with co-generation plans.
4.1.5 Qualitative Question: Have the provincial policy announcements related to climate
change and electricity, including the proposed coal retirement schedule impacted your
decision to install new or additional co-generation at a future date? Half of the respondents replied “No” to this question; however, some indicated that more detail may
impact the decision. For those who answered “Yes”, uncertainty remained the biggest factor in their
decision.
4.1.6 Qualitative Question: Given the announcement of focus on renewable energy additions
and their dispatchability issues, would you be able to provide grid support by varying
generation exports and/or demand or by installing storage capacity? The majority of respondents indicated that the existing configurations may not be able to support the
grid without facility modifications. Certainty around economic justifications would be required to
pursue this option.
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4.2 Project Related Questions
4.2.1 Background and Analysis Notes This section presents basic results of all project related questions. The questions were divided into
following categories:
1) General project related
2) Production forecast
3) Power cogeneration
4) Generation status
5) Regulatory
6) Reliability
7) Other on site power generation and general notes
Responses to production forecast and power cogeneration questions display the common issue in that a
large number of respondents did not provide data past 2023. In fact, there is little consistency in those
responses that include forecasts till 2023 but not past 2023 making an option of flat lining at 2023 values
questionable. For example, the response may include the electrical load past 2023 and not production
levels. Consequently, the data are generally reported only till 2023.
Most of the project focused questions are asked for three forecast scenarios: high, mid and low. While
the majority of respondents answered all questions for all three scenarios, a few answered only for the
mid scenario. This would have skewed the result in a sense that high scenario would show lower results
than mid. Out of the two options, either to reject the response or to assume high and low forecasts the
same as mid the analysis is based on the latter, namely to assume mid forecast in all three scenarios.
Several respondents also indicated different 2014 and 2015 actual values for high and low category. This
was probably a result of pre-filling the questionnaires with data from 2014 survey. To ensure that the
actuals do not differ between the high, mid and low, the high and low responses were forced to equal
the mid responses whenever different.
Discounting of the electric power demand and of export opportunity was done using the project overall
status. While the original intent was to use the status indicated with each unit, the response rate to this
question was low and the relationship between the yearly forecasts and the unit sizes could not be
established in a meaningful manner. For example, the responses often did not have any units specified
even though there was forecast of cogeneration considered. The primary reason was difficulty in
specifying the unit size for future developments. In several cases, the combined capacity of identified
units fell short of the forecasted co-generation.
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4.2.2 General Project Related Questions The following questions were related to individual projects.
4.2.2.1 Project Locations
There are three oil sands deposit regions in Alberta; Peace River, Athabasca, and Cold Lake as shown in
Figure 6: Alberta Oil Send Regions. The Athabasca region contains both heritage and new mining and in-
situ developments. This region is the largest and most active.
For the purposes of this study, the Athabasca region has been further divided into three areas; North of
Fort McMurray and East of the Athabasca River, North of Fort McMurray and West of the Athabasca
River, and South of Fort McMurray. The Cold Lake region is found to the southeast of the Athabasca
region while the Peace River region is located to the west. The Cold Lake and Peace River areas focus on
in-situ operations. The Wabasca and Red Earth/Other regions contain those few outliers not located in
the traditional three oil sands areas.
Figure 6: Alberta Oil Send Regions
The results of this question should assist the AESO in planning for future transmission growth by
identifying the location and number of existing and forecast co-generation units and the anticipated
cogeneration operating capacity in each region. Values in Table 6 show number of projects with Co-
Generation and the nameplate capacity over the considered planning horizon. Both the number of
projects and the capacity approximately doubled since 2010.
12 | P a g e
Table 6: Number of Projects with Co-Generation and the Generation Capacity by Location
Location
No of Projects with
Cogeneration1
Generating Capacity
Cold Lake 5 755
North of Fort McMurray and East of the Athabasca River 11 1084
North of Fort McMurray and West of the Athabasca River 25 2806
South of Fort McMurray 29 1703
Red Earth / Other 3 49
Grand Total 732 6398
The majority of projects are located in Athabasca region and so is the majority of generating capacity
either in operation or planned.
The above locations are used in the subsequent sections to categorize the responses.
4.2.2.2 Project Type
Summary of project type is shown in Table 7. The vast majority of surveyed projects were of in-situ type
and out of these SAGD is the most prevalent.
Table 7: Project Type
Type of Project No of
Projects
In-Situ 67
CSS 1
Electric Heating 2
SAGD 38
SAGD + solvent 1
Not Specified 25
Mining 7
Mining, In-Situ & Upg 1
Grand Total 75
Geographical breakdown of the type of operation is shown in Table 8. All mining projects are in the Fort
McMurray area.
1 Total number of projects with project section answered. 2 The data presented in breakdowns are affected by whether respondents answered a particular question (in this case location). Consequently, the totals differ from table to table.
13 | P a g e
Table 8: Geographical Breakdown of the Project Type
Project Type
Cold Lake
North of Fort McMurray and
East of the Athabasca River
North of Fort McMurray and
West of the Athabasca River
Red Earth / Other
South of Fort
McMurray Grand Total
In-Situ 5 7 23 3 29 67
Mining 5 2 7 Mining, In-Situ & Upg 1 1
Grand Total 5 12 26 3 29 75
4.2.2.3 Project Status and Estimated Start Date
Respondents were asked to categorize the project status. This is not to be confused with the status of
approval/construction of the individual generating units.
