InSITE Project
The InSITE Project – INtelligent energy management of Smart grids based on IoT and edge/cloud Technologies aims to design and develop an innovative edge-to-cloud-based system for controlling and managing smart grids to cover the entire energy management chain, from collecting and analysing information produced by individual elements in the field to decision support and system implementation in the cloud.
This project aims to overcome the limitations of traditional SCADA-based control systems and to prototype a solution that, leveraging the potential of cloud-edge computing and IoT, is able to:
- reduce energy costs for users;
- increase the resilience and security of the electrical grid;
- encourage and optimise the use of renewable sources for energy production.
The partners involved
Politecnico di Bari – Dipartimento di Ingegneria Elettrica e dell’Informazione
Idea 75 Srl:
Exprivia SpA.
The InSITE Project – INtelligent energy management of Smart grids based on IoT and edge/cloud Technologies aims to design and develop an innovative edge-to-cloud-based system for controlling and managing smart grids to cover the entire energy management chain, from collecting and analysing information produced by individual elements in the field to decision support and system implementation in the cloud.
This project aims to overcome the limitations of traditional SCADA-based control systems and to prototype a solution that, leveraging the potential of cloud-edge computing and IoT, is able to:
reduce energy costs for users; increase the resilience and security of the electrical grid; encourage and optimise the use of renewable sources for energy production.
You can download the documentation related to the project from the following links:
Publications related to the InSITE project
- Brescia, E., Massenio, P. R., Di Nardo, M., Cascella, G. L., Gerada, C., & Cupertino, F. (2023). Parameter Estimation of Isotropic PMSMs Based on Multiple Steady-State Measurements Collected During Regular Operations. IEEE Transactions on Energy Conversion.
- Brescia, E., Palmieri, M., Massenio, P. R., Cascella, G. L., & Cupertino, F. (2023). Cogging torque suppression of modular permanent magnet machines using a semi-analytical approach and artificial intelligence. IEEE Access, 11, 39405-39417.
- Amato, G., De Tuglie, E. E., & Montegiglio, P. (2024). Geometrical method for a fast practical static stability region evaluation of a Smart MicroGrid. Sustainable Energy, Grids and Networks, 101387.
- Wróblewski, W., Rasolomampionona, D., Klos, M., Pai, H. Y., Montegiglio, P., & De Tuglie, E. E. (2023, June). The Influence of Renewable Energy Source Penetration on Load Frequency Control. In 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-6). IEEE.
- Amato, G., De Tuglie, E. E., Martella, A., Montegiglio, P., Rasolomampionona, D., & Pai, H. Y. (2023, June). Static Security Assessment for Smart Microgrids based on unstable working points clustering. In 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-6). IEEE.
- Pai, H. Y., Liu, Y. H., Chen, G. J., Rasolomampionona, D., Brescia, E., & De Tuglie, E. (2023, June). A novel parameters identification method of Lithium-ion battery equivalent circuit model under dynamic stress test. In 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-5). IEEE.
