Distributed robust optimization for low-carbon dispatch of wind-thermal power under uncertainties

Area of Science:

• Power Systems Engineering
• Optimization Theory
• Environmental Science

Background:

• Growing demand for carbon emission reduction in the power industry.
• Integration of renewable energy sources like wind power presents challenges.
• Uncertainties in wind power generation and carbon reduction strategies impact grid stability and efficiency.

Purpose of the Study:

• To develop a robust optimization model for low-carbon power dispatch.
• To address uncertainties associated with wind power integration and thermal power carbon reduction.
• To balance robustness, economic viability, and environmental objectives in power dispatch.

Main Methods:

• Presentation of a distributed robust optimization model.
• Detailed discussion of wind power characterization and scenario management.
• Exploration of initial carbon emission rights allocation algorithms.

Main Results:

• The model effectively handles wind power uncertainties, reducing operating costs.
• It addresses uncertainties in carbon reduction modes, leading to lower carbon emissions.
• Demonstrated ability to achieve low-carbon dispatch strategies balancing multiple objectives.

Conclusions:

• The proposed model offers a scientific and reasonable approach to minimize uncertainties in low-carbon power dispatch.
• It facilitates the achievement of robust, economic, and environmentally friendly power dispatch strategies.
• Empirical analysis validates the model's effectiveness in complex power system operations.