Mesh distribution system analysis in presence of distributed generation with time varying load model

Murty, V. V. S. N.; Kumar, Ashwani

doi:10.1016/j.ijepes.2014.05.034

Cited by 56 publications

(33 citation statements)

References 29 publications

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“…Big bang big crunch method was implemented for the placement of DG in Hegazy et al (2014). Murty and Kumar (2014) uses mesh distribution system analysis for the placement of distributed generation with time varying load model. Probabilistic approach with DG penetration was discussed in Kolenc et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Suresh

Edward

2018

Renewables

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Distributed generation (DG) is small generating plants which are connected to consumers in distribution systems to improve the voltage profile, voltage regulation, stability, reduction in power losses and economic benefits. The above benefits can be achieved by optimal placement of DGs. A novel nature-inspired algorithm called Dragonfly algorithm is used to determine the optimal DG units size in this paper. It has been developed based on the peculiar behavior of dragonflies in nature. This algorithm mainly focused on the dragonflies how they look for food or away from enemies. The proposed algorithm is tested on IEEE 15, 33 and 69 test systems. The results obtained by the proposed algorithm are compared with other evolutionary algorithms. When compared with other algorithms the Dragonfly algorithm gives best results. Best results are obtained from type III DG unit operating at 0.9 pf.

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Section: Introductionmentioning

confidence: 99%

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Suresh

Edward

2018

Renewables

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“…and Kaur et al (2014) proposed hybrid evolutionary algorithm for DG placement. Mesh distribution system analysis with time-varying load model was presented in Qian et al (2011) and Murty and Kumar (2014). The backtracking search optimization algorithm (BSOA) was used in DS planning with multitype DGs in El-Fergany (2015); BSOA was proposed for DG placement with various load models.…”

Section: Open Accessmentioning

confidence: 99%

Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

2017

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Distributed generator (DG) resources are small scale electric power generating plants that can provide power to homes, businesses or industrial facilities in distribution systems. Power loss reductions, voltage profile improvement and increasing reliability are some advantages of DG units. The above benefits can be achieved by optimal placement of DGs. Whale optimization algorithm (WOA), a novel metaheuristic algorithm, is used to determine the optimal DG size. WOA is modeled based on the unique hunting behavior of humpback whales. The WOA is evaluated on IEEE 15, 33, 69 and 85-bus test systems. WOA was compared with different types of DGs and other evolutionary algorithms. When compared with voltage sensitivity index method, WOA and index vector methods gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.

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“…The normal topologies in MDN include weakly mesh or all mesh [77,78]. The former mode is realized with topologies attain by closing a selective number of TS, whereas the later mode works under normal operation by closing all TS [12,14,26,75].…”

Section: Mesh Distribution Network (Mdn)mentioning

confidence: 99%

“…The SDN concept on the basis of voltage stability, interconnected topology, reliability to consumers and high DG penetration favors loop and mesh topologies, aiming primarily at advanced power distribution configuration in SG [13,14]. Gamarra et al [15] presented a technical review of the literature on optimization techniques for planning, applied to one of the most popular SDN concepts, known as a microgrid (MG).…”

Section: Introductionmentioning

confidence: 99%

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Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective

et al. 2017

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Smart grids (SGs), as an emerging grid modernization concept, is spreading across diverse research areas for revolutionizing power systems. SGs realize new key concepts with intelligent technologies, maximizing achieved objectives and addressing critical issues that are limited in conventional grids. The SG modernization is more noticeable at the distribution grid level. Thus, the transformation of the traditional distribution network (DN) into an intelligent one, is a vital dimension of SG research. Since future DNs are expected to be interconnected in nature and operation, hence traditional planning methods and tools may no longer be applicable. In this paper, the smart distribution network (SDN) concept under the SG paradigm, has presented and reviewed from the planning perspective. Also, developments in the SDN planning process have been surveyed on the basis of SG package (SGP). The package presents a SDN planning foundation via major SG-enabling technologies (SGTF), anticipated functionalities (SGAF), new consumption models (MDC) as potential SDN candidates, associated policies and pilot projects and multi-objective planning (MOP) as a real-world optimization problem. In addition, the need for an aggregated SDN planning model has also been highlighted. The paper discusses recent notable related works, implementation activities, various issues/challenges and potential future research directions; all aiming at SDN planning.

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Mesh distribution system analysis in presence of distributed generation with time varying load model

Cited by 56 publications

References 29 publications

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective

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