Teaching learning based optimization to solve economic and emission scheduling problems

This paper presents a modern technique called teaching learning based algorithm (TLBO) to solve a multi objective of the economic and emission load dispatch (EELD) problem. The emission of pollutants such as NOx, power demand equality constraint and operating limit constraint are considered here. A recently developed population based evolutionary algorithm TLBO has been implemented to search for the optimum solution. TLBO uses two different phases `Teacher Phase' and `Learner Phase'. TLBO uses the mean value of the population to update the solution. The operation of TLBO is simpler compared to other optimization techniques due to absence of parameters to be tuned. Therefore, in the present paper Teaching-Learning-Based Optimization (TLBO) is tested on IEEE 30-bus 6 generator system and 10 generator system efficiently and effectively in order to achieve superior quality solution in computationally robust way. Simulation results show that the performance of proposed approach is superior compared to several already existing optimization techniques.

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