Deployment and demonstration of wide area monitoring system in power system of Great Britain

The creation of a suitable wide area monitoring system(WAMS) is widely recognized as an essential aspect of delivering a power system that will be secure,efficient and sustainable for the foreseeable future. In Great Britain(GB), the deployment of the first WAMS to monitor the entire power system in real time was the responsibility of the visualization of real time system dynamics using enhanced monitoring(VISOR) project. The core scope of the VISOR project is to deploy this WAMS and demonstrate how WAMS applications can in the near term provide system operators and planners with clear, actionable information. This paper presents the wider scope of the VISOR project and the GB wide WAMS that has been deployed. Furthermore, the paper describes some of the WAMS applications that have been deployed and provides examples of the measurement device performance issues that have been encountered during the project.

Analysis and Control of MVDC Demonstration Project in the UK:ANGLE-DC

The emerging medium voltage direct current(MVDC)distribution networks are becoming more attractive due to their flexible power flow control and lower losses compared to traditional AC networks.This will significantly increase the wide uptake of renewable energy sources.The optimum utilization of the existing assets is an important aspect in grid upgrading and planning.One feasible option is to convert existing MVAC lines into MVDC operation.One of the practical demonstrations is the“ANGLE-DC”project which is also the first MVDC link in the UK.This paper highlights the innovative approach,challenges and key benefits delivered by the ANGLE-DC project.

Prediction of voltage distribution using deep learning and identified key smart meter locations

The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.