PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022
TITOLO DEL PROGETTO: HERA (Holistic Energy Recovery Agent)
CUP CODE: D53D23003910006
Budget: € 68.500
U.R. Coordinator: Prof. Umberto Berardi
Other Research Units or any Sub-Units:
University of Naples Federico II (P.I.: Prof. Annamaria Buonomano)
Sapienza University of Rome (U.R. Coordinator – Prof. Davide Astiaso Garcia)
Brief project description
The HERA project (Holistic Energy Recovery Agent) is situated within the context of the urban energy transition, addressing the growing need to reduce emissions, improve energy efficiency and increase the resilience of urban systems. In particular, the project focuses on developing a holistic approach to the design and management of sustainable energy districts, understood as clusters of interconnected buildings capable of interacting intelligently with energy networks. These systems integrate renewable energy sources, storage systems and advanced energy management strategies, also incorporating the role of electric mobility and user behaviour. The proposed approach is based on a multi-scale dynamic simulation platform that makes it possible to model buildings, energy infrastructures and energy flows, assessing their performance in energy, environmental and economic terms.
Purpose
The aims of the project are geared towards supporting the design and optimisation of resilient, flexible and low-emission energy districts, promoting the integration of urban planning and energy. In this regard, HERA aims to address existing gaps in urban modelling tools by developing solutions capable of representing buildings, energy systems and networks in an integrated manner, and of supporting decision-making processes through advanced simulations and optimisation algorithms. The project also aims to encourage the adoption of innovative strategies such as demand response, energy sharing and sector coupling, helping to improve the energy self-sufficiency of districts and reduce dependence on fossil fuels.
Expected results
The expected results of the project include, first and foremost, the development of an innovative simulation and optimisation platform capable of analysing the energy behaviour of complex urban districts and identifying the most efficient configurations in terms of technologies, systems and management strategies. This will be complemented by the definition of advanced models for representing buildings and energy systems, including simplified models (Reduced Order Models) to reduce computational times and facilitate large-scale application. The project also envisages the development and analysis of real and representative case studies, which will be useful for validating the methodologies developed and for generating transferable datasets and knowledge. A further outcome will be the development of guidelines and design criteria for the implementation and management of sustainable energy districts, as well as the development of advanced energy management strategies based on optimisation, artificial intelligence and predictive models.
Achieved results
At the conclusion of the project activities, the HERA project achieved significant results in both scientific and applied terms, making a concrete contribution to the advancement of knowledge and tools for the energy transition of urban systems.
First, an innovative dynamic simulation and optimization platform was developed, capable of integrally modelling the energy behaviour of building clusters and complex urban districts, based on a long-term multi-node optimization model (H2RES). This platform makes it possible to analyse the interactions between energy demand, renewable energy production, storage systems and network infrastructure, thereby identifying optimal configurations in terms of energy efficiency, emissions reduction and system flexibility.
In parallel, the project led to the development of advanced models for representing buildings and energy systems, including simplified models (Reduced Order Models) capable of ensuring a good balance between accuracy and computational time. These tools made it possible to efficiently simulate entire urban districts, enabling the analysis of complex scenarios and the exploration of different retrofit and energy management strategies.
A further result concerns the definition and implementation of advanced energy management strategies based on demand response, energy sharing and multi-vector integration (electricity, thermal energy, mobility). In this context, optimization algorithms and approaches based on artificial intelligence and machine learning were developed, capable of improving the management of energy flows and increasing the self-sufficiency and resilience of districts, including in the presence of intermittent renewable energy sources.
