Adaptive Swarm Intelligence-Based Optimization Approach for Smart Grids Power Dispatch

Approximately 70-90 percent of today's energy usage is derived from fossil fuels. Due to global warming and countless air pollutants, the atmosphere is polluted, compelling us to use new computer paradigms for efficient dispatching of smart energy grids. In order to solve the difficult nonlinear, non-convex, and constrained confined EEDs, a new whale optimization algorithm (WOA) is devised for the minimization of total cost and emission. The proposed method takes into account the social behaviour of humpback whales, with an emphasis on the nature of suggested meta-heuristic optimization algorithms. The proposed optimization paradigm generated data demonstrating AWSOA superiority to other advanced approaches. To ensure the efficacy of the proposed method, we compare it to other approaches and consider a number of technological constraints; the findings demonstrate that our approach is both cost-effective and accurate. In addition, the research assists electric energy firms in controlling emissions as required by emission regulatory agencies and saving the annual operational cost of thermal plants.