A Distributed MILP-ADMM Framework for Italian Energy Communities: Shared Energy Incentives, Renewables, and Shiftable Loads⁎⁎Work supported and funded by the Italian Ministry for Research and Education (MUR) under the National Recovery and Resilience Plan (NRRP)-MISSION 4 COMPONENT 2, Investment 1.3. Investment 1.1. Notice Prin 2022 - DD N. 104 del 2/2/2022, from title ECODREAM Energy COmmunity management: DistRibutEd AlgorithMs and toolboxes for efficient and sustainable operations, proposal co

• Published in: IFAC-PapersOnLine Vol. 59; no. 9; pp. 19 - 24
• Main Authors: Messilem, A. Mohamed, Brumali, Riccardo, Carnevale, Guido, Notarstefano, Giuseppe, Carli, Ruggero
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2025
• Subjects: Distributed control; Networked Control Systems; Shared Energy; Smart Grids; Networked Control Systems; Distributed control; Shared Energy; Smart Grids
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2025.08.106

In this paper, we propose a distributed optimization framework for Italian energy communities that take advantage of state incentives linked to the amount of shared energy consumed. The framework models the collective use of renewable energy generated by community members, allowing those without direct access to renewable sources to benefit from energy at a reduced cost, in line with regulatory incentives. We formulate the problem as a Mixed-Integer Linear Program (MILP) that considers different user configurations, including those with renewable energy sources and battery energy storage systems (BESS). A key feature of our approach is the integration of shiftable loads, enabling flexible energy consumption adjustments to optimize overall system performance. To solve this problem, we employ a distributed optimization approach combining MILP with the Alternating Direction Method of Multipliers (ADMM), facilitating decentralized decision-making, enhancing data privacy, and reducing computational complexity. This makes the solution more scalable and efficient for larger communities. Numerical results show that our approach significantly reduces the community’s overall energy costs and demonstrates the effectiveness of cooperative BESS management and dynamic energy consumption optimization through shiftable loads.