Recently, researchers have begun to study hybrid approaches to Microgrid techno-economic planning, where a reduced model optimizes the DER selection and sizing is combined with a full model that optimizes operation and dispatch. Though providing significant computation time savings, these hybrid models are susceptible to infeasibilities, when the size of the DER is insufficient to meet the energy balance in the full model during macrogrid outages. In this work, a novel hybrid optimization framework is introduced, specifically designed for resilience to macrogrid outages. The framework solves the same optimization problem twice, where the second solution using full data is informed by the first solution using representative data to size and select DER. This framework includes a novel constraint on the state of charge for storage devices, which allows the representation of multiple repeated days of grid outage, despite a single 24-h profile being optimized in the representative model. Multiple approaches to the hybrid optimization are compared in terms of their computation time, optimality, and robustness against infeasibilities. Through a case study on three real Microgrid designs, we show that allowing optimizing the DER sizing in both stages of the hybrid design, dubbed minimum investment optimization (MIO), provides the greatest degree of optimality, guarantees robustness, and provides significant time savings over the benchmark optimization.

最近，研究人员开始研究微电网技术经济计划的混合方法，其中简化的模型优化了DER的选择，规模确定与优化运营和调度的完整模型相结合。尽管DER的大小不足以在大型电网中断期间满足完整模型中的能量平衡，但是这些混合模型虽然可节省大量的计算时间，但它们容易受到不可行性的影响。在这项工作中，引入了一种新颖的混合优化框架，该框架专门设计用于应对大型电网中断。该框架两次解决了相同的优化问题，其中使用完整数据的第二个解决方案由使用代表性数据进行大小调整并选择DER的第一个解决方案告知。该框架包括对存储设备充电状态的新颖约束，尽管在代表性模型中对单个24小时配置文件进行了优化，但仍可以表示电网中断的多个重复天数。比较了混合优化的多种方法的计算时间，最优性和针对不可行性的鲁棒性。通过对三个实际微电网设计的案例研究，我们发现在混合设计的两个阶段都允许优化DER的大小，称为最小投资优化（MIO），可提供最大程度的最优性，确保鲁棒性，并节省大量时间基准优化。最佳性和针对不可行性的鲁棒性。通过对三个实际微电网设计的案例研究，我们发现在混合设计的两个阶段都允许优化DER的大小，称为最小投资优化（MIO），可提供最大程度的最优性，确保鲁棒性，并节省大量时间基准优化。最佳性和针对不可行性的鲁棒性。通过对三个实际微电网设计的案例研究，我们发现在混合设计的两个阶段都允许优化DER的大小，称为最小投资优化（MIO），可提供最大程度的最优性，确保鲁棒性，并节省大量时间基准优化。