Weather Sensitive High Spatio-Temporal Resolution Transportation Electric Load Profiles For Multiple Decarbonization Pathways

Electrification of transport compounded with climate change will transform hourly load profiles and their response to weather. Power system operators and EV charging stakeholders require such high-resolution load profiles for their planning studies. However, such profiles accounting whole transportation sector is lacking. Thus, we present a novel approach to generating hourly electric load profiles that considers charging strategies and evolving sensitivity to temperature. The approach consists of downscaling annual state-scale sectoral load projections from the multi-sectoral Global Change Analysis Model (GCAM) into hourly electric load profiles leveraging high resolution climate and population datasets. Profiles are developed and evaluated at the Balancing Authority scale, with a 5-year increment until 2050 over the Western U.S. Interconnect for multiple decarbonization pathways and climate scenarios. The datasets are readily available for production cost model analysis. Our open source approach is transferable to other regions

GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County

<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>.</p><p>GCAM-USA scenarios (see <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details):</p><ul><li>`BAU_Climate` - a business-as-usual scenario without IRA incentives</li><li>`business_as_usual_ira_ccs_climate` - a business-as-usual scenario with IRA incentives for CCS technology</li><li>`NetZeroNoCCS_Climate` - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>`net_zero_ira_ccs_climate` - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><p>Climate pathways (see <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details):</p><ul><li>`rcp45cooler` - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>`rcp85hotter` - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><p>Fields in the data files:</p><ul><li>`time` - hourly timestamp in UTC representing the preceding hour of data</li><li>`load_MWh` - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>`temperature_celsius` - mean temperature within this county and balancing authority in degrees Celsius</li><li>`FIPS` - FIPS code for the county</li><li>`balancing_authority` - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>`State` - the state abbreviation for this county</li></ul><p> </p><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). </p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p&gt

GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County

<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>. The code used to produce this data is available at <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>. Note that fleet sizes at the state scale are derived from the GCAM-USA scenario output. Downscaling to the county scale uses the electric vehicle penetration rates found in the appendix of <a href="https://www.pnnl.gov/sites/default/files/media/file/EV-AT-SCALE_1_IMPACTS_final.pdf">M. Kintner-Meyer, S. Davis, S. Sridhar, D. Bhatnagar, S. Mahserejian and M. Ghosal, "Electric vehicles at scale-phase I analysis: High EV adoption impacts on the western US power grid", Tech. Rep., 2020</a>. To harmonize the state scale LDV energy use from the GCAM-USA scenarios with the LDV load calculated with EV-Pro Lite, a scale factor was applied to the county level loads, so note that if the reported scale factor is much different than 1.0 there is potentially some disagreement between the load and the fleet size. This scale factor has already been applied to the loads reported in these files (but has NOT been applied to the fleet sizes).</p><h4><strong>GCAM-USA scenarios</strong></h4><p>See <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details</p><ul><li>BAU_Climate - a business-as-usual scenario without IRA incentives</li><li>business_as_usual_ira_ccs_climate - a business-as-usual scenario with IRA incentives for CCS technology</li><li>NetZeroNoCCS_Climate - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>net_zero_ira_ccs_climate - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><h4><strong>Climate pathways</strong></h4><p>See <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details</p><ul><li>rcp45cooler - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>rcp85hotter - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><h4><strong>Fields in the data files:</strong></h4><ul><li>time - hourly timestamp in UTC representing the preceding hour of data</li><li>county - the county name</li><li>State - the state abbreviation for this county</li><li>FIPS - FIPS code for the county</li><li>balancing_authority - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>load_MWh - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>temperature_celsius - mean temperature within this county and balancing authority in degrees Celsius</li><li>fleet_size - number of electrified LDV cars within this county and balancing authority</li><li>daily_miles - average number of miles traveled per day per LDV within this county and balancing authority in miles/day</li><li>scale_factor - the values in the load_MWh field have been scaled by this multiplier in order to harmonize the state scale LDV loads with the GCAM-USA scenarios</li></ul><h4><strong>Changelog</strong></h4><ul><li>v1.0.1 - added fleet_size, daily_miles, and scale_factor to the output, and updated the README accordingly</li></ul><h4><strong>Acknowledgements</strong></h4><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p&gt

Grid Reserve and Flexibility Planning Tool (GRAF-Plan) for Assessing Resource Balancing Capability Under High Renewable Penetration

High penetration of intermittent generation increases uncertainty and variability in balancing reserve needs. New tools are needed to help the balancing authority system operator plan for intraday and intra-hour balance between generation and load. The Grid Reserve and Flexibility Planning tool (GRAF-Plan) helps plan for adequate balancing reserves for future years or seasons for expected wind and solar generation. It also assesses the flexibility of the scheduled generation fleet to meet such requirements. The estimations are based on utilities’ operational practices (e.g., forecasting and time frame of reserve deployment), and it incorporates detailed data from renewable generation and load. Application of the tool in estimating reserve requirements in Central America under different levels of renewable generation (high and low) and for the Western Electricity Coordinating Council (WECC) 2030 Anchor Data Set scenario is discussed