In the quest to make smart buildings more economically efficient and environmentally friendly, a team of researchers led by YE Aoshuang from the State Grid Pudong Power Supply Company in Shanghai has developed a novel approach to optimize energy management. Their work, published in the journal “Zhejiang Electric Power” (translated from its original Chinese title), introduces an adaptive quantum particle swarm optimization (AQPSO) strategy for smart building microgrids, promising to revolutionize how we think about energy consumption in urban environments.
Smart buildings, equipped with microgrids that integrate photovoltaic (PV) systems, wind power, and electric vehicle (EV) charging and discharging capabilities, are becoming increasingly common. However, managing these complex systems to minimize costs and carbon emissions while maintaining comfort and reliability is a significant challenge. YE Aoshuang and her team have tackled this issue head-on by developing a model that considers various constraints, such as power balance and thermal comfort, to minimize energy costs over a building’s operational cycle.
The researchers improved upon the traditional quantum particle swarm optimization (QPSO) algorithm by introducing adaptive parameter control, creating the AQPSO. This enhanced algorithm was then used to solve the model, demonstrating superior convergence speed and optimization capability compared to traditional methods. “The AQPSO algorithm significantly outperforms conventional optimization techniques, providing a more efficient and effective solution for smart building microgrid management,” YE Aoshuang explained.
To validate their approach, the team set up four different scenarios for case analysis. The results were impressive, showing that the proposed model and optimal scheduling strategy could effectively reduce building operating costs, carbon emissions, and carbon emission costs while improving the utilization rate of clean energy. This research has significant implications for the energy sector, particularly in urban areas where smart buildings are becoming more prevalent.
The commercial impacts of this research are substantial. By optimizing the use of renewable energy sources and EV integration, building operators can reduce their energy costs and carbon footprint, making them more competitive and sustainable. Moreover, the improved utilization of clean energy can contribute to the overall decarbonization of the energy sector, aligning with global efforts to combat climate change.
The research led by YE Aoshuang highlights the potential of advanced optimization algorithms in enhancing the performance of smart building microgrids. As the world continues to urbanize and the demand for sustainable energy solutions grows, such innovations will be crucial in shaping the future of the energy sector. By providing a more efficient and effective way to manage smart building microgrids, this research paves the way for a more sustainable and economically viable future.