A hybrid teaching–learning-based optimization technique for optimal DG sizing and placement in radial distribution systems

Quadri, Imran Ahmad; Bhowmick, Suman; Joshi, Dheeraj

doi:10.1007/s00500-018-3544-8

Cited by 22 publications

(11 citation statements)

References 39 publications

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“…5. The line data and bus data of such system is taken from [29]. The total active power and reactive power demand of the network is 3715 kW and 2300 kVAR respectively.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Coordinated optimal planning of electric vehicle charging stations and capacitors in distribution systems with vehicle-to-grid facility

Bilal

Rizwan

2021

Preprint

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Deployment of electric vehicles as a future mode of transport is essential to lower the greenhouse gas emissions and other harmful gases instigated by conventional engine driven vehicles. Installation of electric vehicle charging stations are one of the prominent aspects for the widespread use of electric vehicles. Improper positioning of electric vehicle charging station creates challenges which in turn affect the electrical network and utility operator. This paper presents a new hybrid approach for investigating the optimal location of electric vehicle charging stations. Reactive power compensation is also provided along with charging station to deal with power loss issues and retain the distribution network's reliability. Moreover, vehicle to grid facility has been comprehensively considered in this paper. The proposed algorithm is the hybrid of grey wolf optimization and particle swarm optimization. The hybridization of two algorithms combines the desirable attributes of both algorithms and maintains a strong balance between the exploration and exploitation capability. The proposed hybrid approach is applied on IEEE-33 bus and 34-bus distribution system for minimizing the active power loss and maximizing the net profit. Furthermore, the outcome attained using the anticipated technique is equated with conventional grey wolf optimization (GWO). For 33-bus system, the proposed technique results in loss reduction of 30.67% and maximizing the net profit by 1142.91$ as compared to GWO. Likewise, power losses have been reduced to 27.6% and maximized net profit by 4111.74$ for 34-bus network. The obtained results prove the supremacy of the proposed approach over grey wolf optimization to evaluate locations of charging stations' and reactive power source in radial distribution network.

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“…5. The line data and bus data of such system is taken from [29]. The total active power and reactive power demand of the network is 3715 kW and 2300 kVAR respectively.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Coordinated optimal planning of electric vehicle charging stations and capacitors in distribution systems with vehicle-to-grid facility

Bilal

Rizwan

2021

Preprint

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“…Quadri, I.A., Bhowmick, S. and Joshi, D. [17] proposed a Hybrid Teaching Learning Based Optimization (HTLBO) method for the ideal allocation of DGs. HTLBO method is able to control both discrete and continuous variables.…”

Section: Literature Workmentioning

confidence: 99%

Optimal Placement of Distributed Generation Units in Radial Distribution System using Hybrid Techniques

Rani,

Reddy

2019

IJEAT

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Reconfiguration is a process that supports to eliminate the power loss from a distribution network and this process have the capability to reduce the losses up to a specific point. Additionally, loss minimization may be calculated through the presentation of Distributed Generation (DG) units. Conversely, the incorporation of DG into the distribution network at an improper position may cause higher in losses and fluctuations in voltage. In the meantime, the uncertainty in voltage may produce partial power failure in the system. For that reason, it is essential to deliberate the stability boundaries in DGs position and sizing in the Radial Distribution System (RDS). In this research paper, hybrid Binary Particle Swarm Optimization (BPSO) with Flower Pollination Algorithm (FPA) is proposed for the ideal reconfiguration process and placing the DG in the 69-bus RDS. BPSO is applied to identify the best DG reconfiguration and FPA is proposed to determine the optimal DG size. This technique narrowly changes the DG location in every load bus of the network that delivers the minimum value of the objective function, which is considered as the finest candidate for DG connection. The simulation outcomes indicate the proposed method is more effective in reducing the power loss from 224.9804 to 27.2183 KW with the reduction of 88.8972% when compared to existing algorithm

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“…An implanted MFO algorithm to reduce active power loss and Voltage Stability Index (VSI) considering various renewable energy-based DG units have been addressed (Settoul et al 2019b). An applied teaching-learning-based optimization technique to minimize voltage deviation, active power loss, and the maximization of VSI has been published (Quadr et al 2019). The authors in (Hassan et al 2019) proposed a multi-verse optimizer algorithm to minimize three indices, which are the annual losses cost, total voltage variation, and apparent power loss.…”

Section: Introductionmentioning

confidence: 99%

Enhancing energy efficiency for optimal multiple photovoltaic distributed generators integration using inertia weight control strategies in PSO algorithms

Zellagui¹,

Lasmari²,

Alaboudy³

et al. 2022

Polityka Energetyczna – Energy Policy Journal

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A hybrid teaching–learning-based optimization technique for optimal DG sizing and placement in radial distribution systems

Cited by 22 publications

References 39 publications

Coordinated optimal planning of electric vehicle charging stations and capacitors in distribution systems with vehicle-to-grid facility

Coordinated optimal planning of electric vehicle charging stations and capacitors in distribution systems with vehicle-to-grid facility

Optimal Placement of Distributed Generation Units in Radial Distribution System using Hybrid Techniques

Enhancing energy efficiency for optimal multiple photovoltaic distributed generators integration using inertia weight control strategies in PSO algorithms

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