Battery technology is progressing slowly and advances in lithium-metal are not yet commercially available
Federal EV battery incentives pertain to countries with US free trade agreements: Australia, Canada, and Chile
Battery supply is constrained by metal mining and production is limited by complex and costly process technologies
More research and product production methods are imminently needed
Battery production for electric vehicles should be a concern. For one, the US has neither the resources nor the production capacity to meet the demand of EV manufacturers. Second, as a national security concern, not having the requisite production infrastructure to support energy transformation leaves the US vulnerable to economic decline and energy price increases. Third, to navigate energy transformation it’s imperative to establish battery production for grid stability and resiliency, particularly when introducing renewable energies.
Currently, lithium-ion batteries are the core foundation for EVs and most vehicle manufacturers are planning to transition to all elective vehicles in the near future. California might ban the sale of new cars running only on gasoline by 2035. The issue is the production of EVs is inextricably linked to the availability of batteries that are limited by supply constraints in both battery metals and production capacity. Our focus is on battery supply chains and production.
Battery Supply Chains
The big issue around EV batteries is assuring an adequate supply of materials at a reasonable price. To better understand the EV supply chain let’s look at the common raw materials namely metals and their associated costs. The four primary metals in a lithium-ion battery commonly used in most EVs are lithium, nickel, cobalt, and manganese. EV batteries use nickel-manganese-cobalt cathodes, with 60% nickel and 20% of cobalt and manganese.
With the bipartisan National Electric Vehicle Infrastructure (NEVI) funding fast approaching, what are the implications on energy demand and the utility grid and why is EV charging compounding the complexities of grid transmission and distribution?
Currently EVs account for about 1% of the global vehicle market and according to EV Adoption, there are approximately 2 million EV on the road in the US. According to the Department of Transportation’s Federal Highway Administration, the average vehicle travels approximately 13,500 miles annually and EV efficiency is roughly 3.5 miles per kWh suggesting annual energy consumption of 3,870 kWh. According to the DOE Energy Information Administration, the average US home consumes roughly 10,900 kWh a year. Therefore, an EV would potentially account for the 35% of the average US home’s electric usage. Most homes can be equipped with a Level 2 EV chargers (240 volts / 50 amps) mitigating any grid impact
So, what does this EV energy transformation mean to consumers? Let’s look at a few key factors in evaluating EVs: economics, driving range, charging time and charging network. For one, it is the understanding of EV economics such as the difference between MPG to miles per kilowatt hour (kWh). Essentially, how far can you drive with a gallon of gas to kWh of energy. According the EPA, the average vehicle fuel efficiency in 2020 was 25.7 MPG. The U.S. Department of Transportation’s Federal Highway Administration states the average person drives around 13,500 milesevery year suggesting an annual fuel cost of over $2,300 at $4.50 per gallon.
The average EV range is approximately 3.5 miles per kWh. One way to assess the economics between MPG and kWh efficiency is to compare the driving costs of traveling 100 miles. With the average fuel cost of $4.50 in the US and 25.7 MPG equates to $17.50. With an EV achieving 3.5 miles per kWh, the 100-mile traveling cost will depend on whether the EV was charged at home or on a charging network station. According to the Energy Information Administration, the average at home cost is roughly $0.14 per kWh. So, the 100-mile EV travel cost equates to $3.91.
However, if the EV requires charging on a public charging network, the cost is significantly higher. The average kWh cost on public charging networks is approximately $0.42 per kWh ranging from $0.25 from Tesla to $0.33-to-$0.60 on other charging networks. At $0.42 per kWh, the 100-miles travel would cost $12.00 in an EV which is still a 30% savings over conventional vehicles.
The US utility grid, comprised of electric generation, transmission (high voltage long distance transport) and distribution (last mile connection to end user) consumes approximately 3.8 trillion kilowatt hours (kWh) with 1.28 trillion kWh in commercial use or roughly 34% of the grid.
In 2021, Electric Vehicles (EV) represented approximately 3% of the registered vehicles in the US. The U.S. Department of Transportation’s Federal Highway Administration states the average person drives around 13,500 miles every year. The average electric car consumes 34.5 kWh per 100 miles. This works out as 0.346 kWh per mile. https://ecocostsavings.com/electric-car-cost-per-mile/ That amounts to 36 billion kWh or 1% of the electric grid.
Vehicle manufacturers are projecting substantial migration to EVs which will increase the impact on the grid. When EVs account for 25% of total vehicles, an additional 7% of grid capacity will be required.
