DeepTwin: A Deep Reinforcement Learning Supported Digital Twin Model for Micro-Grids

Abstract

This paper presents the development and application of a Digital Twin (DT) model for the optimization of micro-grid operations. With the increasing integration of renewable energy resources (RERs) into power grids, micro-grids are essential for enhancing grid resilience and sustainability. The proposed DT model, enhanced with Deep Reinforcement Learning (DRL), simulates and optimizes key micro-grid functions, such as battery scheduling and load balancing, to improve energy efficiency and reduce operational costs. The model incorporates real-time monitoring, service-oriented simulations, cloud-based deployments, "what-if"analyses, advanced data analytics, and security features to enable comprehensive management of DTs. An optimization scenario was conducted to evaluate the effectiveness of the DT and DRL in improving micro-grid performance. The results demonstrated significant revenue improvements: 81.7% for PPO and 56.12% for SAC compared to the baseline. These findings highlight both the promising potential of DT technology and the critical importance of incorporating DRL techniques into the DTs to improve system performance and resilience.