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TRCLC 17-07: Phase -II : Community-Aware Charging Station Network Design for Electrified Vehicles in Urban Areas: Reducing Congestion, Emissions, Improving Accessibility, and Promoting Walking, Bicycling, and use of Public Transportation

Summary:

The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of accessible charging stations due to mutual dependence of EV sales and public infrastructure deployment. Such infrastructure deployment also presents a number of unique opportunities for promoting livability while helping to reduce the negative side-effects of transportation (e.g., congestion and emissions). During the proposed second phase of this research, apart from solidifying our findings for the current EV charging station network design modeling framework (MF) developed during the previous phase of the project, we intend to develop additional modules that also account for human behavioral aspects to enrich the current MF. We believe that these extensions will greatly improve the practical usability of the MF and provide additional insights for the decision makers. The second phase will enhance the MF in following ways: a) Explicitly account for accessibility range to EVs within a community during charging station network design and support minimum coverage requirements, assess the impact of uncovered regions within a community, and facilitate effective tradeoffs between accessibility, utilization and budgets, and include different charger levels for the design b) Inclusion of interaction between multimodal transportation and EV charging stations network planning and develop a framework to study their synergy in improving overall transportation options in a community, c) Design a pricing scheme template based on proposed network design, price/demand elasticity, utilization and     demand pattern to incentivize the stakeholders involved in maintaining the infrastructure, and d) Evaluation of the MF through a community case study in partnership with a regional planning agency such as SEMCOG. The proposed research relates and contributes to the attainment of strategic goals of the U.S. Department of Transportation and the U.S. Department of Energy. It contributes to the fostering of livable communities by increasing the access to transportation with EVs, improves adoption of EVs, promotes sustainable transportation and provides increased transportation choice. It further contributes to environmental sustainability through reduced carbon footprint of transport. Lastly, it contributes to the economic competitiveness through increased transportation productivity and more efficient utilization of existing system resources.

Problem:

The electric charging station design problem is formulated as a scenario based, two-stage non-linear stochastic programming model considering the randomness arising from dwell time, willingness to walk, EV market penetration, demand pattern in weekdays vs weekend and state of charge. The solution approach for the problem is as follow:

	Figure 1: Parking lot locations used in Test Cases’ analysis
	Figure 2: Average aggregated utility for each type of EVSE in each parking location
	Figure 3: Distribution of EVSE Level 1 and level 2 based on limited budget of $50,000
	Figure 4: Accessibility % fluctuation due to different capacity and budget
	Figure 5: # of installed Level 1 and level 2 chargers for different budgets at capacity 5, labeled by accessibility percentage.
	Figure 6: EV driver’s utility for different price of level 3 charging
	Figure 7: The framework of multi-Modal network to determine the location of parking-charging infrastructures

 Results:

In this project, we use a choice modeling approach embedded in a two-stage stochastic programming model for network design of EV in a given community. Various sources of uncertainties such as total EV flows, arrival and dwell times, batteries’ SOCs at the time of arrival, and EV drivers’ willingness to walk are considered. Using many factors such as charging price, cost at home, range charge, total trip distance, dwell time, we capture the BEV drivers’ behavior toward charging choices. The results showed that with increasing the budget and capacity, we could increase the accessibility of EV owner to the charging stations. Also, charger level 2 is most preferable charging type among EV owner. The proposed model indicates the robustness to any future changes in the community’s pattern of willingness to walk. It provides a matrix of relationship among budget, accessibility, capacity and choices of chargers. Also, using data collected from SEMCOG, we show how the current model is used in the multi-modal network to locate charging parking infrastructures. The developed tools are expected to be used by planning agencies.