Optimal Distributed Generation Location and Sizing Using Genetic Algorithms

Pisică, Ioana; Bulac, Constantin; Eremia, Mircea

doi:10.1109/isap.2009.5352936

Cited by 72 publications

(27 citation statements)

References 5 publications

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“…GA proceeds in the direction of maximization of fitness function in the multi dimensional search space and hence the solution proceeds toward global optimum. The main advantage of GA is, it works with binary coding of parameters rather than parameters themselves and hence it is capable of handling multiobjective functions containing both integer and real parameters simultaneously which is more useful for power system applications [36]. GA has been successfully applied to many power system problems because of its capability of handling both discrete and continuous variables simultaneously compared to other evolutionary techniques like particle swarm optimization [45,46].…”

Section: Problem Formulation With the Multiobjective Functionmentioning

confidence: 99%

“…GA has been successfully applied to many power system problems because of its capability of handling both discrete and continuous variables simultaneously compared to other evolutionary techniques like particle swarm optimization [45,46]. Recently many authors applied GA for optimum allocation and sizing of DG units [11,13,31,36,43].…”

Section: Problem Formulation With the Multiobjective Functionmentioning

confidence: 99%

“…Arefifar et al [35] have proposed a methodology for optimal sizing and allocation of DG units considering the probabilistic nature of renewable energy sources and the hourly variations in load profile in order to minimize the total annual energy losses for designing microgrids with optimum supply adequacy. Pisica and Eremia [36] have proposed GA based approach for optimum sizing and location of DG units in distribution systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimum placement and sizing of DGs considering average hourly variations of load

Gampa

Das

2015

International Journal of Electrical Power & Energy Systems

143

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Section: Problem Formulation With the Multiobjective Functionmentioning

confidence: 99%

Section: Problem Formulation With the Multiobjective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimum placement and sizing of DGs considering average hourly variations of load

Gampa

Das

2015

International Journal of Electrical Power & Energy Systems

143

View full text Add to dashboard Cite

“…In [2], a linear programming approach to determine optimal allocation of embedded generation on distribution networks is proposed. Optimal location and sizing of distributed generation in a distribution networks using Genetic Algorithm (GA) is discussed in [3]. The allocation and sizing of DGs for social welfare maximization and profit maximization using Locational marginal price (LMP) is proposed in [4].…”

Section: Introductionmentioning

confidence: 99%

Combined Optimal Placement of Solar, Wind and Fuel cell Based DGs Using AHP

Singh¹,

Parida²

2011

Linköping Electronic Conference Proceedings

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Abstract:The integration of distributed generations (DGs) into grid has a great importance in improving system reliability. Many methods were proposed in the literature for finding best locations for DG placement considering various criteria. Sometime, it becomes difficult for combined placement of different kinds of renewable based DGs, such as solar, wind and fuel cell. The criterion of minimizing total system cost was used previously by many researchers for locating the optimal sites for DGs using OPF formulations. In this case, three different cost functions are formulated for different kinds of renewable energy sources (RESs). By taking combined cost function of all the RESs in the OPF to identify location for each different kind of sources becomes very cumbersome task. It would be difficult to find the exact locations for various kinds of RESs that is where to place which type of RESs. In order to solve this difficulty, three different objectives have been considered separately for determining the optimal locations for each kind of RESs using mixed integer nonlinear programming (MINLP) method. Having many alternatives with these three objectives, analytic hierarchy process (AHP) has been used to make a decision over getting the optimal locations for these different kinds of RESs. The proposed method for finding the optimal locations of solar, wind and fuel cell based DG placement has been demonstrated on 15 node distribution systems.

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“…Many papers which discusses about the optimal planning and operation of the DGs are available in the literature. A genetic algorithm (GA) based method was used to determine the optimal location and sizing of DGs in distribution networks [1]. In [2], the distribution system planning framework for minimizing the total planning cost including investment and variable cost was used to obtain the optimal DG sizing and siting.…”

mentioning

confidence: 99%