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 on improving system resilience, but fail to quantify the improvements. In this paper, we propose a novel workflow that allows increases in resilience to be measured quantitatively. It incorporates out-of-sample testing following optimisation, and compares the impacts of demand and power interruption uncertainty on both risk-unaware and risk-aware district energy system models. To ensure we encompass the full range of impacts caused by uncertainty, we consider nine distinct objectives encompassing differences in: investment and operation costs, CO${}_2$ emissions, and aversion to risk.

We apply the workflow in a case study in Bangalore, India, and demonstrate that scenario optimisation improves system resilience by one to two orders of magnitude. However, systems designed for resilience to demand uncertainty are not able to gracefully extend to managing risk from extreme shocks to the system, such as power interruptions. We show that shock-induced instability can be addressed by specific measures to reduce grid dependence. Finally, by studying out-of-sample test results, we identify an objective which balances cost, CO${}_2$ emissions, and system resilience; this balance is achieved by novel application of the Conditional Value at Risk measure. These results expose the need for out-of-sample testing whenever uncertainty is considered in energy system modelling, and we provide the framework with which it can be undertaken.

随着我们面临依靠不可分派技术和部门整合实现脱碳的挑战，设计弹性能源系统的需求变得越来越明显。建模工作越来越着重于提高系统弹性，但无法量化改进。在本文中，我们提出了一种新颖的工作流程，该工作流程可以定量测量弹性的提高。它整合了优化后的样本外测试，并比较了需求和断电不确定性对无风险意识和有风险意识的区域能源系统模型的影响。为确保我们涵盖不确定性所造成的全部影响，我们考虑了九个不同的目标，其中包括以下方面的差异：投资和运营成本，CO${}_2$ 排放和厌恶风险。

我们将工作流应用于印度班加罗尔的案例研究中，并演示了方案优化可将系统弹性提高一到两个数量级。但是，专为应对需求不确定性而设计的系统无法优雅地扩展到管理系统极端冲击（例如电源中断）的风险。我们表明，可以通过减少网格依赖性的特定措施来解决激振引起的不稳定性。最后，通过研究样本外测试结果，我们确定了一个平衡成本，CO的目标。${}_2$排放和系统弹性；这种平衡是通过新颖地应用条件风险价值度量来实现的。这些结果表明，无论何时在能源系统建模中考虑不确定性时，都需要进行样本外测试，我们提供了可以进行不确定性测试的框架。