Power Optimization Operation Based on Energy-Saving Dispatching and Demand-Side Management

In order to meet the challenges of power optimization operation research, in view of the shortcomings of the existing dynamic programming algorithms, this study proposes an innovative power optimization operation research method based on energy-saving scheduling and demand-side management. This new scheme uses the principles of allocation scheduling theory to accurately identify and locate key influencing factors, and accordingly makes a wise classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of energy-saving scheduling and demand-side management, this scheme cleverly constructs a design strategy to optimize operations. The empirical results show that this scheme shows a significant improvement compared with the traditional dynamic programming algorithm in terms of the accuracy of power optimization operation research and the processing efficiency of key factors, showing its obvious strong advantages. In the intelligent power system, power optimization operation research plays a vital role, which can accurately predict and optimize the growth trend and output results of power optimization operation research. However, in the face of complex simulation tasks, traditional dynamic programming algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of power optimization operation based on energy-saving scheduling and demand-side management optimization, and accurately controls the influencing parameters through the allocation scheduling theory, which is used as the road map for index allocation, and then uses energy-saving scheduling and demand-side management to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the research process of power optimization operation, the simulation scheme based on energy-saving scheduling and demand-side management successfully overcomes the shortcomings of traditional dynamic programming algorithms, and significantly improves the accuracy and operation efficiency of simulation.