Project status was used primarily for probabilistic discounting of various forecasted values.
Table 9: Project Status
Status Count
Operating 17
Construction 4
Approved 7
Regulatory 7
Regulatory Approval of Scheme 1
Announced 11
Conceptual 25
Cancelled 1
Other 2
Grand Total 75
4.2.3 Project Expected In Service Date Project expected In Service Dates (ISD) are shown in Figure 7. The figure comprises 34 responses; 40
respondents indicated either TBD or did not answer the question. The project completion peaks in 2017
and again in 2020 with 4 projects completed in each year.
14 | P a g e
Figure 7: Expected Project ISD
4.2.4 Large Electrical Load Oil sands development is typically electrical demand intensive. Respondents were asked to identify the
typical large electrical demand centers. The results are shown in Table 10. Please note that the values
add to more than 75, the number of projects, since projects have more than one type of load.
Table 10: Large Electric Loads
Electric Load Water evaporation technology 19
Pumping Station or facility 29
Camp site with Electric Space heating 18
Electric Heat Tracing 11
Total Number of Large Electrical Loads 77
Not surprisingly, the pump drives account for the large part of electric demand in most projects. Table
11 shows presence of large electric loads broken down by project type.
Table 11: Large Electric Load Broken Down by Type of Project
Electric Load In-Situ Mining Mining, In-Situ &
Upg
Water evaporation technology 18 0 1
Pumping Station or facility 25 3 1
Camp site with Electric Space heating 17 1 0
Electric Heat Tracing 11 0 0
Total projects of this type 71 4 2
0
1
2
3
4
5
6
7
2009 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2024 2025 2027 2022 +
Pro
ject
Co
un
t
Project ISD
15 | P a g e
4.3 Power Co-Generation Focused Questions
4.3.1 On site Power Demand Oil sands developments have significant on-site power demands. As the incremental project and stage
sizes keep increasing so does the power demand.
The core of this power load is typical industrial-related demand. However, the project related aspects of
oil sands developments, such as water treatment, worker camps, and on-site pumping facilities, can also
lead to significantly increased electricity demand. Figure 8 shows the reported forecasted load demand.
Figure 9 shows the annual and cumulative increase in project load demand. The annual increases peak
at 16% in 2016 and taper off to 2% by 2022.
The electrical demand increase comprises both electrical demand increases at the same site (new stages
of same project) and new electrical load of new projects.
The dip in low scenario demand from 2015 to 2016 is a result of forcing the 2014 and 2015 demands to
medium values.
The significant increases in load in 2016 and 2017 are mainly due to new projects being commissioned.
Compared to surveys from previous years, the forecast of power demand dropped similar to the drop in
forecast of production.
Figure 8 On-Site Power Demand:
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Ele
ctri
cal D
em
and
[M
W]
Year
High
Low
Medium
Historical Forecast
16 | P a g e
Figure 9: Annual Forecasted Load Growth
4.3.2 Options Considered to Supply Load On site power demand can be in principle supplied by:
1) On site co-generation only
2) Interconnection with the provincial power grid (AEIS)
3) Combination of the two options above
4) Other means
Co-generation implies that the waste heat from electrical power generation is utilized in the process.
The typical arrangement consists of single cycle gas turbine with boiler or OTSG (Once Trough Steam
Generator) supplied from the GT waste heat. However, reciprocating engines can be used in a similar
arrangement depending on the heat amount and quality requirements. In its simplest form, the co-
generation can supply all on site power demand and the interconnection with the AEIS is not required.
The main advantages of co-generation include reduced carbon impact, increased efficiency and reduced
long term cost. The major disadvantages include increased complexity and capital cost.
Interconnection with the provincial power grid (AEIS) is a conventional approach to supplying power
requirements. Major advantages include relative simplicity, historically high reliability and ease of
operation. The typical disadvantages include potentially long lead times, high cost of interconnection,
higher carbon impact and little control over the long term costs.
Combination of the two supply options, on site co-generation with grid backup option is intended to
address major shortcoming of each approach.
Other category was specified for the first time in 2016 to solicit information about load being supplied
from non co-cogeneration sources such as renewables and other options.
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
2014 2015 2016 2017 2018 2019 2020 2021 2022
An
nu
al L
oad
Gro
wth
Year
Historical Forecast
17 | P a g e
Co-generation with grid as a backup is by far the most selected option. Its relative representation has
increased over previous years where purchase from the grid only was a close second.
Consideration of co-generation only (with no grid backup) has dropped compared to previous years.
Consideration of other means how to supply power did not include further elaboration as to what those
means would be. Given the recent increase in renewable resources one can only speculate whether
renewable generation is considered.
It should be pointed out that number of respondents indicated that they are considering all of the main
three options.
The results of the question are shown in Figure 10.
Figure 10: Options Considered to Supply on Site Demand
4.3.3 Installed Capacity If considering the co-generation, respondents were asked to indicate the total size of onsite installed
generation for each project.
The summation of the results is shown in Figure 11. Annual percentage increase is shown in Figure 12.
