dc.contributor.author | Bringedal, Amanda Sæbø | |
dc.contributor.author | Søvikhagen, Anne-Marthe | |
dc.contributor.author | Aasgård, Ellen Krohn | |
dc.contributor.author | Fleten, Stein-Erik | |
dc.date.accessioned | 2021-11-04T12:18:39Z | |
dc.date.available | 2021-11-04T12:18:39Z | |
dc.date.created | 2021-10-28T09:59:02Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1868-3967 | |
dc.identifier.uri | https://hdl.handle.net/11250/2827880 | |
dc.description.abstract | A stochastic programming model for a price-taking, profit-maximizing hydropower producer participating in the Nordic day-ahead and balancing market is developed and evaluated by backtesting over 200 historical days. We find that the producer may gain 0.07% by coordinating its trades in the day-ahead and balancing market, compared to considering the two markets sequentially. It is thus questionable whether a coordinated bidding strategy is worthwhile. However, the gain from coordinating trades is dependent on the quality of the forecasts for the balancing market. The limited gain of 0.07% comes from using an artificial neural network prediction model that is trained on historical data on seasonal effects, day-ahead market price, wind and temperature forecasts. To quantify the effect of the forecasting model on the gain of coordination, we therefore develop a benchmarking framework for two additional prediction models: a naive forecast predicting zero imbalance in expectation, and a perfect information forecast. Using the naive method, we estimate the lower bound of coordination to be 0.0% which coincides with theory. When having perfect information, we find that the upper bound for the gain is 3.8% which indicates that a substantial gain in profits can be obtained by coordinated bidding if accurate prediction methods could be developed. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Backtesting coordinated hydropower bidding using neural network forecasting | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Energy Systems, Springer Verlag | en_US |
dc.identifier.doi | 10.1007/s12667-021-00490-4 | |
dc.identifier.cristin | 1949156 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |