Simulation and Optimization Analysis of Integrated Energy System Considering Load Forecasting

To improve the economic performance of system operation and better coordinate the operation of various equipment in an integrated energy system, a day-ahead operation optimization for the integrated energy system considering load forecasting is proposed. Firstly, the CloudPSS IESLab platform is utilized to establish an integrated energy system model that encompasses photovoltaic, heating pump, and energy storage systems. The effectiveness of this model and the accuracy of the calculations are verified; Subsequently, utilizing the CloudPSS IESLab SDK along with statistical methods, machine learning, and neural network models, an integrated energy system load forecasting model is developed; Finally, the optimize the operation is implemented with the goal of minimizing the operating and maintenance costs of the integrated energy system based on predicted load scenario. The results show that the day-ahead operation optimization based on load forecasting method proposed in this article can significantly improve system economy performance, improve system efficiency, and achieve low-carbon operation.