Analyzing power network vulnerability considering spatial heterogeneous demand under extreme heat

Abstract

Power networks are facing significant challenges from frequent extreme heat in terms of operational stress and the risk of cascading failures. This paper proposes a vulnerability analysis framework for power networks exposed to extreme heat scenarios, taking into account spatial heterogeneous demand. Based on the complex distribution of power demand and the varying social impacts of service disruptions, we introduce the Service Disruption–Social Vulnerability Index (SD-SVI) to build a spatial demand model at the level of urban functional zones. Using power network and cascading failure modeling, we apply the SD-SVI weighted method to assess the network’s vulnerability. In addition, we model load growth and line failure rates as driven by extreme heat and design a dual-objective optimization model that considers both vulnerability and failure probability. Case study results show that the SD-SVI weighting significantly affects the vulnerability of the power network, with the continued temperature increase due to climate change making the network more vulnerable. Furthermore, analysis of the obtained Pareto front solutions reveals that extreme heat has a nonlinear effect on system vulnerability, and when temperatures exceed 36°C, single or double branch failures dominate the Pareto front of the power network. Based on the “average frequency × inferred vulnerability” composite index, our analysis shows that protecting vulnerable lines can greatly improve network performance and its ability to adapt to multiple extreme heat scenarios. This study provides theoretical insights and practical guidance for power network vulnerability analysis, risk management and climate change adaptation.