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Smart Grids
Energy infrastructure This research is critical as it addresses the increasing complexity and demand on electricity grids driven by the rise of renewable energy sources, which are intermittent and seasonal. Optimising smart grid distribution networks through real-time monitoring and control of electricity flows, and integrating renewable energy sources, is essential for creating a sustainable and […]
This research is critical as it addresses the increasing complexity and demand on electricity grids driven by the rise of renewable energy sources, which are intermittent and seasonal. Optimising smart grid distribution networks through real-time monitoring and control of electricity flows, and integrating renewable energy sources, is essential for creating a sustainable and efficient energy infrastructure. The advancements in TVWS technology for smart grid communication not only improve the reliability and efficiency of electricity distribution but also support the development of smart charging networks for electric vehicles, ultimately contributing to the reduction of carbon emissions and enhancing energy security.
The research and development of TV White-Space (TVWS) for Smart Grid communication represents a significant technical advancement in the field of smart grid optimisation and energy management. TVWS offers substantial advantages in terms of range and penetration capabilities, outperforming traditional RF Mesh and PLC systems. This allows for a point-to-multipoint network architecture, simplifying network management and maintaining system throughput. The ability to cover up to 40 times the area of conventional systems with the same transmission power makes TVWS highly efficient for last-mile communication. The integration of TVWS with other communication modules via a Universal Asynchronous Receiver/Transmitter (UART) interface allows for a hybrid system that maximises both range and penetration, ensuring robust and reliable communication even in challenging environments.
The importance of research in smart grid optimisation lies in addressing the evolving demands and challenges faced by modern electricity grids, driven by climate change, geopolitical energy security concerns, and the decreasing costs of renewable energy. Traditional grids, designed for predictable energy dispatch from thermal plants, struggle with the intermittency and variability of renewable sources like solar and wind. These fluctuations can cause severe issues such as harmonic distortions, supply-demand imbalances, and overall grid instability. To adapt, distribution networks must evolve into smart grids capable of real-time monitoring and control of electricity flows. This involves advanced metering infrastructure (AMI) for precise electricity usage data, distribution automation (DA) for dynamic flow management, energy management systems (EMS) for integrating renewable sources, and dynamic demand-side management (DDM) to balance local supply and demand using household and EV batteries.
ANE is at the forefront of this innovative research and development, pioneering methods to optimise these smart-grid components. Our work focuses on ensuring that these technologies work together seamlessly to enhance efficiency, reliability, and sustainability. ANE's research aims to create a resilient energy infrastructure that supports the widespread adoption of renewable energy and effectively meets future energy demands. The practical market contributions of this research could be substantial, leading to the development of more efficient and reliable electricity distribution networks, reduced carbon emissions, and improved energy security. By optimising smart grids, ANE's advancements could also facilitate the broader implementation of electric vehicle charging networks and the integration of distributed renewable energy sources, driving significant progress in the global shift towards sustainable energy solutions.
The below academic articles relating to our research is available for those who want to better understand the potential of our work.