Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

Ahmed, Walaa; Selim, Ali; Kamel, Salah; Yu, Juan; Jurado, Francisco

doi:10.9781/ijimai.2018.11.001

Cited by 15 publications

(12 citation statements)

References 28 publications

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“…Several methods have been used and compared in the context of their work. These are NSGA II, MOPSO, PESA II and SPEA-2 [9]. The IEEE 33 and IEEE 69 networks are the test networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Contribution to the Economic and Optimal Planning of Multi-GED and a FACTS in a Distribution Network by Genetics Algorithms

Oloulade¹,

Imano²,

Fifatin³

et al. 2020

EPES

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The distribution networks are more and more heavily loaded due to economic growth, industrial development and housing. The operation of these networks under these conditions generates voltage instabilities and excessive power losses. The present work consisted in the optimal integration of multi-GED (Decentralized Energy Generators) (Photovoltaic (PV), Fuel Cell (FC or PAC) and Wind Generator (WG)) and FACTS (SVC) in a Medium Voltage distribution's departure of the Beninese Electrical Energy Company (SBEE), with a view to improve its technical performances. The diagnostic study of the Ouidah 122-nodes test network, before optimization, revealed that the active and reactive losses are 457.34588 kW and 625.41503 kVAr respectively. This network has high voltage instability with a minimum voltage of 0.80455 p.u. and a minimum VSI of 0.41897 p.u. The optimization of the size and positioning of GED and FACTS was based on the Nondominated Sorting Genetic Algoritm II (NSGA II). After optimization with the NSGA II, a comparative study of the different combinations between the three GEDs and the SVC, made it possible to choose that of the placement of a 121 kW Wind Generator at node 75, a PV of 131 kW at node 51, a system of Fuel Cell (FC, PAC in french) of 700 kW at node 34, and an SVC of 2.126 MVAr at node 94 of the network. This positioning enabled a reduction of 65.11% in active losses and 65.12% in reactive losses. The voltage profile and the voltage stability are clearly improved, with a minimum voltage of 0.96993 p.u. and a minimum VSI of 0.88505 p.u. The initial investment for this project is seven hundred and seven million three hundred and fifty-two thousand three hundred and fifty-eight point seven CFA francs (707,352,358.7 CFA francs). The technical and economic evaluation shows that the payback period is approximately 4 years 6 months and 14 days. The relevant results obtained show that the method used is efficient and effective, and can be applied to other MV departures of the SBEE.

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“…Several methods have been used and compared in the context of their work. These are NSGA II, MOPSO, PESA II and SPEA-2 [9]. The IEEE 33 and IEEE 69 networks are the test networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Contribution to the Economic and Optimal Planning of Multi-GED and a FACTS in a Distribution Network by Genetics Algorithms

Oloulade¹,

Imano²,

Fifatin³

et al. 2020

EPES

View full text Add to dashboard Cite

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“…Many contributions were introduced for detecting the optimal size and location of certain FACTS devices to achieve certain objective functions. These contributions were achieved by many optimization techniques, like particle swarm optimization (PSO) and its modifications [2][3][4][5], biography-based optimization (BBO) [5], moth flame optimization (MFO) [6], gray wolf optimization (GWO) [7], improved harmony search (IHS) algorithm [8], cuckoo search algorithm (CSA) [9], teaching learning-based optimization (TLBO) [10,11], the dragonfly algorithm (DA) [12], and the Pareto envelope-based selection algorithm [13]. Also, some of the contributions involving FACTS devices were achieved by hybrid techniques, like the hybridizations between artificial bee colony (ABC) and the gravitational search algorithm (GSA) in [14]; differential evolution (DE) and BBO, known as the hybrid DE-based BBO algorithm, in [15]; and chemical reaction optimization (CRO) with quasi-oppositional-based optimization in [16].…”

Section: Introductionmentioning

confidence: 99%

Optimal SVC allocation in power systems using lightning attachment procedure optimization

Awad¹,

Kamel²,

Youssef³

2020

Turk J Elec Eng & Comp Sci

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Flexible AC transmission systems (FACTS) technology is widely adopted and utilized to maintain the performance of power systems. However, the improvements of power system performance achieved by FACTS devices depend on the right sizing and allocation of such devices. For technical and economic considerations, a FACTS device's location and size should be selected very carefully in order to maximize its benefits to the power system. In this paper, the sizing and location of a static VAR compensator (SVC) are optimally determined using a new optimization technique called lightning attachment procedure optimization (LAPO). The optimal allocation of the SVC is determined regarding the improvement of voltage deviation index and the reduction of total active power losses. The system is optimized in two cases: once without SVC installation in order to find out the optimal settings of the system that achieve the objective functions, and another time with SVC installation to determine its optimum sizes and locations by which the required objective functions are achieved. Then the system performance is analyzed after optimization with and without SVC devices to show the impact of the optimum sizing and location of the SVC on the system. The study is validated using the standard IEEE 30-bus system, while the developed LAPO is performed by MATLAB M-Files and the system performance analysis in different cases is performed by NEPLAN software.

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“…The random placement of DGs and capacitors in DS can cause more voltage drop and higher power losses than losses without them [5], [6]. Therefore, determining the proper placement and capacity of DGs in DS becomes a crucial task for obtaining their maximum possible advantages.…”

Section: Introductionmentioning

confidence: 99%

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

Mohamed¹,

Mohamed²,

Mitani³

2019

IJIMAI

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This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units.

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Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

Cited by 15 publications

References 28 publications

Contribution to the Economic and Optimal Planning of Multi-GED and a FACTS in a Distribution Network by Genetics Algorithms

Contribution to the Economic and Optimal Planning of Multi-GED and a FACTS in a Distribution Network by Genetics Algorithms

Optimal SVC allocation in power systems using lightning attachment procedure optimization

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

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