BRIDGES - Building a Resilient and Intelligent Dynamic Grid Enhancement System

Started

Other Power and Storage Technologies(Electricity transmission and distribution)

Applied Research and Development

ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering)

Not Cross-cutting

Project Contact
National Grid Electricity Transmission

Network Innovation Allowance

Ofgem

01 March 2025

30 September 2026

ENA months

£1,392,000

Power

London

Network Innovation Allowance

Project Contact, National Grid Electricity Transmission

The proposed project aims to investigate the feasibility of integrating multiple Grid Enhancing Technologies (GETs) such as DLR and optimal power flow control into a new system architecture centred around the GridNode controller to provide Dynamic System Rating (DSR) solution that enhances the utilisation of existing capacity and improves network control capability.The system will gather real-time thermal ratings from DLR systems, as well as current and voltage measurements from Phasor Measurement Units (PMUs) or other suitable devices. This data will be sent to the GridNode controller. The controller will then assess the steady-state angular and voltage stability in real-time and provide a dynamic system rating (DSR) based on simultaneous monitoring of thermal and stability constraints. Using this information, the system will calculate control commands to enable fast-acting zonal autonomous control. It will adjust the operation of tap changers for super grid transformers, reactive power compensation devices, and/or FACTs devices to optimize power flow. The goal is to alleviate congestion and maximise the capacity for active power flow transfer within the grid zone.This project will primarily focus on developing the scientific model of the DSR software functions, serving as a proof of concept for the proposed solution. Once the concept is validated, the algorithms will be prepared for hardware implementation. They can then be directly converted to code and implemented into the Zonal control devices.If feasible, the developed system will be tested using real-world data from 5-7 substations, preferably those connected to circuits with DLR enabled.Data Quality Statement (DQS):??The project will be delivered under the NIA framework in line with OFGEM, ENA and NGGT / NGET internal policy. Data produced as part of this project will be subject to quality assurance to ensure that the information produced with each deliverable is accurate to the best of our knowledge and sources of information are appropriately documented. All deliverables and project outputs will be stored on our internal Sharepoint platform ensuring access control, backup and version management. Relevant project documentation and reports will also be made available on the ENA Smarter Networks Portal and dissemination material will be shared with the relevant stakeholders.??Measurement Quality Statement (MQS):??The methodology used in this project will be subject to the suppliers own quality assurance regime. Quality assurance processes and the source of data, measurement processes and equipment as well as data processing will be clearly documented and verifiable. The measurements, designs and economic assessments will also be clearly documented in the relevant deliverables and final project report and will be made available for review.??Risk Assessment:??TRL Change = 1?Cost = 2?Supplier = 1?Data = 2?Total risk score = 6 Low (L) ?? The scope of this project is divided into four work packages, which encompass all the necessary tasks from network modelling to Hardware-in-Loop tests, culminating in a comprehensive project report with recommendations.Work Package 1: Construction of a Network modelIdentify potential areas or zones in the network for future implementation, which can increase the capacity of the existing network and benefit consumers.Gather the required data, including network data, simulated software PMU data, and historical weather data, to develop the necessary network model for the selected network zone. This model should incorporate thermal response, angular and voltage stability responses, as well as relevant zonal control actions.Deliverables:Development of a network model for the selected areas/zones, with accurate representation of thermal response, angular and voltage stability responses, and related zonal control actions. This model should incorporate simulated software PMU data and historical weather data inputs.Presentation slides detailing the development of the network modelCybersecurity Architectural Assessment for DSR.Work Package 2: Conceptual development and validation of DSR solution (5 months)Describe the proposed Dynamic System Rating (DSR) methodology.Develop the integrated DSR solution with automatic zonal control, including:Fundamental algorithms to configure thermal limits, steady-state voltage limits, and angular stability limits, and determine the maximum power flow that circuits can handle without compromising system stability.Automatic zonal control algorithm to optimise power flow control by adjusting the operation of tap changers of super grid transformers, reactive power compensation devices, and/or FACTs devices.Validate the developed dynamic system rating approach and automatic zonal control algorithm against the network model through four major tests: DLR thermal limit, voltage stability limit, angular stability limit, and maximum dynamic power limit (DSR limit). This validation process, also known as software-in-loop tests, may involve modifying and fine-tuning the algorithms if necessary.Deliverables:Presentation slides explaining the development and configuration of the DSR and automatic zonal control algorithms.Workshop to share key findings and validation results of the developed algorithms to key stakeholders, accompanied by presentation slides.Technical report detailing the development of the DSR and automatic zonal control algorithms, as well as their validation against the network model.Work Package 3: Hardware Deployment and Hardware in Loop testingConvert the developed DSR with zonal control function into a scientific model and prepare for hardware implementation.Conduct Hardware-in-Loop tests using actual PMUs and Zonal Controllers. These real-time tests will verify the correct computation of DSRs, latency, and accurate decision-making for control actions. The tests will be conducted both with and without controllable assets to determine the impact of autonomous control on reducing network congestion.Assess cybersecurity risks and provide recommendations for final deployment architectures, including the implementation of security controls and the security review of data sets and sources to ensure that unauthorized access to data is prevented, and integrity of the data used.Deliverables:A workshop to share the setup, key findings, and validation results of the Real-Time Digital Simulator (RTDS) testing of the developed algorithms.Technical report detailing the hardware deployment and Hardware-in-Loop testing.A collaborative workshop between NGET and GE Vernova to assess the cybersecurity architecture, identifying potential risks and recommending security reviews of data sets and sources, as well as security controls.An Architectural Assessment outlining future cybersecurity risks and recommendations, including the implementation of security controls and, if feasible, continuous security monitoring.Work Package 4: Performance and Benefit Evaluation andFuture WorksEvaluate the performance of DSR in comparison with conventional static seasonal rating and DLR performance.Conduct a cost-benefit analysis based on the calculated increase in network capacity and reduction in curtailed renewable generated power.Identify future work required for implementation and propose implement plan.Provide user guidance and training on DSR with zonal autonomous control.Produce a final project report that covers the key work from all work packages, test results, performance evaluations, cost-benefit analysis, and recommendations for future implementation.Deliverables:Final project report, user guidance, and training materials on DSR with zonal autonomous control.Organization of dissemination events to share the key outcomes and learnings from the project. The key objectives of the project are as follow:Explore the feasibility of integrating multiple Grid Enhancing Technologies (GETs) such as DLR and optimal power flow control within a novel system architecture centerd around the GridNode controller.Develop and validate a Dynamic System Rating (DSR) approach that enhances the utilization of existing capacity and improves network control capability.Evaluate the performance of DSR solution with ZAC and assess the cost-benefits of future application.Develop an in depth understanding of potential implementation of DSR with ZAC and further works required that lead to implementation.

This project aims to develop an advanced Dynamic System Rating (DSR) solution that maximises the use of the existing transmission network, allowing circuits to operate closer to their true capacity without compromising system stability. The proposed DSR solution integrates with regional autonomous control to alleviate network congestion and enhance power transfer capability. Once validated in a laboratory environment, the system will be prepared for hardware deployment. This technology will reduce the need for extensive network reinforcement, providing cost savings to consumers and facilitating the efficient integration of renewables. Ultimately, it will enhance grid controllability and flexibility, supporting the transition to a Net Zero-carbon energy system.

02/04/25