Prasenjit Shil, PhD, Ameren; Tom Anderson, SAS Institute Inc.
Reducing carbon emissions globally requires more than the reduction of traditional energy sources. It also requires implementation of electric transportation and advancement of distributed sources, such as solar, wind and battery. Carbon economy is a complex topic and evaluating net carbon emissions is even more complex especially when evaluating how well the utility is performing. This presentation will discuss electricity consumption, installed renewables and other sources of generation in the utilitys footprint, along with electric transportation statistics to estimate the net carbon footprint in the service area. The goal is to assign a carbon score based on these parameters within the service footprint. This paper will also introduce a duck curve analysis that provides a visualization of the net carbon impact on an hourly basis. With the climate emergency declaration for carbon neutral, carbon reduction becomes a main focus for many utilities worldwide. While there are many ways to analyze this problem, the methodology presented here will provide a starting point for many utilities as they continue to address this ever-important challenge. This presentation highlights reporting and analytics methodologies to benchmark and score a utilitys net zero carbon initiatives. SAS Visual Analytics and SAS Visual Statistics are used for analysis and presentation.
Watch Decisioning for a Low Carbon Future as presented by the authors on the SAS Users YouTube channel.
Carbon emissions among utilities in general are declining. Based on EIA's reporting, CO2 emissions declined 28% since 2005 level and will continue to drop. Many states and cities are aggressively pushing the low carbon initiatives as well. One of the useful ways of comparing CO2 reduction is Per capita carbon dioxide emissions. Many factors contribute to variation in the amount of emissions per capita, including climate, the structure of the state economy, population density, energy sources, building standards, carbon intensity and explicit state policies to reduce emissions. States with higher carbon intensity, which reflects the energy fuel mix, tend to be the states with higher per capita carbon emissions. Tracking impacts of relevant carbon reduction measures are little complex which highlights the necessity for innovative reporting and analytics for better stakeholder engagements. This presentation focuses on innovative presentation of the carbon economy in the utility spectrum.
Before the paper discusses the details of analytical visualization, we discuss some of the important calculations. As noted earlier, the data and calculations represented here do not represent any utility per se, its mere showcase of the analytical ability utilities have to track its own progress in reducing the carbon footprint. The presentation highlights how such measures can be tracked at a micro level such zip level or at the circuit level which helps in distribution planning.
Many large companies, utilities and non-utilities have taken initiatives to reduce carbon footprint. Utilities such as Ameren Entergy, Southern Company etc. are adopting to net zero carbon strategies
With changing consumer expectations and frankly, to improve its own cost basis for energy consumption, non-utilities too are targeting zero-carbon future. Examples of non-utilities targeting zero carbon future are: Microsoft, Mercedes-Benz AG, Natura &Co, NIKE, Inc., Starbucks, Unilever, and Wipro, as well as Environmental Defense Fund (EDF). Corporations are also taking measures to comply with various state and federal regulations as well as stakeholder's demand to reduce carbon footprint.
Utilities in USA, one of the largest emitters of carbon among various industry classes, are taking measures to reduce its carbon footprint in the coming days. Figure 1 shows the trend in carbon dioxide emissions among various US electric power corporations. Utilities are doing so by decommissioning the coal based power plants and increasing usage of photovoltaics (PV), promoting energy efficiency (EE), demand response (DR) and electrification measures such as electric vehicles (EV). The following section discusses how Ameren Missouri, a St Louis, Missouri based large regulated utility company, is planning to be a net zero carbon utility by 2050.
In its most recent IRP publication in 2020, Ameren Missouri has established a net-zero carbon emissions goal by 2050 across all its operations in Missouri. The goal has been established for Ameren Illinois as well. One of the key objectives of Ameren Missouri's strategy for transforming its generation assets is ensuring customer affordability. Customer affordability is the driving force behind Ameren Missouri's plan for meeting its customers' future energy needs. This plan significantly increases investments in wind and solar generation, advancing the retirement of the coal-fired energy centers, and putting the company on a path to net-zero carbon dioxide (CO2) emissions by 2050. The 2020 IRP represents a step change in the execution and realization of the generation strategy that guided the resource planning for most of the last decade.
