A Scalable River Flow Forecast and Basin Optimization System for Hydropower Plants

Buhan, Serkan; Küçük, Dilek; Çinar, Muhammet Serkan; Güvengir, Umut; Demirci, Turan; Yılmaz, Yasemin; Malkoc, Filiz; Eminoglu, Erkan; Yildirim, Mehmet Uğur

doi:10.1109/tste.2019.2952450

Cited by 19 publications

(2 citation statements)

References 27 publications

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“…These results are detailed in Figure 5. It is important to clarify that the article regarding turbine efficiency curves adjustment [48] does not have a specific river system application. Thus, we did not compute this paper in any river system type.…”

Section: Results Analysismentioning

confidence: 99%

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Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Bernardes

Santos

Abreu

et al. 2022

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The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

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Section: Results Analysismentioning

confidence: 99%

“…An important exception is a work with ML application done in [48], where the turbine efficiency curves are adjusted. It is also essential to point out that one article deals with water flow and sediment forecast [45].…”

Section: Results Analysismentioning

confidence: 99%