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Power producers use a wide range of decision support systems to manage andplan for sales in the day-ahead electricity market, and they are often facedwith the challenge of choosing the most advantageous bidding strategy for anygiven day. The optimal solution is not known until after spot clearing. Resultsfrom the models and strategy used, and their impact on profitability, caneither continuously be registered, or simulated with use of historic data.Access to an increasing amount of data opens for the application of machinelearning models to predict the best combination of models and strategy for anygiven day. In this article, historical performance of two given biddingstrategies over several years have been analyzed with a combination of domainknowledge and machine learning techniques (gradient boosting and neuralnetworks). A wide range of variables accessible to the models prior to biddinghave been evaluated to predict the optimal strategy for a given day. Resultsindicate that a machine learning model can learn to slightly outperform astatic strategy where one bidding method is chosen based on overall historicperformance.

A gradient boosting approach for optimal selection of bidding strategies in reservoir hydro