Ameren's stated goal to achieve Net-zero carbon emission is the first in the industry to establish commitments to significant long-term reductions in CO2 emissions. Ameren Missouri invested in 700 megawatts (MW) of new wind generation which will be in operation and will provide clean energy to our customers in 2021. Ameren Missouri’s transformative plan includes adding nearly 3,100 megawatts of wind and solar generation by 2030, 5,500 MWs of wind and solar generation by 2040, retire all of remaining coal-fired generations by 2042, and achieve net-zero CO2 emissions by 2050. In doing so, Ameren Missouri will also support the de-carbonization of the region's economy through efficient electrification of transportation and other sectors that currently require fossil fuels.
As discussed in Figure 2, Ameren Missouri is targeting 50% reductions in CO2 emissions by 2030 and 85 percent by 2040 with a goal of achieving net-zero CO2 emission by 2050. These reductions are based on based on 2005 levels. Ameren Missouri's planned renewable resource additions will not only help it achieve its net zero carbon target, but also will result in significant additional carbon emission reductions across the region. Additionally, energy efficiency and demand response programs are expected to contribute to carbon reduction by reducing total demand. By 2040, these programs are expected to result in nearly 2,000 MW of peak demand savings. In addition, the efficient electrification will help in transforming traditional carbon emitting sources of energy for heating and transportation purposes.
As utilities and non-utilities do their parts, the carbon intensity of the energy supply will reduce across the state. Per EIA, West Virginia, Wyoming, Kentucky are among the top carbon intensive states. The carbon intensity of the energy supply essentially highlights the energy fuel mix within a state and its no surprise that the coal dominant states rank high in such ranking. Such states also rank high on the basis of per capita carbon dioxide emissions.
As utilities install various measures, it's necessary for them to appropriately measure the outcome. This paper proposes a set of analytical presentations that would help in visualizing the outcome. This paper proposes dashboards which will summarize total EVs and PVs in the system and will provide simulation tools to estimate carbon reduction statistics from various levels of EVs or PVs. This dashboard enables the users to view the statistics at the zip code level. Such ability will result in better stakeholder engagement.
This paper introduces Carbon Score which is essentially the CO2 reduction per capita. Carbon intensity score is the total carbon emission per KWh of output. Total carbon reduction is calculated using the estimated carbon reduction per KWh production, as published by EIA. The analytics presented here also demonstrate "Duck curve analytics." Duck Curve analytics presented in the dashboards considers overall electric demand including those from EVs and generations from PV during the course of the day. Figure 3 showcases some essential formulae behind the calculations.
The computation of the reduction in CO2 emissions from the EVs is little bit complicated as it requires a few assumptions. This paper follows EIA'S methodology for this purpose. The EV related Carbon Reduction is calculated based on averages. This calculation is based on EIA's estimates of CO2 emissions per year from a typical vehicle per vehicle driven. The detail assumptions are shown here for your reference.
Figure 4-Figure 9 represent a set of dashboards prepared in SAS VA to demonstrate the visualization. For example, the bottom part of the Figure 4 shows the breakdown of the major carbon stats by EV and PV installation in the footprint. The dashboard also shows the system load shape with PVs and EVs added on, and highlights the "Duck curve" (Figure 5).
These dashboards also provide ability to simulate various EV and PV scenarios which are very helpful for distribution planning or even for integrated distribution planning purpose. For example, the slide bars shown on Figure 4 and 5 provides an ability to estimate carbon score for a desired amount of EVs and PVs. The Duck curve simulates accordingly. Additionally, the dashboards also provides opportunity to visualize the carbon footprint at the zip level by highlighting the area in the Geo Map section. The dashboard also incorporates a forecast of energy production from installed PV capacity in the footprint or in a geographic area. Additionally, the paper demonstrates that the grid topology can be layered into the analysis which will provide the distribution planners understand peak hour characteristics such as available generation mix, power flow, loadings on circuit etc. (Figure 9).
Over last decade or two, analytics and internet of things have improved so much that big data analytics can be easily done in near real time or real time. Machine learning AI based optimization can potentially chart the lifecycle of every kilowatt-hour generated and consumed, predict user behavior and consumption pattern, thereby identifying opportunities to minimize the carbon footprint in systematic manner while maintaining grid reliability at its highest point. This paper is merely a tipping point. This paper showcases the power of analytics in the decision making process for carbon economy!
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