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

F. Liu, R. Sen, J. Talusan, A. Pettet, A. Kandel, Y. Suzue, A. Mukhopadhyay, and A. Dubey, Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards, in Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2025, Detroit, Michigan, Richland, SC, 2025.
```
@inproceedings{liu2024reinforcement,
  author = {Liu, Fangqi and Sen, Rishav and Talusan, Jose and Pettet, Ava and Kandel, Aaron and Suzue, Yoshinori and Mukhopadhyay, Ayan and Dubey, Abhishek},
  booktitle = {Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2025, Detroit, Michigan},
  title = {Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards},
  year = {2025},
  address = {Richland, SC},
  note = {nominated for best paper},
  organization = {International Conference on Autonomous Agents and Multi-Agent Systems},
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  series = {AAMAS '25},
  acceptance = {24.5},
  category = {selective},
  contribution = {lead},
  location = {Detroit, Michigan}
}
```
Strategic aggregation of electric vehicle batteries as energy reservoirs can optimize power grid demand, benefiting smart and connected communities, especially large office buildings that offer workplace charging. This involves optimizing charging and discharging to reduce peak energy costs and net peak demand, monitored over extended periods (e.g., a month), which involves making sequential decisions under uncertainty and delayed and sparse rewards, a continuous action space, and the complexity of ensuring generalization across diverse conditions. Existing algorithmic approaches, e.g., heuristic-based strategies, fall short in addressing real-time decision-making under dynamic conditions, and traditional reinforcement learning (RL) models struggle with large stateaction spaces, multi-agent settings, and the need for long-term reward optimization. To address these challenges, we introduce a novel RL framework that combines the Deep Deterministic Policy Gradient approach (DDPG) with action masking and efficient MILP-driven policy guidance. Our approach balances the exploration of continuous action spaces to meet user charging demands. Using real-world data from a major electric vehicle manufacturer, we show that our approach comprehensively outperforms many well-established baselines and several scalable heuristic approaches, achieving significant cost savings while meeting all charging requirements. Our results show that the proposed approach is one of the first scalable and general approaches to solving the V2B energy management challenge.
R. Sen, Y. Zhang, F. Liu, J. P. Talusan, A. Pettet, Y. Suzue, A. Mukhopadhyay, and A. Dubey, Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems, in Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (ICCPS), New York, NY, USA, 2025.
```
@inproceedings{sen2025iccps,
  author = {Sen, Rishav and Zhang, Yunuo and Liu, Fangqi and Talusan, Jose Paolo and Pettet, Ava and Suzue, Yoshinori and Mukhopadhyay, Ayan and Dubey, Abhishek},
  booktitle = {Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (ICCPS)},
  title = {Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems},
  year = {2025},
  address = {New York, NY, USA},
  publisher = {Association for Computing Machinery},
  series = {ICCPS '25},
  acceptance = {28.4},
  category = {selective},
  contribution = {lead},
  location = {California, USA},
  numpages = {10},
  ranking = {rank1}
}
```
Vehicle-to-building (V2B) systems combine physical infrastructure such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital control mechanisms to manage energy use. By utilizing EVs as flexible energy reservoirs, buildings can dynamically charge and discharge EVs to effectively manage energy usage, and reduce costs under time-variable pricing and demand charge policies. This setup leads to the V2B optimization problem, where buildings coordinate EV charging and discharging to minimize total electricity costs while meeting users’ charging requirements. However, the V2B optimization problem is difficult due to: 1) fluctuating electricity pricing, which includes both energy charges (/kWh) and demand charges (/kW); 2) long planning horizons (usually over 30 days); 3) heterogeneous chargers with differing charging rates, controllability, and directionality (unidirectional or bidirectional); and 4) user-specific battery levels at departure to ensure user requirements are met. While existing approaches often model this setting as a single-shot combinatorial optimization problem, we highlight critical limitations in prior work and instead model the V2B optimization problem as a Markov decision process, i.e., a stochastic control process. Solving the resulting MDP is challenging due to the large state and action spaces. To address the challenges of the large state space, we leverage online search, and we counter the action space by using domain-specific heuristics to prune unpromising actions. We validate our approach in collaboration with an EV manufacturer and a smart building operator in California, United States, showing that the proposed framework significantly outperforms state-of-the-art methods.
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.
```
@inproceedings{talusan2024smartcomp,
  author = {Talusan, Jose Paolo and Sen, Rishav and Ava Pettet, Aaron Kandel and Suzue, Yoshinori and Pedersen, Liam and Mukhopadhyay, Ayan and Dubey, Abhishek},
  booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)},
  title = {OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization},
  year = {2024},
  month = jun,
  acceptance = {32.9},
  contribution = {lead}
}
```
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.
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.
```
@inproceedings{rishavITSC2024,
  author = {Sen, Rishav and Sivagnanam, Amutheezan and Laszka, Aron and Mukhopadhyay, Ayan and Dubey, Abhishek},
  booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)},
  title = {Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit},
  year = {2024},
  contribution = {lead},
  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.
R. Sen, T. Tran, S. Khaleghian, P. Pugliese, M. Sartipi, H. Neema, and A. Dubey, BTE-Sim: Fast Simulation Environment For Public Transportation , in 2022 IEEE International Conference on Big Data (Big Data) , Los Alamitos, CA, USA, 2022, pp. 2886–2894.
```
@inproceedings{sen2022,
  author = {Sen, Rishav and Tran, Toan and Khaleghian, Seyedmehdi and Pugliese, Philip and Sartipi, Mina and Neema, Himanshu and Dubey, Abhishek},
  booktitle = { 2022 IEEE International Conference on Big Data (Big Data) },
  title = {{ BTE-Sim: Fast Simulation Environment For Public Transportation }},
  year = {2022},
  address = {Los Alamitos, CA, USA},
  month = dec,
  pages = {2886-2894},
  publisher = {IEEE Computer Society},
  contribution = {lead},
  doi = {10.1109/BigData55660.2022.10020973},
  keywords = {Measurement;Energy consumption;Roads;Sociology;Big Data;Market research;Reliability},
  url = {https://doi.ieeecomputersociety.org/10.1109/BigData55660.2022.10020973}
}
```
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.
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.
```
@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},
  booktitle = {Proceedings of the Thirteenth ACM International Conference on Future Energy Systems},
  title = {E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations},
  year = {2022},
  address = {New York, NY, USA},
  pages = {532–541},
  publisher = {Association for Computing Machinery},
  series = {e-Energy '22},
  contribution = {lead},
  doi = {10.1145/3538637.3539586},
  isbn = {9781450393973},
  keywords = {model-integration, cyber-physical systems, co-simulation, powergrid simulation, traffic simulation},
  location = {Virtual Event},
  numpages = {10},
  url = {https://doi.org/10.1145/3538637.3539586}
}
```
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.

ScopeLab - Design, Operation and Optimization of Smart Cyber-Physical Systems

Rishav Sen Publications

ScopeLab
- Design, Operation and Optimization of Smart Cyber-Physical Systems