Master Thesis: Data-Driven Stack Models for Electricity Price Forecasting
Master Thesis: Data-Driven Stack Models for Electricity Price Forecasting
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1 Vacancy
Job Description
Extending Data-Driven Merit Order Models for Multi-Country Electricity Price Forecasting with Flow and Storage Integration
About the thesis:
Objectives
This project aims to extend the existing framework in two key directions:
- Multi-Country Modelling: Include multiple EPEX SPOT SDAC countries; shift from regression-based import/export to implicit flow modelling.
- Storage Integration: Add pumped hydro and other storage as market players; use deterministic/stochastic optimization with a price-forward curve.
Research Questions
- Can the hybrid model outperform Axpos operational model on price and flow forecasts
- How accurate can it capture inter-country flows and storage operation without explicitly modelling FBMC like EUPHEMIA
- What is the value-add of integrated storage optimization for forecast accuracy and realism
Methodology
- Extend to a multi-node framework with transmission constraints and flow variables.
- Integrate storage dispatch via optimization using price-forward curves.
- Benchmark against Axpos operational model used in asset-backed trading with out-of-sample backtests and error metrics for prices and flows.
Expected Impact
- Demonstrate feasibility and benefits of the models for interconnected European markets.
- Provide actionable insights for Axpos traders and analysts on price and flow forecasting.
Your profile:
- Masters student with experience in Energy Markets or a related field (e.g. Engineering Mathematics Energy Science Data Science) enrolled at a Swiss university or university of applied sciences (FH)
- Proficient in Python
- Solid understanding of optimization methods
Starting Date: As soon as possible
Employment Type
Full Time
