Signal 4: Using Realtime Grid Carbon Data, Carbon Pricing, and IoT to Cut GHG Emissions
For decades carbon pricing has been held as a holy grail of climate solutions. Capturing the externality cost that society bears from GHG emissions and attaching it to the use of fossil fuels is believed to be the most efficient and robust approach to regulating carbon. But determining a carbon price is not the only challenge in this concept. We also deal with uncertainty around the carbon intensity of our power supply.
GHG accounting relies on emission factors to determine the tons of GHGs emitted from using energy. These factors are static for the combustion of fossil fuels like natural gas or diesel. However, our regional power grids are served by hundreds to thousands of power generators, each with different combinations of fuel type, combustion efficiency, transmission constraints, and price. The carbon intensity of the power we use varies not just city to city, but minute to minute, as dirtier “peaking” plants are dispatched or curtailed to follow fluctuations in demand. When an energy efficiency or a renewable energy project comes online, they displace these dirtiest, or “marginal”, power plants.
Enter WattTime and their real time marginal emission factor. WattTime uses real-time data from power grid operators and the EPA, local weather data, and machine learning algorithms to generate a real time marginal emission factor for power grids across the united states and makes this data available through an API. The tool presents potential benefits in a number of scenarios where carbon is valued. With this real-time emission factor, regulators and utilities could price power based on carbon intensity, thereby rewarding the use of cleaner power and directly addressing the dirtiest resources. This data could then also be processed by IoT enabled appliances and equipment to optimize their use for grid carbon intensity. This realtime data combined with a carbon price could mean the difference between charging an EV fleet using dirty peaker plants that emit the most carbon and local air pollutants, and utilizing the most efficient resources on the grid. It could also allow batteries, which have the benefit of near instantaneous response, to supply cleaner power in peak demand periods without waiting to be called by a dispatcher. The effect could be akin to Uber’s congestion pricing, but applied to carbon emissions.
On November 6th, Washington State will vote on a carbon tax proposal. Accurate and granular data on the carbon intensity will be a requisite for this or any carbon pricing scheme to realize its full potential.
