A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

AC transmission expansion planning (ACTEP) is one of the most critical issues in electric power system expansion planning. In existing research on ACTEP, the reduction of power losses is often overlooked due to the significant computational workload associated with ACTEP problem. While in some insta...

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Veröffentlicht in:Electrical engineering Jg. 107; H. 1; S. 987 - 1007
Hauptverfasser: Duong, Thanh Long, Bui, Nguyen Duc Huy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
Springer Nature B.V
Schlagworte:
Alternative energy sources
Economics and Management
Effectiveness
Electric power loss
Electric power systems
Electrical Engineering
Electrical Machines and Networks
Energy Policy
Engineering
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic
Integer programming
Loss reduction
Machine learning
Methods
Optimization
Optimization algorithms
Original Paper
Power Electronics
Reactive power
Series compensators
Simulation
Strategy
Swarm intelligence
Thyristors
Transmission lines
Wind power
Power loss
Reactive power planning
AC transmission expansion planning
Meta-heuristic
Thyristor-controlled series compensators
ISSN:0948-7921, 1432-0487
Online-Zugang:Volltext
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Zusammenfassung:AC transmission expansion planning (ACTEP) is one of the most critical issues in electric power system expansion planning. In existing research on ACTEP, the reduction of power losses is often overlooked due to the significant computational workload associated with ACTEP problem. While in some instances, minimizing power loss is included as an objective function in the TEP problem, this approach may impact the addition of new lines. Consequently, to address this issue, a four-stage strategy is proposed in this paper for resolving ACTEP problem while considering power loss reduction. Specifically, the reduction of power losses is addressed after the deployment of new transmission lines. Moreover, a hybrid approach, referred to as differential evolution (DE) combined with teaching–learning-based optimization (TLBO) algorithms, called (DE-TLBO), is proposed for optimizing reactive power planning and determining the size of thyristor-controlled series compensators (TCSC) to minimize power loss in ACTEP problem. Simulation results conducted on Graver 6 bus, IEEE 24 bus, and modified IEEE 118 bus systems demonstrate the efficacy of the proposed algorithm when compared to conventional methods such as differential evolution (DE), modified artificial bee colony, and real genetic algorithms (RGA). Additionally, the proposed method also illustrates the effectiveness of utilizing TCSC to reduce power loss in the Graver 6 bus and the IEEE 24 bus systems by 3.72% and 11.95%, respectively.
Bibliographie:ObjectType-Article-1
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ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-024-02566-7