A New Bayesian-Based Source Load Coordination and Scheduling Method for Power Systems Under the Background of Carbon Reduction

With the advancement of the national “dual carbon” strategy goals and the planning of new energy systems, the proportion of new energy connected to the power grid is rapidly increasing. Therefore, it is urgent to accelerate the planning and construction of energy storage. In this context, from the perspective of energy storage as a regulatory resource, this paper aims to build a set of energy storage state estimation and residual life prediction methods with high estimation accuracy and low computational complexity, and achieve energy storage management on the energy storage management system of edge computing architecture, so as to meet the safe, reliable, lasting and efficient operation needs of the energy storage management system, while promoting wind and solar energy consumption as a means. Taking carbon emission reduction as the optimal goal, energy storage management and utilization methods and built. Through simulation experiments, it is verified that this optimal carbon reduction and energy storage management method has good experimental results.