A way to improve energy management is to perform balancing both at the Peer-to-peer (P2P) level and then at the Virtual Microgrid-to-Virtual Microgrid (VMG2VMG) level, while considering the intermittency of available Renewable Energy Source (RES). This paper proposes an interdisciplinary analytics-based approach for the formation of VMGs addressing energy balancing. Our approach incorporates Computer Science methods to address an Energy sector problem, utilizing data preprocessing techniques and Machine Learning concepts. It features P2P balancing, where each peer is a prosumer perceived as an individual entity, and Virtual Microgrids (VMGs) as clusters of peers. We conducted several simulations utilizing clustering and binning algorithms for preprocessing energy data. Our approach offers options for generating VMGs of prosumers, prior to using a customized Exhaustive brute-force Balancing Algorithm (EBA). EBA performs balancing at the cluster-to-cluster level, perceived as VMG2VMG balancing. To that end, the study simulates on data from 94 prosumers, and reports outcomes, biases, and prospects for scaling up and expanding this work. Finally, this paper outlines potential ideal usages for the approach, either standalone or integrated with other toolkits and technologies.

改善能源管理的一种方法是在对等（P2P）级别然后在虚拟微电网到虚拟微电网（VMG2VMG）级别执行平衡，同时考虑可用可再生能源（RES）的间歇性。本文提出了一种基于跨学科分析的方法来形成VMG，以解决能源平衡问题。我们的方法结合了计算机科学方法，利用数据预处理技术和机器学习概念来解决能源行业的问题。它具有P2P平衡功能，其中每个对等方都是被视为单个实体的生产者，而虚拟微电网（VMG）则是对等方的集群。我们利用聚类和分箱算法对能源数据进行了多次仿真。我们的方法提供了用于生成生产者VMG的选项，在使用自定义的穷举蛮力平衡算法（EBA）之前。EBA在群集到群集级别执行平衡，称为VMG2VMG平衡。为此，该研究模拟了来自94个生产者的数据，并报告了扩大，扩展这项工作的结果，偏见和前景。最后，本文概述了该方法的潜在理想用法，既可以独立使用，也可以与其他工具箱和技术集成。