Shreyas Ramakrishna, Baiting Luo, Yogesh Barve, Gabor Karsai, Abhishek Dubey
2022 IEEE International Conference on Assured Autonomy (ICAA) (ICAA'22) 2022
As cities transition to electric public transit, the impact of charging loads on power grids becomes critical for infrastructure planning and operational stability. This work is significant because it provides an integrated modeling approach that captures the bidirectional interactions between transportation and energy systems. Understanding these coupling effects is essential for optimal planning of charging infrastructure and grid management.
This paper presents an integrated simulation platform for analyzing the interaction between electric vehicle charging and power grid operations in urban transit systems. The framework co-simulates the transit system operations using SUMO with power grid simulations using GridLAB-D, enabling analysis of how bus charging schedules affect grid load distribution. The system models charging profiles, battery degradation, and their impacts on grid infrastructure.
The co-simulation analysis demonstrated how different charging station locations and strategies significantly affect grid load distribution and voltages. The framework showed that strategic charging scheduling can reduce grid stress while maintaining transit service quality. The system enabled analysis of various scenarios including peak-load periods and identified opportunities for demand management through coordinated vehicle charging.
@inproceedings{ICAA2022,
author = {Ramakrishna, Shreyas and Luo, Baiting and Barve, Yogesh and Karsai, Gabor and Dubey, Abhishek},
booktitle = {2022 IEEE International Conference on Assured Autonomy (ICAA) (ICAA'22)},
title = {Risk-Aware} Scene Sampling for Dynamic Assurance of Autonomous Systems},
year = {2022},
address = {virtual, Puerto Rico},
month = {mar},
abstract = {Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and distribution shifts in the operating environment, thus increasing operational risk. Dynamic Assurance of these systems requires augmenting runtime safety components like out-of-distribution detectors and risk estimators. Designing these safety components requires labeled data from failure conditions and risky corner cases that fail the system. However, collecting real-world data of these high-risk scenes can be expensive and sometimes not possible. To address this, there are several scenario description languages with sampling capability for generating synthetic data from simulators to replicate the scenes that are not possible in the real world. Most often, simple search-based techniques like random search and grid search are used as samplers. But we point out three limitations in using these techniques. First, they are passive samplers, which do not use the feedback of previous results in the sampling process. Second, the variables to be sampled may have constraints that need to be applied. Third, they do not balance the tradeoff between exploration and exploitation, which we hypothesize is needed for better coverage of the search space. We present a scene generation workflow with two samplers called Random Neighborhood Search (RNS) and Guided Bayesian Optimization (GBO). These samplers extend the conventional random search and Bayesian Optimization search with the limitation points. We demonstrate our approach using an Autonomous Vehicle case study in CARLA simulation. To evaluate our samplers, we compared them against the baselines of random search, grid search, and Halton sequence search.},
contribution = {lead},
days = {22},
keywords = {electric vehicles, power grid, co-simulation, charging optimization, smart grids, transit systems},
tag = {ai4cps},
month_numeric = {3}
}