Optimized Convolutional Neural Network for Deregulated Congestion Management

Congestion in power systems primarily caused by deregulation of the power system and rising electricity consumption. Grid congestion occurrences are possible due to increased levels of distributed energy resources (DER) penetration and result in serious harm to power networks, including potential blackouts. Thus, one of the main objectives for assuring network dependability and security is the development of creative operation planning methodologies and control tools for grid congestion management. In order to reduce congestion and support Independent System Operators (ISOs), this work presents a Congestion Management (CM) technique utilizing Convolution Neural Network (CNN). Using a generation rescheduling-based strategy, the primary goal of CM is to ease transmission line congestion while addressing all limitations with least amount of congestion expense. The control of congestion in public transmission and distribution systems continues to be a difficult problem. Traditionally, load shedding and generator rescheduling have been employed to accomplish this. The suggested work uses Improved Artificial Bee Colony Algorithm (IABCA)-optimized CNN, which helps to deliver considerable results for congestion management with lower error. The suggested approach improves a number of current optimization techniques in terms of congestion cost and losses by producing more accurate prediction outputs. For the IEEE 30 Bus system, simulation outcomes based on IABCA are presented using MATLAB simulation software.