£1.3 million of new funding for net zero-driven energy data application winners

electrical grid

Close to £1.3 million of new funding across nine new digital energy data projects has been announced by UK Research & Innovataion (UKRI). The non-departmental public body is awarding the funding through its ‘Modernising Energy Data Applications phase 1’ competition, overseen by the Prospering From the Energy Revolution challenge.

The competition requires projects to develop data applications that address the challenges faced by organisations and individuals to deliver net zero local energy systems. The nine winning applicants are: Urbantide Ltd; Alian Ltd; Brits Energy Ltd; City Science Corporation Ltd; Power My Hub Ltd; Zuhlke Engineering Ltd; GL Industrial Services UK Ltd; Mind Foundry Ltd; and Advanced Infrastructure Technology Ltd.

The competition ran through Autumn 2020 and is the first of a two-phase opportunity that will be followed by the development and testing of prototypes from these winning projects. Projects were required to demonstrate their benefits to the users of local energy systems, provide scalable commercial opportunities, and utilise the Open Energy data architecture being developed through projects funded under the previous Modernising Energy Data Access competition.

The successful projects include Urbantide’s solution to integrate the smart meter system data across the UK to identify those who would benefit from energy efficient measures – and offer appropriate solutions, and Brits Energy’s technology platform for maximising the benefits of digitally-controlled crop production by managing the energy input.

Rob Saunders, challenge director for the Prospering From the Energy Revolution challenge, said: “The quality and range of concepts put forward in this initial competition not only shows what is possible by using some of the newest ideas in data and technology, but also highlights the variety of inefficiencies in the current energy landscape that can be improved as we move towards our net zero ambitions in the UK. Whether it’s industry, farming, investment or efficiency for people’s homes, there is a part to play for innovative technology that reduces usage for us all. We are now looking forward to helping these projects move to the next phase of bringing their solutions to life through testing and development of the ideas.”

Further details on each of the projects are included below, in some cases written in the words of individual developers.

Supporting fuel poor households through data integration and AI (led by Urbantide Ltd) – £149,512
This project will, for the very first time, combine UK-wide smart meter system data with multiple cross-sector data sources to:

• identify households which would benefit from Energy Efficiency Programmes;
• identify households in fuel poverty in a completely novel way; and
• propose an intervention approach which can maximise energy reduction toward Net Zero whilst reducing fuel poverty.

Our project seeks to build upon this knowledge and, with other data sources, integrate new real-time data and AI to accelerate the process of identification and impact assessment.

AI for Low Carbon Technology Site Optimisation (led by Alian Ltd) – £145,176
Farad AI (FAI) has developed a digital twin of the UK’s low voltage electricity grid at substation level, providing Low Carbon Technology project developers (such as electric vehicle charge point or renewable energy developers) with significantly better visibility of their grid connection opportunities. Based on customer research, business development activities to date and the research outputs of prior MEDA winners, our users would greatly benefit from the integration of a series of other non-energy datasets to provide them with a more holistic picture of their connection opportunities.

Building on the work undertaken during by previous MEDA competition winners, we are proposing to integrate a series of new datasets into our platform to provide our users with visibility of the above variables. In doing so, we will enable our users to better assess connection probabilities and project risk.

Net Zero Operation Map (led by Brits Energy Ltd) – £149,793
This project will develop an integrated technological platform to maximise the benefits of digitally controlled crop production (speed up or slow down crop growth by managing the energy input) and linking this with local grid energy flexibility requirements.

The approach is to identify the flexible demand opportunities and use them to enhance resource efficiency of local energy systems, act as a bridge by integrating this flexible demand with grid demand-side response programmes in order to reduce the total cost of electricity and emitted CO2. Analysis estimates that 30% cost and 100% CO2 reduction is possible with this suggested approach.

AI Generative Design Tool for Low-Cost District Heating Networks (led by City Science Corporation Ltd) – £148,552
The project objective is to build a generative design tool that will automate the optimal design of district heat energy networks. Currently, design is costly and time-consuming and often lacks sufficiently detailed data to ensure system efficiency. In many cases this leads to over-sizing of equipment which increases capital costs and makes projects unviable. By developing a new, cutting-edge, generative design tool our project will accelerate district heat:

• using an automated, computer-driven process, we will radically reduce the up-front costs of modelling and system sizing;
• using the latest AI techniques combined with detailed geographic data we will optimise system design to increase the affordability of heat networks and maximise their viability.