The required grid buildout will be complicated further by the adding huge numbers of physical EV charging locations. The Federal Government has just allotted $5 billion to assist states that have aggressive charging station construction plans. Bottom line: the EV transformation will have a trillion-dollar impact on the economy — driven by the 30%-to-60% energy efficiency gain of EVs over internal combustion engines.
Currently, there are over 160,000 fueling locations around the country such as gas stations and convenience stores. EV charging units, not individual locations, just a power connector, are estimated to be at around 36,000. What is important to note is that the majority of these legacy EV charging systems are Level 1 and Level 2 type requiring charge times of an hour to go 100 miles. These legacy EV charging systems are not conducive for vehicle commuting behavior. Who can wait an hour to charge their vehicle?
The trend is for next generation fast charge (FC) and extreme fast charge (EFC) EV charging systems that are capable of extending range and providing faster charge times more indicative of the average gas refueling time. The limitation is that the number of FC and EFC charging locations is minute. Tesla operates over 20,000 Supercharger connections globally but only 908 physical US locations.
The bottom line is that the buildout to support fast charging EVs will require extensive capital investment and generation capacity that is further complicated by managing distributed energy resources such as DC power conversion, energy storage and renewable energy.
While EVs are changing the utility landscape, digital transformation – where greater reliance is required by expanding data centers that consume substantially more energy than manufacturing facilities – is consuming energy at an even faster rate. The economics of cloud computing, machine learning, AI chips, and analytics-driven business models are only accelerating this digital transformation and dependence on data centers. When one adds crypto currency mining to the mix, the utility grid will predictably undergo substantial change. At current growth projections, Green Econometrics forecasts that EVs, data centers and crypto mining will require an additional 11% energy generation and grid capacity by 2027.
From the inception of the Industrial Revolution several core ingredients enabled the transformation and growth of industry. Among these core building blocks of the Industrial Revolution namely: access to risk capital, visionary entrepreneurs, available labor, technology, resources and energy. Technology and energy play a crucial role in not only growing industry but enable scale. Technology can open new markets and provide advantage through product differentiation and economies of scale. Energy is literally the fuel that scales operations.
Today technology, built from knowledge and data, is how companies compete. Energy now emerges as even more integral in scaling operations. Just as James Watt developed the first steam powered engine in 1606 commencing the Industrial Revolution, it was the access to available coal with the use of the steam powered pump, invented by Thomas Savery in 1698, that allowed greater access to coal that gave scale to industry.
Most recently, the pending transaction of Salesforce’s (CRM) acquisition of Slack (WORK) after acquiring Tableau last year serves as a reference in valuing the importance of technology is to sustaining market value. The market value of seven companies accounts for 27% of the approximately $31.6 trillion for the S&P 500. Evaluating the industry and market impact of innovative technologies can be viewed through the lens of stock valuations, particularly as it applies to mergers and acquisitions. This article reviews the companies and the technologies from the perspective of market sales opportunity and the economic impact of the technologies based on the price/performance disruption to the industry.
So why are we focusing on energy and data today? Energy, predominantly hydrocarbon fuels such as oil, natural gas and even coal is how people heat their homes and buildings, facilitate transportation, and generate electricity to run lights, computers, machines and equipment. In addition, there is substantial investment focus on the digital economy, Environmental and Social Governance (ESG), and innovative technologies. A common thread among these themes is energy and data.
Data and Energy are the pillars of the digital economy. Energy efficiency can reduce carbon emissions, thereby improve ESG sustainability initiatives. Innovative technologies around energy and data are opening new markets and processes from formulating new business models to structuring and operating businesses.
The climate imperative and investing in energy infrastructure and environmental ESGs are predicted on energy efficiency and relevant performance metrics to evaluate investment allocation decisions. Therefore, our initial emphasis begins with a background on energy consumption with focus on electric consumption trends, carbon footprint, Green House Gas (GHG) emissions, sustainability, electric grid resilience, and technologies that impact energy including Electric Vehicles (EV), energy storage, and Autonomous Driving (AD). Data technologies encompass cloud architecture, Software as a Service (SaaS), Machine Learning (ML) analytics, and the importance of data as the digital transformation gives rise to the digital economy.
Digital Economy Performance Metrics
Before we dive into the financial and competitive analysis, let’s review business models that are disruptive to the status quo. That is are innovative technologies capable of rapid scale and efficiency gains that change the economics of the market and business profitability. In addition, disruptive events, driven primarily by technology, often appear as waves as the adoption of innovative technologies expands through the market.
