Microgrids are localized energy systems that provide stable power supply, especially in remote or disaster-prone areas. As the world transitions to renewable energy sources like solar and wind power, microgrids are becoming more essential. However, managing these systems is challenging due to the uncertainties in energy supply and demand, such as power outages or fluctuations in energy usage, and stochastic islanding — situations where parts of the power grid unexpectedly become isolated, disrupting the power supply.
To address these challenges, a team of researchers from Incheon National University, Korea, led by Assistant Professor Jongheon Lee, has developed a new optimization model to improve the operation of microgrids under uncertain conditions. These models not only boost the efficiency and reliability of microgrids but also offer scalable solutions for the real world. Their findings were made available online on August 2, 2024 and was published in Volume 374 of Applied Energy on November 15, 2024.
Traditional methods for optimizing microgrid operations, such as multistage models, are computationally expensive and impractical for real-world use. These models consider different scenarios over time, but the complexity increases exponentially, making their application difficult at a large scale. The researchers have simplified these models while maintaining their effectiveness, by reducing the number of possible scenarios and introducing a process called replanning, where the optimization model adapts over time as new information emerges. This new approach significantly reduced the computational burden, enabling them to be more efficient in real-world settings.
"Our goal was to create a method that makes microgrid operation more adaptable and cost-effective, especially in regions with unreliable grids or frequent disruptions," says Dr. Lee. "By simplifying the models and using replanning, we can achieve effective operation plan without the heavy computational cost."
Microgrids act as essential backup energy source in remote and rural areas where stable grid access is unreliable, ensuring continuous power during outages or natural disasters. With the new models, these microgrids can operate more efficiently, minimizing energy waste and overproduction. Dr. Lee explains, "As renewable energy sources like solar and wind are often unpredictable, balancing these fluctuations is crucial. Our models help manage these uncertainties, ensuring a more stable energy supply."
Additionally, these solutions are beneficial to cities as well, where the energy demand is rising, and grids are under strain. Scalable optimization models can improve the overall energy management. Adapting to changes in supply and demand in real time helps boost grid resilience, supporting the transition to sustainable energy. Moreover, these models are flexible, making them suitable for both small and large systems.
"These optimization methods will be vital for improving energy security, particularly in areas with unreliable power. They also support global sustainability goals by promoting renewable energy," highlights Dr. Lee.
In conclusion, this study represents a step forward in creating smarter and more sustainable energy systems, ensuring stable and efficient power for communities around the world.
