Structural analysis of wholesale electricity market with SVAR models: Assessment of effects of renewable energy sources on the level and variability of electricity prices
Grant no.: 2016/21/D/HS4/00515
Funding agency: National Science Centre (NCN), Poland
Funding scheme: SONATA
Funding period: 01.03.2017-28.02.2020 (36 months)
Budget: 148 450 PLN
Title in Polish: Analiza hurtowego rynku energii elektrycznej przy wykorzystaniu strukturalnych modeli wektorowej autoregresji SVAR: Ocena wpływu odnawialnych źródeł energii na poziom i zmienność cen
Principal Investigator (Kierownik):
Aims and scope:
The main goal of the project is to analyze wholesale electricity markets with multivariate time series models. The project focuses on two main issues:
- Conducting a structural analysis of the electricity market, with the aim of assessing the impact of renewable energy sources (RES) on the level and the variability of electricity prices. In the research, Structural Vector Autoregressive (SVAR) and Conditional Heteroscedastic SVAR (CH-SVAR) models will be applied.
- Development of a new type of a structural model: CH-SVAR. The model has not been so far discussed in the literature. It can be viewed as an interesting alternative for a more complicated GARCH-SVAR (Generalized Autoregressive Conditional Heteroskedastic SVAR) models.
This project is a response to the observed structural changes of electricity markets induced by an introduction of the Climate Policy 3×20. This policy obligated the EU member states to reduce the CO2 emissions by 20% and increase the share of EU energy produced from renewable resources to 20% by the year 2020. These goals have resulted in a rapid growth of the amount of electricity generated from wind and solar energy, which has influenced electricity prices due to the merit order effect. Therefore, the project aims at verifying the following hypotheses:
- Increase of the share of RES leads to a significant fall of wholesale electricity prices.
- RES generation influences the variability of the wholesale electricity prices. The effect can be positive (a reduction of the variance) or negative (an increase of the variance) conditional on the level of demand and the level of RES.
- Application of SVAR models to the analysis of electricity markets and to the assessment of the impact of RES on the level and the variability of electricity prices.
- Development of a new type of an SVAR model with conditional heteroscedasticity and investigation of its identification conditions.
- Application of CH-SVAR (Conditional Heteroscedastic SVAR) models to the evaluation of the influence of RES on the level and the variability of electricity prices.
Peer-reviewed articles in JCR-listed journals
2020 (1+), 2019 (1+), 2017-2018 (0)
- K. Maciejowska (2020) Assessing the impact of renewable energy sources on the electricity price level and variability – a Quantile Regression approach, Energy Economics 85, 104532 (doi: 10.1016/j.eneco.2019.104532)
- K. Maciejowska, W. Nitka, T. Weron (2019) Day-ahead vs. intraday – Forecasting the price spread to maximize economic benefits, Energies 12(4), 631 (doi: 10.3390/en12040631)
- K. Maciejowska, R. Weron (2019) Electricity price forecasting, in “Wiley StatsRef: Statistics Reference Online”, Wiley, DOI: 10.1002/9781118445112.stat08215. Working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1901.html
Forthcoming publications, submitted papers and work in progress