Multiagent based Power Management for Grid-connected Photovoltaic Source Using the Optimized Network Parameters From Butterworth Inertia Weight Particle Swarm Optimization

An efficient power management technique of a grid-connected renewable source proficiently coordinates the various controllable units necessary in the power system operation. It is achieved by responding to the dynamic load demand through efficient communication and advanced control structures. This paper presents a decentralized multiagent power management technique for a grid-connected photovoltaic/energy storage system using the optimized network parameters from the Butterworth inertial weight particle swarm optimization method. The power network is coordinated by intelligent agents and structured into a zonal generation and load multiagent system to update the load and power injected at different network buses. However, Butterworth inertial weighting function particle swarm optimization determines the optimized network parameters and the capacity of the connected energy sources fed into the multiagent system. The inertial weight of the optimization technique is patterned along the Butterworth filtering curve for holistic space search and improved convergence. Hence, the proposed technique solves the problem of inefficient optimization methods and provides a robust control and management system with agents capable of reorganizing and coping with the system's dynamic changes. The performance analysis of the IEEE 33-Bus distribution system shows an improved network coordinating method. The power loss reduction appreciated significantly from 65.42% to 68.58%, while the voltage deviation improved from 88.19% to 89.95% by integrating a renewable battery system. The voltage is maintained within the operational constraints of daily simulations. The method is targeted at efficient operation of distribution networks.