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When electrified transit systems make grid aware choices, improved social welfare is achieved by scheduling charging at low grid impact locations and times causing reduced loss, minimal power quality issues and reduced grid stress. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs, etc., that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid’s power flow and therefore cannot account for the power grid’s needs in its day to day operation. In this paper we propose a framework of transportation-grid co-simulation analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day’s traffic from Chattanooga city’s transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to the transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of Transit electrification that further necessitates such an integrated Transportation-Grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.
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Calibrating Real-World City Traffic Simulation Model Using Vehicle Speed Data
Large-scale traffic simulations are necessary for the planning, design, and operation of city-scale transportation systems. These simulations enable novel and complex transportation technology and services such as optimization of traffic control systems, supporting on-demand transit, and redesigning regional transit systems for better energy efficiency and emissions. For a city-wide simulation model, big data from multiple sources such as Open Street Map (OSM), traffic surveys, geo-location traces, vehicular traffic data, and transit details are integrated to create a unique and accurate representation. However, in order to accurately identify the model structure and have reliable simulation results, these traffic simulation models must be thoroughly calibrated and validated against real-world data. This paper presents a novel calibration approach for a city-scale traffic simulation model based on limited real-world speed data. The simulation model runs a microscopic and mesoscopic realistic traffic simulation from Chattanooga, TN (US) for a 24-hour period and includes various transport modes such as transit buses, passenger cars, and trucks. The experiment results presented demonstrate the effectiveness of our approach for calibrating large-scale traffic networks using only real-world speed data. This paper presents our proposed calibration approach that utilizes 2160 real-world speed data points, performs sensitivity analysis of the simulation model to input parameters, and genetic algorithm for optimizing the model for calibration.
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- Award ID(s):
- 1952011
- PAR ID:
- 10466142
- Date Published:
- Journal Name:
- 2023 IEEE International Conference on Smart Computing (SMARTCOMP)
- Page Range / eLocation ID:
- 303 to 308
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMARTCOMP58114.2023.00076
