1. 电力设备电气绝缘国家重点实验室(西安交通大学), 陕西省西安市 710049;2. 陕西省智能电网重点实验室(西安交通大学), 陕西省西安市 710049;3. 西安交通大学电气工程学院, 陕西省西安市 710049
1. State Key Laboratory of Electrical Insulation and Power Equipment(Xi'an Jiaotong University), Xi'an 710049, China;2. Shaanxi Key Laboratory of Smart Grid(Xi'an Jiaotong University), Xi'an 710049, China;3. School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
The integrated energy system(IES)realizes the coupling and interaction of various energy systems such as electricity, heat and natural gas. The contingency number of IES increases dramatically compared with the traditional simplex energy system. It has become a concern for the risk assessment of IES how to screen the most serious contingencies from the mass number of contingencies fast and accurately, which is important for improving the efficiency of risk assessment. Focusing on this topic, a contingency intelligent screening and ranking approach for IES is proposed based on genetic algorithm. Firstly, considering the constraints of various energy networks in IES, a bi-level optimization model is established to depict the expected loss of IES caused by contingencies, which reflects the influences of contingency probability and economic loss caused by contingency. Furthermore, a solution algorithm of the bi-level model is proposed based on genetic algorithm. With the improvement of traditional genetic algorithm, the proposed solution algorithm can simultaneously screen multiple contingencies with highest expected loss. Finally, the case study is based on an IES test case which consists of the modified IEEE 33-bus electric system, Barry Island heating system and Belgian gas network. Numerical results indicate that the computational efficiency of proposed approach is obviously higher than that of traditional enumeration method and Monte Carlo method, which verifies the effectiveness of the proposed approach.
WANG Can, BIE Zhaohong, PAN Chaoqiong,et al.Contingency Intelligent Screening and Ranking Approach for Integrated Energy System Considering Expected Loss[J].Automation of Electric Power Systems,2019,43(21):44-53. DOI:10.7500/AEPS20190119004.