Abstract
The sooty tern optimization algorithm (STOA) has been used in this study to solve and optimize the dynamic economic emission dispatch (DEED) problem. The main aim of the DEED model is to minimize total fuel cost and emission of pollutant gases from thermal generators for 24 hours. The various operating constraints like valve point loading effect, ramp rate limit, transmission losses, operating conditions, and power balance constraints have been considered in this study to get a closer practical system. The swarm intelligence-based STOA method has been inspired by the migration and attacking behaviors of sea bird sooty tern. The exploration and exploitation approach of the proposed algorithm help to get an optimum solution in less convergence time. The algorithm has been tested in 5 and 10 thermal generating units to verify the algorithm's performance. The results obtained by the proposed algorithm have been compared with results obtained by other recently developed algorithms.
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