A mixed-integer LP model for the optimal allocation of voltage regulators and capacitors in radial distribution systems

2013 ◽  
Vol 48 ◽  
pp. 123-130 ◽  
Author(s):  
John F. Franco ◽  
Marcos J. Rider ◽  
Marina Lavorato ◽  
Rubén Romero
Keyword(s):  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Voltage Regulators ◽  
Mixed Integer ◽  
Lp Model

scholarly journals Optimal Placement of Capacitors, Voltage Regulators, and Distributed Generators in Electric Power Distribution Systems

Ingeniería ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 334-354
Author(s):  
Luis Alfonso Gallego Pareja ◽  
Jesus Maria Lopez Lezama ◽  
Oscar Gomez Carmona
Keyword(s):  
Distribution Systems ◽  
Optimal Allocation ◽  
Test System ◽  
Voltage Regulators ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Distributed Generators ◽  
Proposed Model ◽  
Power Loss Reduction ◽  
Optimal Implementation

Context: With the advent of the smart grid paradigm, electrical distribution network (EDN) operators are making efforts to modernize their power grids through the optimal implementation of distributed generators (DGs) and other devices such as capacitors (CAs) and voltage regulators (VRs). The optimal allocation of such devices is a challenging task involving discrete and integer decision variables. Method: This paper presents an approach for the optimal placement of CAs, VRs and DGs in EDNs. The distinctive feature of the proposed model is the fact that it can be used to optimize the allocation of all of these elements together, in pairs, or separately. The optimal implementation of these elements is formulated as a mixed integer nonlinear programming (MINLP) problem, and it is solved by means of a specialized genetic algorithm (SGA). Results: The proposed methodology was tested on the IEEE 69-bus test system. The results were compared with previous works from the specialized literature, showing the effectiveness and robustness of the model. Conclusions: It was found that the appropriate allocation of CAs, VRs, and DGs results in a significant power loss reduction. It was also found that the proposed model is faster than other techniques proposed in the specialized literature. Acknowledgements: The authors gratefully acknowledge the support from the Colombia Científica program, within the framework of the Ecosistéma Científico (Contract No. FP44842- 218-2018). The authors also acknowledge the support of the State University of Londrina and Universidad Tecnológica de Pereira (UTP).

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

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