Optimal renewable resources placement in distribution networks by combined power loss index and whale optimization algorithms

Reddy, P. Dinakara Prasad; Reddy, Vijay; Manohar, T. Gowri

doi:10.1016/j.jesit.2017.05.006

Cited by 113 publications

(7 citation statements)

References 32 publications

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“…The backtracking search optimization algorithm (BSOA) was used in DS planning with multi-type DGs in El-Fergany (2015), BSOA was proposed for DG placement with various load models. Reddy et al (2017a) and Reddy et al (2017b) proposed whale optimization and Ant Lion optimization algorithm for sizing of DGs. In most of the studies economic analysis has not been taken.…”

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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“…It comprises of a slack bus, 117 branches and load buses. 25 It has a total power load of 22 709 kW and 17 041 kVAr. 26 The simulation is of two parts: the first part presents the distribution system analysis at static load conditions.…”

Section: Test System and Simulation Casesmentioning

confidence: 99%

Real‐time multiperiod voltage control algorithm with OLTC and switched capacitors for smart distribution networks in the presence of energy storage system

Olatunde

Hassan

Abdullah

et al. 2020

Int Trans Electr Energ Syst

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Summary Conventionally, voltage profile are regulated by controllers that are not operational flexible to the system dynamism and challenges offered by the integration of renewable energy sources. Consequently, there is a need for the adaptive coordinating mechanism as an interface between the passive network and the new active network. This will provide a platform for the smooth integration of distributed energy resources in most developing countries where traditional network is still operational. This paper proposes an algorithm for real‐time multiperiod adaptive compensation of network reactive power support based on hierarchical structure and decentralized layered multiagent system voltage coordinating scheme. A multiperiod voltage control algorithm replicates the daily load flow evaluation aiming at ameliorating voltage fluctuation over a 24‐hour simulation period. The algorithm applies the decentralized voltage control mechanism to monitor the network parameters and dynamically evaluate the capacity of the capacitor banks (CBs) to be injected for voltage compensation. This mitigates the challenges associated with under and over reactive power compensation experienced in the day‐ahead programming and manual switching operation. The algorithm works in conjunction with an on‐load tap changing transformer (OLTC) and energy storage system (ESS). The ESS complements the OLTC during switching delay, dip in its output root mean square and voltage fluctuation as network loading condition varies. The effectiveness of the proposed method is verified on standard IEEE 33 and 118‐bus distribution systems. The results show an improved voltage performance and reduction in stress/complexity associated with load and renewable generation forecast in manual switching operations.

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“…Apart from the classical and analytical approaches, large numbers of meta-heuristic and heuristic algorithms have been proposed. The heuristic approaches are harmony search algorithm (Maleki et al , 2020), artificial bee colony algorithm (Das et al , 2018; Al-ammar et al , 2020), bacterial forging optimization (Devabalaji and Ravi, 2015), invasive weed optimization algorithm (Prabha and Jayabarathi, 2015), cuckoo search algorithm (Mohamed et al , 2018), symbiotic organism search (The et al , 2020), backtracking search optimization algorithm (El-Fergany, 2015), whale optimization algorithm (Prasasd Reddy et al , 2018), flower pollination algorithm (Sudabattula and Kowsalya, 2016), elephant herd optimization algorithm (Vijay, 2018), krill herd algorithm (Sultana and Roy, 2016) considering the drop in system losses and enhancement in voltage outline across all the buses of the network. Improvements in heuristic approaches have been eliminated by the hybridization of heuristic approaches with analytical approaches.…”

Section: Introductionmentioning

confidence: 99%

Optimal location and sizing of various DG units in real distribution substation using heuristic approach

M.L.¹,

Suja²,

2022

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Purpose The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO). Design/methodology/approach The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value. Findings The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints. Originality/value A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

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Optimal renewable resources placement in distribution networks by combined power loss index and whale optimization algorithms

Cited by 113 publications

References 32 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

Real‐time multiperiod voltage control algorithm with OLTC and switched capacitors for smart distribution networks in the presence of energy storage system

Optimal location and sizing of various DG units in real distribution substation using heuristic approach

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