Learning objectives
The student will acquire
(i) specific knowledge related to conventional and innovative energy conversion systems and their integration into energy networks;
(ii) specific knowledge related to advanced control techniques and optimization algorithms for smart management of complex Energy Systems;
(iii) the fundamental notions and theoretical tools that can be used for the dynamic simulation of complex systems;
(iv) the ability to build mathematical models for the simulation of system components of different type and configuration, and the ability to evaluate and define the most appropriate level of detail by adopting the appropriate simplifications to obtain results of adequate accuracy (also in relation to the accuracy of the measures available for calibration, validation and comparison);
(v) the ability to apply the fundamental knowledge and the methods of analysis learned for the further and continuous study of the subject at a higher level with particular reference to the evolution of the most complex Energy Systems and the most advanced control techniques in the context of a sustainable energy transition.
Prerequisites
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Course unit content
The course aims to provide the knowledge and skills necessary for the analysis and implementation of integrated Energy Systems and their smart management and control algorithms, in the context of the sustainable energy transition.
After an introduction to Energy Systems and solutions for their integration into complex energy networks, we will review the characteristics and limitations of the conversion technologies currently used. In particular, we will analyze programmable and non-programmable renewable energy technologies, advanced solutions such as heat pumps, Power-to-X systems and waste energy recovery, and the role of energy storage technologies in increasing the flexibility and resilience of Energy Systems.
Moreover, we will investigate advanced management and optimization techniques for complex Energy Systems (including control strategies based on Model Predictive Control) and the procedures for the development and application of the mathematical models used for the simulation of Energy Systems, the networks in which they are integrated and their control algorithms.
Full programme
1. Introduction and sustainable energy transition: decarbonization, decentralization, integration, electrification, digitalization.
2. Decarbonization and energy conversion technologies. Limitations of traditional energy technologies. Renewable energy technologies: programmable (biomass, hydropower, geothermal) and non-programmable (wind, photovoltaic, solar thermal, wave power).
3. Integration and energy networks. District heating and cooling networks. Gas networks. Electrical networks. Sustainable mobility. Technologies for network integration (cogeneration, trigeneration, Vehicle-to-Grid, etc).
4. Electrification. Heat pumps and their role in waste heat recovery. Refrigeration plants. Power-to-gas and production of electrofuels: electrolyzers and fuel cells (alcalyne, PEM, AEM, SOFC), methanation.
5. Flexibility and energy storage. Systems for direct storage: hydraulic, electrochemical storage, LAES/CAES, thermal storage. Innovative solutions for “indirect” storage (in buildings and industrial processes). Demand side management.
6. Digitalization and smart management of Energy Systems. Dynamic systems. Traditional and innovative control solutions. Model Predictive Control (MPC). Optmization algorithms (LP, MILP, DP). Model-in-the-Loop architectures for control verification.
7. Mathematical models of Energy Systems. The mathematical modeling process and its phases. Classification of models. State-space models and causality. Linearization and parametric identification.
Bibliography
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Teaching methods
Learning activities will be developed in the form of frontal lectures (online if requested by COVID containment measures).
Educational material will be periodically uploaded on the Elly platform to support and deepen the contents of the lectures.
To access these contents (which are part of the course) it is necessary to register for the on-line course.
The teacher is available during the reception hours and by appointment (e-mail) for explanations on the contents of the course.
Assessment methods and criteria
The assessment of learning is carried out through an oral exam consisting of the presentation of the theoretical in-depth analysis of a topic (planned with the teacher) and the discussion of a project.
The project consists of the development of a model of a fluid or thermodynamic system and its implementation in the Matlab computing environment.
In case of limitations due to the containment of the COVID-19 contagion, the exam will be administered online via Teams platform.
Other information
Lectures attendance is highly recommended.
Non-attending students are invited to consult the Elly platform on which the topics actually presented in class will be periodically listed.
