Over-limit risk assessment method of integrated energy system considering source-load correlation

In an integrated energy system, source-load multiple uncertainties and correlations lead to an over-limit risk in operating state, including voltage, temperature, and pressure over-limit. Therefore, efficient probabilistic energy flow calculation methods and risk assessment theories applicable to in...

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Bibliographic Details
Main Authors: Ying Wang, Xiaojun Wang, Yizhi Zhang, Yigang Zhang, Zekai Xu
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2023-12-01
Series:Global Energy Interconnection
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2096511723000920
Description
Summary:In an integrated energy system, source-load multiple uncertainties and correlations lead to an over-limit risk in operating state, including voltage, temperature, and pressure over-limit. Therefore, efficient probabilistic energy flow calculation methods and risk assessment theories applicable to integrated energy systems are crucial. This study proposed a probabilistic energy flow calculation method based on polynomial chaos expansion for an electric-heat-gas integrated energy system. The method accurately and efficiently calculated the over-limit probability of the system state variables, considering the coupling conditions of electricity, heat, and gas, as well as uncertainties and correlations in renewable energy unit outputs and multiple types of loads. To further evaluate and quantify the impact of uncertainty factors on the over-limit risk, a global sensitivity analysis method for the integrated energy system based on the analysis of covariance theory is proposed. This method considered the source-load correlation and aimed to identify the key uncertainty factors that influence stable operation. Simulation results demonstrated that the proposed method achieved accuracy to that of the Monte Carlo method while significantly reducing calculation time. It effectively quantified the over-limit risk under the presence of multiple source-load uncertainties.
ISSN:2096-5117