The installed capacity rate of increase peaks in 2016 at 28% increase with number of new projects being
put in service. The subsequent increases taper off with the maximum of 10% in 2020.
Compared to the surveys from the previous years, the forecast of installed generating capacity is
significantly lower; approximately half when compared with 2014 forecast.
0
5
10
15
20
25
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Nu
mb
er
of
Re
spo
nse
s
Year
Co-Generation (no grid back-up or stand-by)
Co-Generation with grid back-up or stand-by
Other (please explain in notes at the bottom of page)
Purchase from the power pool (grid only)Historical Forecast
18 | P a g e
Figure 11: Forecast of Installed Capacity
Figure 12: Annual installed capacity increase
-
500.00
1,000.00
1,500.00
2,000.00
2,500.00
3,000.00
3,500.00
4,000.00
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Inst
alle
d C
apac
ity
[MW
]
Year
High
Low
Historical Forecast
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
2015 2016 2017 2018 2019 2020 2021 2022 2023
Gen
erat
ion
Gro
wth
Year
Historical Forecast
19 | P a g e
4.3.4 Exports to the grid Respondents were asked to estimate their available surplus capacity that they intend to export into the
grid. In the simplest sense, this is difference between the on-site installed capacity and the project
demand. However, respondents were asked to override the above relationship where they would not
export.
The results are shown in Figure 13. The annual growth and the cumulative growth of capacity available
for export are shown in Figure 14.
The results indicate that after 30% increase in 2016, the subsequent generating capacity additions
match the demand and the exports are not increasing.
Compared with the results of the survey from the previous years, the 2016 forecast of surplus export
capacity is both lower in absolute terms and in terms of its growth. The tempering of the growth was
already observed in results of 2014 survey; however the negative growth is apparent now in pre 2020
period.
Figure 13: Capacity Available for Export
0
200
400
600
800
1,000
1,200
1,400
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Cap
acit
y A
vaila
ble
fo
r Ex
po
rt [
MW
]
Year
High
Low
Medium
Historical Forecast
20 | P a g e
Figure 14: Growth of Capacity Available for Export
4.3.5 Pricing of Exports Respondents were asked to indicate how do they or how are they planning to price their export
capability. Out of the three presented options, no one indicated that the amount of export would be
price sensitive. This means that all co-generation oil sand operations that responded to the survey are
price takers with some having internal constraints that dictate the amount of exports.
This response is generally expected from industrial co-generation for bulk of their generation since the
generation output is typically dictated by the heat requirements. It is however notable that co-
generators do not operate some small portion of their generation in market price sensitive mode.
The response to pricing of exports is summarized in Figure 15.
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
2015 2016 2017 2018 2019 2020 2021 2022 2023
Load
Gro
wth
Year
Historical Forecast
21 | P a g e
Figure 15: Pricing of Exports
4.3.6 Standby Power Requirements If planning co-generation, the respondents were asked to identify what part of their demand, if any
would be backed up by the grid.
The results of the grid backup reliance is shown in Figure 16. The demand for grid backup shows a
steady increase.
Compared with the results of the survey from the previous years, the 2016 forecast of grid backup
reliance dropped by a similar ratio as was the drop in demand (about half).
Figure 17 shows comparison of the standby requirement with the demand growth and growth in
installed co-generation capacity. While the growth in absolute terms trails the demand growth, the
cumulative percentage growth in grid backup actually exceeds the growth in both installed co-
generation capacity and demand as shown in Figure 18.
0
2
4
6
8
10
12
14
16
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
No
of
Res
po
nd
ets
Year
Price Independent (Generator Must Run) Generation Price Response Dependent on the internal oil sands operations
Historical Forecast
22 | P a g e
Figure 16: Standby Power Requirement
Figure 17: Stand-by Requirement Compared with Electric Power Demand
-
200.00
400.00
600.00
800.00
1,000.00
1,200.00
1,400.00
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Stan
db
y R
equ
irem
ent
[MW
]
Year
High
Low
Medium
Historical Forecast
-
500.00
1,000.00
1,500.00
2,000.00
2,500.00
3,000.00
3,500.00
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Stan
db
y an
d D
eman
d R
equ
irem
ents
[M
W]
Year
Demand
Standby Required
Co-Gen
Historical Forecast
23 | P a g e
Figure 18: Cumulative Growth of Grid Standby Requirement compared to Growth in Demand and Co-Generation
4.3.7 Heat Recovery Respondent were asked for an estimate of how much of the project heat requirement is or is planning to
be related to production of electricity. For example, if all the heat requirement is generated in gas
turbine(s) and no supplemental firing or external boilers are used, the answer would be 100%.
The question is intended to provide information about potential of oil sands projects to generate
additional electrical energy for AIES.
The results which vary between 20% and 30% suggest that there is still considerable thermal potential
for additional electric power generation. It should be emphasized that not all of this heat potential can
be utilized for various reasons including but not limited to the plant operational requirement and
difficulties retrofitting existing projects.
-20%
0%
20%
40%
60%
80%
100%
120%
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Cu
mm
ula
tive
Gro
wth
Year
Demand Standby Required Co-Gen
Historical Forecast
24 | P a g e
Figure 19: Percentage of Project Heat Requirement that is related to Electricity Production.
