Anagrafe della ricerca

Smart Cities and Energy communities are receiving a special attention from the EU, as confirmed by the several projects under development in Italy such as STARDUST in Trento, RUGGEDISED in Parma, SmartEnCity in Lecce and Sharing Cities in Milan [1]. A Smart City is an urban area that uses different types of methods and sensors to collect data from citizens, buildings, systems (e.g., gas and electricity grid, generators) and services (e.g., public transportation, lighting, hospitals) with the goal of optimizing the management/planning of resources, systems and services to citizens. Thanks to the synergies between the different systems, such a systematic approach allows improving the quality of life [2] and reducing the consumption of fossil fuels and emissions of CO2 and emissions [3]. Similar benefits occur in an energy community which aggregates different prosumers sharing the renewable energy resources and energy storage systems. Through a local smart energy network, the different prosumers can exchange energy with the final goal of minimizing the energy cost/consumption of the community. A variety of terms is used in the current literature for the energy system serving smart cities/districts: multi-energy systems [4], energy hubs/districts [5] [6], microgrids [7] and aggregators [8]. While they might feature some differences depending on the application (e.g., providing or not balancing services to the electric grid), their common feature is the presence of multiple energy conversion technologies and energy storages which are aggregated and managed in a synergic manner by an integrated Energy Management Strategy (EMS) algorithm. For this reason, a more general definition is Aggregated Energy System (AES). The EMS uses optimization methods to determine the most efficient operational strategy of the power generation units, energy distribution networks and loads subject to demand-side management programs [9]. The development of EMS is a very active research area with several hundreds of publications per year. EMS can be classified depending on the number of decision makers: centralized EMS and distributed EMS. Centralized EMS are suitable for applications where a single decision maker plans the operation of the AES and state of the art approaches rely on Mixed Integer Linear Programming (MILP) formulations [10] which can provide optimality guarantees of the solution. Distributed EMS algorithms enable prosumers to locally optimize the operation of their own generation/consumption units while cooperating to determine the optimal solution for the community. Coordination mechanisms can be price-based [11] (price signals are communicated to prosumers/owners [12]), incentive-based or based on decomposition algorithms [13].While most researchers focus.....