scholarly journals Sensitivity Based Approach for the Optimal Sizing and Allocation of Distributed Generation in a Radial Network

2020 ◽  
Vol 5 (6) ◽  
pp. 751-756
Author(s):  
Paul C. Maduforo ◽  
Jonas N. Emechebe ◽  
Emmanuel M. Eronu ◽  
Stephen Adole Benson
Keyword(s):  
Distributed Generation ◽  
Power Reduction ◽  
Sensitivity Index ◽  
Voltage Sensitivity ◽  
Voltage Profile ◽  
Unity Power Factor ◽  
Step Size ◽  
Index Method ◽  
Sensitivity Method ◽  
Radial Network

Electric power has remained a basic essential for the advancement of any nation's economy. Increasing human exercises because of innovative advancement combined with populace development has made the interest for power dramatically increasing continuously, subsequently widening the gap between power generated and the demand by the consumers. This work provides sensitivity based method for the allocation of a distributed generation in a distribution network aimed at improving the voltage profile and reduce power loss in the quest to narrow the gap between power generated and that demanded by the consumers. Using the Loss sensitivity method, a DG of 153kW was allocated to bus 5 that sees a power reduction of 46% with voltage profile improved within constraints. Voltage sensitivity index was calculated at all nodes. Bus 17 was found to have the minimum VSI. In this case DG sizes were taken in step size of 17.5kW starting from 30 kW till 170 kW at different power factors of 1.0, 0.9, 0.85, and 0.8. The DG sizes were tested at the selected power for various DG sizes. 135kW DG at unity power factor was installed. After comparing the two methods it can be concluded that loss reduction in loss sensitivity method is more and it is better in terms of selecting the optimal location for the placement of DG. For the purpose of sizing the voltage sensitivity analysis index method is a better option.

scholarly journals Optimal Placement and Sizing of Distributed Generation in Distribution System using Analytical Method

2019 ◽  
Vol 8 (4) ◽  
pp. 6357-6363
Keyword(s):  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Network ◽  
Optimal Placement ◽  
Small Scale ◽  
Voltage Profile ◽  
Distribution Grid ◽  
Step Size ◽  
System Power

The reliability of distribution network can be improved with the penetration of small scale distributed generation (DG) unit to the distribution grid. Nevertheless, the location and sizing of the DG in the distribution network have always become a topic of debate. This problem arises as different capacity of DG at various location can affect the performance of the entire system. The main objective of this study is to recommend a suitable size of DG to be placed at the most appropriate location for better voltage profile and minimum power loss. Therefore, this paper presents an analytical approach with a fixed DG step size of 500 kW up to 4500 kW DG to analyses the effect of a single P-type DG in IEEE 33 bus system with consideration of system power loss and voltage profile. Four scenarios have been selected for discussions where Scenario 1: 3500 kW DG placed at node 3; Scenario 2: 2500 kW DG placed at node 6; Scenario 3: 1000 kW DG placed at node 18 and Scenario 4: 3000 kW DG placed at node 7. Results show that all the four scenarios are able to reduce the power loss and improve the voltage profile however Scenario 4 has better performance where it complies with minimum voltage requirement and minimizing the system power loss.

scholarly journals Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique

2019 ◽  
Vol 4 (4) ◽  
pp. 83-89 ◽  
Author(s):  
Evans Chinemezu Ashigwuike ◽  
Stephen Adole Benson
Keyword(s):  
Fuzzy Logic ◽  
Distributed Generation ◽  
Reducing Power ◽  
Voltage Profile ◽  
Power Losses ◽  
Optimal Position ◽  
Neuro Fuzzy ◽  
Bus Voltage ◽  
Radial Network ◽  
Fuzzy Logic Technique

The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile at the buses. The method was simulated using ANFIS toolbox MATLAB R2013b (8.2.0.701) 64-bit software and tested using Gwagwalada injection sub-station feeder 1 system. The results obtained were compared to that obtained using ANN. It was observed that adaptive neuro fuzzy logic technique performed better in terms of reducing power losses compared to ANN technique. The percentage reduction in the power loss at the buses cumulatively is 48.96% for ANN while adaptive neuro fuzzy logic technique is 49.21%. The voltage profile of the networks after optimizing the DG location and sizes using adaptive neuro fuzzy logic technique were also found to be much improved with the lowest bus voltage improved from 0.9284 to 1.05pu.

