Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Mathematics > Optimization and Control

arXiv:1804.02580 (math)
[Submitted on 7 Apr 2018]

Title:Comprehensive Modeling of Electric Vehicles in California Demand Response Markets

Authors:Bin Wang, Rongxin Yin, Doug Black
View PDF
Abstract:Electric vehicle (EV) is a significant type of distributed energy resources (DERs), that provide flexibilities to grid operators to achieve a myriad of objectives. This paper presents a comprehensive modeling framework of EVs under multiple real-world demand response (DR) markets in California and provides combined strategies to maximize the revenues via unidirectional EV-Grid integrations (V1G). EV itinerary and usage information from a commercial demonstration site is utilized to model the EV flexibilities, based on which, we modeled the heterogeneous market rules using mixed-integer programming approaches. The system cost-saving performance is analyzed with respect to fleet properties and market constraints, including flexibility, participation threshold, and baseline calculaton, etc.
Subjects: Optimization and Control (math.OC); Systems and Control (eess.SY)
Cite as: arXiv:1804.02580 [math.OC]
  (or arXiv:1804.02580v1 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.1804.02580

Submission history

From: Bin Wang [view email]
[v1] Sat, 7 Apr 2018 17:50:41 UTC (689 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
view license
Current browse context:
math.OC
< prev   |   next >
new | recent | 2018-04
Change to browse by:
cs
cs.SY
math

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?)