Introducing CarbonFlow: tracing GHG emissions from source to consumption
Electricity generation is responsible for 25% of total U.S. greenhouse gas (GHG) emissions (EPA). Although that pollution is emitted at power plants, the power produced by a plant can be consumed by loads hundreds of miles away. Singularity’s CarbonFlow technology, demonstrated here on a model of the U.S. grid, enables granular tracing of emissions from source to consumption and improves on previous lower resolution approaches. CarbonFlow provides a powerful new tool for understanding how emissions flow across the grid and the emissions associated with electricity consumption.
Previous approaches to calculating consumed emissions use import and export data between grid operators, along with information about the fuels used to generate electricity within each grid operator’s territory, to estimate region-wide consumed emission rates (see, for example, Chalendar et.al’s work). There are many advantages to this — the data needed is freely available in near real time, and important large-scale inter-region trends are captured.
However, regional data can’t tell us how consumed emission rates vary within regions. Even at the regional level, regional consumed emission data is not always accurate, because it assumes that power is uniformly mixed within regions and does not account for how power flows on each transmission corridor may affect consumed emissions. To account for these important granular effects, CarbonFlow uses power flow tracing to identify consumed emission rates at each line and load on an electrical grid.
One context where CarbonFlow can be valuable is in determining emissions factors for emission accounting laws such as New York City’s Local Law 97, which requires large power users to reduce the emissions from their power consumption. NYC is a large load center within NYISO, and relies heavily on power imported both from other parts of the state as well as surrounding regions. Even in a state like New York with aggressive clean energy targets, consumed emission rates vary within the state, and NYISO-wide numbers can be misleading. In this blog post, we’ll discuss two important trends affecting NYC’s electricity emission rate:
- New York’s consumed emission rates vary significantly by county. The average emission rate in NYC over a modeled year is 1.5 times the statewide average.
- Temporal patterns in emission rate vary across the grid. Within NYC, the highest emission hour in a modeled year has an emission rate 1.6 times the lowest emission rate hour.
The data in this blog comes from a simulated model of the U.S. transmission system constructed by Breakthrough Energy (Xu et al., 2020), which was designed to approximate and validated against the 2020 U.S. grid, including regional temporal and spatial patterns. The emissions data in this model shouldn’t be thought of as matching to a specific transmission bus and time on the real grid, but instead as giving us insight the overall behavior of the 2020 grid. Singularity is working with grid operators to apply CarbonFlow to real grid topology and generation and load data.
Regional grid variability in NYISO
Insight 1: Consumed emissions intensity varies across the NYISO grid.
When we break down average consumed emission rates by each county in New York state, shown below, we see wide variation. In the southern part of the state, relatively high emission power plants combine with coal imports from Pennsylvania to result in higher average consumed emission rates than the state as a whole. In upstate New York, where there is more hydro generation, the opposite is true. (The model we use is limited to the U.S., but in the real grid, hydro imports from Quebec likely lead to even cleaner power in northern New York.)
In New York City (“Zone J”, for readers familiar with NYISO’s load zones), emissions are high throughout the year compared to the NYISO-wide average. This difference has significant implications for emissions accounting, because electricity users in New York City using a state-wide emission rate would be undercounting their emissions.
When we zoom in, we see that even within the counties making up NYC (Bronx, New York (Manhattan), Kings (Brooklyn), Queens, and Richmond (Staten Island)), average emission rates vary widely. In the 2020 model used here, these trends were driven largely by the Indian Point nuclear plant north of NYC, which stopped operating in 2021. The northeastern part of the city (Bronx and Queens) received more nuclear power from Indian Point, which drove lower average emission rates relative to the southwestern part of the city (Manhattan, Brooklyn, and Richmond), which received more fossil power from Pennsylvania. The decommissioning of Indian Point has led to increased natural gas generation in the lower Hudson Valley north of NYC, so these spatial patterns have shifted in the years since 2020, with all boroughs in NYC relying more on fossil fuel power.
While revealing, these annual averages don’t tell the whole story. Next, we’ll dive into daily and seasonal trends across New York and within NYC.
Insight 2: Temporal patterns of consumed emission rate vary across New York
The average emission rate of consumed electricity across NYISO varies with a predictable daily pattern, shown in the figure below, where each day is represented by a column with hours represented vertically. Emission rates are low overnight when load is low, rise with load in the mornings, drop with solar generation mid-day, and rise sharply in the evening as decreasing solar production aligns with the evening spike in load as people get home from work. There is also a strong seasonal trend, with summers having higher emission electricity as high demand for cooling drives more generation from high emissions plants.
With CarbonFlow, we can zoom in from this grid-wide picture to look at smaller regions or even specific transmission substations (each transmission substation feeds a distribution network that connects to homes, businesses, and other electricity consumers). When we do this, we see that the New York-wide temporal pattern conceals sub-regional variation.
The daily pattern and regional patterns in NYC are similar to the state-wide patterns, with dirtier summertime electricity and an evening peak in emission rate. The temporal variability is smaller in magnitude than the state-wide trend, because NYC’s fuel mix is more consistent over the day and year than the state as a whole.
Finally, we can zoom in even further, to boroughs within NYC, with the Bronx and Staten Island shown in Figure 2. At this level, because we’re using a synthetic model, the trends we see are less likely to match the patterns we’d see in real data. However, our work with actual transmission system data supports the overall insight that temporal trends can vary even across small spatial scales like New York City’s neighborhoods.
Although NYC as a whole has relatively uniform emission rates over the model year, the Bronx in this 2020 model actually had more temporal variability than the state overall. Electricity consumers in the Bronx consumed carbon free energy (mostly from Indian Point nuclear plant, which ceased operation in 2021) overnight during much of the year but saw a relatively large shift to higher emission electricity during summer afternoons. In contrast, Richmond county (Staten Island), on the southern side of the city, has consistently high emission rates and lower temporal variability than the city-wide trend.
How CarbonFlow works: tracing electricity from generation to load
Now that we’ve demonstrated what CarbonFlow can do, we’ll provide a brief overview of how it works. CarbonFlow is a power flow tracing algorithm, an approach with a long history of applications in market design and power systems research. Figure 3 shows how this differs from previous approaches, allowing a uniquely granular view into the transport and consumption of electricity.
We start with a model of the topology of the electrical grid, including the locations and physical parameters of its lines, transformers, and other features, along with data describing the generation and load at each transmission bus.
We assign each MWh generated on the grid an emission rate, from 0 lbs CO2/MWh at clean energy plants to 2000 lbs CO2/MWh or more at oil and coal plants. The locations where these emissions are generated are important for understanding pollution and related equity concerns, but they don’t show which end users are actually consuming electricity and therefore responsible for the associated emissions.
Consumed emission rates fill this gap, telling us what emissions were associated with the generation of electricity consumed at a given point on the grid. As generators and loads change their production and demand, power flows across transmission lines in constantly varying configurations. To calculate consumed emission rates, we need to model the dynamics of the entire system, including the electrical parameters of its lines, transformers, and other components, and map emissions data onto the electrical grid. We can then trace the power from each plant, and its associated emissions, as it travels to the loads it serves, and report the emission rate at each of those loads.
Although CarbonFlow gives us a consumed emission rate at each load, we can aggregate it to more user-friendly scales, like the counties discussed in this blog post.
Applications of CarbonFlow
CarbonFlow can be applied to use cases across carbon accounting, policy, and grid decarbonization applications. Singularity is working with partners including MISO and others on use cases including:
- Location-based Scope 2 Emissions accounting. Data about the emissions intensity of consumed electricity allows consumers of electricity both big and small to account for the proportion of generated emissions they are responsible for, often called “location-based” carbon accounting. CarbonFlow could enable more granular and accurate location-based Scope 2 accounting, and also help inform more accurate residual mix estimates for market-based accounting. MISO worked with Singularity to explore nodal-level consumed carbon emissions via CarbonFlow for the first time.
- Quantifying clean energy deliverability. CarbonFlow can trace not only emissions but also, as demonstrated above, key drivers of emissions, including fuel mix and even power from individual generators. We’ve published a preprint introducing how tracing of individual generators can help answer whether a generator’s power is deliverable to a specific load, which informed our whitepaper on how to define deliverability in clean energy markets.
- Data-driven insights for policymaking. Because nodal CarbonFlow results can be aggregated to any geographic resolution, they open new opportunities for accurate consumption-based insights at the city-level, county-level, state-level, or other administrative boundaries that don’t necessarily align with grid or utility boundaries. Such results could tell policymakers more about total consumed emissions within their jurisdiction, or transfers of emissions with neighboring jurisdictions.
