AIMS AND CONTENT

LEARNING OUTCOMES

The module presents the theoretical, methodological, and implementation aspects of basic optimization techniques for the planning, management, and control of electrical systems. The main objectives of the module are to recognize, formulate, and implement (in software environment) optimization techniques in the context of electrical systems (unit commitment/dispatch, optimal power flow, energy management systems, etc.); to identify the main properties of solution algorithms; and to implement forecasting techniques for load and renewable generation.

AIMS AND LEARNING OUTCOMES

The aim of the course is to introduce the principles, solution methods, and software‑based implementation techniques for optimization problems in the field of power and energy systems. Several classes of problems will be considered: (1) unconstrained optimization, (2) constrained optimization, (3) convex optimization, (4) linear programming, (5) quadratic optimization, (6) nonlinear problems, and (7) mixed‑integer programming. For each class, the main solution methodologies will be presented, together with the software tools used for formulating and solving optimization problems (for example: Matlab Optimization Toolbox, GAMS – General Algebraic Modeling System, and the MATPOWER library for Matlab). Relevant applications to power systems will also be illustrated, such as power plant dispatching, dynamic economic dispatch, unit commitment, microgrid energy management systems, AC/DC optimal power flow, and security‑constrained optimal power flow.

Finally, both theoretical and implementation aspects of forecasting techniques — including methods based on artificial intelligence — will be introduced for applications involving electric load and renewable energy generation, and the concept of model predictive control applied to optimization problems will be presented.

PREREQUISITES

Knowledge of Mathematical Analysis is required. In addition, students should have successfully completed the Machine Learning for Pattern Recognition course in the first year of the Master's Degree Programme.

TEACHING METHODS

The teaching activities are evenly divided between:

• Theoretical lectures, dedicated to presenting the mathematical foundations required for modeling optimization problems and understanding the main solution methods.
• Classroom exercises, during which application‑oriented problems related to electrical systems are developed and solved using specialized software (for example: power plant dispatching, dynamic economic dispatch, unit commitment, microgrid energy management systems, AC/DC optimal power flow, and security‑constrained optimal power flow).

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the School's disability contact person can be found at the following link Comitato di Ateneo per l’inclusione delle studentesse e degli studenti con disabilità o con DSA | UniGe | Università di Genova.

SYLLABUS/CONTENT

• Elements of unconstrained, linear, quadratic, mixed‑integer, and nonlinear optimization
• Matlab Optimization Toolbox
• GAMS software (General Algebraic Modeling Language)
• Power plant dispatching
• Dynamic economic dispatch
• Unit commitment
• Microgrid energy management
• AC/DC optimal power flow, security‑constrained optimal power flow
• Load and generation forecasting
• Model predictive control techniques

RECOMMENDED READING/BIBLIOGRAPHY

• Course handouts available on Aulaweb
• J. Nocedal, S. J. Wright, “Numerical Optimization”, Springer, 1999
• Matlab Manual: https://it.mathworks.com/help/
• GAMS Manual: User's Guide (gams.com)
• A. Soroudi, “Power System Optimization Modeling in GAMS”, Springer, 2018
• Matpower Manual: https://matpower.org/docs/MATPOWER-manual.pdf

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11955/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral Exam

ASSESSMENT METHODS

Verification of the acquisition of theoretical and practical knowledge of the calculation methodologies in a software environment related to the optimization problems addressed in the lessons. The oral exam will verify the student's ability to reproduce and discuss the theoretical and applied methods covered during the course. The quality of the presentation and the correct use of specialized terminology will also be evaluated, as well as the student's reasoning ability, autonomy, and recall of the previously defined cultural prerequisites.

FURTHER INFORMATION

Ask the professor for other information not included in the teaching schedule.