We are becoming a separate company

In April 2019, the Electricity System Operator is becoming a separate company within the National Grid Group. Find out more about what this change will mean.

Network Innovation Allowance (NIA)

The Network Innovation Allowance (NIA) provides an annual allowance to fund innovation projects that create value for our customers. You can find out more about our NIA-funded projects on this page.

You can find a discussion of our innovation projects in our Innovation Annual Summary.

Download the Innovation Annual Summary document

The following is a list of all NIA projects we are currently delivering.

GB Non-renewable Embedded Generation Forecasting Study
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA costStart TRLEnd TRL
NIA_NGSO0002GB Non-renewable Embedded Generation Forecasting Study 2, 4, 13Smith Institute£91,50034

Find out more on the Smarter Networks website.

Improving Forecasts with Advanced Modelling 

Two NIA projects are helping the System Operator to develop more accurate short-term forecasting for embedded generation.

Great Britain’s electricity system has seen more embedded generation connecting in recent years. This is generation connected at local level to the electricity distribution network rather than the transmission network. It includes both renewable and non-renewable sources of energy. Growth in embedded generation makes running the system more challenging because it is not visible to the SO. This lack of visibility makes accurate national transmission system demand forecasts more difficult, as the demand is reduced by any embedded generation running. Ultimately, better demand forecasting helps to reduce the cost of managing the system and savings being shared with our customers.

Smith Institute: Non-renewable generation

The first project was undertaken by the Smith Institute for Industrial Mathematics and System Engineering. It studied how to improve short-term forecasts of up to seven days for non-renewable embedded generation. The study assessed 4.7 GW of generation from technologies including biomass, gas (combined), hydro, landfill and waste. The aim was to create forecasting models for one, two and seven days ahead. The study used four years of data on embedded generation. It also investigated the impact of electricity prices on the one-day-ahead model. Electricity prices have not previously been used in National Grid’s demand forecasts.

Results and potential benefits

The project has given us a better understanding about the types of non-renewable embedded generation connected to the grid and how each contributes. The project also showed that forecast electricity price was a major factor in the level of embedded generation. The next step is to validate the models and to assess how they could improve demand forecasting accuracy. Generation from these technologies is most important in the winter season, when output tends to peak.

Alan Turing Institute: Renewable generation

Meanwhile, the Alan Turing Institute led a forecasting study focused on renewable generation. It looked specifically at embedded solar PV and wind, again up to seven days ahead. The project also examined ways to detect partial outages on wind farms and the effect of shutdowns caused by high wind speeds

Results and potential benefits The Turing project has developed models that will improve the way both embedded solar PV and wind generation are forecast. For example, at the end of the project, the proposed forecasting method for national PV generation showed a 10% improvement in Mean Absolute Error (MAE) compared with existing methods for seven-day ahead forecasts. Since the project completed, we have continued development of the Turing PV model. An enhanced version of the model is planned for this summer. The model showed further benefits, particularly for regions with sparse weather forecasts. The software will now be tested and validated. It could then be added to our demand forecasting pipeline.

 

These two projects are helping us to understand more clearly the role of embedded generation.

 

- Kevin Tilley, Project Lead

Optimisation of Energy Forecasting through analysis of datasets of metered embedded wind and PV generation
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA costStart TRLEnd TRL
NIA_NGSO0001Optimisation of Energy Forecasting through analysis of datasets of metered embedded wind and PV generation2, 4, 13The Alan Turing Institute£34,15037

 

Find out more on the Smarter Networks website.

Improving Forecasts with Advanced Modelling

Two NIA projects are helping the System Operator to develop more accurate short-term forecasting for embedded generation.

Great Britain’s electricity system has seen more embedded generation connecting in recent years. This is generation connected at local level to the electricity distribution network rather than the transmission network. It includes both renewable and non-renewable sources of energy. Growth in embedded generation makes running the system more challenging because it is not visible to the SO. This lack of visibility makes accurate national transmission system demand forecasts more difficult, as the demand is reduced by any embedded generation running. Ultimately, better demand forecasting helps to reduce the cost of managing the system and savings being shared with our customers.

