In the rapidly evolving landscape of renewable energy, the integration of distributed energy resources (DERs) into microgrids is becoming increasingly crucial. However, ensuring efficient power sharing among these resources has been a persistent challenge. A groundbreaking study published by Seyedmohammad Hasheminasab, a researcher from the Department of Electronics at Carleton University’s Intelligent Robotic and Energy Systems (IRES) Research Group in Ottawa, Canada, offers a promising solution to this problem. His work, published in the IEEE Access journal, introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based virtual impedance controller that could revolutionize the way microgrids operate.
Traditional droop control techniques, which are essential for regulating active and reactive power exchange in inverter-interfaced microgrids, often fall short due to varying feeder impedance and slow responses to dynamic load changes. This leads to inaccuracies in power sharing, a significant hurdle for the seamless integration of DERs. Hasheminasab’s innovative controller addresses these issues by dynamically adjusting a virtual voltage to compensate for impedance mismatches, thereby modifying the reference voltage of the inverter. “This enables precise power tracking with minimal deviation from the defined reference values and a faster response under transient conditions,” Hasheminasab explains.
The ANFIS framework, which integrates fuzzy logic and neural networks, eliminates the limitations of manual and separate tuning in conventional controllers. This integration allows for improved performance in nonlinear systems, a common characteristic of modern energy grids. The controller’s effectiveness was validated on an IEEE 39-bus test system under various scenarios, including charging, discharging, and transient disturbances. The system was tested with three different battery sizes (1 MW, 96 kW, and 75 kW) to assess scalability, ensuring that the controller can adapt to different capacities and distributed generators.
The implications of this research are far-reaching for the energy sector. As the world moves towards a more decentralized energy system, the ability to efficiently manage and share power among distributed resources will be paramount. Hasheminasab’s controller offers a robust solution that could enhance the reliability and efficiency of microgrids, making them more attractive for commercial and industrial applications. “The results demonstrate the controller’s superior effectiveness compared to traditional methods,” Hasheminasab notes, highlighting the potential for significant improvements in power sharing accuracy and response times.
The commercial impacts of this research could be substantial. Energy companies and grid operators could benefit from reduced operational costs and improved system stability. Moreover, the scalability of the controller means that it can be applied to a wide range of microgrid configurations, from small community grids to large industrial complexes. This flexibility could accelerate the adoption of DERs, paving the way for a more sustainable and resilient energy future.
As the energy sector continues to evolve, innovations like Hasheminasab’s ANFIS-based virtual impedance controller will play a crucial role in shaping the future of power distribution. By addressing the challenges of power sharing in microgrids, this research opens up new possibilities for the integration of renewable energy sources and the development of smarter, more efficient energy systems. The study, published in the IEEE Access journal, titled “An Adaptive Neuro-Fuzzy Controller to Enhance Power Sharing in Distributed Energy Resources Applications,” marks a significant step forward in the quest for a more sustainable energy landscape.