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Polanowski, Peter
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UNIVERSITY OF CALGARY
An Outlook of Utility and Residential Solar Photovoltaic Growth Potential in Alberta from
2020-2030
By
Peter Polanowski
A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE REQUREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
GRADUATE PROGRAM IN SUSTAINABLE ENERGY DEVELOPMENT
CALGARY, ALBERTA
AUGUST, 2020
© Peter Polanowski 2020
Abstract
In this paper, I present a review of literature assessing the key factors in solar PV adoption, while also estimating installed capacity for utility-scale and residential PV installations across
Alberta by 2030. Several economic and socio-demographic variables are known to contribute to solar photovoltaic (PV) distribution, such as the resource availability, installed costs, household median income, policy instruments and the availability of financial incentives. Total employment potential from resulting estimates suggest that over 17, 000 jobs (in job-years) can be created in construction and installation of solar PV installations across the province.
Resulting GHG avoided emissions were also calculated from projected installed utility-scale projects. Taken together, these results will help policymakers in what policy measures can be taken to facilitate further growth in the industry, as well as provide training organizations foresight on where best to apply training programs for workers transitioning to the renewable energy economy.
Key words: Solar photovoltaic Installations GHG emissions Job growth
ii
Acknowledgements
I would like to express my sincere gratitude to the following people and organizations for their support in this project:
• Dr. Irene Herremans – Thank you for your continuous feedback, support and motivation,
especially during challenging times.
• Dr. Ganesh Doulaweerawatta – Thank you for supervising this project and your
continuous feedback.
• Adam Lynes-Ford and Iron & Earth – Thank you Adam for spurring interest in this project
and enabling me to contribute to Iron & Earth’s research.
• Crystal Hickey – Thank you for motivating me to ‘stick with the program’.
• Kelvin Tan – Thank you for your support and feedback throughout the project.
iii
Table of Contents
Approval Page ...... i
Abstract ...... ii
Acknowledgements ...... iii
Table of Contents ...... iv
List of Tables ...... vi
List of Figures ...... vi
List of Abbreviations ...... vii
Chapter 1 - Introduction ...... 1
1.1 Interdisciplinary Research Areas ...... 2
1.2 Research Objectives ...... 3
1.3 Solar PV Systems ...... 3
Chapter 2 - Related Literature ...... 5
2.1 Solar resource availability ...... 5
2.2 Economic Factors ...... 7
2.3 Policy and Incentive Programs ...... 8
2.4 Socio-demographic factors affecting residential solar PV installations ...... 12
2.5 Variables affecting commercial, community and utility-scale projects ...... 14
Chapter 3 – Solar PV Job Growth Potential ...... 15
3.1 Current trends in solar PV job growth ...... 15
3.2 Methods to analyze job creation potential of solar PV ...... 15
iv
3.4 Solar PV value chain job distribution ...... 18
3.5 Research Gaps ...... 19
Chapter 4 - Methodology and data collection ...... 20
4.1 AESO Project List Methodology and Data Collection – Utility-Scale, centralized and distribution- connected projects ...... 20
4.2 Residential PV Methodology and Data collection ...... 25
Chapter 5 - Results and Analysis ...... 28
5.1 AESO Project List results ...... 28
5.2 Residential Solar PV results ...... 31
Chapter 6 - Conclusion, Limitations and Future Research ...... 34
6.1 Limitations ...... 34
6.2 Future Research ...... 35
6.3 Conclusion ...... 37
References ...... 40
Appendix A – July 2020 AESO Project List: All solar PV installations ...... 53
Appendix B - Census Data for Albertan Population Centres ...... 56
v
List of Tables
Table 1: Installed Capacity and employment figures from AESO Project List analysis ...... 30
Table 2: Results of Rooftop residential PV installed capacity figures ...... 31
Table 3: Installed capacity and job figures results for residential rooftop PV ...... 33
List of Figures
Figure 1: Alberta Solar Resources Map ...... 6
Figure 2: Total PV system cost benchmarks from 2010-2018 in U.S. inflation-adjusted prices ...... 7
Figure 3: U.S. Solar Employment by Sector ...... 19
Figure 4: AESO Transmission Planning Areas ...... 23
Figure 5: AESO Project List – Projected Installed Capacity by Planning Area (MW) ...... 29
Figure 6: Avoided GHG emissions from AESO Solar PV Projects – 2020-2045 (Mt CO2) ...... 29
Figure 7: Alberta Population Centres with annual average capacity factors >13% and median income >
$36,000...... 32
vi
List of Abbreviations
AICEP: Alberta Indigenous Community Energy Program
AISP: Alberta Indigenous Solar Program
GHG: Greenhouse Gases
MW: Megawatt(s)
IEA: International Energy Agency
IRENA: International Renewable Energy Agency
LCOE: Levelized Cost of Electricity kW: kilowatts kWh: kilowatt-hour(s)
PV: Photovoltaic
MCCAC: Municipal Cilimate Change Action Centre
NREL: National Renewable Energy Laboratory
EEG: Renewable Energy Resource Act
RESOP: Renewable Energy Standard Office Program
GEGEA: Green Energy and Green Economy Act
FIT: Feed-in Tariff
GEIA: Green Energy Investment Agreement
LRP: Large Renewable Program
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NASEO: National Association of State Energy Officials
EFI: Energy Futures Initiative
I-O: Input-Output model
JEDI: Jobs and Economic Development Impacts
O&M: Operations and Maintenance
FTE: Full-time Equivalent (jobs)
AESO: Alberta Electricity Systems Operator
SASR: System access Service Request
LTO: Long-term Outlook
viii
Chapter 1 - Introduction
The growth of renewable energy technologies is necessary to reduce greenhouse gas emissions
(GHG) and mitigate the adverse effects of climate change. Decarbonization strategies, combined with falling costs, economies of scale, increased efficiencies and strong policy incentives have led to a global boom in solar photovoltaic installations across the globe
(International Energy Agency [IEA], 2019). Canada is no exception, installing over 3 095
Megawatts (MW) of solar PV capacity by the end of 2018, with the majority (96% in the
Province of Ontario (Baldus-Jeursen, Poissant & Johnson, 2018).
In contrast, the province of Alberta is the heart of the Canadian oil and gas industry, not normally known for its renewable energy resources. This paradigm is shifting. Since 2009, the province has gone from 1 MW to over 90 MW of installed centralized and distributed capacity.
The future of solar PV looks bright in Alberta (Howell, 2018; Alberta Electricity Systems
Operator [AESO], 2019).
My research questions are: What are the key factors in predicting solar PV adoption? In addition, what is the growth potential in solar PV installations across Alberta in the next decade? More specifically, I have sought to predict where and what kind of growth we can see in utility-scale and residential PV installations. By being able to analyze where solar PV growth is most likely to occur, this research aims to provide insights on local job growth potential.
Moreover, it will help organizations like Iron & Earth in the development of specialized training programs that facilitate fossil fuel and indigenous workers to transition into the renewable energy economy.
1
Increasingly, Alberta is taking a lead in solar PV and wind energy generation through effective policies and a spur of public and private investments (Palmer-Wilson et al., 2019). Alberta has the second highest solar production potential in Canada, meaning that an average solar PV system installed would produce 1276 kilowatt-hours, per kilowatt, per year (kWh/kW/yr)
(NRCAN, 2020). Additionally, there are a host of other variables that affect the concentration and distribution of solar PV systems. Economic variables such as the Levelized Cost of Electricity
(LCOE), as well as socio-economic variables like household median income and the number of local installation companies or organizations (Kurdgelashvili, Shih, Yang & Garg, 2019; Schaffer
& Brun, 2015; (Sommerfeld, Buys, Mergersen & Vine, 2017). Analyzing these variables will provide a basis for predicting what areas of the province will see growth in PV installations over the next decade.
1.1 Interdisciplinary Research Areas
This project is multidisciplinary in nature, encompassing the fields of socio-demographics, energy, and the environment. First, a review of literature was undertaken to assess the key economic and socio-demographic factors that affect solar PV adoption. Literature of job creation methods and statistics was also presented. Following this, cumulative installed capacity for utility scale and residential projects was calculated using Alberta Electricity Systems
Operator (AESO) data (AESO, 2019; AESO, n.d.c.), Statistics Canada (2016) information, and additional reports forecasting future solar PV installed capacity in Alberta (Solas, 2018). Job creation potential and GHG emissions avoided from solar PV installations displacing fossil fuel generation were also estimated.
2
The following research seeks to provide policymakers with insights on what additional incentive measures can be put forth across jurisdictions in Alberta to help facilitate further growth in the industry. It will also provide solar installation organizations and non-governmental organizations with insights on where growth likely is to happen, along with where training opportunities may be most applicable to enhance job growth capacity in the sector.
1.2 Research Objectives
● Analyze main contributing economic and socio-demographic variables affecting solar PV
distribution
● Estimate installed capacity, and job creation potential, and GHG emissions offset of
large-scale distribution and transmission connected projects in Alberta from 2020-2030
● Estimate installed capacity, job creation potential and GHG emissions offset of
residential PV installations in Alberta by 2030
1.3 Solar PV Systems
All solar PV systems are either grid-connected or off-grid. The majority of installations in
Canada are grid-connected. No nation-wide data was found on the capacity of off-grid installations across Canada (Baldus-Jeursen et al., 2018).
