Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model
by
Chen, Dapeng
, Tan, Huijuan
, Jing, Zhaoxia
in
aggregator
/ bidding strategy
/ business model
/ Costs
/ Customer services
/ Electric vehicles
/ Electricity
/ Electricity distribution
/ Energy industry
/ Energy prices
/ Opportunity costs
/ Optimization
/ Participation
/ plug-in electric vehicle (PEV)
/ Scheduling
/ Smart grid technology
/ stochastic optimization
2019
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model
by
Chen, Dapeng
, Tan, Huijuan
, Jing, Zhaoxia
in
aggregator
/ bidding strategy
/ business model
/ Costs
/ Customer services
/ Electric vehicles
/ Electricity
/ Electricity distribution
/ Energy industry
/ Energy prices
/ Opportunity costs
/ Optimization
/ Participation
/ plug-in electric vehicle (PEV)
/ Scheduling
/ Smart grid technology
/ stochastic optimization
2019
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model
by
Chen, Dapeng
, Tan, Huijuan
, Jing, Zhaoxia
in
aggregator
/ bidding strategy
/ business model
/ Costs
/ Customer services
/ Electric vehicles
/ Electricity
/ Electricity distribution
/ Energy industry
/ Energy prices
/ Opportunity costs
/ Optimization
/ Participation
/ plug-in electric vehicle (PEV)
/ Scheduling
/ Smart grid technology
/ stochastic optimization
2019
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model
Journal Article
Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model
2019
Request Book From Autostore
and Choose the Collection Method
Overview
Realizing the full potential of plug-in electric vehicle (PEVs) in power systems requires the development of business models for PEV owners and electric vehicle aggregators (EVAs). Most business models neglect the significant economic potential of PEV demand response. This paper addresses this challenge by proposing a novel business model to optimize the charging energy of PEVs for maximizing the owners’ profits. The proposed business model aims to overcome the opportunity cost neglect for PEV owners, whose charging energy and charging profiles are optimized with full consideration of the demand curves and market conditions. Lagrangian relaxation technology is used for the relaxation of the constraint of satisfying the charging demand, and as a result, the optimization potential becomes greater. The bidding/offering strategy is formulated as a two-stage stochastic optimization problem, considering the different market prices and initial and target state of energy (SOE) of the PEVs. By case studies and analyses, we demonstrate that the proposed business model can effectively overcome the opportunity cost neglect and increase the PEV owners’ profits. Furthermore, we demonstrate that the proposed business model is incentive-compatible. The PEV owners will be attracted by the proposed business model.
Publisher
MDPI AG
Subject
This website uses cookies to ensure you get the best experience on our website.