
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid

Peer-to-peer trading in energy networks is expected to be exclusively conducted by the prosumers of the network with negligible influence from the grid. This raises the critical question: how can enough prosumers be encouraged to participate in peer-to-peer trading so as to make its operation sustainable and beneficial to the overall electricity network? To this end, this paper proposes how a motivational psychology framework can be used effectively to design peer-to-peer energy trading to increase user participation. To do so, first, the state-of-the-art of peer-to-peer energy trading literature is discussed by following a systematic classification, and gaps in existing studies are identified. Second, a motivation psychology framework is introduced, which consists of a number of motivational models that a prosumer needs to satisfy before being convinced to participate in energy trading. Third, a game-theoretic peer-to-peer energy trading scheme is developed, its relevant properties are studied, and it is shown that the coalition among different prosumers is a stable coalition. Fourth, through numerical case studies, it is shown that the proposed model can reduce carbon emissions by 18.38% and 9.82% in a single day in Summer and Winter respectively compared to a feed-in-tariff scheme. The proposed scheme is also shown to reduce the cost of energy up to 118 cents and 87 cents per day in Summer and Winter respectively. Finally, how the outcomes of the scheme satisfy all the motivational psychology models is discussed, which subsequently shows its potential to attract users to participate in energy trading.
7 Figures, 5 Tables, Accepted in Applied Energy
- University of Queensland Australia
- Princeton University Pure Princeton United States
- University of Queensland Australia
- Singapore University of Technology and Design Singapore
- University of Queensland Australia
2100 Energy, 690, FOS: Computer and information sciences, Monitoring, 2210 Mechanical Engineering, FFR, Computer Science - Networking and Internet Architecture, Computer Science - Computer Science and Game Theory, Civil and Structural Engineering, Networking and Internet Architecture (cs.NI), Policy and Law, Mechanical Engineering, Building and Construction, Management, General Energy, 2215 Building and Construction, 2308 Management, Computer Science and Game Theory (cs.GT)
2100 Energy, 690, FOS: Computer and information sciences, Monitoring, 2210 Mechanical Engineering, FFR, Computer Science - Networking and Internet Architecture, Computer Science - Computer Science and Game Theory, Civil and Structural Engineering, Networking and Internet Architecture (cs.NI), Policy and Law, Mechanical Engineering, Building and Construction, Management, General Energy, 2215 Building and Construction, 2308 Management, Computer Science and Game Theory (cs.GT)
citations 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).314 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 0.1% 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 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.1%
