Optimization Model for Comprehensive Energy Grid Consumption Capacity Under Health Monitoring

The real-time operational status information redundancy of the integrated energy system leads to significant deviations between the relevant characteristics of the integrated power grid and the electricity consumption characteristics of the user side, which affects the health of the integrated energy grid. To address this, a comprehensive energy grid electricity capacity optimization model under health monitoring is proposed. By determining the energy consumption optimization indicators, the K-means clustering algorithm is applied to find the sample day with the highest similarity to the optimal consumption day. Introducing a fusion factor and combining it with the SVR model to optimize the electricity consumption capacity and achieve the optimization of the comprehensive electricity consumption capacity of the power grid. The experimental results show that this research method can monitor the unbalanced current of integrated energy grid equipment, accurately monitor the health status of integrated energy network equipment, and increase the consumption rate to 93%.