Capacity optimization configuration of multi-source independent microgrids based on TLC-GOLD-GWO algorithm

To guarantee the economic efficiency and reliability of standalone microgrids, this paper introduces an enhanced Grey Wolf optimization algorithm (GWO) [1] that leverages chaos mapping techniques, nonlinear convergence factor, and golden operator for solving multi-source capacity allocation problems. Taking the reliability of the power supply as a significant constraint, an optimization model is constructed with the minimization of the annual total economic cost as the objective function. Given the issues of local convergence and premature convergence encountered during the solution process of the fundamental Grey Wolf optimization algorithm, we employ chaotic mapping technology to address the challenge of unequal initial population distribution. Subsequently, a nonlinear convergence factor is introduced to expedite the convergence rate, enhancing the algorithm’s performance. Lastly, to update the grey wolf’s position, the golden sine operator is used, thereby refining the solution space and enhancing the effectiveness of the optimization process. The simulation outcomes reveal that the enhanced Grey Wolf optimization algorithm notably enhances the precision of the objective function, delivering superior performance. Compared to the Grey Wolf optimization algorithm and Whale optimization algorithm (WOA) [2], the enhanced algorithm demonstrates superiority in terms of reducing economic costs, enhancing the capability for global optimal search, and accelerating the optimization process.