文献来源：
Binglong Han a , Hangxin Li a , b , * , Shengwei Wang a , b , *
a
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
b Research Institute for Smart Energy, The Hong Kong Polytechnic University, Kowloon, Hong Kong
/2024.11.22.pdf
文章摘要：Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantiffcation of building energy ffexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated ffexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the ffexibility of individual buildings, eliminating the need of time-consuming iterative and ffnite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building ffexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantiffes the aggregated ffexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves signiffcantly higher accuracy.
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