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77923-01 - Vorlesung: Electricity Market Crash Course (3 KP)
| Semester | Frühjahrsemester 2026 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende | |
| Inhalt | This course introduces participants to empirical and computational tools for analyzing the challenges of the energy transition, with a primary focus on electricity markets. It combines econometric, optimization, and machine-learning approaches to evaluate how markets and policies adapt to decarbonization. Students will develop applied skills in data analysis and quantitative modeling, working primarily in Jupyter notebooks. Module 1. Quantitative Tools and Context Overview of energy transition data and trends. Introduction to quantitative analysis in electricity markets. Review of econometric techniques (regression analysis, event studies, regression discontinuity). Introduction to Python programming for empirical analysis. Module 2. Modeling Supply-Side Dynamics Development of economic models of electricity markets. Optimization under constraints and mixed-integer programming. Dimensionality reduction for computational tractability. Distinction between short- and long-run perspectives. Introduction to Python for electricity markets modeling. Module 3. Climate Policy and Market Design Integration of environmental and leakage policies into market models. Transmission constraints and the role of flexibility. Incorporation of storage technologies such as batteries. Modeling carbon pricing and renewable integration policies. Module 4. Demand-Side Analysis Empirical modeling of electricity consumption across industrial, commercial, and residential sectors. Evaluation of demand-response and pricing policies using smart-meter data. Equity and distributional impacts of energy policies. Module 5. Integration and Project Presentations Synthesis of supply and demand modules. Group project involving an applied empirical or computational analysis related to energy transition. Students are encouraged to use real data and explore open research questions with reproducible code. |
| Lernziele | Learning Outcomes ☐ Apply econometric, optimization, and machine learning techniques to energy market analysis. ☐ Develop and solve optimization problems relevant to market design and climate policy. ☐ Implement reproducible empirical analyses using Python notebooks. ☐ Critically evaluate evidence on energy transition policies and communicate results effectively. |
| Literatur | tba |
| Bemerkungen | Lecturer: Professor Mar Reguant is ICREA Researcher at CSIC-IAE and Professor at Northwestern University |
| Teilnahmevoraussetzungen | Knowledge of econometrics and some coding experience (any language). |
| Anmeldung zur Lehrveranstaltung | Please register for the course and the subsequent assignment by email to gsbe@unibas.ch by February 4, 2026, 24:00 at the latest and stating your student ID number (Matrikelnummer). Mobility students of other Swiss Universities first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (email address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html Applies to everyone: Enrolment = Registration for the course and the exam/assignment! |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| täglich | Siehe Einzeltermine | ||
Einzeltermine
| Datum | Zeit | Raum |
|---|---|---|
| Montag 09.02.2026 | 10.15-16.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S13 HG.35 |
| Dienstag 10.02.2026 | 10.15-16.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S13 HG.35 |
| Mittwoch 11.02.2026 | 10.15-16.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S13 HG.35 |
| Donnerstag 12.02.2026 | 10.15-16.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S13 HG.35 |
| Freitag 13.02.2026 | 10.15-16.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S13 HG.35 |
| Module |
Modul: Fachlich-methodische Ausbildung (Promotionsfach: Wirtschaftswissenschaften) Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät (Studienbeginn vor 01.02.2024)) |
| Prüfung | Leistungsnachweis |
| Hinweise zur Prüfung | Assessment will be based on participation, assignments, a group project, and a final exam: • Participation and mini-assignments: 30% • Final in-class presentation: 20% • Group project: 50% All coding exercises and projects must be completed using Python and reproducible notebooks. |
| An-/Abmeldung zur Prüfung | An- und Abmelden: Dozierende |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | Pass / Fail |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |