A modified mutative scale chaotic optimization algorithm for economic load dispatch

A modified mutative scale chaotic optimization (MMSCO) algorithm for economic load dispatch among power generation units is proposed in this paper. The MSCO does not require derivative information and uses stochastic random search instead of a gradient search. In MSCO, mutative scale chaotic sequences are changed into generation load variables through load maps for calculation of the cost function. But these load variables do not always correspond with load constraints, searching becomes random and sightless. In MMSCO, a novel load map is built after the chaotic search to ensure the load variables fit the feasible region. MMSCO is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. MMSCO is simple in concept, few in parameters, and easy in implementation. The proposed modified method outperforms other chaotic search algorithms in solving load dispatch problems with the valve-point effect.

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