A novel distributed generation planning algorithm via graphically-based network reconfiguration and soft open points placement using Archimedes optimization algorithm

The optimal sizing and placement of distributed generators have recently drawn a considerable attention to itself. This paper proposes an evolutionary cuckoo optimization algorithm (COA) for optimal placement of distributed generation (DG) in a distribution system. The optimal DG placement problem is formulated as a cost function of network losses, voltage profile, and DG expenses. The proposed method is validated on a 13-bus distribution system. The results show that any variation in the parameter’s weight in the objective function leads to a significant change in the prediction of the DG’s location and capacity.

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Application of Improved Particle Swarm Optimization Algorithm in the Location and Capacity Determination of Distributed Generation

2020 23rd International Conference on Electrical Machines and Systems (ICEMS) ◽

10.23919/icems50442.2020.9291190 ◽

2020 ◽

Author(s):

Chunxue Wen ◽

Qi Xiong ◽

Xichao Zhou ◽

Jianlin Li ◽

Jinghua Zhou

Keyword(s):

Particle Swarm Optimization ◽

Distributed Generation ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Capacity Determination

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Optimal feeder reconfiguration in distributed generation environment under time-varying loading condition

SN Applied Sciences ◽

10.1007/s42452-021-04557-w ◽

2021 ◽

Vol 3 (6) ◽

Author(s):

Yanrenthung Odyuo ◽

Dipu Sarkar ◽

Lilika Sumi

Keyword(s):

Graph Theory ◽

Distributed Generation ◽

Spanning Tree ◽

Voltage Stability ◽

Electrical Network ◽

List Type ◽

Network Reconfiguration ◽

Time Varying ◽

Electrical Networks ◽

Tree Algorithm

Abstract The development and planning of optimal network reconfiguration strategies for electrical networks is greatly improved with proper application of graph theory techniques. This paper investigates the application of Kruskal's maximal spanning tree algorithm in finding the optimal radial networks for different loading scenarios from an interconnected meshed electrical network integrated with distributed generation (DG). The work is done with an objective to assess the prowess of Kruskal's algorithm to compute, obtain or derive an optimal radial network (optimal maximal spanning tree) that gives improved voltage stability and highest loss minimization from among all the possible radial networks obtainable from the DG-integrated mesh network for different time-varying loading scenarios. The proposed technique has been demonstrated on a multiple test systems considering time-varying load levels to investigate the performance and effectiveness of the suggested method. For interconnected electrical networks with the presence of distributed generation, it was found that application of Kruskal's algorithm quickly computes optimal radial configurations that gives the least amount of power losses and better voltage stability even under varying load conditions. Article Highlights Investigated network reconfiguration strategies for electrical networks with the presence of Distributed Generation for time-varying loading conditions. Investigated the application of graph theory techniques in electrical networks for developing and planning reconfiguration strategies. Applied Kruskal’s maximal spanning tree algorithm to obtain the optimal radial electrical networks for different loading scenarios from DG-integrated meshed electrical network.

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Optimal placement of distributed generation using colliding bodies optimization

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.32.15589 ◽

2018 ◽

Vol 7 (3.3) ◽

pp. 168

Author(s):

Gera Kalidas Babu ◽

P V. Ramana rao

Keyword(s):

Distributed Generation ◽

Optimization Algorithm ◽

Distribution Networks ◽

Optimal Placement ◽

Solar Photovoltaic ◽

Load Power ◽

Colliding Bodies Optimization ◽

Power Loss Reduction ◽

Radial Distribution Networks ◽

Distribution Generation

The present paper foremost objective is to resolve best practicable location of solar photovoltaic distribution generation (DG) of several cases using different distribution load power factors and to analyze power loss reduction. This objective achieved by a recent developed method, the so called colliding bodies’ optimization algorithm, to perceive optimum location. Performances of colliding bodies’ optimization algorithm have been evaluated and compared with other search algorithms. The execution to test viability and efficiency, the proposed collid-ing bodies’ optimization is simulated on standard IEEE 38 bus radial distribution networks. The acquired outcome from colliding bodies Optimization algorithm exhibits the possible location of distributed generation through different pre assumed load power factors compared to the other stochastic search bat and genetic algorithm.

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