Optimal allocation of FACTS device to improve voltage profile and power loss using evolutionary programming technique

2016 ◽  
Author(s):  
Nur Ashida Binti Salim ◽  
Josepely Maika
Keyword(s):  
Power Loss ◽  
Optimal Allocation ◽  
Evolutionary Programming ◽  
Voltage Profile ◽  
Programming Technique

scholarly journals Voltage Control and Power Loss Reduction in Distribution Networks using Distributed Generation

2019 ◽  
Vol 8 (12) ◽  
pp. 2863-2867
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Voltage Profile ◽  
Vector Method ◽  
Index Vector ◽  
Power Loss Reduction ◽  
Whale Optimization

Distributed generation (DG) units can provide many benefits when they are incorporated along the distribution network/system. These benefits are more if DG units are connected at suitable nodes with appropriate rating otherwise, they may cause to increased power loss and poor voltage profile. In this work, optimal allocation (both location and size) problem is solved by considering power loss minimization as an objective function. An analytical method “index vector method (IVM)” is applied to find DG location. A new optimization algorithm “Whale Optimization Algorithm (WOA)” is employed to determine the DG rating. Two popularly known test systems “IEEE 33 & IEEE 69”bus systems are used to evaluate the efficacy of IVM and WOA.

scholarly journals Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

2019 ◽  
Vol 8 (3) ◽  
pp. 978-984
Author(s):  
Nur Ainna Shakinah Abas ◽  
Ismail Musirin ◽  
Shahrizal Jelani ◽  
Mohd Helmi Mansor ◽  
Naeem M. S. Honnoon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Distributed Generation ◽  
Distribution System ◽  
Optimization Technique ◽  
Evolutionary Programming ◽  
Test System ◽  
Voltage Profile ◽  
Power Losses ◽  
Programming Technique ◽  
Distributed Generations

This paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system.

Optimization of Automatic Transformer Tap Changer Using Cascaded-Evolutionary Programming Technique

2015 ◽  
Vol 785 ◽  
pp. 429-434
Author(s):  
Mohamad Ariff Nur Hakim Mohamad Zahir ◽  
Nur Ain binti Abd Manap ◽  
Harizan Che Mat Haris ◽  
Ismail Musirin ◽  
Mohamad Fadhil Mohd Kamal
Keyword(s):  
Evolutionary Programming ◽  
Test System ◽  
Load Flow ◽  
Fine Tuning ◽  
Computational Time ◽  
Voltage Profile ◽  
Instability Condition ◽  
Programming Technique ◽  
Acceptable Range ◽  
Tap Changer

In power system, load variation can cause instability condition. Transformer Tap Changer (TTC) adjustment can be used to alleviate this condition. This paper proposes an algorithm to optimize the adjustment of TTC termed as Cascaded-Evolutionary Programming (EP). This is to allow fine-tuning process on the results if the optimized results are beyond the acceptable range. The Tests were conducted on IEEE 14-Bus Reliable Test System (RTS) to determine minimum Voltage Profile (VP),Vmin at selected load bus, using ordinary Load Flow thus maintaining the voltage at 0.9 p.u. to 1.5 p.u. The result demonstrates the pre-optimization and post-optimization of the minimum VP, Vmin in the system and proves that the proposed algorithm technique identifies the optimum value of TTC whilst reducing computational time.

scholarly journals Optimal integration of wind energy with a shunt-FACTS controller for reductions in electrical power loss

2021 ◽  
Vol 23 (1) ◽  
pp. 41
Author(s):  
I Made Wartana ◽  
Ni Putu Agustini ◽  
Sasidharan Sreedharan
Keyword(s):  
Genetic Algorithm ◽  
Wind Energy ◽  
Power Loss ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Test System ◽  
Optimal Size ◽  
Voltage Profile ◽  
Nsga Ii ◽  
Multi Objective

The integration of distributed generators (DGs) with flexible alternating current transmission systems (FACTS) can improve the performance of the grid system. In this study, we determine the location and optimal size of one type of DG, based on wind energy, with a shunt-FACTS control device called a static var compensator (SVC). The voltage profile is increase and the power loss reduced due to an improvement in performance from the maximizing load bus system scenario. Newton-Raphson power flow with a wind turbine generator (WTG) and SVC are formulated as a multi-objective problem called MLB system and minimizing system power loss (Ploss) by satisfying various system constraints, namely the loading limits, generation limits, voltage limits, and the small-signal stability. A variant of the genetic algorithm, called the non-dominated sorting genetic algorithm II (NSGA-II), is used to solve these conflicting multi-objective optimization problems. Modifications to the IEEE 14-bus standard and practical test system integrated to the WTG and SVC in the PSAT software are used as a test system. The simulation results indicate that the optimal allocation of the WTG and SVC, determined using the proposed technique, results in improved system performance, since all the specified constraints are met.

scholarly journals Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdullahi Bala Kunya ◽  
Gaddafi Sani Shehu ◽  
Usman Muhammad Hassan ◽  
Abdurrahman Umar Lawal
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Combinatorial Optimization Problems ◽  
Power Loss Reduction ◽  
Real Coded Genetic Algorithm

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations.  To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.  

Optimal allocation of Multiple Distributed Generators and Shunt Capacitors in a Distribution System using Political Optimization Algorithm

2021 ◽  
pp. 1478-1488
Keyword(s):  
Optimization Algorithm ◽  
Distribution System ◽  
Power Loss ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Sensitivity Factor ◽  
Combined Approach ◽  
Voltage Profile ◽  
Distributed Generators ◽  
Shunt Capacitors

To meet the increasing real & reactive power demand of a distribution system (DS), it is essential to allocate the Distributed Generators (DGs) and Shunt capacitors (SCs) optimally. In this article, multiple DGs and SCs are allocated simultaneously in the DS aiming minimal power loss (PL), improved voltage stability index (VSI) and voltage profile of the system. A combined approach considering loss sensitivity factor (LSF) and political optimization algorithm (POA) is proposed to solve the allocation and sizing of DGs and SCs. The analysis is performed on an IEEE 33 bus system considering 9 different scenarios and results are compared with other Meta heuristic techniques. The analysis is extended for a 24 hour case study to prove the efficacy of the proposed combined approach. From all the performed simulations it can be observed that the combined approach helps in minimizing power loss and improving voltage profile and VSI for dynamic load variations effectively.

scholarly journals Optimal Allocation and Sizing of Multi DG Units Including Different Load Model Using Evolutionary Programming

2021 ◽  
Vol 1878 (1) ◽  
pp. 012036
Author(s):  
M. F. Mohammed ◽  
S R A Rahim ◽  
A. Azmi ◽  
W. Z. Wan Zanudin ◽  
M H Hussain ◽  
...  
Keyword(s):  
Optimal Allocation ◽  
Evolutionary Programming ◽  
Load Model

scholarly journals A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Varaprasad Janamala
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Optimal Allocation ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Voltage Profile ◽  
Pv System ◽  
Solar Photovoltaic System ◽  
The Impact

AbstractA new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

Sign in / Sign up

Export Citation Format

Share Document