Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target. (1st April 2022) - Li, Jinghua; Luo, Yichen; Wei, Shanyang
Abstract: Currently, system dynamics (SDs) is thought to be an effective method to forecast the evolution process of electricity consumption, which is of great significance for the long-term planning of the power system. However, for the traditional SDs forecast model, the interactions between system variables are not considered, resulting in that it may be invalid in the carbon-neutral situation where the inter-relationship between system variables is complex. Moreover, the electricity consumption of various industries is only simply superimposed in the traditional SDs model. This also leads to that the changes of future electricity consumption cannot be understood and grasped as a whole. Thus, a long-term load forecasting method involving carbon-neutral situation is proposed in the current work. First, a new system of influencing factors for electricity consumption containing carbon-neutral factors is constructed. Then, based on the information feedback mechanism, a new SDs forecast model is established, which can more accurately reflect the interaction of system variables. In addition, an electricity consumption identity is also developed to reveal the quantitative relationship between factors, electricity consumption, and carbon emissions at a holistic level. Finally, the evolution process of electricity consumption under various carbon-neutral scenarios is forecasted based on the new established forecast model. Correspondingly, the forecast results are expected to guide power system planning in the future carbon-neutral situation. Highlights: A load influencing factors system with carbon-neutral factors added is proposed. A dynamic long-term load forecasting model with modified feedback is established. The relationship of load and factors is established to avoid qualitative analysis. The evolution process of load under various carbon-neutral scenarios is forecasted.