Optimal Shunt Capacitors’ Placement and Sizing in Radial Distribution Systems Using Multiverse Optimizer

Mtonga, Thomson; Kaberere, Keren K.; Irungu, George K.

doi:10.1109/icjece.2020.3012041

Cited by 28 publications

(18 citation statements)

References 29 publications

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“…The OCP problem is considered as a nonlinear mixed integer programming problem in the work carried out in (Riaño et al 2021), which is solved using Chu and Beasley genetic algorithm (CBGA). Whereas, in (Mtonga et al 2021), the authors employ the multiverse optimizer (MO) for the OCP issue. The approach used offers reduced total system cost and improved system performance.…”

Section: Nomenclaturementioning

confidence: 99%

A Novel Quasi-opposition Based Sine Cosine Algorithm for Optimal Allocation and Sizing of Capacitor in Radial Distribution Systems

Biswal

Shankar

2021

Preprint

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Increasing trend in load demand has introduced many problems in distribution systems like more line losses, low power factor, voltage fluctuations and so on. These issues have become a vital challenge for the power utilities to resolve and maintain the system under healthy conditions. For handling these issues, optimal capacitor placement (OCP) in radial distribution systems employing an optimization approach is explored in this work. The present work proposes a novel application of quasi-opposition based sine cosine algorithm for solving OCP problem. The effectiveness and superiority of the proposed algorithm is verified over other algorithms using different standard benchmark test functions. For solving OCP problem, at first, the most deserving candidate buses for the OCP are identified using a new proposed sensitivity index that helps in reducing search space for the optimization process. Thereafter, by minimizing the losses and maximizing the net annual profit of the system, the optimal location and selection of the fixed-step capacitor banks are obtained. The efficacy of the proposed algorithm has been verified by comparing the results obtained with that of other state-of-the-art algorithms on the standard IEEE 85 bus and 118 bus radial distribution test systems considering full load and variable load scenarios.

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Section: Nomenclaturementioning

confidence: 99%

A Novel Quasi-opposition Based Sine Cosine Algorithm for Optimal Allocation and Sizing of Capacitor in Radial Distribution Systems

Biswal

Shankar

2021

Preprint

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“…The problem of optimal integration of RE based DGs and CBs in EDSs has been addressed using various heuristic approaches in the literature. Gravitational search algorithm (GSA) [1], shark smell optimization (SSO) [2], flower pollination algorithm (FPA) [3], whale optimization algorithm [4], polar bear optimization algorithm [5], spotted hyena optimizer (SHO) [6], salp swarm algorithm (SSA) [7], water cycle algorithm (WCA) [8], multiverse optimizer [9], and vortex search algorithm (VSA) [10], are recent examples of CBs allocation. A comprehensive literature survey on other related works can be seen in [11].…”

Section: Introductionmentioning

confidence: 99%

“…CBs and/or DGs can improve EDS performance through DG allocation work in terms of loss reduction , voltage profile improvement , VDI reduction [12,19,20,21], voltage stability enhancement [2,8,12,14,17,18,20,21,24], GHG emission reduction [8,14,23,24], net savings maximisation [14,15,17,18], and reliability improvement [22,23]. In addition, major objective functions handled in the CBs allocation problem include the cost of real power distribution loss [1][2][3][4][5][6][7][8][9][10], operational cost [2][3][4][5][6][7][8][9][10], and installation cost [1,2,5,7]. Some works use real power loss sensitivity factors (RLSFs) [1,9,…”

Section: Introductionmentioning

confidence: 99%

“…In addition, major objective functions handled in the CBs allocation problem include the cost of real power distribution loss [1][2][3][4][5][6][7][8][9][10], operational cost [2][3][4][5][6][7][8][9][10], and installation cost [1,2,5,7]. Some works use real power loss sensitivity factors (RLSFs) [1,9,20], reactive power loss sensitivity factors (QLSFs) [5,6], and normalised voltage magnitudes (NVMs) [1] to determine the pre-defined search space in order to improve the performance of heuristic algorithms. For the CB/DG allocation problem to be solved, both discrete variables (like locations) and continuous variables (like sizes) must be taken into account.…”

