Smart Energy in Cities and Communities for 2050

Abstract:

The objective of the ECCO-2050 project is to develop three novel approaches and tools for optimizing the design and operation of the integrated energy system serving smart cities and energy communities.

The Investment Planning optimization tool will perform the optimization of the long-term investment planning required to meet the 2050 CO2 emission targets in the most economically and environmentally sustainable way. The code will include an accurate mathematical model of the smart city/energy community with related energy systems, renewable energy resources, energy distribution networks, and main energy users. The mathematical model will define the constraints of the investment planning optimization problem to determine the decisions to be taken over the future years to meet the 2050 goal of zero CO2 emissions. For example, these actions may include the disposal of old energy systems, the installation of new energy systems (e.g., PV, wind, heat pumps, waste to energy plants, H2-fired fuel cell), the adoption of low temperature district heating network, the construction of H2 networks, or the requalification of buildings. Such planning tool is original from a scientific point of view (no previous work addresses such multi-year planning problem) and its development is quite ambitious because of the large number of variables and constraints of the optimization problem.

The Monitoring and Diagnostic tool will use data-driven and first-principle models to build digital twins of the integrated energy system. The digital twins will allow monitoring, diagnosing faults and identifying efficiency losses at component level.

The Energy Management System (EMS) tool will optimize the operation of integrated energy system. Starting from the forecast of the demands and the feedback of the monitoring and diagnostic tool, the tool will define the optimal management of energy conversion technologies over the prediction horizon. Compared to existing EMSs, the integration with the monitoring and diagnostic tool represents a significant step forward since it allows updating the performance model parameters and availability status of the energy technologies in order to find effective correction actions.

All tools will integrate accurate models of the energy technologies/energy users/networks within ad hoc formulated optimization problems (i.e., the investment planning problem and the EMS will be formulated as Mixed Integer Linear Programs, while the monitoring and diagnosis tool will combine nonlinear optimization and machine learning techniques) which will be solved with state-of-the-art solvers.

The tools will be tested on two case studies defined with the help of the supporting external institutes: RSE, SIRAM-Veolia and the municipalities of Cremona and Vigolzone (see the attached support letters).

The tools developed in the project will be extremely useful for optimizing the energy planning of all Italian municipalities.