Early warning and proactive control strategies for power blackouts caused by gas network malfunctions

The global transition to net-zero energy systems necessitates a significant role for gas-fired generators, both as a replacement for coal and as a flexibility service provider. This increased reliance on gas, however, heightens the risk of large-scale power outages stemming from gas network malfunctions. The interdependence between gas and electricity networks is substantial and growing, particularly as the transition from coal and oil accelerates. A malfunction in the gas network can trigger a cascading failure in the electricity system, with the speed of information transmission and power generation redispatch often outpacing the escalation of the gas network problem itself (e.g., pressure drops). This critical time difference forms the basis for the proposed gas-electric early warning system. The system aims to reduce the negative consequences of gas network malfunctions on power systems by providing early warnings and enabling proactive control strategies. The research utilizes case studies from a real coupled gas-electricity system in China to validate the effectiveness of this approach. The increasing use of gas turbines, despite the move toward carbon neutrality, is due to their efficiency, lower emissions compared to coal and oil, and higher ramp rates, facilitating renewable energy integration. Even with the eventual reduction of natural gas use, existing gas infrastructure will be repurposed for alternative fuels like green hydrogen, highlighting the continued importance of gas networks. However, energy system security has not received the same attention as carbon emission reduction. The study addresses this gap by presenting a system to prevent cascading failures. Past incidents like the 2021 US blackouts illustrate the significant impact of gas network issues on power systems; similar events have occurred repeatedly. Factors such as extreme weather, pipeline leaks, and geopolitical tensions all contribute to increased risk.

Existing research on energy security and resilience often focuses on single energy sources or examines resilience from the perspective of internal (e.g., gas leakage) or external disruptions (e.g., extreme weather). Chronological approaches examine preparedness, absorption, adaptation, and recovery stages. While integrated energy system resilience has seen progress, most lack real-time coordination between gas and electricity systems. The key gap identified is the lack of utilization of the inherent time delay between a gas network malfunction and its impact on the power system. The authors draw an analogy to earthquake early warning systems in geology and disaster response to illustrate the feasibility of leveraging this propagation delay for proactive control in the gas-electricity system.

The proposed gas-electric early warning system leverages the time delay between a gas network malfunction and its impact on the power system. This delay allows for proactive control actions to be taken before the failure significantly impacts electricity generation. The system comprises modules for sensing, analysis, and decision-making, allowing the electric power control center (EPCC) to act proactively rather than reactively. Two early warning indicators are introduced: Available Escape Time (AET) and Available Line Pack (ALP). AET represents the remaining time before inlet pressure drops to critical levels for impacted gas turbines. ALP quantifies the remaining gas in the pipeline available for the impacted turbine. These indicators are calculated by the Gas Dispatch Center (GDC) and transmitted to the EPCC. The EPCC uses these indicators to formulate a proactive control strategy, aiming to minimize power deficits by adjusting the generation of other power plants before impacted gas turbines are affected. This proactive control is modeled as a linear programming problem, minimizing the total energy deficit while considering constraints like power balance, generator capacity, ramp rates, and available line pack. Static proactive control uses initial AET estimates, while dynamic proactive control iteratively extends control times based on updated AET values to further minimize deficits. The methodology is validated using real-world data from a coupled gas-electricity system in Zhejiang Province, China. The hydraulic simulation of the gas network utilizes an isothermal model and a finite-difference time-domain (FDTD) method to solve a set of partial differential algebraic equations (PDAEs) to model the dynamic behavior of the gas pipelines. The Weymouth equation is also utilized for steady-state calculations. Calculations for AET and ALP leverage an approximation that the average pressure in the terminal pipeline segment represents the turbine inlet pressure; this is validated in supplementary information.

The study demonstrates the feasibility and effectiveness of the gas-electric early warning system. Case studies show that the proactive control strategy significantly reduces or eliminates power deficits compared to passive control. Using the Zhejiang Province system, the impact of various gas network failures was analyzed. The study demonstrates that the system successfully mitigates the impact of single and multiple gas system failures. The initial AET, or SAET (Static Available Escape Time), determines the urgency of the gas failure. The study highlights that the closer the gas turbine is to the failure, the lower the SAET, and the more urgent the need for proactive control. The use of both AET and ALP as early warning indicators provides sufficient information to the EPCC for proactive control, reducing or eliminating the need to access detailed gas network information, ensuring the system respects data privacy requirements between gas and electricity entities. The static proactive control method effectively eliminated power deficits in cases where the SAET was sufficient. In situations with insufficient SAET, the dynamic proactive control approach, which iteratively extends the control time, further reduced deficits, leveraging the remaining available line pack. A comparative analysis of passive versus proactive control strategies across various scenarios (including single pipeline failures, multiple failures in a city-level system, and a provincial-level system simulation involving multiple simultaneous gas source and pipeline failures along with wind farm malfunctions) demonstrates the advantages of proactive management in minimizing power deficits and avoiding blackouts. Results indicate that dynamic proactive control significantly outperforms static control and passive control, notably reducing energy deficit during multiple gas network disruptions.

The findings demonstrate that incorporating information from gas networks into electricity system control significantly enhances grid resilience and prevents cascading failures due to gas network malfunctions. The proposed gas-electric early warning system offers a practical solution to a growing problem in modern energy systems. The system's success hinges on effectively utilizing the time delay between gas network failures and their impact on electricity generation. This work advances current methods by enabling proactive, rather than reactive, responses to gas network disruptions. The results strongly support the integration of this early warning system into existing electricity grid management practices, potentially preventing costly and disruptive power blackouts. The methodology is applicable to different regions and scales of coupled gas-electricity systems, and the modular nature of the early warning system makes it adaptable to various network configurations.

This paper proposes a novel gas-electric early warning system that leverages the time delay between gas network malfunctions and their impact on power systems for proactive control. The system uses two key indicators, AET and ALP, to inform control strategies, effectively minimizing power deficits. Case studies demonstrate the effectiveness of this approach. Future work could explore the integration of the system with more detailed cascading failure models and its application to other coupled energy systems.

The study's accuracy relies on the accuracy of the gas network and power system models. The simplified gas network model assumes isothermal conditions and uses approximations for AET and ALP calculations. Real-world gas networks are more complex, and unforeseen events could affect the system's performance. Future research should focus on refining the models and incorporating more variables to account for these limitations.