Out of grid-connected systems, solar PV installations can be either centralized or distribution- generated (DG). Centralized electricity generation produces electricity at a central source. The electricity is fed into high-voltage transmission lines and distributed to multiple end-users
(Tamimi, Cańizares & Bhattacharya, 2013). Centralized solar typically refers to large, utility-scale
3 installations that are 5 MW or greater. The 17 MW Brooks Solar farm in Brooks, Alberta is a utility-scale, centralized solar facility.
DG electricity generation produces electricity at or near the source of consumption (Doluweera et al., 2020). A rooftop installation on a house or commercial building are examples of DG. The resulting installation can serve a single house or building or can be fed back into the distribution grid.
Systems can be further categorized into residential, commercial and utility-scale systems.
Residential solar PV systems are usually installed on rooftops and range from approximately 3 kilowatts (kW) to 10 kW in size. Commercial and industrial solar PV systems are also predominantly installed on rooftops and typically range from 10 kW to 2 megawatts (MW) in size. Utility-scale systems are typically ground-mounted and on average provide a capacity of greater than 2 MW (Fu et al., 2018; Municipal Climate Change and Action Centre [MCCAC],
2019).
This report mainly focuses on Alberta’s estimated installed capacity of utility-scale and residential PV systems, and the resulting job impacts and GHG avoided emissions.
4
Chapter 2 - Related Literature
The decision to adopt a solar PV installation involves a complex interplay of institutional, environmental, economic and socio-demographic factors (Karakaya, Hidalgo and Nuur, 2015;
Schelley & Letzelter, 2020; Sigrin, Pless and Drury, 2015). These factors can vary depending on the type of system installed. Regional and local differences also have an effect on solar PV adoption and distribution (Mundaca & Samahita, 2020; Robinson & Rai, 2015).
An extensive amount of literature now exists on adoption factors affecting solar PV adoption, diffusion, and geographical distribution. A review of literature was undertaken in this study to examine what key variables affect adoption rates of residential, commercial, utility-scale and community-level solar PV installations.
2.1 Solar resource availability
Solar radiation, or solar potential are terms that are used to describe the electromagnetic radiation that is emitted by the sun. Sunlight that passes through the atmosphere and is scattered, absorbed, or reflected is called diffuse solar radiation. Sunlight that reaches the surface of the Earth without interference is called direct solar radiation. Taken together, these forms or sunlight are called global solar radiation. Solar energy can be measured by the total radiation on a horizonal surface, such as a solar PV panel. Solar resource potential for solar PV systems is usually represented in kilowatt-hours per square metre (kWh/m2) (Office of Energy
Efficiency and Renewable Energy [energy.gov], 2013). Alberta has some of the best solar resource potential in Canada, with southeastern jurisdictions showing the most energy potential per kW installed. Several studies show a high correlation with strong levels of solar insolation and the prevalence of solar PV technology (Crago and Koegler 2018; Kwan 2012).
5
Figure 1: Alberta Solar Resources Map
Note: Map provides an estimate of solar energy available for power
Generation. Represented by average daily/yearly sum of global horizontal
Irradiation (GHI). Adapted from Solargis (n.d.)
6
2.2 Economic Factors
Total installed costs of solar PV systems are one of the major factors that affect adoption in residential, commercial, and utility-scale systems and have seen dramatic declines in the last decade. Total costs represent ‘hard costs’ such as module prices along with balance of system components (steel structure, electrical components), as well as ‘soft costs’, which include factors such as installation costs, permitting, inspection, design and construction (Fu et al.,
2018; Barbose et al., 2019). According to the National Renewable Energy Laboratory’s (NREL)
Tracking the Sun Report, the average total installed costs for residential systems were $3.11 per watt AC (Wac), $2.10 Wac for commercial systems and $1.44 for fixed-tilt utility scale systems
(Barbose et al. 2019).
Figure 2: Total PV system cost benchmarks from 2010-2018 in U.S. inflation-adjusted prices
Source: (Barbose et al., 2019).
7
Another metric for highlighting solar PV costs is the Levelized Cost of Electricity (LCOE), which is the all-in cost of the power produced by the system. It includes both hard and soft costs, as well as well as the estimated power produced by the solar PV array over the lifetime of the system
(typically around 25 years). The main contributors to LCOE for solar PV systems are the capital cost as well as solar resource availability (Doluweera et al., 2018). Data from the U.S. notes that the average, unsubsidized LCOE costs have declined by over 70% from 2010-2018 to $0.12-
0.16/kWh for residential systems, $0.09-0.12/kWh for commercial systems and $0.04-0.06 for utility-scale systems (Barbose et al., 2019; Fu et al., 2018; IRENA, 2019a). These cost declines represent declines in both hard and soft costs in solar PV arrays, driven by technological efficiencies, as well as increased efficiencies in labour and installation.
Globally, the weighted LCOE for solar PV decreased by 77% from 2010 to 2018 and solar PV installations have reached a total of 480 Gigawatts (GW) of installed capacity worldwide
(International Renewable Energy Agency [IRENA], 2019a).
2.3 Policy and Incentive Programs
National and state-level policies and incentive programs have been important instruments in lowering initial cost barriers to solar PV adoption across market segments throughout the world. Government financed incentives in the form of feed-in tariffs, rebates and tax credits have played a pivotal role in the increase of residential, commercial and industrial PV installations (Kwan, 2012; O’Shaughnessey, 2019; Wang, Yu & Johnson, 2017).
8
Germany’s Energiewende – the transition to a low-carbon economy, has resulted in rapid growth in renewable energy sources. Key to this transition has been the Renewable Energy
Sources Act (EEG), which came into force in 2000 and has been amended several times since
(Renewable Energy Sources Act [EEG], 2017). The main objective of the Act was to increase
Germany’s share of renewable electricity generation sources as a percentage of total consumption between 40 and 45 percent by 2025, 55 to 60 percent by 2035, reaching 80 percent or higher by 2050 (Buchholz & Eichenseer, 2019). As part of EEG’s policy measures, EEG gives priority to renewable energy generation projects by guaranteeing competitive feed-in tariffs. Twenty years of EEG policy has resulted in 49 GW of installed capacity across 1.8 million installations (Sahu, 2015).
In Canada, the province of Ontario has led growth in renewable energy procurement as a result of five main contract programs:
The Ontario Power Authority initiated the Renewable Energy Standard Offer Program (RESOP) in 2006. The program eased access for small-scale renewable energy generating facilities in the electricity supply market by providing stable pricing under twenty-year contracts.
The Green Energy and Green Economy Act (GEGEA), 2009 which included the FIT and microFIT programs (Mabee, Mannion & Carpenter, 2012). These programs offered feed-in tariffs – guaranteed prices for fixed contract terms for renewable energy electricity generation. Solar PV contract periods were for 20 years under both programs, with tariff rates being reduced as module prices decreased over time (Stokes, 2013). The programs also included provisions for local content requirements as well as a “price adder” for indigenous communities wishing to
9 install a renewable energy facility (Baldus-Jeursen et al., 2018). The final year for FIT and microFIT applications concluded in 2016.
The Green Energy Investment Agreement, 2010 (GEIA) was an agreement between the
Government of Ontario, Samsung and Korea Electric Power Corp. The basis of the agreement was to develop 2.5 GW of solar and wind capacity along with expanding the renewable energy manufacturing base in the province (Government of Ontario, 2015).
The Large Renewable Procurement Program of 2014 (LRP) was the successor to the FIT program, intended for projects that exceeded 500kW of capacity. The program was designed to secure a better price for large renewable energy systems and to manage the amount of installation projects (Government of Ontario, 2015).
Although the above-mentioned policies and programs were deemed to be controversial in terms of high tariffs and lack of consultation, they still led to almost 3000MW of installed PV capacity by the end of 2018 (Baldus-Jeursen et al., 2018).
In Alberta, the Climate Leadership Plan (CLP), introduced by the former provincial New
Democratic Party (NDP) government, paved the way for solar PV industry in the province
(Government of Alberta, 2018a).
The Renewable Electricity Program was created to facilitate renewable electricity generation capacity of the province to 30% by 2030 (Government of Alberta, n.d.). The program was meant for large community or utility-scale projects. It offered 20-year contract terms through an indexed RE credit payment system (Government of Alberta, 2016).
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The Residential and Commercial Solar Program offered direct financial incentives for homeowners, business owners and non-profit organizations to incentivize adoption of residential and commercial-scale systems in the province. The program provided rates of $
$0.90/w for residential systems, $0.75/w for commercial installations and $1.00/w for non- profit installations. The maximum grant contribution for each level was 35% of the eligible system cost. The Municipal Solar Program offered rebates per watt of installed capacity up to a maximum of 30% of eligible system costs for municipal facilities (Government of Alberta, 2019).
Under the CLP, the NDP government launched two programs to facilitate the development of renewable energy projects, the Alberta Indigenous Solar Program (AISP) and the Alberta
Indigenous Community Energy Program (AICEP) (Government of Alberta, 2018a).
AISP provided financial incentives in the form of grants to Indigenous organizations and communities wishing to install solar PV systems on community-owned facilities from 2 kW to 1
MW in capacity. Incentives up to $200,000 or up to 60% of eligible costs were covered under the program (Government of Alberta, 2017). AICEP provided resources and financial incentives to increase energy literacy among indigenous communities and organizations and identify cost savings (Government of Alberta, 2018b).
The policies and programs under the CLP helped the province’s solar PV industry grow by 800 percent through over 3 100 solar PV installations. As a result, solar PV capacity increased from
6MW in 2015 to over 50MW by the end of 2018 (Government of Alberta, 2018a). In 2019, the current provincial government under the leadership of the United Conservative Party cancelled these rebate and incentive programs. Although The renewable energy policy future for Alberta
11 remains unclear, some projects are being financed through the private sector, subsidy-free (The
Canadian Press, 2019).
