From Coupling to Resilience: Quantifying the Impact of Interconnection in Energy Carrier Grids

TL;DR

This paper develops a Monte Carlo framework to quantify resilience in coupled energy grids (electricity, gas, and heating) by defining a load-shedding–based resilience indicator $R^G_{LS}$ and component- and carrier-level resilience metrics. The authors model MES with steady-state AC power flow for electricity, Weymouth and Darcy–Weisbach for gas and water, and three coupling points (CHP, P2H, P2G), then map the network to a directed weighted multigraph to enable complex-network analysis. They introduce high-impact event generation, a load-shedding optimization via NLP, and resilience metrics (Overall, Single Component Impact, Carrier Grid Impact) to quantify inter-grid influences, validating the approach on a benchmark case and exploring the role of CP density and topology. Key findings show that coupling can improve system resilience under certain conditions but may reduce resilience of individual carrier grids, with correlations between topology (notably Katz centrality and betweenness) and resilience impacts in some components. The work offers a practical framework for planning MES deployments and highlights the importance of considering inter-grid coupling and topological attributes in resilience evaluations, while suggesting avenues for faster simulations and richer data in future work.

Abstract

Due to the increasing share of renewable energy resources and the emergence of couplings of different energy carrier grids, which may support the electricity networks by providing additional flexibility, conducting research on the properties of multi-energy systems is necessary. Primarily to keep stable grid operation and provide efficient planning, the resilience of such systems against low-probability, high-impact events is central. Previous steady-state resilience studies of electricity grids also involved investigating the topological attributes from a complex network theory perspective. However, this work aims to determine the influence of complex topological attributes on the resilience of coupled energy grids. To achieve this, we set up a Monte Carlo simulation to calculate the load-shedding performance indicator for the grids when affected by high-impact events. This indicator is used to calculate resilience metrics, which express the influence of the grids on each other. The metrics are the base to search for correlations between centrality/vitality metrics and the resilience impact metric. We apply our method to a case study based on a benchmark electricity grid. Our results show that, first, our impact metric is feasible for determining the influences of the network on each other. Second, we show that increasing coupling densities can lead to lower resilience in single-carrier grids. Third, it is apparent that centrality influences the impact of the grid components' resilience.

Figures (7)

• Figure 1: Flow of the Monte-Carlo simulation for calculating the resilience of the MES
• Figure 2: Performance drop by scenario and carrier; x-axis single labels: event characteristic; x-axis group labels: CP density
• Figure 3: Impacts on the different carrier grids -- electricity heating gas
• Figure 4: Network visualization of the multi-grid system with different carrier-impacts of the components as main-color; the carrier of the nodes are depicted as the shape (rectangle = electricity, triangle = gas, pentagon = heat, cp = diamond
• Figure 5: Relation between graph metrics and the impact of the components on the resilience