Dynamic assessment of distribution network-VPP interaction: an LSTM-entropy hybrid methodology

The integration of renewable energy into power systems has introduced significant complexity and dynamism, particularly in the interaction between distribution network and VPP. Existing methods struggle to capture the complex and dynamic characteristics, while machine learning techniques like LSTM remain underutilized in this context. This study proposes a methodology for evaluating distribution network-VPP interaction in uncertain environments. The methodology integrates a multi-dimensional evaluation index system with a dynamic weighting approach that combines the entropy method for initial weight generation and LSTM for optimization. The evaluation index system covers economic, safety, and flexibility dimensions, with specific indicators designed to capture the complex interdependencies and dynamic characteristics. The LSTM, leveraging its ability to process sequential data and capture temporal dependencies, dynamically adjusts the weights of evaluation indicators based on historical operational patterns, thereby enhancing the accuracy and adaptability of the assessment. Implementation results demonstrate that the proposed method achieves high accuracy and reliability, with MSE of 0.0012, MAE of 0.0056, and WRC of 96.2%. Testing using real-world operational data from a regional distribution network confirms a 95.0% match with expert argumentation, highlighting the practical applicability and robustness of the methodology. This study contributes to the advancement of data-driven decision-making frameworks for power system planning and operation, particularly in the context of integrating distributed energy resources and achieving carbon neutrality goals.