Benefit of PARMA Modeling for Long-Term Hydroelectric Scheduling Using Stochastic Dual Dynamic Programming
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 3
Abstract
In the long-term management of large hydropower systems, operators generally have to maximize the benefits from energy production while ensuring they satisfy a minimal energy profile throughout the year. Enhanced hydrological information can be critical to improving the operations of the system. This need encourages the use of a more complex representation of inflow series. Stochastic dual dynamic programming (SDDP) is a commonly used method for optimizing multireservoir operations of hydropower systems. Within SDDP, inflow uncertainty is usually modeled using statistical time-series models such as the family of periodic autoregressive (PAR) models, which have the required linear structure to implement SDDP. Although often used in hydrological modeling for its ability to represent long-term spatiotemporal relationships, periodic autoregressive and moving average (PARMA) has yet been little used in a SDDP framework. The additional moving average component of PARMA models over PAR models provides PARMA with a deeper memory than PAR, thanks to a more complex correlation structure. This paper compares policies generated by PARMA and PAR to manage the Manicouagan hydropower system in Quebec, Canada. The comparison is made between PAR and PARMA models of the same autoregressive order to illustrate the advantages of including the moving average component. Simulations over historical scenarios are performed and reveal that PARMA derives policies that can better manage the interannual capacity present in the system.
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Data Availability Statement
The data used for the study are private industrial data of a Hydro-Quebec Production, the authority in charge of the generation of hydroelectric in Quebec’s region. They are not available for the general public. For all data requests, the agreement of Hydro-Quebec Production is required and should be asked accordingly.
Acknowledgments
The authors thank the reviewers, the Associate Editor, and the Editor for their constructive suggestions, which helped improve the quality of the present paper. The authors thank Laura Fagherazzi at Hydro-Quebec’s production planning unit, who provided the tools to calibrate and analyze the different hydrological models used in the paper. Finally, the authors thank the optimization practitioners of the team Planification and Strategies for their critical comments, insights, and disponibility throughout the study.
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Journal of Water Resources Planning and Management
Volume 147 • Issue 3 • March 2021
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© 2021 American Society of Civil Engineers.
History
Received: Feb 13, 2020
Accepted: Sep 18, 2020
Published online: Jan 15, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 15, 2021
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