4.3.8 Status of Co-Generation Projects The above reported generation capacity does not reflect the forecasted capacity past 2013 for reasons
stated previously. The simple summation of size of all generating units identified by the respondents is
shown in Table 12. The total vastly exceeds the total of the forecasted generation in 2023.
Extensive interpretation of the respondent’s intention would be required to align the forecast of project
generation with the sum of unit capacity3. Question was therefore not further analyzed.
Table 12: Generating Capacity by Stage of Development
Sum of Size [MW]
Built and/or operating 2913
Under construction 210
Has been fully approved by the Regulatory Boards 233
Has been fully approved by the Company Boards 101
Under development 68
Announced only 15 Conceptual Planning Stage 2460
Grand Total 6000
3 It is suspected that the previous year analysis may have reported results of this question and that at least some of difference between 2014 and 2016 results is due to this discrepancy. However, despite considerable effort this could not be confirmed.
0%
5%
10%
15%
20%
25%
30%
35%
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Pe
rce
nt
of
He
at R
eq
uir
em
en
t R
ela
ted
to
El
ect
rici
ty P
rod
uct
ion
Year
Historical Forecast
25 | P a g e
4.3.9 Regulatory Status Respondents were asked the status of the regulatory application with respect to the EUA Section 101
release and ISD designation for each project. The results are shown below.
Planned Application
Filed Application Approved
Disco EUA section 101 approval 12 0 10
Industrial System Designation 20 1 12
Grand Total 32 1 22
4.3.10 Reliability Only two respondents in three projects identified that there were constraints in 2014 and 2015 but did
not specify the frequency or duration. Since it is unlikely that there were no power outages of any kind
experienced by the other projects, it is assumed that most respondents did not answer this question.
4.3.11 Other On-site Power Generation Respondents were asked whether they are considering other material on site power generation.
The results are shown in Table 13. There was very low response rate to this question (six) indicating that
there are not much of an interest in non-cogeneration power supply.
The type of generation was diesel in most cases and diesel & gas.
Table 13: Other Generation
Row Labels 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Are you considering (or do you already have) other material on-site generation (e.g. diesel, solar, peak shaving etc.)
3 3 5 5 5 5 6 6 6 6
Please specify size (MW) 8.2 5 39 39 39 40 45 45 45 45
Grand Total 8.2 5 39 39 39 40 45 45 45 45
26 | P a g e
5 Duration Curve Analysis Both electrical power demand and electrical power generation are typically expressed in terms of peaks.
Electrical power demand (load) is characterized by its annual4 peak and generation by its installed
capacity, i.e. the maximum generating capability. However, peaks provide a crucial but partial picture. In
practice, the electrical load does not stay at its peak level for the whole year. Furthermore, different
customers’ peaks occur at different times reducing the aggregate peak (coincident peak) when
compared with the sum of individual peaks (noncoincident peak).
One index which reflects the above is Load Factor which is a ratio of average load and annual peak. For
example, a load factor of 0.5 indicates that the average load is only 50% of the annual peak.
While indices such as the load factor improve the understanding of the load variability, a much better
indication comes from use of duration curves. Duration curves show in sequential order the length of
time each power level was present. When showing load, duration curves are called load duration curves
and when generation, generation duration curves. Duration curves can be prepared for other variables
as well. Examples include export duration curve or power deficiency duration curve.
In this analysis, annual duration curves with granularity of one hour are used to show the various
characteristics of AEIS in general and Fort McMurray in particular.
5.1 Load Duration Curves
5.1.1 Alberta Interconnected Load Load duration curves for Alberta interconnected load (AIL) are shown in Figure 20 for the 2010 to 2015
time period. The summary statistics for the same are shown in Table 14. The data demonstrates the
importance of the supplementary information in addition to annual peak data. For example, the peak
has changed little between 2010-2011 and 2013-2014 yet the energy continued to grow at the healthy
rate of 2.6% and 3.2% respectively.
By comparison, the 2014 to 2015 period experienced almost no load growth with the peak increasing by
0.5% and energy by 0.4%.
Table 14: AIL Peak Load and Energy Growth
Year 2010 2011 2012 2013 2014 2015
Peak [MW] 10,196 10,226 10,609 11,139 11,169 11,229
Load Factor 80% 82% 81% 79% 82% 82%
Load Growth 0.29% 3.75% 5.00% 0.27% 0.54%
Energy [GWh] 71,568 73,448 75,249 77,287 79,781 80,089
Energy Growth 2.63% 2.45% 2.71% 3.23% 0.39%
The slope of the AIL middle section of the load duration curves shows little change from year to year.
4 One year is a common time period to consider but monthly peak, seasonal peak and other indices are in common use as well.
27 | P a g e
Figure 20: Load Duration Curves - Alberta Interconnected Load Demand
5.1.2 Fort McMurray Fort McMurray load is discussed in three steps. The municipal load is discussed first followed by the oil
sand load. The two are subsequently combined to reflect the load of the whole Fort McMurray area.
The load profiles were obtained from AESO and are the measured Point of Delivery (POD) loads.
Consequently, it should be recognized, that the Fort McMurray urban load is expected to be
approximately the actual installed load while the Fort McMurray oil sand load comprises customers with
significant amount of co-generation. For these customers, the load is the difference between Behind the
Fence (BTF) load and BTF generation.
