Energy Consumption Control and Optimization of Large Power Grid Operation Based on Artificial Neural Network Algorithm

Abstract

Green, energy-saving, efficient and reliable smart grids have become one of the most important development areas in the world. An artificial neural network algorithm based on cranial nerve principle proposed in this paper provides reference for power flow analysis, reasonable dispatching and control decision-making of smart grids. At the same time, new EMS (Energy Management System) structure and strategy are introduced to optimize resource allocation and energy management. Based on the single-layer network control theory of large power grid, BP neural network including genetic algorithm is constructed and combined with regression analysis to realize the optimization of power analysis. In addition, a neuron controller is set up in the distributed unit to collect, monitor and control the parameters and send them to the high-level central controller, forming a multi-level three-dimensional network to analyze and make decisions, accurately predict the energy consumption of power grids, and improve the control level of the energy consumption of smart grids.