Optimum siting and sizing of a large distributed generator in a mesh connected system

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

doi:10.1016/j.epsr.2009.10.034

Cited by 84 publications

(46 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Even though there are many proven algorithms presented for optimal DG siting and sizing, most of them are deterministic methods [2][3][4][5][6][7][8][9][10]. Power loss minimization or cost minimization is the only objective of the above-mentioned methods, without focusing on transmission congestion relief.…”

Section: Resultsmentioning

confidence: 99%

“…Presently, an immense volume of research papers are available to address the optimal siting and sizing of DG units; nevertheless, most of them are deterministic methods [2][3][4][5][6][7][8][9][10]. Analytical methods are presented to determine the optimal location of DG in radial and networked systems with the objective of minimization of the losses [3].…”

Section: Introductionmentioning

confidence: 99%

“…An equivalent current injection-based loss sensitivity factor with the objective of minimizing total power losses is used to determine the optimal locations and sizes of DG units [9]. A new approach was proposed to optimally determine the locations and sizes of DG units in a large mesh-connected system [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

Sasiraja¹,

Kumar²,

Ponmani³

2015

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load demand processes. Among the above methods generation rescheduling and load shedding are normally preferred, since the other methods require additional investments.However, some critical cases require improved methods to alleviate congestion. With the extensive application of distributed generation (DG), congestion management is also accomplished by the optimal placement of multiple DG units. It is well known that incorrect sizes and improper locations of DG undoubtedly create higher power losses and an undesirable voltage profile. Hence, this research effort employs the line flow sensitivity index to establish the optimal location of DG units and genetic algorithm-based optimization for determining the optimal sizes of DG units. The objective of this research is to minimize the total losses and real power flow performance index and to improve the voltage shape of the modified IEEE 30-bus test system. The results of this proposed approach are encouraging and help in anticipating higher efficiency by satisfying all the objectives.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

Sasiraja¹,

Kumar²,

Ponmani³

2015

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…These studies have considered a variety of technical issues including voltage profile [11], [12], thermal limits of conductors [13], substation capacity [14], three phase and single phase to ground short circuit [13], [15], and load modeling [9]. The reported models for DG planning can be divided into two major categories: static and dynamic models.…”

Section: B Literature Reviewmentioning

confidence: 99%

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Soroudi

Afrasiab

2012

IET Renew. Power Gener.

151

View full text Add to dashboard Cite

A scenario reduction technique is used to reduce the computational burden of the model. The Pareto optimal solutions of the problem are found using a binary PSO algorithm and finally a fuzzy satisfying method is applied to select the optimal solution considering the desires of the planner. The effectiveness of the proposed model is demonstrated by applying it to a realistic 201-node distribution network. Index TermsDistributed generation, PSO, Dynamic planning, scenario reduction, Pareto optimal front. NOMENCLATURE Constants C ℓCost of reinforcement of feeder ℓ C

show abstract

“…Distributed generation (DG) is defined as electrical power resources that are directly connected to the network [1]. These resources include renewable and nonrenewable energies.…”

Section: Introductionmentioning

confidence: 99%

Optimal sizing and siting distributed generation resources using a multi objective algorithm

Hosseini¹,

Madahi²,

Razavi

2013

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:The restructuring of the electrical market, improvement in the technologies of energy production, and energy crisis have paved the way for increasing applications of distributed generation (DG) resources in recent years. Installing DG units in a distribution network may result in positive impacts, such as voltage profile improvement and loss reduction, and negative impacts, such as an increase in the short-circuit level. These impacts depend on the type, capacity, and place of these resources. Therefore, finding the optimal place and capacity of DG resources is of crucial importance. Accordingly, this paper is aimed at finding the optimal place and capacity of DG resources, in order to improve the technical parameters of the network, including the power losses, voltage profile, and short-circuit level. The proposed formulation of this paper significantly increases the convergence and the speed of the finding the answers. Furthermore, to select the optimal weighting coefficients, an algorithm is proposed. The weighting coefficients are decided on according to the requirements of each network and deciding on them optimally prevents the arbitrarily selection of these resources.The genetic algorithm is used to minimize the objective function and to find the best answers during the investigation.Finally, the proposed algorithm is tested on the Zanjan Province distribution network in Iran and the simulation results are presented and discussed.

show abstract

Optimum siting and sizing of a large distributed generator in a mesh connected system

Cited by 84 publications

References 2 publications

An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Optimal sizing and siting distributed generation resources using a multi objective algorithm

Contact Info

Product

Resources

About