Efficiency of Peer-to-Peer Trading Coalitions in Energy Communities

Master thesis (2022)

Authors

Y. Zhang Electrical Engineering, Mathematics and Computer Science

Contributors

Valentin Robu Algorithmics - EEMCS (mentor)
N. Yorke-Smith Algorithmics - EEMCS (graduation committee member)
Simon H. Tindemans Intelligent Electrical Power Grids - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Peter Zhang
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Published Date

12-07-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Peer-to-peer trading and energy communities have garnered much attention over the last few years due to the wider spread of distributed energy resources. Much research has been performed on the mechanisms and methodologies behind their implementation and realisation. However, the efficiency and micro-structure of trading in such markets raise many important challenges. To analyse the efficiency of peer-to-peer energy markets, we consider two different popular approaches to peer-to-peer trading, i.e. centralised and decentralised and explore the economic benefits these models bring given optimal trading schedules computed by a joint schedule optimizer. In both these modes, benefits can be realised mainly due to the diversity in consumption behaviour and renewable energy generation between prosumers in an energy community.
This diversity decreases quickly as more peer-to-peer energy contracts are established and more prosumers join the market, leading to significantly diminishing returns. In this work, we aim to quantify such effects using large-scale real-world data from two trials in the UK, i.e. the Low Carbon London project and the Thames Valley Vision project.
We show that only a small number of peer-to-peer contracts and a fraction of the prosumers are needed to realise the majority of the Gains from Trade.