Key Technologies for User Side Load Storage Interactive Regulation of New Power Systems Based on Deep Learning Algorithms

In the current energy environment, new power systems have become the development direction of future power systems due to their high efficiency, reliability, and intelligence. As an important component of the new power system, user side load storage can achieve functions such as load peak valley balance, energy scheduling, and supply-demand matching in the power system. In order to better achieve the function of user side load storage, it is necessary to develop new interactive adjustment technologies. This article proposes a novel user side load storage interactive regulation technology based on deep learning algorithms for power systems, aiming to improve the reliability and economy of the power system. This technology predicts and optimizes the electricity consumption and energy storage behavior of user side load storage through deep learning algorithms, thereby achieving dynamic regulation and optimization of the power system. Specifically, this technology first collects electricity consumption and energy storage data from the user’s side load storage, and uses deep learning algorithms to analyze and predict it. Secondly, according to the prediction results, the power consumption and energy storage behavior of the user side load storage are optimized and adjusted to achieve Dynamic equilibrium and optimization of the power system. Finally, through experimental verification, the peak electricity consumption was reduced by 20% and the energy utilization rate was increased by 15% after using this technology, proving the effectiveness and feasibility of this technology in the power system. The research results of this article can provide new ideas and methods for the operation and management of power systems. At the same time, this technology can also provide strong support and guarantee for the application and development of user side load storage.