Abstract
There is growing interest in deploying the sharing-economy-based business model to energy systems, with modalities like peer-to-peer (P2P) energy trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). This paper exploits the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. We employ theoretical concepts of online spatial crowdsourcing, reinforcement learning, and reverse auction to devise this novel platform. Experimental results using real data from New York City taxi trips and energy consumption show that e-Uber performs close to the optimum and outperforms a state-of-the-art approach.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 |
| Pages | 359-365 |
| Number of pages | 7 |
| ISBN (Electronic) | 9798350324334 |
| DOIs | |
| State | Published - 2023 |
| Event | 20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 - Toronto, Canada Duration: Sep 25 2023 → Sep 27 2023 |
Publication series
| Name | Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 |
|---|
Conference
| Conference | 20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 |
|---|---|
| Country/Territory | Canada |
| City | Toronto |
| Period | 9/25/23 → 9/27/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- combinatorial multi-armed bandit
- energy-sharing
- Online spatial crowdsourcing
- personalized recommendation
- ride-sharing
- V2G
ASJC Scopus subject areas
- Control and Optimization
- Modeling and Simulation
- Instrumentation
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems and Management