5.1.2.1 Fort McMurray Urban Area Load
Fort McMurray urban area load shows a drop in 2015 to pre-2013 levels in both peak and energy. The
change over 2014 is -4% in terms of peak demand and -3.7% in terms of energy. The load duration
curves are shown in Figure 21 and the load summary is shown in Table 15.
6000
7000
8000
9000
10000
11000
12000
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Load
[M
W]
Hours
2010 2011 2012 2013 2014 2015
28 | P a g e
Table 15: Fort McMurray Municipal Demand Peak Load and Energy Growth
Year 2010 2011 2012 2013 2014 2015
Peak [MW] 94 96 99 104 106 102
Load Factor 61% 64% 64% 64% 64% 65%
Load Growth 1.75% 3.32% 4.96% 2.08% -4.00%
Energy [GWh] 504 537 559 585 598 576
Energy Growth 6.67% 3.97% 4.67% 2.30% -3.67%
Figure 21: Load Duration Curves – Fort McMurray Municipal Load
5.1.2.2 Fort McMurray Area Oil Sands Load
The load duration curves are shown in Figure 22 and the load summary is shown in Table 16.
The load has grown consistently from 2010 to 2015 in terms of both energy and peak except for 2013 to
2014 time period where the annual peak drops. However, this is caused more by the significant increase
in peak in 2013 (33%) and corresponding drop in load factor (60%) than by any intrinsic trends.
The atypical load duration curve in 2013 is caused by the significant project additions during the year.
An interesting trend surrounds the year by year increase in load factor without a corresponding
reduction in the apparent load duration curve slope. The explanation of the reduction in load factor
relates to the relative flattening of the few peak hours of the duration curve which most likely relates to
increase in both load and amount of co-generation. The peak hours of the oil sands duration curves are
0.0
20.0
40.0
60.0
80.0
100.0
120.0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Load
[M
W]
Hours
2010 2011 2012 2013 2014 2015
29 | P a g e
influenced by the backup power, unexpected loss of BTF generating units without a corresponding drop
in BTF load and other similar events. As the size of the local electrical system increases relative to the
average unit size, the loss of any one unit has a smaller relative effect. The trend is documented in Table
17 by showing the load factor calculated for the 500 highest load hours and load factor calculated for
the middle section of the duration curve with the first and last 500 hours excluded. The former is
showing significant increase while the latter remains relatively constant (except for 2013 for reasons
previously discussed).
Table 16: Fort McMurray Oil Sands Demand Peak Load and Energy Growth
Year 2010 2011 2012 2013 2014 2015
Peak [MW] 410 477 503 673 660 692
Load Factor 63% 62% 64% 60% 70% 70%
Load Growth 16.25% 5.35% 33.89% -1.86% 4.83%
Energy [GWh] 2,269 2,581 2,820 3,517 4,017 4,225
Energy Growth 13.77% 9.24% 24.72% 14.22% 5.19%
Figure 22: Load Duration Curves – Fort McMurray Oil Sands Load
0.00
100.00
200.00
300.00
400.00
500.00
600.00
700.00
800.00
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Load
[M
W]
Hours
2010 2011 2012 2013 2014 2015
30 | P a g e
Table 17: Fort McMurray Oil Sands Load Factor for the First 500 Hours and for the Middle Section of the Load Duration Curve
Year 2010 2011 2012 2013 2014 2015
Load Factor for the first 500 Hours 78.4% 79.1% 87.7% 82.8% 86.2% 90.5% Load Factor excluding the first 500 Hours 78.3% 79.6% 76.8% 71.9% 80.6% 79.6%
5.1.2.3 Combined Fort McMurray Area Load
Load data for Fort McMurray area show that the load growth in the area significantly exceeds the rest of
Alberta. The data also shows an increase in load factor as the large industrial load component increases
much faster than the municipal composition of commercial, small industrial and residential load.
Interestingly, the peak in 2013 exceeded the peak in 2014, however the energy shows more orderly
growth at 14.2% in 2014 and 5.2% in 2015.
The ratio of large industrial load to municipal load in Fort McMurray area reaches approximately 6:1
ratio in 2015; a significant increase over approximately 3:1 in 2010.
Table 18: Fort McMurray Area Peak Load and Energy Growth
Year 2010 2011 2012 2013 2014 2015
Peak [MW] 410 477 503 673 660 692
Load Factor 63% 62% 64% 60% 70% 70%
Load Growth 16.25% 5.35% 33.89% -1.86% 4.83%
Energy [GWh] 2,269 2,581 2,820 3,517 4,017 4,225
Energy Growth 13.77% 9.24% 24.72% 14.22% 5.19%
31 | P a g e
Figure 23: Load Duration Curves – Fort McMurray Area
5.2 Generation Duration Curves Generation duration curves were not available directly from AESO. Instead, the curves were prepared
for Fort McMurray area by superimposing the area export on top of the load. It should be stressed that
the resulting generation is in fact a net injection into the AIES; the installed generation in the area is
considerably larger. The generation duration curves are shown in Figure 24 and their summary is shown
in Table 19.
The generation injection into the AIES shows significant increase in the 2010 to 2015 timeframe of 370
MW (43%), however the year over year increase oscillates. This is to be expected as the load additions
do not necessarily match the generation additions.