2.4 Socio-demographic factors affecting residential solar PV installations
While the above-mentioned factors affect solar PV adoption across market segments (Utility- scale, commercial, community, residential), the decision to adopt a solar PV installation is complex and also includes the behavioural and social characteristics of individuals and organizations (Abreu, Wingarz & Hardy, 2019; Dharshing, 2017; Schaffer & Brun, 2015).
The growth in residential solar PV installations has been concentrated around higher income households. Home ownership and higher median household income have been found to be significant factors in adoption of solar PV (Barbose, Darghouth, Hoen & Wiser, 2018; Diaz-
Rainey & Ashton, 2011)
Higher levels of education have also been found to be significant factors for solar PV adoption
(Sommerfield et al., 2017). Rai & McAndrews (2012) reported that survey respondents were more educated and had higher income than the median citizen in Texas. Kwan (2012) also found a median income of $25 000 or greater a significant factor of residential solar PV adoption in California.
Social influences also affect update of residential solar installations. Peer effects are the influences of a person’s social network (neighbours, co-workers, friends) on their behaviour.
Several quantitative and qualitative studies have identified peer effects in residential PV adoption (Bollinger & Gillingham, 2012; Graziano, Fiaschetti & Atkinson-Palombo, 2019).
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Bollinger and Gillingham (2012) were some of the first researchers to demonstrate that one household’s PV adoption increased the likelihood of more adoptions in the same neighbourhood. Other studies have found similar findings in Germany, employing an epidemic diffusion model (Dharshing, 2017; Rode & Weber, 2016).
Pro-environmental attitudes have also found to be a contributing factor for residential solar PV uptake. Concern for the environment showed significant effects on the willingness-to-pay for renewable energy in the UK and other OECD countries (Tsantopoulos, Arabatzis & Tampakis,
2014). In addition, research has shown that individual characteristics, including moral obligations for addressing environmental issues, combined with how innovative an individual is can work together to determine beliefs about the pros and cons of PV adoption (Schelly, 2014).
Simply put, people who have pro-environmental attitudes while also showing keen interest in new technologies can have more positive perceptions of solar PV and are then more likely to adopt a PV system earlier than individuals without these characteristics. Literature has also supported the notion that pro-environmental attitudes are one of the key drivers of early adopters of environmentally friendly innovations such as solar PV (Islam, 2014; Jager, 2006).
Other research has identified the role that middle-actors, or local organizations play in the spatial distribution of residential PV. Schaffer & Brun (2015) found that government agencies, energy service companies and non-governmental organizations or industry associations affect differences in regional growth in local German solar photovoltaic markets. Other studies have found that proximity to solar organizations (industry associations and installers) affects the distribution of solar PV. A study by Noll, Dawes & Rai (2014) found that community-based
13 organizations facilitating residential PV adoption in the U.S. were able to influence peer effects through their campaigns in engaging with local residents.
Finally, age has also shown to be a demographic variable in willingness to adopt solar installations. Some studies have noted a negative correlation with age and solar PV adoption
(Kwan, 2012), while others note that the age brackets of 30-44, and 55 and older show higher tendency to install solar than their peers (Sommerfield et al., 2017; Araújo, Boucher & Aphale,
2019).
2.5 Variables affecting commercial, community and utility-scale projects
Socio-demographic variables are not as significant in community, commercial and utility-scale solar PV installations, as they are primarily driven by falling installation costs, incentives, and solar insolation potential, although public perception of environmental effects of utility-scale
PV can play a factor in siting considerations (Brewer and Brewer 2015; Crago and Koegler 2018).
Socio-demographic and socio-technical factors also play a role in the deployment of community-based solar installation, particularly in remote indigenous communities. A complex interplay of Indigenous governance politics, relations with provincial and federal governments, and energy literacy influence adoption of solar PV or hybrid microgrid systems in these communities (Karanasios and Parker 2018).
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Chapter 3 – Solar PV Job Growth Potential
3.1 Current trends in solar PV job growth
Globally, the solar PV is the highest employer in the renewable energy sector, employing approximately 3.6 million persons as of 2018. China is the lead employer, employing two-thirds of the world’s PV workforce at 2.2 million jobs (IRENA, 2019b). The U.S. solar PV labour market, which ranks third globally, reported approximately 248 000 full-time and 97 000 part-time workers in the industry as of 2019. This represents a 2.3 percent increase from 2018 in full-time employment (NASEO & EFI, 2020).
The installation of renewable energy technologies like solar PV are driven by concerns of climate change, energy insecurity and air pollution. The reduction of CO2 emissions, the need for energy independence and decreasing air pollution are strong regional and national drivers and touted benefits for the adoption of solar PV systems. The job creation potential of the solar
PV industry has been another benefit that has been promoted across academia, governments and the private sector (IRENA, 2019b; Ram, Aghahosseini & Breyer, 2020). There were an estimated 3.6 million persons employed in the global solar PV sector as of 2018. This includes employment across the value chain, including research and development, manufacturing and installation (IRENA, 2019a).
3.2 Methods to analyze job creation potential of solar PV
While explaining in detail all of the approaches to job estimation, along with the extensive literature on the subject area is beyond the scope of this paper, it is important to briefly discuss
15 the methods and variance in estimates that are brought forth when discussing job creation potential in the solar PV industry.
Studies that analyze employment impacts of renewable energy typically use one of two main methods: input-output models (I-O) or analytical models.
I-O methods can show interdependencies of different sectors and subsectors of an industry.
The methods allow the ability to analyze employment impacts across the economy, by adjusting demand in a sector and are also able to calculate the number of direct and indirect employment. (Sooriyaarachchi, Tsai, Khatib, El Khatib, Farib & Mezher, 2015). Several international and national employment estimates use this model (Garret-Peltier, 2017; National
Association of State Energy Officials [NASEO] & Energy Future Initiative [EFI], 2020).
For example, in using the I-O method, the International Renewable Energy Agency (IRENA
2019b) has three main categories it uses to allocate job opportunities in across renewable energy sectors based on the relevance of employment impacts. They are as follows:
• Direct employment: Jobs resulting from the research and design, construction,
installation and maintenance of RE technologies
• Indirect employment: Jobs resulting from the manufacturing of RE equipment, including
materials and services for the generation of the facility (including upstream processes
that provide raw materials and services to the manufacturing segment); the financial
institutions that provide support for the construction and operation of a RE facility.
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• Induced employment: The jobs created outside of the RE sector in-question, as a result
of the spending of employees who are directly or indirectly employed in the particular
RE sector.
The U.S. National Renewable Energy Laboratory (NREL) has developed a Jobs and Economic
Development Impact Model (JEDI). This model takes in user-entered, project-specific inputs to estimate the number of jobs as well as other economic impacts in the construction and operation of a power plant (National Renewable Energy Laboratory [NREL], n.d.).
In contrast to I-O studies, analytical methods for analyzing job impacts are simpler. They also have a more regional or provincial scope. Moreover, they focus on direct employment and usually involve extensive employment surveys to supplement their data (Cameron & Van Der
Zwaar, 2015). An example of this methodology is used in the 2018 National Solar Jobs Census by the U.S. Solar Foundation.
There are limitations to both employment impact methods. For instance, the I-O method relies on static coefficients which often do not factor in technological advancements, or fluctuations in consumption and prices. In addition, the data used for I-O studies comes from national statistics and makes it difficult to analyze regional or local impacts (Bae & Dall’erba, 2016). For analytical studies which rely on extensive surveys, the accuracy of the data depends on the objective nature of respondents Cameron & Van Der Zwaar, 2015).
Literature pertaining to job creation in the renewable energy sector describes various metrics that can be used, such as jobs per MW installed capacity, person-years per MW installed, job- years per MW installed. The most widely used according to literature are jobs per MW installed
17 and person-years per MW installed. One person-year is meant to imply one year of full-time employment for one person.
The representation of these employment factors varies depending on where on the value chain the jobs are located. For example, jobs in construction and installation are most often represented in person-years per MW. This is meant to reflect the temporary nature of these jobs. Conversely, jobs in the operations and maintenance (O&M) phase, are most often represented in jobs per MW (Cameron & Van Der Zwaar, 2015).
3.4 Solar PV value chain job distribution
Solar PV employs people across various value chain segments, ranging from research and development, production and manufacturing, construction and installation, project management and sales. There are several different ways to categorize solar PV sector employment. One way that employment opportunities can be categorized is by using the technology value chain model. This includes research and development, product manufacturing and distribution, project development, construction, and installation, as well as operations and maintenance (O&M) (Sooriyaarachchi et al., 2015). Categorization methods vary widely among authors, organizations and countries.
The distribution of employment by sector varies around the globe. For example, most China’s jobs in solar PV are in manufacturing, as China and other countries in Asia are the main manufacturers of solar PV modules. Conversely, in the United States, the majority of solar jobs are found in construction, installation and project management. Similarly, in Canada, the
18 majority jobs are found in the installation of solar PV systems. A study by Solas Energy
Consulting (2018) identified the potential of over 8,800 annual full-time equivalent (FTE) jobs by
2030 in the in the province of Alberta.
Figure 3: U.S. Solar Employment by Sector
All others Manufacturing 5% 14%
O&M 5%
Wholesale Trade & Distribution Installation & 12% Project Development 64%
Data source: Solar Foundation (2018).
