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| Reference Number | NIA2_NGESO051 | |
| Title | MinGFM | |
| Status | Started | |
| Energy Categories | Renewable Energy Sources (Wind Energy) 20%; Other Power and Storage Technologies (Electric power conversion) 40%; Other Power and Storage Technologies (Electricity transmission and distribution) 40%; |
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| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 100% | |
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given National Grid plc |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 September 2023 | |
| End Date | 28 February 2025 | |
| Duration | ENA months | |
| Total Grant Value | £415,000 | |
| Industrial Sectors | Power | |
| Region | London | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , National Grid plc (100.000%) |
| Industrial Collaborator | Project Contact , National Grid plc (0.000%) |
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| Web Site | https://smarter.energynetworks.org/projects/NIA2_NGESO051 |
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| Objectives | The project will be delivered in three work packages:WP1: Development of individual wind farm models using Grid Following (GFL), Standard GFM with Energy Storage (ES) and MinGFM (without ES) as proposed, as well as system studies including stability and fault level assessment (6 months) WP2: Development of data-driven smart controller for offshore wind turbines (with/without HVDC systems) using GFL, standard GFM, MinGFM as well as system studies including stability and fault level assessment (9 months).In WP2, a data-driven intelligent smart controller will be developed to unlock IBRs MinGFM control capabilities. Drawing upon recent research findings, it has been demonstrated that the implementation of a MinGFM approach is feasible without the need for additional investments in energy storage. WP3: Techno-economic comparisons of those IBRs with Grid Following Control GFL, GFM+ES, MinGFM and their optimised combinations in different trial regional networks for secure, economic and coordinated system operations (3 months) System testing will be carried out in Real Time Digital Simulation (RTDS) across WP1-3. This approach will utilise best practice techniques identified in the NIA project D3 - Data-driven Network Dynamic Representation for Derisking the HVDC and Offshore Wind, in addition to International open-sourced best practices e.g. Institute of Electrical and Electronics Engineers (IEEE) and the International Council on Large Electric Systems (CIGRE). The approach will also be informed by in-house best practices for IEEE R&D publications and other study results for existing publications. The ESO will not share any non-public data with the University of Birmingham as per the approach adopted in the D3 project.The outputs of the project will be validated against an in-house model library co-developed by the University of Birmingham and National Grid in 2017. Some emerging models will be further developed based on the existing outcomes of IEEE publication as well recognised by international experts. Model performances will be further validated from knowledge collected from the ESOs engagement with international associations and organisations e.g. CIGRE and the Global Power System Transformation Consortium (ESIG/G-PST) and internal capability developed during the MinGFM project. Project deliverables include:Report on wind turbine models with stability control capability using GFL, Standard GFM and MinGFM (Month 6), based on WP1Report on assessment of data-driven smart controller for offshore wind turbines using GFL, Standard GFM and MinGFM (Month 15), based on WP2A comprehensive report on the techno-economic comparisons of Grid Following Control (GFL), standard Grid Forming (GFM), MinGFM (without energy storage) along with recommendations (due at Month 18). This report will be based on the insights and findings derived from Work Package 3 (WP3). To harness the substantial potential control capabilities of IBRs such as offshore wind farms and interconnectors, and to advance the emerging concept of GFM control for IBRs as a solution for declining inertia and fault level challenges, it is crucial to develop new mathematical models and tools. These tools will help to unlock the control potential of renewable energy sources without requiring additional investment in energy storage. Investigating data-driven smart controller design methods will enable the realisation of grid forming control capabilities. A techno-economic framework will be employed to devise optimised combinations of control strategies in various trial regional networks to ensure secure, cost-efficient, and coordinated system operation. This project will yield the following benefits: By negating the need for additional energy storage investments particularly in offshore wind farms where space is limited, the constraints associated with these investments will be reduced.The implementation of MinGFM stability services, which will rely on software upgrades rather than additional hardware (energy storage) installations, can significantly reduce associated costs. Unlike standard GFM, which requires substantial investment in energy storage, MinGFM stability services are expected to become basic grid connection requirements for wind farms, thus greatly reducing the associated service costs.The outcomes will also help shape new ESO policies and strategies for creating a portfolio of stability control services utilising GFM, thereby supporting the industry in achieving net-zero targets.Increased competition in the offshore wind market through the facilitation of appropriate entry requirements will benefit both generators and consumers through reduced costs. Appropriately setting market entry requirements will help capture value for all participants in the value chain.The contribution to incentives will significantly accelerate the net-zero energy transition in the UK. Investigating the stability service capability of wind farms employing MinGFM control through the sole upgrade of wind farm control systems (primarily software updates) without the need for additional energy storage investment.Defining the implementation of GFM control by unlocking the control capabilities of IBRs, allowing them to release certain amounts of stored energy within wind turbines through data-driven smart control strategies.Conducting economic comparisons between Grid Following Control (GFL), standard GFM (with energy storage), and MinGFM (without energy storage), subsequently proposing a roadmap for implementing MinGFM services under electricity market environments and recommending changes to the Grid Code | |
| Abstract | The UK Government has set ambitious targets of 50GW of offshore wind installed on the GB transmission system by 2030. Increasing these inverter-based resources provides new opportunities for stability services via grid forming control (GFM) of power electronic converters. The GFM control can help deal with issues synonymous with future electricity systems, such as low inertia and low fault levels. However, while using a GFM approach has benefits, significant energy storage investment is needed. This project will investigate new methods and control strategies for when additional energy storage is not needed. In particular, this project will help develop an understanding of the potential for data-driven intelligent control of wind turbines while delivering a techno-economic comparison of various control strategies. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/10/24 | |
