Attack-Resilient Cyber-Physical System State Estimation for Smart Grid Digital Twin Design

Before implementing the microgrid testbed and SCADA electricity monitoring systems, computer aided tools can be used to design and validate technical specifications and performance. In this way, the system and product can be implemented digitally reducing cost, time, efforts, and visualizing expected quality. In real-time, designing and implementing the smart grid incorporating renewable microgrids is also a critical and challenging task due to random generation patterns of foreseeable green energy. In order to solve this impending problem, the microgrid digital twin incorporating renewable distributed energy resources is designed using physical and governing laws such as Kirchhoff’s laws, and input-output relationships. After modeling the distribution grid into a set of first-order differential equations, the microgrid digital framework is transformed into a compact state-space representation. Using a set of IoT sensors, the measurements are collected from the distribution grid at common coupling points. Indeed, the increased rate of cyber-attacks on the smart grid communication network requires for innovative solutions to ensure its resiliency and operations. When the IoT sensing information is under cyber attacks, designing the optimal smart grid state estimation algorithm that can tolerate false data injection attacks is a crucial task for energy management systems. To address aforementioned issue, this article had proposed a physics-informed based optimal grid state estimation. The simulation results have to be demonstrated the improved performance in grid state estimation accuracy, and computational efficiency compared to the traditional method. The availability of smart grid digital twin model can assist in monitoring the grid status which is precursor for controller design to regulate grid voltage at common coupling points.