A chaotic hybrid optimization technique for solution of dynamic generation scheduling problem considering effect of renewable energy sources

This research introduces a novel hybrid optimizer using two well-known metaheuristic algorithms, SMA and SCA. The suggested methodology was used to answer the problem of optimal dynamic generation scheduling for the thermal generation unit along with thermal unit integrated with renewable sources such as wind, solar, and electric vehicles. The problem is solved using a unique hybrid CSMA-SCA optimizer in three steps: first, the units are prioritized based on the average full load cost, and the unit scheduling solution is used without consideration of the many constraints that have an impact on the solutions. The second step is the establishment of a heuristic constraints repair mechanism, which forces previous solutions to comply with inescapable constraints. The third step is the implementation of an optimal power generation share allocation for all participating units. To model the stochastic behavior of wind speed and solar radiation, the Weibull probability distribution and Beta PDF functions are used. To avoid the algorithm from slipping into local minima and achieve a better balance between exploration and exploitation, a novel chaotic position updating method called Singer map-based position updating is proposed. The suggested method has proven effective in small-, medium-, and large-scale thermal power systems as well as thermal systems that integrate wind power. The extensive studies demonstrate that the CSMA-SCA methodology presented in this research outperforms most current methods in terms of producing high-quality solutions around global minima. Graphical abstract