Solving the economic dispatch problem with tabu search algorithm

Khamsawang, S.; Boonseng, C.; Pothiya, S.

doi:10.1109/icit.2002.1189906

Cited by 27 publications

(9 citation statements)

References 12 publications

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“…For solving ED problem, the classical MPC have to solve We then discuss how to find a feasible solution of the optimal control problem in Step 2 of the decomposed MPC. As mentioned in previous work [1][2][3][4][5][6], the random solution can be initialized and be repaired if infeasible. This way is time consuming because the repairing needs a heuristic method.…”

Section: Classical Mpc Algorithm For Ed Problemmentioning

confidence: 99%

“…Many researchers have proposed their work for solving ED problems. Due to highly nonlinear characteristics of ED problems, several meta-heuristic methods such as genetic algorithm [1], artificial neural networks [2], tabu search [3], evolutionary programming [4,8], particle swarm optimization [5] and differential evolution [6], have been developed and applied successfully to ED problems. Generally, ED problems can be modeled in two types of formulas, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Decomposed Model Predictive Control for Economic Dispatch problems

Xia

Zhang

2013

2013 Africon

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Model Predictive Control (MPC) was applied in Economic Dispatching (ED) problem, but its computation burden was large due to high complexity and dimension of problems. In this paper, a decomposed MPC approach is proposed to reduce the computational time without decreasing the performance of convergence and robustness. The decomposed MPC uses the "divide and conquer" concept, and divides the original problem into two less difficult sub-problems. The proposed MPC has been tested on a 6-unit system, and the results have been shown that decomposition can deliver promising solutions with less computation time.

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Section: Classical Mpc Algorithm For Ed Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decomposed Model Predictive Control for Economic Dispatch problems

Xia

Zhang

2013

2013 Africon

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“…Therefore, the classical solution methods have difficulty in dealing with this problem. In recent years, many methods have been applied to solve the non-convex ED problem such as evolutionary programming (EP) for solving the ED problem with multiple fuel cost function has been discussed in [1,2], genetic algorithm (GA) for soling the ED problem with many types of fuel cost function, GA for solving the ED problem with valve-point effects was proposed in [3], tabu search algorithm (TSA) for solving the ED problem with multiple minima [4], TSA for solving the ED problem consider valve-point effects was proposed in [5], simulated annealing (SA) and the hybrid GA/SA for dealing with classical ED problems [6,7], particle swarm optimization (PSO) with improvements and a new PSO hybrid with local search [8], etc. However, these methods have large number of iterations to achieve solution and easily affected by the relevant control parameters.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Differential Evolution and Gravitational Search Algorithm for Nonconvex Economic Dispatch

et al. 2015

Proceedings in Adaptation, Learning and Optimization

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Abstract. The hybrid differential evolution and gravitational search algorithm (DEGSA) to solve economic dispatch (ED) problems with non-convex cost functions is presented in this paper with various generator constraints in power systems. The proposed DEGSA method is an improved differential evolution method based on the gravitational search algorithm scheme. The DEGSA method has the flexible adjustment of the parameters to get a better optimal solution. Moreover, an effective constraint handling framework in the method is employed for properly handling equality and inequality constraints of the problems. The proposed DEGSA has been tested on three systems with 13, 15, 40 units and the obtained results from the DEGSA algorithm have been compared to those from other methods in the literature. The result comparison has indicated that the proposed DEGSA method is more effective than many other methods for obtaining better optimal solution for the test systems. Therefore, the proposed DEGSA is a very favorable method for solving the non-convex ED problems.

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“…ECD with valve point loading effect and multiple fuel option (ECD-VPL-MF) [31,32]. Multiobjective formulation includes combined emission economic dispatch CEED [19,14,21], multi-area emission economic dispatch MAEED [29,30], power generation under different utilities [22].…”

Section: Eld Problem Formulationmentioning

confidence: 99%

“…The optimal ELD should meet load demand, generation limit, ramp rate, prohibited operating zone, [1,2] etc. For solving economic load dispatch various conventional methods like bundle method [3], non linear programming , mixed integer linear programming [4][5][6][7], dynamic programming [5], quadratic programming [6] , Lagrange relaxation method [8], network flow method [9], direct search method [10] are used to solve such problems. When compared with the conventional (classical) techniques [10][11], modern heuristic optimization techniques based on operational research and artificial intelligence concepts, such as evolutionary algorithms [12][13], simulated annealing [14,15], artificial neural networks [16][17][18], and taboo search [19,20] have been given attention by many researchers due to their ability to find an almost global optimal solution for ELD problems with operation constraints.…”

Section: Iintroductionmentioning

confidence: 99%

Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load Dispatch

Khan¹,

Mahala²

2014

IJAREEIE

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ABSTRACT:In this paper ,various inertia weight strategy particle swarm optimization is used to obtain the optimal power dispatch for 6 unit generator system with constraints satisfaction and minimizing the operating cost. The results are compared among classical Particle Swarm Optimization(CPSO),e1PSO, e2PSO methods. The numerical results affirmed the robustness and proficiency of proposed approach over other existing method.KEYWORDS: E1-PSO, E2-PSO,Economic load dispatch, PSO, Inertia weight, Prohibited operating zones. I.INTRODUCTIONThe economic load dispatch (ELD) of power generating units has always occupied an important position in the electric power industry. The primary objective of ELD is to schedule the committed generating units output so as to meet the required load demand at minimum cost satisfying all unit and system operational constraints. The optimal ELD should meet load demand, generation limit, ramp rate, prohibited operating zone, [1,2] [19,20] have been given attention by many researchers due to their ability to find an almost global optimal solution for ELD problems with operation constraints. ELD problem is non linear, non convex type with multiple local optimal point due to the inclusion of valve point loading effect, multiple fuel options with diverse equality and inequality constraints. Dynamic programming method is one of the approaches to solve the non-linear and discontinuous ELD problem, but it suffers from the problem of "curse of dimensionality" or local optimality. Thus the conventional methods have failed to solve such problems as they are sensitive to initial estimates and converge into local optimal solution and computational complexity. Modern heuristic optimization techniques based on operational research and artificial intelligence concepts, such as simulated annealing [14][15], evolutionary programming [13] , genetic algorithm, tabu search [19][20], neural network, particle swarm optimization provides better solution. The PSO originally developed by Eberhart and Kennedy in 1995[25], is a population based stochastic algorithm. The PSO is an evolutionary optimization tool of swarm intelligence field based on a swarm (population), where each member seen as a particle and each particle is a potential solution to the problem under analysis. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem, and implements the simulation of social behavior. PSO however, allows each particle to maintain a memory of the best solution that it has found and the best solution found in the particle's neighborhood is swarm. The main advantage of PSO algorithm is summarized as: simple concept, easy implementation, robustness to control parameters, and computational efficiency when compared with mathematical algorithms and other heuristic optimization techniques. PSO can be easily applied to nonlinear and non-continuous optimization problem.

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Solving the economic dispatch problem with tabu search algorithm

Cited by 27 publications

References 12 publications

Decomposed Model Predictive Control for Economic Dispatch problems

Decomposed Model Predictive Control for Economic Dispatch problems

Hybrid Differential Evolution and Gravitational Search Algorithm for Nonconvex Economic Dispatch

Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load Dispatch

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