Smart cities can harness machine learning for greener grids, revolutionizing urban energy management with renewables聽
Integrating renewable energy sources and electric vehicles (EVs) into modern cities is a necessary evolution for the urban landscape. However, the variability introduced by these green technologies poses significant challenges for traditional energy management systems.
A team of researchers including Khalifa University鈥檚 Prof. Ahmed Al-Durra has introduced an innovative approach that could redefine energy management in smart cities. Prof. Al-Durra collaborated with researchers from Politecnico di Milano University, Italy; Islamic Azad University, Iran; Arman Niroo Hormozgan Company, Iran; and Aalborg University, Denmark, to develop an intelligent energy management strategy for networked microgrids (NMGs) in smart cities considering renewable energy source uncertainties and power fluctuations. Their approach leverages a sophisticated combination of technologies including neural networks and deep reinforcement learning algorithms.
The team published their research in, a top 1% journal.
Smart cities are increasingly turning to NMGs as a solution to enhance energy reliability and efficiency. These microgrids can operate independently or in conjunction with the main power grid and are essential for integrating renewable energy sources and electric vehicles effectively. However, managing them in real-time, considering the unpredictable nature of solar power, for example, and EV battery usage, requires a robust, adaptable solution.
The research team鈥檚 solution leverages the power of machine learning to manage the active power and frequency of NMGs dynamically. Key to this strategy is its dual structure: offline training for the algorithm and decentralized operation for real-world application. This setup allows for continuous adjustment based on the operational data each microgrid collects, ensuring optimal decisions for frequency and power control.
The system can adapt in real-time. Offline training fine-tunes the algorithm鈥檚 responsiveness, and the decentralized operation allows for individual microgrids to make autonomous decisions based on local data.
The team鈥檚 system demonstrated a computation accuracy exceeding 98 percent, significantly outperforming traditional methods, with a 7.82 percent reduction in computation burden and a 61.1 percent decrease in computation time. These enhancements mean that NMGs can operate more smoothly and efficiently, with less downtime and faster responses to changes in energy demand or supply. This is particularly important in urban settings where energy demands can be unpredictable.
For urban planners and energy managers, this represents a step towards more sustainable urban energy practices, where green technology integration is efficient and reliable. The potential for scalability and further development opens new pathways for even smarter, more responsive urban energy grids, powered by the capabilities of machine learning.
Jade Sterling
Science Writer
26 June 2024
