A Distributed Algorithm for Coordination in Energy Communities *

In this paper, we consider an energy trading problem in a network of interconnected MicroGrids. We consider a model in which each unit can produce, consume, or store energy and is classified as a seller or buyer, depending on its energy status. Indeed, the sellers have an excess of energy to be sold or stored, while the buyers, instead, need to buy energy from the other units or the main grid to satisfy their energy demand. In this setting, we formulate a cooperative optimization problem with the aim of finding the best tradeoff between the competitive objectives of (i) maximizing the sellers' revenue, (ii) ensuring storage, (iii) minimizing the buyers' energy cost, and (iv) satisfying the energy demand. Then, we recast the obtained problem in the so-called aggregative optimization scenario, a recently emerged framework in which a network of agents aims at cooperatively minimizing the sum of local functions each depending on both global (the so-called aggregative variable) and local quantities. Hence, we propose a distributed scheme tailored for aggregative optimization. The numerical simulations confirm the effectiveness of our approach showing the convergence of the chosen distributed algorithm to a stationary point of the problem. Finally, we test the flexibility of the model by considering scenarios where agents have different preferences.