Electricification Archives - Energeia https://energeia-usa.com/tag/electrification/ Pioneering the future of energy Thu, 11 Sep 2025 02:41:22 +0000 en-US hourly 1 https://wordpress.org/?v=6.8.3 https://energeia-usa.com/wp-content/uploads/2023/08/cropped-Energeia-logo-white-space-added-32x32.png Electricification Archives - Energeia https://energeia-usa.com/tag/electrification/ 32 32 Bridging the Skills Gap: Workforces for Electrification https://energeia-usa.com/workforces-for-electrification/ Tue, 20 May 2025 23:01:19 +0000 https://energeia-usa.com/?p=5419 Electrification is one of the most important strategies for reducing carbon emissions in the future, as it assists in transitioning away from fossil fuels across a range of sectors, including residential, commercial, industry and transport. A key question is how will these changes be enabled by and impact the workforce, and how can it be best positioned?

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Electrification is one of the most important strategies for reducing carbon emissions in the future, as it assists in transitioning away from fossil fuels across a range of sectors, including residential, commercial, industry and transport. A key question is how will these changes be enabled by and impact the workforce, and how can it be best positioned?

Figure 1 below shows leading state and federal CO2 targets, with California, Colorado, Massachusetts, and Maryland undertaking some of the most comprehensive climate action plans, driven by state policy.

Figure 1 – Leading State and Federal CO2 Targets, Source: US EIA (2023)

Electrification is one of the most important strategies for reducing carbon emissions in the future, as it assists in transitioning away from fossil fuels across a range of sectors, including residential, commercial, industry and transport. Understanding the opportunities and challenges related to electrification is key to ensuring that policies supporting electrification are effective, and workforce development is increasingly at the top of this list.

Energeia’s analysis of workforce related issues and best practices is broken down into a workforce impact assessment, which assesses the change in workforce requirements over time due to multi-sector electrification, and workforce solution development, which captures the solutions available to meet the workforce needs.

Workforce Impact

The following sections provide examples of workforce requirements analysis we have completed across key decarbonization pathways, and highlight the associated key methods, issues and insights:

  • Building electrification
  • Gas sector decarbonization
  • Future electricity utilities
  • Gas infrastructure

Building Electrification

One of the Australian state governments was considering policy options to achieve faster gas system decarbonization. following charts show Energeia’s analysis of the number of Full-time Equivalent (FTE) personnel required under different policy options related to electrifying appliances.

The analysis considered the impact of banning gas connections in new premises, and/or banning the sale of new gas appliances, for existing appliances at the end of their life. The modeling showed that bans would have significant impacts on trade jobs, with Figure 2 showing the business-as-usual outcome, and Figures 3 and 4 showing the policy options gas connection ban and gas appliance ban, respectively.

Figure 2 – Business as Usual, Source: Energeia Modelling
Figure 3 – No Gas in New Premises from 2023, Source: Energeia Modeling
Figure 4 – No New Gas Appliances Sold from 2024, Source: Energeia Modeling

The results of the scenario analysis above show that each policy option results in a large increase in electric-related work hours and a decrease in gas-related work hours. Both the gas connection ban and appliance ban scenarios result in changes to workforce needs.

A key recommendation was to set bans far enough in the future to allow the market to adjust as well as stagger bans. For example, start with residential water heaters, followed by space heaters, then commercial water heaters, then space heaters, and so on.

Gas Sector Decarbonization

A US state was considering a wide range of options to decarbonize its gas sector, and not just electrification. The following charts show Energeia’s analysis of the number of FTEs required for different gas system pathway options.

Energeia modeled the impact of each pathway on sector utility and trade jobs. The impacts were mainly estimated using throughput and installation labor estimates. The analysis highlights the significant differences in potential impact depending on the pathway chosen.

Figure 5 – Forecast Change in Gas Trade Jobs, Source: Energeia analysis, Note: BaU = Business as Usual, CF = Cleaner Fuels, SDF = Statewide Dual Fuel, LCF/EE = Low Carbon Fuel/Energy Efficiency, CZA = Climate Zone Approaches, HEF = High Electrification Future, TU = Thermal Utility
Figure 6 – Forecast Change in Electric Trade Jobs, Source: Energeia analysis, Note: BaU = Business as Usual, CF = Cleaner Fuels, SDF = Statewide Dual Fuel, LCF/EE = Low Carbon Fuel/Energy Efficiency, CZA = Climate Zone Approaches, HEF = High Electrification Future, TU = Thermal Utility

The above analysis highlights the high level of variation of skilled labor requirements across different sectors due to policy decisions and the importance of considering potential workforce shortages on policy implementation.

 

 

Future Electricity Utilities

Electricity utilities are also impacted by the increasing decarbonization of consumer end uses and the grid transitions.

The following analysis was developed through an engagement with a US utility that is aiming for 100% renewable energy in the next 10 years. Energeia developed workforce estimates from strategic plans, human resources and finance datasets. The project identified the workforce needs vs. supply gap based on current recruitment and training capacity, and developed a strategy for addressing them.

Future FTEs by class were estimated over time, based on planned investments in the distribution, transmission, generation and BTM program capacity and capabilities. Figure 7 below reports on the forecast change in the number of employees needed for the utility.

 

Figure 7 – Forecast Change in FTEs Needed Over Time, Source: Energeia analysis, Note: Field = Field Services, EE/BE/PV = Energy efficiency, building electrification and photovoltaics, Eng = Engineering, Major = Major Works, Dx = Distribution, Gx = Generation

Moving to a 100% renewable grid is likely to involve a more decentralized system, requiring a large shift in focus and reallocation of spending and workforce composition. Key recommendations from this work included the need to enhance their understanding of:​

  • Future changes in job and skills mix​
  • Integration of relevant data systems, including HR, finance, and system planning
  • Strategies to increase capacity and capability​
  • Strategies for optimizing the make vs. buy decision

Gas Infrastructure Decommissioning

The government of the Australian Capital Territory (ACT) was considering methods of shutting down the gas network over time, as well as different policy options that would assist in transitioning people off gas.

Figures 8 and 9 below show two different methods for shutting down the gas network. The first is to shut down portions of the gas network once consumption has fallen below a certain threshold, and the second is to shut down an equal portion each year. The modeling outcomes below show that different decommissioning strategies would have very different impacts on demand for gas and electricity.

Figure 8 – Gas Consumption Over Time Using a Threshold Based Decommissioning Approach, Source: Energeia
Figure 9 – Gas Consumption Over Time Using a Phased Decommissioning Approach, Source: Energeia

The two methods, which are achieving similar outcomes, have very different gas and electric sector workforce impacts. The difference in these two graphics shows the importance of considering the impacts of policies on skilled labor requirements.

