scholarly journals Genetic Algorithm Based Small Distributed Generation Units Addition Technique To Reduce Real Power Losses

2011 ◽  
Vol 19 (4) ◽  
pp. 20-29
Author(s):  
Saraa Esmaeel Khaleel
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Power Losses ◽  
Real Power

scholarly journals Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

2019 ◽  
Vol 16 (2) ◽  
pp. 956
Author(s):  
Abdulhamid Musa ◽  
Tengku Juhana Tengku Hashim
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Total Power ◽  
Power Losses ◽  
Loss Minimization ◽  
Power Constraints ◽  
Voltage Deviation ◽  
Power Limits

This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0 – 2 MW and 0 – 3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0 – 2 MW power limit is consistent to the 0 – 3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses.

scholarly journals Penetration effects of various kinds of distributed generation systems on the distribution system

2019 ◽  
Vol 8 (9) ◽  
pp. 596-600
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Reactive Power ◽  
Load Flow ◽  
Voltage Profile ◽  
Power Losses ◽  
Generation System ◽  
Radial Distribution System ◽  
Real Power

Distributed generation system penetration in the existing distribution system is done for minimizing the losses and improving the voltage profile. There are total five types of distributed generation systems exist based on their power delivery like distributed generation system injecting real and reactive power, supplying real power only, supplying reactive power only, absorbing reactive power only , supplying real power and absorbing reactive power. All these five types of distributed generation systems have different penetration effects on the radial distribution system. We get different voltage profiles and power losses for different types of distributed generation systems. The testing of these five types of distributed generation systems will be done on IEEE 33 bus radial distribution system. For computing, the line parameters and power losses of the above testing system the forward-backward sweep load flow method will be applied

A fast non-dominated sorting guided genetic algorithm for multi-objective power distribution system reconfiguration problem

2015 ◽  
Author(s):  
◽  
Awad M. Eldurssi
Keyword(s):  
Genetic Algorithm ◽  
Power Distribution ◽  
Distribution System ◽  
Normal Operation ◽  
Computational Time ◽  
Power Losses ◽  
Real Power ◽  
Multi Objective ◽  
System Reconfiguration ◽  
Distribution System Reconfiguration

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Power distribution systems are designed and constructed as closed loops, but they are operated in a radial topology by choosing suitable open tie switches. Radial configurations are used because they satisfy various operational and protection requirements. Distribution system reconfiguration (DSR) determines the status of both sectionalizing switches (which are normally closed) and tie switches (which are normally open). DSR has great benefits in both normal and abnormal operations (outages). DSR is a multi-objective, non-linear problem. A new, fast, non-dominated sorting genetic algorithm (FNSGA) is introduced for solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively small numbers of population size and generations and short computational time. The dissertation describes creative contributions to genetic algorithm science for the DSR problem and describes results of applying the FNSGA to a standard IEEE test system. The results show the efficiency of this algorithm as compared to other methods in terms of both achieving all the goals and minimizing the computational time with reasonable population and generation sizes. The objectives of the problem in normal operation are to optimize the system performance and efficiency in terms of maximizing the operating voltage and minimizing the branch loading. The operation cost will be reduced by minimizing the real power losses. This should be achieved with a small number of switching operations. The objectives of the problem in normal operation are to minimize real power losses and improve the voltage profile and load-balancing index with minimum switching operations. In this dissertation, a load shedding strategy based on priority customers, minimization of the number of affected buses, and minimization of the number of switching operations is introduced. To test the algorithm, it was applied to three widely studied test systems and a real one. The results show the efficiency of this algorithm as compared to other methods in terms of achieving all the objectives simultaneously with reasonable population and generation sizes and without using a mutation rate, which is usually problem-dependent.

scholarly journals Optimal sizing of distributed generation using firefly algorithm and loss sensitivity for voltage stability improvement

2020 ◽  
Vol 17 (2) ◽  
pp. 720
Author(s):  
Zulkiffli Bin Abdul Hamid ◽  
Sylvester Jipinus ◽  
Ismail Musirin ◽  
Muhammad Murtadha Othman ◽  
Rahmatul Hidayah Salimin
Keyword(s):  
Power System ◽  
Distributed Generation ◽  
Heuristic Algorithm ◽  
Distribution System ◽  
Voltage Stability ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Optimal Size ◽  
Power Losses ◽  
Real Power

This paper proposes an optimization technique for distributed generation (DG) sizing in power system. The DG placement was done through Loss Sensitive (LS) technique to determine the suitable locations. The LS index is calculated such that the change in power losses is divided with generation increment and a rank of buses is obtained to identify the suitable locations for DG placement.  Subsequently, a meta-heuristic algorithm, known as Firefly Algorithm (FA) was run to obtain the optimal size or capacity of the DG. The installation takes into consideration the aspect of voltage stability in terms of total real power losses and voltage profiles to be improved in the distribution system. Based on the experiment, the real power losses and voltage profiles were improved significantly as a result of the DG placement. In addition, the installation could prevent the power system from collapse as the reactive loading was increased to maximum.

scholarly journals An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Eagle Strategy

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

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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