1. College of Electrical Engineering and Automation, Shandong University of Science and Technology,Qingdao,China
2. Contribution: Conceptualization, Resources, Writing - review & editing
3. Contribution: Data curation, Validation, Writing - original draft
4. School of Electrical and Electronic Engineering, Nanyang Technological University,Singapore,Singapore
5. Contribution: Formal analysis, Investigation, Supervision, Writing - review & editing
6. Contribution: Conceptualization, Resources, Supervision, Writing - review & editing
7. Department of Electrical and Electronics Engineering, RMIT,Victoria,Melbourne,Australia
8. Contribution: Data curation, Supervision
9. College of Energy Storage Technology, Shandong University of Science and Technology,Qingdao,China
10. Contribution: Formal analysis, Validation, Writing - review & editing
纸质出版:2025
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Yumin Zhang, Yongchen Zhang, Xizhen Xue, 等. Affinely adjustable robust optimal scheduling of a power system considering the flexibility of supply and demand[J]. Energy Conversion and Economics, 2025,6(3).
Yumin Zhang, Yongchen Zhang, Xizhen Xue, et al. Affinely adjustable robust optimal scheduling of a power system considering the flexibility of supply and demand[J]. Energy Conversion and Economics, 2025, 6(3).
Yumin Zhang, Yongchen Zhang, Xizhen Xue, 等. Affinely adjustable robust optimal scheduling of a power system considering the flexibility of supply and demand[J]. Energy Conversion and Economics, 2025,6(3). DOI: 10.1049/enc2.70011.
Yumin Zhang, Yongchen Zhang, Xizhen Xue, et al. Affinely adjustable robust optimal scheduling of a power system considering the flexibility of supply and demand[J]. Energy Conversion and Economics, 2025, 6(3). DOI: 10.1049/enc2.70011.
As the integration of renewable energy sources
such as wind power and photovoltaics
continues
the issue of system uncertainty has become more pronounced. This paper proposes a stochastic power system dispatch method based on affinely adjustable robust optimization (AARO) with a generalized linear polyhedron (GLP) uncertainty set that can accurately quantify the flexibility of the power system supply and demand as well as enhance the optimality of dispatch strategies. First
a GLP uncertainty set was established to characterize both the temporal stochasticity and spatial correlation of multiple renewable energy outputs. A correlation envelope was employed to reflect renewable energy outputs from historical data
and a polyhedral set was proposed to accurately describe the uncertainty for model formulation
which can effectively reduce model conservatism by minimizing empty regions. Furthermore
the range of net load variations was analysed to build a demand flexibility quantification model for the power system. Next
based on the expected operational value
a robust optimization dispatch model that considers the flexible supply and demand balance is developed within the affine strategy framework. Finally
simulations of a modified 6-bus system and modified IEEE 57-bus system validate the effectiveness of the proposed GLP-AARO method for power system flexibility quantification and dispatch strategy optimization.
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