Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area
Abstract
:1. Introduction
2. Materials and Methods
2.1. Co-Simulation Setup
2.2. Distribution Grid Simulation
2.3. Distribution Scenarios
2.4. Transportation Simulation
2.5. Transportation Scenarios
- 1.
- Base (2035)
- ○
- Vehicle cost and performance consistent with the U.S. Energy Information Administration’s Annual Energy Outlook 2018;
- ○
- A sales ban on non-zero emission vehicles (non-ZEVs) starting in 2035 (consistent with the phasing out in California of non-ZEVs);
- ○
- Technology deployment levels consistent with the 2035 fleet (less turnover of vehicle stock).
- 2.
- Base—Scenario 1
- ○
- All agents with EVs have access to a home charging;
- ○
- Unlimited home, work, and public charging infrastructure;
- ○
- Destination charging only;
- ○
- No co-simulation of the transportation and grid models.
- 3.
- Base—Scenario 2
- ○
- All agents with EVs have access to home charging;
- ○
- Constrained home, work, and public charging infrastructure;
- ○
- Destination charging only;
- ○
- Co-simulation of the transportation and grid models.
- 4.
- Base—Scenario 3
- ○
- Approximately 87% of agents with EVs have access to home charging;
- ○
- Constrained home, work, and public charging infrastructure;
- ○
- En route and destination charging;
- ○
- Co-simulation of the transportation and grid models.
- 5.
- High EV Adoption (2040)
- ○
- Vehicle cost and performance improvements for PEVs based on the National Renewable Energy Laboratory’s (NREL’s) Annual Technology Baseline’s 2020 Advanced scenarios;
- ○
- Non-ZEV sales ban starting in 2035;
- ○
- Technology deployment levels consistent with the 2040 fleet (i.e., higher turnover of vehicle stock).
- 6.
- Advanced Mobility (2040)
- ○
- Advanced ride-hailing: 50% reductions in cost and time;
- ○
- Option for households to drop personally owned vehicles;
- ○
- Automation assumed for ride-hailing fleets (i.e., reduced cost).
- 7.
- Max EV Adoption (2050)
- ○
- Technology deployment levels consistent with the 2050 fleet—almost full turnover of vehicles after the non-ZEV ban.
2.6. EV Charging Infrastructure
3. Results and Discussion
3.1. Simulation Scenarios
- Base (2035)—Scenario 1: No EV charging;
- Base (2035)—Scenario 2: Base EV charging;
- Base (2035)—Scenario 3: Base EV charging with increased en route charging.
3.2. Charging Loads and Grid Impacts
3.3. Spatial Analysis
3.4. Discussion and Future Work
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
Term | Definition |
EV | Electric Vehicle |
SMART-DS | Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios |
BEAM | Behavior, Energy, Autonomy, and Mobility |
GEMINI | Grid-Enhanced, Mobility-Integrated NetworkInfrastructures for Extreme Fast Charging |
PyDSS | Python Distribution System Simulator |
HELICS | Hierarchical Engine for Large-scale Infrastructure Co-Simulation |
AWS | Amazon Web Services |
HPC | High-Performance Computer |
NREL | National Renewable Energy Laboratory |
DER | Distributed Energy Resources |
PV | Photovoltaic |
XFC | Extreme Fast Charging or Charger |
SFO | San Francisco |
BESS | Battery Energy Storage System |
LBNL | Lawrence Berkeley National Laboratory |
SOC | State-of-Charge |
TAZ | Transportation Analysis Zone |
JSON | JavaScript Object Notation |
DCFC | Direct Current Fast Charger |
TEMPO | Transportation Energy and Mobility Pathway Options |
ZEV | Zero-Emissions Vehicle |
CA | California |
ATB | Annual Technology Baseline |
ANSI | American National Standards Institute |
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Scenario | Percentage of Loads Selected by Number | Max. kW Distributed Solar Installed (% of Feeder Peak kW) | Percentage of Feeders with One Utility PV Installation | Percentage of Feeders with Two Utility PV Installations | Max. kW Utility Solar Installed (% of Feeder Peak kW) |
---|---|---|---|---|---|
Medium Solar | 35% | 75% | 50% | 0% | 33% |
High Solar | 65% | 150% | 100% | 75% | 80% |
Scenario | Percentage of Loads Selected | Percentage of Substations with One Utility BESS Installation | Percentage of Substations with Two Utility BESS Installations |
---|---|---|---|
Low Batteries | 5% | 50% | 0% |
High Batteries | 35% | 100% | 75% |
Scenario | Results Summary |
---|---|
Scenario 1: No EV charging | Minimal voltage excursions, no sharp load spikes |
Scenario 2: Base EV charging | Voltage excursions especially in morning, load spikes during morning and midday |
Scenario 3: Base EV charging with increased en route charging | Voltage excursions especially in morning and midday, load spikes during morning and midday, especially in urban centers |
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Share and Cite
Panossian, N.V.; Laarabi, H.; Moffat, K.; Chang, H.; Palmintier, B.; Meintz, A.; Lipman, T.E.; Waraich, R.A. Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area. Energies 2023, 16, 2189. https://doi.org/10.3390/en16052189
Panossian NV, Laarabi H, Moffat K, Chang H, Palmintier B, Meintz A, Lipman TE, Waraich RA. Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area. Energies. 2023; 16(5):2189. https://doi.org/10.3390/en16052189
Chicago/Turabian StylePanossian, Nadia V., Haitam Laarabi, Keith Moffat, Heather Chang, Bryan Palmintier, Andrew Meintz, Timothy E. Lipman, and Rashid A. Waraich. 2023. "Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area" Energies 16, no. 5: 2189. https://doi.org/10.3390/en16052189
APA StylePanossian, N. V., Laarabi, H., Moffat, K., Chang, H., Palmintier, B., Meintz, A., Lipman, T. E., & Waraich, R. A. (2023). Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area. Energies, 16(5), 2189. https://doi.org/10.3390/en16052189