Abstract
This paper demonstrates how deep learning can be used to find optimal reservoir operating policies in hydropower river systems. The method that we propose is based on the implicit stochastic optimization (ISO) framework, using direct policy search methods combined with deep neural networks (DNN). The findings from a real-world two-reservoir hydropower system in southern Norway suggest that DNNs can learn how to map input (price, inflow, starting reservoir levels) to the optimal production pattern directly. Due to the speed of evaluating the DNN, this approach is from an operational standpoint computationally inexpensive and may potentially address the long-standing problem of high dimensionality in hydropower optimization. Further on, our method may be used as an input for decision-theoretic planning, suggesting the policy that will give the highest expected profit. The approach also permits for a broader use of pre-trained neural networks in historical reanalysis of production patterns and studies of climate change effects.
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Acknowledgements
This research has been supported by Agder Energi, the Norwegian Research Council (ENERGIX program), and the University of Agder. We also thank Jarand Roeynstrand at Agder Energi for input on an early version of this article.
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Matheussen, B.V., Granmo, OC., Sharma, J. (2019). Hydropower Optimization Using Deep Learning. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_11
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