Hydropower station scheduling with ship arrival prediction and energy storage
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 pa...
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ftpubmed:oai:pubmedcentral.nih.gov:10624911 2023-12-10T09:47:14+01:00 Hydropower station scheduling with ship arrival prediction and energy storage Zhou, Enjiang Liu, Xiao Meng, Zhihang Yu, Song Mei, Jinxiu Qu, Qiang 2023-11-03 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624911/ http://www.ncbi.nlm.nih.gov/pubmed/37923883 https://doi.org/10.1038/s41598-023-45995-3 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624911/ http://www.ncbi.nlm.nih.gov/pubmed/37923883 http://dx.doi.org/10.1038/s41598-023-45995-3 © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . Sci Rep Article Text 2023 ftpubmed https://doi.org/10.1038/s41598-023-45995-3 2023-11-12T01:42:56Z 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. Text Beluga Beluga whale Beluga* PubMed Central (PMC) Scientific Reports 13 1 |
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Article Zhou, Enjiang Liu, Xiao Meng, Zhihang Yu, Song Mei, Jinxiu Qu, Qiang Hydropower station scheduling with ship arrival prediction and energy storage |
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Article |
description |
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 |
Text |
author |
Zhou, Enjiang Liu, Xiao Meng, Zhihang Yu, Song Mei, Jinxiu Qu, Qiang |
author_facet |
Zhou, Enjiang Liu, Xiao Meng, Zhihang Yu, Song Mei, Jinxiu Qu, Qiang |
author_sort |
Zhou, Enjiang |
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 Publishing Group UK |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624911/ http://www.ncbi.nlm.nih.gov/pubmed/37923883 https://doi.org/10.1038/s41598-023-45995-3 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Sci Rep |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624911/ http://www.ncbi.nlm.nih.gov/pubmed/37923883 http://dx.doi.org/10.1038/s41598-023-45995-3 |
op_rights |
© The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
op_doi |
https://doi.org/10.1038/s41598-023-45995-3 |
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