1. Dean of International School of Software, Wuhan University, China
Simulating the PHEV Ownership Distribution and its Impacts on Power Grid
Xiaohui (Sean) Cui, PhD
Dean of International School of Software, Wuhan University, China
武汉大学国际软件学院

2. OUTLINE
Model spatial distribution of PHEV ownership at local residential level
Find PHEV hot zones (region with highly concentrate PHEV owners)
Analyze impact on the local electric grid with different incitement policies and PHEV user charging strategies

3. PHEV
Plug-in hybrid electric vehicles (PHEVs) offers promise to replace a significant portion of the US’s current fuel-based light vehicle fleet before electronic vehicle battery recharging infrastructure is fully deployed nationwide.
The general assumption is that the electric power grid is built to support peak loads and, as a consequence, suffers from low asset utilization rates in off-peak periods.

4. PHEV Impact on Power Grid
PHEV vehicle users will most likely charge their vehicles when convenient, rather than waiting for power grid off-peak periods.
Un-controlled charging strategy would place increased pressure on power grid but no additional generation capacity would be required if PHEVs charging cycles started in the off-peak periods. (Lilienthal and Brown 2007)
Most US regions would need to build additional generation capacity to meet the added electric demand when PHEVs are charged in the evening. (Hadley and Tsvetkova, 2009)
Variability in charging times for vehicles may have a critical impact on the local electric grid infrastructure (Letendre and Watts, 2009)

5. Challenges
Most efforts ignore the possibility of spatially variable PHEV penetration in different residential areas
PHEV penetration rate is expected to vary with household demographic and socioeconomic attributes such as income, travel distance, age, household size, education.
Specific points along some electric distribution lines may face overload if local patterns of electricity demand change significantly because of PHEV recharging
Variation of these household demographic attributes in residential areas will generate PHEV penetration rate patterns.

6. Charging with Renewable Energy
全球10米高度风力
通过使用美国USGS以及欧洲ESA提供的地表覆盖300M图以及由NOAA提供的全球气象观察点数据成功建立全球风能与太阳能分布信息
通过使用美国USGS以及欧洲ESA提供的地表覆盖300M图以及由NOAA提供的全球气象观察点数据
研究利用超级计算机建立社会集群模型并研究气候变化对美国能源供给安全影响

7. Agent-based Framework for Modeling Spatial Distribution of PHEVs
Develop an agent-based framework for modeling spatial distribution of PHEV household adoption in residential areas
Evaluate the impacts of vehicles charging load on a residential electric distribution network with different charging strategies
Discover the ‘‘PHEV hot zones’’ where PHEV ownership may quickly increase in the near future
Use Knox County, Tennessee as a case study to show the simulation results of this agent-based model (ABM) framework

8. The Design of Desired Collective Behaviors with Agent Based Simulation
Simple but predictable local interactions among many intelligent agents can generate familiar but enigmatic social patterns, such as stock market crashes, revolutions, fads and feeding frenzies.
By carefully tuning the interaction between agents, it is possible generate desired collective behaviors
Desired Behaviors
Emergent Behaviors
Collective
Interaction
Local
Interaction
Decision
Behavior
Action
Perception

9. High Fidelity Household Demographic Data
The high fidelity input data for agent-based simulation is the primary assurance for the simulation to generate meaningful results
High fidelity household characteristics, individual locations and behaviors are used in the ABM simulation for estimating each individual household (agent) vehicle choice behavior.
There are 190,965 households in the Knox County, which means 190,965 agents are created in this ABM simulation platform.

10. LandScan (synthetic household data)
ORNL's LandScan is the community standard for global and USA population distribution. The finest resolution global population distribution data available.
LandScanUSA: 3” (90 meters x 90 meters) resolution.
LandScanGlobal: approximately 1 km2 resolution (30" X 30”)

11. Synthetic Population
Synthesizing population and personal demographic data from surveyed samples with behaviorally realistic and theoretically sound approaches

12. Typical Person Travel Behaviors
Public Trans
Public Trans
Office
Office
Cafeteria
Shopping Mall
Home
Car
Car
Home
Coffee shop
Working site
Car
Bar
Car
Bus
School
Bus
8 AM
time
10 PM

13. 2. DATA Validation
Without correct data, no system can function well. It is garbage in and then garbage out.
ABM need validated high fidelity and accurate data
Population distribution data validation
Transportation
Social Networks validation
Travel behaviors validation

14. Consumer Decision Model in ABM
A PHEV vehicle ownership distribution model at high resolution (Household) level
Individual households with different characteristics are represented by agents
Decision Models
Consumer choice model (Lin and Greene, 2010) for PHEVs choice
University of Michigan Transportation Research Institute (UMTRI) model (Sullivan et al., 2009) for estimating the time when consumers start searching for a new car
Stigmergy-based neighborhood effect model (Cui et al., 2009) for estimating the probability of consumer’s selection for different PHEV.
Vehicles consumer can choose from
PHEV-10
PHEV-20
PHEV-40
others, which include hybrid electric vehicles and traditional Internal Combustion Engine (ICE) vehicles)
The combination of the three decision models described above will help each agent make an independent choice about whether to buy a PHEV or not and the kind of PHEV to buy.

