Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

Kanwar, Neeraj; Gupta, Nikhil; Niazi, K. R.; Swarnkar, Anil

doi:10.1016/j.ijepes.2015.05.049

Cited by 80 publications

(43 citation statements)

References 37 publications

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“…For improvement of solution search quality and computational speed, there have been attempts in modifying the existing algorithms, such as variable discretisation during crossover and mutation mechanisms in modified Genetic Algorithm (GA) [341], improved solution re-initialisation algorithm and incorporation of time-varying acceleration coefficient (TVAC) in Self-organising Hierarchical Binary Particle Swarm Optimisation (SOHBPSO) [264], incorporation of star topology and symbol vector space in Meta Particle Swarm Optimisation (MPSO) [342], infusion of Quasi-Opposition based Learning (QOBL) element into Swine Influenza Model-based Optimisation with Quarantine (SIMBO-Q) in QuasiOppositional Swine Influenza Model-based Optimisation with Quarantine (QOSIMBO -Q) [339], modification with dispersion elimination in modified Bacterial Foraging Algorithm (BFA) [343], improved PSO with local escape algorithm, modified GA with brute force cross-over and acquiescent mutation, and seeking and tracing mode revision for improved Cat Swarm Optimisation (CSO) [344]. New nature-inspired methods such as Grey Wolf Table 16 Summary of model equations for tidal/wave energy conversion.…”

Section: Mathematical Modelling Optimisation Techniquesmentioning

confidence: 99%

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Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

243

105

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Section: Mathematical Modelling Optimisation Techniquesmentioning

confidence: 99%

“…Esfahani et al [313] Optimisation (GWO) [345], Krill Herd Algorithm (KHA) [338], Invasive Weed Optimisation (IWO) [346], and Cat Swarm Optimisation (CSO) [344] are introduced with enhanced optimisation performance. Hybridisation of modern mathematical modelling optimisation methods is another strategy for performance improvement.…”

Section: Maleki and Askarzadehmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

243

105

View full text Add to dashboard Cite

“…Maximum number of branches (C5) For radial networks, the number of branches can be obtained as a constraint as shown in (24), where nbr is the number of branches and N is the total number of nodes:…”

Section: 25mentioning

confidence: 99%

“…The minimum number of PMUs to install in the power system is calculated according to (24), where f(x) refers to the minimum quantity of units to install:…”

Section: Minimum Pmus Installed (C6)mentioning

confidence: 99%

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Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topic.

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Bilevel optimization framework for impact analysis of DR on optimal accommodation of PV and BESS in distribution system

Sharma

Niazi

Verma

et al. 2019

Int Trans Electr Energ Syst

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Summary The optimal integration of emerging distributed energy resources (DERs) such as energy storage systems and distributed generations (DGs) can improve the performance of distribution systems (DS) significantly. However, increased penetration of DGs may adversely affect the system performance under certain conditions in terms of increased energy losses, voltage violation, power mismatch, reverse power flow etc. The demand response (DR) is one of the operational technologies which may help overcome such issues related to increased DG penetration. In this paper, the role of DR is investigated to optimally accommodate multiple photovoltaics (PV) and battery energy storage systems (BESS) in DS. The problem is formulated as bilevel constrained optimization problem which has been solved using genetic algorithm (GA). The level 1 of optimization determines the optimal nodes and sizes of multiple BESS and PVs which are to be integrated in the system. Thereafter, dispatch of BESS in coordination with DR is optimized in the level 2 optimization. The simulation results highlight the effectiveness of DR in accommodating multiple PV and BESS in DS.

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Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

Cited by 80 publications

References 37 publications

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Bilevel optimization framework for impact analysis of DR on optimal accommodation of PV and BESS in distribution system

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