Incremental Fuel Cost Function Research Articles

Dynamic economic dispatch based on improved differential evolution algorithm

Dynamic economic dispatch optimum scheduling of power plant generation is of great importance to electric utility systems, it is difficult to solve because of its complex structure, variable parameter, nonlinear characteristics et al. Based on analysis of DE searching mechanism, an improved differential evolution (IDE) algorithm based DE/target-to-best is presented,which adopts an improved mutation strategy that a random vector and the previous best vector is used instead of the current vector in case the DE algorithm may be in early maturity or decline in convergence speed.The algorithm is applied to solve the generators dynamic load economic dispatch problems taking into account the incremental fuel cost function and the valve-point effects. Computer simulation test shows that IDE algorithm provides better solution of less cost In the case of less generations and outperforms GA, PSO and DE.

Economic Dispatch Optimization Using Imperialist Competitive Algorithm (ICA) and compare with PSO algorithm result

Measurement Imperialist Competitive Algorithm (ICA) is a population based stochastic optimization technique, originallydeveloped by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In ICA, the system (imperialists) is initialized with a population of random solutions (colonies) and searches for optimal using cognitive and social factors by updating generations. ICA has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the ICA, this paper discusses the use of ICA approach to optimize performance of economic dispatch problems. The proposed method is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects

Artificial bee colony optimization for economic dispatch with valve point effect

In recent years, various heuristic optimization methods have been proposed to solve economic dispatch (ED) problem in power systems. This paper presents the well-known power system ED problem solution considering valve-point effect by a new optimization algorithm called artificial bee colony (ABC). The proposed approach has been applied to various test systems with incremental fuel cost function, taking into account the valve-point effects. The results show that the proposed approach is efficient and robust when compared with other optimization algorithms reported in literature.

Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect

This paper addresses a hybrid solution methodology involving modified shuffled frog leaping algorithm (MSFLA) with genetic algorithm (GA) crossover for the economic load dispatch problem of generating units considering the valve-point effects. The MSFLA uses a more dynamic and less stochastic approach to problem solving than classical non-traditional algorithms, such as genetic algorithm, and evolutionary programming. The potentiality of MSFLA includes its simple structure, ease of use, convergence property, quality of solution, and robustness. In order to overcome the defects of shuffled frog leaping algorithm (SFLA), such as slow searching speed in the late evolution and getting trapped easily into local iteration, MSFLA with GA cross-over is put forward in this paper. MSFLA with GA cross-over produces better possibilities of getting the best result in much less global as well as local iteration as one has strong local search capability while the other is good at global search. This paper proposes a new approach for solving economic load dispatch problems with valve-point effect where the cost function of the generating units exhibits non-convex characteristics, as the valve-point effects are modeled and imposed as rectified sinusoid components. The combined methodology and its variants are validated for the following four test systems: IEEE standard 30 bus test system, a practical Eastern Indian power grid system of 203 buses, 264 lines, and 23 generators, and 13 and 40 thermal units systems whose incremental fuel cost function take into account the valve-point loading effects. The results are quite promising and effective compared with several benchmark methods.

Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms

A chaotic sequence based differential evolution (DE) approach for solving the dynamic economic dispatch problem (DEDP) with valve-point effect is presented in this paper. The proposed method combines the DE algorithm with the local search technique to improve the performance of the algorithm. DE is the main optimizer, while an approximated model for local search is applied to fine tune in the solution of the DE run. To accelerate convergence of DE, a series of constraints handling rules are adopted. An initial population obtained by using chaotic sequence exerts optimal performance of the proposed algorithm. The combined algorithm is validated for two test systems consisting of 10 and 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other algorithms reported in literatures for DEDP considering valve-point effects.

An improved harmony search algorithm for power economic load dispatch

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature.

Economic Power Dispatch with Valve Point Effects Using Bee Optimization Algorithm

This paper presents a newly developed optimization algorithm, the Bee Optimization Algorithm (BeeOA), to solve the economic power dispatch (EPD) problem. The authors have developed a derivative free and global optimization technique based on the working of the honey bee. The economic power dispatch problem is a nonlinear constrained optimization problem. Classical optimization techniques fail to provide a global solution and evolutionary algorithms provide only a good enough solution. The proposed approach has been examined and tested on two test systems with different objectives. A simple power dispatch problem is tested first on 6 generators and then the algorithm is demonstrated on 13 thermal unit systems whose incremental fuel cost function takes into account the value point loading effect. The results are promising and show the effectiveness and robustness of the proposed approach over recently reported methods.

Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects

Particle swarm optimization (PSO) algorithm is population-based heuristic global search algorithm inspired by social behavior patterns of organisms that live and interact within large groups. The PSO is based on researches on swarms such as fish schooling and bird flocking. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-inspired version of the PSO (QPSO) using the harmonic oscillator potential well (HQPSO) to solve economic dispatch problems. A 13-units test system with incremental fuel cost function that takes into account the valve-point loading effects is used to illustrate the effectiveness of the proposed HQPSO method compared with the simulation results based on the classical PSO, the QPSO, and other optimization algorithms reported in the literature.

Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units

The economic dispatch problem (EDP) is an optimization problem useful in power systems operation. The objective of the EDP of electric power generation, whose characteristics are complex and highly non-linear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying system constraints. Recently, as an alternative to the conventional mathematical approaches, modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. As special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used as optimization technique in EDPs. Based on the chaos theory, this paper discusses the design and validation of an optimization procedure based on a chaotic artificial immune network approach based on Zaslavsky’s map. The optimization approach based on chaotic artificial immune network is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results and comparisons show that the chaotic artificial immune network approach is competitive in performance with other optimization approaches presented in literature and is also an attractive tool to be used on applications in the power systems field.