3.5 Research Gaps
Although extensive research has been done on factors that affect solar PV distribution in the US and Europe, little information is available in the Canadian context. Furthermore, the volatility of the oil and gas sector along with the rapid growth of the solar PV industry in Alberta creates the need for research to be done on the potential job growth that the industry can facilitate in the province. The following research attempts to address these gaps by focusing on the growth potential of solar PV installations across Alberta.
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Chapter 4 - Methodology and data collection
My methodology for estimating growth in the solar PV sector in Alberta followed a two-step approach, each following separate methodologies.
The first part of my research consisted of using the Alberta Electricity Operator (AESO) project lists to project installed capacity, resulting jobs per MW installed, plotting out the installed capacity across planning regions, and calculating avoided GHG emissions. The AESO Project lists contain system access service (SAS) requests for, utility-scale and community level projects
(Alberta Electricity Systems Operator [AESO], 2020 n.d.a.).
The second part of my research consisted of using census-level data, along with AESO and Solas
Consulting growth scenarios for rooftop residential PV to project installed capacity by 2030, jobs per MW, avoided GHG emissions resulting from the installed projects, as well as estimating what population centres across the province were likely to see the greatest growth.
Commercial and farm solar installations were not part of the study but their potential growth in
Alberta is discussed in the discussion part of the paper.
4.1 AESO Project List Methodology and Data Collection – Utility-Scale, centralized and distribution-connected projects
Market participants wishing to initiate projects and connect to the Alberta’s transmission system must go through the AESO Connection Process. Projects under this process are broadly grouped into two categories: connection projects and behind-the-fence projects. Connection projects require transmission infrastructure to be build or altered by the designated
20 transmission facility owner (TFO) in addition to building the generation facility. BTF projects involve market participants who wish to make alterations to their existing facilities already connected to the transmission system (AESO, n.d.b.).
AESO Project lists contain all active SASR for large transmission-connected and large distribution connected proposed generation and load projects (5MW or greater). Small distribution-connected projects and microgeneration projects (<5MW) are not governed by the connection process and thus were not included.
The connection process has seven stages, from 0 to 6:
• Stage 0: In this stage, the market participant submits a SASR to AESO for a new
connection or BTF project, or an alteration to an existing facility.
• Stage 1: After the SASR has been approved, scoping of the project is initiated, which
includes the Connection Plan and Connection Study scope, along with determining
responsibilities among all stakeholders.
• Stage 2: The Connection Proposal is submitted and reviewed. If accepted, projects
move to stage 3.
• Stage 3: AESO determines the filing strategy with the Alberta Utilities Commission
(AUC). The Facility Application is completed by the TFO.
• Stage 4: The AESO and TFO submit the project application with the AUC. Upon
approval, permits and licenses are issued for the project.
• Stage 5: Construction commences on the project. The market participant and the
AESO sign the SAS Agreement
21
• Stage 6: The in-service date of the project is finalized, and commissioning of the
generation facility takes place prior to commercial operation.
(AESO, n.d.b.)
Once projects have their Connection Proposal approved and clear stage two, they are put on the Connection Queue List.
The July 2020 AESO Project List was used for this study. To view the list and all solar PV projects through stages 1-6, see Appendix A. The Project list headings are as follows:
Project – denotes project name and number
Project Type – Connection or BTF.
Planning Area – What AESO planning area the project is located.
STS MW Change – The proposed supply transmission service, in MW – the service provided to generators, for access to Alberta’s transmission system. Also denotes the capacity of the project or project amendment.
DTS MW Change – Demand Transmission Service, in MW – the service provided to loads for access to Alberta’s transmission system.
MW Type – Type of generation (ex. Wind, solar, cogeneration, gas turbine), or load (denoted by
‘load’).
Process Stage – What AESO connection process stage the project is currently on.
Planned ISD – The planned in-service date.
Applied on – When the SASR was submitted.
22
Figure 4: AESO Transmission Planning Areas
Source: (AESO, n.d.e.)
Next, all solar projects were isolated, then organized by process stage and planning area.
In total there were 51 BTF projects and 21 Connection projects across stages 1 to 5.
23
Not all projects in stage 1 and 2 of the connection process proceed to the connection queue and become energized. Based off 2019 Project List data, a probability factor of 0.7 was assigned for all projects in stage 1 and 2 of being actualized. Solar projects in stages 3-5 were assumed to proceed to stage 6.
Next, cumulative installed capacity of all solar projects was calculated. Seventy percent of the planned cumulative capacity was used for projects in stage 1 and 2 to account for not all the projects or capacity entering the connection queue.
To calculate the total electricity generated, and avoided CO2 emissions, each project was assumed to have an operational lifespan of 25 years from its planned ISD. An annual average capacity factor of 15% was assumed for all projects.
Solar PV installations are emission-free technologies during the generation of electricity. As such, GHG emissions that are attributed to electricity generation using fossil fuels can be avoided. GHG avoided emissions were calculated in Megatonnes of CO2 per MWh for the lifetime of each project. The emissions factors used estimate projected C02 emissions from
Alberta’s electricity generation mix from 2020-2040. Emissions factors were obtained from the
Canadian Energy Research Institute’s (CERI) study on Opportunities and Challenges for
Distributed Electricity Generation in Canada (Doluweera, Gallardo, Rahmanifard, Bartholameuz,
2020).
Annual GHG avoided emissions and cumulative yearly GHG avoided emissions were graphed over the lifetime of all projects.
24
To estimate local job growth, an employment factor of 3.9 was used. Employment factors vary across solar PV segments (utility, commercial, residential, community), along with what value segments are represented. A study by Jones, Phillips and Zabin (2016) used JEDI to produce the factor of 3.9 job-years per MW of installed solar PV capacity for the utility sector. Similarly, a report by Bridge, Gilbert and Beresford (2017) also used the same factor when estimating job growth in the solar PV sector for Canada’s Building Trades Union by 2030. Other literature has cited similar figures in job-years per MW installed for utility-scale installations. Since primary studies and reports have used similar employment factors, and since the majority of planned projects in the AESO project list were utility-scale projects, I assumed this employment factor to be sufficient for the purpose of this study.
4.2 Residential PV Methodology and Data collection
To estimate the potential installed capacity of residential solar PV installations, a projection was made based off on AESO and Solas Consulting reports.
The AESO LTO for 2019 has four economic and load growth scenarios, which includes projections for all rooftop installation capacity by 2039. The reference case scenario projects load and economic growth based on the Conference Board of Canada’s twenty-year GDP forecast of 1.9% annual GDP growth and projects a total rooftop installed capacity of 241 MW.
The high growth scenario sees a 3.3% CAGR GDP growth rate, with rebounded oil sands activity and increased pipeline capacity, where rooftop solar installations remain at 241 MW. The diversification scenario sees Alberta’s economy diversify away from oil sands production and
25 projects rooftop solar installations at 241 MW. Under the low growth scenario, precipitated by low GDP growth as a result of stagnating oil sands production, installed rooftop solar capacity is projected to reach 728 MW by 2039 (AESO, 2019).
Solas Consulting’s Alberta Solar Market Outlook (2017) estimated total residential rooftop capacity by 2030 under three growth scenarios – C, D, E. Scenario C assumes current deployment rates for solar PV and projects residential PV deployment to reach 120 MW.
Scenario D assumes double the growth rates of scenario C and projects residential market deployment at 207 MW, while Scenario E assumes U.S. residential PV deployment growth rates and puts residential solar capacity at 566 MW by 2030.
Taking the previous reports into account, a capacity of 200 MW of residential solar installations by 2030 was assumed for this study.
Research notes that residential rooftop installations lead to more jobs per MW of installed capacity than larger commercial and utility-scale projects (Sooriyaarachchi et al., 2015;
Steinberg, Porro & Goldberg, 2012).
An employment factor of 38.7 jobs per MW installed was to estimate job creation potential of residential PV solar in Alberta by 2030. This factor was obtained from U.S. Solar Foundation
National Solar Job Census, 2018. The U.S. Solar Foundation calculated jobs per MW from employer interviews in their 2017 National Solar Jobs Census. The job figures are based on all workers per sector (utility, non-residential, residential).
Information on population, number of detached households and median income was obtained from Statistics Canada 2016 Census on 107 population centres across Alberta (Statistics Canada,
26
2017). Median household income and annual average capacity factors were used as the key variables predicting the temporal distribution of residential solar installations. A capacity factor of 13% and median income of less than $36, 000 were used as cut off points for this analysis.
The resulting population centres with capacity factors of 13% or greater, and median income of
$35, 000 or greater were plotted on a map.
To estimate the percent of households that would haver a residential solar installation the following assumptions were made:
- A 5 kWdc system was assumed, as an average size of residential rooftop system
- The installations were on single-detached homes
Using Census 2016 data, total detached homes across population centres were calculated. The projected installed capacity of 200 MW was then divided by 5 kWdc, then the total households.
Finally, indigenous, community-owned installations were totalled for installed capacity and plotted on a map.
27
Chapter 5 - Results and Analysis
5.1 AESO Project List results
In total there were 22 connection-type and 29 BTF-type projects analyzed over 23 AESO planning areas. Total installed capacity from all solar PV projects in the July 2020 Project List was calculated to be 3587.1 MW. Over 70% of the projected installed capacity is concentrated in just four regions – Brooks, Stavely, Vauxhall and Medicine Hat. The majority of projects are also located in the central and southern planning regions, which also have higher solar resource potential. Figure 5 illustrates the geographical distribution of the projects.
