Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services

Hernandez Cedillo, Monica; Sun, Hongjian

Abstract

The transition to a low carbon transportation system has brought many challenges for researchers, one major challenge is how to ensure power system reliability as a result of high load demands to supply energy to Electric Vehicles (EVs) while coping with increasing distributed and renewable sources of energy. Consequently, energy management strategies have become very important in the future smart grid design. An aggregator could play a critical role when integrating management strategies between EVs and the grid, based on emerging market opportunities and different variables from the stakeholders involved such as EV requirements, balancing services and profitability of the Charging Station (CS). This paper proposes a data-driven optimisation algorithm with pricing and control modules that communicate with each other to achieve a successful integration with the grid by charging at the right price and at the right time. The results show customers can be positively engaged with pricing signals while providing support to the power system. In conclusion, this paper can be used as a foundation to a commercial CS that may enhance an effective integration of EVs with the grid.

Citation

Hernandez Cedillo, M., & Sun, H. (2020, November). Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services. Presented at 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
• Start Date: Nov 11, 2020
• End Date: Nov 13, 2020
• Acceptance Date: Aug 30, 2020
• Online Publication Date: Dec 30, 2020
• Publication Date: 2020
• Deposit Date: Apr 28, 2021
• Publicly Available Date: Apr 28, 2021
• DOI: https://doi.org/10.1109/smartgridcomm47815.2020.9303018
• Public URL: https://durham-repository.worktribe.com/output/1138894