Smith Institute: Non-renewable generation

The first project was undertaken by the Smith Institute for Industrial Mathematics and System Engineering. It studied how to improve short-term forecasts of up to seven days for non-renewable embedded generation. The study assessed 4.7 GW of generation from technologies including biomass, gas (combined), hydro, landfill and waste. The aim was to create forecasting models for one, two and seven days ahead. The study used four years of data on embedded generation. It also investigated the impact of electricity prices on the one-day-ahead model. Electricity prices have not previously been used in National Grid’s demand forecasts.

Results and potential benefits

The project has given us a better understanding about the types of non-renewable embedded generation connected to the grid and how each contributes. The project also showed that forecast electricity price was a major factor in the level of embedded generation. The next step is to validate the models and to assess how they could improve demand forecasting accuracy. Generation from these technologies is most important in the winter season, when output tends to peak.

Alan Turing Institute: Renewable generation

Meanwhile, the Alan Turing Institute led a forecasting study focused on renewable generation. It looked specifically at embedded solar PV and wind, again up to seven days ahead. The project also examined ways to detect partial outages on wind farms and the effect of shutdowns caused by high wind speeds.

Results and potential benefits

The Turing project has developed models that will improve the way both embedded solar PV and wind generation are forecast. For example, at the end of the project, the proposed forecasting method for national PV generation showed a 10% improvement in Mean Absolute Error (MAE) compared with existing methods for seven-day ahead forecasts. Since the project completed, we have continued development of the Turing PV model. An enhanced version of the model is planned for this summer. The model showed further benefits, particularly for regions with sparse weather forecasts. The software will now be tested and validated. It could then be added to our demand forecasting pipeline.

These two projects are helping us to understand more clearly the role of embedded generation.

- Kevin Tilley, Project Lead

Virtual Synchronous Machine Demonstration
Project referenceProject nameInnovation priority (see Annual SummarySuppliersPEA costStart TRLEnd TRL
NIA_NGSO0004Virtual Synchronous Machine Demonstration3, 6Nottingham University£456,00046

Find out more on the Smarter Networks website.

Project overview

Most traditional generators turn at the same rate and are synchronised with the power grid. Renewable generation, however, utilise non-synchronous technologies and are connected through power inverters. This gives renewable generation the freedom to harvest power in all conditions without, for example, wind turbines being restricted by a need to turn at the same frequency as the system. Using non-synchronous technologies has allowed more low-carbon energy onto the system at lower connection costs. However, as it continues to grow and replaces traditional synchronous machines, it could pose significant challenges for the day-to-day operation of the grid.

We’re attempting to develop and specify a technology to solve this, called Virtual Synchronous Machines (VSM). It uses advanced electronic converter controls so non-synchronous generation can inherit key features of conventional synchronous machines.

Results and learning

Our study with University of Strathclyde showed that VSM is a practical solution for any mix of up to 100 per cent renewable energy on to Britain’s power grid. We’re now beginning to build a full prototype of VSM technology.

Consumer benefits

The technology has huge potential and is expected to allow a system to run with even higher penetrations of nonsynchronous generation. If successful, it will help us hit our targets for cutting carbon at lower cost to consumers.

Dynamic Modelling Development (PLL)
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA costStart TRLEnd TRL
NIA_NGSO0005Dynamic Modelling Development (PLL)3, 7, 9, (4)*Power System Consulting£80,00034

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Project overview

An increasing amount of Britain’s electricity demand is met by Non-Synchronous Generation (NSG). This is power generated in non-traditional ways, such as from renewable sources, batteries and interconnectors. NSG uses converters to change its power into a form that’s compatible with our system.

The process involves a control system called Phase Locked Loop (PLL), which tells the generator how to stay in step with the demands of the electricity network. It’s vital we understand how these PLLs behave, yet our existing planning software isn’t able to fully interpret their behaviour. Through this project, we’re doing the missing detective work and applying everything we learn to improve our planning.

Results and learning

We carried out a review of the PLL systems, both in use and under development, and researched their hallmark behaviours. We uncovered that PLLs don’t react to disturbances as fast as we’d thought and that there are a variety of different types of behaviour, which means we can better reflect how new generation can respond to system events. We’ll now update our planning software to reflect these insights. We will also be preparing guidance based on this work to inform other Transmission and Distribution Network Owners and Operators so they can reflect these insights within their own modelling.

