Buildings are among the most polluting entities, as research conducted by the European Commission found that they are responsible for approximately 40% of the overall energy consumption and 36% greenhouse emissions in the EU. It is therefore clear that an improvement in Buildings Energy Management (BEM) practices could have a significant impact in terms of energy savings and pollution reduction. The physical renovation of existing buildings and the adoption of the most advanced techniques for the construction of new ones should be cou- pled with radical innovations in the adoption of Digital Technologies for BEM, therefore creating so called Smart Homes, on which SHADER multidisciplinary project is focused.
SHADER has a significant role in the Building Energy Management (BEM) transition, as it aims at developing a platform that, by integrating heterogeneous data sources and creating a digital twin of a physical system, allows to run multiple scenario analyses, thus allowing to define the optimal control strategy for the Energy Appliances over time minimizing energy consumption and costs for the final user.
A thorough Political, Economic, Socio-Cultural, and Technological (PEST) analysis was conducted in order to better understand the potential opportunities and threats that may affect the Smart Home market in the European and Italian economic context, suggesting that huge opportunities can be exploited thanks to European Programs such as Next Generation EU. On the other side, potential threats derive mostly from the retail public’s resistance to adopt such high-level technologies into their homes. Moreover, an in-depth analysis of the Smart Home industry was conducted, with a special focus on the Smart Energy Appliances segment, with the aim of understanding which are the Key Success Factors (KSF) required to successfully compete in this industry, as well as the main players in the market.
The development of an effective solution started with the analysis of the simulation frameworks developed by the PoliTo research group and by the Ariston-PoliMi Joint Research Centre. The first simulation consists of different models, each developed in a domain specific software, while the second was implemented in Modelica language creating the so called “AristonBuildingHVAC” Modelica library. Particular attention was given to the AristonBuildingHVAC library, where a sensitivity analysis on several parameters, which influence the heating control strategy of an apartment, was performed in order to find the optimal setting that maximizes the user comfort and minimizes the energy consumption.
Once the simulation parameters were optimised, different subsystems coming from the two different models were integrated by exploiting co-simulation, which was identified as the optimal methodology to run scenario analyses using digital twins of buildings and power grids. Co-simulation offers the possibility of modelling the system as a group of independent units communicating with each other. Our objective is to perform large scale simulations of energy consumption and production entities while offering the possibility of easily changing the components that make up the studied energy system. HELICS is the co-simulation framework of choice because of its performance and scalability. Functional Mock-up Interface (FMI) is the industry standard for the definition of a common interface for communicating with simulators, allowing to perform simulation steps, set options, and performing input and output operations. The framework was extended with several components developed in Python in order to provide a uniform and easy to set up common interface between HELICS and FMI. These components allow the easy definition of the single simulations to be orchestrated, together with the data to be interchanged between them.
Several tests were conducted in order to validate the feasibility of co-simulation, evaluating its effectiveness compared to traditional simulation methodologies on performance and accuracy. A first test demonstrated the accuracy of results obtained through co-simulation compared to the ones obtained through a traditional simulation while using different discretization periods. A second test demonstrated the possibility of scaling the simulation of multiple buildings by using a single machine, showing the possibility of predicting the change in performance given the hardware. A third test demonstrated an advantage of co-simulation in the possibility of extending the computation to multiple machines.
Lastly, a mathematical overview of the Model Predictive Control (MPC) framework, which is the main tool used to put in action the optimal control strategy that resulted from the co-simulated data, was carried out with a focus on the stochastic version of the MPC (i.e., Stochastic MPC), showing how it could be integrated into SHA- DER’s co-simulation framework in order to become an active agent in the process.
Concluding, it was demonstrated how the international political landscape actively supports the vision of SHADER by providing huge economic incentives targeted at promoting ecological transition and digitalization, categories that SHADER falls into. Possible limitations due to technology and resistance to adoption were also investigated. The co-simulation platform that was developed proved to have several advantages in terms of scalability compared to the traditional simulation, and a clear path for future developments was set in order to further improve it. SHADER managed to validate co-simulation as a viable technology for the analysis and control of complex energy systems and to set the path for major innovations in Building Energy Management practices, which will strongly contribute to reduce carbon emissions and energy sources exploitation in the future.
Principal Academic Tutors
Alessandro Margara, DEIB, Politecnico di Milano
Academic Tutor
Rossano Scoccia, DENG, Politecnico di Milano
Edoardo Patti, DAUIN, Politecnico di Torino
Luca Barbierato, DAUIN, Politecnico di Torino
Daniele Schiera, DENERG, Politecnico di Torino
Rossella Alesci, DENG, Politecnico di Milano
External Institutions
Ariston SpA
External Tutor
Francesco Perticaroli, Ariston SpA
Team members
Virginia Capone, Architecture – Built Environment – Interiors, Politecnico di Milano
Yi Yu Ivan Chen, Mathematical Engineering, Politecnico di Torino
Simone Corti, Automation and Control Engineering, Politecnico di Milano
Luigi Fusco, Computer Science and Engineering, Politecnico di Milano
Pablo Pozo, Structural Engineering, Politecnico di Milano
Silvia Trimarchi, Energy Engineering, Politecnico di Milano
Francesco Zanon, Management Engineering, Politecnico di Milano