A new study from Sense and Singularity Energy has demonstrated the potential for significant carbon reductions from electric vehicle (EV) charging using a combination of smart home automation and location- and time-based carbon emissions data from the power grid. The study found that by automating charging to minimize carbon impact, carbon emissions from EV charging could be reduced 8-14% on average across the U.S.
The potential reductions in California are more dramatic, with a potential for 43% carbon savings. California’s grid relies on renewable energy for nearly half of its electricity, much of it from low-carbon sources such as solar and wind, which contribute to significant variations in carbon intensity, a measure of carbon emissions per unit of energy consumed. As states increase their reliance on renewable energy sources, their variability will increase, too, offering similar opportunities to shift usage to times when carbon intensity is lowest.
Carbon reductions from automated EV charging could have a significant impact on reaching carbon emissions goals to slow climate change, and while EV charging is the most obvious case, similar opportunities for savings apply to other large loads in the home. The best opportunities for load shaping are activities that can be scheduled flexibly, like running a dishwasher or washing machine during overnight hours to have clean clothes and dishes ready when they’re needed in the morning. For these cases, automation can provide the right balance of meeting consumer needs and optimizing cost, carbon emissions, and constraints of the grid.
The study examined consumers’ EV charging patterns using over 100,000 sessions of in-field EV charging data and time-based carbon intensity data for 30 major regional grid balancing authorities for utilities. It found that charging dynamically to minimize carbon utilization was consistently more effective at reducing carbon than Time of Use rates.
“This EV study is an example of what can be done as we add intelligence to home infrastructure,” said Sense CEO Mike Phillips. “As we work on decarbonizing the grid, because of the increased use of intermittent low-carbon energy sources, it is becoming increasingly important to influence not only how much power is being used, but when it is used. Fortunately, there are many things in the home where people only care about the result – not when the energy is used. EV charging is a great example, but automation can extend to other key consumers of energy as we build intelligence into the infrastructure of the home.”
The results show that smart home automation can dynamically adjust energy usage to address both grid constraints and carbon emissions goals. A separate study of 1100 California homes conducted by Sense found that 55% of electricity usage in the evening time frame could be shifted to other times during the day or reduced. Using an automated, dynamic approach, utilities can incentivize customers to reduce peak emissions by shifting their activities, including EV charging, similar to the current incentives to reduce peak demand.
Carbon reductions are influenced by the regional mix of energy sources, with some regions offering a potential for higher reductions because of greater variability of carbon intensity in their fuel sources. Among the top 10 balancing authorities, CAISO (California Independent System Operator) had the highest variation in carbon intensity at 307%, followed by SWPP (Southwest Power Pool) at 259%, ERCOT Electric Reliability Council of Texas) at 197% and BPAT (Bonneville Power Authority Transmission) at 181%. For more details, see the complete study.
The analysis showed that most regions can achieve significant carbon reductions by automating EV charging to take advantage of the cleanest energy sources as they come onto the grid. As more states and regions increase the share of energy produced by renewable sources, the carbon savings potential will increase across the country.
Said Wenbo Shi, CEO and co-founder of Singularity Energy: “This study demonstrates the potential of data-driven carbon intelligence to improve energy management strategies and cost-effectively reduce carbon emissions. We are filling a gap between decarbonization targets measured in tons of carbon and existing energy management strategies that are still kWh and cost driven. There is a massive opportunity to apply the technology to EVs and other smart devices at scale to rapidly accelerate the transition towards a clean energy future.”
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