Bees Two-Hive Algorithm for Optimal Power Flow

2013 ◽  
Vol 313-314 ◽  
pp. 870-875
Author(s):  
Nopbhorn Leeprechanon ◽  
Prakornchai Phonrattanasak
Keyword(s):  
Energy Balance ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Bees Algorithm ◽  
Fuel Cost ◽  
Physical Constraints ◽  
Optimal Power ◽  
Transmission Limits ◽  
Simulation Results

This paper presents bees two-hive algorithm for solving the optimal power flow (OPF) problem with various constraints. The objective of the proposed technique is to improve the quality solution of the conventional bees algorithm that minimize the total fuel cost subject to operational and physical constraints i.e. energy balance, generation and transmission limits including security constraints. The proposed methodology is tested on the IEEE 30-bus test system. The results obtained using the proposed approach are compared to GA, PSO, BA and other conventional. The comparison of quality solution with other algorithms confirms performance of proposed technique. Simulation results demonstrate that bees two-hive algorithm provides better results than other heuristic techniques.

scholarly journals Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

2020 ◽  
Vol 12 (2) ◽  
pp. 518
Author(s):  
Yue Chen ◽  
Zhizhong Guo ◽  
Hongbo Li ◽  
Yi Yang ◽  
Abebe Tilahun Tadie ◽  
...  
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Power Sources ◽  
Renewable Power ◽  
Point Estimate Method ◽  
Optimal Power ◽  
Simulation Results ◽  
Point Estimation Method

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

Optimal TCSC placement for optimal power flow

2012 ◽  
Vol 63 (5) ◽  
pp. 316-321 ◽  
Author(s):  
Fatiha Lakdja ◽  
Fatima Zohra Gherbi ◽  
Redouane Berber ◽  
Houari Boudjella
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Analysis ◽  
Test System ◽  
Facts Devices ◽  
Optimal Power ◽  
Current Transmission ◽  
Flexible Alternating Current Transmission

Very few publications have been focused on the mathematical modeling of Flexible Alternating Current Transmission Systems (FACTS) -devices in optimal power flow analysis. A Thyristor Controlled Series Capacitors (TCSC) model has been proposed, and the model has been implemented in a successive QP. The mathematical models for TCSC have been established, and the Optimal Power Flow (OPF) problem with these FACTS-devices is solved by Newtons method. This article employs the Newton- based OPF-TCSC solver of MATLAB Simulator, thus it is essential to understand the development of OPF and the suitability of Newton-based algorithms for solving OPF-TCSC problem. The proposed concept was tested and validated with TCSC in twenty six-bus test system. Result shows that, when TCSC is used to relieve congestion in the system and the investment on TCSC can be recovered, with a new and original idea of integration.

Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem

2016 ◽  
pp. 177-207 ◽  
Author(s):  
Jirawadee Polprasert ◽  
Weerakorn Ongsakul ◽  
Vo Ngoc Dieu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Stability Index ◽  
Gradient Search ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Optimal Power ◽  
Voltage Deviation

This paper proposes an improved pseudo-gradient search particle swarm optimization (IPG-PSO) for solving optimal power flow (OPF) with non-convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The proposed IPG-PSO method is an improved PSO by chaotic weight factor and guided by pseudo-gradient search for particle's movement in an appropriate direction. Test results on the IEEE 30-bus and 118-bus systems indicate that IPG-PSO method is superior to other methods in terms of lower generator fuel cost, smaller voltage deviation, and lower voltage stability index.

Optimal Power Flow Problem Solution Based on Hybrid Firefly Krill Herd Method

2019 ◽  
Vol 44 ◽  
pp. 213-228 ◽  
Author(s):  
Aboubakr Khelifi ◽  
Bachir Bentouati ◽  
Saliha Chettih
Keyword(s):  
Light Intensity ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Problems ◽  
Test System ◽  
Optimization Techniques ◽  
System Control ◽  
Problem Solution ◽  
Optimal Power ◽  
Krill Herd

Optimal Power Flow (OPF) problem is one of the most important and widely studied nonlinear optimization problems in power system operation. This study presents the implementation of a new technology based on the hybrid Firefly and krill herd method (FKH), which has been provided and used for OPF problems in power systems. In FKH, an improved formulation of the attractiveness and adjustment of light intensity operator initially employed in FA, named attractiveness and light intensity the update operator (ALIU), is inserted into the KH approach as a local search perform. The FKH is prove with the solving of the OPF problem for various types of single-objective and multi-objective functions such as generation cost, reduced emission, active power losses and voltage deviation which are optimized simultaneously on exam system, viz the IEEE-30 Bus test system, which is used to test and confirm the efficiency of the proposed FKH technique. By comparing with several optimization techniques, the results produced by using the recommended FKH technique are provided in detail. The results obtained in this study appear that the FKH technique can be efficiency used to solve the non-linear and non-convex problems and high performance compared with other optimization methods in the literature. This study can achieve a minimum objective by finding the optimum setting for system control variables.

Decomposition Model Based on Optimal Power Flow in Interconnected Electricity Networks

2010 ◽  
Vol 34-35 ◽  
pp. 785-789
Author(s):  
Qing Ran Wang ◽  
Li Zi Zhang
Keyword(s):  
Quadratic Programming ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Model ◽  
Test System ◽  
Regional Network ◽  
Electricity Networks ◽  
Model Based ◽  
Optimal Power ◽  
Multi Level

In order to adapt to the current multi-level dispatching management system and to promote the operational efficiency of interconnected electricity networks, this paper proposes a decomposition collaborative model based on optimal power flow theory. The model is a quadratic programming question used to solve optimal power flow model. The information of interchanging between regions is communication price and boundary nodal bus phase angle. IEEE 30-bus test system demonstrates the validity and novelty of the model that the regional network can be calculated reasonably and the development of cross-regional electricity transaction is promoted effectively.

scholarly journals Artificial Bee Colony Algorithm Based onK-Means Clustering for Multiobjective Optimal Power Flow Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Liling Sun ◽  
Jingtao Hu ◽  
Hanning Chen
Keyword(s):  
Information Exchange ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Population Diversity ◽  
Particle Swarm Optimizer ◽  
Nsga Ii ◽  
Bee Colony ◽  
Optimal Power ◽  
The Cost

An improved multiobjective ABC algorithm based onK-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based onK-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II), the multiobjective particle swarm optimizer (MOPSO), and the multiobjective ABC (MOABC). Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

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