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

Samrat Acharya, Malini Ghosal, Travis Thurber, Casey D. Burleyson, Yang Ou, Allison Campbell, Gokul Iyer, Nathalie Voisin, Jason Fuller

Abstract

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.

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

Abstract

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.
Paper Structure (19 sections, 6 figures, 1 table)

This paper contains 19 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Data and Tools
  3. Global Change Analysis Model (GCAM)
  4. Thermodynamic Global Warming Simulations (TGW)
  5. Total ELectricity Loads Model (TELL)
  6. EVI-Pro Lite
  7. Fleet DNA Data
  8. Non-Road Vehicles Data
  9. Methodology
  10. Developing LDV Charging Load Profiles
  11. Developing MHDV Charging Load Profiles
  12. Developing Non-Road Vehicles Charging Load Profiles
  13. Case Study and Results
  14. Sensitivity of LDV Load Profiles
  15. Sensitivity of MHDV Load Profiles
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Energy use of LDVs, MDVs, HDVs, and non-road vehicles in the western U.S. interconnection for the Business As Usual (BAU) and Net Zero (NZ) decarbonization pathways in the Global Change Analysis Model.
  • Figure 2: Workflow for generating transportation charging load profiles across BAs in the western U.S. interconnection. Tempr.: Temperature, WRF: Weather Research and Forecasting Model, TELL: Total ELectricity Loads Model.
  • Figure 3: Sensitivity of LDV average daily charging load profiles (00:00 to 24:00 hours in PDT) to (a) charging strategies, (b) temperature, and (c) weekends and weekdays. In (a), C1 and C2 are immediate (min_delay) and load levelling (load_level) charging strategies. The results in (a)-(c) are simulated for King County, Washington in 2035 using the NZ decarbonization pathway.
  • Figure 4: Sensitivity of MHDV average daily charging profiles (00:00 to 24:00 hours in PDT), (a)-(c): MDV and (d)-(f): HDV, with (a) and (d): charging strategies, (b) and (e): charger capacities, and (c) and (f): number of sample fleets for stochastic simulation. In (a) and (d), Imm: Immediate charging, Del: Delayed charging, Min: Minimum power charging, Ours: 40% Imm + 10% Del + 50% Min strategies. In (b) and (e), Ours: 5% 50 kW + 5% 125 kW + 10% 250 kW + 40% 350 kW + 40% 500 kW chargers. The profiles in (a)-(f) are simulated for the CISO BA in 2035 using the BAU pathway.
  • Figure 5: Average daily transportation charging load profiles (00:00 to 23:00 UTC hours) in the western U.S. interconnection across months in (a) 2035 and (b) 2050 for the NZ decarbonization pathway and RCP 4.5 climate scenario.
  • ...and 1 more figures