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Improved Genetic Algorithm for Power Economic Dispatch of Units With Valve-Point Effects and Multiple Fuels

IEEE Transactions on Power Systems · 2005 · Vol. 20(4) · pp. 1690–1699
C.-L. Chiang

Abstract

This paper presents an improved genetic algorithm with multiplier updating (IGA/spl I.bar/MU) to solve power economic dispatch (PED) problems of units with valve-point effects and multiple fuels. The proposed IGA/spl I.bar/MU integrates the improved genetic algorithm (IGA) and the multiplier updating (MU). The IGA equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions, and the MU is employed to handle the equality and inequality constraints of the PED problem. Few PED problem-related studies have seldom addressed both valve-point loadings and change fuels. To show the advantages of the proposed algorithm, which was applied to test PED problems with one example considering valve-point effects, one example considering multiple fuels, and one example addressing both valve-point effects and multiple fuels. Additionally, the proposed algorithm was compared with previous methods and the conventional genetic algorithm (CGA) with the MU (CGA/spl I.bar/MU), revealing that the proposed IGA/spl I.bar/MU is more effective than previous approaches, and applies the realistic PED problem more efficiently than does the CGA/spl I.bar/MU. Especially, the proposed algorithm is highly promising for the large-scale system of the actual PED operation.

Electric Power System OptimizationOptimal Power Flow DistributionMicrogrid Control and OptimizationEconomic dispatchGenetic algorithmAlgorithmMultiplier (economics)Mathematical optimizationElectric power systemPower (physics)Computer scienceMathematics
Citations
708
FWCI
13.42
field-weighted impact
References
27
Percentile
99%
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Citations per year
References
Genetic algorithm solution of economic dispatch with valve point loading
IEEE Transactions on Power Systems · 1993 · 1,200 citations
Genetic algorithms in search, optimization, and machine learning
Choice Reviews Online · 1989 · 49,283 citations
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