A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect

An efficient hybrid genetic algorithm (HGA) approach for solving the economic dispatch problem (EDP) with valve-point effect is presented in this paper. The proposed method combines the GA algorithm with the differential evolution (DE) and sequential quadratic programming (SQP) technique to improve the performance of the algorithm. GA is the main optimizer, while the DE and SQP are used to fine tune in the solution of the GA run. To improve the performance of the SQP, the cost function of EDP is approximated by using a smooth and differentiable function based on the maximum entropy principle. An initial population obtained by using uniform design exerts optimal performance of the proposed hybrid algorithm. The combined algorithm is validated for two test systems consisting of 13 and 40 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other algorithms reported in literatures (EP, EP–SQP, PSO, PSO–SQP) for EDP considering valve-point effects.

Hybrid genetic algorithm for economic dispatch with valve-point effect

This paper presents an efficient method for solving the economic dispatch problem (EDP) through combination of genetic algorithm (GA), the sequential quadratic programming (SQP) technique, uniform design technique, the maximum entropy principle, simplex crossover and non-uniform mutation. The proposed hybrid technique uses GA as the main optimizer, the SQP to fine tune in the solution of the GA run. Based on the maximum entropy principle, the cost function of EDP is approximated by using a smooth and differentiable function to improve the performance of the SQP. An initial population obtained by using uniform design exerts optimal performance of the proposed hybrid algorithm. The effectiveness of the proposed method is validated by carrying out extensive tests on two different EDP with incremental fuel-cost function taking into account the valve-point loadings effects. The result shows that the proposed hybrid genetic algorithm improves the solution accuracy and reliability compared to other techniques for EDP considering valve-point effects.

A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the chaotic systems theory, this paper proposed a novel chaotic PSO combined with an implicit filtering (IF) local search method to solve economic dispatch problems. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed PSO introduces chaos mapping using Hénon map sequences which increases its convergence rate and resulting precision. The chaotic PSO approach is used to produce good potential solutions, and the IF is used to fine-tune of final solution of PSO. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results are promising and show the effectiveness of the proposed approach.

Evolução diferencial híbrida com programação quadrática aplicada ao problema de despacho econômico de energia elétrica

Neste artigo propõe-se uma nova metodologia híbrida para resolução do problema de despacho econômico de energia elétrica com o efeito do ponto de válvula. O método híbrido proposto integra evolução diferencial (ED) com a técnica de programação quadrática seqüencial (SQP). A ED é utilizada como otimizador global e a SQP é utilizada para o ajuste fino da otimização, realizada pela ED. A metodologia híbrida e suas variantes são validadas para sistemas teste de 3, 13 e 40 unidades geradoras térmicas com funções incrementais de custo de combustível que levam em consideração o efeito de carregamento devido ao ponto de válvula. O método híbrido proposto supera e providencia soluções promissoras, em termos de eficiência, se comparadas com as obtidas pela ED e SQP sozinhas bem como de outras técnicas propostas na literatura para o problema de despacho econômico de carga com o efeito do ponto de válvula.

Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect

Evolutionary algorithms are heuristic methods that have yielded promising results for solving nonlinear, nondifferentiable, and multi-modal optimization problems in the power systems area. The differential evolution (DE) algorithm is an evolutionary algorithm that uses a rather greedy and less stochastic approach to problem solving than do classical evolutionary algorithms, such as genetic algorithms, evolutionary programming, and evolution strategies. DE also incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution, and robustness. This paper proposes a new approach for solving economic load dispatch problems with valve-point effect. The proposed method combines the DE algorithm with the generator of chaos sequences and sequential quadratic programming (SQP) technique to optimize the performance of economic dispatch problems. The DE with chaos sequences is the global optimizer, and the SQP is used to fine-tune the DE run in a sequential manner. The combined methodology and its variants are validated for two test systems consisting of 13 and 40 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other state-of-the-art algorithms in solving load dispatch problems with the valve-point effect.

06/00913 A step-by-step application of Sandia method in developing typical meteorological years for different locations in Oman: Sawaqed, N. M. et al. International Journal of Energy Research, 2005, 29, (8), 723–737.

An efficient hybrid genetic algorithm (HGA) approach for solving the economic dispatch problem (EDP) with valve-point effect is presented in this paper. The proposed method combines the GA algorithm with the differential evolution (DE) and sequential quadratic programming (SQP) technique to improve the performance of the algorithm. GA is the main optimizer, while the DE and SQP are used to fine tune in the solution of the GA run. To improve the performance of the SQP, the cost function of EDP is approximated by using a smooth and differentiable function based on the maximum entropy principle. An initial population obtained by using uniform design exerts optimal performance of the proposed hybrid algorithm. The combined algorithm is validated for two test systems consisting of 13 and 40 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other algorithms reported in literatures (EP, EP–SQP, PSO, PSO–SQP) for EDP considering valve-point effects.

Hybrid PSO–SQP for economic dispatch with valve-point effect

This paper presents a novel and efficient method for solving the economic dispatch problem (EDP), by integrating the particle swarm optimization (PSO) technique with the sequential quadratic programming (SQP) technique. PSO is the main optimizer and the SQP is used to fine tune for every improvement in the solution of the PSO run. PSO is a derivative free optimization technique which produces results quickly and proves itself fit for solving large-scale complex EDP without considering the nature of the incremental fuel cost function it minimizes. SQP is a nonlinear programming method which starts from a single searching point and finds a solution using the gradient information. The effectiveness of the proposed method is validated by carrying out extensive tests on three different EDP with incremental fuel cost function takes into account the valve-point loadings effects. The proposed method out-performs and provides quality solutions compared to other existing techniques for EDP considering valve-point effects are shown in general.