Optimal sensor deployment and data analytics for power distribution network visibility and control

改造网络测量设备（如CT/VT/PMU）以获得网络可观测性可能成本高昂，并且需要系统访问（中断），这很难规划，通常/目前需要多年的规划。测量装置通常用于保护、计量和控制目的，目前或通常不用于实时网络可观测性。家庭和工业负荷的特点正在演变；由于近年来新兴的DER安装，网络基础设施也在不断发展。网络的可观测性必须通过新的工具来建立，这些工具不应该过多地依赖历史负荷预测和网络模型，而应该依赖智能传感器的数据。配电网运营商（DNO）希望主动网络管理（ANM）能够为DSO做好准备。新的和现有的测量和控制设备在主动网络管理中发挥着至关重要的作用，例如了解所有馈线的电压稳定性和热极限。由于高昂的资金成本和系统访问要求，在每个馈线末端安装测量装置是不切实际的-可以探索测量装置的战略放置。这将构成电压和无功控制（VVC）的主干，涉及慢速和快速电压控制装置。首先需要新的工具来评估各种数量，如电压、有功/无功功率和流量，然后可以在主要控制中建立网络运行情况图，这需要必须应对需求侧发生的变化的状态估计器。目标是：1。Costeffectivesensorplacementinthenetwork 2。数据压缩算法，减少向控制中心传输的数据量。电压和无功控制，通过混合整数非线性规划的凸化，将缓慢的离散电压控制与快速的平滑电压控制结合起来。传感器的最优分配需要鲁棒的算法，该算法涵盖了配电网流中所有不断变化的未来场景。数据分析将以预测建模为基础，这是一种新的模型，将不再依赖于网络拓扑处理。在处理连续（DG无功控制）和离散（OLTC，以及可切换并联电容器和电抗器组）决策变量时，VVC的凸化应尊重DG的功率特性，以在有功配电网中提供最佳的电压和潮流控制支持。

Retrofitting Network Measurement devices such as CT/VT/PMU to gain network observability can be costly and require system access (outages) which can be difficult to plan and typically/currently require years of planning. Measuring devices are typically used for protection, metering and control purpose and currently or often not used for real time network observability. Characteristics of domestic and industrial load is evolving; network infrastructure is also evolving due to emerging DER installations in recent years. Network observability must be established by new tools that should not rely much on historical load forecast and network model rather on the data from smart sensor. It is in Distribution Network Operator's (DNO) interest to have Active network management (ANM) in place for DSO readiness. New and existing measurement and control devices play a vital role in active network management such as knowing voltage stability and thermal limits of all feeders. It is impractical to install measurement devices at end of every feeder due to high capital cost and system access requirement - strategic placement of measurement devices can be explored. This will form the backbone of voltage and var control (VVC) involving slow and fast voltage control devices. New tools are needed first for the evaluation of various quantities such as voltage, active/reactive power and flow which can then establish a network operating situation map in the primary control which needs state estimators that must cope with the changes that is taking place in the demand side.The objectives are:1.Costeffectivesensorplacementinthenetwork 2. Data compression algorithm to reduce the volume of data transmission to control centre 3. Voltage and var control which will address slow discrete voltage control with fast smooth voltage control from power converter through convexification of mixed integer non-linear programming. The optimal sensor allocation requires robust algorithm covering all evolving future scenarios in distribution network flow. The data analytics will be based on predictive modelling which is new and will not rely much on network topology processing. The convexification of VVC should respect the DG power capability characteristic while handling both continuous (DG reactive power control) and discrete (OLTC, and switchable shunt capacitor and reactor banks) decision variables to provide optimum voltage and power flow control support in active distribution network.