In this paper, an improved metaheuristic optimization algorithm based on the firefly algorithm, called multidimensional firefly algorithm (MDFA), is presented for solving day-ahead scheduling optimization in a microgrid. The proposed algorithm takes the output of power generations among a quantity of distributed energy resources during 24 h together rather than a single hour as a firefly separately. The proposed algorithm is combined with strategy of solving equality constraint replacing the use of the penalty-function technique. It is also enhanced by using a novel method in parameters self-adaption instead of applying fixed values, resulting in avoiding tuning frequently the algorithm parameters during the process of optimization. The MDFA is utilized for optimization of energy production cost in a microgrid. The superiority of the MDFA is demonstrated by using the classic test power system proved in the previous literature. The solutions obtained by MDFA are compared with the results found by five famous optimization algorithms. The high performance of MDFA is established by the quality with the minimum total cost, the reliability of gained solutions, the speed of convergence, and the ability to satisfy various constraints.

本文提出了一种基于萤火虫算法的改进的元启发式优化算法，称为多维萤火虫算法（MDFA），用于解决微电网中的日前调度优化问题。所提出的算法将发电量的输出集中在24小时内的大量分布式能源中，而不是将单个小时的萤火虫分开。提出的算法与求解等式约束的策略相结合，取代了惩罚函数技术的应用。通过在参数自适应中使用新颖的方法而不是应用固定值，也可以增强此功能，从而避免在优化过程中频繁调整算法参数。MDFA用于优化微电网中的能源生产成本。通过使用先前文献中证明的经典测试电源系统，可以证明MDFA的优越性。将通过MDFA获得的解决方案与通过五种著名的优化算法得出的结果进行比较。MDFA的高性能是由最低的总成本，获得的解决方案的可靠性，收敛的速度以及满足各种约束条件的能力所决定的。