Prominent technological waves such as the personal computer (PC), followed by the internet and smartphones and most recently social media and cloud computer all manifested themselves in engendering new business models and creating new market opportunities that dramatically changed the status quo among leading companies at the time. We will use the internet and mobile technology waves to explain how the introduction of innovative technologies offering vastly improved means of commerce enabled the development of new services that changed the business landscape.
Most recent advances in technology appear as waves and give rise to new business models and markets. The internet is one example. The internet enables the connection and process of communication over a new channel. The internet allowed one-to-one and one to many communications and the ability to engage, transact and scale using a digital platform that tremendously lowered the cost of engagement. Scale is among the most important attributes of the internet because the cost of digital replication is close to zero.
Mobile and smartphones began a new era in the digital world. The smartphone allowed a large portion of the world to interact with the internet for the first time on a mobile device. The mobile wave provided platform that enabled the introduction of a host of new business models. The introduction of the Apple iPhone gave way to several new services and industries all from your cell phone.
Let’s review the business model impact of innovative technologies as it applies to cost structure.
Cost Structure and Disruptive Innovation
As explained by ARK Investment Management’s Catherine Wood, the rate of cost decline can be used as a proxy for evaluating the disruptive impact of innovative technology. Cost structure improves as unit production expands. As first postulated by Theodore Wright, an aerospace engineer, who postulated that “for every accumulated doubling of aircraft production, costs fell by about 20 percent”. Wright’s Law as it is now known is also called the Learning Curve or Experience Curve and it is found across industries that experience different rates of declining costs.
What is important from the perspective of investment firms such as Ark is that the magnitude of disruptive impact can be gleaned from these declining cost curves. Revenue growth can then be correlated from these declining cost curves. Essentially, demand elasticity and future sales can be derived from the rate of product cost declines.
This is why price/performance and scientific metrics play an important role in evaluating products, services and company competitive positions. For example, the average cellular price per gigabyte (GB) of data is approximately $12.37 in 2020 according to Small business trends. Another example in science, is the physical performance of an LED light assessed by lumens the light output to the amount of energy consumed in watts such as lumens/watt (Lm/W). These metrics are points in time. For more context, the changes over time and magnitude of change provide insight into inflection points, trends, patterns and relationships.
As devices become complex, encompassing separate processors for communications, computing, power, video and various sensors, it is the integration and orchestration of the overall device performance that becomes of greater value to the user. So, price/performance, scientific understanding and economics become more attuned to relationships among these varied and interdependent components.
A data analytics framework is applicable to insight discovery; provides a roadmap towards innovation; and enables capabilities that can optimize approaches to new business models and opportunities. The following paper provides examples revealing how and why to apply visual analytics for discovery, innovation and evaluating new opportunities.
Discover how waveforms and patterns are applied to science and finance, and how customer usage patterns can reveal new approaches to market micro-segmentation and persona classifications. Lastly we’ll reveal how the deployment of IoT devices across the enterprise fuels data flow in the physical world regarding the performance and conditions of business assets.
Introduction
Our theme is applying visual data analytics as a tool for discovery, innovation and evaluating market opportunities. We show how two metrics, price and volume, are able to convey insight and establish price targets for technical analysis. Why energy consumption patterns and waveforms lend themselves to understanding science and classifying human behavior. How proxy metrics can serve as measures for physical events. Why linking granular visibility into processes and the monitoring of conditions and operating performance help build an advantage in the digital economy.
Green Econometrics relies on visual analytics as a core fabric in our data analytics frameworks because visual analytics are integral to discovery, innovation and new opportunity development. Visual insights are easy to understand – allowing business objective and performance metrics to seamlessly transfer across business units. So how do we do it?
The value of IoT is its ability to monitor, control, and compile data. Data derived from IoT sensors when combined with analytics can lower operating costs, enable new business models, and improve productivity. Embedded sensors monitor, measure, and manage connected devices with limited human interaction. Less human interaction translates into higher productivity. Sensors that can monitor and control devices can also minimize maintenance costs, reduce energy costs, optimize resources allocation and process flow.
For instance, photo and occupancy sensors that can control lighting typically save 20% of a building’s lighting cost. On average, lighting accounts for 25% of the buildings energy costs or approximately $0.70 per square foot according to the DOE. When lighting controls sensors are connected to the Internet, they enable remote diagnostics, device control, and collect data.
