Reliability Assessment Method of Integrated Energy System Based on Cluster Analysis and Model Driven

Large-scale wind power connected gas-electric integrated energy system (GEIES) has high randomness and strong gas-electricity coupling, which adversely affects the safe operation of the system and brings technical challenges to reliability assessment. A GEIES reliability evaluation method based on K-means clustering analysis and model driven is proposed in this paper. Firstly, a large number of historical wind power data is clustered based on K-means to divide different wind power operation scenarios. Secondly, the mathematical model of power system, natural gas system and power equipment belonging to GEIES is established, as well as the system operation power model and operation constraint model. Then, the reliability indicators of GEIES energy supply insufficient risk and operation overrun risk are established, and the analytical method is used to solve them. The weak nodes of the system are identified. Study the impact of power to gas (P2G) devices on reliability. Finally, the effectiveness of the algorithm is verified by IEEE39 and Belgian natural gas 20 node system.