Research on Microgrid Operation Prediction Based on Grey BP-NNSP Algorithm

With the profound transformation of the global energy structure and the rapid development of smart grid technology, microgrids, as an important part of distributed energy systems, have a significant impact on the stability and economy of the power system. Especially in the context of large-scale integration of renewable energy, accurate prediction of microgrids can not only improve the flexibility of the power system but also effectively alleviate the problems brought by the intermittence and uncertainty of new energy generation. This paper proposes an innovative Grey BP-NNSP algorithm to address the issue of decreased prediction accuracy caused by spatial differences between meteorological forecast data and actual geographic locations. By combining active network management theory, it achieves high-precision prediction of microgrid operation and accurate calculation of deep peak regulation requirements. Experimental results show that the algorithm can significantly improve the prediction accuracy of regional load and photovoltaic output, optimize the allocation of grid resources, reduce the electricity cost for users during peak hours, and enhance the capacity of new energy to be carried and the self-healing ability of the distribution network.