Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

scholarly journals Optimal Allocation of Multiple Distributed Generations including Uncertainties in Distribution Networks by k-Means Clustering and Particle Swarm Optimization Algorithms

2021 ◽  
Vol 19 ◽  
pp. 79-84
Author(s):  
Onur Hakki Eyüboglu ◽  
◽  
Ömer Ömer Gül
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Optimal Allocation ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Power Losses ◽  
Swarm Optimization ◽  
Distributed Generations ◽  
Distributed Power Generation ◽  
The One

Climate change is the one of the most important issues faced globally and reasons of it must be reduced immediately in every area. Installing distributed power generation (DG) is one of the powerful options for reducing carbon emissions in power generation. However, improper allocation of these assets has several drawbacks. Efficient, novel and robust algorithm which is combination of both k-Means clustering and Particle Swarm Optimization is proposed in order to allocate DGs. Proposed algorithm clusters distribution network buses and selects to most proper cluster to allocate DG in this way it reduces possible buses. Furthermore, sizing and generation constraints of DGs are quite important for allocation. Therefore, several cases including different DG sizes and types are implemented to obtain the best results. Moreover, multiple DG cases are included in the study. Finally, DGs have considered as wind turbines for best cases and cases have analysed in 24 hourly bases including uncertainties both demand and production side. 33 Bus test feeder power losses are reduced up to 69%, 86%, 90% at best cases and 39%, 53%, 55% at including uncertainties by proposed algorithm for cases 1, 2, 3 DG installed, respectively.

scholarly journals Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3112
Author(s):  
Donghyeon Lee ◽  
Seungwan Son ◽  
Insu Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Power ◽  
Distributed Generation ◽  
Optimal Allocation ◽  
Power Grid ◽  
Particle Swarm ◽  
Renewable Energy Resources ◽  
Swarm Optimization ◽  
Large Capacity ◽  
Electric Power Grid

Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.

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