A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production
Hydropower is the largest source of renewable energy in the world and currently dominates flexible electricity production capacity. However, climate variations remain major challenges for efficient production planning, especially the annual forecasting of periodically variable inflows and their effe...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/15/8/1559 |
| _version_ | 1797603177456992256 |
|---|---|
| author | Shuang Hao Anders Wörman Joakim Riml Andrea Bottacin-Busolin |
| author_facet | Shuang Hao Anders Wörman Joakim Riml Andrea Bottacin-Busolin |
| author_sort | Shuang Hao |
| collection | DOAJ |
| description | Hydropower is the largest source of renewable energy in the world and currently dominates flexible electricity production capacity. However, climate variations remain major challenges for efficient production planning, especially the annual forecasting of periodically variable inflows and their effects on electricity generation. This study presents a model that assesses the impact of forecast quality on the efficiency of hydropower operations. The model uses ensemble forecasting and stepwise linear optimisation combined with receding horizon control to simulate runoff and the operation of a cascading hydropower system. In the first application, the model framework is applied to the Dalälven River basin in Sweden. The efficiency of hydropower operations is found to depend significantly on the linkage between the representative biannual hydrologic regime and the regime actually realised in a future scenario. The forecasting error decreases when considering periodic hydroclimate fluctuations, such as the dry–wet year variability evident in the runoff in the Dalälven River, which ultimately increases production efficiency by approximately 2% (at its largest), as is shown in scenarios 1 and 2. The corresponding potential hydropower production is found to vary by 80 GWh/year. The reduction in forecasting error when considering biennial periodicity corresponds to a production efficiency improvement of about 0.33% (or 13.2 GWh/year). |
| first_indexed | 2024-03-11T04:26:43Z |
| format | Article |
| id | doaj.art-ffe98ebc962a478fb4b4b9a025963661 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T04:26:43Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-ffe98ebc962a478fb4b4b9a0259636612023-11-17T21:48:50ZengMDPI AGWater2073-44412023-04-01158155910.3390/w15081559A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower ProductionShuang Hao0Anders Wörman1Joakim Riml2Andrea Bottacin-Busolin3Department of Sustainable Development Environmental Science and Engineering, Royal Institute of Technology (KTH), 100 44 Stockholm, SwedenDepartment of Sustainable Development Environmental Science and Engineering, Royal Institute of Technology (KTH), 100 44 Stockholm, SwedenDepartment of Sustainable Development Environmental Science and Engineering, Royal Institute of Technology (KTH), 100 44 Stockholm, SwedenDepartment of Industrial Engineering, University of Padua, Via Venezia 1, 35121 Padova, ItalyHydropower is the largest source of renewable energy in the world and currently dominates flexible electricity production capacity. However, climate variations remain major challenges for efficient production planning, especially the annual forecasting of periodically variable inflows and their effects on electricity generation. This study presents a model that assesses the impact of forecast quality on the efficiency of hydropower operations. The model uses ensemble forecasting and stepwise linear optimisation combined with receding horizon control to simulate runoff and the operation of a cascading hydropower system. In the first application, the model framework is applied to the Dalälven River basin in Sweden. The efficiency of hydropower operations is found to depend significantly on the linkage between the representative biannual hydrologic regime and the regime actually realised in a future scenario. The forecasting error decreases when considering periodic hydroclimate fluctuations, such as the dry–wet year variability evident in the runoff in the Dalälven River, which ultimately increases production efficiency by approximately 2% (at its largest), as is shown in scenarios 1 and 2. The corresponding potential hydropower production is found to vary by 80 GWh/year. The reduction in forecasting error when considering biennial periodicity corresponds to a production efficiency improvement of about 0.33% (or 13.2 GWh/year).https://www.mdpi.com/2073-4441/15/8/1559ensemble forecastingbiennial periodic climatehydropower optimisationhydropower managementproduction efficiencyforecasting error |
| spellingShingle | Shuang Hao Anders Wörman Joakim Riml Andrea Bottacin-Busolin A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production Water ensemble forecasting biennial periodic climate hydropower optimisation hydropower management production efficiency forecasting error |
| title | A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production |
| title_full | A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production |
| title_fullStr | A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production |
| title_full_unstemmed | A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production |
| title_short | A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production |
| title_sort | model for assessing the importance of runoff forecasts in periodic climate on hydropower production |
| topic | ensemble forecasting biennial periodic climate hydropower optimisation hydropower management production efficiency forecasting error |
| url | https://www.mdpi.com/2073-4441/15/8/1559 |
| work_keys_str_mv | AT shuanghao amodelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction AT andersworman amodelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction AT joakimriml amodelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction AT andreabottacinbusolin amodelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction AT shuanghao modelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction AT andersworman modelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction AT joakimriml modelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction AT andreabottacinbusolin modelforassessingtheimportanceofrunoffforecastsinperiodicclimateonhydropowerproduction |