Distributionally Robust Co-optimization of Regulation Resources for Flexible Distribution Networks

Aiming at the problems that the existing flexible distribution network planning does not fully take the collaboration between flexible resources and traditional regulation equipment into account, which is mainly based on symmetric networks without fine modelling of the three-phase imbalance characte...

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Bibliographic Details
Published in:2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2) pp. 2949 - 2955
Main Authors: Zhao, Mintong, Huan, Jiajia, Guo, Zuogang, Tan, Yingjie
Format: Conference Proceeding
Language:English
Published: IEEE 29.11.2024
Subjects:
CC&G algorithm (key words)
Collaboration
Costs
Distribution networks
distribution robust model
flexible distribution network
Planning
Power supplies
Probability distribution
Regulation
soft open point
Static VAr compensators
System integration
Uncertainty
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Summary:Aiming at the problems that the existing flexible distribution network planning does not fully take the collaboration between flexible resources and traditional regulation equipment into account, which is mainly based on symmetric networks without fine modelling of the three-phase imbalance characteristics of the distribution network, a distributional robust optimization method is proposed for the collaborative planning of SOPs, ESSs and SVCs. Firstly, considering the source-load output uncertainty, a mixed probability distribution uncertainty model based on 1-parameter and ∞-parameter is constructed using the data of historical output scenarios. Secondly, considering the investment cost and operation cost of SOPs, ESSs, and SVCs, a scenario probability-driven collaborative planning model is established. Then, a main problem-subproblem iterative algorithm based on the generation of columns and constraints is adopted to achieve the solution by a commercial solver. Finally, the effectiveness and practicality of the proposed method in the paper are verified by the IEEE 33-node arithmetic example.
DOI:10.1109/EI264398.2024.10991626