Years 2012 and 2013 show gradual step change in the 1000 to 2000 hour range. This step is caused by
large generation additions.
Table 19: Fort McMurray Area Generation Growth
Year 2010 2011 2012 2013 2014 2015
Peak [MW] 855 814 1,067 1,360 1,259 1,225
Generation Factor 69% 64% 60% 66% 74% 72%
Growth -4.87% 31.18% 27.40% -7.41% -2.75%
Energy [GWh] 5,181 4,568 5,581 7,799 8,095 7,753
Energy Growth -11.84% 22.17% 39.76% 3.79% -4.23%
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
800.0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Load
[M
W]
Hours
2010 2011 2012 2013 2014 2015
32 | P a g e
Figure 24: Generation Duration Curves – Fort McMurray Area
5.3 Fort McMurray Area Export The Fort McMurray area export is defined as the sum of the flows on the following power system
elements (southerly direction is defined as positive export):
1) Transmission line 9L74,
2) Transmission line 9L07,
3) Transmission line 9L23,
4) Transmission line 9L84,
5) Transformer 901T location within Ruth Lake 848S substation, and
6) Transformer 902T located within the Ruth Lake 848S substation.
Fort McMurray area export is shown both in terms of export variation curve and export duration curve
for years 2014 and 2015. Load variation curves are shown in Figure 25 and Figure 26 while the load
duration curves are shown in Figure 27. The summary statistics are shown in Table 20.
The Fort McMurray area export shows significant increase from 2010 to 2015 both in terms of peak and
energy (57% vs. 59% respectively). Similar to net generation injection, the export fluctuates from year to
year as the generation additions do not match exactly the timing of load additions.
-
200
400
600
800
1,000
1,200
1,400
1,600
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Load
[M
W]
Hours
2010 2011 2012 2013 2014 2015
33 | P a g e
Figure 25: Fort McMurray Area Export Variation Curve - 2014
Figure 26: Fort McMurray Area Export Variation Curve - 2015
-600.0
-400.0
-200.0
0.0
200.0
400.0
600.0
800.0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
FMM
Exp
ort
[M
W]
Hour Ending
Import Limit
Export Limit
-600.0
-400.0
-200.0
0.0
200.0
400.0
600.0
800.0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
FMM
Exp
ort
[M
W]
Hour Ending
Export Limit
Import Limit
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Figure 27: Fort McMurray Area Export Duration Curve for 2010 to 2015
Table 20: Fort McMurray Area Export Statistics
Year 2010 2011 2012 2013 2014 2015
Peak [MW] 451 431 636 732 737 709
Export Factor 56% 41% 50% 67% 63% 57%
Growth -4.48% 47.73% 15.00% 0.73% -3.79%
Energy [GWh] 2,214 1,528 2,765 4,286 4,082 3,530
Energy Growth -30.98% 80.97% 55.01% -4.77% -13.51%
Min 0 (117) (93) (113) 64 116
Average 253 175 316 491 467 404
Two important observations can be made from the data:
1) Both 2014 and 2015 shows exports only (i.e. no negative values)
2) Peak Exports correspond to the AESO export limits (presently 730 MW under the most favorable
conditions).
(600)
(400)
(200)
-
200
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800
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
FMM
Exp
ort
[M
W]
Hour Ending
2010 2011 2012 2013 2014 2015
Export Limit
Import Limit
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5.4 Fort McMurray Area Forecast Survey respondents were asked to identify the existing and forecast future co-generation projects as
well as electrical power demand. The results were presented earlier in the report. Figure 28 show the
results for Fort McMurray area.
Figure 28: Forecast of Load and Generation Based on Survey Results
Both the load growth and co-generation growth significantly exceeds the growth anticipated in AESO
forecast in the area. Comparison of the load forecast obtained from the survey with the three most
recent forecast presented in AESO Long Term Outlook is shown in Figure 29.
Forecast of the Fort McMurray area export was prepared using the 2015 load and generation data
obtained from AESO and the relative load and generation increase in 2020 and 2023 obtained from the
surveys. The results are shown in Figure 30.
The results indicate that in the absence of the Fort McMurray East 500kV Transmission Project5 the
surplus generation available for export from the Fort McMurray area will exceed the available maximum
transfer capability in more than 1000 hours per year.
5 Based on 2017 LTO, AESO is deferring the Fort McMurray East 500kV Transmission Project (FMM
500kV line).
0%
20%
40%
60%
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120%
140%
160%
180%
0
500
1000
1500
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2500
2015 2016 2017 2018 2019 2020 2021 2022 2023
Cu
mm
ula
tive
Gro
wth
[%
]
MW
Year
Load Generation
Load Growth Generation Growth
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Figure 29: Comparison of Load Growth Forecast from Survey with AESO Forecast from Recent Long Term Outlook
Figure 30: Forecast of Fort McMurray Area Export Duration Curve for 2020 and 2023
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
2016 2017 2018 2019 2020 2021 2022 2023
Cu
mm
ula
tive
Gro
wth
Year
Load Growth AESO 2017 LTO AESO 2016 LTO AESO 2014 LTO
-600
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0
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0 1000 2000 3000 4000 5000 6000 7000 8000 9000
FMM
Exp
ort
[M
W]
Hour Ending
2020 2023
Present Export Limit
Import Limit
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6 Probabilistic Analysis The forecasting analysis shown in the previous section is based on measurement data for area load and
area export flows as supplied by AESO. While the load data is expected to accurately represent the
present duration curves and can be used as the basis for future load modeling, the export data (and
consequently the generation data) may suffer from several constraints.
