Abstract
The introduction of more renewable energy sources into the energy system increases the variability and weather dependence of electricity generation.
Power system simulations are used to assess the adequacy and reliability of the electricity grid over decades, but often become computational intractable for such long simulation periods with high technical detail.
To alleviate this computational burden, we investigate the use of outlier detection algorithms to find periods of extreme renewable energy generation which enables detailed modelling of the performance of power systems under these circumstances.
Specifically, we apply the Maximum Divergent Intervals (MDI) algorithm to power generation time series that have been derived from ERA5 historical climate reanalysis covering the period from 1950 through 2019.
By applying the MDI algorithm on these time series, we identified intervals of extreme low and high energy production.
To determine the outlierness of an interval different divergence measures can be used.
Where the cross-entropy measure results in shorter and strongly peaking outliers, the unbiased Kullback-Leibler divergence tends to detect longer and more persistent intervals.
These intervals are regarded as potential risks for the electricity grid by domain experts, showcasing the capability of the MDI algorithm to detect critical events in these time series.
For the historical period analysed, we found no trend in outlier intensity, or shift and lengthening of the outliers that could be attributed to climate change.
By applying MDI on climate model output, power system modellers can investigate the adequacy and possible changes of risk for the current and future electricity grid under a wider range of scenarios.
Power system simulations are used to assess the adequacy and reliability of the electricity grid over decades, but often become computational intractable for such long simulation periods with high technical detail.
To alleviate this computational burden, we investigate the use of outlier detection algorithms to find periods of extreme renewable energy generation which enables detailed modelling of the performance of power systems under these circumstances.
Specifically, we apply the Maximum Divergent Intervals (MDI) algorithm to power generation time series that have been derived from ERA5 historical climate reanalysis covering the period from 1950 through 2019.
By applying the MDI algorithm on these time series, we identified intervals of extreme low and high energy production.
To determine the outlierness of an interval different divergence measures can be used.
Where the cross-entropy measure results in shorter and strongly peaking outliers, the unbiased Kullback-Leibler divergence tends to detect longer and more persistent intervals.
These intervals are regarded as potential risks for the electricity grid by domain experts, showcasing the capability of the MDI algorithm to detect critical events in these time series.
For the historical period analysed, we found no trend in outlier intensity, or shift and lengthening of the outliers that could be attributed to climate change.
By applying MDI on climate model output, power system modellers can investigate the adequacy and possible changes of risk for the current and future electricity grid under a wider range of scenarios.
| Original language | English |
|---|---|
| Title of host publication | Advanced Analytics and Learning on Temporal Data |
| Subtitle of host publication | 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers |
| Editors | Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim |
| Publisher | Springer |
| Pages | 104-119 |
| Number of pages | 16 |
| Edition | 1 |
| ISBN (Electronic) | 978-3-030-91445-5 |
| ISBN (Print) | 978-3-030-91444-8 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 6th International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2021 - Virtual, Online Duration: 13 Sept 2021 → 17 Sept 2021 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 13114 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 6th International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2021 |
|---|---|
| City | Virtual, Online |
| Period | 13/09/21 → 17/09/21 |
Bibliographical note
Funding Information:The data used in the experiments contains modified Copernicus Climate Change Service information, doi.org/10.24381/cds.adbb2d47 (2020). This research received funding from the Netherlands Organisation for Scientific Research (NWO) under grant number 647.003.005. The methodology presented here was developed as part of the IS-ENES3 project that has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement No. 824084.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
Funding
The data used in the experiments contains modified Copernicus Climate Change Service information, doi.org/10.24381/cds.adbb2d47 (2020). This research received funding from the Netherlands Organisation for Scientific Research (NWO) under grant number 647.003.005. The methodology presented here was developed as part of the IS-ENES3 project that has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement No. 824084.
Keywords
- Energy climate
- Power system modelling
- Outlier detection
- Time series
- Climate change
- Anomaly detection
- High impact events
