Abstract
The recent introduction of Energy Communities into European legislation is set to change the paradigm of energy production and consumption. Multiple users can now aggregate to form groups of so-called prosumers, a term that identifies entities which are both producers and consumers of energy. Prosumers share loads, renewable energy production, and storage resources within the energy community, thereby achieving environmental and economic benefits. One of the objectives of introducing energy communities is to stimulate a hybrid form of distributed generation systems based on renewable energy technologies. These energy systems share a connection to the national power grid, but at the same time, they are encouraged to produce and consume their energy locally; to alleviate the stress and potential stability problems that a high share of renewable energy can cause on the power grid. However, due to the complexity of these energy systems and the high number of variables involved, the creation of such communities must overcome several challenges to be effective and to generate benefits for their members. For example, the fluctuating nature of renewable energy sources, such as solar power, introduces a component of unpredictability that must be addressed with storage systems. Moreover, the possibility of injecting a portion of the self-produced energy into the grid may generate additional revenues by exploiting periods of high electricity prices. In this context, one of the most significant challenges is the management of the community's assets—such as controllable loads and battery storage systems—to ensure optimal exploitation of renewable energy sources and maximum economic gain. To this end, this work analyses the case study of a photovoltaic energy community located in Northern Italy, composed of eight prosumers, and proposes an optimization strategy for the allocation of controllable assets, such as loads and battery systems, with the objective of maximizing economic benefit. The results demonstrate the technical and economic feasibility of these renewable energy system solutions. The energy community has been modelled in a Python programming environment, and an optimization tool based on a Mixed-Integer Linear Programming solver has been developed.