Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels

This paper presents an improved genetic algorithm with multiplier updating (IGA_MU) to solve power economic dispatch (PED) problems of units with valve-point effects and multiple fuels. The proposed IGA_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_MU), revealing that the proposed IGA_MU is more effective than previous approaches, and applies the realistic PED problem more efficiently than does the CGA_MU. Especially, the proposed algorithm is highly promising for the large-scale system of the actual PED operation.