Optimal capacitor placement in distorted distribution networks with different load models using Penalty Free Genetic Algorithm

Vuletić, Jovica; Todorovski, Mirko

doi:10.1016/j.ijepes.2015.11.065

Cited by 43 publications

(24 citation statements)

References 30 publications

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“…The subscripts used in the above formula indicate that smoothed values should be used, which are defined as (3). In Reference 20, the observation intervals and the limiting values for P st and P lt are specified in Table 1.

\begin{matrix} lefttrue \\ P_{50, smoothed} = \frac{((), P_{30} + P_{50} + P_{80})}{3} \\ P_{10, smoothed} = \frac{((), P_{6} + P_{8} + P_{10} + P_{13} + P_{17})}{5} \\ P_{3, smoothed} = \frac{((), P_{2.2} + P_{3} + P_{4})}{3} \\ P_{1, smoothed} = \frac{((), P_{0.7} + P_{1} + P_{1.5})}{3} \end{matrix}

…”

Section: Basic Concepts and Proposed Methodsmentioning

confidence: 99%

Reconfiguration of distribution network using discrete particle swarm optimization to reduce voltage fluctuations

Behbahani

Jalilian

Amini

2020

Int Trans Electr Energ Syst

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Summary Reconfiguration of the distribution network (DN) is the changing of status of normally open and close switches for a specific purpose. In this paper, a situation is studied in which a fluctuating load (such as a welder) violates the network power quality (PQ) standards. Therefore, in order to mitigate the emission of the generated flicker, an optimization problem is developed for improving the PQ of the network comprising voltage flicker level and voltage profile in addition to the network power loss as a traditional objective of the DN reconfiguration. The optimization procedure is performed by changing the network configuration using tie and sectionalizing switches. Discrete particle swarm optimization is utilized to optimize the network configuration. The proposed method is evaluated in two DNs. IEEE standard 69‐bus DN and 95‐bus practical Iranian DN are considered as case study networks. The results show the effectiveness of the proposed method for improving the network PQ, especially in case of voltage flicker. The presented studies and procedure will guide the utility engineers in making decisions associated with the choice of reasonable mitigation technique to overcome the identified PQ problems.

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\begin{matrix} lefttrue \\ P_{50, smoothed} = \frac{((), P_{30} + P_{50} + P_{80})}{3} \\ P_{10, smoothed} = \frac{((), P_{6} + P_{8} + P_{10} + P_{13} + P_{17})}{5} \\ P_{3, smoothed} = \frac{((), P_{2.2} + P_{3} + P_{4})}{3} \\ P_{1, smoothed} = \frac{((), P_{0.7} + P_{1} + P_{1.5})}{3} \end{matrix}

…”

Section: Basic Concepts and Proposed Methodsmentioning

confidence: 99%

Reconfiguration of distribution network using discrete particle swarm optimization to reduce voltage fluctuations

Behbahani

Jalilian

Amini

2020

Int Trans Electr Energ Syst

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“…A.M. Othman developed the EBFO technique that incorporates optimal SCBs sizing and sitting with thermal cable evaluation on a practical 110kV sub-transmission line [121]. PFGA algorithm has provided Cost reduction & power quality improvement in radial distribution systems [122].…”

Section: Heuristics Methodsmentioning

confidence: 99%

An Extensive Literature Review and New Proposal on Optimal Capacitor Placement in Distribution Systems

Mostafa

Shingh

Haque

2020

JEA

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The main goal of power utilities is to supply reliable and quality power to the end-users and fulfill their total demands at all possible locations. Most of the loads are connected in the distribution systems are inductive. The excessive reactive power demand over the distribution network causes tremendous reactive power losses and changes the voltage profile, hence the system's reliability. Shunt Capacitor Bank (SCB) is widely used in the distribution system for reactive power support, voltage profile, and system performance improvement. But there are some challenges to employ SCB in the distribution network; among them, ensuring the most optimum location and size is a big challenge to get the maximum benefits. Some existing techniques showed better loss reduction but needed either larger SCBs sizes or cause improper node voltage. In this research study, the first section provides an extensive literature review of optimal SCBs placement and sizing. Later on, a new technique called Combinatorial Method has been developed for sizing and sitting of optimal Shunt Capacitors to reduce the distribution loss significantly. The developed method was tested for different case studies using Indian practical 22-bus and IEEE-69-bus network. The results were compared with DSA, Fuzzy GA, and TLBO method and found better distribution feeder loss minimization and voltage profile improvement.

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“…In [7], genetic algorithm (GA) is utilized to determine the optimal location and capacity of capacitor banks for reducing the power system loss and improving the voltage profile. Optimal placement of parallel fixed capacitors is studied by using GA for minimizing the power losses and reducing the cost of the capacitors and the network [8]. In [9], a mixed integer non-linear programming (MINLP) approach is presented for optimal placement of capacitors for decreasing the investment cost and minimizing the power loss.…”

Section: Introductionmentioning

confidence: 99%

A Two-Loop Hybrid Method for Optimal Placement and Scheduling of Switched Capacitors in Distribution Networks

et al. 2020

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This paper presents a method to find the optimal size and place of the switched capacitors using a hybrid optimization algorithm. The objective function includes the active and reactive power of power plants, the capital and maintenance costs of capacitor banks, and the cost of active and reactive power losses in distribution lines and transformers. The impact of the load model on the optimal sizing and placement of switched capacitors is studied using three different scenarios: In the first scenario, all loads are voltage-dependent; in the second scenario, only a portion of loads are voltage-dependent; in the third scenario, all loads are voltage-independent. The proposed hybrid algorithm incorporates an outer and two inner optimization layers. The outer layer is executed by a genetic algorithm (GA), while the inner layer is performed by a GA, an exchange market algorithm (EMA), or a particle swarm optimization (PSO). The performance of GA-GA, GA-EMA, and GA-PSO hybrid schemes are compared on an IEEE 33-bus test system. Moreover, IEEE 33-bus and 69-bus networks are used to verify the effectiveness of proposed hybrid scheme against the gravitational search algorithm (GSA), a combination of PSO and GSA (PSOGSA), cuckoo search algorithm (CSA), teaching learning-based optimization (TLBO), and flower pollination algorithm (FPA). The results highlight the advantage of the proposed hybrid optimization scheme over the other optimization algorithms. INDEX TERMS Exchange market algorithm (EMA), genetic algorithm (GA), particle swarm optimization (PSO), radial distribution system (RDS), switched capacitors.

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Optimal capacitor placement in distorted distribution networks with different load models using Penalty Free Genetic Algorithm

Cited by 43 publications

References 30 publications

Reconfiguration of distribution network using discrete particle swarm optimization to reduce voltage fluctuations

Reconfiguration of distribution network using discrete particle swarm optimization to reduce voltage fluctuations

An Extensive Literature Review and New Proposal on Optimal Capacitor Placement in Distribution Systems

A Two-Loop Hybrid Method for Optimal Placement and Scheduling of Switched Capacitors in Distribution Networks

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