Optimum planning of large distributed resources in a mesh connected system based on artificial neural networks

Elnashar, M.; Shatshat, Ramadan El; Salama, M.M.A.

doi:10.1109/pes.2009.5275669

Cited by 4 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Not only can they handle noisy or incomplete data and solve nonlinear problems, but also they can perform predictions and classification at a high speed, due to their significant merits of learning from examples. Besides, weighting factors in multi‐objective function can be estimated by ANN algorithms with less bias 147 . However, more convergence time would be required in the face of massive data 152,153 …”

Section: Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Yang

Chen

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

Summary Distributed generation (DG) has attracted significant attention due to its great potential for enhancing economical and technical performance of power systems and reducing dependence on fossil fuels. Optimal sizing and placement are critical for stimulating such potential, about which a considerable number of models and algorithms have been proposed in past literature. This paper attempts to undertake a comprehensive review on optimal sizing and placement of DG via a systematic methodology procedure, including definition and classifications of DG, modelling and problem formulation with different technical and economic criteria, and summary of optimization algorithms. Common features and distinctive characteristics of both models and methods are identified, followed by evaluations and comparisons based on their practical performance in various test systems. Selection of DG techniques with respect to application scenarios, indispensable and optional considerations in DG planning models, and pros and cons of algorithms are listed in tables for a clearer understanding. Lastly, a total of 107 algorithms are addressed, which are classified into five categories. Particular, hybrid methods can deal with complex engineering problems with multiple objective functions and constraints most effectively and robustly. Future research trends are also highlighted with the aim of providing a comprehensive and state‐of‐the‐art survey for researchers, engineers, and other stakeholders.

show abstract

Section: Algorithmsmentioning

confidence: 99%

“…Besides, weighting factors in multi-objective function can be estimated by ANN algorithms with less bias. 147 However, more convergence time would be required in the face of massive data. 152,153…”

Section: Columnmentioning

confidence: 99%

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Yang

Chen

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…Distributed generator usually connected to the distribution system, this provides numerous benefits such as improved reliability, power quality and increased efficiency [1]. A challenging part for most of utilities is how to determine sitting and sizing of installing the distribution generator (DG) resources.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…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. The optimum size and location of large capacity DR (around 10% of the system capacity) without considering the capital costs and running cost of DG units [1]. 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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ismail

Chen

Xia

2011

2011 International Conference on Electrical and Control Engineering

View full text Add to dashboard Cite

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.

show abstract

Multiple-distributed generation planning under load uncertainty and different penetration levels

Ugranlı

Karatepe

2013

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Optimum planning of large distributed resources in a mesh connected system based on artificial neural networks

Cited by 4 publications

References 13 publications

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

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

Multiple-distributed generation planning under load uncertainty and different penetration levels

Contact Info

Product

Resources

About