Optimal economic emission dispatch of hydrothermal power systems

This study develops an improved genetic algorithm-based multi-objective approach for the optimal economic emission dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The improved genetic algorithm (IGA) equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the ε-constraint technique is employed to manage the multi-objective problem. To show the advantages of the proposed algorithm, which is applied to test EED problems of the HPS with one example considering the best cost, one example considering the best emission, and one example addressing the best compromise. The proposed approach integrates the IGA, the MU and the ε-constraint technique, revealing that the proposed approach has the following merits – ease of implementation; applicability to non-smooth fuel cost and emission level functions; better effectiveness than the previous method; better efficiency than genetic algorithm with the MU (GA-MU), and the requirement for only a small population in applying the optimal EED problem of the HPS.