HYBRID GENETIC APPROACHES TO RAMPING RATE CONSTRAINED DYNAMIC ECONOMIC-DISPATCH

Hybrid genetic algorithms (HGAs) are proposed in this paper to determi ne the economic scheduling of electric power generation over a fixed t ime period under various system and operational constraints. The propo sed technique can outperform conventional genetic algorithms (CGAs) in the sense in that HGAs make it possible to improve both the quality o f the solution and reduce the computing expenses. In contrast, any car efully designed GA is only able to balance the exploration and the exp loitation of the search effort, which means that an increase in the ac curacy of a solution can only come at the sacrifice of convergent spee d, and vice visa. It is unlikely that both of them can be improved sim ultaneously. The proposed hybrid scheme is developed in such a way tha t a simple GA is acting as a base level search, which makes a quick de cision to direct the search towards the optimal region, and a local se arch method (gradient search technique) is next employed to do the fin e tuning. The aim of the strategy is to achieve the cost reduction wit hin a reasonable computing time. The effectiveness of the proposed hyb rid technique is verified on a real public electricity supply system w ith 25 generator units. The simulation results obtained with the HGAs for the real system are very encouraging with regard to the computatio nal expenses and the cost reduction of power generation. (C) 1997 Else vier Science S.A.