By analyzing data from IoT devices, new business models can be created. Analytics play a crucial role developing these new business models. Uber uses analytics to know user demand by the minute. Palantir Technologies provides visual analysis using disparate transactional activities to detect fraud. IoT devices allow greater detail in data capture and faster timing responses. IoT sensors that enable device control and data capture will engender new business models.
Advances in technology such as seismic imaging with Dawson Geophysical and horizontal drilling with Schlumberger have dramatically changed the economics of oil and gas extraction. The change in oil economics is so profound that the cost structure of hydrocarbon fuels will reverberate through the global energy market and impact pricing of renewables energies and investment decisions. So profound are these changes that the US has surpassed Saudi Arabia and emerged as the world’s largest oil producer.
With the price of oil falling as a result of large production gains in US oil production. The price of oil is may fall below $40 per barrel according to an article in Barron’s The Case for $35 a barrel Oil suggesting further oil price declines are possible.
Global oil demand grew 0.6% in 2012 and over the last ten years oil consumption grew at a compounded annual growth rate (CAGR) of 1.3%. With near term oil demand at a lower level then the trend for the past ten years suggests the pace in oil consumption is slowing.
According to the Energy Information Administration (EIA), EIA the trend in oil consumption is pointing towards slower if not anemic growth. In the two largest areas, the US and Europe, demand is for oil is declining. While the increasing demand for oil in China and India is significant, the rate of growth is slower.
Figure 1 Global Oil Demand
In the US, oil demand declined 2.1% in 2012 and over the last ten years oil consumption is down 0.6%. The oil consumption trend in the US suggests the decline maybe more structural, particularly as vehicle fuel efficiency is improving and high oil prices may change consumer-driving habits.
Figure 2 Oil Consumption – Major Countries
While the economic weakness in Europe and moderating growth in China, it is not surprising to see weakness in global oil demand. The trend is lower oil consumption might just be the result of short term economic weakness.
Europe and the US account for over 37% of the global demand for oil and that demand has declined over the last ten years. While the US was down 0.6%, demand for oil in Europe was down 1.1% in the last ten years.
Figure 3 Oil Consumption Perspective
There is still strong demand for oil in China and India, but the rate of growth has slowed. China and India represent 15% of the global demand for oil. China and India have one-year oil demand growth rates below their respective ten-year rates.
Figure 4 Oil Consumption Trends
The bottom line is that is demand for oil has slowed and it maybe at a point where oil prices may soon reflect slowing demand.
The latest data on oil consumption suggest the dip in consumption that appeared in 2008 after the global financial crisis quickly reversed. The contraction in oil has now turned to expansion with consumption up 4% y/y globally.
According to the latest reported information from the Energy Information Administration (EIA), EIA oil consumption is up 4% for 2010 from 2009. The data oil consumption data suggests the global economy has recovered from the financial crisis and is translated into higher oil demand.
Figure 1 Global Oil Demand
WE have seen economic contraction result into declines in oil demand before. Oil demand dropped in the 1979 to 1983 period with of a 10% decline per year. On a global basis, oil demand declined approximately 2% in 2009 from 2008, but is not up nearly 4% in 2010
In the US, oil demand dropped 5.7% in 2008 and 3.7% in 2009 with demand in 2010 increasing 3.8%. The oil consumption trend in the US suggests the decline in oil demand was cyclical as apposed to any structural changes in US consumer demand.
Figure 2 US China & India Oil Consumption
The real story is the growing demand for oil from China and India. According to data from The Centre for Global Energy Studies (CGES) , the demand for oil from China is up 100% from in the last ten years. China’s oil consumption rate has grown from 4.8 million barrels per day (MBPD) to 9.6 MBPD amounting to half of the total US consumption. In 2010 the growth in oil demand in China is up 17%.
The demand for oil in India is also increasing. Oil consumption in India is up 58% in the last ten years and up 8% in 2010.
Figure 3 China and India Oil Demand
The bottom line is that is demand for oil continues to increase and we expect further increase in oil prices.
After several months in Silicon Valley three factors resonate clearly in the process of innovation: access to data, applied analytics, and time to insight. Innovative ideas and technology can just as easily be spawned in New Jersey or Milan as in Silicon Valley. Our focus is why investment into infrastructure that facilitates access to energy or commerce, is the critical factor in game changing events.
