Double-layer optimization model for integrated energy system under multiple robustness.

Area of Science:

• Renewable Energy Systems
• Optimization Theory
• Energy Economics

Background:

• Renewable energy integration is hindered by output instability and randomness.
• Comprehensive energy systems enhance efficiency but face challenges with intermittent sources.
• Uncertainty in distributed energy output and load impacts system reliability and cost.

Purpose of the Study:

• To develop a robust optimization model for comprehensive energy systems.
• To address the uncertainty of distributed renewable energy output and load.
• To minimize system operating costs while ensuring reliability.

Main Methods:

• Formulation of a bilevel programming optimization model incorporating robustness.
• Utilization of the firefly algorithm for solving the complex optimization problem.
• Analysis of system performance under varying distributed wind power output intervals.

Main Results:

• Optimizing for uncertainty significantly reduces actual system operating costs, with a maximum reduction of 14.43%.
• A dynamic balance between cost and consumption rate is achievable within a specific distributed wind power output range ([0, 190]).
• The proposed model demonstrates the effectiveness of robust optimization in managing renewable energy integration.

Conclusions:

• Robust optimization is a viable strategy for mitigating the economic impact of renewable energy uncertainty.
• The firefly algorithm provides an effective solution for complex comprehensive energy system optimization problems.
• Achieving a balance between cost and consumption is critical for the successful large-scale deployment of renewable energy.