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The increasing coupling between natural gas and electricity systems by gas-fired generation units brings new challenges to system analysis, such as pressure variations due to consumption perturbations of generation units. The emerging issues require revolutionary modeling and analysis techniques.This paper proposes a novel model to quantify gas pressure variations due to gas-fired power unit ramping and the impact of constraints from natural gas pressure change on ramp rates of gas-fired plants. By utilizing Laplace transform to resolve the governing equations of gas networks, the proposed model can significantly reduce modeling complexity and computational burden. The dynamic behaviors in time scale in s-domain and spatial partial differential equations are transformed into finite difference equations. By introducing the concept of transfer matrices, the relation between states at each node of gas systems can be expressed by transfer parameter matrices. Additionally, a simplified model is introduced to simply the analysis. The explicit expressions of nodal pressure variations in response to demand change are very convenient for analyzing system dynamic performance under disturbances, identifying the most influential factors. The new models are extensively demonstrated on three natural gas networks and benchmarked with traditional simulation approaches. Results illustrate that they produce very close results with the simulation approach, particularly when gas pipelines are long and enter steady states.

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In order to mitigate cascading failure blackout risks in power systems, the critical components whose failures lead to high blackout risks should be identified. In this paper, such critical components are identified by the statefailure network (SF-network) formed by cascading failure chain and loss data, which can be gathered from either utilities or simulations. The failures along the chains are recombined in the SF-network, where each failure is allocated a value that can reveal the blackout risks after their occurrences. Thus, critical failures can be identified in the SF-network where the failures raise up blackout risks, and thus the critical components can be found based on their critical failure risks. The simulation results validate the effectiveness of the proposed method.