Key recommendations from the work included banning new appliances as soon as possible to avoid future asset-stranding costs, pushing out decommissioning as far as possible to minimize stranding of existing assets, and managing price increases to avoid uneconomic switching over time​.

Workforce impact analysis was not in scope, but the difference in network shutdowns by scenario shows the potential for rapid change in utility and trade jobs between the gas and electric systems​.

Energeia’s detailed report can be found here.

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Workforce Solution Development

The following section outlines key dynamics in the current US workforce, including current trends, challenges and solutions for meeting the required increase in the workforce.

Key Barriers

Energeia has summarised the key workforce barriers below, which were determined from both contractor interviews and research. Interviewees cited barriers such as the competitiveness of wages, blue-collar job stigma, retraining incentive availability and uncertainty around demand for green jobs.

Table 1 below summarizes the identified barriers, which include a decline in energy job interest, a small pool of workers, a lack of training access, and high entry requirements.

Table 1 - Key Workforce Barriers, Source: Energeia Research

Career Attractiveness

Energeia analyzed hourly wages for blue-collar jobs and compared them to the hourly wages of the contractors who install building electrification appliances. The findings showed that plumbers, electricians, and HVAC professionals all make significantly more than most non-BE blue-collar jobs in this example.

  • Plumbers make ~$46/hr on average.
  • Electricians make ~$45/hr on average.
  • HVAC professionals make ~$36/hr on average.

As shown in Figure 10, the only non-building electrification blue-collar job that makes more than contractors related to building electrification technologies is construction, which makes ~$1 more per hour than HVAC professionals. The key takeaway here is that plumbers, electricians, and HVAC professionals have competitive salaries when compared to other trade jobs.

Figure 10 – Contractor Wages Compared to Blue Collar Trades, Source: Employment Development Department (EDD), Energeia analysis

Workforce Development

The following table summarizes the challenges and associated strategies to overcome these challenges in the electrification workforce, based on Energeia research.

Table 2 - Strategies to Increase Building Electrification Workforce, Source: Energeia Research

On the basis of the above challenges and solutions, Energeia developed a targeted workforce strategy for a Community Choice Aggregator (CCA) that addressed key workforce needs and barriers. Table 3 below captures the estimated costs and workforce impacts of selected key solutions from one of our recent engagements in California.

  • Entry-level bonus reflects the average signing incentives provided to new entrants upon completing training in trades such as HVAC, electrical, and plumbing, based primarily on actual industry data.
  • The retraining bonus is based on a Silicon Valley Clean Energy’s (SVCE) Future Fit Fundamentals Program, which offers a $500 training incentive to those who completed a training course.
  • Energeia also developed an estimate of the cost of process improvements and advertising campaigns based on experience, but these numbers should be validated on a case-by-case basis.
Table 3 – Workforce Development Strategies, Costs and Impacts, Source: Energeia Analysis. Percentage impacts represent estimated values

The resulting, illustrative workforce development program impacts are shown in Figure 8. They include strategies for increasing the efficiency of the existing workforce, e.g., instant permits and streamlining. In this case, the plan was smoothed to reflect the realities of hiring and program implementation (ideally, it should ramp up). The resulting, illustrative workforce transition forecast is shown below against the target needed to hit the identified building electrification goals.

Figure 11 – Workforce Need Above BaU vs Modeled Supply, Source: Energeia Analysis

Energeia recommended revisiting workforce development plans each year to validate assumptions and to refine programs as required.

Key Takeaways and Recommendations

Energeia’s key takeaways and recommendations for tackling the skills gap in the workforce and implementing decarbonization are summarized below.

Key Takeaways:

  • Achieving federal, state and city decarbonization targets will require significant workforce development.
  • The impacts across the electric, natural gas, refined product, and utility sectors vary significantly by pathway.
  • Best practice transmission planning includes assessment of impacts, and workforce development strategies and plans.
  • Workforce planning can effectively manage the transition in a timely and equitable basis.
  • Workforce development infrastructure will need to grow and change to meet expected workforce development needs.
  • Workforce development funding will need to grow to meet growing transition targets, and is a key gap at the moment.

Key Recommendations:

  • Ensure that transition planning includes workforce impact and optimization assessments, development strategies, plans and funding.
  • Engage with the workforce early to identify and address local issues and potential strategies, or risk significant pushback.
  • Understand the timing and mix of workforce impacts, and develop strategies to ensure an equitable, timely transition.
  • Significant education and support are likely to be needed across a wide range of sectors to ensure programs and funding are commensurate.
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]]> Industrial Decarbonization: Hard-to-Abate Sectors https://energeia-usa.com/hard-to-abate-sectors/ Mon, 24 Mar 2025 19:53:13 +0000 https://energeia-usa.com/?p=5328 Examining the specific CO₂ generation activities, fuel inputs, and viable decarbonization options—including electrification—of hard-to-abate sectors is required to provide an accurate outlook on this critical aspect of the energy transition.

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Examining the specific CO₂ generation activities, fuel inputs, and viable decarbonization options—including electrification—of hard-to-abate sectors is required to provide an accurate outlook on this critical aspect of the energy transition.

Hard-to-abate emissions continue to be forecast as minimally changing to 2050 across various sectors in the United States, including transportation and industrial sectors which account for two-thirds of total baseline emissions, as shown in Figure 1. These industries rely on energy-intensive processes that are difficult to decarbonize, such as high-temperature furnaces, heavy machinery, and chemical reactions.

Figure 1 – Reference Case Emissions Projections by Sector, Source: US EIA (2023)

Figure 2 below shows the emissions targets federally and by state, which show the misalignment between the forecast emissions over time shown in Figure 1. Understanding the factors driving industrial emissions, and the unique abatement options of these sectors, including their potential for energy efficiency, fuel switching or post-emissions abatement is crucial for utilities, policymakers, and industry leaders.

Figure 2 – Leading State and Federal CO2 Targets, Source: US EIA (2023)

The following sections summarize Energeia’s latest research into hard-to-abate industrial emissions, including the factors contributing to their persistence, strategies for improving energy efficiency and reducing emissions, and opportunities for implementing carbon capture and alternative technologies. These sections offer actionable insights and recommendations designed to help stakeholders address the complexities of decarbonizing hard-to-abate industries while maintaining economic competitiveness.

US Industrial Energy Consumption

Industrial energy usage is shown below in Figure 3 by industrial segment and fuel type along with corresponding emissions. Bulk chemicals, mining, refining, and construction have the highest total energy use.

