▶Japanese
Post to SNS
- Academic unit or major
- Tokyo Tech Academy of Energy and Informatics program
- Instructor(s)
- Ohashi Kazuhiko Yamamoto Yohei
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Thr3-4()
- Group
- -
- Course number
- ENI.H402
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- 2021/7/27
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
This course covers finance (by Ohashi) and data analysis (by Yamamoto) in energy markets. More precisely, in the former, we study the basics of finance and economics necessary for understanding the structure of energy - especially electricity - markets, the characteristics and models of prices and demand, and the use of derivatives for risk management. In the latter, we study the theory of time-series data analysis of market prices and demand volumes, which is necessary for quantitative analysis, along with analytical examples, including trend and cycle decomposition of time-series data, evaluation of future forecasts, identification and estimation of time-series models, and inference of the effects of policies and external factors.
With the liberalization of electricity market, the managerial decisions by power companies on electricity generation and transaction are now based on market prices. In this context, changes in the power supply mix due to the increase in renewable energy generation and the shift away from coal due to decarbonization, as well as the development of demand management technologies such as demand response, are significantly changing the characteristics of electricity prices and supply/demand. Knowledge and technology to quantitatively assess electricity prices and supply/demand, and to identify and manage risks, are needed more than ever in management related to electricity markets. This lecture aims to provide the basic knowledge of finance, economics, and data analysis used for this purpose.
Student learning outcomes
This course is designed to help students to acquire the following skills:
1) Explain the structure of electricity market and the risks that the suppliers and demanders face.
2) Explain the characteristics of price and demand of electricity, the factors that determine them, and the relation with other energy prices.
3) Explain the valuation and management of risks and the use of derivatives.
4) Conduct forecasting and statistical inference using power market data.
Keywords
electricity price, demand, risk, derivatives, time-series analysis
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Students will gain an understanding of the workings of the electricity market, the challenges faced by market participants, and how they utilize knowledge of finance, economics, and data analysis to solve those challenges, moving back and forth between social issues and academic methods to find solutions.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | (Ohashi) Structure of electricity market and risks faced by electricity suppliers and demanders | Understand the economic role of price fluctuation and risks faced by suppliers and demanders. |
| Class 2 | (Ohashi) Characteristics of electricity prices and demand, determinants, and relation with other energy prices | Understand the characteristics and determinants of fluctuations in electricity prices and demand, and their relationship with others. |
| Class 3 | (Ohashi) Evaluation and management of risks related to electricity price and demand, and how to use derivatives | Understand how to evaluate and manage risk and use derivatives. |
| Class 4 | (Yamamoto) Software basics, trend and cycle decomposition of time series data, frequency analysis, patterns of electricity market data | Understand how to extract seasonal and trend factors from electricity market data. |
| Class 5 | (Yamamoto) Autoregressive model, prediction interval, vector autoregressive model, impulse response function, out-of-sample analysis and prediction accuracy | Understand how to use time series models to make forecasts. |
| Class 6 | (Yamamoto) Multiple regression analysis, causal inference, case studies of the Great East Japan Earthquake and electricity demand crunch in 2021, etc. | Understand how to analyze the effects of policies and external factors on electricity markets. |
| Class 7 | Summary and Q&A |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Lecture materials will be shared through the learning system.
Reference books, course materials, etc.
Reference books will be introduced in the lecture as appropriate due to the wide range of topics.
Assessment criteria and methods
Grade is based on a 7-10 page report in which a student selects a topic related to the electricity market, discusses the significance of the issue, and draws conclusion based on data analysis.
Related courses
- ENI.H401 ： Marketing for Value Creation
- ENI.H403 ： Economic Development and Energy Policies
- TAL.S502 ： Professionals and Value Creation A
- TAL.S503 ： Professionals and Value Creation B
- ENI.A602 ： InfoSyEnergy Product-service design
- ENI.A603 ： InfoSyEnergy Policy-making workshop
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Basic knowledge of microeconomics, statistical inference (estimation and hypothesis testing), and software (MATLAB) is desirable.
