Hydropower station scheduling with ship arrival prediction and energy storage
Abstract Effectiveness improvement in power generation and navigation for grid-connected hydropower stations have emerged as a significant concern due to the challenges such as discrepancies between declared and actual ship arrival times, as well as unstable power generation. To address these issues...
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ftdoajarticles:oai:doaj.org/article:1835903e06874001996a6783dad30a71 2023-12-10T09:47:14+01:00 Hydropower station scheduling with ship arrival prediction and energy storage Enjiang Zhou Xiao Liu Zhihang Meng Song Yu Jinxiu Mei Qiang Qu 2023-11-01T00:00:00Z https://doi.org/10.1038/s41598-023-45995-3 https://doaj.org/article/1835903e06874001996a6783dad30a71 EN eng Nature Portfolio https://doi.org/10.1038/s41598-023-45995-3 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-023-45995-3 2045-2322 https://doaj.org/article/1835903e06874001996a6783dad30a71 Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023) Medicine R Science Q article 2023 ftdoajarticles https://doi.org/10.1038/s41598-023-45995-3 2023-11-12T01:40:47Z Abstract Effectiveness improvement in power generation and navigation for grid-connected hydropower stations have emerged as a significant concern due to the challenges such as discrepancies between declared and actual ship arrival times, as well as unstable power generation. To address these issues, this paper proposes a multi-objective real-time scheduling model. The proposed model incorporates energy storage and ship arrival prediction. An energy storage mechanism is introduced to stabilize power generation by charging the power storage equipment during surplus generation and discharging it during periods of insufficient generation at the hydropower stations. To facilitate the scheduling with the eneragy storage mechanism, the arrival time of ships to the stations are predicted. We use the maximization of generation minus grid load demand and the maximization of navigability assurance rate as two objective functions in the scheduling process. The model uses the Non-Dominated Sorting Beluga Whale Optimization (NSBWO) algorithm to optimize and solve the real-time discharge flow scheduling of the hydropower stations in different time periods. The NSBWO algorithm combines the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Beluga Whale Optimization (BWO). The experimental results show that the proposed method has advantages in predicting the expected arrival time of ships and scheduling the discharge flow. The prediction using XGBoost model reaches accuracy with more than 0.9, and the discharged flow obtained from scheduling meets the demand of hydropower stations grid load while also improves the navigation benefits. This study provides theoretical analysis with its practical applications in a real hyropower station as a case study for solving hydropower scheduling problems. Article in Journal/Newspaper Beluga Beluga whale Beluga* Directory of Open Access Journals: DOAJ Articles Scientific Reports 13 1 |
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Medicine R Science Q Enjiang Zhou Xiao Liu Zhihang Meng Song Yu Jinxiu Mei Qiang Qu Hydropower station scheduling with ship arrival prediction and energy storage |
topic_facet |
Medicine R Science Q |
description |
Abstract Effectiveness improvement in power generation and navigation for grid-connected hydropower stations have emerged as a significant concern due to the challenges such as discrepancies between declared and actual ship arrival times, as well as unstable power generation. To address these issues, this paper proposes a multi-objective real-time scheduling model. The proposed model incorporates energy storage and ship arrival prediction. An energy storage mechanism is introduced to stabilize power generation by charging the power storage equipment during surplus generation and discharging it during periods of insufficient generation at the hydropower stations. To facilitate the scheduling with the eneragy storage mechanism, the arrival time of ships to the stations are predicted. We use the maximization of generation minus grid load demand and the maximization of navigability assurance rate as two objective functions in the scheduling process. The model uses the Non-Dominated Sorting Beluga Whale Optimization (NSBWO) algorithm to optimize and solve the real-time discharge flow scheduling of the hydropower stations in different time periods. The NSBWO algorithm combines the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Beluga Whale Optimization (BWO). The experimental results show that the proposed method has advantages in predicting the expected arrival time of ships and scheduling the discharge flow. The prediction using XGBoost model reaches accuracy with more than 0.9, and the discharged flow obtained from scheduling meets the demand of hydropower stations grid load while also improves the navigation benefits. This study provides theoretical analysis with its practical applications in a real hyropower station as a case study for solving hydropower scheduling problems. |
format |
Article in Journal/Newspaper |
author |
Enjiang Zhou Xiao Liu Zhihang Meng Song Yu Jinxiu Mei Qiang Qu |
author_facet |
Enjiang Zhou Xiao Liu Zhihang Meng Song Yu Jinxiu Mei Qiang Qu |
author_sort |
Enjiang Zhou |
title |
Hydropower station scheduling with ship arrival prediction and energy storage |
title_short |
Hydropower station scheduling with ship arrival prediction and energy storage |
title_full |
Hydropower station scheduling with ship arrival prediction and energy storage |
title_fullStr |
Hydropower station scheduling with ship arrival prediction and energy storage |
title_full_unstemmed |
Hydropower station scheduling with ship arrival prediction and energy storage |
title_sort |
hydropower station scheduling with ship arrival prediction and energy storage |
publisher |
Nature Portfolio |
publishDate |
2023 |
url |
https://doi.org/10.1038/s41598-023-45995-3 https://doaj.org/article/1835903e06874001996a6783dad30a71 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023) |
op_relation |
https://doi.org/10.1038/s41598-023-45995-3 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-023-45995-3 2045-2322 https://doaj.org/article/1835903e06874001996a6783dad30a71 |
op_doi |
https://doi.org/10.1038/s41598-023-45995-3 |
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Scientific Reports |
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13 |
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1 |
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1784890829192560640 |