Enabling Efficient Scheduling of Multi-Type Sources in Power Systems via Uncertainty Monitoring and Nonlinear Constraint Processing

Zhang, Di; Li, Qionglin; Han, Ji; Tian, Chunsun; Li, Yebin

doi:10.3390/s25216564

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Enabling Efficient Scheduling of Multi-Type Sources in Power Systems via Uncertainty Monitoring and Nonlinear Constraint Processing

by

Di Zhang

¹,

Qionglin Li

¹,

Ji Han

^2,*

,

Chunsun Tian

¹ and

Yebin Li

²

¹

Electric Power Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450000, China

²

College of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China

^*

Author to whom correspondence should be addressed.

Sensors 2025, 25(21), 6564; https://doi.org/10.3390/s25216564 (registering DOI)

Submission received: 24 September 2025 / Revised: 19 October 2025 / Accepted: 20 October 2025 / Published: 24 October 2025

(This article belongs to the Special Issue Advanced Sensors and Artificial Intelligence for Condition Monitoring of Power Electronic Systems)

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Abstract

The large-scale integration of renewable energy sources introduces significant uncertainty into modern power systems, posing new challenges for reliable and economical operation. Effective scheduling therefore requires accurate monitoring of uncertainty and efficient handling of nonlinear system dynamics. This paper proposes an optimization-based scheduling method that combines sensor-informed monitoring of photovoltaic (PV) uncertainty with advanced processing of nonlinear hydropower characteristics. A detailed hydropower model is incorporated into the framework to represent water balance, reservoir dynamics, and head–discharge–power relationships with improved accuracy. Nonlinear constraints and uncertainty are addressed through a unified approximation scheme that ensures computational tractability. Case studies on the modified IEEE−39 system show that the proposed method achieves effective multi-source coordination, reduces operating costs by up to 2.9%, and enhances renewable energy utilization across different uncertainty levels and PV penetration scenarios.

Keywords: uncertainty monitoring; nonlinear constraint processing; optimization-based scheduling; renewable energy integration; hydropower modeling; multi-source coordination

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MDPI and ACS Style

Zhang, D.; Li, Q.; Han, J.; Tian, C.; Li, Y. Enabling Efficient Scheduling of Multi-Type Sources in Power Systems via Uncertainty Monitoring and Nonlinear Constraint Processing. Sensors 2025, 25, 6564. https://doi.org/10.3390/s25216564

AMA Style

Zhang D, Li Q, Han J, Tian C, Li Y. Enabling Efficient Scheduling of Multi-Type Sources in Power Systems via Uncertainty Monitoring and Nonlinear Constraint Processing. Sensors. 2025; 25(21):6564. https://doi.org/10.3390/s25216564

Chicago/Turabian Style

Zhang, Di, Qionglin Li, Ji Han, Chunsun Tian, and Yebin Li. 2025. "Enabling Efficient Scheduling of Multi-Type Sources in Power Systems via Uncertainty Monitoring and Nonlinear Constraint Processing" Sensors 25, no. 21: 6564. https://doi.org/10.3390/s25216564

APA Style

Zhang, D., Li, Q., Han, J., Tian, C., & Li, Y. (2025). Enabling Efficient Scheduling of Multi-Type Sources in Power Systems via Uncertainty Monitoring and Nonlinear Constraint Processing. Sensors, 25(21), 6564. https://doi.org/10.3390/s25216564

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Enabling Efficient Scheduling of Multi-Type Sources in Power Systems via Uncertainty Monitoring and Nonlinear Constraint Processing

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