Value-based distributed generator placements for service quality improvements

Teng, Jen‐Hao; Liu, Yi-Hwa; Chen, Chia‐Yen; Chen, Chi-Fa

doi:10.1016/j.ijepes.2006.07.008

Cited by 97 publications

(66 citation statements)

References 8 publications

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“…The operating cost consists of labor cost, taxes and fuel cost etc. [17,18]. Fourth place after the implementation of algorithms for distributed generation units is proposed (The bus 11 and 18 and 19 and 25, respectively, with capacities of 600, 300, 300, 900kW) that network will reach the best reliability.…”

Section: Resultsmentioning

confidence: 99%

Value-based Distributed Generation Placements for Reliability Criteria Improvement

Heidari¹,

Banejad²

2013

Journal of Electrical Engineering and Technology

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-Restructuring and recent developments in the power system and problems arising from construction and maintenance of large power plants, increasing amount of interest in distributed generation (DG) source. Distributed generation units due to specifications, technology and location network connectivity can improve system and load point reliability indices. In this paper, the allocation and sizing of DG in distribution networks are determined using optimization. The objective function of the proposed method is to improve customer-based reliability indices at lowest cost. The placement and size of DGs are optimized using a Genetic Algorithm (GA). To evaluate the proposed algorithm, 34-bus IEEE test system, is used. The results illustrate efficiency of the proposed method.

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Section: Resultsmentioning

confidence: 99%

Value-based Distributed Generation Placements for Reliability Criteria Improvement

Heidari¹,

Banejad²

2013

Journal of Electrical Engineering and Technology

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Section: Introductionmentioning

confidence: 99%

“…A GA methodology is implemented to optimally allocate renewable DG units in distribution network to maximize the worth of the connection to the local distribution company as well as the customers connected to the system [5]. A value-based approach, taking into account the benefits and costs of DGs, is developed and solved by a GA that computes the optimal number, type, location, and size of DGs [6].Tabu search (TS) is used to solve the optimal sizing and siting of DG units simultaneously with the optimal placement of reactive power sources in [7]. A stochastic multiple DGs optimal sizes and locations are determined for cost minimization by a combined TS and scatter search [8].…”

mentioning

confidence: 99%

A Modified Firefly Algorithm for Optimal Sizing and Siting of Voltage Controlled Distributed Generators in Distribution Networks

Othman¹,

Hegazy²,

Abdelaziz³

2015

Period. Polytech. Elec. Eng. Comp. Sci.

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Keywords distributed generators, Firefly algorithm, optimization IntroductionArtificial intelligence optimization methods occupy a prominent place in solving the optimization problems concerning the optimal distributed generation (DG) placement and sizing. Several methods are used for solving the aforementioned optimization problem. In this context, genetic algorithm (GA), tabu search (TS), particle swarm optimization (PSO), ant colony optimization, artificial bee colony (ABC), harmony search (HS) and Firefly Algorithm (FA) are from the most efficient methods used.Genetic algorithm (GA) is proposed in [1] for evaluating the optimal size of multiple DGs in order to minimize the system power loss. GA is applied to solve an optimal multiple DGs sizing and siting problem with reliability constraints in [2]. Paper [3] proposes a GA based method for optimal sizing and siting of DGs in radial as well as networked systems for the sake of power loss minimization. A GA is utilized in [4] to solve the optimization problem that maximizes the profit of the system by the optimal placement of DGs. A GA methodology is implemented to optimally allocate renewable DG units in distribution network to maximize the worth of the connection to the local distribution company as well as the customers connected to the system [5]. A value-based approach, taking into account the benefits and costs of DGs, is developed and solved by a GA that computes the optimal number, type, location, and size of DGs [6].Tabu search (TS) is used to solve the optimal sizing and siting of DG units simultaneously with the optimal placement of reactive power sources in [7]. A stochastic multiple DGs optimal sizes and locations are determined for cost minimization by a combined TS and scatter search [8].A multiobjective with weight method based on particle swarm optimization (PSO) is applied for determining the optimal size and location of multiple DGS in distribution system with non unity power factor considering variable power load models [9]. PSO is used for optimal selection of types, locations and sizes in order to maximize the DG penetration considering standard harmonic limits and protection coordination constraints [10]. A PSO is utilized for cost minimization through the optimal sizing and placement of multiple DG units [11].

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“…The DG units change the flow over the feeders of the distribution network by injecting active and reactive power to their interconnection node. They may cause different benefits for Distribution Network Operators (DNOs) as reported in the literature, such as: network investment deferral [1], [2], active loss reduction [3]- [6], environmental emission reduction [7] and reliability improvement [8]. These benefits are obtained just if they are connected in the appropriate sizes and locations.…”

Section: Introductionmentioning

confidence: 99%

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Soroudi

Ehsan

2010

Int Trans Elec Energy Syst

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This paper proposes a hybrid heuristic optimization method based on genetic algorithm and immune systems to maximize the benefits of distribution network operators (DNOs) accrued due to sizing and placement of distributed generation (DG) units in distribution networks. The effects of DG units in reducing the reinforcement costs and active power losses of distribution network have been investigated. In the presented method, the integration of DG units in distribution network is done considering both technical and economical aspects. The strength of the proposed method is evaluated by applying it on a small and a realistic large scale distribution network and the results are compared with analytical classic and other heuristic methods and discussed.

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Value-based distributed generator placements for service quality improvements

Cited by 97 publications

References 8 publications

Value-based Distributed Generation Placements for Reliability Criteria Improvement

Value-based Distributed Generation Placements for Reliability Criteria Improvement

A Modified Firefly Algorithm for Optimal Sizing and Siting of Voltage Controlled Distributed Generators in Distribution Networks

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

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