A robust optimization model for microgrid considering hybrid renewable energy sources under uncertainties.

Area of Science:

• Electrical Engineering
• Operations Research
• Sustainable Energy

Background:

• Future electricity generation relies on hybrid renewable energy sources and microgrids.
• Evaluating uncertain intermittent power output is crucial for sustainable and reliable microgrid operations.
• Growing energy demands necessitate advanced methods for managing renewable energy integration.

Purpose of the Study:

• To propose a robust mixed-integer linear programming model for microgrids to minimize day-ahead operational costs.
• To address uncertainties in wind turbine, photovoltaic, and electrical load outputs.
• To validate the model's effectiveness in managing energy systems with integrated renewable sources.

Main Methods:

• Developed a robust mixed-integer linear programming model.
• Utilized piecewise linear curves to handle uncertainties in renewable energy generation and load.
• Employed robust optimization techniques, including a robust worst-case scenario and max-min robust optimization.
• Validated the model through a case study on the IEEE 33-node system.

Main Results:

• The proposed robust optimization model effectively minimizes day-ahead costs under uncertainty.
• The model demonstrates superior cost-effectiveness compared to alternative optimization techniques.
• Adjusting the Uncertainty Budget Set allows for optimal decision-making in controlling load demand and renewable energy uncertainty.
• The methodology proved effective and advantageous in the IEEE 33-node system case study.

Conclusions:

• The robust optimization approach ensures high solutions for microgrid availability and cost-effectiveness.
• The study provides managerial insights into integrating renewable energy sources into microgrids.
• The proposed method offers an efficient and reliable solution for managing uncertain energy systems.