scholarly journals Optimal Sizing and Siting of DG in Electrical Networks based on Systematic method

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2674-2683
Keyword(s):  
Distributed Generation ◽  
Power Factor ◽  
Distribution Systems ◽  
Electrical Network ◽  
Loss Reduction ◽  
Electrical Networks ◽  
Voltage Profile ◽  
Unity Power Factor ◽  
Electrical Distribution Systems ◽  
Voltage Profile Improvement

In this paper a simple and an efficient technique for determining the size(s) and site(s) for Distributed Generation systems in electrical distribution systems is presented for power loss saving and voltage profile improvement, giving suitable weighing factors to each one of the considered objectives. For this purpose a method of analytic has been developed and used, which is based on change in real and reactive parts in the branch currents caused by the DG located, and is tested on a 69-bus electrical network. Obtained results shows best loss reduction as well as voltage profile enhancement of the network under consideration. Among various power factors assumed, the operation of Distributed Generation corresponding to load power factor can enhances the system performance greatly, compared to that at unity power factor.

scholarly journals Voltage profile improvement and losses minimization for Hayin Rigasa radial network Kaduna using distributed generation

2021 ◽  
Vol 5 (1) ◽  
pp. 20-36
Author(s):  
Idris A. Araga ◽  
Abel E. Airoboman ◽  
Simon A. Auta
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Reactive Power ◽  
Circuit Breaker ◽  
Research Work ◽  
Sensitivity Index ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Active And Reactive Power ◽  
Power Loss Reduction

This research work has presented the application of distributed generation (DG) units in a simultaneous placement approach on IEEE 33 radial test systems for validation of the technique with further implementation on 56-Bus Hayin Rigasa feeder. The genetic algorithm (GA) is employed in obtaining the optimal sizes and load loss sensitivity index for locations of the DGs for entire active and reactive power loss reduction. The voltage profile index is computed for each bus of the networks to ascertain the weakest voltage bus of the network before and after DG and circuit breaker allocation. The simultaneous placement approach of the DGs is tested with the IEEE 33-bus test networks and Hayin Rigasa feeder network and the results obtained are confirmed by comparing with the results gotten from separate DGs allocation on the networks. For IEEE 33-bus system, the simultaneous allocation of DGs and of optimal sizes 750 kW, 800 kW and at locations of buses 2 and 6 respectively, lead to a 66.49 % and 68.64 % drop in active and reactive power loss and 3.02 % improvement in voltage profile. For the 56-bus Hayin Rigasa network in Kaduna distribution network, the simultaneous placement of DGs of sizes 1,470 kW and 1490 kW at locations of bus 16 and 23 respectively, lead to a 79.54 % and 73.98 % drop in active and reactive power loss and 15.94 % improvement in voltage profile. From results comparison, it is evident that the allocation of DGs using the combination GA and load loss sensitivity index, gives an improved performance in relations to power loss reduction and voltage profile improvements of networks when compared to without DGs.

scholarly journals CONGESTION MITIGATION IN DISTRIBUTION NETWORK BY INTEGRATED DISTRIBUTED GENERATIONS FOR IMPROVING VOLTAGE PROFILES AND MINIMIZING THE LOSSES

2021 ◽  
Vol 25 (02) ◽  
pp. 78-87
Author(s):  
Ihsan M. Jawad ◽  
◽  
Wafaa S. Majeed ◽  
Keyword(s):  
Power Systems ◽  
Distributed Generation ◽  
Transmission Lines ◽  
Power Flow ◽  
Electrical Power ◽  
Distribution Network ◽  
Sensitivity Index ◽  
Sudden Increase ◽  
Voltage Profile ◽  
Active Power Flow

In electrical power systems, unexpected outage of transmission systems, sudden increase of loads, the exit of generators from service, and equipment failure, leads to a contingency occurring on one or several transmission lines. The loads must be within the specified state and the transmission lines should not exceed the thermal limits. One of the important methods used to alleviate the contingency and reduce the congestion lines by injected a Distributed Generation (DG) within an optimal siting and optimal sizing in the distribution network that achieves improvement of the voltage profile as well as leads to reduce the losses. First, to achieve the best goals in this paper that is determined contingency lines, an index has been used called (Active Power Flow Performance Index) (PIRPF) and an equation called (Line Flow Sensitivity Index) (LFSI) is used for finding the optimum site for Distributed Generation. Second, to determine the optimum size for distributed generators, the Genetic Algorithm (GA) is used. Also, this research was distinguished by choosing new sites and sizes according to the GA to obtain the best desired results. Finally, these methodologies were applied to the IEEE-30 bus ring network using the MATPOWER Version 6.0, 16-Dec-2016 program within MATLAP R2018a environment.