Consumer benefits

Past experience from improving dynamic models has illustrated that as we gain new insights in modelling, this allows us to operate the system more efficiently across a full range of electrical faults and disturbances. This could potentially deliver millions of pounds of savings for consumers over future years as we apply these new modelling techniques to support the development of a lower carbon energy system

This could potentially deliver millions of pounds of savings for consumers over future years.

- Ben Marshal, Project Lead

Investigation & Modelling of Fast Frequency Phenomena (“F2P”)
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA CostStart TRLEnd TRL
NIA_NGSO0007Investigation & Modelling of Fast Frequency Phenomena (“F2P”)3, 6, (4)*Brunel University£340,00036

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Project overview

In the past, when traditional forms of generation supplied most of our electricity demand, we were able to treat system frequency as one value across the whole country. Traditional generators were large, heavy machines spinning at 3,000rpm, and these rotating masses provided lots of angular momentum or “inertia” to the power system. One of the major impacts of having more renewables connected to the network has been a reduction in inertia, which previously provided a natural aid for keeping the system stable. We now see larger and more rapid frequency changes following a disturbance, which vary from region to region. This creates new challenges for how we operate the system, so we need to make sure we understand and can accurately forecast how fast frequency fluctuations spread across the network. In this project, we’re investigating how well our existing planning tools – which weren’t specifically designed to model this effect – can model fast frequency phenomena.

Results and learning

We have recruited a research fellow to investigate how well our existing models correlate with measurements taken using GPS signals and other technology. This will be followed by recommendations on whether to improve our existing tools or introduce more sophisticated software.

Consumer benefits

By improving our understanding and modelling of fast frequency phenomena, we’ll reduce the costs of managing this challenge, which currently amounts to around £5m a month.

By improving our understanding and modelling of F2P, we’ll reduce the costs of managing this challenge.

- Martin Bradley, Project Lead

System Impacts of Embedded Storage
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA CostStart TRLEnd TRL
NIA_NGSO0006System Impacts of Embedded Storage12, 14, (10)*The Carbon Trust£222,000`23

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

The electricity storage market has developed significantly in the past year and is expected to continue to grow rapidly. Up to now, most analysis has focused on how storage can help address some of the challenges facing the system. However, as the market continues to develop quickly, particularly within the distribution networks, there may be some risks to system operation from unexpected behaviour of embedded storage. This project will identify the largest and most urgent risks and provide recommendations for innovation projects that can help us manage them.

Results and learning

With the project in its early stages, we’re now exploring which areas of storage may have the biggest impacts on system operation. Once we’ve made this evaluation we’ll look to propose further activities to address these issues.

Benefits to consumers

By consolidating our understanding of risks around storage, we’ll have a strong foundation to propose solutions that offer the best value for consumers.

WI-POD- Wind turbine control Interaction with Power Oscillation Damping control approaches
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA costStart TRLEnd TRL
NIA_NGET0188WI-POD- Wind turbine control Interaction with Power Oscillation Damping control approaches6University of Warwick£350,00023

Find out more on the Smarter Networks website.

Update: Power oscillation damping

Project overview

During certain disturbances on the network, such as a major loss of generation or trip of a transmission line, power swings between different parts of the system. We need to make sure that the oscillations caused by these swings aren’t too large and are quickly reduced, which we call damping.

Traditionally, this damping has been met by the natural behaviour of conventional generators. However, as more wind generation replaces traditional generation, we need to find new ways of providing this.

One option is to introduce Power Oscillation Damping (POD) on wind farms. Through this project, we’re exploring the risks involved – both to wind farms and the broader grid – of introducing PODs and suggesting ways to manage them.

Results and learning

Following a literature review and preliminary simulations, we’ve found that POD control for wind turbines is possible and can effectively reduce oscillations on the power system.

Benefits to consumers

Fewer oscillations to manage means we’ll need to make fewer expensive interventions, which will cut costs for consumers. It will also create a stable environment for more wind farms to connect.