Section: Introductionmentioning

confidence: 99%

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Self-Adaptive Dragonfly Algorithm for Optimal Allocation of Multiple Energy Storage Systems with VAr Support for Islanded Microgrid Operation

2022

IJIES

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Microgrid (MG) operation is one of the operational requirements of modern utilities for not only maintaining reliable and uninterrupted supply under faulty conditions but also for achieving desired economic goals in the competitive electricity market environment. Under faulty conditions, the electrical distribution system (EDS) becomes islanded microgrid (IMG) with either micro turbine (MT) or renewable energy (RE) based distribution generation (DG) units. However, the power generation from RE based DGs is stochastic nature and it may become either surplus or deficit to the network connected load. Also, the reactive power (VAr) support from DGs is limited. Under this scenario, there is a need for the integration of energy storage systems (ESSs) and reactive power compensators like switched/ fixed capacitor banks (CBs). In this paper, a novel optimization approach for determining the locations and capacities of ESSs combined with CBs along with DGs is proposed based on improved variant of dragonfly algorithm (DFA). Different performance variables of basic DFA are tuned by a self-adaptive mechanism in SADFA for attaining the global solution with least computational efforts. Simulation results on IEEE 33-bus are compared with literature works and also other algorithms namely basic DFA, PSO, BOA, and FSA. The islanded network is suffered with a loss of 97.1229 kW, whereas, it is reduced to 74.2305 kW, 47.3380 kW and 37.6792 kW, by integrating CBs, DGs and simultaneous CBs and DGs, respectively. Based on the comparative analysis, SAFDA is dominated literature works and all other simulated algorithms. Also, the proposed method for ESSs, CBs and DGs integration is shown its effectiveness for serving the IMG energy requirements with reduced losses and increased economic benefits and its suitability for practical applications.

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“…Increases in power losses at the consumer's end necessitates an increase in the generation of energy, and its effects on the power system are manifold. While it increases the operation and maintenance costs, it also increases the stress on the power system, leading to the voltage profile deterioration and several other challenges [1]. Increasing the generation may also require additional time to respond to the problem and incur additional costs for installation of newer generation plants/facilities.…”

Section: Introductionmentioning

confidence: 99%

Annual Cost and Loss Minimization in a Radial Distribution Network by Capacitor Allocation Using PSO

et al. 2021

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Increasing power demand from passive distribution networks has led to deteriorated voltage profiles and increased line flows. This has increased the annual operations and installation costs due to unavoidable reinforcement equipment. This work proposes the reduction in annual costs by optimal placement of capacitors used to alleviate power loss in radial distribution networks (RDNs). The optimization objective function is formulated for the reduction in operation costs by (i) reducing the active and reactive power losses, and (ii) the cost and installation of capacitors, necessary to provide the reactive power support and maintain the voltage profile. Initially, the network buses are ranked according to two loss sensitivity indices (LSIs), i.e., active loss sensitivity with respect to node voltage (LSI1) and reactive power injection (LSI2). The sorted bus list is then fed to the particle swarm optimization (PSO) for solving the objective function. The efficacy of the proposed work is tested on different IEEE standard networks (34 and 85 nodes) for different use cases and load conditions. In use case 1, the values finalized by the algorithm are selected without considering their market availability, whereas in use case 2, market-available capacitor sizes close to the optimal solution are selected. Furthermore, the static and seasonal load profiles are considered. The results are compared with recent methods and have shown significant improvement in terms of annual cost, losses and line flows reduction, and voltage profile.

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Optimal Shunt Capacitors’ Placement and Sizing in Radial Distribution Systems Using Multiverse Optimizer

Cited by 28 publications

References 29 publications

A Novel Quasi-opposition Based Sine Cosine Algorithm for Optimal Allocation and Sizing of Capacitor in Radial Distribution Systems

A Novel Quasi-opposition Based Sine Cosine Algorithm for Optimal Allocation and Sizing of Capacitor in Radial Distribution Systems

Self-Adaptive Dragonfly Algorithm for Optimal Allocation of Multiple Energy Storage Systems with VAr Support for Islanded Microgrid Operation

Annual Cost and Loss Minimization in a Radial Distribution Network by Capacitor Allocation Using PSO

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