Combined economic and emission dispatch using Whale Optimization Algorithm

Power plants give the most to environmental pollution, another important factor nowadays. Power stations must hold carbon credits and follow tight carbon emission restrictions. This is crucial for minimizing global warming and sustaining life. Electric power system planning and operation must meet load demand reliably, cost-effectively, and environmentally. Planners and operators use optimisation tools to attain these goals. In this study, the performance of two new optimisation methods, like the Whale Optimisation Algorithm (WOA), is compared to the performance of two older optimisation methods, like the Moth Flame Optimisation (MFO) and the Ant Lion Optimisation (ALO). When compared to the other two optimisation method, the results from the new optimisation method are better. It is obvious that there are competing goals that must be met. One cannot reasonably expect to achieve both the goal of reducing fuel costs and that of reducing gaseous emissions. In order to aid decision-makers in making the best choices, multi objective optimisation techniques are used to derive trade-off relationships between these incompatible goal functions. In this study, we examine the economic load dispatching issues that arise in the operation of power systems. The objective function of the issue is first analysed as a multi-objective function, with power dispatch and environmental considerations each being addressed as a distinct goal. Both the single- and multi-objective variants are examples of high-dimensional, nonlinear, non-convex constrained optimisation problems. Because of this, employing any optimisation strategy is extremely difficult. Several algorithms, including those that take their cues from nature, have been implemented to help us get as near as possible to optimum solutions tools.