An Adjustable Robust Economic Energy and Reserve Dispatch Problem Incorporating Large-Scale Wind Farms
The integration of large-scale wind farms creates significant uncertainty and remarkable technical challenges for power systems due to their intermittency. Comprehensive wind power dependence structure modeling is essential for uncertainty management in the optimal operation of modern power systems...
Published in: | IEEE Access |
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Main Author: | |
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2022
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Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134230952&doi=10.1109%2fACCESS.2022.3190070&partnerID=40&md5=69069648fa202feceee0e685ceaef60e |
Summary: | The integration of large-scale wind farms creates significant uncertainty and remarkable technical challenges for power systems due to their intermittency. Comprehensive wind power dependence structure modeling is essential for uncertainty management in the optimal operation of modern power systems with many wind farms. This paper presents an efficient adjustable robust optimization framework by which the complex dependence structure of wind farms can be modeled in energy and reserve dispatch problems. Efficient dependence structure modeling and its incorporation in wind generation and reserve dispatch scheduling can avoid wind curtailment and load shedding incidents. The proposed approach utilizes a canonical vine copula as a flexible and appropriate statistical model to consider both the joint and conditional distributions of any number of wind farms. The results of the suggested canonical vine copula-based dependence structure modeling are then utilized as efficient inputs for the presented adjustable robust economic energy and reserve dispatch problem. The suggested approach is examined on the IEEE 118-bus system, and simulation results demonstrate that the proposed methodology by efficient dependence structure modeling is effective and also show the effect of the wind power correlation level on the total cost. © 2013 IEEE. |
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ISSN: | 21693536 |
DOI: | 10.1109/ACCESS.2022.3190070 |