14 封面專題 • COVER STORY 澳大新語 • 2025 UMAGAZINE 32 stability and meet carbon reduction goals. Yonghua Song, rector of UM and director of SKL-IOTSC, was among the first to propose a new ‘load follows source’ model. This innovative approach shifts the focus of regulation from power generation to demand-side management, allowing electricity consumption to adjust more dynamically to the availability of renewable energy. This model facilitates the utilisation of intermittent sources such as wind and solar power while ensuring safe and cost-effective operation of the grid. For example, commercial buildings can adjust air-conditioning systems by taking advantage of their thermal inertia, and electric vehicles (EVs) can be charged during off-peak hours. By tapping into these invisible resources, the model plays a vital role in advancing SDG13 (Climate Action). Decarbonising Urban Power Systems Cities across the Greater Bay Area, including Macao, are experiencing rapid economic growth, large populations, and high power load density. Moreover, public buildings such as hotels and office towers are densely packed and highly energy-intensive, creating an urgent need to enhance grid reliability and develop low-carbon, energy-efficient regulation strategies. A research team led by Hui Hongxun, assistant professor in SKL-IOTSC, has made important progress in addressing these challenges. The team has developed ‘IoT-based regulation technologies for flexible loads in urban power grids’, achieving breakthroughs in holographic information sensing, time-varying building modelling, and optimised scheduling control. The team’s approach integrates both the electrical characteristics of flexible resources and non-electrical factors such as external environmental conditions, occupant comfort, and building thermal inertia. This method has enabled the development of advanced IoT-driven technologies for managing flexible loads in urban power systems. Key innovations include cross-district information exchange mechanisms, multi-timescale modelling techniques, and quantitative tools for assessing regulation capacity. In addition, to address the increasing connection between the electricity market and the carbon market, the team has developed resource scheduling control technologies. These technologies were first applied on the UM campus, where the team established a smart energy hybrid real-time simulation platform. This platform integrates photovoltaic systems, energy storage, charging systems, and centralised cooling resources, enabling microsecond-level real-time regulation of the campus power system. According to Prof Hui, the platform has helped reduce UM’s energy costs by about 10%, providing a practical ‘UM model’ for urban carbon reduction. Beyond the campus, the team has also supported Shenzhen in developing a ‘building energy consumption monitoring platform’ and a ‘controllable load resource management platform’. Leveraging Shenzhen’s ‘dual carbon’ platform, their work has been deployed across multiple building clusters in the city, improving grid 利用人工智能實現電網系統的智能控制 UM researchers use AI-based technologies to achieve intelligent control of power grids 張洪財教授 Prof Zhang Hongcai
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