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Hybrid genetic/simulated annealing approach to short-term multiple-fuel-constrained generation scheduling

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2 Author(s)
Kit Po Wong ; Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia ; Yin Wa Wong

This paper develops a new formulation for short-term multiple-fuel-constrained generation scheduling. In the formulation, the power balance constraint, generator operation limits, fuel availability factors of generators, efficiency factors of fuels and the supply limits of fuels are taken fully into account. A fuzzy set approach is included in the formulation to find the fuel schedules, which meet the take-or-pay fuel consumption as closely as possible or maximise the utilisation of the cheap fuels, within a generation schedule. The new formulation is combined with genetic algorithms, simulated-annealing and hybrid genetic/simulated-annealing optimisation methods to establish new algorithms for solving the problem. A method for forming the initial candidate solutions in the genetic-based and hybrid-based algorithms is also developed. This method has also been incorporated into the simulated-annealing-based algorithm. The new algorithms are demonstrated by applying them to determine the most economical generation schedule for 25 generators in a local power system and its fuel schedule for 4 different types of fuels

Published in:

Power Systems, IEEE Transactions on  (Volume:12 ,  Issue: 2 )

Date of Publication:

May 1997

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