Next-generation smart metering and energy equity

Income and financial position alone may be a strong indicator of energy poverty. The relationship between income and energy consumption is usually discussed as either high consumption due to energy inefficiencies or an inability to afford baseline utility payments, resulting in accrued debt due to utility arrears. According to the Department of Energy’s (DOE) Low-Income Energy Affordability Data (LEAD) Tool, the national average energy burden for low-income households is 8.6%, three times higher than for non-low-income households, estimated at 3%. The inability to change residences plays a particularly crucial role in long-term energy poverty when considered alongside the increased inadequacy of energy efficiencies in private rentals.

One of the most impactful new capabilities of AMI 2.0 is the ability to identify individual devices consuming energy via disaggregation. Through artificial intelligence and machine learning (AI/ML), the meter can compare a customer’s appliance usage against models with millions of data points from other appliances and identify inefficient appliances—a possible indicator of financial hardship.

The key to recognizing customer income factors is making identification of the problem as easy as possible and the resolution as specific as possible. Next-generation smart meters can do that.

The next factor we considered as a measure of energy equity is how energy is used for comfort and health. For developed countries, often those in the Northern Hemisphere, literature emphasizes maintaining a certain level of internal heating as a primary metric. This metric is problematic, however, when applied to more temperate locations, as it may not encompass energy insecurity in hotter or mild climates. Due to record summer temperatures and global climate change, continued monitoring of both heating and cooling needs for safe living environments is necessary for policymakers.

Smart metering will be able to connect directly to Wi-Fi–enabled smart thermostats, record temperature settings, and compare them against others in the same neighborhood. But even if a residence doesn’t have a smart thermostat, smart metering will be able, through disaggregation, to detect when HVAC equipment is running, inferring if a household is sufficiently maintaining a comfortable and healthy temperature. Through observations and comparisons of other similar dwellings, the next-gen smart meter can pinpoint candidates for either payment assistance or energy-efficient HVAC equipment or suggest specific weatherization options.

Demographic characteristics such as age, disability, and chronic illness may be useful metrics to identify populations vulnerable to energy poverty. In addition to the health risks of poorly heated or cooled households for older populations, energy poverty has been linked to worsened outcomes for those with mental illness, disability, pregnancy, and chronic illness. While outcomes for these populations may be generally negatively affected by their low-income status, the added challenge of energy poverty increases the severity of symptoms or leads to dangerous health outcomes, such as increased risk of hypothermia in populations with restrictions on physical activity.

Similarly, single-parent households may fare worse than dual-parent households. In addition to carrying an increased financial burden due to the relationship between fuel poverty and single heads of households, a 2019 study published in Energy Research & Social Science on gendered partitioning of household labor showed that, if household labor falls to single women, they are more likely to be required to manage load-shifting their household workload patterns to adjust for time-of-day rate structures that may lead to lost economic opportunity.

With next-generation smart metering, single female parents who have shifted their workloads to off-peak times to save money, potentially affecting their ability to generate greater income, can be identified through energy consumption patterns that are indicative of a single-parent household.

Structural issues in buildings can lead to leaks, dampness, and drafts that are often associated with decreases in thermal comfort, leading to either higher costs in energy consumption or unhealthy household temperatures. But measuring internal household temperatures is invasive and often infeasible for researchers or for wide-scale application of social programs.

Being able to remotely assess the energy efficiency of existing residencies, apartments, mobile homes, and public housing projects could provide a key metric in understanding those at highest risk of energy poverty and the interventions most likely to be successful.

Using disaggregated energy data, coupled with weather and demographic data run through an AI learning model that includes not only other local similar dwellings but also millions of others as more utilities adopt smart metering, AMI 2.0 is able to identify potential signs of structural concerns for customers. Utilities also often have weatherization inspection and remediation services that could be provided to LMI customers at little or no charge.

Existing electricity tariff structures were established to recoup expensive fuel costs. However, as renewable energy projects come online, the cost requirements are reversed: Households will be charged a higher fixed fee and charged less per kilowatt consumed, resulting in higher-consumption consumers paying less per kilowatt-hour and lower-consumption consumers paying more per kilowatt-hour, creating an inequality among consumers.

Dr. Paolo Mastropietro of Comillas Pontifical University points out that when there is a utility support charge to cover more solar energy, the effect is, in fact, a regressive tariff on the LMI individual, who is paying a substantially higher portion of income toward their energy bill. Mastropietro suggests alternative solutions, such as including the surcharge in other fees like gasoline taxes, income taxes, or emission auction schemes. However, these have their own pitfalls.

Instead, smart metering could tag the energy put onto the grid or into an electric vehicle using a distributed electronic ledger, aka blockchain. Energy could then be traced to its source of origin, such as a customer’s solar or home battery system. These units of energy could then be sold as carbon offset credits or RECs, generating revenue for utilities to offset the reduced revenue from solar-generating customers while eliminating the need for a special tax on lower-income and eco-friendly customers.