Optimal location and capacity of distributed generation based on scenario probability

Xiaoqun, Ding; Zhao, Feng

doi:10.1109/supergen.2009.5348224

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…This paper has considered distribution network investment and maintenance costs, DG investment and operating costs, grid consumption costs and power purchase costs to establish an economic model of distribution network planning [4,5].…”

Section: Economy Model Of Distribution Network Planning Containing Di...mentioning

confidence: 99%

Study on the Algorithm of Distribution Network Planning Containing Distributed Power

Huang

Peng

et al. 2013

AMR

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Access distributed power will take great influence to distribution network's safe and economic operation. This paper established a distribution network planning economic model and analyzed the advantages and disadvantages of various planning algorithms, and propose an adaptive parallel genetic algorithm. This algorithm can overcome premature phenomenon of standard genetic algorithm, and the ability is better to seek the global optimal solution.

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Section: Economy Model Of Distribution Network Planning Containing Di...mentioning

confidence: 99%

Study on the Algorithm of Distribution Network Planning Containing Distributed Power

Huang

Peng

et al. 2013

AMR

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“…The objective is to minimize the real power losses and improve the voltage profile. Some technical and economic impacts of DGs using some heuristic methods have been studied in ref [9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm

Rostamzadeh¹,

Valipour²,

Shenava³

et al. 2012

AIR

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This paper proposes an Imperialist Competitive Algorithm (ICA) for optimal multiple distributed generations (DGs) placement and sizing in a distribution system. The objective is to minimize the total real power losses and improve the voltage profile within real and reactive power generation and voltage limits. Three types of DG are considered and the ICA is used to find the better sizes and locations of DGs for maximum real power losses reduction and voltage improvement for given number of DG units in each type. Both integer and continuous variables are considered in ICA, integer variable for locations and continues variable for sizes. The total real power losses and voltage profile evaluation are based on a power flow method for radial distribution system with the representation of DGs. The proposed method has been demonstrated on 33 bus radial distribution system. The efficiency of the ICA in reducing the total power losses and improving voltage is validated by comparing the obtained results with Particle Swarm Optimization (PSO) algorithm.

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“…Proper allocation of DG units is one of the most important aspects of DG planning [2]. Many studies have been performed to plan distributed generation in the networks so as to minimize power loss [1][2][3][4][5]. The proposed algorithm uses Artificial Neural Network (ANN) to determine the appropriate weighting factors of each parameter included in the optimization problem like (voltage level, the total system losses and short circuit level) in order to choose the optimal distribution resource (DR) allocation and its corresponding sizing.…”

Section: Introductionmentioning

confidence: 99%

“…A probabilistic planning technique was proposed for optimal allocation for different types of (wind-based, solar, and biomass) DG's in to the distribution system, so as to minimize annual energy losses which includes all possible operating conditions, but does not involve the capital costs and running cost of DG units. The optimal PV model or wind turbine is selected based on the highest capacity factor (CF) criteria; therefore CF itself is not good indicator of overall economics for the wind plant [2][3].…”

Section: Introductionmentioning

confidence: 99%

“…The PSO as an optimization tool provide a population-based search procedure in which individuals called particle change their position with time. By considering uncertainties on DG power production use the methodology of probability of scene occurrence, investment cost and power loss of distribution network but load changes did not take in to account [3]. In this paper, an algorithm by using particle swarm intelligence for optimally allocation and sizing of renewable distributed generator units (wind) is proposed in the distribution network, so as to minimize real power loss and to improve system voltage profile.…”

Section: Introductionmentioning

confidence: 99%

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Optimal planning for power distribution network with distributed generation in Zanzibar Island

Ismail

Chen

Xia

2011

2011 International Conference on Electrical and Control Engineering

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As a renewable energy, wind power generation has become an important development direction for power industries due to its non-pollutant and inexhaustible resource. Reliability improvement, operational cost reduction and power loss decreasing are some of the main benefits of distribution generator (DG) allocation in distribution systems. In this paper, an optimal planning model is presented for the distribution network at Zanzibar Island. The investigation of electric power demand and planning of micro-grid in Mpendae feeder has been conducted. The particle swarm algorithm was used for optimal allocation and sizing of wind distributed generator unit, so as to minimize real power loss and to improve system voltage profile. Simulation results show the effectiveness of the planning model.

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Optimal location and capacity of distributed generation based on scenario probability

Cited by 6 publications

References 18 publications

Study on the Algorithm of Distribution Network Planning Containing Distributed Power

Study on the Algorithm of Distribution Network Planning Containing Distributed Power

Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm

Optimal planning for power distribution network with distributed generation in Zanzibar Island

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