Deep Time Series Aggregation for Extracting Operating Scenarios in New Power Systems

What is it about?

This paper presents a novel deep learning framework that automatically extracts typical operating scenarios from new power systems. It converts historical one-dimensional operational snapshots into three-dimensional images using a Gramian Angular Summation Field encoder. Then, a deep time series aggregation scheme (DTSAs) clusters these images to reveal the spatiotemporal characteristics of grid operations under varying renewable energy penetration. This method preserves the rich dispatch experience and helps identify fine-grained dispatch schemes.

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Photo by Andrey Metelev on Unsplash

Photo by Andrey Metelev on Unsplash

Why is it important?

As power systems incorporate higher levels of renewable energy and flexible loads, operating conditions become increasingly complex and dynamic. Traditional methods struggle to capture these high-dimensional, rapidly changing scenarios, which can hinder effective grid dispatch and control. This work is crucial because it provides a robust, data-driven approach to automatically extract and cluster typical operating scenarios. This enables grid operators to better understand historical operation patterns, adapt to real-time changes, and improve the economic, safe, and low-carbon operation of power systems.

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