15. ABM Simulation Estimate PHEV ownership distribution in year 2025
Use the two scenarios, Base Case and FreedomCARGoals Case defined in Lin and Greene (2010)
Apply US Energy Information Administration’s Annual Energy Outlook 2010 report for 2012 – 2025 energy estimating prices
Aggregate the individual household PHEV number based on the census block group (234 BGs in the Knox County) in which individual households are located
Discover the ‘‘PHEV hot zone’’ – the highest PHEV ownership concentration community

16. Distribution of PHEV Ownership
The distribution of the PHEV in Knox County for 2025 based on the base scenario
The height of the bars in different BGs represents the number of PHEVs in the corresponding BGs.

17. Distribution of PHEV Ownership
The distribution of the PHEV in Knox County for 2025 based on
The new energy car incitement scenario

18. Comparing of Two Scenarios
The “new energy car incitement scenario” such as FreedomCARGoals scenario will have a higher PHEV market penetration than Base Case.
Both scenarios indicate that the southwest portion of the county (which is the Town of Farragut) will have the highest PHEV concentration.

19. Simulation Results
Four census block groups (46, 57, 58 and 62) from the 234 BGs in Knox County, have the highest estimated PHEV penetration under the FreedomCARGoals Scenario.
The evening peak charging load for these BGs can reach 3625 kW, 32.6% of the vehicle charging load generated by the fleet in the Knox County. These BGs can be considered as the ‘‘PHEV hot zones’’.
Residential neighborhoods, where multiple PHEV consumers share a given circuit to recharge their plug-in vehicles, could increase peak demand locally and require utilities to upgrade the distribution infrastructure.
Four census block groups (46, 57, 58 and 62) with 2670 vehicles each, from the 234 BGs in Knox County, have the highest estimated PHEV penetration under the FreedomCARGoals Scenario. According to the simulation output, the evening peak charging load for these BGs can reach 3625 kW, 32.6% of the vehicle charging load generated by the fleet in the Knox County. These BGs can be considered as the ‘‘PHEV hot zones’’.
Residential neighborhoods, where multiple PHEV consumers share a given circuit to recharge their plug-in vehicles, could increase peak demand locally and require utilities to upgrade the distribution infrastructure.

20. Conclusion
In a region the overall electric generation and grid capacity may be underutilized
However, PHEV hot zone could increase peak electric demand locally and cause system disruptions and eventually require upgrading of the electric distribution infrastructure.

21. Issues with ABM Validity Human agents
Every model serves its own unique purpose
Must be built at the right level of description with the appropriate amount of complexity
Human agents
Complex Psychology
Irrational Behavior
Subjective Choices

22. All Models Are Wrong but Some are Useful
Practical Question: How wrong do model has to be to not be useful?
New Model Validating Protocol – “Trustability Level of the ABM”
Exploring the model validating protocol that can providing the numerical trustability level of the ABM for a spectrum of scenarios
Provide a numerical value about the predictability quality of an ABM
Limitation of existing verification and validation methods
Only try to answer whether the test object is correct or incorrect
This simple white-or-black thinking may NOT completely reflect what we expect from the testing result for agent-based disease spread models

23. Practical Validation*
Perform a series of experiments to invalidate the model
Models shown to be invalid are salvageable with further work
Models may have parameter settings resulting in valid and invalid end states
End result of validation exercises
A model that has passed some validation tests (not a totally validated model)
A better understanding of the model’s capabilities, limitations, and usefulness
* Excerpt from Macal, C.M., Model Verification and Validation (presentation); Workshop on Threat Anticipation: Social Science Methods and Models, University of Chicago and Argonne National Laboratory, April 2005.
5/2/2019

24. Issues with ABM Qualitative vs. Quantitative
Varying degree of accuracy and completeness in input (data, expertise, etc)
Use qualitative data to learn about the system
Capturing the behavior of all constituent units
Lower level description can extremely computationally intense, and time consuming
Heterogeneous units

25. Contact Information:
Xiaohui (Sean) Cui, PhD
Computational Data Analytics Group, Oak Ridge National Lab, USA
International School of Software, Wuhan University, China
USA Phone: (865)
China Phone: