Automation

The course provides the basic methodological elements and operational tools for the management and supervision of smart electricity grids

Knowledge and understanding

Fundamentals of analysis of linear dynamical systems. Elements of analysis and implementation of digital and event control systems. Control system architectures: levels of command and supervision for the management of electrical networks. SCADA systems. 

Ability to apply knowledge and understanding

Evaluate the performance of electrical regulation systems in terms of dynamic response, also using simulation tools. Implement digital control systems for energy management. Operate with supervisory systems for the management of electrical grids.

Artificial intelligence

The course provides the basic theoretical and practical knowledge for the use of artificial intelligence methods for machine learning and data analysis.

Knowledge and understanding

Fundamentals and methods for learning patterns and extracting insights using data analysis. Know which artificial intelligence methods to use on the basis of available data and the needs of the application context. Data regularization techniques. Supervised and unsupervised learning paradigms. Artificial intelligence software tools; performance metrics.

Ability to apply knowledge and understanding

Knowing how to select and configure artificial intelligence methods to develop management, planning, optimization and prediction algorithms according to application needs. Extract information from datasets, even large ones, with regression and classification.

Batteries and energy storage

The course provides the methodological and operational tools for the analysis and sizing of batteries and, more generally, of energy storage systems and their integration into electricity distribution networks.

Knowledge and understanding

Characteristics and performance of batteries and energy storage systems. Technology, modeling and electrical simulation of rechargeable batteries. Battery management system and battery interfacing systems. Sizing criteria for batteries and energy storage systems. Regulation of energy flows in batteries and storage systems in smart grids. Monitoring and diagnostics of rechargeable batteries.

Ability to apply knowledge and understanding

Size battery storage systems. Evaluate and predict performance as a function of operating conditions. Apply regulation techniques of interface systems for the management of battery storage systems. Evaluate the impact of storage systems on power flows and functional parameters of the electricity grid.

Communication Networks

The course provides the methodological and technological knowledge to understand the functioning of data transmission networks

Knowledge and understanding

Operating principles of communication devices and networks. Main architectures and data transmission protocols for electrical systems and smart grids. Encoding and data processing for the reliability and security of network communications. Operating principles of intelligent devices for smart metering. Types of data exchange between devices and other network infrastructures.

Ability to apply knowledge and understanding

Choose the most suitable devices and architectures for data transmission. Configure simple data transmission networks in electrical systems for interconnection of meters, control equipment and smart devices. Select smart metering systems based on intelligent sensors for data acquisition in electrical applications.

Cybersecurity

The course provides the basic methodological and operational tools for managing the security of processing systems and computer networks, through the basic knowledge of cryptography techniques, authentication algorithms and protocols, the main protocols for secure communications and possible network vulnerabilities and main protection mechanisms.

Knowledge and understanding

Theoretical and practical aspects of the security of processing systems and computer networks; encryption-based data protection and authentication schemes; intrusion and intrusion detection techniques; classification of viruses and malware and techniques and tools for their analysis and identification.

Ability to apply knowledge and understanding

Analyze the security properties of a system. Determine secure data exchange and authentication mechanisms for a network. Analyze and compare possible solutions for the defense of networked information systems.

Electric circuits

The course provides the methodological knowledge and operational tools for the numerical analysis and simulation of electrical circuits and distribution networks operating in steady state, in transient and in fault conditions.

Knowledge and understanding

Electrical quantities and measuring devices. Analysis of transient electric circuits (in time and Laplace domain). Elements of numerical methods for the simulation of steady state and transient electrical circuits. Circuit simulation software. Circuit and block simulation for circuit and system analysis. Three-phase, three- and four-wire circuits under normal and fault conditions.

Ability to apply knowledge and understanding

Analyze a circuit in the time and Laplace domain. Analyze three-phase and four-wire circuits under normal and fault conditions. Use circuit simulators and block simulation software for numerical processing of complex circuits. Evaluate the behavior of a real electrical system based on simulation data. Choosing measurement devices in energy distribution networks.

Electric machines

The course provides theoretical knowledge on the functioning of static and rotating electric machines; methodologies for the modeling and regulation of electric machines and for the study of three-phase circuits.

