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While ML algorithms have improved conventional ES optimization models, new models using ML techniques have become increasingly important [[55]]. These ML algorithms that have focused on the optimization of ESs aim to accelerate the conventional ES optimization models, decentralize the centralized optimization models, or speed them up by approximating the iteration processes and parameters related to optimization. This editorial overviews the contents of the Special Issue "Machine Learning for Energy Systems 2021" and review the trends in machine learning (ML) techniques for energy system (ES) optimization. In other words, a few ML algorithms have improved the optimization of current ES optimization models rather than been used in the development of new models. [Extracted from the article]

The power requirements of grids have risen as artificial intelligence and electric vehicle technologies have been used. Thus, the installation of distributed generators (DGs) has become an essential factor to streamline power grids. The objective of this study is to optimize the capacity and location of DGs. For this purpose, an objective function was defined, which takes into account the fault current and the levelized cost of energy, and a modified particle swarm optimization method was applied. Then, we analyzed a case of a single line-to-ground fault with a test feeder (i.e., the IEEE 30 bus system) with no DGs connected, as well as a case where the DGs are optimally connected. The effect of the optimally allocated DGs on the system was analyzed. We discuss an optimal layout method that takes the economic efficiency of the DG installation into account. [ABSTRACT FROM AUTHOR]