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

Lead Research Organisation: Imperial College London
Department Name: Electrical and Electronic Engineering


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.

Planned Impact

This Centre will train students in the blend of traditional and emerging power network concepts and advances in information and communication technologies, consumer and demand side technologies, and integrated energy systems required to deliver future power networks. This targets the skills challenge in the electrical power networks industry, and the lack of high quality graduates able to deliver the smart grid. The training will deliver doctoral level engineers that are prepared for key technical tasks within the power networks and utility industry, and this is a positive impact for society.

A number of industrial partners have agreed to provide placements in which projects are undertaken with the company and on their premises. This will provide an immediate industrial impact where research concepts, systems and approaches can be delivered as knowledge exchange impact, leading to enhanced performance of the UK power networks industry. Direct engagement with the industrial partners, and their funding of the research programme and strong engagement, will lead to new intellectual property that can be capitalised upon by UK manufacturers (new products), consultancies and service providers (new offerings, analyses, services) and network operators (increased efficiencies and reduced capital and operational expenditure). Overall, this will lead to the impact of reduced energy costs for the UK consumer.

Academic impact will be achieved through the internationally leading and novel research activities planned for the Centre. Extensive links and engagement with leading international academics are being put in place to underpin this.

Society will benefit directly by the CDT helping to elevate the standing of the engineering profession and producing more engineers aware of the implications of their technical work for policy and their wider responsibilities to the public, with a particular emphasis on energy. The CDT's impact on policy will be accentuated by the key roles played by our senior staff in government-industry steering groups such as ETI Strategic Advisory Groups, Ofgem Innovation Working Group, IET Power Networks Joint Vision Group, Scottish Grid and Economics Group, and the Scottish Smart Grid Sector Strategy Group to name a few. Our international links through CIGRE, CIRED, and the IEEE will ensure that our outcomes influence a global community.

Our CDT cohorts, alongside our early career research communities, are central to our ambitions to inspire a generation through impact and engagement. Strategic engagement initiatives, such as Strathclyde's Technology and Innovation Centre, are intended to transform the way in which universities work with industry and communicate effectively with all stakeholders, including the public. The CDT cohort will benefit from interactions within this environment, leading to further uptake of the research among stakeholders.


10 25 50