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New model finds the best locations for electric vehicle charging stations

Parking space features sign specifying it is for charging electric vehicles only

Researchers at North Carolina State University have developed a computational model that can be used to determine the optimal locations for locating electric vehicle (EV) charging facilities and how powerful the charging stations can be without straining the local power grid. . †

“Ultimately, we think the model can be used to inform the development of charging infrastructure for electric cars at multiple levels, from projects aimed at supporting local commuters to charging facilities that enable highway transportation,” said Leila Hajibabai , corresponding author of a paper on the work and an assistant professor in NC State’s Fitts Department of Industrial and Systems Engineering.

Identifying the best locations for charging facilities is a complex process, as it must take into account travel flow and user demand, as well as the needs of the regional energy infrastructure. In other words, where will people use it? And can it be supported by the power grid?

“We’ve developed a model that allows planners to optimize these decisions and serve the largest number of people without straining the energy system,” Hajibabai says.

While much work has gone into implementing charging facilities for EVs, the researchers found that most previous efforts have focused on placing these facilities based on what would work best for the power system, or what would work best from a transport position.

“Very little work has been done that addresses both,” Hajibabai says. “And those cases where both power and transportation systems were looked at didn’t take into account the decisions users make. Where do they want to charge their vehicles? What are their travel plans?

“The best location for a charging point from an electricity system point of view is often not the best location from a transport systems point of view. And the best location from the user’s point of view is often a third option. Our model looks at energy systems, transportation systems and user decision-making to find the best compromise.”

The power system component of the model takes into account the limitations of the power distribution network – the power supply, voltage, current and so on. The transportation component of the overarching model takes into account the number of travelers, the routes they take while traveling and how far their vehicles can go before they need to be recharged. To account for user decision-making, the model attempts to identify locations that minimize travel time for users.

“People often don’t want to go out of their way to charge their vehicles, so our model takes that into account,” says Hajibabai.

The researchers are currently in talks with state and local government officials, as well as energy companies, to use the model to inform the development of EV charging infrastructure in North Carolina.

The newspaper, “Joint design of electric mobility power distribution and charging network with user balancing decisions”, is published open access in the magazine Computer Aided Civil Engineering and Infrastructure† The paper was co-authored by Asya Atik, a Ph.D. student at NC State, and Amir Mirheli, a former Ph.D. student at NC State.

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Note for editors: The study summary follows.

“Joint design of power distribution and charging network for electrified mobility with user balancing decisions”

authors: Leila Hajibabai, Asya Atik and Amir Mirheli, North Carolina State University

published: June the 6th Computer Aided Civil Engineering and Infrastructure

DOI: 10.1111/mice.12854

Abstract: Rapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV-dominated transport systems are both economically and technically intertwined with the operation of the electricity grid. High penetration of EVs in the future can increase the charging load and cause a wide range of operational problems in power distribution networks (PDNs). This paper aims to design an EV charging network with a built-in PDN layout to take into account energy distribution and underlying traffic flows in urban transport networks that support electric mobility in the near future. A two-level multi-integer model is proposed with the location of the EV charging facility and PDN energy decisions at the upper level and the traffic allocation of the user balance at the lower level, taking into account an uncertain charging demand. The aim is to minimize the costs of PDN operations, loading facility implementations and transportation. The proposed problem is solved using a column and constraint generation algorithm while implementing a macroscopic fundamental diagram concept to estimate the arc travel times. The methodology is applied to a hypothetical and two real-world case study networks, and the solutions are compared against a Benders decomposition benchmark. The results of the east coast analysis indicate a 77.3% reduction in computation time. Moreover, the benchmark technique achieves an optimality gap of 1.15%, while the C&CG algorithm yields a gap of 0.61%. The numerical experiments demonstrate the robustness of the proposed methodology. In addition, a series of sensitivity analyzes was performed to study the impact of input parameters on the proposed methodology and to obtain management insights.

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