需求响应
数学优化
可再生能源
稳健优化
计算机科学
稳健性(进化)
随机规划
模棱两可
电
工程类
数学
生物化学
基因
电气工程
化学
程序设计语言
作者
Yang Li,Meng Han,Mohammad Shahidehpour,Jiazheng Li,Chao Long
出处
期刊:Applied Energy
[Elsevier BV]
日期:2023-02-04
卷期号:335: 120749-120749
被引量:110
标识
DOI:10.1016/j.apenergy.2023.120749
摘要
A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.
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