Figure 3 – Industrial Energy Usage by Segment and Fuel Type in 2025, Source: EIA (2023)

Refining and bulk chemicals generate more than double the level of CO2 than most other sectors. This investigation into hard-to-abate sectors has focused on a subset of these, where more than electrification is likely to be required for a range of reasons.

Figure 4 – Industrial CO2 Emissions by Segment in 2025, Source: EIA (2023)

Which Sectors and Processes are Hard to Abate

Hard-to-abate processes occur in a number of different sectors. Each of the hard-to-abate end uess typically involves one of the following:

  • Processes with CO2 as a feedstock
  • Processes with CO2 as a byproduct
  • Processes requiring a light-weight fuel, typically aviation
  • Processes requiring a dense fuel, typically shipping
  • Processes at very high temperatures, which have historically been difficult and/or costly to achieve with electricity

Table 1 below summarizes the sectors and their corresponding processes which classify them as hard to abate.

Table 1 – Summary of Hard-to-Abate Industry Sectors, Source: Energeia Research

Solutions for Abatement

The following table summarizes the different solutions which aim to abate carbon emissions from processes.

Table 2 – Summary of Solutions for Hard-to-Abate Industry Sectors

Different hard-to-abate sectors benefit from different solutions for emissions abatement. Mixed solutions may be required for different processes within the same industry. Energy efficiency, and alternative processes may not abate all emissions.

Table 3 – Potential Solutions by Section, Source: Energeia Research

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Cost to Abate

Energeia researched and modeled the cost per metric ton of CO2 abatement to decarbonize different sectors. Energeia’s analysis shows a wide range of costs among potential decarbonization pathways for hard-to-abate industry sectors.

Industrial Sector

Figure 5 below shows the outcomes of the modeling for iron and steel production. Key iron and steel abatement options can be extremely expensive at over $3,000/CO2e, with carbon capture, utilization and storage (CCUS) or offsets potentially being more cost-effective solution.

Figure 5 – Iron and Steel Production Costs by Key Abatement Solution, Source: Zuberi et al. (2022), IEA (2021 & 2020), ARENA (2021), Note * indicates a solution that can address all stages of production, ^ indicates a simplified levelized cost

For aluminum, shown in Figure 6 below, there are a number of options at much lower cost, but as is the case for iron and steel, CCUS is the only solution (other than offsets) capable of achieving 100% abatement net of lower-cost alternative process solutions.

Figure 6 – Aluminum Production Costs by Key Abatement Solution, Source: Zuberi et al. (2022), IEA (2021 & 2020), ARENA (2021), Note * indicates a solution that can address all stages of production, ^ indicates a simplified levelized cost

For cement production, costs rise compared to aluminum, and improved thermal efficiency is the lowest cost solution to abatement, however, this solution is not capable of abating all emissions. Note that the CCUS costs are vastly different between steel and iron vs. cement, mainly due to the difference in capture and utilization costs.

Figure 7 – Cement Production Costs by Abatement Solution, Source: Zuberi et al. (2022), Mission Possible (2022), ARENA (2022) Note * indicates a solution that can address all stages of production, Source: European Commission (2022), European Parliament (2023), freethink (2024), Energeia Research

Petrochemical and chemical production have very high abatement costs, as shown in Figure 8 below, with carbon capture the lowest cost solution to full abatement, and hydrogen alternatives the highest cost solution of any hard-to-abate solutions considered.

Figure 8 – Petrochemical and Chemical Production Costs by Abatement Solution, Source: IEA (2021 & 2023). Note * indicates a solution that can address all stages of production

Importantly, many of the key solutions identified here will not be able to reduce 100% of sector emissions, instead requiring a portfolio approach and/or offsets.

Transport Sector

Transport remains one of the largest emitting sectors that many countries are looking to decarbonize moving forward. Figures 9 – 11 show the cost to abate emissions for heavy-duty road transport, shipping and aviation.

For heavy-duty road transport, electrification is the least cost solution for short-distance applications, however, current battery electric technology is constrained by energy density. Biofuels provide a cost-effective solution for reducing emissions in existing fleets across all sectors despite their lower energy density. Alternative fuels (ie. hydrogen) offer a potential technical solution to the energy density challenge for vehicle range, however, face infrastructure and scalability issues due to the immaturity of the technology. Alternate fuels are also currently modeled to be the highest-cost solution.

Figure 9 – Heavy Duty Transport Production Costs by Abatement Solution

Heavy duty transport and shipping appear capable of decarbonization without CCUS or offsets, however, aviation remains high cost.

Figure 10 – Shipping Production Costs by Abatement Solution, Source: ARENA (2024), Concawe (2022), Rony et al. (2023), NatureEnergy (2024), Energeia Research
Figure 11 – Aviation Production Costs by Abatement Solution, Source: ARENA (2024)

Future Directions

Uncertainty in costs additionally drives the complexity of decarbonization of hard-to-abate sectors. Figures 12 and 13 below show the range in estimates of solution costs depending on the technology and the forecast year.

Figure 12 – Carbon Capture and Storage (CCS), Source: IEA (2022), Wood Mackenzie (2021)
Figure 13 – Land Use, Land-Use Change and Forestry (LULUCF), Source: MIT (2024)

Key Takeaways and Recommendations

Energeia’s key takeaways and recommendations for tackling emissions reductions in hard-to-abate industries (derived from Energeia’s best practice research and innovative analysis) are summarized below.

Key Takeaways:

  • Industry and transport sectors (excluding light duty) represent 2/3 of baseline emissions in the US
  • Of these, a large proportion of them are not suited to electrification for a range of reasons
  • While specific solutions are being developed in each case, they can be very high-cost
  • CCS/CCUS and offsets are general approaches that may be needed to achieve abatement targets
  • A key question is how accurate the CCS / CCUS cost estimates are

Key Recommendations:

  • Electric high temperature heat technologies are a key solution that will be essential to the transition​
  • R&D focus will be key to bringing its cost down​
  • While biofuels are relatively low cost, there are not enough of them to meet all needs​
  • Green hydrogen will be needed to provide feedstock, and the focus should be on this application​
  • Much is riding on CCS / CCUS and LULUCF, and additional effort should be focused on them to bring costs down and ensure capacity
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]]> The Future of Data Center Electrical Grid Impacts https://energeia-usa.com/data-center-grid-impacts/ Mon, 27 Jan 2025 20:36:20 +0000 https://energeia-usa.com/?p=5245 The rapid expansion of data centers in the U.S. to support AI, cloud computing, and digitization is reshaping electricity demand and challenging grid planning. Energeia's research highlights the drivers of data center growth, their unique energy profiles, and strategies for efficient grid integration.

