A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models

Yammani, Chandrasekhar; Sydulu, M.; Kumari, M. Sailaja

doi:10.1016/j.ijepes.2016.01.003

Cited by 102 publications

(41 citation statements)

References 30 publications

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“…(1) Traditional LF solvers: The traditional LF solution methods, aiming at solving equality and inequality constraints, predominately include: • Other LF methods and frameworks as in [33,[42][43][44]51,53,66,80,90,92]; in addition to LF frameworks as multi-criteria stochastic planning model (MCSPM) with central limit theorem (CLT) [34] and IBVT in [56].…”

Section: Load Flow Solution Methods (S)mentioning

confidence: 99%

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

2017

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Distribution networks (DNWs) are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP) strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN) needs to consider choices, primarily distributed generation (DG), network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP) in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs) based on mult-objective optimizations (MOOs) in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT) have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at the multi-objective-based frameworks, are also presented in this paper.Keywords: active distribution network (ADN); distributed generation (DG); distributed energy resources (DERs); distribution network planning (DP); multi-objective optimization (MOO); multi-criteria decision analysis (MCDA); distributed generation placement (DGP); volt-ampere reactive power (VAR) compensation and power quality (VPQ); component reinforcement and up gradation (CRU); network (distribution) topology change and reconfiguration (NTR); planning techniques (PT); multiple objective planning (MOP); multi-objective planning techniques (MOPTs); future distribution networks (FDNs)

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Section: Load Flow Solution Methods (S)mentioning

confidence: 99%

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

2017

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“…Some of the Paretobased MOO algorithms that were utilized in solving the DG allocation problem include improved multiobjective HSA, 27 multiobjective shuffled bat algorithm, 28 improved differential search algorithm, 29 improved nondominated sorting GA-II (NSGA-II), 30 multiobjective Taguchi approach (MOTA), 31 Pareto frontier-based DE, 32 and nondominated sorting TLBO. Hence, the problem of allocating DGs in RDS becomes a complex multiobjective optimization (MOO) problem since it is quite hard to simultaneously optimize multiple conflicting objectives.…”

Section: Introductionmentioning

confidence: 99%

“…In view of the considered DG allocation problem, the Pareto approach is more suitable than the conventional weighted sum approach because, in Pareto approach, the trade-off nondominated solutions are obtained in just a single run whereas the weighted sum approach requires multiple runs by varying the weights (usually, in steps) to find the trade-off nondominated solutions. Some of the Paretobased MOO algorithms that were utilized in solving the DG allocation problem include improved multiobjective HSA, 27 multiobjective shuffled bat algorithm, 28 improved differential search algorithm, 29 improved nondominated sorting GA-II (NSGA-II), 30 multiobjective Taguchi approach (MOTA), 31 Pareto frontier-based DE, 32 and nondominated sorting TLBO. 33 It may be observed from literature survey that only a few number of studies are available that have utilized the Pareto-based MOO methods 17,[27][28][29][30][31][32][33] as compared with the conventional weighted sum method.…”

Section: Introductionmentioning

confidence: 99%

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

Saha

Mukherjee

2019

Int Trans Electr Energ Syst

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Summary This article presents a new method to decide the optimal locations and sizes of distributed generators (DGs) in radial distribution system (RDS). This task is associated with minimization of total voltage deviation and power loss and maximization of voltage stability index of the RDS. To solve this multiobjective DG allocation problem, a novel metaheuristic algorithm, namely, multiobjective chaotic symbiotic organisms search (SOS) (MOCSOS), is proposed in this article. The proposed MOCSOS is an improved version of the traditional SOS, where a chaotic local search technique is incorporated within the traditional SOS framework. The MOCSOS utilizes a fast nondominated sorting strategy to achieve the Pareto solutions. The constraints associated with this DG allocation problem are handled using a binary tournament selection–based approach. The performance of MOCSOS is validated on 33‐ and 69‐node RDSs. The efficacy of the MOCSOS is established by comparing its performance with other nature‐inspired state‐of‐the‐art algorithms.

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“…Sequential quadratic programming has been employed in [27] to obtain set of optimal solutions to minimize total real power loss and cost of DG. In addition to loss and voltage deviation minimization, cost minimization is also considered as objective [28]. Shuffled Bat algorithm has been employed in solving optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Pareto front has been obtained in both and compromised solution has been selected from set of solutions. The best solution has been selected by fuzzy decision-making in [27] and while it has been done by max-min technique in [28].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Approach for Placement of Multiple DGs for Voltage Sensitive Loads

Kaur

Jain

2017

Energies

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This paper presents the optimal placement of multiple Dispersed Generators using multi-objective optimization. The optimization is carried out with objectives namely active power loss, reactive power loss, voltage deviation and overall economy. The multi-objective optimization and accounting conflicting objectives are realized through Particle Swarm Optimization with fuzzy decision approach to find the optimal sizes and sites of Dispersed Generators for voltage dependent residential, commercial and industrial loads. The clusters of buses are formulated from base case load flow to limit the search space for finding the placement of the Dispersed Generators. The effectiveness of the proposed approach is tested on a 69-bus radial distribution. It is found that the optimal placement of the Dispersed Generators improves the overall performance of the system and the optimal allocation is affected by the type of load.

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A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models

Cited by 102 publications

References 30 publications

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

Multi-Objective Optimization Approach for Placement of Multiple DGs for Voltage Sensitive Loads

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