ELEC-E8425 - Energy System Modelling and Optimization D, Lecture, 28.2.2022-23.5.2022

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

After successfully completing this course, the students will:

  1. Understand the basic concepts of optimization, implementation, and solution approaches.
  2. Formulate their ideas and decide which model is the most computationally efficient way.
  3. Recast their original model into LP, MILP, or convex models, if required.
  4. How to struggle with the operating and planning problems and make their model as solver-friendly as possible.
  5. Understand the basic concept of convex optimization, check the convexity of a model, and how to convexify a non-convex model.
  6. Consider the existing uncertainties in the model via stochastic or robust programming approaches.
  7. How to interpret the outcomes of the models. 

Credits: 5

Schedule: 28.02.2022 - 23.05.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Matti Lehtonen, Mahdi Pourakbari Kasmaei

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    The course will introduce Linear Programming (LP) problems and methodology, bilevel optimization, mixed-integer linear programming, the formulation and solving of non-linear programming and mixed integer nonlinear programming problems. Convex programming is covered, as are the non-deterministic techniques, stochastic and robust programming.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    The course evaluation will be based on graded homework exercises and assignments.

     

Workload

  • valid for whole curriculum period:

    • Contact teaching 24 h.
    • Material review and independent studies and work-based learning 48 h.
    • Assignments 30 h (for seven assignments). 
    • Excercise 8 h.
    • Exercise review 8 h.

     

DETAILS

Study Material

  • valid for whole curriculum period:

    Presentation slides, recommended text books.

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    4 Quality Education

    7 Affordable and Clean Energy

    11 Sustainable Cities and Communities

    12 Responsible Production and Consumption

    13 Climate Action

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    The student has to confirm his/her participation in the course by attending the first lecture.

    Teaching Period:

    2020-2021 Spring IV-V

    2021-2022 Spring IV-V

    Course Homepage: https://mycourses.aalto.fi/course/search.php?search=ELEC-E8425

    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

    WebOodi.

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof