See how EVOLVE Intelligence combines marginal emissions rates from WattTime with energy savings data to help lower GHG emissions.
The energy savings valuation landscape in the United States is likely to undergo a sea change in the coming years. Some states and utilities are exploring the potential of rewarding GHG emissions reductions. Many regulators and utilities already view their current energy efficiency programs as levers to reduce greenhouse gases. And, DNV's Energy Transition Outlook posits that to achieve our current climate goals we need to maintain the emission reductions experienced in 2020 due to COVID 19, and to meet or exceed similar reductions every year through 2050. While there is a myriad of factors involved in accelerating the energy transition, one thing is abundantly clear: energy savings is no longer the only key performance metric to inform demand-side clean energy program success.
Recently, DNV and WattTime announced an innovative partnership that is giving us deeper insights into GHG emissions than ever before. WattTime has championed the use of a marginal operating emissions rate (MOER) as a more relevant way to understand and evaluate the actual real-time environmental impact of a particular energy use.
By incorporating marginal emissions rates from WattTime into EVOLVE Intelligence, we are able to see the true impact of energy use on GHG emissions—and determine with a high degree of accuracy which technologies have the greatest potential to drive energy savings while optimizing for emissions reductions.
In this article, I’ll briefly explore why the energy industry needs to embrace advanced data-driven methods for measuring GHG emissions impact, and how we are incorporating marginal emissions to help utilities and their customers understand and take action to reduce GHG emissions.
Limitations of Measuring kWh Saved and Why MOER Is Better
Traditional energy efficiency valuation takes a one-kWh-saved-fits-all approach, whereby estimated energy savings are multiplied by the number of energy efficiency widgets. But as we move beyond energy saved to also look at reducing GHG emissions, utilities must begin factoring in variables such as climate, seasonality, time of day, and geography to accurately assess emissions impact. The reality is that energy saved and GHG emissions avoided are not always proportionate. Although a customer may have a mid-day load peak driven by business operations or air conditioning use, that same customer’s emissions peak may be in the evening, after business hours, when more of the grid is relying on dirtier scalable energy generation (see Figures 1 and 2). When utilities and their customers understand the combined impact of energy savings and emissions avoidance, they can make more informed decisions and investments to accelerate clean energy programs based on either form of energy savings valuation.
Considering when and where on the grid energy is saved is where MOERs come into play. A MOER increases our data veracity around emissions by calculating those emissions based on each unit of electricity demanded from a grid at a specific time and place. And, a MOER measures how a grid meets demand based on time, place, season, climate, generation efficiency, and generation fuel mix. This method has the benefit of accurately assessing the environmental impact that an energy-saving decision has on a power grid by apportioning GHG emissions to users based on when and where they are increasing or decreasing their demand on the grid.
How EVOLVE Intelligence + WattTime Drives Better GHG Emissions Outcomes
One of the most significant business problems I anticipate for utilities in the changing landscape of energy savings valuation will be the measuring and accounting of avoided GHG emissions, and how to optimize a clean energy program for both energy saved, and emissions avoided. In conjunction with other DNV services, EVOLVE Intelligence can specifically address that business problem. Enhanced by machine learning, EVOLVE Intelligence analyzes multiple, unique datasets to deliver real-time, actionable insights that help us to increase the accuracy of planning, forecasting, and targeting. (For a quick tour, I recommend you check out this article from my colleague, Thomas Quasarano.)
By incorporating marginal emissions rates from WattTime into EVOLVE Intelligence, we are able to accurately apportion emissions impacts for an entire energy efficiency program, a single customer portfolio, and down to a specific measure or technology. From the massive scale of billions of data points collected from utility customers to the targeted use of a library of technology load shapes and advanced time series analysis, EVOLVE Intelligence can provide insight into how the emissions impact of a particular building’s energy efficiency improvement changes by geography or grid region (see Figure 3).
This combined capability enables real-time insights and can be used to help customers understand the impact of specific solutions depending on the time of day or the day of the week. These kinds of insights provide a solid foundation for specific recommendations around solutions that can help customers reduce GHG emissions in addition to saving energy. When climate-driven customers – and customers not yet as climate motivated – see this kind of contextualized data, they are more likely to take action.
Embracing advanced, data-driven methods for measuring GHG emissions is critical in providing clean energy programs with the flexibility to adjust to a changing energy savings valuation landscape. It is also critical for making forward momentum on our long-term climate goals. Thanks to our partnership with WattTime, we are helping utilities and their customers get the contextualized insights they need to make informed investments to reduce GHG emissions ultimately delivering new choices in energy management. If you would like to learn more about transforming your clean energy program impact with data insights, I urge you to explore EVOLVE Intelligence.
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