SGRC Grid Impact Models Forecast EV, Electrification and Extreme Climate Hourly Load Impacts for Utility, ZIP & Neighborhood Areas |
Background: Electric Grid Threats from EV, Electrification and Climate Extremes
Electric utilities are facing new unprecedented challenges providing peak
period power in geographic areas with rapidly increasing EV ownership and increasing electrification.
A single level 2 charger on a transformer that is close to maximum capacity can result in voltage sags with flickering lights, reduced transformer lifetimes and even transformer failures. The NREL forecasts a 7-fold EV ownership increase by 2030 (from 2.6% to 18%) - that is 1 in 6 US households will own an EV in 5 years. In addition, new construction is trending towards more electric appliances contributing disproportionately to loads in critical utility peak periods (think water heaters, ovens). And finally, extreme weather can boost AC and space heating load contributions at system peak time, an impact that becomes more important with new load additions from EV charging and increased electrification. This substation/feeder/transformer capacity problem is a new wrinkle on grid management. Grid designs have been based on “typical” household kW load served. Load diversity among individual households averages out load spikes so design load maximums were reasonably easy to calculate and incorporate in original grid design. Grid capacity based on this traditional planning (i.e., most established residential areas) may no longer be sufficient with increasing EV ownership and electrification. Example: A single 25 kVA transformer serves 10 houses in RI with relatively low AC loads. Before: Average 2 kVA design peak load across 10 customers = 20 kVA so a 25 kVA transformer has a 25 % safety margin. After: One typical level-2 EV charger added: Add 7.7 kVA in the peak hour results in 27.7 kVA assuming the design average, 11% above the transformer rating which is enough to cause low-voltage light flickering when the EV charger is activated. Two EVs chargers activated within the same hour on this transformer boosts peak kVA to 35.4 kWV, 42 % above the transformer rating causing a significant low voltage situation and shortened transformer life or even failure. Plus: Adding an extreme AC weather event makes transformer overloading even worse. Depending on the extent of new EV ownership and increased electrification plus potential weather impacts, many existing substations, feeders, and transformers will require upgrading or active demand management to avoid low voltage problems and/or premature equipment failures. SGRC Impact Model (GIM) SummaryThe new SGRC Impact Model (GIM) identifies potential EV, electrification and weather grid threats for individual ZIP and neighborhood areas along with demand management program analysis to mitigate these threats.The GIM model is provided to individual electric utilities populated with actual utility-specific household data for immediate in-house applications in easy-to-apply Excel workbooks. GIM model development is supported by all consortium members avoiding the cost of expensive one-off consultant engagements. GIM Models are designed for in-house applications. Model software is encapsulated in an Excel workbook providing easy-to-use option selections and output presentations. Each Model progresses through three processes: The remainder of this Web page desribes the GIM model and its application in more detail in the following sections:
Example Grid Impact Model (GIM) Analysis Results and ReportsThe GIM modeling is embeded in an Excel workbook providing easy-to-use option selections, output presentations and the ability for users, if desired, to conduct their own analysis and presentation material.This section provides screen shots of worksheets associated with an example EV analysis application for all ZIP code areas in Rhode Island. 
BEGIN TABThe BEGIN tab selects forecast analysis options including EVs, demand management, electrification, price response and weather extremes. These options are described in more detail below.The selected options in this example are Electric Vehicles with a Forecast Population Option CP2030 specifying a 2030 analysis for the current population of dwelling units. Clicking on the "Execute Forecast/Analysis" button transfers control to the EV SETUP tab. EV SETUP TAB
The two analysis parameters in the EV SETUP tab include:
The household income filter can include all or a subset of households based on household income in the analysis. Households tend to reflect locational income clusters within ZIPs. This "filter" parameter provides the ability to conduct analysis for an "income neighborhood" within ZIP code areas. HOURLY LOAD GRID IMPACT RESULTS TABClicking on the "Execute Forecast/Analysis" in the EV SETUP tab executes the SGRC Grid Impact Model forecasting EV ownership and hourly load impacts specified by the user. Control is passed to the RESULTS worksheet.Forecast and analysis results presented in the GIM Workbook RESULTS tab are presented in three sections:
Service area summary results
The Service area summary section provides a summary of forecast/analysis including:
Summary results for each ZIP code
This section includes a map reflecting forecast year (2030 in this example) EV ZIP saturations along with a table of ZIP-detailed summary results. Data for each ZIP includes:
Detailed results for individual ZIP code areas
This section provides users with the ability to drill down to individual ZIP codes to view hourly load profiles with and without EV charging loads. Users enter a ZIP code and click the UPDATE button to populate the charts and tables. The ZIP code name and county name are provided along with a map of the selected ZIP code. Summary data are provided for the selected ZIP code area along with the three seasonal load profiles and tabular data. Modeling Methodology SummaryThe MAISY AI agent-based model is intuitively appealing; behavior of each household (i.e., each agent) in a representative sample of actual households is modeled providing a forecast of the entire population of households. Data on each household is available in the 7+ million household database collected by the US Census Department in its American Community Survey (ACS).Information on income, demographics, dwelling unit, appliances, commuting characteristics, vehicle ownership, and other characteristics is available for each household record. An AI process determines the probability of EV ownership for each household in the ACS database. The EV purchase probability is estimated with an AI KNN “nearest neighbor” algorithm that draws on a separate household/EV (HH/EV) database of more than 26,000 individual households where each household record includes income, demographics, other variables and most importantly EV ownership. The KNN algorithm matches each ACS household with a group of similar households in the HH/EV database and calculates an EV ownership probability for the ACS household. 
More detail on EV ownership and EV charging data development is available here. How to Get a Smart Grid Research Consoritum Grid Impact Model for Your UtilityThe SGRC 2.0 consortium provides forecasting and analysis benefits to members based on shared objectives and shared financial support. Each member receives products/services at a fraction of the cost required in a traditional consulting project.
Additional Model Application DetailThis section provides a summary description of options presented in the GIM model BEGIN, SETUP and POPULATION workbook tabs that define model analysis.BEGIN TABThe BEGIN tab selects one or more of the five basic analysis functions, the forecast horizon, and reference household population. GIM BEGIN worksheet selections allow users to customize forecasts by enabling or disabling specific settings.
SETUP TABSAfter selecting initial forecast check-box options and clicking on the "Execute Forecast/Analysis" button in the BEGIN tab, control is transferred to individual SETUP worksheet tabs to provide additional parameters for each selected forecast option.
POPULATION TABThe GIM model provides two forecast horizons, 2030 and 2035. Historical five-year ZIP household growth rates for each ZIP code area are provided as default parameters. Users can change any of these growth rates to reflect alternative assumptions.Features That Make the SGRC Grid Impact Model (GIM) Utility Applications Unique
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Click Here to see advantages of MAISY/SGRC data/analysis compared to Department of Energy, NREL and other engineering model-based sources. |