Energy system modelling can help us understand how technologies like electric vehicles (EVs) and heat pumps (HPs) can affect energy demand, energy system dynamics, costs, and GHG emissions associated with the energy system.
Many energy system models are designed to identify least-cost solutions. This can lead to them to overestimate how fast and at what scale new technologies will be adopted if things like consumer preferences, supply chain constraints, availability of skilled workers, and existence of supporting infrastructure aren’t taken into account. For example, unless otherwise instructed, an energy system model wouldn’t account for the impact that supply chain constraints (such as those that have recently resulted in long wait times for the delivery of electric vehicles to Atlantic Canadians) could have on EV uptake.
However, users of energy system models like the Atlantic Canada Energy System model (ACES) can define constraints within the model’s input files to help ensure that technology adoption rates better reflect enabling and/or inhibiting sociocultural, economic, and technical conditions.
Net Zero Atlantic created and modelled three scenarios to demonstrate how to constrain technology adoption in the ACES model and to enhance understanding around how limiting the adoption of HPs and EVs would impact the costs and GHG emissions associated with Atlantic Canada’s energy system.
The following table provides an overview of these scenarios and indicates which policies are enacted in each scenario and whether technology adoption has been limited.
Table 1: Description of the Scenarios Modelled to Explore the Impacts of Limiting EV and HP Adoption
In the “Slow Adoption” and “Fast Adoption” scenarios, the adoption rates of EVs were informed by targets reported in IEA’s Global EV Outlook. HPs were modelled to have the same adoption rates as EVs, in the absence of readily available data. Figure 1 shows the number of EVs modelled within the system under unlimited, slow, and fast adoption rates.
For more information on how the ACES model represents the Atlantic Canada energy system, check out the ACES Model Documentation.
Figure 1: Adoption rate of light-duty EVs over 2020 to 2050 in the “Unconstrained”, “Slow Adoption”, and “Fast Adoption” scenarios
Results and Discussion
Adoption rates were constrained for all end use technologies listed in Table 1. To articulate the key insights of the scenario analysis, discussion will be limited to:
1) impacts to the light-duty vehicle, heavy-duty vehicle, and residential heating sectors,
2) impacts to electricity sector activity, and
3) impacts to the total energy system GHGs and costs.
When EV adoption is not limited, the light-duty passenger vehicle sector is fully electrified by 2040 (see Figure 2). When the buildout of EVs is constrained (in both the “Slow Adoption” and “Fast Adoption” scenarios), the ACES model chooses to replace gasoline vehicles with PHEVs. If the buildout of PHEVs was also constrained, the ACES model would be forced to continue to use gasoline vehicles to meet light-duty transportation demands.
Figure 2: Activity of the light-duty vehicle sector under different technology adoption rates and the federal carbon tax policy, in billions km travelled (bkm)
The heavy-duty vehicle sector similarly experiences rapid electrification when the adoption rate is “Unconstrained” (see Figure 3). Unlike the light-duty vehicle sector, PHEVs weren’t included as a technology option within the heavy-duty vehicle sector. As a result, when constraints are applied to adoption rates in the “Slow Adoption” and “Fast Adoption” scenarios, diesel engine vehicles continue to make up the majority of the market share of the heavy-duty vehicle sector from 2020 to 2050. While hydrogen fuel cell vehicles do not appear in the “Unconstrained” scenario, they begin to penetrate the heavy-duty vehicle sector starting in 2040 in the “Slow Adoption” and “Fast Adoption” scenarios, in which the adoption of EVs is constrained.
Figure 3: Activity of the heavy-duty vehicle sector under different technology adoption rates and the federal carbon tax policy
In the residential space heating sector, the stock rapidly converts to HPs (Figure 3). Applying constraints to the adoption rates of HPs in the “Slow Adoption” and “Fast Adoption” scenarios forces the ACES model to build out more electric baseboard/furnace units to meet the space heat demand.
Figure 4: Activity of the residential heating sector under different technology adoption rates and the federal carbon tax policy
As EV adoption increases in the scenarios, so does the demand for electricity. This was not the case for HPs as increasing their buildout slightly decreased electricity demand. This is because HPs have a higher energy efficiency than electric resistance heating units, more of which were built out by the ACES model when HP adoption was limited.
Limiting adoption of EVs and HPs leads to an increase in cumulative greenhouse gas emissions (GHGs) between 2020 and 2055 (see Figure 4), as GHG-emitting end-use technologies stay in the system longer. It also increases the total discounted system cost of the Atlantic Canadian energy system (see Table 2), as imposed adoption rates for EVs and HPs constrain the cost-optimal use of end-use technologies.
The results observed in this modelling exercise demonstrate that the rate of EV and HP adoption has a significant impact on the costs and GHG emissions associated with the Atlantic Canadian energy system. This emphasizes that it is important to consider how technology adoption rates could impact results when carrying out an energy system modelling exercise. It also indicates that technology adoption will be a significant lever in reducing GHG emissions and meeting climate change targets.
Table 2: Total discounted energy system costs and cumulative GHG emissions under different technology adoption rates and the federal carbon tax policy
About the ACES Model
The Atlantic Canada Energy System (ACES) model was created as a tool Atlantic Canadians can use to help answer “what if” scenario questions regarding the future long-term development of our energy system. The model covers all four Atlantic provinces and six greenhouse gas-emitting sectors: electricity, buildings, transportation, industry, agriculture, and waste. The ACES model provides least-cost solutions that satisfy the energy demand for each time period included in the model. ACES model users can customize and analyze possible scenarios to test outcomes that are best suited for their needs, while also tracking emissions and economic key performance indicators.
The ACES model uses the open-source modelling framework, Temoa (Tools for Energy Model Optimization Analysis). With the objective of optimizing capacity and minimizing the cost of energy supply, Temoa acts as the “engine” of ACES to produce scenario results based on Atlantic Canada-specific values and parameters. Using an open-source model like ACES provides researchers, policymakers, industry, and NGOs with the insight needed to effectively reduce emissions and beneficially participate in the energy system transition.
You can learn more about how to run the ACES model here.
Who We Are
Net Zero Atlantic seeks to advance the goal of a sustainable and inclusive transition to a carbon-neutral future in Atlantic Canada. We produce credible and objective data to inform sound policies and decisions related to critical topics currently including, but not limited to, hydrogen, offshore wind, geothermal energy, and energy system modeling.
At Net Zero Atlantic, our mandate includes assessing the impact different types of energy technologies have on the natural and social environment. Our goal is to manage research in an objective way and to put the results into the public domain to help with discussion. We believe an energy system model like the ACES model will enable us to identify areas requiring more study and help to shape our research priorities moving forward.
Learn more about Net Zero Atlantic here.