This research area focusses on models and scenarios for energy systems analysis and energy planning. The adopted approach is interdisciplinary, technological, economical and environmental and is performed through an integrated analysis of energy system design and control.
One important part of the research is devoted to the study and the technical-economical optimisation of polygeneration systems composed for example by Photovoltaic plants, Electric batteries, Cogeneration plants and Heat Pumps.
On this kind of systems, a fundamental role to ensure the efficiency and sustainability, is the implementation of an optimal control. The main objective of optimal control is to optimize the energy balance of the entire system, maximizing the use of renewable sources and minimizing the environmental impact. In the field two techniques seem to be very promising and are under implementation and analysis in the research area: Reinforcement Learning and Model Predictive Control.
Through the use of these learning algorithms, energy systems can dynamically adapt to changing conditions and optimize decisions, learning from the results of the actions taken. This innovative approach offers promising prospects for addressing the complex challenges of the energy sector, further improving the efficiency and optimization of energy systems.
Examples of recent and currently on-going research activities include:
- Analysis and comparison of high efficiency polygeneration systems based on innovative technologies.
- Development of planning and operational tools for optimising energy flows and synergies, between energy networks, through the application of cogeneration and P2X technologies.
- Implementation of models and scenarios for energy planning and for energy systems analysis.
- Development of optimized control systems for cogeneration plants through Reinforcement Learning and Model Predictive Control techniques
Contatti:
Prof. Marco Badami: https://www.polito.it/en/staff?p=marco.badami