兰州大学机构库
Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling
Ma, Yixiang1; Yu, Lean2,3; Zhang, Guoxing4; Lu, Zhiming4; Wu, Jiaqian5
2023-12
Source PublicationRENEWABLE ENERGY   Impact Factor & Quartile
ISSN0960-1481 ; 1879-0682
EISSN1879-0682
Volume219
page numbers15
AbstractThe uncertainty of the source-load data, accompanied by the contradiction between different goal orientations, poses a challenge to the decision-making of the scheduling scheme. To solve the issue of multi-objective optimal scheduling under the condition of source-load uncertainty, this paper proposes a multi-objective multi-energy complementary optimal scheduling scheme based on source-load uncertainty. In the proposed method, four main steps: uncertainty analysis of the source-load data, design of multi-energy complementary scheduling scheme, optimal calculation of the scheduling scheme, and multi-scenario analysis, are involved. In addition, the effect of the source-load prediction on the optimal scheduling scheme is further analyzed for management implications. In the empirical analysis, the source-load data with 15-min intervals is introduced as the sample data, and different optimization algorithms and compromise solution determination methods are selected for comparative analysis. Compared with other optimization algorithms, the proposed method has an average decrease of 22.699%, 7.587% and 22.149% in the total cost of generation (TCG), the spinning reserve cost (CSR) and the carbon emission (CE), respectively, and the average increase in the rate of new energy generation (RNE) is 11.969%. The empirical analysis shows that the proposed method outperforms all benchmark methods, which can provide valuable insights for intraday rolling scheduling under the condition of source-load uncertainty and multiobjective optimization.
KeywordIntraday rolling scheduling Source-load uncertainty Multi-objective optimization Compromise solution Multi-energy complementary Prediction driven-decision making
PublisherPERGAMON-ELSEVIER SCIENCE LTD
DOI10.1016/j.renene.2023.119483
Indexed BySCIE
Language英语
WOS Research AreaScience & Technology - Other Topics ; Energy & Fuels
WOS SubjectGreen & Sustainable Science & Technology ; Energy & Fuels
WOS IDWOS:001102398300001
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/568811
Collection兰州大学
Corresponding AuthorYu, Lean; Wu, Jiaqian
Affiliation
1.Beijing Univ Chem Technol, Sch Econ & Management, Beijing 100029, Peoples R China;
2.Sichuan Univ, Business Sch, Chengdu 610065, Peoples R China;
3.Shenzhen Inst Technol, Sch Business, Shenzhen 518116, Peoples R China;
4.Lanzhou Univ, Sch Management, Lanzhou 730000, Peoples R China;
5.Zhengzhou Univ, Sch Management, Zhengzhou 450001, Peoples R China
Recommended Citation
GB/T 7714
Ma, Yixiang,Yu, Lean,Zhang, Guoxing,et al. Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling[J]. RENEWABLE ENERGY,2023,219.
APA Ma, Yixiang,Yu, Lean,Zhang, Guoxing,Lu, Zhiming,&Wu, Jiaqian.(2023).Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling.RENEWABLE ENERGY,219.
MLA Ma, Yixiang,et al."Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling".RENEWABLE ENERGY 219(2023).
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