Power Loss Minimization by the Placement of DG in Distribution System Using GA

Sattianadan, D.; Sudhakaran, M.; Dash, Subhransu Sekhar; Vijayakumar, K.

doi:10.1007/978-3-642-35380-2_31

Cited by 6 publications

(9 citation statements)

References 12 publications

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“…In this paper, load models with DG integration is divided into two types, first one is theoretical load model which varies linearly and with square of the voltage (Constant Current Load & Constant Impedance Load) [10][11][12][13] and the other type is termed as practical load model i.e., loads which are majorly used in practical distribution system (Residential, Commercial and Industrial) [15][16][17][18][19][20][21][22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sattianadan et al [12] studied the effect of theoretical load model to improve the voltage profile and increase the percentage power loss reduction with DG. Whereas Roy et al [11] investigated his approach only on voltage profile improvement with Distributed Wind Generators on static and composite loads.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, an investigation is performed on DG optimization problem with inappropriate loads. Manikanta et al [13] also studied the effect of DG with same load model (theoretical load model) [12] to maximize the percentage power loss reduction in distribution system. Manikanta et al [13] used multiple DGs, whereas Sattianadan et al [12] used a single DG.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Manikanta et al [13] also studied the effect of DG with same load model (theoretical load model) [12] to maximize the percentage power loss reduction in distribution system. Manikanta et al [13] used multiple DGs, whereas Sattianadan et al [12] used a single DG. The overall power losses obtained in Ref [13] is low as compared with Ref [12].…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System

Manikanta

Mani

Singh

et al. 2020

mjee

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Distribution system supplies power to variety of load depending upon the consumer's demand, which is increasing day by day and lead to high power losses and poor voltage regulation. The increase in demand can be met by integrating Distributed Generators (DG) into the distribution system. Optimal location and capacity of DG plays an important role in distribution network to minimize the power losses. Some researchers have studied this important optimization problem with constant power load which is independent of voltage. However, majority of consumers at load center uses voltage dependent load models, which are primarily dependent on magnitude of supply voltage. In practical distribution network, the assumption of constant power load can significantly affect the location and size of DG, which in turn can lead to higher power losses and poor voltage regulation. In this study, an investigation has been performed to find the increase in power loss due to the use of inappropriate load models, while solving the optimization problem. Furthermore, an attempt has been made in this study to reduce power losses occurring in large test bus systems with loads being dependent on voltage rather than the constant power load. Different test cases are created to analyse the power losses with appropriate load model and in-appropriate load model (constant power load model). The load at distribution network is not mainly dependent on any single type of load model, it is a combination of all load models. In this study, a class of mix load viz., combination of residential, industrial, constant power, and commercial load, is also considered. In order to solve this critical combinatorial optimization problem with voltage dependent load model, which requires an extensive search, Adaptive Quantum inspired Evolutionary Algorithm (AQiEA) is used. The proposed algorithm uses entanglement and superposition principles, which does not require an operator to avoid premature convergence and tuning parameters for improving the convergence rate. A Quantum Rotation inspired Adaptive Crossover operator has been used as a variation operator for a better convergence. The effectiveness of AQiEA is demonstrated and computer simulations are carried out on two standard benchmark large test bus systems viz., 85 bus system and 118 bus system. In addition to AQiEA, four other algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), and Ecogeography-based Optimization (EBO) with Classification based on Multiple Association Rules (CMAR)) have also been employed for comparison. Tabulated results show that the location and size of DGs determined using in-appropriate load model (constant power load model) has significantly high power losses when applied in distribution system with different load model (other voltage dependent load models) as compared with the location and size of DGs determined using the appropriate load model. Experimental results indicate that AQiEA has a better pe...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System

Manikanta

Mani

Singh

et al. 2020

mjee

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“…The energycost savings due to loss reduction by DG placement using GA is addressed in [10]. PSO is another technique to find out the optimal size and placement of DG by changing location and varying sizes of DGs [11,12]. PSO method possesses some disadvantages in terms of regulation of particle velocity and hence sometimes leads to the inaccuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Distributed Generator Sizing and Placement by Analytical Method and Fuzzy Expert System: a Case Study in Tezpur University, India

Manas

Saikia²,

Baruah

2018

Technol Econ Smart Grids Sustain Energy

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Loss minimization in a distribution network is a vital component for enhancing the efficiency and reliability of a power system. Integration of Distributed Generators (DGs) in the existing power system is comparatively an efficient method for loss reduction than other known solutions. Challenges lie in the optimal sizing and placement of distributed generator. Installation of DGs of higher size at non optimal buses leads to higher system losses. In this paper a two stage methodology is proposed for optimal sizing and placement of DGs. At first, the sizing of DGs is done by analytical approach based on exact loss formula for minimum loss at the allied buses. After that Fuzzy expert system (FES) is used for optimal Placement using Distribution Loss Reduction Index (DLRI) and Voltage Deviation Reduction Index (VDRI) as its input parameters. The proposed methodology is tested for a practical distribution system of Tezpur University, India. The results show the effectiveness of the algorithm in terms of the loss reduction and voltage profile improvement of the distribution system.

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Optimization Techniques for Minimizing Power Loss in Radial Distribution Systems by Placing Wind and Solar Systems

Angalaeswari

Subbulekshmi

Deepa

2023

Power Converters, Drives and Controls for Sustainable Operations

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Power Loss Minimization by the Placement of DG in Distribution System Using GA

Cited by 6 publications

References 12 publications

Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System

Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System

Optimal Distributed Generator Sizing and Placement by Analytical Method and Fuzzy Expert System: a Case Study in Tezpur University, India

Optimization Techniques for Minimizing Power Loss in Radial Distribution Systems by Placing Wind and Solar Systems

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