Firstly, the data is based on metered data; BTF generation offsetting load will result in measurement of
net load or net generation underestimating both.
Secondly, it is reasonable to assume that the actual performance is affected by the known system
constraints. Trying to assess the system potential from actual data that reflects system constraint
creates a circular dependency and would lead to the wrong conclusions.
To address this concern, a detailed probabilistic analysis was prepared that reflects the typical
performance of the existing and planned units and this analysis was combined with the load model
prepared from the survey results and AESO data.
The probabilistic analysis used follows the industry standard probabilistic power system reliability
analysis principles6.
6.1 Probabilistic Reliability Analysis Basics The objective of the probabilistic reliability analysis is typically to determine the reliability of power
supply to the load using one of the reliability indices such as the Loss of Load Expectation (LOLE), Loss of
Load Probability (LOLP), Expected Energy Not Supplied (EENS) or Frequency and Duration of outages
(F&D). However the same techniques can be used to ascertain probabilistically the reliance of one
service area on another or the frequency of exceeding the power transfer limits between areas.
To better understand the concepts used in the analysis, this section presents the very basics of
probabilistic reliability modeling and assessment.
The probabilistic quantitative reliability evaluation methods utilized in this study permit reliability
indices for any electric power system to be computed from the knowledge of the reliability performance
of the individual system components.
The term “reliability” in respect to power system performance is defined here as the ability of the
system to provide an adequate supply of electrical energy. System reliability is divided into two
domains:
System adequacy related to the existence of sufficient facilities within the system to satisfy the
load demand. Included are the facilities necessary to generate sufficient energy and the facilities
required to transmit and distribute the energy to the load points. Adequacy is therefore
associated with static conditions, which do not include system disturbances.
System Security related to the ability of the system to respond to disturbances arising within
that system.
6 These should not be confused with the deterministic reliability modeling presently used by AESO.
38 | P a g e
While the two domains are closely interconnected and from the end user perspective there is little
difference between power outages or shortages caused by either, from the planning perspective it is
useful to divide them. To plan for a power system with sufficient adequacy requires both significant lead
time and financial resources. By comparison, system security, given the sufficient adequacy, can be
typically addressed by means with comparatively short lead times and much lesser financial resources.
For these reasons, consideration of adequacy should precede other considerations in any properly
planned power system.
Probabilistic reliability modeling and calculations can be daunting even given the advances in modern
computers and computational techniques. For these reasons as well as other reasons, the industry
practice is to divide the system into functional zones (hierarchical levels):
1. Hierarchical Level I (HLI) defines the reliability of generation only. In HLI studies, the
transmission and distribution are assumed to be 100% reliable.
2. Hierarchical Level II (HLII) defines the reliability of both generation and transmission. Thus, HLII
studies determine the reliability as seen at the substations.
3. Hierarchical Level III (HLIII) utilizes the results of HLII study to determinate reliability indices at
the load delivery points.
6.1.1 Probabilistic Approach Used on This Study The first probabilistic model used in this study is a modified HLI model. In this approach, the generation
Fort McMurray area is modeled using Composite Outage Probability Table (COPT) using Frequency and
Duration (F&D) model.
COPT aggregates the generation to form a probabilistic distribution showing likelihood of availability of
certain generation level. The model can answer questions such as what is the probability of generation
falling short of a certain level and what is its expected frequency and duration.
Once combined with forecasted load profile, probability, frequency and duration of power shortage can
be determined. Furthermore, the probability of power shortages can be determined with various
degrees of severity thus allowing for the computation of indices such as Expected Energy Not Supplied
(EENS).
In this analysis, the COPT model was expanded to investigate a number of additional issues such as the
unconstrained potential of FMM with respect to export to the rest of AIES and the magnitude of the
present constraint.
6.1.2 Fort McMurray area Reliability and Export Potential The objective of the analysis was to:
1) determine the reliability of supply given the local generation
2) show probabilistically the potential of Fort McMurray to export power to AIES after supplying
the area load.
For the purpose of the analysis it is assumed that no congestion exists between the Fort McMurray area
and the rest of the AIES.
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The model used in the analysis is shown in Figure 31. The probabilistic generation model is combined
with the deterministic area load model. COPT is used for the generation model. Load variation curve
comprising 8,760 hourly values is used for the load model.
GenerationCOPT
Load
Fort McMurray System AIES
Export
Import
Figure 31: Probabilistic Model
6.1.2.1 Generation Model
Generation model was derived from all units in the area, their nominal generation capability (MCR),
representative average failure rates, average repair times and the annualized maintenance requirement.
The summary of area generation which was used as the basis for the model and the peak load forecast is
shown in Table 21. The 2015 reserve margin of 50% is considered very high by typical utility standards
which typically range between 15% to 24%. Consequently, the relibility is expected to be very high and
resulting export potential significant.
