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

Area of Science:

• Power Systems Engineering
• Optimization Theory
• Renewable Energy Integration

Background:

• Large-scale renewable energy integration causes uncertainty in power systems.
• Reliable and economical operation demands accurate uncertainty monitoring and nonlinear dynamics handling.

Purpose of the Study:

• To propose an optimization-based scheduling method for power systems with high renewable energy penetration.
• To effectively manage photovoltaic (PV) uncertainty and nonlinear hydropower characteristics.

Main Methods:

• Developed an optimization-based scheduling framework.
• Integrated sensor-informed monitoring of PV uncertainty.
• Incorporated a detailed hydropower model with nonlinear dynamics.
• Employed a unified approximation scheme for nonlinear constraints and uncertainty.

Main Results:

• Achieved effective multi-source coordination in case studies.
• Reduced operating costs by up to 2.9%.
• Enhanced renewable energy utilization under various uncertainty levels and PV penetration scenarios.

Conclusions:

• The proposed method offers a computationally tractable solution for scheduling in complex power systems.
• It effectively balances reliability, economy, and renewable energy integration.
• Demonstrated significant improvements in operational efficiency and cost reduction.