Optimal Location and Sizing of DG and Shunt Capacitors Using Differential Evolution

Gunda, Jagadeesh; Khan, Nasim Ali

doi:10.3923/ijscomp.2011.128.135

Cited by 15 publications

(5 citation statements)

References 8 publications

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“…The PSO for line loss reduction in power system subjected to voltage constraints for optimum placement of DG was performed by Wong et al [5]. Jagadeesh Gunda et al (2011) had applied the differential evaluation algorithm (DEA) to determine the optimal location and size of shunt capacitors and DGs in RDN for minimum real power loss on Indian Electricity Board benchmark 25 bus distribution systems [6]. The distributed generations are mainly classified according to the main fuel used.…”

Section: Introductionmentioning

confidence: 99%

Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network

Rajeswaran¹,

Kamaraj²

2016

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Nowadays the optimal allocation of distributed generation (DG) in the distribution network becomes the popular research area in restructuring of power system. The capacitor banks introduced in the distribution networks for reactive power compensation also have the capacity to minimize the real and reactive power losses occurred in the system. Hence, this research integrates the allocation of renewable energy DG and capacitor banks in the radial distribution network to minimize the real power loss occurred in the system. A two-stage methodology is used for simultaneous allocation of renewable DG and capacitor banks. The optimum location of renewable energy DG and capacitor banks is determined using the distributed generation sitting index (DGSI) ranking method and the optimum sizing of DG and capacitor banks is found out for simultaneous placement using weight improved particle swarm optimization algorithm (WIPSO) and self adaptive differential evolution algorithm (SADE). This two-stage methodology reduces the burden of SADE and WIPSO algorithm, by using the DGSI index in determining the optimal location. Hence the computational time gets reduced which makes them suitable for online applications. By using the above methodology, a comprehensive performance analysis is done on IEEE 33 bus and 69 bus RDNs and the results are discussed in detail.

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Section: Introductionmentioning

confidence: 99%

Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network

Rajeswaran¹,

Kamaraj²

2016

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“…Furthermore, numerous other artificial intelligencebased optimization algorithms that have been proposed in literature for the simultaneous DG/SC allocation includes ant lion optimization (ALO) [25], biogeography-based optimization (BBO) [26], backtracking search algorithm (BSA) [27,28], bacterial foraging optimization algorithm (BFOA) [29], binary collective animal behavior optimization (BCAO) [30], binary GSA (BGSA) [31], cuckoo search algorithm (CSA) [32], differential evolutionary algorithm (DEA) [33], discrete imperialistic competition algorithm (DICA) [34], intersect mutation differential evolution (IDME) [35], G best -guided artificial bee colony algorithm (GABC) [36], memetic algorithm (MA) [37], symbiotic organisms search (SOS) [38], tabu search (TS) [39], and teaching-learning-based optimization [40].…”

Section: Existing Research and Itsmentioning

confidence: 99%

Parameter-Free Improved Best-Worst Optimizers and Their Application for Simultaneous Distributed Generation and Shunt Capacitors Allocation in Distribution Networks

Leghari

Hussain

Memon

et al. 2022

International Transactions on Electrical Energy Systems

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This paper proposes parameter-free two best-worst optimizers (BWOs) that combine the searching capabilities of Jaya and Rao-1 algorithms to intensify their exploration and exploitation capabilities. For the proposed first optimizer, BWO-1, parallel taxonomy has been adopted for the Jaya and Rao-1 combination to obtain dual sets of updated solutions for a given current solution set. For the proposed second optimizer, BWO-2, along with the parallel taxonomy, a subloop of random scaling factors has been introduced in the solution updating mechanism of both standard Jaya and Rao-1 algorithms to generate multiple sets of updated solutions in a single iteration. The best solutions from obtained multiple solution-sets will survive only and lead to the next iteration. Hence, the proposed solution updating mechanisms for BWO-1 and BWO-2 increase the probability of getting a quality solution under set conditions. The ability and scalability of BWO-1 and BWO-2 were assessed, solving the optimization problem of power loss reduction and voltage deviation minimization in the IEEE 33-bus and 69-bus test systems. The results show that the BWO-2 exhibited a lead of 2.97%, 1.83%, and 0.72% over the Rao-1, Jaya, and BWO-1 techniques, respectively. Besides, the BWO-2 achieves up to 38.76% more reduction in power losses against the existing standard, improved, and hybrid optimization techniques.

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“…After placing the estimated shunt capacitive value at the most preferred bus (i.e., at a candidate bus), again DEA is executed for fixing the optimum capacitor size at the next preferred candidate/potential bus [26]. This procedure is repeated until no additional compensation is required and any further compensation increases the losses in the distribution network.…”

Section: Selection Operationmentioning

confidence: 99%

Optimal Location and Sizing of Shunt Capacitors with Distributed Generation in Distribution Systems

Salkuti

2021

ECTI-EEC

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This paper proposes a novel approach to determine an optimal location and sizing of shunt capacitors for reactive power compensation in distribution systems with distributed generation. Here, the optimal location for installing the shunt capacitors is determined using the loss sensitivity factor approach. The stochastic nature of distributed generation (i.e., wind and solar PV powers) has been considered in this paper. Optimal sizing of shunt capacitors has been determined by considering the power loss minimization as objective function and this problem is solved using the differential evolution algorithm (DEA). The results obtained with DEA are also compared with genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. The effectiveness of this proposed approach has been simulated on the IEEE 15, 33, 69 and 85 bus test systems.

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Optimal Location and Sizing of DG and Shunt Capacitors Using Differential Evolution

Cited by 15 publications

References 8 publications

Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network

Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network

Parameter-Free Improved Best-Worst Optimizers and Their Application for Simultaneous Distributed Generation and Shunt Capacitors Allocation in Distribution Networks

Optimal Location and Sizing of Shunt Capacitors with Distributed Generation in Distribution Systems

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