In addition, it was found that three of the 71 projects on the AESO Project List were on First
Nations Reserves – A 40 MW Solar project on Piikani First Nations land, near Fort McLeod, a 40
MW project on Chiniki First Nations near Seebe, and a 37 MW facility on Paul First Nations land near Wabamun.
This is not an exhaustive list of indigenous, community-led solar projects. Further research is needed to identify upcoming and potential centralized and DG solar projects across Alberta.
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Figure 5: AESO Project List – Projected Installed Capacity by Planning Area (MW)
1400 100% 90% 1200 80% 1000 70% 800 60% 50% 600 40% 400 30% 20% 200 10% 0 0% Seebe Hanna Airdrie Brooks - Stavely - Calgary - Provost … … - Caroline - - Empress - - Vauxhall - - - Deer Red - 44 42 Vegreville - 47 High River High Sheerness Glenwood Wabamun Edmonton 57 - Lethbridge 37 49 - 45 36 - - - - 38 48 06 Wetaskiwin 52 - 35 - 56 Medicine Hat Medicine 46 Macleod Fort 43 55 40 60 - 54 - 31 53 04
(Source: adapted from - AESO, n.d.a.) Assuming a 15% capacity factor across all projects, and a 25-year operational lifespan from the planned ISD, the cumulative GHG-avoided emissions of all projects by 2030 is projected at 17.4
Mt CO2, and reach a total of 40.8 Mt by 2040.
Figure 6: Avoided GHG emissions from AESO Solar PV Projects – 2020-2045 (Mt CO2)
45 40 35 30 25 20 15 10 5 0 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045
MtCO2 avoided emissions Cumulative MtC02 avoided emissions
(Source: Polanowski, 2020)
29
Excluding stage 5 projects that are in the process of becoming constructed, the total installed capacity for projects in stages 1-3 was calculated to be 2,593 MW. In total, 10,113 jobs in construction, installation and project management are predicted for upcoming projects on the
AESO Project List. It is important to note that these figures are broad estimates based on employment factors consistent with other studies. In addition, the installed capacity and resulting employment figures assume that 70% of projects in stages 1 and 2 will end up being energized. Statistical methodologies are recommended to more accurately forecast job estimates in large-scale solar installations in Alberta.
Table 1: Installed Capacity and employment figures from AESO Project List analysis
Total Installed Capacity for AESO Project List Solar PV Projects 2,593
stages 1-3 MW
Employment factor (Job-years per MW installed) 3.9
Total Job-years for construction, installation, and project- 10,113
management jobs
Source: Polanowski, 2020).
30
5.2 Residential Solar PV results
The estimated 200 MW capacity divided by the assumed average residential PV system size of 5 kW resulted in 40,000 systems applied over 773,345 residences. This represents approximately
5% of all detached residences of the 107 population centres surveyed.
Table 2: Results of Rooftop residential PV installed capacity figures
Estimated residential rooftop solar PV across Alberta by 2030 200 MW
Assume average size of system 5 kW
Number of systems 40, 000
Number of detached households 773, 435
% of all detached households with residential rooftop solar PV 5%
(Souce: Polanowski, 2020).
Excluding all population centres with capacity factors <13% and median income <$36,000, 51 population centres remained. These centres are mapped out in figure 7. For the complete set of population centres and data, please refer to Appendix B.
31
Figure 7: Alberta Population Centres with annual average capacity factors >13% and median income > $36,000.
(Source: Adapted from - Google Maps n.d.).
Total construction, installation and project management jobs totalled 7,740 for residential solar
PV installations by 2030.
32
Table 3: Installed capacity and job figures results for residential rooftop PV
Total Installed Capacity for AESO Project List 200 MW
Solar PV Projects stages 1-3
Employment factor (Job-years per MW 38.7
installed)
Total Jobs per MW for construction, 7,740
installation, and project-management jobs
(Source: Polanowski, 2020).
33
Chapter 6 - Conclusion, Limitations and Future Research
6.1 Limitations
The aforementioned results are broad estimates of what utility-scale and residential solar PV installation capacity, job potential and GHG avoided emissions are to be expected. A total cumulative installed capacity of 3,587.1 MW of utility-scale projects from the AESO list alone means that Alberta’s solar PV capacity will expand by over a factor of 30 in this sector alone.
This is an optimistic estimate and should be seen as a measure of capacity potential that exists in the utility-scale segment. There are other limitations that were noted throughout this research as follows:
• Median income and capacity factors were the only variables used to predict the
geographical distribution of solar PV systems. Although several studies have listed these
factors as contributing to solar PV distribution, a statistical analysis would have yielded
more accurate results.
• Although some community-scale projects were included on in the AESO Project List,
commercial, farm and community-scale projects were excluded in the scope of this
study.
• Due to the broad nature of this report, rigorous statistical methods were not employed
while analyzing adoption factors, or in the estimation or forecasting of solar PV
installations across Alberta.
• Average capacity factors were used. Modeling software such as RETScreen could have
been used to more accurately account for regional differences in capacity factors,
annual kWh output and resulting GHG avoided emissions.
34
• Employment factors were taken from other primary and secondary sources of
information.
6.2 Future Research
It is recommended that future research pertaining to adoption factors and estimation of solar
PV installed capacity in Alberta adopt a more rigorous statistical approach. For adoption factors, multi-linear regression analysis and diffusion models can be used. For example, Drury et al.,
(2012) used a multi-linear regression analysis to study how various demographic variables such as age, education and household income affected solar PV adoption in California. Kwan (2012) also used a regression model to analyze effects on various independent variables on the solar
PV concentration within a zip code in the US. Kurdgelashvili et al., (2019) also employed regression analysis when finding associations between diffusion parameters and socio- demographic variables on solar PV adoption across counties in California. Bollinger &
Gillingham (2012), Islam, (2014) and Kurdgelashvili et al., (2019) used diffusion models to examine adoption factors and future installation capacity of residential PV installations.
Furthermore, to forecast future geographical and temporal distribution of solar PV systems more accurately in Alberta, it is necessary to obtain comprehensive datasets pertaining on current installations in the province. Obtaining such data proved difficult during this research project.
Future research estimating solar PV job creation potential would benefit from. Using an I-O or analytical model. For example, Inputting Alberta-specific data into JEDI would lead to more
35 accurate analysis for Alberta. Extensive job industry surveys would also be recommended to supplement any statistical models.
Furthermore, research examining the replacement of local fossil fuel jobs by renewable energy jobs is scarce (Pai, Zerriffi, Jewell & Pathak, 2020). Over 6,000 MW of coal electricity generation is to come offline by 2030, with the majority expected to be offline by 2023 (Hussey & Jackson,
2019). Thus, examining the techno-economic feasibility of current and former coal industry workers would be of great benefit to facilitating the transition to the renewable energy economy in the province and help ensure that fossil fuel workers do not bear a disproportionate amount of responsibility in these challenging times
Future research should also explore factors that affect adoption of commercial and community- scale solar PV installations, as most research has been focused on residential factors. Of particular interest would be an analysis of factors that influence adoption in Alberta’s rural and indigenous communities.
Finally, the COVID-19 global pandemic has had severe repercussions on not only the global economy, but global energy demand as well. Imposed economic restrictions, combined with decreased corporate investment and consumer disposable income have resulted in a contraction in GDP as well as global energy demand, which declined by 3.8% in the first quarter of 2020. While coal, natural gas and oil demand have declined significantly since the pandemic, renewables energy sources seem to be more resilient to the shocks. Projects coming to completion and priority dispatch for renewable electricity sources have led to a 3% increase in renewable energy generation in the first quarter of 2020 (IEA, 2020).
36
Low oil prices combined with the economic effects of the pandemic have created uncertainty for Alberta’s energy future. Will decreased oilsands output lead to increases in renewable energy investment? Could this represent a tipping point for the renewable energy sector in
Alberta? Future research is needed to address the effects of COVID-19 on Alberta’s economy and energy sector.
6.3 Conclusion
Deciding to install a solar PV system at any size is dependent on a complex interplay of economic and socio-demographic variables. Understanding the impact these variables have on solar PV adoption is particularly useful for solar PV installers, organizations, and policymakers.
Forecasting the geographical and temporal distribution of solar PV capacity is a challenging endeavour. It is made more difficult by a lack of federal and provincial comprehensive datasets on solar PV installations in Canada. Notwithstanding, Albertans can be confident that the province will see growth in installed capacity over the next decade in all sectors of the solar PV market leading to thousands of jobs in construction, installation and project management. Even though Alberta’s policy future regarding renewable energy remains uncertain after the cancellation of the CLP, Alberta’s coal phase-out, combined with the continued downturn in oil sands growth means workers will continue to be displaced by this transition in large numbers, many of which reside in rural and Indigenous communities. The public and private sector must work together in providing support to families and communities affected by this transition. One way to do this is to provide community and industry-tailored training programs to help these workers transition to jobs in the renewable energy sector.
37
Iron & Earth has made great strides in this transition already. The organization was started in
2015 by oilsands workers when oil prices began to crash, and mass layoffs began in the oilsands industry. They understand the importance the oilsands play in Alberta and Canada’s economy, but they work to advocate and provide support for former fossil fuel workers wishing for a career in renewable energy jobs. Their SOLAR SKILLS renewable energy training program prepares trainees to work in the solar PV industry, helping transition their already valuable skills. For Iron&Earth, being able to know where growth in the industry is most likely to take place can help them in creating local, community-based training programs.