Solar PV Forecasting Phase 1
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA costStart TRLEnd TRL
NIA_NGET0177Solar PV Forecasting Phase 12, 3, (4)*The Met Office£440,00037

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Update: Solar Photovoltaic (PV) Generation Forecasting

Phase 1 of the Solar PV Forecasting project with the Met office aims to develop more accurate short-term forecasting for embedded solar. The work looks at improving one of the biggest drivers of PV forecasting error, solar radiation forecasts. Within-day errors from PV forecasts alone are around 400MW.

The project has helped to develop an optimal blend of Met Office forecasts. This led to a 100MW improvement in the PV forecast error.

In the past year the team investigated a technique for statistical post-processing of forecasts. It compares and corrects the initial forecasts, considering recent observations. The work is now in its final stages of development. If successful, it could potentially reduce forecasting error by another 40MW.

Phase 2 of the Solar PV Forecasting project with the University of Reading addresses the variability of solar PV and other renewable sources and examines ways to improve PV forecasting models.

The team developed a 38-year hourly time series of wind, solar and demand. This was used to explore the scale and frequency of high-impact events such as low renewable output, high demand days. Data has already been used to aid Capacity Market planning and will be an excellent reference to support system operability, helping to reduce system balancing costs for customers.

The work also proposed a new model, for PV forecasting techniques which took account of time of day and season. The team looked at potential improvements by combining forecasts with observations of PV outturn. The proposed method out-performed simpler models at all timeframes from one to six hours ahead.

Solar PV Forecasting Phase 2
Project referenceProject nameInnovation priority (see Annual Summary)SuppliersPEA costStart TRLEnd TRL
NIA_NGET0183Solar PV Forecasting Phase 23, 12, (4)*University of Reading£300,00046

 

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Update: Solar Photovoltaic (PV) Generation Forecasting

Phase 1 of the Solar PV Forecasting project with the Met office aims to develop more accurate short-term forecasting for embedded solar. The work looks at improving one of the biggest drivers of PV forecasting error, solar radiation forecasts. Within-day errors from PV forecasts alone are around 400MW.

The project has helped to develop an optimal blend of Met Office forecasts. This led to a 100MW improvement in the PV forecast error.

In the past year the team investigated a technique for statistical post-processing of forecasts. It compares and corrects the initial forecasts, considering recent observations. The work is now in its final stages of development. If successful, it could potentially reduce forecasting error by another 40MW.

Phase 2 of the Solar PV Forecasting project with the University of Reading addresses the variability of solar PV and other renewable sources and examines ways to improve PV forecasting models.

The team developed a 38-year hourly time series of wind, solar and demand. This was used to explore the scale and frequency of high-impact events such as low renewable output, high demand days. Data has already been used to aid Capacity Market planning and will be an excellent reference to support system operability, helping to reduce system balancing costs for customers.

The work also proposed a new model, for PV forecasting techniques which took account of time of day and season. The team looked at potential improvements by combining forecasts with observations of PV outturn. The proposed method out-performed simpler models at all timeframes from one to six hours ahead.

A Combined Approach to Wind Profile Prediction
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0039A Combined Approach to Wind Profile Prediction2, 6, (4)*

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Mathematics of Balancing Energy Networks Under Uncertainty
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0052Mathematics of Balancing Energy Networks Under Uncertainty4, 13

Find out more on the Smarter Networks website.

Scalable Computational Tools and Infrastructure for Interoperable and Secure Control of Power System
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0058Scalable Computational Tools and Infrastructure for Interoperable and Secure Control of Power System4, 5, 11

Find out more on the Smarter Networks website.

Protection and Fault Handling in Offshore HVDC grids
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0059Protection and Fault Handling in Offshore HVDC grids(13)*

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Enhanced Weather Modelling for Dynamic Line Rating
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0105Protection and Fault Handling in Offshore HVDC grids8, (4)*

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Control and Protection Challenges in Future Converter Dominated Power Systems
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0106Control and Protection Challenges in Future Converter Dominated Power Systems3, 6

Find out more on the Smarter Networks website.

