Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1109/smartg...
Article . 2019 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Conference object
Data sources: DBLP
versions View all 2 versions
addClaim

Energy Trading with Demand Response in a Community-based P2P Energy Market

Authors: Min Zhang 0058; Frank Eliassen; Amir Taherkordi; Hans-Arno Jacobsen; Hwei-Ming Chung; Yan Zhang 0002;

Energy Trading with Demand Response in a Community-based P2P Energy Market

Abstract

Peer-to-peer (P2P) energy trading among neighbouring prosumers is considered as a promising trading method for the future smart grid. Demand response management becomes a critical challenge due to increased penetration of renewable energy. Earlier work mostly considers P2P trading models with only prosumers, while we believe there will still be a role to play for electricity suppliers in local energy markets in the foreseeable future. This paper therefore proposes a trading model in a community based P2P electric energy market that includes local energy suppliers and a community coordinator as market participants, in addition to pure energy consumers, and prosumers. We develop a demand response mechanism for the proposed trading model, in which dynamic pricing for suppliers is used. The community coordinator negotiates with suppliers on the external energy price and trades with them on behalf of the households within the P2P energy market. In our proposed trading model, the behaviour of the suppliers and the community households are modelled as two non-cooperative games. We propose a distributed algorithm to determine the equilibrium of the games. Simulation results show that our model has great effect on reducing the net peak load and increasing the market participants’ profit. Additionally, the proposed mechanism is shown to act as an efficient incentive for pure energy consumers to become prosumers.

Related Organizations
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    27
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Top 10%
Top 10%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!