Quantifying resilience in energy systems with out-of-sample testing

The need to design resilient energy systems becomes ever more apparent as we face the challenge of decarbonising through reliance on non-dispatchable technologies and sectoral integration. Increasingly, modelling efforts focus improving system resilience, but fail quantify improvements. In this paper, propose a novel workflow that allows increases in resilience be measured quantitatively. It incorporates out-of-sample testing following optimisation, compares impacts demand power interruption uncertainty both risk-unaware risk-aware district models. To ensure encompass full range caused by uncertainty, consider nine distinct objectives encompassing differences in: investment operation costs, CO2 emissions, aversion risk. We apply case study Bangalore, India, demonstrate scenario optimisation improves one two orders magnitude. However, designed for are not able gracefully extend managing risk from extreme shocks system, such interruptions. show shock-induced instability can addressed specific measures reduce grid dependence. Finally, studying test results, identify an objective which balances cost, resilience; balance is achieved application Conditional Value at Risk measure. These results expose whenever considered modelling, provide framework with it undertaken.