In addition to job creation potential, the increased deployment of solar PV in Alberta will play an important role in the reductions of GHG emissions in the province. Due to the reliance on fossil-fuel generation, Alberta’s electricity sector produces more GHG emissions than any other sector (Canada Energy Regulator [CER, 2020]). The result of this research show that the utility and residential PV sectors alone could reduce GHG emissions by over 40 Mt through the deployment of forecasted installations.
GHG emissions have reduced globally as economic output fell due to the COVID-19. The pandemic has also adversely affected global energy demand. Renewable energy has so far proven to be mostly resilient to this crisis. The policies and economic incentives that governments enact going forward will determine the pace of recovery across the world and will be key in shaping the energy future for Alberta.
38
Recent global events have once again proven that our energy systems are not isolated and subject to unforeseen effects. Whatever the economic outlook may be for Alberta in the next decade, it seems like the future of solar PV is bright.
39
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52
Appendix A – July 2020 AESO Project List: All solar PV installations
AESO Solar Project List July 2020 STS DTS Project MW MW MW Process Project Subproject Type Planning Area Change Change Type Stage Planned ISD Applied On P2216 FortisAlberta Chappice Lake 649S DER Solar 1 BTF 04-Medicine Hat 10.5 0.0 Solar 3 Dec 1, 2020 Mar 22, 2019 P2266 ATCO Seal Lake 869S DER Solar 1 BTF 19-Peace River 0.0 0.0 Solar 3 May 31, 2021 Aug 14, 2019 P2231 FortisAlberta Ponoka 331S DER Solar 1 BTF 31-Wetaskiwin 4.0 0.0 Solar 5 Aug 20, 2020 May 3, 2019 P2314 FortisAlberta Joffre 535S DER Solar 1 BTF 35-Red Deer 22.8 0.0 Solar 1 Jul 1, 2021 Jan 17, 2020 P2315 FortisAlberta Joffre 535S DER Solar 1 BTF 35-Red Deer 18.0 0.0 Solar 1 Jul 1, 2021 Jan 17, 2020 P2177 FortisAlberta Red Deer 63S DER Solar 1 BTF 35-Red Deer 18.3 0.0 Solar 2 Dec 31, 2021 Oct 31, 2018 P2259 FortisAlberta Metiskow 648S DER Solar 1 BTF 37-Provost 22.0 0.0 Solar 2 May 1, 2021 Jul 17, 2019 P2292 FortisAlberta Killarney Lake 267S DER Solar/Battery Storage 1 BTF 37-Provost 18.0 0.0 Solar 2 May 1, 2021 Oct 28, 2019 P2155 ATCO Monitor 2 774S DER Solar 1 BTF 38-Caroline 21.6 0.0 Solar 2 Nov 1, 2022 Sep 14, 2018 P1995 ATCO Coronation 773S Solar DG 1 BTF 42-Hanna 12.0 0.0 Solar 2 Sep 30, 2020 Aug 30, 2017 P2209 ATCO Monitor 1 774S DER Solar 1 BTF 42-Hanna 4.0 0.0 Solar 2 Nov 1, 2022 Feb 15, 2019 P2248 ATCO Michichi Creek 802S DER Solar 1 BTF 42-Hanna 13.5 0.0 Solar 2 Apr 1, 2021 Jun 26, 2019 P1978 ATCO Michichi DER Solar 1 BTF 42-Hanna 61.0 0.0 Solar 3 Apr 1, 2021 Jun 20, 2017 P2059 ATCO Three Hills 770S DER Solar 1 1 BTF 42-Hanna 14.0 0.0 Solar 3 Sep 30, 2020 Feb 20, 2018 P2061 ATCO Michichi Creek 802S DER Solar 1 BTF 42-Hanna 11.0 0.0 Solar 3 Sep 30, 2020 Feb 20, 2018 P2351 ATCO Bullpound 803S DER Solar 1 BTF 43-Sheerness 13.8 0.0 Solar 1 Jun 30, 2021 May 5, 2020 45- P1984 FortisAlberta Gleichen DG Solar 1 BTF Strathmore/Blackie 17.0 0.0 Solar 3 Oct 15, 2021 Jul 17, 2017 45- P2029 FortisAlberta Strathmore 151S DER Solar 1 1 BTF Strathmore/Blackie 16.7 0.0 Solar 3 Aug 15, 2021 Dec 6, 2017 45- P2030 FortisAlberta Strathmore 151S DER Solar 2 1 BTF Strathmore/Blackie 19.5 0.0 Solar 3 Aug 15, 2021 Dec 7, 2017 45- P1932 FortisAlberta Namaka DER Solar 1 BTF Strathmore/Blackie 15.5 0.0 Solar 5 Apr 1, 2021 Apr 10, 2017 P2195 FortisAlberta Bassano 435S DER Solar 1 BTF 47-Brooks 9.3 0.0 Solar 3 Sep 1, 2021 Jan 7, 2019 P2086 FortisAlberta Brooks 121S DER Solar (83L) 1 BTF 47-Brooks 13.0 0.0 Solar 5 Dec 31, 2020 May 14, 2018 P2092 FortisAlberta Brooks 121S DER Solar (257LE) 1 BTF 47-Brooks 12.0 0.0 Solar 5 Dec 31, 2020 May 14, 2018 P2249 FortisAlberta Empress 394S DER Solar 1 1 BTF 48-Empress 22.0 0.0 Solar 2 May 1, 2021 Jun 28, 2019 P2250 FortisAlberta Empress 394S DER Solar 2 1 BTF 48-Empress 15.5 0.0 Solar 2 May 1, 2021 Jun 28, 2019
53
P1840 Fortis 275S Jenner Solar DER 1 BTF 48-Empress 23.0 0.0 Solar 5 Nov 12, 2020 Aug 5, 2016 P2302 EDF Vulcan Solar 1 BTF 49-Stavely 77.5 0.0 Solar 1 Dec 8, 2023 Dec 2, 2019 P2335 Fortis Vulcan 255S DER Solar 1 BTF 49-Stavely 12.6 0.0 Solar 1 Jun 1, 2021 Mar 9, 2020 P2341 Travers Solar Phase 2 1 BTF 49-Stavely 65.0 0.5 Solar 1 Apr 1, 2022 Mar 30, 2020 P1831 Fortis 255S Vulcan Faribault Farms DG PV 1 BTF 49-Stavely 17.0 0.0 Solar 5 May 31, 2021 Aug 5, 2016 P1870 Fortis Stavely 349S DER Solar 1 BTF 49-Stavely 13.0 0.0 Solar 5 May 31, 2021 Oct 5, 2016 P2297 FortisAlberta Burdett 368S DER Solar Battery Storage 1 BTF 52-Vauxhall 17.5 0.0 Solar 1 May 1, 2021 Nov 15, 2019 P2323 Fortis Taber 83S DER Solar 1 1 BTF 52-Vauxhall 16.4 0.0 Solar 1 Nov 1, 2022 Feb 6, 2020 P2324 Fortis Taber 83S DER Solar 2 1 BTF 52-Vauxhall 15.0 0.0 Solar 1 Nov 1, 2022 Feb 6, 2020 P2325 Fortis Taber 83S DER Solar 3 1 BTF 52-Vauxhall 13.3 0.0 Solar 1 Nov 1, 2022 Feb 6, 2020 P2326 Fortis Taber 83S DER Solar 4 1 BTF 52-Vauxhall 9.2 0.0 Solar 1 Nov 1, 2022 Feb 6, 2020 P1837 Fortis 498S Tilley DG PV 1 BTF 52-Vauxhall 13.0 0.0 Solar 5 Oct 28, 2020 Aug 5, 2016 P1839 Fortis 421S Hays DG PV 1 BTF 52-Vauxhall 13.4 0.0 Solar 5 Oct 15, 2020 Aug 5, 2016 P1839 Fortis 421S Hays DG PV 2 BTF 52-Vauxhall 0.0 0.0 Solar 5 Mar 4, 2021 Aug 5, 2016 P1849 Fortis Burdett 368S DG P/V 1 BTF 52-Vauxhall 10.5 0.0 Solar 5 Dec 15, 2020 Aug 24, 2016 P1918 FortisAlberta Conrad DER Solar 1 1 BTF 52-Vauxhall 18.4 0.0 Solar 5 Apr 15, 2021 Feb 6, 2017 P1959 FortisAlberta Conrad DER Solar 2 1 BTF 52-Vauxhall 18.0 0.0 Solar 5 Apr 15, 2021 May 8, 2017 P2171 FortisAlberta Westfield 107S DER Solar 1 BTF 52-Vauxhall 18.4 9.3 Solar 5 Nov 9, 2020 Oct 22, 2018 P2241 FortisAlberta 368S Burdett DER Solar 1 BTF 52-Vauxhall 15.0 0.0 Solar 5 Nov 2, 2020 Jun 5, 2019 P1850 Fortis Coaldale 254S DER Solar 3 1 BTF 54-Lethbridge 22.0 0.0 Solar 5 May 31, 2021 Aug 26, 2016 P1851 Fortis Monarch 492S DER Solar 1 BTF 54-Lethbridge 20.0 0.0 Solar 5 May 31, 2021 Aug 26, 2016 P1862 Fortis Spring Coulee 385S Solar DG 1 BTF 55-Glenwood 28.5 0.0 Solar 5 Oct 15, 2021 Sep 28, 2016 P2346 ATCO Vilna 777S DER Solar 1 BTF 56-Vegreville 2.7 0.0 Solar 1 Oct 19, 2021 Apr 9, 2020 P2194 FortisAlberta East Crossfield 64S DER Solar 1 BTF 57-Airdrie 1.6 0.0 Solar 5 Jul 15, 2021 Jan 4, 2019 P2176 FortisAlberta Acheson 305S DER Solar 1 BTF 60-Edmonton 8.5 0.0 Solar 2 Nov 1, 2020 Oct 29, 2018 P1982 EDTI DG Solar BTF 1 BTF 60-Edmonton 0.0 0.0 Solar 3 Jan 31, 2021 Jul 7, 2017
