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

优化传感器部署和数据分析，实现配电网络可视性和控制

• 批准号: 1971077
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1971077
• 关键词: Optimal; sensor; deployment; data; analytics

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.

改造诸如CT/VT/PMU之类的网络测量设备以获得网络可观测性可能是昂贵的，并且需要系统访问（中断），这可能难以规划并且通常/当前需要多年的规划。测量设备通常用于保护、计量和控制目的，并且当前或经常不用于真实的时间网络可观测性。家庭和工业负荷的特点正在演变;由于近年来出现DER安装，网络基础设施也在演变。网络的可观测性必须通过新的工具来建立，这些工具不应该太依赖于历史负荷预测和网络模型，而应该依赖于来自智能传感器的数据。为DSO做好准备，实施主动网络管理（ANM）符合配电网络运营商（DNO）的利益。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。由于高资本成本和系统接入要求，在每个馈线的末端安装测量设备是不切实际的-可以探索测量设备的战略布局。这将形成电压和无功控制（VVC）的骨干，包括慢速和快速电压控制设备。首先需要新的工具来评估各种量，如电压，有功/无功功率和流量，然后在初级控制中建立网络运行情况图，这需要状态估计器，必须科普需求侧发生的变化。目标是：1.数据压缩算法，减少传输到控制中心的数据量.电压和无功控制，通过混合整数非线性规划的凸化，将解决来自功率转换器的具有快速平滑电压控制的缓慢离散电压控制。传感器的优化配置需要鲁棒的算法覆盖配电网络流中所有不断变化的未来场景。数据分析将基于预测建模，这是新的，不会太依赖于网络拓扑处理。VVC的凸化应尊重DG功率容量特性，同时处理连续（DG无功功率控制）和离散（OLTC，可切换并联电容器和电抗器组）决策变量，以提供最佳的电压和潮流控制支持在主动配电网。