United States Department of Transportation
i
A Microscopic Approach for Electric Vehicle Demand Estimation
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2024-10-01
[PDF-2.58 MB]
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Edition:Final Report
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Abstract:As the market penetration of electric vehicles (EVs) increases, the surge of charging demand could potentially overload the power grid and disrupt infrastructure planning. Hence, an efficient deployment strategy of electrical vehicle supply equipment (EVSE) is much needed. This project attempted to address the EVSE problem from a microscopic perspective by formulating the problem in two steps: public charging demand simulation and charging station location optimization. Specifically, we applied an agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, we performed an EV assignment based on socioeconomic attributes to determine EV adopters. An energy consumption model and a public charging rule were specified for generating synthetic public charging demand, and such demand was validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, we applied a location approach — the capacitated maximal coverage location problem (CMCLP) model — to reallocate existing charging stations with the objective of maximizing the coverage of total charging demands generated from the previous step under the budget and load capacity constraints. The entire framework is capable of modeling the spatiotemporal distribution of public charging demand in a bottom-up fashion, and provides practical support for future public EVSE installations.
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