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The rapid expansion of data centers in the U.S. to support AI, cloud computing, and digitization is reshaping electricity demand and challenging grid planning. Energeia's research highlights the drivers of data center growth, their unique energy profiles, and strategies for efficient grid integration.

As new data center developments expand rapidly across the United States to accommodate growth in IT-intensive sectors like artificial intelligence (AI), cloud computing, and e-commerce, they are reshaping electricity demand and presenting new challenges for grid planning and distribution system integration. These sectors require power-intensive servers, data storage, cooling systems and more. Understanding the drivers behind data center load growth, the unique energy load profiles of data centers and their potential flexibility, and their integration with distribution systems is essential for utilities, policymakers, and industry leaders.

The following sections summarize Energeia’s latest research into data center load growth, including the factors driving their development, strategies for efficient integration into grid infrastructure, and opportunities for increasing load flexibility and energy efficiency. The following sections

Drivers of Data Center Growth

Generative AI, blockchain, social media, gaming, and virtual reality are among the top sectors driving data center growth, as listed in Table 1. Each sector presents unique energy demands, from the computational intensity of AI training to the consistent, baseline uptime required by e-commerce platforms. These sector-specific characteristics influence energy intensity, synchronicity, and the right strategies for least-cost integration with the grid. The following section dives into detail for a selection of key industries driving data center growth.

Table 1– Key Sectors Driving Data Center Growth

E-commerce, one of the oldest IT applications, has served as a foundational driver for cloud computing platforms like AWS. Initially growing in parallel with the U.S. economy until approximately 2010, the sector has since accelerated significantly. E-commerce exhibits notable load flexibility, due to factors such as inventory management, allowing for asynchronous operation. However, as transactions are continually digitized, Figure 1 suggests electricity demand in this sector could grow sevenfold over the next 10–20 years, assuming an 80% sales market saturation.

Figure 1 – US Retail & Wholesale vs. E-Commerce Sales

Whereas e-commerce shows significant, consistent growth potential up to reasonable market saturation, cryptocurrency mining is a volatile, energy-intensive activity subject to economic factors like market prices and hash rates. Figure 2 below shows a relationship between Bitcoin price and hash rates over time and implies a $0.14/kWh average mining revenue.

Figure 2 – Bitcoin Price vs. Hash Rate

Crypto mining operations across the US are geographically ; the largest mining operations are not necessarily located in regions offering favorable electricity rates or land costs[1].

Utilities face challenges in predicting energy demand due to the volatile nature of cryptocurrency markets and the sporadic nature of mining operations.

Artificial intelligence has grown exponentially in recent years as well, with model training consuming ten times the energy of typical cloud processes. Figure 3 shows OpenAI’s ChatGPT-4 consumes up to 7.2 GWh in its training processes.

Figure 3 – AI Training Energy Consumption by Model

Similarly, Figure 4 shows that an AI-powered Google Search uses as much as 25x the energy as a typical Google Search. As AI becomes embedded in more applications, its power and energy requirements are projected to rise significantly. Data centers supporting AI training and utilization will require advanced infrastructure to balance real-time processing needs with grid constraints. Key questions facing the industry include:

  1. How many more commercial AI models will be trained
  2. Which industries will apply AI to automate processes at scale?
Figure 4 – Energy Consumption by Query/Search Type

Forecasts for U.S. data center energy consumption in 2030 vary widely, ranging from 120 TWh to over 600 TWh by 2030, as shown in Figure 5. This lack of consensus highlights the uncertainty in estimating data center growth, driven by differing assumptions about efficiency improvements and sectoral expansion. Both top-down estimates based on historical growth rates and bottom-up forecast methods based on processor sales and power requirements have their limitations, further complicating grid system planning.

Figure 5 – US Data Center Consumption Forecast Comparison

The following section helps demystify the details of data center energy intensity, subloads, flexibility, energy efficiency options and more, providing insight into the tools required to develop a sound outlook for data center growth.

Data Center Subloads, Energy Efficiency, and Flexibility

In a typical data center today, more than 70% of energy is consumed by , servers, and storage, with power conversion and network hardware contributing to approximately 25% of load, while lighting typically accounts for less than 3%, as shown in Figure 6.

Figure 6 – Typical Data Center Consumption Mix by Source (PUE: 1.56)

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Power Usage Effectiveness (PUE), a key metric for non-IT load efficiency, measures the ratio of total data center power usage to critical IT power, which includes servers, storage, and network hardware. While average PUEs fell substantially until 2013, progress has since plateaued. Industry leaders like NREL, Google, and Meta have achieved PUEs as low as 1.03 through innovations like liquid cooling, though the broader industry has yet to match these [2]

Figure 7 shows various outlooks for more efficient data centers relative to a base scenario of 100%, via both non-IT and processor efficiency improvements. It is critical to note that the specific processor and cooling technologies present in each data center, or even server rack, will directly impact its power requirement.

Figure 7 – Data Center Energy Efficiency Scenarios

An estimated data center load profile within the AI sector is shown in Figure 8, with varying levels of training, utilization, and load shifting. Flat, flexible load typically represents asynchronous processing, while synchronous processing drives profile shape.

The relatively flat profile below shows very little weather sensitivity in its cooling systems, and may only reflect critical IT loads. Other literature in data center subload analysis, such as Ghatikar et. al (), suggests data center load shapes include more weather sensitivity on daily and seasonal bases to maintain safe operational temperatures within these facilities[3].

Figure 8 – Estimated AI Data Center Average Hourly Load

Data on the actual amount of synchronous vs. asynchronous processing by IT-intensive sector is not widely available. Smart metering data analysis could provide key insight into the synchronicity of data center load requirements and the associated flexibility, providing utilities with a clearer view of data center load shapes and their coincidence with asset and system peak demands.

Data Center Siting and Sizing

Data centers come in a wide variety of types, categorized into four main groups, as shown in Table 2. The largest hyperscale facilities are typically developed by major companies like Google and Meta. While their total capacity can exceed 300 MW, these centers are usually constructed in modular blocks of around 25 MW over time. This phased development approach is similar to that of traditional industrial.

Table 2 – Overview of Data Center Types and Sizes

Key data center hot spots in the US include Northern Virginia, Phoenix, Dallas, Atlanta, and Silicon Valley, with average data centers in these areas requiring from 5 up to 14 MW per site. Land and electricity costs drive the fundamental economics of siting, as telecommunications speeds and user proximity can dictate the type and load shape of data centers. Edge data centers are optimized for higher levels of synchronicity than hyperscale centers, and can be located in different proximity to end users. Figure 9 below summarizes the key factors impacting data center siting.

Figure 9 – Drivers of Data Center Siting

Industry Levers for Least Cost Integration

Load serving entities face significant risk in integrating data centers at least cost in the coming years. The following Table 3 describes a series of key potential levers for utility system planners to best manage data center integration, from rate design to demand response programs, distributed renewable development and defining an entirely new customer class.

Table 3 – Key Industry Levers for Integrated Data Centers at Least Cost

Key Takeaways and Recommendations

Energeia’s key takeaways and recommendations for integrating data centers into distribution systems (derived from Energeia’s best practice research and innovative analysis) are summarized below.

Key Takeaways:

  • Growth in server intensive industries is uncertain, but the fundamentals suggest it has legs for at least the next 10 years
  • Growth will be uneven, focused on areas near to major population centers, fiber links, low real estate and electricity costs
  • New connections will vary in size, with the largest connections likely near to population centers (synchronous) or major fiber links with low-cost land and electricity (asynchronous) – the latter is for overflow only after load sharing
  • A significant portion of the load seems likely to reflect underlying economic and demographic patterns
  • There is still significant potential for energy efficiency to reduce consumption per compute/storge activity, with AC and standby power opportunities well

Key Recommendations:

  • Determine the nature of your utility’s likely share of IT intensive industry load, to allocate appropriate levels of effort:
    • How close are you to population or business centers?
    • How good is your fiber connectivity?
    • How low are your land and electricity prices?
  • How much spare capacity do you have the in medium voltage and sub-transmission networks in areas of low land prices, connected to the fiber optic backbone?
  • Consider opportunities for strategic planning and connection policies, e.g. like for renewable energy
  • Be proactive with cost reflective rates and associated demand response programs, best practice here does not yet exist

[1]Tracking electricity consumption from U.S. cryptocurrency mining operations (2024), Tracking electricity consumption from U.S. cryptocurrency mining operations – U.S. Energy Information Administration (EIA)

[2]High-Performance Computing Data Center, High-Performance Computing Data Center | Computational Science | NREL

[3]Demand Response and Open Automated Demand Response Opportunities for Data Centers (2010), Demand Response and Open Automated Demand Response Opportunities for Data Centers

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]]> Optimizing Behind-The-Meter (BTM) Rates and Incentives https://energeia-usa.com/btm-rates-and-incentives/ Tue, 18 Jun 2024 17:31:47 +0000 https://energeia-usa.com/?p=5036 As rooftop solar PV, battery storage and ultimately, vehicle-to-x technologies, create generation alternatives and the means to store energy, artificial intelligence (AI) has the potential respond to pricing fluctuations. All this indicates none of the above characteristics of price inelasticity will remain true in the very near future.

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As rooftop solar PV, battery storage and ultimately, vehicle-to-x technologies, create generation alternatives and the means to store energy, artificial intelligence (AI) has the potential respond to pricing fluctuations. All this indicates none of the above characteristics of price inelasticity will remain true in the very near future.

Electricity has historically exhibited what economists call inelastic price elasticity of demand, in that a change in pricing has not historically led to a change in demand, which has governed how electricity has been priced for around 150 years. Due to advances in technology, this is all changing dramatically, thus emphasizing the need to maxmise efficient consumer energy resource rates and tariffs.

Price Elasticity of Demand

The characteristics of traditional electricity economics are driven by the lack of energy substitutes, the inability to store large volumes of electricity along with the high costs to respond efficiently to high prices.

Figure 1 – Examples of Custimer Responses to Price Changes Source: EconomicsOnline, (20 Jan 2020) Price Elasticity of Demand, economicsonline.co.uk/definitions/ped.html/

As rooftop solar PV, battery storage and ultimately, vehicle-to-x technologies, create generation alternatives and the means to store energy, artificial intelligence (AI) has the potential respond to pricing fluctuations. All this indicates none of the above characteristics of price inelasticity will remain true in the very near future.

As we move towards a future where customer agents, such as AI, can respond in real-time to electricity price signals and control more of our energy demand and supply, it is critical that these price signals are efficient. Otherwise, consumer energy resources (DERs) could be wasted or even operate to increase system costs.

For example, if all DERs respond to the same off-peak price signal, they may all start generating electricity at the same time, creating a new peak. This would be inefficient and would increase costs for everyone.

To avoid this, it is important to have electricity price signals that accurately reflect the real-time cost of generating and delivering electricity. This will help to ensure that DERs are used in the most efficient way possible.

Economic Efficiency in Energy

Economists agree on three complementary measures of economic efficiency: productive, allocative, and dynamic, that account for fluctuations and causal relationships between cost, prices, demand and technological progress. Using Gregory Mankiw, author of Principles of Economics, who defines these measures, we can overlay these principles onto an electricity system.

Productive efficiency is defined as the state of affairs in which the inputs used to produce a given output are minimized and thus operating on its production possibility frontier.

In terms of energy decarbonization, an example of productive efficiency can be represented in any least cost CO2 pathway to net zero emissions. In other words, that will reduce greenhouse gas emissions to a target level at the lowest possible cost.

The figure below shows a range of combinations of centralised (bulk) resources and decentralised resources, each sitting on the efficient frontier. If centralised resources are used more than the ratio described in theory below, the result will be lower system efficiency.

The figure below reports on the projected net benefits of implementing NREL’s identified strategies, with space and water heating measures the majority contributors to savings. The data also shows that most of the net benefits will occur in the 2030 to 2050 timeframe.

Figure 3 – Why Marginal Revenue (Price) Should Equal Marginal Cost Source: Energeia Research

Allocative efficiency is defined as the state of affairs in which the quantity of each good or service produced is equal to the amount that consumers are willing to purchase at the prevailing prices.

In terms of efficient Consumer Energy Resources, we need to take the quantity versus price one step further and examine the rate of change between these two variables, better known as marginal cost and marginal demand. Previously, without alternative energy options, energy prices peaked with system peak demand, based on the marginal cost to meet the demand. Now that alternatives and storage systems are available, during peak demand periods/high price periods consumers are able to switch to stored energy, mitigating high prices and lowering the demand on the grid. The goal of efficient rate and incentive design is to balance marginal cost to equal marginal demand.

Figure 4 – Illustration of Ineffiecenty Solar PV Incentives Source: Energeia Analysis

Where this is not the case, due to monopoly power, for example, then we will see under consumption where prices are too high, and under consumption where prices are too low, resulting in what economists term deadweight loss. We can also call this avoidable inefficiency, as shown in the figure above.

