Abstract This paper expresses the planning model of the backup distributed generation (DG) and lines hardening and tie lines in distribution networks according to resilient architecture (RA) strategy under natural disaster conditions such as earthquakes and floods. Indeed, the proposed deterministic problem of resilient distribution system planning considers the minimization of the daily investment, operation and resiliency (repair and load shedding) costs as objective functions subject to constraints of AC power flow equations, system operation limits, planning and operation model of backup DG and hardening and tie lines, as well as network reconfiguration formulation. The problem formulation is based on a mixed integer non-linear programming (MINLP) model, which is converted to a mixed integer linear programming (MILP) model on the basis of Benders decomposition (BD) approach using linearization approaches to achieve the optimal solution with the lower computational efforts and error. Besides, a hybrid stochastic/robust optimization (HSRO) based on the bounded uncertainty-based robust optimization (BURO) and a scenario-based stochastic optimization is used to model the uncertainties of load, energy price and availability of the network equipment under the extreme weather conditions. Finally, the proposed RA strategy is applied on 33-bus and 119-bus distribution test systems to investigate its capabilities in different case studies.

中文翻译：

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针对极端天气条件的配电网络弹性架构的混合随机/稳健优化模型

摘要 本文根据地震、洪水等自然灾害条件下的弹性架构（RA）策略，表达了配电网中备用分布式发电（DG）和线路硬化和联络线的规划模型。实际上，弹性配电系统规划的确定性问题将日常投资、运行和弹性（维修和减载）成本的最小化视为受交流潮流方程、系统运行限制、规划和运行模型约束的目标函数。备份 DG 和加固和联络线，以及网络重构公式。问题公式基于混合整数非线性规划 (MINLP) 模型，在 Benders 分解 (BD) 方法的基础上，使用线性化方法将其转换为混合整数线性规划 (MILP) 模型，以实现具有较低计算量和误差的最优解。此外，基于有界不确定性鲁棒优化（BURO）和基于场景的随机优化的混合随机/鲁棒优化（HSRO）用于模拟极端条件下网络设备的负载、能源价格和可用性的不确定性。天气状况。最后，将提出的 RA 策略应用于 33 总线和 119 总线配电测试系统，以研究其在不同案例研究中的能力。基于有界不确定性鲁棒优化（BURO）和基于场景的随机优化的混合随机/鲁棒优化（HSRO）用于模拟极端天气条件下网络设备的负载、能源价格和可用性的不确定性. 最后，将提出的 RA 策略应用于 33 总线和 119 总线配电测试系统，以研究其在不同案例研究中的能力。基于有界不确定性鲁棒优化（BURO）和基于场景的随机优化的混合随机/鲁棒优化（HSRO）用于模拟极端天气条件下网络设备的负载、能源价格和可用性的不确定性. 最后，将提出的 RA 策略应用于 33 总线和 119 总线配电测试系统，以研究其在不同案例研究中的能力。