Combined economic and emission dispatch using Whale Optimization Algorithm
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Author |
M. Chandrashekhar and P. K. Dhal
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e-ISSN |
1819-6608 |
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On Pages
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2692-2707
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Volume No. |
18
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Issue No. |
24
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Issue Date |
February 29, 2024
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DOI |
https://doi.org/10.59018/1223321
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Keywords |
economic load dispatch (ELD), fuel cost, emission, whale optimization algorithm (WOA), moth flame optimization (MFO), ant lion optimization (ALO).
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Abstract
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.
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