A tie line planning method and system based on deep learning

CN111709550BActive Publication Date: 2026-07-24CHINA ELECTRIC POWER RESEARCH INSTITUTE CO LTD +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA ELECTRIC POWER RESEARCH INSTITUTE CO LTD
Filing Date
2020-05-07
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

The different planning methods used by dispatch centers at various levels make it impossible to effectively grasp the operation mode and planning calculation methods of power grids at all levels, resulting in repeated iterations and modifications to tie line plans.

Method used

A deep learning-based long short-term memory network model is adopted. By training historical load data, clean energy power data and tie-line planned power data, a mapping relationship between system load, clean energy power and tie-line planned power is established, and tie-line plans are directly generated using prediction data.

Benefits of technology

It improves the decision-making accuracy and adaptability of tie-line planning, reduces iterative modifications, and has the characteristics of self-evolution and self-learning, making it suitable for computational problems in complex interconnected large power grids.

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Abstract

The application provides a tie line planning method based on deep learning, comprising the following steps: obtaining load prediction data and clean energy prediction data of a day to be calculated; inputting the load prediction data and the clean energy prediction data of the day to be calculated into a pre-trained long short-term memory network deep learning model to obtain a tie line plan; wherein the long short-term memory network deep learning model is obtained based on training of historical load data, clean energy power data and historical tie line plan power; the mapping relationship among the known input load data, clean energy power and tie line plan power is directly constructed by using massive historical decision data for training, without the need of studying the internal mechanism of power grid operation.
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