 

Electrical Demand Archetype Model (EDAM2)
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0110Electrical Demand Archetype Model (EDAM2)3, 12, (4)*, (6)

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

Granular Voltage Control
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0134Granular Voltage Control3, 7, 11, 14

Find out more on the Smarter Networks website.

Integrated Electricity and Gas Transmission Network Operating Model
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0144Integrated Electricity and Gas Transmission Network Operating Model4, 8, 12

Find out more on the Smarter Networks website.

DNO Investigation into Voltage Interaction and Dependancy Expectation (DIVIDE)
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0156DNO Investigation into Voltage Interaction and Dependancy Expectation (DIVIDE)1

Find out more on the Smarter Networks website.

 

Detection and Control of Inter-Area Oscillations (DACIAO)
Project referenceProject nameInnovation priority (see Annual Summary)
NIA_NGET0161Detection and Control of Inter-Area Oscillations (DACIAO)3, 4

Find out more on the Smarter Networks website.

 

South East Smart Grids (SESG)

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0167South East Smart Grids (SESG)1, 7, 8, 10, 11, 14, (2)*, (4), (13)

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

 

Transmission Network Topology Optimisation

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0169Transmission Network Topology Optimisation4, 8, 10, 11

Find out more on the Smarter Networks website.

 

PV Monitoring Phase 2

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0170PV Monitoring Phase 213, (4)

Find out more on the Smarter Networks website.

Embedded Cyber Risks with Procurement

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0174Embedded Cyber Risks with Procurement5

Find out more on the Smarter Networks website.

Improving Cyber Security Culture

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0175Improving Cyber Security Culture5

Find out more on the Smarter Networks website.

 

Transient Voltage Stability of Invertor Dominated Grids and Options to Improve Stability

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0187Transient Voltage Stability of Invertor Dominated Grids and Options to Improve Stability4, 7

Find out more on the Smarter Networks website.

 

SIM - Samuel Inertia Element

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0192SIM - Samuel Inertia Element3, 11

Find out more on the Smarter Networks website.

 

DESERT (Hybrid Battery and Solar Enhanced Frequency Control)

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0193DESERT (Hybrid Battery and Solar Enhanced Frequency Control) 3, 8, 14, (12)*

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

 

Vector Shift Initial Performance Assessment

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGET0205Vector Shift Initial Performance Assessment6

Find out more on the Smarter Networks website.

Assessment of Operation of Small-Scale Inverter Connected PV Generation During Under-Voltage and Voltage Vector Shift Conditions

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGSO0003Assessment of Operation of Small-Scale Inverter Connected PV Generation During Under-Voltage and Voltage Vector Shift Conditions3, 6, (13)*

*The innovation priority numbers in brackets are indirectly linked to the projects

Find out more on the Smarter Networks website.

 

Solar PV Monitoring Phase 3

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGSO0008Solar PV Monitoring Phase 32, 13

Find out more on the Smarter Networks website.

 

EPRI - Situational Awareness Using Comprehensive Information (39.011)

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGSO0010EPRI - Situational Awareness Using Comprehensive Information (39.011)1, 4, 8, 13

Find out more on the Smarter Networks website.

 

EPRI - Application of New Computing Technologies and Solution Methodologies in Grid Operations (39.014)

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGSO0011EPRI - Application of New Computing Technologies and Solution Methodologies in Grid Operations (39.014)1, 4, 11

Find out more on the Smarter Networks website.

 

EPRI - Risk-Based Analysis into Planning and Resiliency Processes (40.022)

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGSO0012EPRI - Risk-Based Analysis into Planning and Resiliency Processes (40.022)1, 6, 7, 8, 14

Find out more on the Smarter Networks website.

 

EPRI - Flexibility and Resource Adequacy for System Planning (PS173C)

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGSO0013EPRI - Flexibility and Resource Adequacy for System Planning (PS173C)3, 10, 14

Find out more on the Smarter Networks website.

 

EPRI - System Planning Methods, Tools, and Analytics (PS173A)

Project reference

Project nameInnovation priority (see Annual Summary)
NIA_NGSO0014EPRI - System Planning Methods, Tools, and Analytics (PS173A)1, 7, 8, 12, 13

Find out more on the Smarter Networks website.