GRA 8550 Financial Decision Making in Energy Projects
GRA 8550 Financial Decision Making in Energy Projects
This course is part of the Master of Management in Energy in cooperation with BI Norwegian Business School and IFP School.
Today's competitive environment and disruptive nature in the energy industry places great demands on a company's ability to understand the commercialization process. The relationship between organizational conduct, optimal decision-making and value generation is critical for many companies.
Upon completion, the student will be able to:
- Assess whether decision biases or noise effect decision making
- Evaluate advanced profitability and investment analyses
- Apply theoretical models for financial analysis, considering their limitations and adapting them to complex real-world
- scenarios
- Assess the significance of contextual analysis in decision-making before model application
- Appraise models for analyzing value generation
Upon completion, the student will be able to:
- Conduct advanced analysis of strategic decisions within the energy field
- Compare and conduct Life cycle costing evaluations and connecting this to the concept of LCOE calculations
- Calculate relevant hurdle rates (cost of capital) for projects with different characteristics such as length of project, industry, ownership diversification, etc.
- Calculate the value of a real options using binomial distributions
- Conduct advanced analysis net present value calculations in connection with real decision problems
Upon completion, the student will be able to:
- Explain how economic/financial outcomes are influenced by decisions and processes, and justify the role of decision analysis in achieving financial results
- Critically evaluate financial/economic analyses, identify their limitations, and argue for the advantages of a data-driven organizational approach
- The decision context and decision biases
- Value generation model
- The connection between strategy, competitive advantage, quality and financial goals
- Profit versus net present value
- Cash flow analysis
- How to handle tax in specific decisions
- The value of real options
- Risk analysis for diversified and non-diversified investors/owners
1 ECTS credit corresponds to a workload of 26-30 hours.
Attendance to all sessions in the course is compulsory. If you have to miss part(s) of the course you must ask in advance for leave of absence. More than 25% absence in a course will require retaking the entire course. It's the student's own responsibility to obtain any information provided in class that is not included on the course homepage/ It's learning or other course materials.
Sessions include lectures, seminars and group work.
Specific information regarding student evaluation beyond the information given in the course description will be provided in class.
In this course students are encouraged to use AI critically. On the exam, students must attach the entire chat history, as well as highlight certain parts of the course paper where they are critical of the answers from the LLM models.
The course is a part of a full Master of Management in Energy (MME) and examination in all courses must be passed in order to obtain a certificate.
In all BI Executive courses and programmes, there is a mutual requirement for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.
Granted admission to the Master of Management in Energy programme. Please consult our student regulations.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: Second Semester Weight: 100 Grouping: Individual Duration: 30 Day(s) Exam code: GRA 85501 Grading scale: ECTS Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 24 Hour(s) | |
Student's own work with learning resources | 60 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 3 ECTS credit corresponds to a workload of at least 80 hours.
Students can use AI but must attach the entire chat history, as well as highlight certain parts of the course paper where they are critical of the answers from the LLM models.