Dynamic efficiency is defined as the state of affairs in which technological progress is maximized. The development and deployment of renewable energy technologies is a classic examples of dynamic efficient in the energy industries. Renewable energy technologies, such as solar and wind power, are becoming increasingly cost-competitive with traditional fossil fuel-based energy sources, increasing adoption and revenue, thus perpetuating the next cycle of investment and lowering costs over time.

The future health of the energy economy will rest is the achievement of all three efficiencies.

Accurate costs are a key precedent to achieving productive efficiency, as is consumer level uptake forecasting or demand for allocative efficient, thus informing the price and marginal revenue, which should circle back around to equal to marginal costs.

Current incentives for solar PV for customers without cost-reflective pricing are a topical example of where Marginal Cost (MC) is not being set to Marginal Revenue (MR). The figure below illustrates how the reduction in the electricity bill, which is greater than avoided costs of delivering that electricity, results in a cross-subsidy, and over consumption of solar PV.

Figure 4 – Illustration of Ineffiecenty Solar PV Incentives Source: Energeia Analysis

It is important to note that Marginal Costs varies depending on the time horizon being considered. Short-run-marginal-cost (SRMC) is typically different to long-run-marginal-cost (LRMC). SRMC only includes variable costs in the short term, while LRMC typically assumes[1] a horizon where all costs are variable.

Due to the capital-intensive nature of centralised electricity supply infrastructure, where grid assets can last 50-70 years, and future costs are lower than historical costs due to technology and industry learning improvements, it is possible for LRMC to be less than historical costs.

Regulated utilities are typically allowed to recover their efficient historical costs, and they can without distorting the MC = MR relationship.

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Social Implications

Differences in upfront costs for gas vs. electric equipment and appliances are a key barrier for anyone with:  

  • capital constraints; or  
  • where there is a split incentive between the owner and the occupier of a premise; or  
  • where the value of the investment cannot be fully recouped, for example due to only being in the premise a few years compared to a 12-year investment horizon. 

The figure below shows illustrative (actual relativities vary by jurisdiction) expenditure relativities between different options for a 4-year investment horizon, which reflects typical residential and commercial lease tenancies – before any government incentives. Gas is generally the least cost option, though results are highly sensitive to local conditions such as gas and electricity relativities. Heat pumps, which may be more cost effective over a 12-year period, are unlikely to be selected by rational investors, due to their bounded conditions such as tenancy duration and the inability to recover the residual value in the resale value of the premise or via a deal with the landlord.

Figure 5 – Frank Ramsey (1903-1930) Source: Wikipaedia, (19 Sept 2023), Frank Ramsey (mathematician), https://en.wikipedia.org/wiki/Frank_Ramsey_(mathematician)

The State of the Art in Maximising Efficient Consumer Energy Resource Rate and Tarrif Design

Energeia’s review of tariff designs and DER incentives in Australia found that they fell short of efficient for the following main reasons:

  • Peak periods not based on forward looking, weather normalised periods of congestion
  • Peak prices not based on LRMC for centralised or decentralised resources
  • Rate designs not reflective of Ramsey pricing
  • Incentives do not reflect LRMC net of tariff impacts

Detailed information regarding efficient peak period design is contained in the webinar, as is information regarding efficient LRMC for centralised and decentralised resources.

Figure 6 – Testing Rate Design forAllocative Efficiency and Ramsey Pricing Source: Energeia Analysis

Selected DER uptake results, representing a short list of the above combination of options, are shown in the figure below. Solar PV adoption in this jurisdiction was around 19% at the start of the period. By the end of the period, it ranged from around 19% to as high as 59%, due to the rate design.

Figure 7 – 10-Year Solar PV and Storage Penetration Rates Source: Energeia Analysis

Adoption rates are one factor, but the real test of the impact of a rate is on the level of DER adoption in terms of capacity, which is reported in the figure below. It tells a very different story than penetration alone, with a range of DER mixes and absolute levels.

Perhaps unsurprisingly, the business-as-usual (BaU) rates, being Inclining Block and Seasonal Time-of-Use energy, the two most popular designs in Australia and the US, shows the highest levels of DER adoption, which are almost entirely solar PV, with very little battery storage.

Figure 8 – Cumulative Solar PV and Storage System Capacity Source: Energeia Analysis

Figure 8 delivers the key result in terms of which of the designs achieves the greatest productive efficiency, which also typically minimise cross-subsidies. Lower cross-subsidies also mean better Ramsey Pricing outcomes.  

Interestingly, no single factor seems to deliver the most efficient outcomes. ATF, OPD and ATE are in the lowest cost as well as highest cost designs. Monthly max demand, one of the most popular peak period pricing mechanisms, tends to result in lower efficiency outcomes.

Figure 9 – Community Cost and Cross Subsidy Impacts by Tariff Design Source: Energeia Analysis

Figue 9 delivers the key result in terms of which of the designs achieves the greatest productive efficiency, which also typically minimise cross-subsidies. Lower cross-subsidies also mean better Ramsey Pricing outcomes.  

Interestingly, no single factor seems to deliver the most efficient outcomes. ATF, OPD and ATE are in the lowest cost as well as highest cost designs. Monthly max demand, one of the most popular peak period pricing mechanisms, tends to result in lower efficiency outcomes.

It is important to note that the above example is from 2017, when solar PV and storage costs were much higher. Recent projects have resulted in significant increases in efficient Consumer Energy Resource resources, as their marginal costs fall relative to centralised system marginal costs.

In situations where it is not possible to achieve major tariff reforms, or as a stop gap measure, incentives such as rebates or annual payments can help send efficient price signals.

The figure below shows how efficient DER incentives should be developed, net of tariff impacts. Solar PV savings (incentives) are above utility savings, and the only way to address that is to use more cost reflective tariff designs. Bill impacts from other DER is under the utility savings, and the role of the efficient incentive is to bridge those gaps.

Figure 10 – Illustration of Using Incentives to Achieve MR = MC by Consumer Energy Resource Source: Energeia Analysis

Takeaways and Recommendations

While most in the energy can agree with and apply Einstein’s mass-energy equivalence, E = mc2, and Newton’s second law of thermodynamics, the industry has been slow to apply the economic principle of MR = MC, marginal revenue equals marginal cost, causes inefficiencies that could have costly and environmentally and socially harmful ramifications.

