Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling
Author
Abstract
Suggested Citation
DOI: 10.1007/s10287-024-00525-y
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Rachunok, Benjamin & Staid, Andrea & Watson, Jean-Paul & Woodruff, David L., 2020. "Assessment of wind power scenario creation methods for stochastic power systems operations," Applied Energy, Elsevier, vol. 268(C).
- Fleten, Stein-Erik & Kristoffersen, Trine Krogh, 2007. "Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer," European Journal of Operational Research, Elsevier, vol. 181(2), pages 916-928, September.
- Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
- Daeho Kim & Hyungkyu Cheon & Dong Gu Choi & Seongbin Im, 2022. "Operations Research Helps the Optimal Bidding of Virtual Power Plants," Interfaces, INFORMS, vol. 52(4), pages 344-362, July.
- Wozabal, David & Rameseder, Gunther, 2020. "Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity," European Journal of Operational Research, Elsevier, vol. 280(2), pages 639-655.
- Ellen Krohn Aasgård & Hans Ivar Skjelbred, 2020. "Progressive hedging for stochastic programs with cross-scenario inequality constraints," Computational Management Science, Springer, vol. 17(1), pages 141-160, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
- Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2020. "Strategic offering of a flexible producer in day-ahead and intraday power markets," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1136-1153.
- Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
- Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
- Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022.
"Short-term risk management of electricity retailers under rising shares of decentralized solar generation,"
Energy Economics, Elsevier, vol. 109(C).
- Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2021. "Short-term risk management for electricity retailers under rising shares of decentralized solar generation," Working Paper Series in Production and Energy 57, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
- Kim, Seokwoo & Choi, Dong Gu, 2024. "A sample robust optimal bidding model for a virtual power plant," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1101-1113.
- Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2023.
"Stochastic optimization of trading strategies in sequential electricity markets,"
European Journal of Operational Research, Elsevier, vol. 308(1), pages 400-421.
- Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2021. "Stochastic optimization of trading strategies in sequential electricity markets," Working Paper Series in Production and Energy 58, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
- Tryggvi Jónsson & Pierre Pinson & Henrik Madsen & Henrik Aalborg Nielsen, 2014. "Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression," Energies, MDPI, vol. 7(9), pages 1-25, August.
- Saeed Hayati & Kenji Fukumizu & Afshin Parvardeh, 2024. "Kernel mean embedding of probability measures and its applications to functional data analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 447-484, June.
- Azar, Pablo D. & Micali, Silvio, 2018. "Computational principal agent problems," Theoretical Economics, Econometric Society, vol. 13(2), May.
- Luis A Barboza & Shu-Wei Chou-Chen & Paola Vásquez & Yury E García & Juan G Calvo & Hugo G Hidalgo & Fabio Sanchez, 2023. "Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 17(1), pages 1-13, January.
- Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
- Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
- Davide Pettenuzzo & Francesco Ravazzolo, 2016.
"Optimal Portfolio Choice Under Decision‐Based Model Combinations,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
- Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers 80, Brandeis University, Department of Economics and International Business School.
- Davide Pettenuzzo & Francesco Ravazzolo, 2015. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers No 9/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal portfolio choice under decision-based model combinations," Working Paper 2014/15, Norges Bank.
- Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
- Basora, Luis & Viens, Arthur & Chao, Manuel Arias & Olive, Xavier, 2025. "A benchmark on uncertainty quantification for deep learning prognostics," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
- Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
- R de Fondeville & A C Davison, 2018. "High-dimensional peaks-over-threshold inference," Biometrika, Biometrika Trust, vol. 105(3), pages 575-592.
- Armantier, Olivier & Treich, Nicolas, 2013.
"Eliciting beliefs: Proper scoring rules, incentives, stakes and hedging,"
European Economic Review, Elsevier, vol. 62(C), pages 17-40.
- Armantier, Olivier & Treich, Nicolas, 2010. "Eliciting Beliefs: Proper Scoring Rules, Incentives, Stakes and Hedging," IDEI Working Papers 643, Institut d'Économie Industrielle (IDEI), Toulouse.
- Armantier, Olivier & Treich, Nicolas, 2010. "Eliciting Beliefs: Proper Scoring Rules, Incentives, Stakes and Hedging," LERNA Working Papers 10.26.332, LERNA, University of Toulouse.
- Armantier, Olivier & Treich, Nicolas, 2010. "Eliciting Beliefs: Proper Scoring Rules, Incentives, Stakes and Hedging," TSE Working Papers 10-213, Toulouse School of Economics (TSE).
- Armantier, Olivier & Treich, Nicolas, 2010. "Eliciting Beliefs: Proper Scoring Rules, Incentives, Stakes and Hedging," TSE Working Papers 10-156, Toulouse School of Economics (TSE).
- Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
More about this item
Keywords
Hydroelectric power; Bidding; Benchmarking; Stochastic optimization;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:21:y:2024:i:2:d:10.1007_s10287-024-00525-y. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.