Analysis of peer-to-peer electricity trading models in a grid-connected microgrid

The thesis proposed an investigation on the implementation of peer-to-peer (P2P) energy transaction platforms in power systems as a possible energy management solution to deal with distributed generation (DG) and renewable energy sources (RES) penetration. Firstly, a state of the art of the current P2P trading technologies development is provided, reviewing and analysing several projects carried out in this field in recent years and doing a comparison of the models, considering their commonalities, strengths and shortcomings, along with.an overview of the main techniques utilized. In the second stage, the focus shifts on the presentation of the structure of the system used in the case study investigated in the project. A multi agent system (MAS) integrated with a micro grid management platform (μGIM) acts in a grid connected microgrid located in an office building, equipped with solar panels (PVs) to operate energy transactions among different agents (prosumers/consumers). Each agent is represented by a tenant of a zone in the building, which owns a part of the total photovoltaic generation. From the starting point of the English auction model, initially used in the trading platform, two new algorithms have been implemented in the system in an attempt to improve the efficiency of the trading process. The algorithms formulation is based on the analysis of the initial model behaviour and results, and is supported by the state of art provided in the first chapter. A specific simulation platform was used to run the model using consumption data recorded from previous week of monitoring, in order to compare different trading algorithms working on the same consumption/generation profile. The developments obtained from this study proves the capabilities of the P2P energy trading to advantage the end users, allowing them to manage their own energy and pursue their personal goals. They also emphasize that this type of models have still a good improvement margin and with further studies they can represent a key element in the future smart grids and decentralized systems