Lévy Opposition-Based Bat Algorithm For Economic And Emission Dispatch With Wind And Solar Power
DOI:
https://doi.org/10.29284/fyh85439Keywords:
Economic Emission Dispatch, Renewable Energy Sources, Bat Algorithm, Lévy Opposition-Based Learning, Emissions Reduction, Security Constraints.Abstract
The Economic Emission Dispatch (EED) problem of a power system is a classical optimization problem that tries to reduce the economic cost and emissions. The Economic Dispatch (ED) problem traditionally has prioritized thermal generators with little consideration of important security limitations. Regarding environmental sustainability, it is necessary to minimize emissions, and an effort is underway to incorporate the use of wind and solar power into the grid. These renewable sources, however, cause some infirmities and intermittency, which significantly compound the dispatch problem. As a result, the goal has shifted to optimize the ED not only regarding emissions but also based on the security limits, being specific to consider the stochastic variability of the renewable energy sources. The LOBL-BA algorithm has been designed to solve these problems. Convergence speed and vulnerability to local optima are common problems that afflict the typical Bat Algorithm (BA). The presented strategy alleviates these problems in three different ways: a Random Inertia Weight is used to make the global search more efficient; a Lévy Opposition-Based Learning is to balance between the exploration and exploitation stages; a Sine function -driven Local search refreshes the candidate solutions and precludes the use of local optima. Applying the LOBL-BA algorithm to the IEEE 30-bus system outperforms in terms of overall costs and overall emissions to the environment in the two test cases, which substantiates the effectiveness of its application in meeting the two-fold goals of economic and environmental efficacy.
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