Investment onto infrastructure to support access to energy enabled New York City to gain prominence over Philadelphia and Boston as the largest economic center in the US. Access to energy can be traced back to 1829 when the first American steam locomotive in Honesdale, PA initiating the American Railroad to transport Anthracite coal mined in nearby Carbondale to a canal network ultimately linking to the Hudson River and New York City. See post Coal: Fueling the American Industrial Revolution to Today’s Electric
As a corollary, in demonstrating the importance of investing into infrastructure to support economic growth, this is the tale of two Southern cities. In the 1950’s, Memphis, TN and Atlanta, GA were roughly the same size. While Memphis enjoyed economic growth from its port on the Mississippi River, Atlanta was land locked. Atlanta strategically invested by focusing on the future of jet aircraft building the infrastructure for the largest airport in the US in 1961. Within 10 years Atlanta had double the population and economic growth of Memphis. Today Atlanta has an economy five times that of Memphis because of innovative thinking and investment into infrastructure of the future.
Figure 1 Infrastructure: Tale of Two Cities Source: Social Science Data Analysis Network
Electric vehicles (EV) and energy storage are perhaps the most important energy strategy second to renewable energy such as solar photovoltaic. The reason EV is so important to a national energy strategy is the fact that oil used for transportation accounts for more than twice the energy required to supply the entire electric needs of the US market. See the Green Econometrics post Energy Perspective The issue is formulating an effective energy strategy that embraces renewable energy and smart grid technologies.
Figure 2 US Electric Vehicles Source: Ward Automotive, Pike Research, Green Econometrics
Just how critical is infrastructure to supporting electric vehicles?
According to information from Tesla Motors’ registration filings with the SEC in June 2010, the charge time on the Tesla Roadster using a 240 volt, 40 amp outlet to full capacity takes approximately 7 hours. Assuming most drivers are in their vehicles for work five days a week and one day on the weekend, the electric energy consumption to charge the electric vehicle amounts to approximately 67 KWH a day and for a six-day per week charging, 20,966 KWH per EV per year.
According to the DOE Energy Information Administration, the average residential home consumes about 11,000 KWH a year. So the electric vehicle is roughly double is energy use of a typical home. Given capacity constraints in electric generation, tripling the electric energy use per house would more exacerbate our already tenuous energy situation,
Figure 3 Smart Grid is Critical for US Electric Vehicles Source: EIA, Green Econometrics
To sustain economic growth and avoid dependence on foreign oil, electric vehicles provide a migration path towards energy independence. To support the adoption of electric vehicles, a tremendous investment in our electric infrastructure is required. A dramatic supply shock to oil could raise substantially the retail price of gas and thereby drive consumer towards EVs at an accelerated rate. If half the vehicles on the road were electric, our electric generating capacity would need to increase dramatically and outfitted with smart grid technologies to stabilize transmission.
The bottom line is vision and innovation require investment into infrastructure and in particular renewable energy generation like solar and wind and the grid to support intelligent transmission and distribution.
The worst global economic recession in since the Great Depression seems to be abating. Given the severity of the financial crisis, it might serve to review what impact the recession has had on oil consumption. In addition, what impact did the decline in oil consumption have on atmospheric CO2 concentration levels?
Since 2006, global oil consumption declined by 1.1 million barrels per day (BPD) from 85.2 in 2006 to 84.0 in 2009. Oil consumption in the US declined 9% to 18.8 million from 20.7 million BPD in 2006. Europe experienced a decline of 7% over this same period with a drop of 16.5 million to 15.2 million BPD. However, over this same period, oil consumption in China and India increased 16% and 13%, respectively. This data was complied from the US Department of Energy Information Administration (EIA) and is displayed in the following charts.
To measure how significant the impact has been, the following charts provide some insights in evaluating how deteriorating world economies may have impacted oil consumption and secondly, whether reduced oil consumption has mitigated heightened CO2 levels.
Figure 1 Global Oil Consumption
Source: EIA
From Figure 1, the impact of the global financial crisis is depicted with the decline in global oil consumption. When a comparison is applied to oil consumption between the US China, and India, the relative drop in oil consumption is less discernable.
Figure 2 US, China, and India
Source: EIA
Figure 2 provides a summary of oil consumption of the US, China, and India. A measurable decline in oil consumption can be seen, but only in the US market.
Figure 3 China and India
Source: EIA
Figure 3 demonstrates the steady and pronounced growth in oil consumption for China and India. Despite the global financial crisis, oil consumption significantly expands in China and India due to secular growth from rapid industrialization in both countries. When measured with respect to the European market, China and India have grown from 15% of the oil consumption rate of Europe in 1980 to over 74% of the consumption level in 2010.
