Backtesting coordinated hydropower bidding using neural network forecasting

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...

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Bibliographic Details
Published in:Energy systems (Berlin. Periodical) 2023-08, Vol.14 (3), p.847-867
Main Authors: Bringedal, Amanda Sæbø, Søvikhagen, Anne-Marthe Liaklev, Aasgård, Ellen Krohn, Fleten, Stein-Erik
Format: Article
Language:English
Subjects:
Artificial neural networks
Balancing
Coordination
Economics and Management
Energy
Energy Policy
Energy Systems
Hydroelectric power
Lower bounds
Neural networks
Operations Research/Decision Theory
Optimization
Original Paper
Prediction models
Predictions
Stochastic models
Stochastic programming
Upper bounds
Weather forecasting
Wind effects
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Summary: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.
ISSN:1868-3967
1868-3975
DOI:10.1007/s12667-021-00490-4