基于SWAN模型的波浪能发电站选址研究

孙单勋; 杨智聪; 王卓恒; 綦晓; 邓慧; 欧阳建友

doi:doi:10.19912/j.0254-0096.tynxb.2024-1593

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

基于SWAN模型的波浪能发电站选址研究

更新时间：2026-02-06

- 基于SWAN模型的波浪能发电站选址研究
- Vol. 47, Issue 1, Pages: 730-737(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1593
  CLC： TM612
- Online First：05 February 2026，
  
  Published：05 February 2026
- 稿件说明：
移动端阅览
孙单勋, 杨智聪, 王卓恒, et al. 基于SWAN模型的波浪能发电站选址研究[J]. 2026, 47(1): 730-737.
DOI：

孙单勋, 杨智聪, 王卓恒, et al. 基于SWAN模型的波浪能发电站选址研究[J]. 2026, 47(1): 730-737. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1593.

摘要

针对现有波浪能电站选址策略单一的问题

提出一种基于SWAN数值模拟模型和平准化度电成本模型的新型波浪能电站选址策略

并应用研究区域的实际数据验证了该方法的可行性;为了进一步解决波浪能电站并网后可能对电网造成的不稳定性问题

采用BiTCN-BiGRU-Attention组合神经网络对最佳选址点的发电功率进行短期预测

可提高波浪能电站后期运营的稳定性。结果表明

该选址策略可为波浪能电站的合理选址与稳定运行提供科学依据。

Abstract

To address the limitations of traditional siting strategies

a novel siting approach based on the SWAN numerical simulation model and the levelized cost of energy （LCOE） model is proposed. This method is validated using real-world data from the study area. Additionally

to mitigate potential grid instability issues caused by the integration of wave energy power stations

a hybrid neural network model

BiTCN-BiGRU-Attention

is employed to predict the short-term power generation at the optimal site

thereby enhancing the stability of subsequent operations. The results indicate that this siting strategy provides a scientific basis for the rational siting and stable operation of wave energy power stations.

关键词

Keywords

references

邵萌, 史宏达, 赵阳, 等. 基于改进层次分析法的海洋能多能互补选址决策模型[J]. 太阳能学报, 2016, 37(9): 2443-2450.SHAO M, SHI H D, ZHAO Y, et al. Location decision model of hybrid marine energy based on improved AHP method[J]. Acta energiae solaris sinica, 2016, 37(9): 2443-2450.

邵萌, 刘盟楚, 陈玉静, 等. 基于GIS-MCDM-GRU的舟山市波浪能电站选址决策研究[J]. 中国海洋大学学报(自然科学版), 2023, 53(10): 95-106.SHAO M, LIU M C, CHEN Y J, et al. Research on site selection methodology of wave power plant for Zhoushan based on GIS-MCDM-GRU[J]. Periodical of Ocean University of China, 2023, 53(10): 95-106.

许杉敏. 海上风能与波浪能联合发电系统布局优化[D]. 长沙: 中南大学, 2023.XU S M. Layout optimization of co-located offshore windand wave energy generation system[D]. Changsha: Central South University, 2023.

PATEL R P, NAGABABU G, KACHHWAHA S S, et al.Techno-economic analysis of wave energy resource for India[J]. Journal of the Indian Society of Remote Sensing, 2023, 51(2): 371-381.

GAO Q, KHAN S S, SERGIIENKO N, et al.Assessment of wind and wave power characteristic and potential for hybrid exploration in Australia[J]. Renewable and sustainable energy reviews, 2022, 168: 112747.

胡聪, 毛海英, 尤再进, 等. 中国海域波浪能资源分布及波浪能发电装置适用性研究[J]. 海洋科学, 2018, 42(3): 142-148.HU C, MAO H Y, YOU Z J, et al. Study on the distribution of wave energy resources in China and the applicability of wave energy generation device[J]. Marine sciences, 2018, 42(3): 142-148.

BABARIT A, HALS J, MULIAWAN M J, et al.Numerical benchmarking study of a selection of wave energy converters[J]. Renewable energy, 2012, 41: 44-63.

M’ZOUGHI F, LEKUBE J, GARRIDO A J, et al. Machine learning-based diagnosis in wave power plants for cost reduction using real measured experimental data: mutriku wave power plant[J]. Ocean engineering, 2024, 293: 116619.

MOJTAHEDI A, BEYRAMZADEH M, SIADATMOUSA VI S M, et al. The role of different wind input, whitecap dissipation and quadruplet wave-wave interaction terms in wave evolution in Lake Michigan[J]. Ocean engineering, 2024, 307: 118149.

