Climatic risk, resilience, and adaptation of power systems in small-island states: the case of Jamaica

Power networks are critical economic infrastructure and are susceptible to natural hazards, such as hurricanes and floods. Disruptions in power systems due to extreme climatic events can cause major detrimental impacts to social systems and large economic losses, and these may worsen due to climate change. Existing methods can be used to spatially map climate risks in energy systems, understand cascading risks, and quantify expected damages in terms of direct and indirect economic losses. Yet, there is a dearth of methods that link operational models of energy networks with climate risk methodologies, making it difficult to assess the impact of system-level operational changes (e.g., increasing renewable energy system (RES) penetration) on climate risk and resilience. In the context of small-island states, such advances are urgently needed as a precursor to producing robust adaptation plans. 

In this work, we introduce a framework to simulate climatic risks in large-scale power networks under uncertain climatic change. We link a highly resolved power system simulation model with a state-of-the-art climate risk analytical framework. We apply our methodology in a national scale case-study from Jamaica. Our results provide the following key insights:

• We spatially map climate risks under varying scenarios of climate change to identify the most vulnerable assets in the power network across the island;
• We quantify the expected direct and indirect damages, as well as the number of customers affected;
• We simulate operational changes in the power system (e.g., increasing RES penetration, adding transmission capacity, changing utility pole materials) and quantify their impact on the risk profiles, and thus informing adaptation.

Our assessment presents a comprehensive assessment of climate risks across Jamaica, which was produced in conjunction with stakeholders from government, industry, and academia from the region.