Design of grid intelligent dispatch system incorporating forecast error feedback mechanisms
TL;DR
Experimental results show that the proposed intelligent grid dispatching system significantly improves load forecasting accuracy and power balance control precision in extreme cold weather conditions, representing a significant improvement on traditional systems.
Abstract
Power system load forecasting poses significant challenges in extreme weather conditions, directly impacting the safe and stable operation of the power grid. This study proposes a design for an intelligent grid dispatching system that incorporates a prediction error feedback mechanism. The aim is to improve the accuracy and reliability of grid dispatching under extreme weather conditions. A core innovation of the system is its adoption of a hierarchical distributed architecture, with the prediction error feedback mechanism integrated into the dispatch optimization algorithm. The study's methodological approach integrates temperature sensitivity analysis and time series feature extraction to establish an adaptive prediction model framework. Additionally, it devises a particle swarm optimization (PSO) algorithm, underpinned by adaptive weights, to enhance dispatch optimisation. Experimental results show that the system significantly improves load forecasting accuracy and power balance control precision in extreme cold weather conditions, representing a significant improvement on traditional systems. Not only does the system reduce generation costs and increase the utilisation rate of renewable energy, it also shortens the response time to load changes. These results are important for making sure the power system can be safely and reliably operated in extreme weather conditions. They also have significant theoretical and practical value in improving the robustness and efficiency of grid scheduling.