In the rapidly evolving landscape of energy systems, the integration of renewable energy sources and power electronics is transforming traditional grids into complex, power electronics-dominated grids (PEDGs). However, this transformation brings with it significant challenges, particularly in accurately estimating the dynamic states of these grids. Enter Ildar Idrisov, a researcher from the Skolkovo Institute of Science and Technology in Moscow, Russia, who has developed a groundbreaking approach to tackle this issue using digital twins.
Idrisov’s research, published in the journal “IEEE Access” (translated from Russian as “IEEE Access”), focuses on enhancing dynamic state estimation (DSE) in PEDGs. Traditional DSE methods often fall short in these modern grids due to their high penetration of distributed generation and energy storage, leading to inaccurate state estimations. “The complexity of PEDGs requires a more sophisticated approach to ensure reliable and efficient grid operation,” Idrisov explains.
The solution lies in leveraging digital twins—a real-time, high-fidelity virtual model of a microgrid. This model includes detailed representations of distributed generation units, power electronics, and load dynamics, enabling accurate state estimation even during transient events. “By creating a digital twin, we can simulate and predict the behavior of the grid under various conditions, providing a more accurate and reliable estimation of its dynamic states,” Idrisov adds.
The proposed approach was validated using a hardware-in-the-loop test bench, demonstrating the digital twin’s accuracy in tracking grid dynamics under diverse operating conditions. Additionally, a sensitivity analysis underscored the importance of precise parameter estimation and minimal communication delays for robust DSE.
The implications of this research are far-reaching. Accurate dynamic state estimation is crucial for advanced grid control, optimization, and predictive maintenance strategies. As the energy sector continues to embrace renewable energy sources and power electronics, the need for reliable and accurate DSE solutions becomes increasingly critical. Idrisov’s work provides a framework for developing these solutions, paving the way for a more reliable, efficient, and sustainable power system.
The commercial impacts are significant. Energy companies can leverage this technology to improve grid stability, reduce downtime, and enhance the integration of renewable energy sources. This, in turn, can lead to cost savings, increased efficiency, and a more resilient energy infrastructure. As the energy sector continues to evolve, the adoption of digital twin-based DSE could become a standard practice, driving innovation and competitiveness in the market.
Idrisov’s research not only addresses the current challenges in PEDGs but also sets the stage for future developments. As the energy landscape continues to change, the need for advanced grid management solutions will only grow. By providing a framework for accurate dynamic state estimation, Idrisov’s work is poised to shape the future of the energy sector, enabling a more sustainable and reliable power system for all.