Knowledge and understanding

Magnetic materials and circuits. Models and characteristics of the main static and rotating electric machines: transformer; asynchronous, synchronous and collector machine; permanent magnet machines. Regulation of torque, speed and power of electric machines. Selection of electric machines and relative regulation system according to the application.

Ability to apply knowledge and understanding

Analyze the functioning of electric machines through their circuit model. Determine the behavior of an electric machine according to the regulation mode. Choose how to connect the electric machine to the network and evaluate its performance. Define the specifications of the electrical machines according to the application.

Electric power systems

The course provides the theoretical and methodological knowledge and operational tools for the study of electrical energy systems and intelligent distribution networks.

Knowledge and understanding

Components of an electrical system and their characteristics: lines, transformers, generators, loads.

Steady-state operation of an electrical system, models with voltage-power equations (load flow) and analysis methods. Simulation of electrical systems. Analysis of failures in electrical systems, quality parameters of electrical quantities. Intelligent power grids: load models, users, load profiles, generation adequacy. Distributed generation and energy storage, flexibility. Connection, microgrids, disconnection protection, demand side management, demand response.

Ability to apply knowledge and understanding

Define the specifications of the components of an electrical system in MV and LV according to the application. Analyze an electrical system when fully operational and in the presence of faults and identify possible solutions to mitigate the effects. Carry out load flow analyzes of an electricity grid in the presence of distributed generation, also with the aid of a simulator. Analyze smart power grids and integrate distributed generation and a storage system into a smart grid. Develop elements of intelligent grid and flexible demand management.

Programming techniques

The course provides the basic elements for solving low-complexity problems through the use of processing systems, using the fundamental elements of a high-level programming language. The course is structured in such a way as to allow students to acquire knowledge relating to the fundamental elements of programming in the Python language, together with the fundamental techniques of "problem solving" through the use of a computer.

Knowledge and understanding

Python language syntax. Main data types and structures. Fundamental constructs of high-level programming languages, fundamental data structures. Standard libraries of the language and for data acquisition, processing and visualization.

Ability to apply knowledge and understanding

Design and implement scripts and simple data processing applications. Implement scripts and applications for reading data from heterogeneous sources. Use libraries for graphical data visualization.

Renewable sources and power converters

The course provides the theoretical and methodological knowledge for the study of generation systems from renewable sources and circuits for the static conversion of electricity.

Knowledge and understanding

Characteristics and models of photovoltaic and wind generators. Optimization and prediction of energy productivity. Design criteria for systems for the generation of electricity from renewable sources. Regulation techniques of generation systems for the management of energy flows. Monitoring and diagnostics. Characteristics of switching power devices. Analysis of circuits for the static conversion of electrical energy. Architectures and models for ac-dc, dc-ac and dc-dc conversion. Circuit and block simulation of converters. Criteria for selecting the conversion circuits according to the application.

Ability to apply knowledge and understanding

Size production plants from renewable sources. Evaluate the performance according to the environmental and operating conditions. Apply regulation techniques for the management of energy produced from renewable sources. Evaluate the impact of distributed generation systems on the power flows of the electricity grid. Analyze switching circuits using simplified models. Analyze the conversion circuits, also by means of a simulator, and evaluate their static and dynamic performance. Choose the circuits for the static conversion of energy according to the application.

Smart grids and energy management

The course provides the methodological and operational tools to determine the impact of production systems from renewable sources, distribution, accumulation and use of energy in the context of the liberalized energy market.

Knowledge and understanding

Architecture of intelligent transmission and distribution networks, smart metering and intelligent functions, prosumers. Models of intelligent networks. Enabling digital technologies. Electricity market models. Electricity grid operation in liberalized markets: portfolio management, load balancing, congestion management. Distributed generation: economic drivers. Aggregation mechanisms for the exploitation of renewable sources.

Ability to apply knowledge and understanding

Analyze an intelligent distribution network that makes use of digital technologies.

Evaluate the technical-economic feasibility of distributed generation plants on the basis of economic, energy data and regulations relating to the energy market. Access forms of incentives that favor the energy transition. Develop energy flow management strategies for the exchange of electricity in the context of the liberalized energy market.