[2209.05658] EV Charging Station Wholesale Market Participation: A Strategic Bidding and Pricing Approach
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Electrical Engineering and Systems Science > Systems and Control

arXiv:2209.05658 (eess)
[Submitted on 13 Sep 2022]

Title:EV Charging Station Wholesale Market Participation: A Strategic Bidding and Pricing Approach

Authors:Mohammad Mousavi, Li "Lisa" Qi, Alexander Brissette, Meng Wu
View PDF
Abstract:This paper presents a framework for simultaneous bidding and pricing strategy for wholesale market participation of electric vehicle (EV) charging stations aggregator. The proposed framework incorporates the EV charging stations' technical constraints as well as EV owners' preferences. A bi-level optimization is adopted to model the problem. In the upper level, the total profit of the EV charging station aggregator is maximized. In the lower-level problem, the EV owner's utility function is maximized. The EV owners' preferences are modeled using the quadratic utility function. The bi-level optimization problem which is non-convex and hard to solve is converted to a mixed-integer convex quadratic programming model by writing the optimal conditions of the lower-level problem that is solvable with commercial solvers. The effectiveness of the proposed framework is investigated by implementing simulation results.
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2209.05658 [eess.SY]
  (or arXiv:2209.05658v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2209.05658

Submission history

From: Mohammad Mousavi [view email]
[v1] Tue, 13 Sep 2022 00:24:51 UTC (550 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
view license
Current browse context:
eess.SY
< prev   |   next >
new | recent | 2022-09
Change to browse by:
cs
cs.SY
eess

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)