Loss minimization and voltage improvement through distributed generation (DG) planning is a well-established problem. However, a careful review of the literature shows that there is still room for the development of efficient algorithms for this purpose. In special, hybridization between optimization techniques is suitable for this complex problem, as it allows taking advantage of the positive features of different approaches. In this work, a novel empirical discrete metaheuristic (EDM) is presented and merged with the steepest descent method to solve the DG allocation problem. The allocation is broken into two subproblems: sitting the DGs and sizing them. The EDM deals with the first subproblem, while the second one is solved by the steepest descent method in an interchangeable optimization structure. The EDM tackles some key limitations of metaheuristic family methods. Relatively, it shows: low results variability in different executions; low initial conditions dependency; and few number parameters to tune. All simulations are performed in a communication scheme using the softwares Matlab and OpenDSS. The obtained results with IEEE 34-bus and IEEE 123-bus distribution test systems were compared to the literature and other metaheuristics, attesting the quality of the proposed approach.

中文翻译：

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通过经验离散元启发式和最速下降法在不平衡径向配电网络中优化分布式发电分配

通过分布式发电 (DG) 规划来最小化损耗和改善电压是一个公认的问题。然而，对文献的仔细审查表明，为此目的仍然有开发有效算法的空间。特别是，优化技术之间的混合适用于这个复杂的问题，因为它允许利用不同方法的积极特征。在这项工作中，提出了一种新颖的经验离散元启发式 (EDM) 并与最速下降法相结合来解决 DG 分配问题。分配分为两个子问题：让 DG 坐下来并调整它们的大小。EDM 处理第一个子问题，而第二个子问题通过可互换优化结构中的最速下降法解决。EDM 解决了元启发式族方法的一些关键限制。相对而言，它表明：不同执行中的结果可变性低；低初始条件依赖性；和几个要调整的数字参数。所有的模拟都是在使用 Matlab 和 OpenDSS 软件的通信方案中进行的。将使用 IEEE 34 总线和 IEEE 123 总线分布测试系统获得的结果与文献和其他元启发式方法进行比较，证明了所提出方法的质量。