Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets

Christie Etukudor, Benoit Couraud, Valentin Robu, Wolf-Gerrit Früh, David Flynn and Chinonso Okereke
Additional contact information
Christie Etukudor: Department of Electrical, Electronic and Computer Engineering, Heriot-Watt University, Edinburgh EH14 4AS, Scotland, UK
Benoit Couraud: Department of Electrical, Electronic and Computer Engineering, Heriot-Watt University, Edinburgh EH14 4AS, Scotland, UK
Valentin Robu: Department of Electrical, Electronic and Computer Engineering, Heriot-Watt University, Edinburgh EH14 4AS, Scotland, UK
Wolf-Gerrit Früh: Department of Electrical, Electronic and Computer Engineering, Heriot-Watt University, Edinburgh EH14 4AS, Scotland, UK
David Flynn: Department of Electrical, Electronic and Computer Engineering, Heriot-Watt University, Edinburgh EH14 4AS, Scotland, UK
Chinonso Okereke: Department of Electrical and Information Engineering, Covenant University, Ota 112233, Ogun State, Nigeria

Energies, 2020, vol. 13, issue 4, 1-19

Abstract: Reliable access to electricity is still a challenge in many developing countries. Indeed, rural areas in sub-Saharan Africa and developing countries such as India still encounter frequent power outages. Local energy markets (LEMs) have emerged as a low-cost solution enabling prosumers with power supply systems such as solar PV to sell their surplus of energy to other members of the local community. This paper proposes a one-to-one automated negotiation framework for peer-to-peer (P2P) local trading of electricity. Our framework uses an autonomous agent model to capture the preferences of both an electricity seller (consumer) and buyer (small local generator or prosumer), in terms of price and electricity quantities to be traded in different periods throughout a day. We develop a bilateral negotiation framework based on the well-known Rubinstein alternating offers protocol, in which the quantity of electricity and the price for different periods are aggregated into daily packages and negotiated between the buyer and seller agent. The framework is then implemented experimentally, with buyers and sellers adopting different negotiation strategies based on negotiation concession algorithms, such as linear heuristic or Boulware. Results show that this framework and agents modelling allow prosumers to increase their revenue while providing electricity access to the community at low cost.

Keywords: automated negotiation; P2P electricity trading; local electricity markets; local energy markets; multi-agent systems; bilateral energy negotiations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/4/920/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/4/920/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:4:p:920-:d:322300

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().