PENG9620 Smarte byer for en bærekraftig energifremtid – fra design til praksis Emneplan

This course covers contemporary topics in smart energy systems such as smart power grid, smart buildings, vehicle-to-grid (V2G) and communication technologies for and network security in smart energy systems, including emerging approaches towards energy intelligence such as machine learning and blockchain.

The course will be offered once a year, provided 5 or more students sign up for the course. If less than 5 students sign up for a course, the course will be cancelled for that year

The course will consist of lectures. In conjunction with the lectures, students will complete exercises related to the lecture topic. A compulsory assignment will be given to the students, to be presented to the other students on the course.

Practical training

The students will be exposed to practical exercises in evidence-based engineering. These exercises will be tailored to the topics that have been discussed and lectured on.

None.

Knowledge

On successful completion of the course, the student:

• is at the forefront of knowledge about smart energy systems, both at the system level and at the specific component/application level.
• understands what different technologies can be used at what level in energy generation, transmission, distribution and consumption networks.
• knows about communication technologies and their performance limits for enabling energy intelligence in smart energy systems.

Skills

On successful completion of the course, the student can:

• solve resource optimisation problems for the energy information network.
• apply optimisation techniques and machine learning-based approaches for residential demand response management and vehicle-to-grid.

General competence

On successful completion of the course, the student can:

• communicate and collaborate with experts from other disciplines on larger interdisciplinary and multidisciplinary research projects.
• Recognise and assess a project's potential and value
• participate in debates and communicate results through recognised international channels, such as academic conferences.
• can construct and develop relevant models and discuss the model's validity.
• Disseminate knowledge to broader audiences

The following required coursework must be approved before the student can take the exam:

Prior to examination it is required that a compulsory assignment be completed, presented to the other students, and approved.

Module 1 and 2 will take the form of a series of lectures. Module 3 will be a combination of hands-on sessions along with the project assignment.

Practical training

The students will solve specific problems using optimisation or machine learning techniques. The students will submit a brief report with results for the problem in the assignment, also describing the process they used for solving the assignment, including the code.

None.

The results for the project assignment, process description, and the code will be assessed by the course leader. The exam can be appealed.

Engineers have to make many important decisions on use of tools, work processes, project organizations, technical frameworks and many other topics throughout their career. Currently, too many of these choices are based on what is fashionable and argumentation made by people with vested interested. This course has as its main goal to teach students how to be evidence-based and make better judgments and decisions in engineering disciplines. To be evidence-based means in this context to base important decisions and judgments on well-formulated decisions and questions, collection of valid, relevant and representative empirical evidence, proper analyses and synthesis of the evidence, and use of the synthesized evidence as input in properly designed judgment and decision processes.

The course will be offered once a year, provided 5 or more students sign up for the course. If less than 5 students sign up for a course, the course will be cancelled for that year.

None.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge:

On successful completion of the course, the student:

• can interpret the philosophy behind principles, design and modelling considerations in using finite element methods (FEM) in analysis and design of structures.
• can describe the general steps used in FEM to model and solve complex nonlinear problems in structural analysis and design.
• has detailed knowledge of solution methods for nonlinear static problems, and some knowledge on solution methods for nonlinear dynamic problems.
• has detailed knowledge of nonlinear geometry and nonlinear material models (elastoplastic and others) and the applications of these models in structural analysis.
• can explore the complex issues in convergence of solutions using nonlinear FEM.

Skills:

On successful completion of the course, the student can:

• demonstrate the ability to use FEM to produce a reliable prediction of displacements and stresses in nonlinear structural problems of relevance to engineering practice.
• create and design complex engineering structures using finite element methods.
• develop expertise in the usage of commercial finite element software for both linear and nonlinear analysis of complex structures.

General competence:

On successful completion of the course, the student can:

• use advanced commercial FEM software.
• understand the importance of verification and validation in research and demonstrate the ability to make critical assessments
• communicate effectively through written reports.