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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Short-term Electricity Price Forcasting using Stack curves |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 50 |
| Date | 2021 |
| Abstract Text | Our paper adds to the vast literature of electricity price forecasting of day-ahead markets. The transformation of electricity markets from state-owned monopolies to competitive actors dominated recently by the rapid development of renewable energy has made the market increasingly unpredictable. To manage the volatility of prices and the risks to the transmission system operators, an accurate model of the day-ahead market is very important. Power producers and traders are also exposed to financial risk. The research aims to develop a short term electricity price forecasting tool useful for trading and managing positions. We propose a LASSO regression using parameters such as fuel prices and physical network inputs. On top of these parameters, we develop a new variable from the stack curves of the daily day-ahead auction process. We believe this variable can enhance the prediction of the non-linear behaviour of electricity price spikes. The lasso regression performs better in terms of fitness compared to an OLS regression. We test our assumption using a trading algorithm with real market data consisting of reconstructed bids and offers. We describe the mechanism of market functioning and test the suggested model with data from the day-ahead EPEX auction for Germany. We find that this algorithm can outperform a naive benchmark in terms of profit and loss and risk measures, even when we include transaction costs. |
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