LAPSE:2023.18349
Published Article
LAPSE:2023.18349
Multi-Level Cooperative Scheduling Based on Robust Optimization Considering Flexibilities and Uncertainties of ADN and MG
Ziqi Zhang, Zhong Chen, Qi Zhao, Puliang Du
March 8, 2023
Abstract
This paper develops the coordination structure and method for utilizing flexibilities in a Micro-Grid (MG), an Active Distribution Network (ADN) and a Transmission Grid (TG), which can play an essential role in addressing the uncertainties caused by renewable energy power generation (REPG). For cooperative dispatching, both flexibilities and uncertainties on the interface of MG−ADN and ADN−TG are portrayed in unified forms utilizing robust optimization (RO), based on the modified equipment-level model of flexible resources. The Constraint-and-Column Generation method is adopted to solve the RO control problems. Simulations on the modified IEEE case-6 and case-33 systems are carried out. The results suggest that the proposed algorithm can exploit flexible resources in both an MG and an ADN, improving the economy and promoting REPG consumption within each level (MG, ADN and TG) while reducing uncertainties and providing flexibilities for superior operators.
Keywords
flexibility and uncertainty, multi-level scheduling, renewable energy power generation, robust optimization (RO)
Suggested Citation
Zhang Z, Chen Z, Zhao Q, Du P. Multi-Level Cooperative Scheduling Based on Robust Optimization Considering Flexibilities and Uncertainties of ADN and MG. (2023). LAPSE:2023.18349
Author Affiliations
Zhang Z: School of Electrical Engineering, Southeast University, Nanjing 210096, China [ORCID]
Chen Z: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Zhao Q: Suzhou Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Suzhou 215004, China
Du P: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Journal Name
Energies
Volume
14
Issue
21
First Page
7376
Year
2021
Publication Date
2021-11-05
ISSN
1996-1073
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Original Submission
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PII: en14217376, Publication Type: Journal Article
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LAPSE:2023.18349
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https://doi.org/10.3390/en14217376
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