Optimal Placement and Sizing of Distributed Generators in Unbalanced Distribution Systems Using Supervised Big Bang-Big Crunch Method

Othman, Mahmoud M.; El-Khattam, Walid; Hegazy, Yasser G.; Abdelaziz, Almoataz Y.

doi:10.1109/tpwrs.2014.2331364

Cited by 82 publications

(49 citation statements)

References 33 publications

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“…Impact of DG penetration on system losses and voltage deviation for load increment is increasing studied [10] For optimal placement and sizing of distributed generators on balanced/ unbalanced distribution networks, big bang big crunch optimization algorithm proposed by Hegazy Y.G.et al [11], whereas a particle swarm optimization-based methodology for optimal sizing and placement of distributed generation is proposed for unbalanced reactive power support by Reddy et al [12].Optimal placement and the sizing problem of the voltage controlled distributed generators is solved using big bang-big crunch method [13]. Dong-Jun Won et al, determined the optimal number of power quality monitors and their locations [14].…”

Section: Related Workmentioning

confidence: 99%

Review and Scientometric Analysis of IEEE 37 Radial Feeder Publications

Sharma¹,

Kamath²

2016

International Journal of Innovative Research in Electrical, Ele

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Abstract:In this paper, a review and scientometric analysis of IEEE 37 Radial Feeder publications is carried out to find out literature growth and researcher interest. An IEEE 37 Radial feeder is a rare, highly unbalance underground feeder in delta configuration and located in California. The publication of study is obtained from IEEE Xplore and Science direct digital library. It is observed that the IEEE Xplore digital library has total 34 publications during 1995 to 2015, while Science direct digital library has only 05 during 2008 to 2015. All these papers are discussed in the literature review. In IEEE Xplore direct digital library, maximum publication on this topic appeared in during 2014 and whereas Science direct digital library have only 01 publications for same. Authors from the USA have contributed the maximum as compared with other countries. In the IEEE Xplore digital library, the relative growth rates (RGR) have continuously decreased from 2003 (0.693) to 2011 (0.095) and maximum doubling time (DT) occurred in 2015 (11.431), while in science direct digital library RGR has continuously decreased from 2011 (0.693) and DT has increased from 1.709 to 3.106 during last three years.

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Section: Related Workmentioning

confidence: 99%

Review and Scientometric Analysis of IEEE 37 Radial Feeder Publications

Sharma¹,

Kamath²

2016

International Journal of Innovative Research in Electrical, Ele

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“…The multi-objective formulation is converted to a single-objective function using the weighted sum of individual objectives [16] (active power loss, reactive power loss, voltage profile and reserve capacity).…”

Section: Introductionmentioning

confidence: 99%

“…Heuristic methods have been also applied, where a genetic algorithm (GA) is applied using chance constrained programming (CCP) [2], tabu-search (TS) [12], particle swarm optimization (PSO) [17], and big bang-big crunch (BB-BC) in [16]. Probabilistic optimal power flow (POPF) models are used in [7,[21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Placement of Energy Storage and Wind Power under Uncertainty

Quevedo

Contreras

2016

Energies

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Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power.

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“…Electrical energy producers began to use small to medium-scale generators that were placed directly in the load center known as distributed generation (DG) [1]. DG is considered as the answer of various limitations on conventional systems, so it is not surprising that DG has reached about 20%-30% of total energy production [2][3][4].Basically, DG is used to improve network reliability, security and power quality to customers [5]. However, with large-scale multi-type DG penetration and improper planning, the network system will face some serious problems [6][7][8] [15].…”

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confidence: 99%

Optimal Expenditure and Benefit Cost Based Location, Size and Type of DGs in Microgrids Systems Using Adaptive Real Coded Genetic Algorithm

et al. 2018

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The Keywords: adaptive real coded GA, benefit cost, expenditure cost, microgridsCopyright © 2018 Universitas Ahmad Dahlan. All rights reserved. IntroductionThe intense competition encourages electric energy producers to do all the effort to provide cheap electrical energy with good quality. Conventional generating systems typically serve loads with centralized power generation. Electrical energy is sent across long transmission lines to the load centers. Recent developments show that this fashion is becoming obsolete. Electrical energy producers began to use small to medium-scale generators that were placed directly in the load center known as distributed generation (DG) [1]. DG is considered as the answer of various limitations on conventional systems, so it is not surprising that DG has reached about 20%-30% of total energy production [2][3][4].Basically, DG is used to improve network reliability, security and power quality to customers [5]. However, with large-scale multi-type DG penetration and improper planning, the network system will face some serious problems [6][7][8] [15]. One of the most discussed elements in almost all DG evaluations is power quality. However, for a comprehensive assessment, various aspects of DG performance need to be examined including economic issues.The economical issue is an essential element to determine whether DG should be installed or not. Techno-economic analysis of PV and wind turbine WT is performed in [16] using HOMER software. This study focuses on the effort to determine the most economical combination of power plants, but the network costs are not taken into account. Optimal planning of renewable energy based DGs has been performed in [17] to maximize the worth of installing DGs. The optimization is conducted using mixed integer programming. The worth of DG installing is determined based on deferral of upgrade investments, cost of energy losses and interruption cost. Optimal planning of DGs with the aim of profit maximization is presented in [18]. Optimal location of DGs is determined using local marginal price (LMP) and Consumer Paymen (CP) index. Benefit cost is determined by LMP index, while expenditure cost is

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Optimal Placement and Sizing of Distributed Generators in Unbalanced Distribution Systems Using Supervised Big Bang-Big Crunch Method

Cited by 82 publications

References 33 publications

Review and Scientometric Analysis of IEEE 37 Radial Feeder Publications

Review and Scientometric Analysis of IEEE 37 Radial Feeder Publications

Optimal Placement of Energy Storage and Wind Power under Uncertainty

Optimal Expenditure and Benefit Cost Based Location, Size and Type of DGs in Microgrids Systems Using Adaptive Real Coded Genetic Algorithm

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