Belkacem Mahdad

Optimal power flow of the algerian network using genetic algorithm/fuzzy rules

This paper presents a combined genetic algorithm and fuzzy logic rules to enhance the optimal power flow (OPF) with consideration of multi shunt flexible AC transmission systems (FACTS). The OPF problem is formulated as a nonlinear constrained objective optimization problem with both equality and inequality constraints. The problem is decomposed into, the optimal power generation subproblem that is searched by a flexible genetic algorithm which crossover and mutation adjusted by fuzzy expert rules and a simple practical reasoning fuzzy rules designed as a second subproblem to control the reactive power exchanged with the network. The proposed method guarantees the near optimal solution and remarkably reduces the computation time. This proposed approach is implemented with Matlab program and applied to the medium 59 bus of the Algerian network. The optimization results are compared to the solution given by the standard GA and ant colony method (ACO). This comparison confirms the efficiency of the proposed approach which makes it promising to solve the OPF with consideration of FACTS devices.

OPF with envirenmental constraints with SVC controller using decomposed parallel GA: Application to the Algerian network

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria up to 90% of the electricity demand produced by thermal generators (vapor, gas), in order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed Parallel GA to solve the multi objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed: the first sub problem related to the active power planning to minimize the total fuel cost, and the second sub problem is a reactive power planning to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed tested on the Algerian 59-bus network test and compared with conventional method and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time.

OPF with Environmental Constraints with Multi Shunt Dynamic Controllers using Decomposed Parallel GA: Application to the Algerian Network

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria, up to 90% of electricity is produced by thermal generators (vapor, gas). In order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods have been proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed Parallel GA to solve the multi-objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two subproblems are proposed: the first subproblem is related to the active power planning to minimize the total fuel cost, and the second subproblem is a reactive power planning design based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed was tested on the Algerian 59-bus network test and compared with conventional methods and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at a critical situation and within a reasonable time.

Optimal power flow for large-scale power system with shunt FACTS using fast parallel GA

The main disadvantage of GAs is the high CPU time execution and the quality of the solution deteriorate with practical large-scale optimal power flow (OPF) problems. This paper presents an efficient parallel GA (EPGA) combined with fuzzy practical rules for the solution of the large-scale OPF. The length of the original chromosome is reduced successively based on the decomposition level and adapted with the topology of the new partition. Partial decomposed active power demand added as a new variable and searched within the active power generation variables of the new decomposed chromosome. In the second step a simple fuzzy rules designed based in practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensators (SVC) to improve the system performances. Numerical results on two test systems IEEE 30-bus and IEEE 118-bus are presented and compared with results of others competitive global approach. The results show that the approach proposed can converge to optimum solution faster than other methods, and obtains the solution with high accuracy.

Strategy based PSO for Dynamic Control of UPFC to Enhance Power System Security

Penetration and installation of a new dynamic technology known as Flexible AC Transmission Systems (FACTS) in a practical and dynamic network requires and force expert engineer to develop robust and flexible strategy for planning and control. Unified Power Flow Controller (UPFC) is one of the recent and effective FACTS devices designed for multi control operation to enhance the power system security. This paper presents a dynamic strategy based on Particle Swarm Optimization (PSO) for optimal parameters setting of UPFC to enhance the system loadability. Firstly, we perform a multi power flow analysis with load incrementation to construct a global database to determine the initial efficient bounds associated to active power and reactive power target vector. Secondly a PSO technique applied to search the new parameters setting of the UPFC within the initial new active power and reactive power target bounds. The proposed approach is implemented with Matlab program and verified with IEEE 30-Bus test network. The results show that the proposed approach can converge to the near optimum solution with accuracy, and confirm that flexible multi-control of this device coordinated with efficient location enhance the system security of power system by eliminating the overloaded lines and the bus voltage violation.

Flexible Methodology Based in Fuzzy Logic Rules for Reactive Power Planning of Multiple Shunt FACTS Devices to Enhance System Loadability

The problem of finding out which positions are the most effective and how many flexible AC transmission system (FACTS) devices have to be installed and controlled in a deregulated environment on economic basis is a question of great significance for the dispatcher. This paper presents a flexible approach based in practical reasoning rules from fuzzy logic theory capable of governing multiple static VAr compensator (SVC) and a group of static compensator (STATCOM) by a flexible adjustment of reactive power injected or absorbed from the network. The main purpose of the presented coordinated strategy is the improvement of index power quality. Reactive index sensitivity coordinated with an expert rules to form a global database as a flexible tool to make an efficient decision about reactive power dispatch and to choose the size of the shunt FACTS controllers. Simulation results show clearly the advantage of this approach to enhance the reactive power planning.

Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm

Display Omitted A new formulation of critical optimal power flow problem based on partitioning structure is proposed.A dynamic adjustment of switching parameter is proposed to enhance the performance of the standard FPA.Three objective functions: fuel cost, power loss, voltage deviation are optimized considering faults.The standard FPA is improved in term of solution quality and number of generation required.Robustness of the proposed strategy adaptive partitioning FPA is tested and validated on IEEE 30-Bus and IEEE 57-Bus. In this paper, a flexible power system planning strategy using a novel population-based metaheuristic algorithm inspired by the pollination process of flowers named adaptive flower pollination algorithm (APFPA) has been proposed. The proposed power system planning strategy implemented and successfully applied for solving the security optimal power flow (OPF) considering faults at critical generating unit. The main particularity of the proposed variant is that the control variables are optimized based on an adaptive and flexible structure. Also the performances of the standard FPA is improved by dynamically adjusting their control parameters, this allows creating diversity and balance between exploration and exploitation during search process. The robustness of the proposed planning strategy, is demonstrated on the IEEE 30-Bus, and IEEE 57-Bus tests power system for different objectives such as fuel cost, power losses, and voltage deviation. Considering the quality of the obtained results compared with various recent methods reported in the literature, the proposed strategy seems to be a competitive tool for solving with accuracy the security OPF considering critical situations.

A Study on Multi-objective Optimal Power Flow under Contingency using Differential Evolution

Toguidethedecisionmakingoftheexpertengineerspecializedinpowersystemo perationandcontrol;�thepracticalOPFsolutionshouldtakeinconsiderationthecriticalsituati onduetosevereloadingconditionsandfaultinpowersystem.�DifferentialEvolution�(DE)�is� oneofthebestEvolutionaryAlgorithms�(EA)�tosolverealvaluedoptimizationproblems.�This� paperpresentssimpleDifferentialEvolution�(DE)�Optimizationalgorithmtosolvingmultiobje ctiveoptimalpowerflow�(OPF)�inthepowersystemwithshuntFACTSdevicesconsideringv oltagedeviation,�powerlosses,�andpowerflowbranch.�Theproposedapproachisexaminedan dtestedonthestandardIEEE�30Buspowersystemtestwithdifferentobjectivefunctionsatcr iticalsituations.�Inaddition,�thenonsmoothcostfunctionduetotheeffectofvalvepointhas� beenconsideredwithinthesecondpracticalnetworktest�(13�generatingunits).�Thesimulation� resultsarecomparedwiththosebytheotherrecenttechniques.�Fromthedifferentcasestudies,� itisobservedthattheresultsdemonstratethepotentialoftheproposedapproachandshowcl earlyitseffectivenesstosolvepracticalOPFundercontingentoperationstates.� �

Optimal Power Flow with Discontinous Fuel Cost Functions Using Decomposed GA Coordinated with Shunt FACTS

This paper presents efficient parallel genetic algorithm (EPGA) based decomposed network for optimal power flow with various kinds of objective functions such as those including prohibited zones, multiple fuels, and multiple areas. Two coordinated sub problems are proposed: the first sub problem is an active power dispatch (APD) based parallel GA; a global database generated containing the best partitioned network: the second subproblem is an optimal setting of control variables such as generators voltages, tap position of tap changing transformers, and the dynamic reactive power of SVC Controllers installed at a critical buses. The proposed approach tested on IEEE 6-bus, IEEE 30-bus and to 15 generating units and compared with global optimization methods (GA, DE, FGA, PSO, MDE, ICA-PSO). The results show that the proposed approach can converge to the near solution and obtain a competitive solution with a reasonable time.

Fuzzy controlled genetic algorithm for environmental/economic dispatch with shunt FACTS devices

In recent year and with the growth in electricity demand, environmental considerations have become one of the major management concerns. This paper presents an hybrid Genetic Algorithm and Fuzzy Logic rules for solving the economic dispatch problem under constrained emission with multi shunt Flexible AC Transmission Systems (FACTS). The economic dispatch problem is formulated as a nonlinear constrained multi objective optimization problem with both equality and inequality constraints. The problem is decomposed into, the optimal power generation subproblem that is searched by a flexible Genetic Algorithm which crossover and mutation adjusted by Fuzzy practical rules and a simple practical reasoning fuzzy rules designed as a second subproblem to control the reactive power exchanged with the network. The proposed method guarantees the near optimal solution and remarkably reduces the computation time. A global database generated based in reactive index sensitivity (RIS) as a flexible tool to choose economically the size of the shunt FACTS devices. This proposed approach is implemented with Matlab program with various case studies, and compared with conventional method and genetic algorithm (GA). The results show that the approach proposed can converge to optimum solution faster than other methods, and obtains the solution with high accuracy.