Reactive power optimization for power distribution networks using mixed-integer Second-order cone programming

The integration of a large number of Distributed Resources (DR) into the power distribution network presents significant challenges to the system’s reactive power and voltage control. This paper comprehensively considers the impacts of network losses, curtailment of renewable energy, and user electr...

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Veröffentlicht in:Journal of physics. Conference series Jg. 2803; H. 1; S. 12012 - 12020
Hauptverfasser: Xiang, Xiaojing, Gao, Shihong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.07.2024
Schlagworte:
Absorption
Complex variables
Demand analysis
Electric power demand
Electric power distribution
Electricity consumption
Energy distribution
Energy management
Mixed integer
Optimization models
Power flow
Reactive power
Renewable energy
Renewable resources
ISSN:1742-6588, 1742-6596
Online-Zugang:Volltext
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Zusammenfassung:The integration of a large number of Distributed Resources (DR) into the power distribution network presents significant challenges to the system’s reactive power and voltage control. This paper comprehensively considers the impacts of network losses, curtailment of renewable energy, and user electricity consumption experiences, and establishes a reactive power optimization model for distribution networks with multiple types of distributed resources. Initially, complex relationships among power flow variables are approximated by linear expressions, transforming the power flow model into a Second-order Cone Programming (SOCP) formulation. Subsequently, quadratic nonlinear terms in the constraints are linearized through the use of auxiliary variable methods. Ultimately, the effectiveness of the proposed model is verified on the IEEE BUS-33 system, with an analysis conducted on the influence of distributed resource capacity, demand response weighting coefficients, and demand response levels on system operation. The study concludes that before the absorption capacity limit of the system, an increase in distributed resource capacity not only reduces system losses but also enhances the user’s electricity consumption experience. However, beyond this absorption capacity limit, further increases in distributed resource capacity not only lead to increased system losses but also result in renewable energy curtailment, thereby causing resource waste.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2803/1/012012