Optimal placement and sizing of voltage controlled distributed generators in unbalanced distribution networks using supervised firefly algorithm

Othman, Mahmoud M.; El-Khattam, Walid; Hegazy, Yasser G.; Abdelaziz, Almoataz Y.

doi:10.1016/j.ijepes.2016.03.010

Cited by 68 publications

(35 citation statements)

References 34 publications

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“…References [280,281] have used FA to find the optimal site and size of multiple DGs on a balanced radial feeder for power loss minimization. Othman et al [282] have modified the traditional FA in order to be able to deal with the practically constrained optimization problems. This new algorithm has many advantages such as, simple concepts, easy implementation and higher stability mechanism compare with traditional FA [282].…”

Section: The Prospective Methodsmentioning

confidence: 99%

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review

Zubo

Mokryani

Rajamani

et al. 2017

Renewable and Sustainable Energy Reviews

180

105

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Section: The Prospective Methodsmentioning

confidence: 99%

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review

Zubo

Mokryani

Rajamani

et al. 2017

Renewable and Sustainable Energy Reviews

180

105

View full text Add to dashboard Cite

show abstract

“…218,219 Nadhir et al 220,221 differently used FA for finding optimal sizes and locations of multiple DGs on a balanced radial network aimed at minimising power loss. Othman et al 222 modified the traditional FA to efficiently solve constrained optimisation problems. The main strengths of the modified FA over the traditional one are ease of implementation, higher stability mechanism, and simpler concepts.…”

Section: Firefly Algorithmmentioning

confidence: 99%

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

2020

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Summary Integrating renewable energy hybrid distributed generation (REHDG) into distribution network systems (DNSs) has become increasingly important because of various technical, economic, and environmental advantages accruing from it. However, the output power of REHDGs from photovoltaic (PV) and wind is highly variable because of its dependency on intermittent parameters such as solar irradiance, temperature, and wind speed. Such variability of generated power from large‐scale REHDGs or load introduces small signal instabilities (oscillations). Meanwhile, different locations of integration and sizes of REHDGs in the DNS affect the system oscillation modes by either improving or depriving the small‐signal stability (SSS) of the network. Consequently, a significant number of research has been conducted on the planning of optimal allocation of REHDGs in DNS. In this regard, this paper reviews the existing planning models, optimisation techniques, and resources' uncertainty modelling employed in REHDGs allocations in terms of their capability in obtaining optimal solutions and enhancing SSS of the system. Planning models with optimisation algorithms are evaluated for modelling renewable resource uncertainties and curtailing SSS variables. Research works on planning of optimal allocation of these generations attain minimum cost, but were unable to satisfy the SSS requirements of the system. The existing models for the planning and design of optimal timing, sizing, and placement of REHDGs will need to be improved to optimally allocate REHDGs and satisfy the SSS of the DNS after the integration.

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“…Indexes of active and reactive power network loss APL and RPL could be further derived as = = (11) Obviously, the active power and power factor (PF) of DWFs have great influences on network loss. In this paper, negative power factor represents capacitive reactive power while positive power factor is on behalf of inductive reactive power according to the convention.…”

Section: Influences On Network Loss and Voltagementioning

confidence: 99%

“…Compared to other intelligent algorithms, the firefly algorithm is faster and the variable parameter adjustment is simplified. But the common disadvantages of these algorithms are premature convergence of the population and slow convergence rate [10,11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Factor Regulation of Dispersed Wind Farms under Diverse Load and Stochastic Wind Conditions Based on Improved Firefly Algorithm

Liu

Wang

Cui

et al. 2018

Mathematical Problems in Engineering

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Aiming at the instability of voltage and large network loss of dispersed wind farms (DWFs) integration into distribution network (DN), an optimal power factor regulation method based on improved firefly algorithm is proposed. Firstly, the generalized load model based on the static voltage characteristics is analyzed. Then reactive power capabilities of DWFs are thoroughly discussed and the influences of DWFs with variable power factor on network loss and voltage profile are presented. In order to reduce network loss and enhance power quality, optimal power factor regulation of DWFs based on improved firefly algorithm (IFA) is proposed. Finally, results of the benchmark IEEE-33 node system show the feasibilities and validities of the proposed method and the superiorities of proposed IFA are revealed by comparison with the existing algorithms.

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Optimal placement and sizing of voltage controlled distributed generators in unbalanced distribution networks using supervised firefly algorithm

Cited by 68 publications

References 34 publications

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

Optimal Power Factor Regulation of Dispersed Wind Farms under Diverse Load and Stochastic Wind Conditions Based on Improved Firefly Algorithm

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