CASTRO-SANTOS L, MARTINS E, GUEDES SOARES C.Economic comparison of technological alternatives to harness offshore wind and wave energies[J]. Energy, 2017, 140: 1121-1130.

王鑫. 波浪能装置阵列布设相关理论方法及其应用研究[D]. 天津: 天津大学, 2019.WANG X. A study on the theory of wave energy converter array layout and its application[D]. Tianjin: Tianjin University, 2019.

LIU J B, SONG D R, LI Q G, et al.Life cycle cost modelling and economic analysis of wind power: a state of art review[J]. Energy conversion and management, 2023, 277: 116628.

O’CONNELL R, KAMIDELIVAND M, FURLONG R, et al. An advanced geospatial assessment of the Levelised cost of energy(LCOE) for wave farms in Irish and western UK waters[J]. Renewable energy, 2024, 221: 119864.

BIAN H H, WANG Q, XU G Z, et al.Research on short-term load forecasting based on accumulated temperature effect and improved temporal convolutional network[J]. Energy reports, 2022, 8: 1482-1491.

卞玉露. 基于BiTCN和预训练的文本情感分析研究[D]. 淮南: 安徽理工大学, 2022.BIAN Y L.Research on Text Emotion Analysis based on BiTCN andPre-training[D]. Huainan: Anhui University of Science & Technology, 2022.

王瑞, 张璐婷, 逯静. 基于新型相似日选取和VMD-NGO-BiGRU的短期光伏功率预测[J]. 湖南大学学报(自然科学版), 2024, 51(2): 68-80.WANG R, ZHANG L T, LU J. Short term photovoltaic power prediction based on new similar day selection and VMD-NGO-BiGRU[J]. Journal of Hunan University (natural sciences), 2024, 51(2): 68-80.

HU M S, ZHENG G Q, SU Z E, et al.Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks[J]. Energy, 2024, 303: 131951.

黄宇, 顾智勇, 李永玲, 等. 基于时间模式注意力机制的CNN-BiGRU短期负荷预测[J]. 华北电力大学学报(自然科学版), 2023, 50(6): 11-20.HUANG Y, GU Z Y, LI Y L, et al. Short-term power load forecasting based on temporal pattern attention mechanism of CNN-BiGRU[J]. Journal of North China Electric Power University (natural science edition), 2023, 50(6): 11-20.

AHMED A A M, JUI S J J, AL-MUSAYLH M S, et al. Hybrid deep learning model for wave height prediction in Australia’s wave energy region[J]. Applied soft computing, 2024, 150: 111003.

WU H, GAO X Z, HENG J N.Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height[J]. Energy, 2024, 294: 130887.

武贺, 方舣洲, 张松, 等. 南海岛礁海域波浪能资源分析及总量评估[J]. 太阳能学报, 2022, 43(9): 416-423.WU H, FANG Y Z, ZHANG S, et al. Wave energy characterization and potential estimation for islands of South China Sea[J]. Acta energiae solaris sinica, 2022, 43(9): 416-423.

张君珏, 苏奋振, 王雯玥. 南海资源环境地理研究综述[J]. 地理科学进展, 2018, 37(11): 1443-1453.ZHANG J J, SU F Z, WANG W Y. A review of geographical information research on resources and environment of the South China Sea region[J]. Progress in geography, 2018, 37(11): 1443-1453.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

融合PCC-LSTM-XGBoost的中长期光伏功率预测模型

ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION

RECENT PROGRESS OF SHORT-TERM FORECASTING OF PHOTOVOLTAIC GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS

ULTRA-SHORT-TERM POWER FORECASTING OF PHOTOVOLTAIC POWER GENERATION BASED ON TWO-STAGE UNCERTAINTY QUANTIZATION

POWER OPTIMIZATION OF DIRECT-DRIVE WAVE POWER SYSTEM BASED ON EXCITATION FORCE ESTIMATION

Related Author

密伟

蒋旭

晁梓博

潘风文

雷宇

Guo Jiqiang

Xu Xudong

Zhang Shuai

Related Institution

College of Electrical and Power Engineering, Taiyuan University of Technology

Shanxi Gemeng Sino-US Clean Energy R & D Center Co., Ltd.

School of Materials Science and Engineering, Xi’an University of Architecture and Technology

School of Information and Control Engineering, Xi’an University of Architecture and Technology

School of Architecture, Xi’an University of Architecture and Technology

AI问答

⁰