Distributed Generation Interconnection Planning: A Wind Power Case Study

Su, Sheng-Yi; Lu, Chan‐Nan; Chang, Rung-Fang; Gutiérrez-Alcaraz, G.

doi:10.1109/tsg.2011.2105895

Cited by 89 publications

(45 citation statements)

References 14 publications

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“…In [111], a method was proposed to introduce a proper number of DG units while considering different stochastic scenarios for variable wind speed. Feeder reconfiguration was presented in three test systems (5-, 19-, and 33-bus) while applying a Binary Particle Swarm Optimization (BPSO) algorithm.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topic.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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“…Chiradeja quantified the benefit of reduced line loss in a radial distribution feeder with a concentrated load [11]. Further, many researchers have used evolutionary computational methods for finding the optimal DG placement [14][15][16][17][18][19]. Mithulananthan used a genetic algorithm (GA) for placement of DGs to reduce the losses [15].…”

Section: --------------(8)mentioning

confidence: 99%

“…Further, many researchers have used evolutionary computational methods for finding the optimal DG placement [14][15][16][17][18][19]. Mithulananthan used a genetic algorithm (GA) for placement of DGs to reduce the losses [15]. Celli and Ghiani used a multi objective Evolutionary algorithm for the sizing and placement of DGs [18].…”

Section: --------------(8)mentioning

confidence: 99%

“…Due to the discrete nature of the sizing and placement problem, the objective function has a number of local minima. The hybrid particle swarm optimization (HPSO) algorithm has applied as a useful tool for engineering optimization, to solve complex optimization problems [10][11][12][13][14][15].This paper presents a novel search approach with respect to the voltage profile for the optimal placement of DGs using the BFO algorithm and compares it with the Bee colony optimization (BCO) algorithm and other methods. Optimal bus locations are determined to obtain the best objective.…”

Section: --------------(8)mentioning

confidence: 99%

“…The DS expansion planning strategy with DG systems with intermittent power generation is presented in [14] to obtain optimal solutions for system planners. A DG placement planning study framework is brought in [15] that includes a coordinated reconfiguration feeder and voltage control to calculate the maximum allowable DG capacity at a given node in the electrical distribution system. Works have been dealing with the DG optimal placing and sizing problem by means of multi-objective optimization (MOO) tools, with a considerable number of them based on heuristic approach.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Placement of Distributed Generation Using Bacterial foraging Optimization in an Electrical Distribution System

Baskar¹,

Hariprakash²,

Vijayakumar³

2017

IJERT

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Abstract-Distributed generation (DG) has been placed in electrical distribution networks widely due to line loss reduction, environmental benefits, better voltage profile, postponement of system upgrading, and increasing reliability. Optimization techniques are tools which can be used for placement and sizing of the DG units in the distribution system. The impacts of DG placement issues, such as power loss minimization and voltage profile, should be analyzed effectively by proposing a method of locating and sizing DG units. This paper proposes Bacterial Foraging Optimization (BFO) algorithm technique for optimal placement of DG units in Distribution System (DS) to minimize the total real power loss, improve power factor and to regulate the voltage profile. BFO is a recently developed nature-inspired optimization technique, which is based on the foraging behavior of E. coli bacteria. To determine the validity of the BFO algorithm, a standard IEEE 69 bus radial distribution feeder system is examined with two test cases. The results show that the proposed BFO algorithm is more efficient, and capable of handling mixed integer nonlinear optimization problems effectively.

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Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

Int Trans Electr Energ Syst

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Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

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Distributed Generation Interconnection Planning: A Wind Power Case Study

Cited by 89 publications

References 14 publications

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Optimal Placement of Distributed Generation Using Bacterial foraging Optimization in an Electrical Distribution System

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

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