Model exploration of grid adjustment and restoration strategy based on intelligent decision system

In the present scenario, Distributed Energy Resources (DERs) are included into power grid distribution networks to withstand power outages. Restoration is a smart and efficient method for self-healing systems to maintain functionality and re-configure themselves in response to outside breakdowns or attacks. Intelligent decision making framework is required for ensuring that the system as a whole survives external breakdowns and attacks by autonomously reconfiguring itself. Further, the power grid faces serious security concerns, network congestion delay, and packet loss due to improper grid adjustments. As a result, in this research Edge computing assisted Intelligent Decision Model (EC-IDM) has been proposed to address the issue of breakdowns and attacks in real time. The emergence of edge computing has substantially improved the smart grid's ability to operate effectively and communicate effectively by reducing the effects of security concerns, packet loss, latency, and overloaded networks. The integration Smart Grid's peripheral index (SSGPI) optimizes the system's performance and operating efficiency. This research presents a real-time EC-IDM for fixing the electricity grid that can automatically develop the restoration plan and identify the blackout scenario. For restoration choices across several time-and-space axes in EC-IDM, an improved hierarchical coordination technique and a local object optimization method are integrated to optimize breakdowns and attacks in real time. EC-IDM utilizes Smart grid self-healing using distributed automation (DA) to address issues of dependability and reliability of power. The simulation results demonstrates that the suggested approach achieves best in security concerns, optimizes network congestion delay, and packet loss security, improves efficiency and performance compared to the other conventional methods.