P2318 EDF North Slope Solar 1 Connection 04-Medicine Hat 200.0 3.0 Solar 1 Aug 1, 2023 Jan 29, 2020
P2337 Dunmore Solar 1 Connection 04-Medicine Hat 216.0 0.5 Solar 1 Apr 1, 2023 Mar 16, 2020
P1828 HEP Capital Alderson Solar 1 Connection 04-Medicine Hat 100.0 1.0 Solar 2 Dec 1, 2022 Jul 29, 2016
P1928 RealPart Calgary Area Solar 1 Connection 06-Calgary 150.0 0.3 Solar 2 Dec 20, 2020 Mar 24, 2017
54
P1893 Red Deer River Solar 1 Connection 35-Red Deer 150.0 0.5 Solar 2 Dec 1, 2020 Nov 15, 2016 36-Alliance/Battle P2002 Soventix Forestburg Area Solar 1 Connection River 40.0 0.1 Solar 2 Oct 1, 2021 Sep 25, 2017
P2140 PBC Paul Band Solar 1 Connection 40-Wabamun 37.0 0.5 Solar 2 Jan 2, 2024 Aug 20, 2018
P2007 Chiniki Solar 1 Connection 44-Seebe 40.0 0.3 Solar 2 Nov 30, 2021 Oct 2, 2017
P2334 TCE Saddlebrook Solar Storage 1 Connection 46-High River 117.5 7.5 Solar 2 Sep 20, 2022 Mar 20, 2020
P1987 Solar Krafte Rainier 1 Connection 47-Brooks 450.0 1.1 Solar 2 Nov 15, 2020 Jul 28, 2017
P2008 Greengate Lathom MPC Solar 1 Connection 47-Brooks 120.0 0.5 Solar 2 Dec 1, 2020 Oct 20, 2017
P2124 Northland Bow City MPC Solar 1 Connection 47-Brooks 400.0 2.0 Solar 2 Nov 1, 2020 Jul 11, 2018
P2218 ENGIE Duchess Solar 1 Connection 47-Brooks 90.0 0.1 Solar 2 Sep 15, 2021 Apr 1, 2019
P1927 Solar Krafte Brooks 1 Connection 47-Brooks 400.0 1.0 Solar 3 Jun 30, 2022 Mar 23, 2017
P2091 EDP Renewables Blue Bridge Solar 1 Connection 48-Empress 150.0 5.0 Solar 2 Oct 1, 2022 May 22, 2018
P2300 RESC Enterprise MPC Solar 1 Connection 49-Stavely 100.0 3.0 Solar 2 Aug 31, 2021 Nov 21, 2019
P1879 Claresholm Solar Connection 1 Connection 49-Stavely 130.0 0.6 Solar 5 Oct 31, 2020 Oct 25, 2016
P2009 Greengate Travers MPC Solar 1 Connection 49-Stavely 400.0 0.5 Solar 5 Dec 1, 2020 Oct 20, 2017
P2348 BluEarth Wheatcrest MPC Solar 1 Connection 52-Vauxhall 60.0 0.2 Solar 1 Mar 31, 2022 May 4, 2020
P2269 Sunset Solar 1 Connection 52-Vauxhall 60.0 0.3 Solar 2 Dec 20, 2021 Aug 28, 2019
P1926 Solar Krafte Vauxhall 1 Connection 52-Vauxhall 150.0 0.4 Solar 3 Dec 31, 2021 Mar 23, 2017 P1891 Archer Piikani Solar 1 Connection 53-Fort Macleod 40.0 0.1 Solar 2 Sep 1, 2021 Nov 7, 2016 BTF denotes a behind the fence project and is not governed by the Connection Process. DTS and STS project types denote active contract change requests and are not governed by the Connection Process.
Source: AESO (n.d.c.)
55
Appendix B - Census Data for Albertan Population Centres
Single- # single Median detached % Of single detached Total PCPUID PCNAME CSDUID CSDNAME CSDTYPE CDUID Population_2016 CF dwellings_2016 house households homes Income Specialized $ 480292 Fort McMurray 4816037 Wood Buffalo municipality 4816 66573 12.55 28567 50 0.5 14283.5 7,481.00 $ 480138 Cardston 4803004 Cardston Town 4803 3258 15.7 1160 90.6 0.906 1050.96 27,755.00 $ 480496 Magrath 4803002 Magrath Town 4803 2179 15.31 719 88.8 0.888 638.472 29,747.00 $ 481302 Two Hills 4810052 Two Hills Town 4810 1198 13.89 414 92.4 0.924 382.536 30,048.00 $ 480183 Claresholm 4803022 Claresholm Town 4803 3424 14.6 1602 76 0.76 1217.52 30,896.00 $ 480693 Raymond 4802008 Raymond Town 4802 3533 15.51 1200 92.1 0.921 1105.2 31,312.00 $ 481012 Westlock 4813031 Westlock Town 4813 4678 12.93 2122 67.1 0.671 1423.862 31,563.00 $ 480708 Rimbey 4808044 Rimbey Town 4808 2086 13.64 978 68.8 0.688 672.864 31,700.00 $ 480934 Three Hills 4805048 Three Hills Town 4805 3078 14.11 1246 66.8 0.668 832.328 31,925.00 $ 480086 Bow Island 4801014 Bow Island Town 4801 1773 15.26 608 81.9 0.819 497.952 32,064.00 $ 481387 Bowden 4808006 Bowden Town 4808 1240 14.36 581 68.6 0.686 398.566 32,704.00 Specialized $ 481347 La Crte 4817095 Mackenzie County municipality 4817 1860 11.74 777 69.5 0.695 540.015 33,109.00 $ 480517 Mayerthorpe 4813002 Mayerthorpe Town 4813 1205 13.04 551 66.7 0.667 367.517 33,323.00 $ 480291 Fort Macleod 4803019 Fort Macleod Town 4803 2708 15.35 1316 78.8 0.788 1037.008 33,419.00 $ 481279 Bassano 4802039 Bassano Town 4802 1196 14.69 552 77.6 0.776 428.352 33,536.00 $ 480042 Barrhead 4813019 Barrhead Town 4813 4387 12.91 1925 69.6 0.696 1339.8 34,144.00 $ 481164 Sundre 4806036 Sundre Town 4806 1866 14.23 805 65.8 0.658 529.69 34,176.00 $ 481015 Wetaskiwin 4811002 Wetaskiwin City 4811 12486 13.78 5503 59 0.59 3246.77 34,344.00 $ 481407 Nobleford 4802014 Nobleford Village 4802 1278 15.04 427 88.9 0.889 379.603 34,496.00
56
Specialized $ 480074 Blairmore 4815007 Crowsnest Pass municipality 4815 1545 15.25 886 84.4 0.844 747.784 34,554.00 $ 480646 Pincher Creek 4803014 Pincher Creek Town 4803 3523 15.53 1540 79.5 0.795 1224.3 34,967.00 $ 481138 Nanton 4803026 Nanton Town 4803 1965 14.55 880 85.9 0.859 755.92 35,174.00 $ 481418 Trochu 4805049 Trochu Town 4805 1058 14.16 470 78.6 0.786 369.42 35,200.00 $ 480643 Picture Butte 4802018 Picture Butte Town 4802 1700 15.21 664 78.5 0.785 521.24 35,328.00 $ 480993 Vulcan 4805006 Vulcan Town 4805 1680 14.62 748 78.3 0.783 585.684 35,616.00 $ 480988 Viking 4810022 Viking Town 4810 1039 14.06 485 85.9 0.859 416.615 35,774.00 $ 481419 Eckville 4808024 Eckville Town 4808 1085 13.93 451 66.3 0.663 299.013 35,904.00 $ 480501 Manning 4817078 Manning Town 4817 1072 11.82 502 72.2 0.722 362.444 40,064.00 $ 480629 Peace River 4819038 Peace River Town 4819 3924 11.96 1812 61.6 0.616 1116.192 47,663.00 $ 480052 Beaverlodge 4819009 Beaverlodge Town 4819 2327 12.09 978 72.3 0.723 707.094 40,046.00 $ 480371 High Level 4817093 High Level Town 4817 2741 12.1 1091 55.7 0.557 607.687 49,312.00 $ 480274 Fairview 4819068 Fairview Town 4819 2598 12.18 1200 74.1 0.741 889.2 39,424.00 $ 480342 Grimshaw 4819074 Grimshaw Town 4819 2599 12.24 1129 74.5 0.745 841.105 45,751.00 $ 481197 Wembley 4819011 Wembley Town 4819 1516 12.27 618 88.5 0.885 546.93 50,176.00 Grande Prairie Municipal $ 481346 Clairmont 4819006 County No. 1 district 4819 1922 12.29 801 73.5 0.735 588.735 52,469.00 Specialized $ 481174 Jasper 4815033 Jasper municipality 4815 3948 12.3 1567 41.9 0.419 656.573 41,600.00 $ 480336 Grande Prairie 4819012 Grande Prairie City 4819 62320 12.3 25948 63.4 0.634 16451.032 48,048.00 $ 481242 Sexsmith 4819014 Sexsmith Town 4819 2461 12.44 868 82.3 0.823 714.364 45,978.00 Municipal $ 480439 Lac la Biche 4812037 Lac la Biche County district 4812 2294 12.65 1040 74.8 0.748 777.92 39,916.00 $ 480372 High Prairie 4817021 High Prairie Town 4817 2264 12.71 975 68.9 0.689 671.775 38,848.00 $ 480972 Valleyview 4818018 Valleyview Town 4818 1421 12.94 663 63.1 0.631 418.353 36,139.00 $ 480764 Slave Lake 4817029 Slave Lake Town 4817 6155 12.97 2557 52.6 0.526 1344.982 46,800.00
