Abstract: Hydro thermal power plants are one of the most important energy sources in the world which are used most in areas called Niagara Falls in Canada. These power plants attempts to generate the energy from the water by using the equipment called turbines which would be placed in the water source to utilize their potential energy which would then be converted into electricity. Hydro thermal scheduling plays a key role in identifying and fixing the required equipments to be activated for the production of energy with less cost. This paper presents a novel analysis on differential evolution for short-term hydrothermal scheduling combined economic emission. The optimization technique called genetic algorithm is used for evolution of hydro thermal scheduling the problem which is formulated with economic emission and without economic emission. The genetic algorithm follows biological behavior of chromosomes which would attempt to find the optimized set of resources that requires to be activated for achieving optimal hydroelectric power plant setup. Hence, the proposed method can well be extended for solving the large-scale hydrothermal scheduling. The experimental tests were conducted on the hydro thermal power with varying set of equipments to be turned, which it is proved that the proposed multi-objective genetic algorithm can provide better and efficient result than the other approaches which has been conducted before.
L.P. Vettrivelan and G. Tholkappiaarasu, 2016. A Novel Analysis of Hydrothermal Scheduling Using Genetic Algorithm with Emission Cost. Asian Journal of Information Technology, 15: 4174-4183.