Optimal Placement of Distributed Generation and D-STATCOM in Radial Distribution Network

Smart Science ◽  
2017 ◽  
Vol 6 (2) ◽  
pp. 125-133 ◽  
Author(s):  
Bushra Weqar ◽  
Mohd Tauseef Khan ◽  
Anwar Shahzad Siddiqui
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution Network ◽  
Optimal Placement

Swarm-Intelligence-Based Optimal Placement and Sizing of Distributed Generation in Distribution Network

2018 ◽  
pp. 29-48 ◽  
Author(s):  
Mahesh Kumar ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Luqman Hakim Rahman
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Voltage Stability ◽  
Reactive Power ◽  
Distribution Network ◽  
Stability Index ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Optimal Placement

In the distribution system, distributed generation (DG) are getting more important because of the electricity demands, fossil fuel depletion and environment concerns. The placement and sizing of DGs have greatly impact on the voltage stability and losses in the distribution network. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. The two i.e. active power and combination of active and reactive power types of DGs are proposed to realize the effect of DG integration. A specific analysis has been applied on IEEE 33 bus system radial distribution networks using MATLAB 2015a software.

scholarly journals Optimal Placement of Distributed Generation Units in Radial Distribution System using Hybrid Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 1682-1688
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Network ◽  
Optimal Placement ◽  
Flower Pollination Algorithm ◽  
Binary Particle Swarm Optimization ◽  
Radial Distribution System ◽  
Hybrid Techniques

Reconfiguration is a process that supports to eliminate the power loss from a distribution network and this process have the capability to reduce the losses up to a specific point. Additionally, loss minimization may be calculated through the presentation of Distributed Generation (DG) units. Conversely, the incorporation of DG into the distribution network at an improper position may cause higher in losses and fluctuations in voltage. In the meantime, the uncertainty in voltage may produce partial power failure in the system. For that reason, it is essential to deliberate the stability boundaries in DGs position and sizing in the Radial Distribution System (RDS). In this research paper, hybrid Binary Particle Swarm Optimization (BPSO) with Flower Pollination Algorithm (FPA) is proposed for the ideal reconfiguration process and placing the DG in the 69-bus RDS. BPSO is applied to identify the best DG reconfiguration and FPA is proposed to determine the optimal DG size. This technique narrowly changes the DG location in every load bus of the network that delivers the minimum value of the objective function, which is considered as the finest candidate for DG connection. The simulation outcomes indicate the proposed method is more effective in reducing the power loss from 224.9804 to 27.2183 KW with the reduction of 88.8972% when compared to existing algorithm

scholarly journals Voltage Profile Enhancement and Loss Minimization Using Optimal Placement and Sizing of Distributed Generation in Reconfigured Network

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 20
Author(s):  
Waseem Haider ◽  
S Jarjees Ul Hassan ◽  
Arif Mehdi ◽  
Arif Hussain ◽  
Gerardo Ondo Micha Adjayeng ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Network ◽  
Optimal Placement ◽  
Total Power ◽  
Voltage Profile ◽  
Penalty Factor ◽  
Before And After

Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software.

Optimal placement of distributed generation based on DISCO’s additional benefit using self adaptive levy flight based black widow optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Naga Lakshmi Gubbala Venkata ◽  
Jaya Laxmi Askani ◽  
Venkataramana Veeramsetty
Keyword(s):  
Distributed Generation ◽  
Distribution Network ◽  
Additional Benefit ◽  
Operating Conditions ◽  
Optimal Placement ◽  
Lévy Flight ◽  
Black Widow ◽  
Levy Flight ◽  
Simulation Performance ◽  
Self Adaptive

Abstract Optimal placement of Distributed Generation (DG) is a crucial challenge for Distribution Companies (DISCO’s) to run the distribution network in good operating conditions. Optimal positioning of DG units is an optimization issue where maximization of DISCO’s additional benefit due to the installation of DG units in the network is considered to be an objective function. In this article, the self adaptive levy flight based black widow optimization algorithm is used as an optimization strategy to find the optimum position and size of the DG units. The proposed algorithm is implemented in the IEEE 15 and PG & E 69 bus management systems in the MATLAB environment. Based on the simulation performance, it has been found that with the correct location and size of the DG modules, the distribution network can be run with maximum DISCO’s additional benefit.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

scholarly journals Optimization of fuzzy controllers for a radial distribution network

2019 ◽  
Vol 13 (1) ◽  
pp. 17-23
Author(s):  
Helbert Eduardo Espitia Cuchango ◽  
Iván Machón González ◽  
Hilario López García ◽  
Domingo Guzmán Díaz González
Keyword(s):  
Energy Distribution ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Distribution Network ◽  
Voltage Profile ◽  
Fuzzy Controllers ◽  
Descent Algorithm ◽  
Distributed Approach

Energy distribution systems present alterations in the voltage profile in their nodes when distributed generation elements are installed. As a consequence, tension can be risen in a level beyond the admissible. This paper presents the optimization to three fuzzy controllers located in a distribution network with radial topology. The optimization of each controller is performed using the maximum descent algorithm, which is separately carried out; thus, having a distributed approach. The interaction between generators is considered to perform this process; the results show that the adjustment of the controllers is achieved

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