Key Takeaways

  • Rates and incentives are a primary driver of DER adoption and operation
  • Virtually all rates and incentives do not reflect key economic principles, resulting in inefficient adoption and operation
  • Key reforms needed include unbundling, locational, accurate LRMC and period calculation, and fair residual cost allocation
  • Efficient tariff and incentives will deliver 2-3 times more DER, in the right places, at the right times, in the right mix
  • Reform will not increase costs for the disadvantaged or undermine efficient network investment or operation

Key Recommendations

  • A rule change is probably needed to unbundle the transmission and distribution portions of tariffs, to ensure an optional tariff is made available and is designed correctly
  • Real-time pricing is nice to have, but 90% of benefits will come from unbundling, and improved cost and period accuracy

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]]> Removing Building Electrification Barriers https://energeia-usa.com/removing-building-electrification-barriers/ Tue, 23 Apr 2024 16:03:07 +0000 https://energeia-usa.com/?p=4924 Buildings account for a significant portion annual emissions, due to burning of gas for water and space heating, and for cooking.

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Removing Building Electrification Barriers

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Buildings account for a significant portion annual emissions, due to burning of gas for water and space heating, and for cooking.

Buildings account for a significant portion of the United States’ annual emissions, mainly due to the burning of gas for water and space heating,  as well as other common uses like cooking. Electrification of these end uses alongside power system decarbonization is a key decarbonization strategy being pursued at the Federal, state and local level. The main barriers to implementing this strategy includes: 

  • Higher cost electric appliances 
  • Consumer preferences (i.e. natural gas for cooking) and low awareness of available technology
  • Higher electricity grid costs 
  • Industry labor capacity limitations

The following sections describe best practice approaches to removing each of these barriers, based on more than 10 projects Energeia has completed in the U.S. and Australia.

U.S. Building Emissions and Electrification

U.S. emission reduction targets are driven by the Paris Agreement, which the U.S rejoined in 2021. U.S. targets include a 50-52% reduction in 2005-level (baseline) emissions by 2030, and a net-zero goal for 2050.

Baseline U.S. emission projections from the EIA are relatively flat, which, given economic and population growth, already reflects some savings from the development and adoption of new technology.

Figure 1 – U.S Emissions Projections by Sector, Source: Energeia Research, US EIA (2022)

The above graphic shows that the majority of U.S. emissions come from the transport and industrial sectors, with residential and commercial end uses accounting for 35% of the U.S. emissions total in 2022 (Lawrence Berkeley National Lab (LBNL), 2023). Most emissions are relatively flat other than for electric power, which reduces over time as the share of renewable energy increases.

A 2024 LBNL study of demand-side pathways for building sector emission reductions found that up to a 91% reduction in building sector CO2 emissions from 2005 levels by 2050 was possible with aggressive implementation of electrification, energy efficiency, and demand flexibility measures. The figure below reports on building sector emissions over time, demonstrating the sequencing of potential CO2 emissions reductions by measure type (HVAC, water heating, etc.)

Figure 2 – Total Building Sector Emissions (MT CO2) Source: LBNL (08/13/23), Demand-side solutions in the US building sector, https://doi.org/10.1016/j.oneear.2023.07.008

The figure below reports on the projected net benefits of implementing NREL’s identified strategies, with space and water heating measures the majority contributors to savings. The data also shows that most of the net benefits will occur in the 2030 to 2050 timeframe.

Figure 3 – Benefits by End Use in an aggressive implementation scenario Source: LBNL (08/13/23), Demand-side solutions in the US building sector, https://doi.org/10.1016/j.oneear.2023.07.008

LBNL’s modeling suggests a total of $107 billion in annual power system cost savings could be achieved by 2050. LBNL’s scenario assumes the majority of these savings, and therefore the uptake of these strategies, occurs between 2030 and 2050, with the majority coming from electrification in the residential sector, as shown in the figure below.

Figure 4 – Benefits by Sector and Measure in an aggressive implementation scenario Note: EL = Electrification, EE = Energy Efficiency, DF = Demand Flexibility Source: LBNL (08/13/23), Demand-side solutions in the US building sector, https://doi.org/10.1016/j.oneear.2023.07.008

While the above national analysis helps point the way, state and local jurisdictions will need to develop their own estimate of the optimal pathway to decarbonizing buildings. The following sections summarize a best practice approach to identifying it.

U.S. Building Electrification Potential

The pace of transition building emissions from the current state to a zero carbon, fully electrified future state, is governed by the rate of premise and equipment turnover, the regulations and incentives in place, and the underlying technology and fuel costs: 

  • Rate of new premises – New buildings are an important market segment for targeting regulations, standards, programs, and incentives. 
  • Rate of building remodeling – This usually triggers new regulations such as no new gas appliances, etc.;  
  • Rate of appliance turnover – Appliance bans can be at point of sale, but incentives can be used encourage voluntary electrification as well; and 
  • Retrofit programs– Almost never used due to their relatively high costs, they will become essential for the orderly decommissioning of the natural gas system. 

The rate of new buildings and premises is largely a function of economic activity and can be relatively easily gleaned from utility growth forecasts. New buildings are subject to prevailing building standards but can also be influenced by programs and incentives.  The figure below is taken from recent work we did on the commercial sector.

Figure 5 – Existing Commercial Premise Replacement Rate, Source: Energeia Research

The rate of appliance turnover outside of major remodels is driven by appliance lifetimes, as most are replaced at end of life. The figure below for water heating technology shows there is not much difference between appliance lifetimes. However, another key insight from this data is that once installed, there will not be another chance to electrify them for over a decade.

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Figure 6 – Water Heating Lifetimes, Source: Energeia Analysis

Turning the above drivers of potential electrification into actual electrification depends on overcoming the key barriers foreshadowed earlier.  

Addressing Appliance Cost Barriers 

Differences in upfront costs for gas vs. electric equipment and appliances are a key barrier for anyone with:  

  • capital constraints; or  
  • where there is a split incentive between the owner and the occupier of a premise; or  
  • where the value of the investment cannot be fully recouped, for example due to only being in the premise a few years compared to a 12-year investment horizon. 

The figure below shows illustrative (actual relativities vary by jurisdiction) expenditure relativities between different options for a 4-year investment horizon, which reflects typical residential and commercial lease tenancies – before any government incentives. Gas is generally the least cost option, though results are highly sensitive to local conditions such as gas and electricity relativities. Heat pumps, which may be more cost effective over a 12-year period, are unlikely to be selected by rational investors, due to their bounded conditions such as tenancy duration and the inability to recover the residual value in the resale value of the premise or via a deal with the landlord. 

Figure 7 - 4-Year Capex and Opex Costs by End Use, Source: Energeia Analysis (2019)

Best practice building electrification programs often ban options that are known to be a bad investment, e.g. resistive ducted or water heating systems, or that would otherwise lock in future costs due to gas network decommissioning. However, this can unfairly force costs on to current premise owners (who may only be there for 4 years),or provide financing or other incentives to better align the costs and benefits over time.  

