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Presentation Details
| A Data-Driven Method for Synthetic Extreme Weather Generation and Solar Impact Assessment Duc-Huy Pham1, 2, Cong Feng1, Jin Tan1. 1National Laboratory of the Rockies, Golden, CO, USA.2North Carolina State University, Raleigh, NC, USA |
Abstract
High-resolution, high-fidelity weather datasets are critical for assessing power system resilience under extreme conditions. Existing datasets, often derived from localized historical events, typically lack the spatiotemporal resolution and scenario diversity required for large-scale system studies. In this work, we present an extreme weather data generation approach that constructs an extreme weather impact factor matrix, enabling the synthesis of events such as hurricanes over targeted areas using publicly available datasets. This method is demonstrated on the Western Electricity Coordinating Council (WECC) 240-bus test system by simulating a hurricane based on Hurricane Kathleen and mapping it onto the WECC buildouts. The hurricane’s trajectory and intensity can be flexibly adjusted to match study objectives. The generated data allow detailed analysis of impacts on solar generation in California, showing that total solar output can drop by over 63% during the peak hurricane period compared to the normal weather, yielding 34-48% of clear-sky levels across different spatial scales in the WECC, consistent with findings from literature. This tool provides a foundation for creating diverse, synthetic-yet-realistic scenarios to support machine learning model training for power system digital twin applications, forecasting, and power system resilience assessment.
No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.
No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.