Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

Kristine Klock Fleten (), Ellen Krohn Aasgård (), Liyuan Xing (), Hanne Høie Grøttum (), Stein-Erik Fleten () and Odd Erik Gundersen ()
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Kristine Klock Fleten: Aneo AS
Ellen Krohn Aasgård: Aneo AS
Liyuan Xing: Aneo AS
Hanne Høie Grøttum: Aneo AS
Stein-Erik Fleten: Norwegian University of Science and Technology
Odd Erik Gundersen: Norwegian University of Science and Technology

Computational Management Science, 2024, vol. 21, issue 2, No 7, 24 pages

Abstract: Abstract Aneo is one of the first Nordic power companies to apply stochastic programming for day-ahead bidding of hydropower. This paper describes our experiences in implementing, testing, and operating a stochastic programming-based bidding method aimed at setting up an automated process for day-ahead bidding. The implementation process has faced challenges such as generating price scenarios for the optimization model, post-processing optimization results to create feasible and understandable bids, and technically integrating these into operational systems. Additionally, comparing the bids from the new stochastic-based method to the existing operator-determined bids has been challenging, which is crucial for building trust in new procedures. Our solution is a rolling horizon comparison, benchmarking the performance of the bidding methods over consecutive two-week periods. Our benchmarking results show that the stochastic method can replicate the current operator-determined bidding strategy. However, additional work is needed before we can fully automate the stochastic bidding setup, particularly in addressing inflow uncertainty and managing special constraints on our watercourses.

Keywords: Hydroelectric power; Bidding; Benchmarking; Stochastic optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10287-024-00525-y

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