Improved genetic algorithm for economic load dispatch with valve-point loadings

Ling, Sai Ho; Lam, Hak‐Keung; Leung, F.H.F.; Lee, Y.S.

doi:10.1109/iecon.2003.1280021

Cited by 28 publications

(13 citation statements)

References 11 publications

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“…However, the classical PSO stuck at local solution after certain iteration. The results obtained by the IPSO method has been compared with the published results: GA [22], IGA [23], PSO [21], SAPSO [21], CPSO [21] and NPSO [21] given in Table 5. It is clearly shows that the proposed IPSO has less generation cost compared to other methods.…”

Section: B Case Study 2: 13 Unit Systemmentioning

confidence: 99%

Economic dispatch with valve point effect using iteration particle swarm optimization

Abdullah

Bakar

Rahim

et al. 2012

2012 47th International Universities Power Engineering Conference (UPEC)

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Abstract-Economic dispatch (ED) is one of the optimization problems in power system operation and planning. The practical ED problems have non-smooth cost functions with equality and inequality constraints. It becomes more complicated problem when valve point effects of generator are considered and results multiple local minima. This makes the ED problems are difficult to find global optimum solution when using any mathematical approach due to non-convex cost function. The Classical particle swarm optimization (PSO) has ability to reach near global optimum solution by tuning some parameters but always stuck at local minima. In this paper, an iteration particle swarm optimization (IPSO) is proposed to solve ED problems with valve point effects. The proposed algorithm is introduced a new parameter in the original velocity equation of PSO in order to enrich searching behaviour, solution quality and avoid being trapped at local minima. The proposed IPSO algorithm has been implemented on two test power systems (consisting 3 and 13 generating units) to validate its effectiveness. The simulation results confirmed that IPSO algorithm has better convergence characteristic and more robust compared to PSO and some published results.Index Terms--Economic dispatch, constraints handling, particle swarm optimization, valve point effect.

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Section: B Case Study 2: 13 Unit Systemmentioning

confidence: 99%

Economic dispatch with valve point effect using iteration particle swarm optimization

Abdullah

Bakar

Rahim

et al. 2012

2012 47th International Universities Power Engineering Conference (UPEC)

View full text Add to dashboard Cite

show abstract

“…As usual, the region defined by the constraint set (4) is called the feasible region. Note that the transmission loss is omitted in (4) since the loss is typically less than 3% in practice and was often neglected in reported numerical experiments with a GA ( [1,3]). The profile of (3) cannot be geographically viewed as it involves three unknowns.…”

mentioning

confidence: 99%

“…However, the comparison to GA is not the issue of this Letter. One of the main reasons is that their stop-criteria are fundamentally different ( [1,3,4]). …”

mentioning

confidence: 99%

“…This is mainly because, in the mathematical programming (MP) discipline, almost all notable algorithms belong to the class of local optimisation methods [2], meaning that they typically stop at a local minimum point of the multimodal function. In the literature, most reported approaches for solving the multimodal ELD problem are based on the genetic algorithm (GA) (see [3] and the references therein). This Letter introduces a new approach, called the filled function method (FFM) [4], and applies it to the multimodal ELD problem.…”

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confidence: 99%

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Application of filled function method to multimodal economic load dispatch problems

Liu¹

2011

Electron. Lett.

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“…In order to verify the performance advantages of QEMA further, the simulation results were compared with that of other optimized algorithm, and the comparison results in Table 3. Algorithm 1 is QEMA which have been applied in this paper; Algorithm 2~Algorithm 5 is evolutionary programming algorithm which have been proposed in [18]; Algorithm 6 is improved genetic algorithm which have been proposed in [19]. Algorithm 7 is EMA.…”

Section: Test Casementioning

confidence: 99%

Quantum-behaved Electromagnetism-like Mechanism Algorithm for Economic Load Dispatch of Power System

Zhang¹,

Gong²,

Duan³

2015

Journal of Electrical Engineering and Technology

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-This paper presents a new algorithm called Quantum-behaved Electromagnetism-like Mechanism Algorithm which is used to solve economic load dispatch of power system. Electromagnetismlike mechanism algorithm simulates attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it move to optimum solution according to certain criteria. Quantum-behaved electromagnetism-like mechanism algorithm merges quantum computing theory with electromagnetism-like mechanism algorithm. Superposition characteristic of quantum methodology can make a single particle present several states, and the characteristic potentially increases population diversity. Probability representation of quantum methodology is to make particle state be presented according to a certain probability. And the quantum rotation gates are used to realize update operation of particles. The algorithm is tested for 13-generator system and 40-generator system, which validates it can effectively solve economic load dispatch problem. Through performance comparison, it is obvious the solution is superior to other optimization algorithm.

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Improved genetic algorithm for economic load dispatch with valve-point loadings

Cited by 28 publications

References 11 publications

Economic dispatch with valve point effect using iteration particle swarm optimization

Economic dispatch with valve point effect using iteration particle swarm optimization

Application of filled function method to multimodal economic load dispatch problems

Quantum-behaved Electromagnetism-like Mechanism Algorithm for Economic Load Dispatch of Power System

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