Figure 4 CO2 Levels
Source: NOAA
With the decline in global oil consumption, perhaps a positive benefit would be a fall in CO2 levels. The atmospheric CO2 readings in part per million (PPM) where taken from the National Oceanic and Atmospheric Administration (NOAA) from the Mauna Loa CO2 Levels monthly measurements. Figure 4 illustrates the average annual atmospheric CO2 concentration readings in Mauna Loa, Hawaii from 1980 through 2010.
The bottom line is even while global oil consumption declined during the recession, growth in China and India remained unabated and subsequently, CO2 concentrations in the atmosphere continue at elevated levels.
In memory of Jamie Kotula – loved by family, friends, teammates, and school.
Insulation is one of the most important factors in improving building energy efficiency. Heating, ventilation and cooling (HVAC) often accounts for more than half the energy expense of a building. Insulation helps to improve the energy efficiency of heating and cooling. Depending on the selected insulating material, the economic impact on heating costs can be quite dramatic.
To understand how insulation helps improve building heating and cooling, it’s helpful to review the dynamics of building heat loss as it applies to building materials and outside actual air temperatures.
To calculate the heating requirements for a building, the overall heat loss from a building can be derived as a function of the combined heat loss of transmission through the roof, walls, windows, doors, and floors, as well as heat loss caused by ventilation and air infiltration. In general, without getting too scientific, the heat loss from transmission through roof, walls, doors, and windows represents the largest impact and is primarily a function of the temperature difference between the inside and outside air and thermal conductance of he building material. For a more detailed review of building heat loss see Heat Loss.
The difference between inside and outside temperature plays a critical role in building heat loss. The first step is to understand heating and cooling requirements from weather data. Heating degree day (HDD) are a measure of energy demand required to heat a building. HDD is derived from the difference between the daily outside temperature observations and the ideal indoor air temperature, say 65 degrees Fahrenheit (18.30 Celsius). The heating requirements for a building in a specific location can be derived from the HDD data in conjunction with building factors such as insulation, windows, solar heat gain, and use. Air conditioning also has a similar metric and is defined as cooling degree day (CDD) and measures the amount of energy used to cool a building.
From the historical data on outside air temperature, an average heating and cooling degree day can be assigned to a specific region. To calculate degree days for both heating and cooling Daily Temperatures can be assessed by zip code to capture historical data on specific climate zones.
When it comes to selecting building materials and insulation, material suppliers often supply two measures – the R-value and C-value. A material’s R-value (thermal resistance) is the measure of its resistance to heat flow. The C-value (thermal conductance) is the reciprocal of thermal resistance and measures the ability of a piece of material to transfer heat per unit time or more specifically, specifies the rate of energy loss through a piece of material.
The US Department of Energy (DOE) has provided revised R-value recommendations based on climate zones. To understand the energy impact of selecting the right R-value insulation material for your building, an on-line heating calculator will help illustrate the heating requirements and associated energy costs for different insulating materials. Building heating requirements are often expressed in BTU (British Thermal Units) per cubic foot.
The Heater Shop BTU Calculator Heating Calculator provides some useful insight into managing energy expenses. The calculations were based on an average of 25 HDD for New York City.
Figure 1 illustrates the heating requirements as measured by BTU per square foot of building space for corresponding insulating materials across ceiling heights from 10 to 40 feet to capture cubic feet. As seen from Figure 1, the heating requirements show significant variance depending on insulation assumptions.
Taking the building heating requirements one-step further, different insulating assumptions (no insulation, average, and good) translate into wide dispersion in operating costs. The on-line heating calculator was used to estimate the building heating requirements based on the following assumptions: 10,000 square foot facility with ceiling height of 10 feet for 25 HDD for no-insulation average insulation, and good insulation. To derive fuel costs, the BTU per square foot for each insulation category was applied to a heating system operating for five heating months with approximately 1,400 hour of operations to coincide with a gas furnace at 90% efficiency and 20-minute on-cycle and 30-minute off-cycle. Gas pricing for heating are based on $17.00 per million BTU.
Figure 2 Heating Energy Cost
Source: Green Econometrics research
Figure 2 demonstrates that heating cost per square foot for good insulation saves approximately $2.90 per square foot in comparison to no-insulation at all. If we compare the heating costs savings to the cost of insulation, the payback period for insulation can be achieved in a year under most circumstances.
Figure 3 Insulation Cost
Source: Green Econometrics research