57
$ 480254 Edson 4814024 Edson Town 4814 8148 13.04 3659 58.9 0.795 2908.905 44,249.00 $ 480301 Fox Creek 4818002 Fox Creek Town 4818 1720 13.11 909 58.1 0.581 528.129 55,040.00 $ 480330 Grand Centre 4812002 Cold Lake City 4812 7256 13.18 3407 66.9 0.669 2279.283 54,144.00 $ 480237 Drayton Valley 4811031 Drayton Valley Town 4811 6867 13.22 2955 64.6 0.646 1908.93 47,146.00 $ 481360 Calmar 4811019 Calmar Town 4811 1849 13.29 717 76.8 0.768 550.656 42,848.00 $ 480697 Redwater 4811065 Redwater Town 4811 1591 13.29 764 81.4 0.814 621.896 43,269.00 $ 480554 Morinville 4811068 Morinville Town 4811 9848 13.32 3611 74 0.74 2672.14 50,353.00 $ 481201 Millet 4811011 Millet Town 4811 1905 13.41 847 69.5 0.695 588.665 39,211.00 $ 480374 Hinton 4814019 Hinton Town 4814 9205 13.41 4073 56.4 0.564 2297.172 44,490.00 $ 481311 Athabasca 4813048 Athabasca Town 4813 1250 13.43 600 59.4 0.594 356.4 37,700.00 $ 480796 St. Paul 4812018 St. Paul Town 4812 5728 13.48 2336 69.8 0.698 1630.528 39,349.00 $ 480465 Leduc 4811016 Leduc City 4811 29556 13.49 12031 63.9 0.639 7687.809 46,538.00 $ 481258 Stony Plain 4811048 Stony Plain Town 4811 16271 13.5 6574 62.8 0.628 4128.472 43,484.00 $ 480780 Spruce Grove 4811049 Spruce Grove City 4811 36135 13.52 14015 64.7 0.647 9067.705 48,600.00 $ 480083 Bonnyville 4812009 Bonnyville Town 4812 5081 13.54 2296 70.5 0.705 1618.68 45,982.00 $ 481141 Gibbons 4811064 Gibbons Town 4811 2661 13.57 1048 89 0.89 932.72 48,768.00 $ 481200 Elk Point 4812016 Elk Point Town 4812 1437 13.59 637 79.2 0.792 504.504 42,240.00 $ 480040 Banff 4815035 Banff Town 4815 7851 13.62 2729 17.3 0.173 472.117 36,998.00 $ 481214 Bon Accord 4811066 Bon Accord Town 4811 1529 13.64 578 84.8 0.848 490.144 50,517.00 $ 481199 Lamont 4810064 Lamont Town 4810 1347 13.67 532 86.5 0.865 460.18 40,875.00 $ 480335 Grande Cache 4818005 Grande Cache Town 4818 3286 13.67 1583 70.4 0.704 1114.432 51,552.00 $ 480226 Devon 4811018 Devon Town 4811 6578 13.69 2493 79.5 0.795 1981.935 45,495.00 $ 481202 Beaumont 4811013 Beaumont Town 4811 17396 13.71 5980 81.3 0.813 4861.74 54,534.00
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$ 480910 Swan Hills 4817024 Swan Hills Town 4817 1275 13.74 698 66.4 0.664 463.472 44,245.00 $ 481165 Tofield 4810018 Tofield Town 4810 1854 13.77 800 80.9 0.809 647.2 37,504.00 $ 480652 Ponoka 4808039 Ponoka Town 4808 6899 13.82 3144 67.4 0.674 2119.056 39,120.00 $ 481022 Whitecourt 4813030 Whitecourt Town 4813 9515 13.84 3966 53.6 0.536 2125.776 46,618.00 Rocky Mountain Rocky Mountain $ 480720 House 4809015 House Town 4809 6429 13.85 2861 61.7 0.617 1765.237 39,923.00 $ 480980 Vegreville 4810028 Vegreville Town 4810 5436 13.92 2617 75.9 0.759 1986.303 36,244.00 $ 480683 Provost 4807002 Provost Town 4807 1945 13.93 807 79.4 0.794 640.758 43,008.00 $ 480123 Camrose 4810011 Camrose City 4810 18520 13.98 8429 61.7 0.617 5200.693 38,525.00 $ 480478 Lloydminster 4810039 Lloydminster (Part) City 4810 31400 13.98 13345 68.5 0.685 9141.325 47,914.00 $ 480446 Lacombe 4808031 Lacombe City 4808 12442 14.03 4796 67.5 0.675 3237.3 40,942.00 $ 480982 Vermilion 4810042 Vermilion Town 4810 3617 14.07 1717 74.6 0.746 1280.882 40,855.00 $ 480252 Edmonton 4811061 Edmonton City 4811 1062643 14.07 436072 49.9 0.499 217599.928 41,836.00 $ 481173 Blackfalds 4808029 Blackfalds Town 4808 8749 14.12 3271 76.1 0.761 2489.231 50,169.00 $ 480125 Canmore 4815023 Canmore Town 4815 11764 14.13 6192 44.3 0.443 2743.056 46,084.00 $ 481139 Black Diamond 4806011 Black Diamond Town 4806 2552 14.18 1048 74.1 0.741 776.568 38,016.00 $ 480915 Sylvan Lake 4808012 Sylvan Lake Town 4808 14942 14.19 6710 68.1 0.681 4569.51 45,097.00 $ 480227 Didsbury 4806032 Didsbury Town 4806 5222 14.2 2098 71.7 0.717 1504.266 36,700.00 $ 481140 Carstairs 4806029 Carstairs Town 4806 3080 14.22 1199 81.9 0.819 981.981 43,410.00 $ 480996 Wainwright 4807054 Wainwright Town 4807 6153 14.23 2714 67.3 0.673 1826.522 42,891.00 $ 480890 Stettler 4807026 Stettler Town 4807 5862 14.26 2572 71.6 0.716 1841.552 37,015.00 $ 480607 Olds 4806034 Olds Town 4806 8944 14.28 3848 60.9 0.609 2343.432 37,910.00 $ 480239 Drumheller 4805026 Drumheller Town 4805 6439 14.28 3006 77.1 0.771 2317.626 38,405.00 $ 481198 Penhold 4808009 Penhold Town 4808 3165 14.28 1252 70.9 0.709 887.668 47,782.00
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$ 480694 Red Deer 4808011 Red Deer City 4808 99718 14.29 41989 52.7 0.527 22128.203 41,109.00 $ 480606 Okotoks 4806012 Okotoks Town 4806 28833 14.36 9815 77.1 0.771 7567.365 46,572.00 $ 480373 High River 4806006 High River Town 4806 13420 14.38 5560 51.2 0.512 2846.72 38,228.00 $ 480387 Innisfail 4808008 Innisfail Town 4808 6927 14.41 3126 64.2 0.642 2006.892 37,728.00 $ 480351 Hanna 4804011 Hanna Town 4804 2332 14.43 1179 81.3 0.813 958.527 38,187.00 $ 481368 Chestermere 4806017 Chestermere City 4806 19472 14.48 6107 79.6 0.796 4861.172 49,384.00 $ 480898 Strathmore 4805018 Strathmore Town 4805 13592 14.49 5275 59.9 0.599 3159.725 40,734.00 $ 481369 Irricana 4806022 Irricana Town 4806 1160 14.5 452 76.6 0.766 346.232 45,376.00 $ 481216 Crossfield 4806026 Crossfield Town 4806 2973 14.54 1165 75.5 0.755 879.575 43,712.00 Municipal $ 481367 Langdon 4806014 Rocky View County district 4806 5060 14.61 1586 93.1 0.931 1476.566 51,984.00 $ 480003 Airdrie 4806021 Airdrie City 4806 61082 14.67 22219 68.1 0.681 15131.139 49,989.00 $ 480101 Brooks 4802034 Brooks City 4802 14436 14.69 5406 55.1 0.551 2978.706 41,358.00 $ 480191 Cochrane 4806019 Cochrane Town 4806 25289 14.71 9959 63 0.63 6274.17 50,896.00 $ 480523 Medicine Hat 4801006 Medicine Hat City 4801 62935 14.85 27766 64.2 0.642 17825.772 36,819.00 $ 480115 Calgary 4806016 Calgary City 4806 1237656 14.89 489271 56.3 0.563 275459.573 43,333.00 $ 480916 Taber 4802022 Taber Town 4802 8548 15.47 3420 72.3 0.723 2472.66 36,112.00 $ 480467 Lethbridge 4802012 Lethbridge City 4802 87572 15.5 37761 63.1 0.631 23827.191 36,938.00 $ 481249 Coalhurst 4802013 Coalhurst Town 4802 2623 15.5 949 78.6 0.786 745.914 38,637.00 $ 480188 Coaldale 4802019 Coaldale Town 4802 8153 15.55 3036 77.4 0.774 2349.864 39,226.00
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