ABSTRACT
The generation of electric power using different fuel resources to meet load demand and losses while satisfying various constraints on the system involves high running cost (cost of fuel). Therefore, it requires electric utilities to minimize the cost of production of electric power by planning and dispatching generating units in an economic and efficient manner to meet the system demand. The aim of the proposed research is to develop an efficient unit commitment based economic load dispatch (ELD) method using the monarch butterfly optimization algorithm in MATLAB R2017a software environment. The above aim was achieved by formulating the economic load dispatch method to minimize cost of generation considering the impact of cost function as system constraints using the application of monarch butterfly optimization algorithm as a tool to optimize the cost. MBO method was used to minimize the cost of supply of electric power to meet increasing demand of customers. The method was modelled and the performance was evaluated by applying the model on the three IEEE standards test system (3-unit IEEE test system, 6-unit test system and the 15-unit). Finally, analysis is done, validated and results. For the 3- unit system, the minimized cost obtained by the MBO model was $1,722.4130/hr for power demand of 150MW and $3,561.3973/hr for 300MW power demand. Similarly, for the 6-unit test system, $9,978.9427/hr was obtained for power demand of 700MW while $17,720.085/hr was obtained for power demand of 1400MW. The model also obtained $32,582.8863/hr for a demand of 2,630MW and $22,797.1231/hr for a demand of 5,260MW on the 15-unit test system. The developed model was finally validated by comparing the result of the 15-unit generator with Differential Evolution Particle Swarm Optimization (DEPSO) technique. For the same power demand of 2,630MW, the DEPSO obtained $32,588.81/h. This comparison showed that the application of MBO ELD model performed better than the DEPSO by 0.0157 (≈ 0.02%) percent in terms of generating cost per hour for load demand of 2,630MW with significant reduction in total power loss when compared with the DEPSO result.