Rishav Sen is a graduate student and a Russel G. Hamilton Scholar in the Department of Electrical Engineering and Computer Science at Vanderbilt University. He completed his Undergraduate in Electronics and Communication from the Heritage Institute of Technology in August, 2020 and had been working as a Software Engineer before starting his Graduate studies.

Rishav Sen Publications

1. R. Sen, A. Sivagnanam, A. Laszka, A. Mukhopadhyay, and A. Dubey, Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit, in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024.
   Bibtex Abstract PDF

   @inproceedings{rishavITSC2024,
     title = {Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit},
     author = {Sen, Rishav and Sivagnanam, Amutheezan and Laszka, Aron and Mukhopadhyay, Ayan and Dubey, Abhishek},
     year = {2024},
     booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)},
     volume = {},
     number = {},
     keywords = {Mixed transit fleet, electrification, dynamic pricing, hierarchical MILP}
   }
   

   The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational chal- lenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.

2. J. P. Talusan, R. Sen, A. K. Ava Pettet, Y. Suzue, L. Pedersen, A. Mukhopadhyay, and A. Dubey, OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization, in 2024 IEEE International Conference on Smart Computing (SMARTCOMP), 2024.
   Bibtex Abstract PDF

   @inproceedings{talusan2024smartcomp,
     title = {OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization},
     author = {Talusan, Jose Paolo and Sen, Rishav and Ava Pettet, Aaron Kandel and Suzue, Yoshinori and Pedersen, Liam and Mukhopadhyay, Ayan and Dubey, Abhishek},
     year = {2024},
     month = jun,
     booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)},
     volume = {},
     number = {}
   }
   

   The increasing popularity of electronic vehicles has spurred a demand for EV charging infrastructure. In the United States alone, over 160,000 public and private charging ports have been installed. This has stoked fear of potential grid issues in the future. Meanwhile, companies, specifically building owners are also seeing the opportunity to leverage EV batteries as energy stores to serve as buffers against the electric grid. The main idea is to influence and control charging behavior to provide a certain level of energy resiliency and demand responsiveness to the building from grid events while ensuring that they meet the demands of EV users. However, managing and co-optimizing energy requirements of EVs and cost-saving measures of building owners is a difficult task. First, user behavior and grid uncertainty contribute greatly to the potential effectiveness of different policies. Second, different charger configurations can have drastically different effects on the cost. Therefore, we propose a complete end-to-end discrete event simulator for vehicle-to-building charging optimization. This software is aimed at building owners and EV manufacturers such as Nissan, looking to deploy their charging stations with state-of-the-art optimization algorithms. We provide a complete solution that allows the owners to train, evaluate, introduce uncertainty, and benchmark policies on their datasets. Lastly, we discuss the potential for extending our work with other vehicle-to-grid deployments.

3. R. Sen, T. Tran, S. Khaleghian, M. Sartipi, H. Neema, and A. Dubey, BTE-Sim: Fast simulation environment for public transportation, 2022 IEEE International Conference on Big Data, 2022.
   Bibtex Abstract PDF

   @article{sen2022,
     author = {Sen, Rishav and Tran, Toan and Khaleghian, Seyedmehdi and Sartipi, Mina and Neema, Himanshu and Dubey, Abhishek},
     title = {BTE-Sim: Fast simulation environment for public transportation},
     year = {2022},
     isbn = {},
     publisher = {IEEE},
     address = {},
     url = {},
     booktitle = {2022 IEEE International Conference on Big Data},
     pages = {},
     numpages = {8},
     keywords = {public transit, fast traffic simulation, model integration, data processing, road speed calibration},
     location = {},
     series = {}
   }
   

   The public commute is essential to all urban centers and is an efficient and environment-friendly way to travel. Transit systems must become more accessible and user-friendly. Since public transit is majorly designed statically, with very few improvements coming over time, it can get stagnated, unable to update itself with changing population trends. To better understand transportation demands and make them more usable, efficient, and demographic-focused, we propose a fast, multi-layered transit simulation that primarily focuses on public transit simulation (BTE-Sim). BTE-Sim is designed based on the population demand, existing traffic conditions, and the road networks that exist in a region. The system is versatile, with the ability to run different configurations of the existing transit routes, or inculcate any new changes that may seem necessary, or even in extreme cases, new transit network design as well. In all situations, it can compare multiple transit networks and provide evaluation metrics for them. It provides detailed data on each transit vehicle, the trips it performs, its on-time performance and other necessary factors. Its highlighting feature is the considerably low computation time it requires to perform all these tasks and provide consistently reliable results.

4. R. Sen, A. K. Bharati, S. Khaleghian, M. Ghosal, M. Wilbur, T. Tran, P. Pugliese, M. Sartipi, H. Neema, and A. Dubey, E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations, in Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, New York, NY, USA, 2022, pp. 532–541.
   Bibtex Abstract DOI PDF

   @inproceedings{rishav2022eEnergy,
     author = {Sen, Rishav and Bharati, Alok Kumar and Khaleghian, Seyedmehdi and Ghosal, Malini and Wilbur, Michael and Tran, Toan and Pugliese, Philip and Sartipi, Mina and Neema, Himanshu and Dubey, Abhishek},
     title = {E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations},
     year = {2022},
     isbn = {9781450393973},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3538637.3539586},
     doi = {10.1145/3538637.3539586},
     booktitle = {Proceedings of the Thirteenth ACM International Conference on Future Energy Systems},
     pages = {532–541},
     numpages = {10},
     keywords = {model-integration, cyber-physical systems, co-simulation, powergrid simulation, traffic simulation},
     location = {Virtual Event},
     series = {e-Energy '22}
   }
   

   When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, 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 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.