We propose a comprehensive framework for the optimal day-ahead scheduling of power distribution systems (PDS), based on a mixed-integer linear programming (MILP) model. The interaction between transmission system operator (TSO) and distribution system operator (DSO) is considered, where TSO informs DSO of the day-ahead forecast concerning the expected power flow (PF) commitment at the TSO-DSO interconnection points and the violation costs. We propose a MILP model to determine the optimal voltage and power settings of distributed energy resources (DERs), such as dispatchable distributed generators and energy storage units and optimal adjustments of capacitor banks controlled by current. The objective function includes the minimization of energy losses, voltage violations, power curtailment of DERs using volt-watt strategy, and violation of TSO-DSO interconnection PF commitment. Furthermore, we propose a method to estimate the degree of uncertainty in the PF commitment. Therefore, the proposed method can help achieve an optimal operation of the distribution system; in addition, the TSO can best model uncertainty at TSO-DSO interface points and thereby procure reduced amounts of resources to address these uncertainties. Numerical results obtained for a system based on real data highlight the several potential applications of the proposed framework.

中文翻译：

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不确定条件下考虑 TSO-DSO 互联潮流承诺的最优日前 PDS 调度的 MILP 模型

我们提出了一个基于混合整数线性规划 (MILP) 模型的配电系统 (PDS) 最佳日前调度的综合框架。考虑输电系统运营商 (TSO) 和配电系统运营商 (DSO) 之间的相互作用，其中 TSO 将有关 TSO-DSO 互连点的预期潮流 (PF) 承诺和违规成本的日前预报通知 DSO。我们提出了一个 MILP 模型来确定分布式能源 (DER) 的最佳电压和功率设置，例如可调度的分布式发电机和储能单元以及电流控制的电容器组的最佳调整。目标函数包括使用伏特策略最小化能量损失、电压违规、DER 的功率削减，违反TSO-DSO互连PF承诺。此外，我们提出了一种方法来估计 PF 承诺的不确定性程度。因此，所提出的方法有助于实现配电系统的优化运行；此外，TSO 可以最好地模拟 TSO-DSO 接口点的不确定性，从而减少采购资源来解决这些不确定性。基于真实数据的系统获得的数值结果突出了所提出框架的几个潜在应用。TSO 可以最好地模拟 TSO-DSO 接口点的不确定性，从而减少用于解决这些不确定性的资源数量。基于真实数据的系统获得的数值结果突出了所提出框架的几个潜在应用。TSO 可以最好地模拟 TSO-DSO 接口点的不确定性，从而减少用于解决这些不确定性的资源数量。基于真实数据的系统获得的数值结果突出了所提出框架的几个潜在应用。