Resilient microgrid modeling in Digital Twin considering demand response and landscape design of renewable energy

Microgrids (MGs) present a promising resolution for bolstering the resilience of distribution grids. Achieving a resilience-oriented (RO) optimal utilization of MGs becomes particularly challenging due to inherent uncertainties, necessitating effective methods for uncertainty modeling. In this study, a hybrid stochastic-robust optimization (HSRO) method is employed to determine the optimal schedule for an MG under both normal and resilient operation scenarios. The optimization method addresses the uncertainties related to the main electrical network cost, wind turbine (WT) power, photovoltaic (PV) power, and reactive/active loads through a combination of stochastic and robust optimization processes. To enhance MG resilience, interruptible and tunable demand response programs (DRPs) are implemented. The proposed method aims to improve MG performance under uncertainties using Monte Carlo simulation during both normal and resilient network operation modes. Additionally, real-time monitoring of the grid is facilitated by deploying Internet of Things (IoT) devices to prepare the grid for worst-case scenarios. The paper also explores conventional and robust methods for RO scheduling in MGs, and the suggested structure is evaluated using a large-scale MG. The integration of Digital Twin technology into smart grid systems has revolutionized the approach to worst-case scenario modeling in the power sector. Functioning as a dynamic virtual replica, the Digital Twin of a Smart Grid accurately mirrors the real-time operations, components, and interactions of the physical grid. Drawing data from IoT devices, sensors, and historical records, this technology provides a precise representation of the grid's current state. In the context of worst-case scenario modeling, the Digital Twin enables the simulation of various disruptions, including extreme weather events, cyber-attacks, equipment failures, and abrupt shifts in energy demand. Decision-makers gain valuable insights into potential vulnerabilities, allowing proactive implementation of mitigation strategies. The Digital Twin also facilitates testing and validation of resilience measures, response plans, and contingency protocols. Predictive capabilities support optimized maintenance schedules, efficient asset management, and the seamless integration of renewable energy sources. Overall, the Digital Twin of a Smart Grid emerges as a transformative tool, enhancing grid resilience, optimizing operations, and shaping the future of intelligent power systems.