Analysis and modeling of peer-to-peer electricity trading systems in the context of energy communities.

Technological advances and cost reductions combined with the need for greater environmental sustainability have led to an enormous increase in installed photovoltaic (PV) capacity in recent years. This deployment involves not only photovoltaic panels but also wind turbines, storage systems, electric vehicles (EVs), and numerous other resources, which together are referred to as Distributed Energy Resources (DER). The increase in these Renewable Energy Sources (RES) encompasses several issues related mainly to their extreme variability and limited predictability, which involve the grid itself. The historical top-down approach of the electricity grid is contrasted with new models for the future in which the owners of RES assume central importance; they no longer behave as mere consumers under the grid control, but on the contrary, act as active prosumers in a decentralized framework. These consumer-centric scenarios can come to life through what is known as a peer-to-peer (P2P) electricity market or community-based structure. Energy communities (ECs) and peer-to-peer energy trading are based on the concept of direct exchanging or sharing energy between individuals (peers), thus allowing prosumers to have more bargaining power over the grid and, at the same time, less dependence on it. A review of the literature on peer-to-peer energy trading and energy communities has been developed in this thesis. The work has been carried out by examining several categories, ranging from the aggregation structures, the market mechanisms, the approaches used to solve it, the sharing policies, and the pricing mechanisms internal to the market. Then a methodology for peer-to-peer energy trading inside a community has been introduced. In particular, this thesis seeks to study how mechanisms for sharing and selling energy, which we can more simply call sharing policies, influence a community with varying internal price mechanisms. In this context, an optimization model has been formulated to optimize the day-ahead scheduling of community members participating in peer-to-peer energy trading. In more detail, a Mixed Integer Linear Programming (MILP) problem, which contains within itself a pricing mechanism, has been developed as a coordinated approach to represent the community model. In this work, simulations were carried out with several community types, investigating different sharing policies and pricing mechanisms within the peer-to-peer market. Finally, the optimization results have been evaluated based on several key parameter indicators (KPI), ranging from the more classical ones concerning the economic aspects to the more technical ones related to energy production, consumption, and self-sufficiency.