Parallel micro genetic algorithm for constrained economic dispatch

Tippayachai, J.; Ongsakul, Weerakorn; Ngamroo, Issarachai

doi:10.1109/tpwrs.2002.800948

Cited by 57 publications

(22 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Network losses In the economic load dispatch, the network losses should be taken into account as functions of respective generating units outputs using B coefficients matrix [8] …”

Section: Transmission Constraintsmentioning

confidence: 99%

“…Among these methods, some of them are genetic algorithm (GA) [7][8], evolutionary programming (EP) [9][10], dynamic programming (DP) [11], tabu search [12], hybrid EP [13], neural network (NN) [14], adaptive Hopfield neural network (AHNN) [15], particle swarm optimization (PSO) [16][17][18], etc. For calculation simplicity, existing methods use A. Y. Saber and M. Higashi are with the Toyota Technological Institute, Hisakata, Nagoya, 486-8511, Japan and A. Yona is with the Engineerin Faculty, University of the Ryukyus, Okinawa, Japan (e-mail: aysaber@yahoo.com).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Economic Load Dispatch of Higher Order Cost Polynomials Using Modified Particle Swarm Optimization

Saber

Yona

Higashi

2007

2007 5th IEEE International Conference on Industrial Informatics

View full text Add to dashboard Cite

This paper presents a modified particle swarm optimization (MPSO) for economic load dispatch (ELD) problem. Considering multi-fuel operations, valve-point effects, accurate curve fitting, etc., real cost function is more complex than a conventional second order cost function in the deregulated changing market. Modified PSO consists of problem dependent variable number of promising values (in velocity vector), unit vector, error-iteration dependent step length, etc. It reliably and accurately tracks a continuously changing solution of the complex cost function and no extra concentration/effort is needed for more complex higher order cost polynomial in ELD. Constraint management is incorporated in the modified PSO. The modified PSO has balance between local and global searching abilities, and an appropriate fitness function helps to converge it quickly. To avoid the method to be frozen, stagnated/idle particles are reset. In this paper, sensitivity of higher order cost polynomial is also analyzed visually to realize the importance of higher order cost polynomial in ELD. Finally, benchmark data and methods are used to show the effectiveness of the proposed method. Index Terms-Modified particle swarm optimization, higher order complex polynomial cost function, unit vector, appropriate fitness function, economic load dispatch.

show abstract

“…Network losses In the economic load dispatch, the network losses should be taken into account as functions of respective generating units outputs using B coefficients matrix [8] …”

Section: Transmission Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic Load Dispatch of Higher Order Cost Polynomials Using Modified Particle Swarm Optimization

Saber

Yona

Higashi

2007

2007 5th IEEE International Conference on Industrial Informatics

View full text Add to dashboard Cite

show abstract

“…Only antibodies that satisfy the constraint are included in the population set. b) The affinity value of each antibody in the population set is evaluated using equation (1). c) Clone the individuals in the population, giving rise to a temporary population of clones.…”

Section: Implementation Of Clonal Selection-based Ais For Solving Ecomentioning

confidence: 99%

“…The procedure to ensure for economic operation of a power system is called Economic Dispatch(ED). Economic Dispatch problem is an optimization problem that determines the optimal output of online generating units so as to meet the load demand with an objective to minimize the total generation cost [1].…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune-Based Optimization Technique for Solving Economic Dispatch in Power System

Rahman

Suliman

Musirin

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents an Artificial Immune-based optimization technique for solving the economic dispatch problem in a power system. The main role of electrical power utility is to ensure that electrical energy requirement from the customer is served. However in doing so, the power utility has also to ensure that the electrical power is generated with minimum cost. Hence, for economic operation of the system, the total demand must be appropriately shared among the generating units with an objective to minimize the total generation cost for the system. Economic Dispatch is a procedure to determine the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfying the load demand simultaneously. The proposed technique implemented Clonal Selection algorithm with several cloning, mutation and selection approaches. These approaches were tested and compared in order to determine the best strategy for solving the economic dispatch problem. The feasibility of the proposed techniques was demonstrated on a system with 18 generating units at various loading conditions. The results show that Artificial Immune System optimization technique that employed adaptive cloning, selective mutation and pair-wise tournament selection has provided the best result in terms of cost minimization and least execution time. A comparative study with λ-iteration optimization method and Genetic Algorithm was also presented.

show abstract

“…Convergence checking with re-initialization: This strategy is introduced to improve a diversity of a search process [19]. To prevent the optimization from wasting excessively computational effort, the convergence of the best solution reached is observed during the search process.…”

Section: Ts Components Encoding and Decodingmentioning

confidence: 99%

Robust decentralized design of power system stabilizers taking into consideration system uncertainties

Ngamroo

Dechanupaprittha

2005

Optim. Control Appl. Meth.

View full text Add to dashboard Cite

SUMMARYIn complex power systems, changes in network configurations, various loading conditions, etc., cause system uncertainties. Without considering such uncertainties in the design, the conventional power system stabilizers (PSSs) may deteriorate the system robust stability. To overcome this problem, the proposed design incorporates the uncertainty model in the system representation. Then, the PSSs in a multi-machine power system are arranged as the decentralized controller in a multi-input multi-output (MIMO) system. The robust stability margin of the closed-loop control system is guaranteed in terms of the MIMO gain margin and phase margin. Control parameters of PSSs are optimized by a tabu search. Non-linear simulation studies confirm the robustness of the designed PSSs against various uncertainties.

show abstract

Parallel micro genetic algorithm for constrained economic dispatch

Cited by 57 publications

References 8 publications

Economic Load Dispatch of Higher Order Cost Polynomials Using Modified Particle Swarm Optimization

Economic Load Dispatch of Higher Order Cost Polynomials Using Modified Particle Swarm Optimization

Artificial Immune-Based Optimization Technique for Solving Economic Dispatch in Power System

Robust decentralized design of power system stabilizers taking into consideration system uncertainties

Contact Info

Product

Resources

About