Smart Grid Automation Technology and Artificial Intelligence Co-optimization

Abstract

With the popularization of energy transition throughout the world and the dual carbon agenda, smart grid, which is the heart of new power system, has an ever-growing scarce requirement on the intensive fusion of automation technology and artificial intelligence. The intelligent grids and their intelligence level are also greatly enhanced by artificial intelligence and its ability to schedule the work, address any faults and allocate resources of the smart grid because of its robust data processing, pattern recognition, and optimization features of the grid decision-making systems. This paper is dedicated to the synergistic optimization of automation and artificial intelligence in smart grids, and one of the priorities is the possible artificial intelligence functioning in the areas of fault diagnostics, renewable energy integration as well as load prediction and resource scheduling. Through the implementation of deep learning tools like LSTM to forecast short-term loads, uniting blockchain technology to ensure credible data exchange and optimization of the scheduling process, and thorough investigation of the consumer behavior of electricity consumption in order to increase the efficiency of resource distribution. The goal is to develop a more adaptable, dependable, and effective smart grid scheduling and operation structure which will offer theoretical foundation and technical aid in the process of intelligent transformation of the grid.