Hui Li, Haoyang Yu, Zhongjian Liu, Fan Li, Xiong Wu, Binrui Cao, Cheng Zhang, Dong Liu
{"title":"Long-term scenario generation of renewable energy generation using attention-based conditional generative adversarial networks","authors":"Hui Li, Haoyang Yu, Zhongjian Liu, Fan Li, Xiong Wu, Binrui Cao, Cheng Zhang, Dong Liu","doi":"10.1049/enc2.12106","DOIUrl":null,"url":null,"abstract":"<p>Long-term scenario generation of renewable energy is regarded as an important part of the optimal planning of renewable energy systems. This study proposes a scenario generation method for generating long-term correlated scenarios of wind and photovoltaic outputs from historical renewable energy data. The generation of scenarios was divided into two processes: long-term yearly sequence generation and intraday scenario generation of wind-solar energy. In the long-term yearly sequence generation process, the <i>k</i>-means clustering algorithm and Markov chain Monte Carlo simulation method were developed to capture the seasonal and long-term features of wind and photovoltaic energies. Furthermore, an attention-based conditional generative adversarial network (ACGAN) was proposed to capture short-term features. An attention structure and conditional classifiers were developed to capture features in the generated scenarios. To accelerate the convergence process and improve the quality of the generated scenarios, a gradient penalty was included in the ACGAN model. Numerical case studies were conducted to verify the validity of the proposed method using a real-world dataset.</p>","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"5 1","pages":"15-27"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.12106","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Conversion and Economics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/enc2.12106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Long-term scenario generation of renewable energy is regarded as an important part of the optimal planning of renewable energy systems. This study proposes a scenario generation method for generating long-term correlated scenarios of wind and photovoltaic outputs from historical renewable energy data. The generation of scenarios was divided into two processes: long-term yearly sequence generation and intraday scenario generation of wind-solar energy. In the long-term yearly sequence generation process, the k-means clustering algorithm and Markov chain Monte Carlo simulation method were developed to capture the seasonal and long-term features of wind and photovoltaic energies. Furthermore, an attention-based conditional generative adversarial network (ACGAN) was proposed to capture short-term features. An attention structure and conditional classifiers were developed to capture features in the generated scenarios. To accelerate the convergence process and improve the quality of the generated scenarios, a gradient penalty was included in the ACGAN model. Numerical case studies were conducted to verify the validity of the proposed method using a real-world dataset.