scholarly journals The Study of the Effect of Capacity Increase and Photovoltaic Placement on Power Loss, Voltage Profile by Considering THDv

2021 ◽  
Vol 1 (2) ◽  
pp. 74-79
Author(s):  
Syukri Yunus ◽  
R.H. Sukma
Keyword(s):  
Power Loss ◽  
Flow Simulation ◽  
Electrical Energy ◽  
Optimal Location ◽  
Electric Power System ◽  
Sensitivity Factor ◽  
Sensitivity Index ◽  
Voltage Sensitivity ◽  
Voltage Profile ◽  
Maximum Capacity

The application of Photovoltaic (PV) is one solution to the increasing demand for electrical energy. However, the application of photovoltaic (PV) must be in the right location and capacity so that the power loss you want to reduce is large and the voltage profile is good. Photovoltaic (PV) generates DC voltage which is then required by an inverter to convert it to AC. The inverter is a non-linear load that produces harmonics. Harmonics in an electric power system can be known from Total Harmonic Distortion (THD). The purpose of this study is to determine the optimal location of placement (PV) and its maximum capacity so that the power loss is smaller. The resulting voltage and THD profile conform to the permitted standards. The methods used in determining the optimal location of photovoltaic (PV) are Loss Sensitivity Factor (LSF) and Voltage Sensitivity Index (VSI). ETAP 16 software is used for power and harmonic flow simulation. From this research, the most optimal photovoltaic (PV) placement is on bus 10 (bus 283 T) with a maximum capacity of 3255 kVA. This placement location provides minimal power loss and a good voltage profile taking into account the permitted standard THDv.

A New Backward/Forward Algorithm for Weakly Meshed Distribution Systems with Distributed Generation

2014 ◽  
Vol 541-542 ◽  
pp. 1027-1031
Author(s):  
Feng Li Jiang ◽  
Zai Lin Piao ◽  
Li Di Wang
Keyword(s):  
Distributed Generation ◽  
Topological Structure ◽  
Distribution Systems ◽  
Reactive Power ◽  
Voltage Profile ◽  
Forward Algorithm ◽  
Dummy Node ◽  
Sweep Algorithm ◽  
Radial Network ◽  
The Impact

According to the analysis of the network topological structure, an improved backward/forward sweep algorithm was proposed. The algorithm can be applied for weakly meshed distribution systems with distributed generation (DG). The method developed an incidence matrixbranch current-bus current injection matrix. A meshed network was converted to a radial network by breaking the loops at the ending nodes of link branches. The mathematical models of DG were established as PV and PQ node. For PV nodes, this paper used a dummy node and dummy branch which inject reactive power to the specified node to maintain the specified voltage value. IEEE 33-bus test feeder was used to verify the correctness and convergence of the proposed algorithm. Moreover, the impact of both weakly meshed and DG on voltage profile and convergence was also investigated.

Optimization of Real Power Loss and Voltage Stability Index for Distribution Systems with Distributed Generation

2017 ◽  
Vol 33 ◽  
pp. 100-114 ◽  
Author(s):  
Mostafa Elshahed ◽  
Mahmoud Dawod ◽  
Zeinab H. Osman
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Reactive Power ◽  
Optimization Problems ◽  
Stability Index ◽  
Mixed Integer ◽  
Voltage Profile ◽  
Voltage Stability Index

Integrating Distributed Generation (DG) units into distribution systems can have an impact on the voltage profile, power flow, power losses, and voltage stability. In this paper, a new methodology for DG location and sizing are developed to minimize system losses and maximize voltage stability index (VSI). A proper allocation of DG has to be determined using the fuzzy ranking method to verify best compromised solutions and achieve maximum benefits. Synchronous machines are utilized and its power factor is optimally determined via genetic optimization to inject reactive power to decrease system losses and improve voltage profile and VSI. The Augmented Lagrangian Genetic Algorithm with nonlinear mixed-integer variables and Non-dominated Sorting Genetic Algorithm have been implemented to solve both single/multi-objective function optimization problems. For proposed methodology effectiveness verification, it is tested on 33-bus and 69-bus radial distribution systems then compared with previous works.

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