This paper presents a temporal decomposition strategy to decompose security-constrained economic dispatch (SCED) over the scheduling horizon with the goal of reducing its computational burden and enhancing its scalability. A set of subproblems, each with respect to demand response, normal constraints, and $N-1$ contingency corrective actions at a subhorizon, is formulated. The proposed decomposition deals with computational complexities originated from intertemporal interdependencies of system equipment, i.e., generators’ ramp constraints and state of charge of storage devices. The concept of overlapping intervals is introduced to make SCED subproblems solvable in parallel. Intertemporal connectivity related to energy storage is also modeled in the context of temporal decomposition. Besides, reserve up and down requirements are formulated as data-driven nonparametric chance constraints to account for wind generation uncertainties. The concept of $\phi -$ divergence is used to convert nonparametric chance constraints to more conservative parametric constraints. A reduced risk level is calculated with respect to wind generation prediction errors to ensure the satisfaction of system constraints with a confidence level after the true realization of uncertainty. Auxiliary problem principle is applied to coordinate SCED subproblems in parallel. Numerical results on three test systems show the effectiveness of the proposed algorithm.

中文翻译：

---

基于时间分解的智能电网能源管理随机经济调度

本文提出了一种时间分解策略，以在调度范围内分解安全约束的经济调度（SCED），以减轻其计算负担并增强其可伸缩性。一组子问题，每个子问题都与需求响应，正常约束和 $ N-1 $ 制定了在超视距下的应急纠正措施。所提出的分解处理了计算复杂性，该计算复杂性源于系统设备的时间间相互依赖性，即发电机的斜坡约束和存储设备的荷电状态。引入重叠间隔的概念是为了使SCED子问题可以并行解决。与能量存储相关的跨时间连接性也在时间分解的背景下建模。此外，储备上，下需求被公式化为数据驱动的非参数机会约束，以解决风力发电的不确定性。概念 $ \ phi-$ 散度用于将非参数机会约束转换为更保守的参数约束。针对风力发电预测误差计算出降低的风险级别，以确保在真正实现不确定性后以置信度满足系统约束条件。辅助问题原理适用于并行协调SCED子问题。在三个测试系统上的数值结果表明了该算法的有效性。