Table 21: Fort McMurray Generation and Load Forecast Assumed in the Probabilistic Analysis
Year 2015 2016 2017 2018 2019 2020 2021 2022 2023
Total Installed Generation 1962 2047 2047 2257 2257 2257 2257 2257 2257
Load Peak [MW] 1299 1357 1466 1626 1667 1750 1765 1892 1890
Reserve Margin 51% 51% 40% 39% 35% 29% 28% 19% 19%
The probabilistic generation model is shown in terms of generation availability duration curve in Figure
32. The change between 2015 to 2023 reflects the forecasted unit additions at Horizon and Fort Hills.
The present system normal export limitation (710 MW) is shown for comparison.
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Figure 32: Generation Probabilistic Model
6.1.2.2 Load Model
The load was derived from the area forecasted peak demand (obtained from surveys) and the load
duration curve obtained from AESO. The oil sands forecasted load was combined with the forecasted
urban Fort McMurray load. The resulting model is shown for years 2015, 2020 and 2023 in terms of load
duration curve in Figure 33.
Figure 33: Load Model
0.0
250.0
500.0
750.0
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1250.0
1500.0
1750.0
2000.0
2250.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Gen
erat
ion
Cap
abili
ty [
MW
]
Probability
Generation Model
COPT 2016-2017 COPT 2015 COPT 2018 - 2023 Export Limit
0
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Load
[M
W]
Probability
Load Model
2020 Series4 2023 2015
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6.1.2.3 Export Model
The chronological load model was combined with the COPT generation model to derive the typical
reliability indices and to determine the Fort McMurray generation export potential.
The basic reliability indices for Fort McMurray are shown in Table 22. The values are determined
without consideration of interconnection to the rest of AIES or its reliability; it shows the reliability of
the generation power supply in Fort McMurray area assuming only the Fort McMurray area generation
and load.
The reliability is high as can be expected from the high reserve margins.
Table 22: Typical Reliability Indices for Fort McMurray Area
2015 2020 2023
Reserve Margin 51% 29% 19%
LOLE [h/y] 0.00006 0.0084 0.2072
EENS [MWh/year] 0.00167 0.3048 8.9836
The export potential is shown in Table 23 together with the estimate of constrained energy based on
present export limits. The same probabilistic capability model is shown in terms of surplus capability
curves in Figure 34.
Since the forecasted load growth exceeds the forecasted generation additions, the expected constrained
energy decreases with time; however it remains material even in later years.
Table 23: Export Potential from Fort McMurray Area
2015 2020 2023
Export Potential [MWh] 8,244,714 8,023,189 7,167,267 Export Constrained [MWh] 2,025,114 1,803,589 947,667
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Figure 34: Probabilistic Surplus Capability Model
6.2 Conclusions The probabilistic generation model was prepared for Fort McMurray area generation. This model was
combined with the hourly load model and typical reliability indices were calculated.
Results show that the area has a significant surplus generation and from the adequacy point of view
does not need to rely on the rest of AIES. The conclusion is collaborated by the previously analyzed
actual data. The observed reliability indices are significantly better when compared to typical utility
systems. It is however acknowledged that the consequences of power supply outages are higher in Fort
McMurray area when compared to a typical utility system.
The analysis also shows that the area generation potential significantly exceeds the available transfer
capability to the rest of AIES. While it is acknowledged that modeling the generating unit output using
the unit MCR is inherently optimistic since it does not consider process related generation constraints
that may limit some of the unit outputs, it is apparent that reducing or eliminating the transfer
capability constraints would improve utilization of the existing generating capacity in Alberta.
0.0
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800.0
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1200.0
1400.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Exp
ort
Cap
abili
ty [
MW
]
Probability
Export Capability Probabilistic Model
2015 2020 2023 Limit
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References [1] Roy Billinton, Ronald N Allan, “Reliability Evaluation of Engineering Systems”, Pitman Books
Limited, 1983
[2] Roy Billinton, Ronald N Allan, “Reliability Evaluation of Power Systems”, Plenum Press, 1984
[3] IEEE Std. 493-1990, “Design of Reliable Industrial and Commercial Power Systems”, IEEE Press, May 1991
[4] IEEE Std. 500-1984, “IEEE Guide to the Collection And presentation of electrical, electronic, sensing component, and mechanical equipment reliability data for nuclear-power generation stations”, IEEE Press, December 1983
[5] Canadian Electrical Association, “Equipment Reliability Information System: Forced Outage Performance of Generation Equipment”, Period January 1, 1991 to December 31, 1995, published October 1996.
[6] Canadian Electrical Association, “Equipment Reliability Information System: Forced Outage Performance of Transmission Equipment”, Period January 1, 1988 to December 31, 1992, published October 1993.
[7] CIGRE 13-06 Working Group, “Summary of CIGRE 13-06 Working Group World Wide Reliability Data, Maintenance cost data, and studies on the worth of improved reliability of high voltage circuit breakers”, Industrial and Commercial Systems Annual Technical Conference, Cleveland, Ohio, May 5-8, 1986.
[8] CIGRE 13-06 Working Group, “Final Report of the second international inquiry on High Voltage Circuit breaker failures and defects in service, CIGRE special publication, June 1994.
[9] E.J. Henley, H. Kumamoto, “Probabilistic Risk Assessment”, IEEE Press, 1992
[10]WASH 1400 “Reactor Safety Study”
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