The Inflation Reduction Act (IRA) enacted in 2022 ,provides tax credits and funds to states, however, it only partially addresses the upfront cost barriers for consumers as shown in the figure . Additional financial strategies at the state and local level are still required to promote equitable and expansive electrification.

Figure 8 – IRA Impacts on Upfront Cost Differentials for CA Single Family Households, Note: SH = Space Heating, WH = Water Heating, LI = Low Income, MI = Middle Income, Source: Energeia Analysis (2024), TECH Clean CA (2024), BEI (2022)

The most appropriate policy and regulatory framework depends on the situation, with the presence of government owned gas and electric utilities leading to a very different optimal policy and regulatory solution than where they are privately held, for example. 

Addressing Grid Impact Barriers

Beyond the owner and occupier barriers, the impact of electrification on the electricity system can be a significant barrier, particularly where tariffs are not cost reflective, and higher electricity system costs fall on existing rather than transitioning demand.  

The figure below provides an illustrative example of average electrification impact by end use. It shows that space heating could lead to a higher peak in the morning, where most electricity peaks are driven by summer air-conditioning load after solar PV output declines. 

As long as the new load is not creating a new peak demand, and prices are cost reflective, it will lead to a general fall in electricity prices, as new demand is able to share the cost of existing infrastructure. However, once the capacity of existing infrastructure is exhausted, electrification could become the key driver of capacity expansion costs. 

Figure 9 – Example Winter Electricity Peak Day Load Profile, Source: Energeia, Analysis (2019)

A key assumption in the above estimate is the mix of resistive vs. heat pump technology and the mix of instant rather than storage water heating technology. Resistive technology uses 2-3 times more electricity for space and water heating, and will drive a much higher morning peak, as does instant compared to storage water heating technology.

Best practice approaches to addressing the above barriers include regulations and incentives that encourage more heat pumps (which are storage based in nature), a reduction in heat pump costs (e.g. via R&D incentives) and discourage electric boosters in them.  

Other best practice approaches focus on driving greater overall electric load flexibility via adoption and operation of the devices shown in the figure below, i.e. rooftop solar PV, batteries, and controllable loads such as at premise electric vehicle chargers and heat pumps.

Figure 10 - Key Technology Options for Reducing Customer Costs, Source: Energeia Research

Implementing the above best practices will have a major impact on the overall cost effectiveness of electrification.

Addressing Industry Capacity Barriers

Another key issue facing policies and regulations that ban appliances that are currently a major portion of the market is the impact on the installation industry and the associated workforce. figure below illustrates this effect on the demand for different trades when implemented all at once in the first few years. It is unlikely that the industry can respond so quickly to the change in demand, leading to labor shortages, unemployment and higher consumer costs.

Figure 11 – Impact on Industry Capacity under Banned Gas Appliance Scenarios, Note: FTE = Full Time Equivalent Assumes new builds are all-electric, gas appliance sales ban, and elec. appliance incentives, Source: Energeia Analysis

Of course, any significant changes to market conditions need to be communicated to the industry in a timely fashion. We have worked with clients to identify and engage with the workforce training elements of the economy to ensure sufficient retraining capacity is available.  

Best practice approaches we have previously developed with clients including starting with incentives to encourage a more gradual increase in electrification, with any future bans on targeted technologies staggered across end of life, new / remodeling and customer segment.  

Addressing Program Funding Barriers

The combined effect of implementing best practice approaches to barrier removal has a dramatic effect on a given community’s costs and benefits from building electrification, as illustrated by the example outcomes across a range of base and optimized policy scenarios shown below.

The whole-of-system and stakeholder impact analysis Energeia conducted showed that 2 out of the 3 base policies resulted in a significant net cost increase. However, implementing best practice mitigation strategies resulted in the lowest overall cost, which was a significant savings compared to the ‘Do Nothing’ scenario (against which other scenarios were compared). 

Figure 12 – 15 year NPV ($M, 2021) by Cost Category, Source: Energeia Analysis

Takeaways and Recommendations

The cost impacts of electrification largely occur upfront, while the benefits occur over a longer period, up to 10-years or more. Costs can also impact on the owner and occupier differently, due to differences in tenancy duration and ownership. Addressing these barriers requires access to financing in a manner that accommodates changes in tenancy.

Best practice, self-funding strategies we have worked with clients to identify and implement include: 

  • Providing rebates or financing that address upfront cost differences, and recovering these costs via higher electricity rates or other techniques to better allocate costs 
  • Accessing CO2 credits or energy efficiency credits (which also translates into CO2 savings once the wholesale market is decarbonized)

We have also seen policymakers look to the tax base, including bonds, to provide financing. However, we have found that this has not been necessary to date, due to the availability of longer-term operating cost savings, and the ability to use energy pricing mechanisms for funding.  

That being said, a key cost that has only been tested in very limited cases in the US is the cost of gas network decommissioning. The gas commissioning process is likely to entail rollout of electric appliances, which will be relatively high cost, and likely need to be compensated.

Energeia encourages careful analysis of net benefits from electrification and the consideration of policies that better capture excess benefits (i.e. windfall gains to certain stakeholder groups) so that any excess benefits may at least partially be used to fund the cost electrification, including gas system decommissioning as appropriate.

While beneficial overall in every jurisdiction that Energeia has analyzed, impacts from building electrification vary widely across premise types, consumer tenancy types, and time.  Best practice policymaking and regulation focuses on mitigating the downsides, in part via capturing a portion of the potential upside. 

Key Takeaways 

  • Building electrification is an essential part of the United State’s decarbonization pathway
  • There are four main electrification triggers: new build, replacement, end-of-life, and retrofit
  • Building electrification impacts different consumers differently
  • Key barriers to electrification are higher upfront costs, higher grid costs, industry capacity constraints, and program funding
    Electrification can impact on electricity sector costs, but these impacts can often be mitigated
  • Bans on appliances in new construction or at end of life can create step changes in workforce requirements

Key Recommendations 

  • Use bottom-up modeling of premises at the sub-load level to provide granularity needed to identify and size the key barriers and solutions 
  • Overcome cost barriers via financing or rebates, recovering these costs from imposts on electricity usage aligned with expected benefits 
  • Overcome electricity grid cost barriers by ensuring cost reflective pricing avoids cost shifting and cross-subsidies, and encouragement of load flexibility and management 
  • A largely unknown key risk is the cost of gas network decommissioning, which Energeia believes could be at least in part funded by repurposing electrification benefits 
  • Address potential industry labor constraints by giving industry plenty of notice, staggering any bans to minimize step changes in demand and ensure retraining capacity 
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