Microgrids offer a promising solution for fortifying the resilience of distribution grids, but achieving optimal utilisation in the face of uncertainties presents a challenge. In a recent study from Shenyang Ligong University in Northern China, a hybrid stochastic-robust optimisation (HSRO) method is proposed to tackle this challenge head-on.
The HSRO method aims to determine the best schedule for an microgrid under both normal and resilient operation scenarios. It addresses uncertainties related to various factors such as electrical network costs, wind turbine and photovoltaic power, and reactive/active loads. By combining stochastic and robust optimisation processes, this method provides a comprehensive approach to enhancing microgrid performance.
One key aspect of this study is the implementation of interruptible and tunable demand response programs (DRPs) to enhance microgrid resilience. These programs allow for dynamic adjustments to energy demand, improving the microgrid’s ability to balance supply and demand even in the face of disruptions.
To facilitate real-time monitoring of the grid, Internet of Things (IoT) devices are deployed. These devices collect data from various sources, including sensors and historical records, providing valuable insights into the grid’s current state. By leveraging this data, grid operators can proactively prepare for worst-case scenarios, ensuring the grid remains resilient and adaptable.
The integration of Digital Twin technology further enhances the resilience of smart grids by providing a dynamic virtual replica of the physical grid. This technology enables utilities and grid operators to simulate various disruptions, allowing for proactive planning and mitigation strategies. Additionally, the Digital Twin facilitates testing and validation of resilience measures, ensuring the grid remains robust in the face of uncertainties.
The study makes significant strides in advancing the resilience of microgrids. By employing innovative optimisation methods, implementing demand response programs, and leveraging cutting-edge technologies like IoT and Digital Twin, the study offers valuable insights and solutions for building a more resilient and adaptive energy infrastructure.
Source
Resilient microgrid modeling in Digital Twin considering demand response and landscape design of renewable energy, Sustainable Energy Technologies and Assessments, 2024-05
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