Buildings and the Grid 101: It Matters, But How Much?

December 21, 2017

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It’s the last days of holiday shopping, and everyone is noticing all of the Internet-connected products out there. But did you know that the rapid growth in the Internet of Things (IoT) can also allow U.S. buildings to become smarter and more connected to their surroundings? Recently, the Building Technologies Office (BTO) introduced a vision for Grid-Interactive Efficient Buildings (GEB) research that can accelerate these trends toward smart, connected buildings. Under this vision, buildings leverage web-connected technologies to enable energy efficiency as a power grid resource, yielding energy and cost savings for both utilities and their customers. The portfolio of technologies that will support GEB – referred to here as GEB technologies – includes smart meters, thermostats, and inverters, building automation systems and wireless sensing networks, distributed energy storage, and connected appliances.

How much of an impact could GEB technologies have on the U.S. building efficiency market? Recent data suggest the current demand for such technologies is small but growing fast. For instance, while an estimated 8.4 billion web-connected IoT devices are currently in use worldwide, only 2% of the global revenue for IoT devices is currently attributed to devices with energy management capabilities.1 However, network connections for such devices grew 41% from 2016 to 2017, and Navigant predicts that global revenue for building energy management technologies will grow from $29.4 billion in 2016 to $123.8 billion in 2026. Further, a recent McKinsey report estimates that by 2025, energy management and monitoring technologies will be adopted in 25-50% of households and 40-70% of offices in advanced economies, providing potential energy savings of 20%.

In some cases, GEB technologies are already capturing significant market share. For example, a recent IAB study estimates that 17% of Americans already own smart home controls and 11% own smart appliances, while another 30% reported interest in purchasing them. Smart thermostats, among the most popular GEB technologies, accounted for 40% of all thermostats sold in the U.S. in 2015. Continued growth of these market shares will require overcoming challenges surrounding first cost, market structure, and operational effectiveness, as outlined in a previous post.

As the market for GEB technologies grows, so does the need to quantify the energy and cost savings potential of these technologies in support of energy policy decisions. Indeed, GEB technologies present unique challenges for such policy-focused analyses. For one, the unit-level energy savings of such technologies are often tied to the actions of occupants, homeowners, and/or building managers, which are difficult to predict. As an example, the energy savings potential of smart meters is contingent upon providing homeowners with actionable, real-time energy use feedback and pricing schemes, which can ultimately yield an average household electricity savings of 4-12%.

Furthermore, the energy and cost savings benefits of GEB technologies may be time-dependent, requiring a distinction between energy saved during peak vs. off-peak energy demand periods. Here, peak energy demand periods – which range from an hour per year to several hours per day – might be valued more highly for their influence on utility generation, transmission, and distribution costs (also known as capacity costs). However, the timing of peak energy demand and the value assigned to peak energy reductions can vary by climate, energy generation mix, and utility rate structure. In the Pacific Northwest utility region, for example, morning heating in the winter contributes 23% to the average daily heating and cooling energy demand profile; in Texas, this contribution is only 11%.2 Regarding rate structure, time-based rate schemes (such as those used in California) may value peak summer electricity generation at almost twice the rate of off-peak generation; however, as of 2014 just 4% of residential utility customers in the U.S. had enrolled in these schemes, making it difficult to generalize regional time-based rates to national-scale analyses.

Recent studies demonstrate the significance of these input assumptions to the outcomes of GEB technology assessments. For example, a Lawrence Berkeley National Lab report found that the value of efficiency measures assessed using the total time-varying value of energy (avoided energy and utility capacity costs) can be up to three times greater than that assessed using avoided energy costs alone. Nevertheless, such time-sensitive efficiency valuations are not currently reflected in BTO analysis tools. For example, the BTO Scout software estimates the annual energy, CO2, and operating cost impacts of building energy conservation measures (ECMs) using national-scale EIA data on baseline energy use and electricity prices. GEB-focused refinements to Scout may require improving the time resolution and geographic granularity of these underlying energy use and pricing data.

To explore GEB-focused improvements to its analysis tools, BTO is conducting a survey of stakeholders working on time-sensitive valuation of energy efficiency (TSV-EE), a key area of GEB research. The survey seeks to establish the most important TSV-EE metrics, determine a reasonable approach for peak and off-peak energy use segmentation, and identify datasets that can support the addition of TSV-EE into Scout and other BTO software programs like EnergyPlus. In a future Buildings 101 post, we’ll talk about some of the insights gleaned from this survey effort and how they are informing new BTO analysis capabilities.

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1 In 2016, energy management IoT global revenue was about $29.4 billion, comprising only 2% of global revenue for all IoT devices ($1.4 trillion).

2 Estimated for a morning period between 6 a.m. - noon; “average daily profile” refers to the EPRI peak season central AC and off-peak season heating load shapes for the residential building sector in the WSCC/NWP and ERCOT utility regions.