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.

The core challenge of the BedBasedEcho BEP project is to create an algorithm to find the heart, and apply it on a robotic echocardiography solution. The team has found multiple complex solutions that are related to this problem, and has extracted useful information from these solutions to apply to this problem. However, some of these complex solutions were too complex, causing the team to run out of physical resources, or to have the solution fail entirely. By taking a step back, and simplifying the solution, the team has managed to create a system that performs marginally better than the complex solutions. The designed product consists of three major components: the data gathering, the learning, and the deployment. When used in this order, the result is an algorithm that can predict which way it should move to gain the optimal view of the heart. The algorithm will be used as a component in a larger automated echocardiography system. Ultimately, the algorithm showed promise by autonomously finding a good view of the heart.