A Framework for Optimal Planning in Large Distribution Networks

Najafi, S.; Hosseinian, Seyed Hossein; Abedi, Mehrdad; Vahidnia, Arash; Abachezadeh, S.

doi:10.1109/tpwrs.2009.2016052

Cited by 105 publications

(54 citation statements)

References 12 publications

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“…Therefore, the power losses reduction and voltage improvement will be obtained from the following objective functions (A & B) in the radial distribution network. The power losses and voltage improvement formulations have obtained from references [15,16].…”

Section: Objective Function and Problem Formulationmentioning

confidence: 99%

“…The total power losses equation can be introduced as follows [15,16] Those equations (10) and (11), represents the main objective function which can be written as follows in equation (12 (12) where, γ is the violation coefficient and DP is the Penalty factor (derived from problem constraint) Z Evaluation function (minimized with PSO).…”

Section: Power Losses Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Optimum Distributed Generation Allocation Using PSO in order to Reduce Losses and Voltage Improvement

Ghani¹,

Hasan²,

Gan³

2014

3rd IET International Conference on Clean Energy and Technology (CEAT) 2014

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The technology advancement in Distributed Generations (DG) has significantly influenced the environmental pollution. In power system, more especially in distribution networks, DGs can able to mitigate the total losses of the network which effectively has significant effects on environmental pollution. Optimal location and size of DG in distribution networks is one of the important issues of the power system. This paper aims to investigate the best solution for optimal operation of distribution networks by taking into consideration of DG. The optimal allocation of DG can be considered as an integer problem that can be formulated by metaheuristic methods. In this paper, the Particle Swarm Optimization (PSO) algorithm has been used to solve the DG placement and sizing. The IEEE 34 bus test system has been utilized to demonstrate the effectiveness of the PSO algorithm on herein mentioned problem. The result illustrates the losses minimization and voltage profile improvement.

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Section: Objective Function and Problem Formulationmentioning

confidence: 99%

Section: Power Losses Formulationmentioning

confidence: 99%

Optimum Distributed Generation Allocation Using PSO in order to Reduce Losses and Voltage Improvement

Ghani¹,

Hasan²,

Gan³

2014

3rd IET International Conference on Clean Energy and Technology (CEAT) 2014

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“…Typically, they use coding based on integer numbers (binary in some cases) to represent a tree, but this type of representation involves the problem of having to define adequate genetic operators to visit only feasible solutions. In this respect, Najafi et al [9] use a binary coding scheme that allows solving a large distribution network that, in practice, is reduced to 90 nodes. Considering a similar representation, Li and Chang [10] propose a new GA supported by a minimal spanning tree algorithm and illustrate the method with a small network.…”

Section: Journal Of Electrical and Computer Engineeringmentioning

confidence: 99%

Evolutionary Optimization of Electric Power Distribution Using the Dandelion Code

Sabattin

Contreras‐Bolton

Arias

et al. 2012

Journal of Electrical and Computer Engineering

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Planning primary electric power distribution involves solving an optimization problem using nonlinear components, which makes it difficult to obtain the optimum solution when the problem has dimensions that are found in reality, in terms of both the installation cost and the power loss cost. To tackle this problem, heuristic methods have been used, but even when sacrificing quality, finding the optimum solution still represents a computational challenge. In this paper, we study this problem using genetic algorithms. With the help of a coding scheme based on the dandelion code, these genetic algorithms allow larger instances of the problem to be solved. With the stated approach, we have solved instances of up to 40,000 consumer nodes when considering 20 substations; the total cost deviates 3.1% with respect to a lower bound that considers only the construction costs of the network.

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“…Design of an optimal power distribution system (PDS) involves simultaneous optimization of frequently competing multiple objectives such as minimization of the installation cost of the new facilities such as substation, distributed generations (DGs), automatic protective devices such as RAs, D-FACT (distribution-FACTs devices such as D-STATCOM: distribution-static compensator), feeders, etc., minimization of operational costs such as energy loss cost and maximization of system reliability to design a cost-effective and reliable network (El-Kady 1984;Samui et al 2012;Kumar et al 2014a, b;Najafi et al 2009;;Rosado and Agustín 2006;Mendoza et al 2006;Carrano et al 2006;Ramírez-Rosado and Domínguez-Navarro 2004;Ganguly et al 2012;Kumar et al 2015a, b;Nekooei et al 2013;Kumar and Samantaray 2014). Research work based on numerical based methods such as ''Mixed integer linear programming approach'' (El-Kady 1984), the ''Direct search approach'' (Samui et al 2012) etc.…”

Section: Introductionmentioning

confidence: 99%

“…for PDS planning have limited scope in practical applications as almost all the practical problems involve objective functions that are non-linear, non-convex, and non-differentiable in nature. In this regard, heuristic-based methods (Najafi et al 2009;Kumar et al 2014b) have distinct advantages such that it can handle complex problems and also it do not require any gradient information. Several multi-objective evolutionary algorithms have been applied and found suitable for PDS planning problems (Rosado and Agustín 2006;Mendoza et al 2006;Carrano et al 2006;Ramírez-Rosado and Domínguez-Navarro 2004;Ganguly et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Design of Advanced Power Distribution Networks Including DGs and D-FACT Devices

Kumar

Samantaray

2016

INAEL

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This paper presents an application of a multiobjective seeker-optimization-algorithm (MOSOA) for solving multi-objective design of advanced power distribution systems (PDS) considering fixed cost, non-linear variable cost and reliability as the objective functions including DGs (distributed generations) and D-FACT (distribution-FACT) devices. The proposed planning methodology uses contingency-load-loss index (CLLI) for reliability evaluation, which is independent of failure rate and fault repair duration of the feeder branches. To enhance the reliability and efficiency of existing PDS, planning strategy includes distribution automation devices such as automatic recloser (RA). Performance of the proposed approach is assessed and illustrated on a 54-bus PDS, considering real-time design practices. Furthermore, a qualitative comparison is made with NSGA-II, showing the efficacy of the proposed planning approach.Keywords Multi-objective seeker-optimizationalgorithm Á Non-dominated sorting genetic algorithm-II (NSGA-II) Á Power distribution system design Á Contingency-load-loss index (CLLI)

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A Framework for Optimal Planning in Large Distribution Networks

Cited by 105 publications

References 12 publications

Optimum Distributed Generation Allocation Using PSO in order to Reduce Losses and Voltage Improvement

Optimum Distributed Generation Allocation Using PSO in order to Reduce Losses and Voltage Improvement

Evolutionary Optimization of Electric Power Distribution Using the Dandelion Code

Design of Advanced Power Distribution Networks Including DGs and D-FACT Devices

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