Two-Stage Adaptive Restoration Decision Support System for a Self-Healing Power Grid

Power outages cost American industries and businesses billions of dollars and jeopardize the lives of hospital patients. The losses can be greatly reduced with a fast, reliable, and flexible self-healing tool. This paper is aimed to tackle the challenging task of developing an adaptive restoration d...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 13; H. 6; S. 2802 - 2812
Hauptverfasser: Golshani, Amir, Sun, Wei, Zhou, Qun, Zheng, Qipeng P., Tong, Jianzhong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.12.2017
Schlagworte:
Adaptive restoration
dynamic reserve
Generators
integer L-shaped algorithm
Integer linear programming
mixed-integer linear programming
Optimization
Power system dynamics
POWER TRANSMISSION AND DISTRIBUTION
Power transmission lines
Real-time systems
two-stage optimization
twostage optimization
ISSN:1551-3203, 1941-0050
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Zusammenfassung:Power outages cost American industries and businesses billions of dollars and jeopardize the lives of hospital patients. The losses can be greatly reduced with a fast, reliable, and flexible self-healing tool. This paper is aimed to tackle the challenging task of developing an adaptive restoration decision support system (RDSS). The proposed RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. The comprehensive formulation encompasses practical constraints including ac power flow, dynamic reserve, and load modeling. The combinatorial problem is decomposed into a two-stage formulation solved by an integer L-shaped algorithm. The two stages are then executed online in the RDSS framework employing a sliding window method. The IEEE 39-bus system has been studied under normal and contingency conditions to demonstrate the effectiveness and efficiency of the proposed online RDSS.
Bibliographie:USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
EE0007998; ECCS-1552073; CMMI-1355939
National Science Foundation (NSF)
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2017.2712147