A Two-level Coordination Strategy for Distribution Network Balancing

Uncertain distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks (DNs), which may harm the health of power equipment and increase the operational cost. There are emerging opportunities to balance three-phase DNs with a number of power electro...

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Veröffentlicht in:IEEE transactions on smart grid Jg. 15; H. 1; S. 1
Hauptverfasser: Cui, Xueyuan, Ruan, Guangchun, Vallee, Francois, Toubeau, Jean-Francois, Wang, Yi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Balancing
Coordination
Costs
Energy sources
hierarchical control
Integer programming
Inverters
Iterative methods
Linear programming
Load management
Load modeling
Mathematical models
Mixed integer
Nonlinear programming
phase switch device
Power transfer
Reactive power
soft open points
successive linearization
Switches
Three-phase unbalance
Voltage control
ISSN:1949-3053, 1949-3061
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Zusammenfassung:Uncertain distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks (DNs), which may harm the health of power equipment and increase the operational cost. There are emerging opportunities to balance three-phase DNs with a number of power electronic devices installed in the system. In this paper, we propose a novel two-level coordination strategy to improve the network balancing performance, where soft open points (SOPs) and phase switch devices (PSDs) are hierarchically coordinated in the network. At the upper level, a new type of SOPs with the function of phase switching is designed to explore the cross-phase power transfer ability; at the lower level, PSDs are utilized to flexibly allocate individual loads to specific phases. The two-level coordination strategy is typically formulated as a mixed-integer nonlinear programming (MINLP) problem. To solve the model accurately and efficiently, we develop a successive linearization algorithm to approximate it to a mixed-integer linear programming (MILP) problem at each iteration. On this basis, we propose a heuristic time-independent fixing algorithm to further ease the computational burden by eliminating a large number of integer variables in the MILP problem. Numerical simulations are conducted to validate the effectiveness, accuracy, and efficiency of the proposed method.
Bibliographie:ObjectType-Article-1
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2023.3274564