Research on Sustainable Management of Grid Companies Based on Artificial Intelligence

With the advancement of power systems, grid operations monitoring has become more complex. To ensure the sustainable operation of the grid, it is important to quickly and dynamically extract a small amount of data from a large amount of real-time data, closely related to grid security, and accurately identify system security risks in a timely manner. In this regard, this paper proposes an artificially intelligent sustainable management system for the grid, which has achieved remarkable results. The main contribution of this paper is the development of a risk identification model for microgrids using artificial intelligence and the DEMATEL method. Firstly, the paper identifies the risk factors involved in the microgrid from four aspects, including the generation side, distribution side, demand side, and human factors, through a comprehensive literature review, expert survey, and brainstorming. Secondly, an artificial intelligence and DEMATEL-based microgrid risk factor identification method is employed to clarify the importance and perturbation relationship of each risk factor of the microgrid. Finally, the paper classifies all factors into two categories, cause factors, and effect factors, and ranks the importance of each factor. To further demonstrate the effectiveness of the proposed model, a wind power prediction algorithm based on data mining technology and an improved SVM algorithm, and a PV power prediction algorithm based on a deep neural network are established. After comparing and analyzing the performance of the constructed algorithms with other algorithms, the DBN prediction model is proposed, which has an absolute error probability of 62.9% within 1%, surpassing the other algorithms and meeting the engineering needs. Moreover, the paper proposes risk control measures on the power generation side, which can significantly reduce the risks involved in power generation. In summary, the paper proposes an artificially intelligent sustainable management system for the grid, which includes a risk identification model for microgrids, and prediction models for wind and PV power generation. The paper's findings provide valuable insights for researchers and practitioners in the field of grid operation and management. The proposed models have shown remarkable results, and the risk control measures can significantly reduce the risks involved in power generation, contributing to the sustainable operation of the grid.