High penetration of renewables in the distribution network brings significant uncertainties, especially during volatile weather conditions. Hence, the network controllers should be designed to account for these uncertainties, and respond to unpredictable events like voltage-dips for reliable voltage control. This paper proposes a control scheme, where inverter-based Distributed Energy Resources (DERs) respond locally with Q(V) control and adapt to set-points assigned by the centralized controller (CC). The Robust Constrained Model Predictive Control (RCMPC) scheme is proposed for centralized voltage control. It robustly deploys control resources from DERs and tap-changers to regulate the lower/upper bound of node voltages within the targeted limit. Moreover, RCMPC ensures minimum resource utilization by relaxing the targeted voltage limit whenever it anticipates significant uncertainties. The CC is implemented in Python, which communicates with the RMS model of the UKGDS network for measurements and dispatching set-points. The performance of RCMPC is compared with deterministic MPC (DMCP) at 5, 10, and 15-minute time-steps of CC. The proposed RCMPC can regulate the node voltage even at a higher degree of uncertainty seen at a 15-minute time-step. In contrast, the DMPC could not contain the node voltages under the targeted limit and worsened at a larger time-step.

中文翻译：

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主动配电网中的鲁棒约束模型预测电压控制


可再生能源在配电网络中的高渗透率带来了巨大的不确定性，特别是在不稳定的天气条件下。因此，网络控制器的设计应考虑到这些不确定性，并对电压骤降等不可预测的事件做出响应，以实现可靠的电压控制。本文提出了一种控制方案，其中基于逆变器的分布式能源（DER）通过 Q(V) 控制进行本地响应，并适应中央控制器（CC）分配的设定点。鲁棒约束模型预测控制（RCMPC）方案被提出用于集中电压控制。它稳健地部署来自分布式电源和分接开关的控制资源，以将节点电压的下限/上限调节在目标限制内。此外，每当 RCMPC 预计出现重大不确定性时，都会放宽目标电压限制，从而确保最低的资源利用率。 CC 使用 Python 实现，与 UKGDS 网络的 RMS 模型进行通信以进行测量和调度设定点。将 RCMPC 的性能与确定性 MPC (DMCP) 在 5、10 和 15 分钟 CC 时间步长下进行比较。即使在 15 分钟时间步长的不确定性较高的情况下，所提出的 RCMPC 也可以调节节点电压。相比之下，DMPC 无法将节点电压控制在目标限制以下，并且在较大的时间步长下会恶化。