A weather integrated neural symbolic dispatch framework for power system operational security
Abstract
Modern power systems face unprecedented challenges from rapid digitalization, electrification of new sectors, and increasing renewable integration. These changes demand real-time operational decisions based on accurate weather forecasting, as traditional dispatch methods cannot handle the speed and complexity of weather-driven variability in renewable generation. This paper thus proposes the Weather-Integrated Neural-Symbolic Dispatch System (WINDS), a novel methodological framework designed to incorporate weather-driven uncertainties into power system dispatch decisions. The WINDS framework combines weather forecasting and neuro-symbolic reasoning, where neural learning methods address weather-related uncertainties and symbolic methods enforce system security constraints, to provide operators with actionable dispatch strategies. Through a detailed set of study cases comprising normal operations, extreme weather events, missing data conditions, and multi-scenario risk analysis, evaluated on both a controlled experiment and measured wind and load data from the Danish DK1 bidding zone, WINDS reduces operational cost by 11.4% to 26.7% compared to static climatology-based dispatch depending on weather volatility, while reducing constraint-violation volume by more than 80% relative to forecast-only dispatch and substantially limiting violations during a measured extreme ramp-down event through adaptive uncertainty margins, with a novel L consistency metric quantifying the alignment between neural predictions and symbolic constraints. WINDS utilizes the strengths of both data-driven and symbolic approaches, enhancing system security and reliability, especially in renewables-rich regions sensitive to weather variability. This work presents a clearly defined methodological framework, and the results provide a proof-of-concept, paving the way for real-world implementation and operational enhancement in weather-sensitive power systems.
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BibTeX
@article{dahal2026weather,
title = {{A weather integrated neural symbolic dispatch framework for power system operational security}},
author = {Swaechchha Dahal and Sambeet Mishra and Gunne John Hegglid and Bhupendra Bimal Chhetri and Shailendra Kumar Jha and Thomas Øyvang},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-73702-5},
url = {https://www.nature.com/articles/s41598-026-73702-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A weather integrated neural symbolic dispatch framework for power system operational security
AU - Swaechchha Dahal
AU - Sambeet Mishra
AU - Gunne John Hegglid
AU - Bhupendra Bimal Chhetri
AU - Shailendra Kumar Jha
AU - Thomas Øyvang
PY - 2026
DA - 2026/10/29
JO - Scientific Reports
DO - 10.1038/s41598-026-73702-5
UR - https://www.nature.com/articles/s41598-026-73702-5
AB - Modern power systems face unprecedented challenges from rapid digitalization, electrification of new sectors, and increasing renewable integration. These changes demand real-time operational decisions based on accurate weather forecasting, as traditional dispatch methods cannot handle the speed and complexity of weather-driven variability in renewable generation. This paper thus proposes the Weather-Integrated Neural-Symbolic Dispatch System (WINDS), a novel methodological framework designed to incorporate weather-driven uncertainties into power system dispatch decisions. The WINDS framework combines weather forecasting and neuro-symbolic reasoning, where neural learning methods address weather-related uncertainties and symbolic methods enforce system security constraints, to provide operators with actionable dispatch strategies. Through a detailed set of study cases comprising normal operations, extreme weather events, missing data conditions, and multi-scenario risk analysis, evaluated on both a controlled experiment and measured wind and load data from the Danish DK1 bidding zone, WINDS reduces operational cost by 11.4% to 26.7% compared to static climatology-based dispatch depending on weather volatility, while reducing constraint-violation volume by more than 80% relative to forecast-only dispatch and substantially limiting violations during a measured extreme ramp-down event through adaptive uncertainty margins, with a novel L consistency metric quantifying the alignment between neural predictions and symbolic constraints. WINDS utilizes the strengths of both data-driven and symbolic approaches, enhancing system security and reliability, especially in renewables-rich regions sensitive to weather variability. This work presents a clearly defined methodological framework, and the results provide a proof-of-concept, paving the way for real-world implementation and operational enhancement in weather-sensitive power systems.
SN - 2045-2322
ER -