Future Energy & Transport Tool (led by Power My Hub Ltd) – £149,508
This project will simplify and enhance the experience of ‘going electric’ and make the electric car the tangible starting point to the way UK consumers engage with the net-zero challenge. This will build on the existing data-driven comparison tool (https://powermyev.co.uk/) that matches consumers to EVs and clean energy solutions using cross-sectoral data, including automotive, mobility, energy, finance and irradiance. This shows the user their potential financial and carbon savings.

This project offers the opportunity to undertake the necessary research to simplify and enhance the experience of “going electric” and objectives are to:

  • investigate, assess and develop energy data and other data sets to enhance the user journey
  • develop the ‘longer journey’ checker features using eergy data and other data
  • undertake user research to explore the evolving market and define user cases

Electric Vehicle Infrastructure Investor App (led by Zuhlke Engineering Ltd) – £149,914
The project is for commercial investors in electric vehicle energy infrastructure who need up-to-date understanding of current and planned initiatives and the energy system as it changes in accordance with achieving the UK’s net zero goals.

The EV Infrastructure Investor App is a collaborative, cloud-based, curated modelling environment with open source aspects and also free-to-use and additional paid premium information/features that cuts through the complexity of acquiring the core knowledge that is inherently held by the technical leadership within Distribution Network Operators of data sources, models, initiatives and recommended practice.The app provides curated access to core standards compliant data and models, with a facility to build run-time simulations of investment models as a foundation for investors to elaborate more sophisticated investment models and intellectual property.

Smart Metering Leak Detection (led by GL Industrial Services UK Ltd) – £109,153
This project aims to analyse the data that is already gathered by smart gas meters and use it for other purposes:

1)To identify potentially dangerous gas leaks in houses. This could be achieved by comparing real time data with previous patterns in gas consumption, while looking for anomalous usage. In particular, unusually large gas flows through the meter could indicate a leak inside the building.

2)In planned maintenance and construction activities on the gas networks. These operations sometimes require portions of the network to be isolated, potentially cutting off the gas supply to a group of customers. Historical gas consumption data could be used to allow more efficient planning and operations.

3)To detect appliances that are performing inefficiently. This has benefits in terms of energy efficiency for financial and environmental reasons. It could also identify appliances that are generating carbon monoxide, which has a health benefit for gas customers.

4)To detect potential problems outside the house, on the gas networks. If multiple customer meters detect issues, then it might be possible to determine gas supply problems or to identify leaks on gas mains outside the properties.

The intention is to use the existing data assets and infrastructure and gas consumption data that is already collected for other purposes, and to provide another incentive for customers to install smart meters. It is possible that other utility usage data could be used in conjunction with the gas meter output.

This technology could be applied to the current natural gas system, but will become even more important if the UK starts moving towards its net zero targets by introducing hydrogen into the gas distribution system. Establishing the technology and methodology to analyse real time gas usage data could provide safety and environmental benefits, without the need to install new hardware in each house. This is a critical step towards the use of net zero energy systems.

If the UK’s gas distribution network is converted to carry hydrogen then new gas meters would be required for each customer, so inclusion of additional functionality at this time would not be a significant additional investment. Standardising the data produced and its format, and the technology employed, would therefore be of benefit to future developments.

It is expected that this technology could also have additional benefits, which will be recorded as part of the study for future evaluation.

Energy-focused geospatial system using multi-sectoral data to deliver net zero (led by Mind Foundry Ltd) – £145,210
The energy sector can undertake a similar approach to sectors such as transport and urban planning that have embarked upon a nimble, focused and data-driven approach in addressing their challenges, by taking data from the energy sector to deliver targeted solutions at the local level.

This project intends to use geospatial data, which combines location information with attribute and often temporal information, to give the ability to analyse sparse or incomplete data with respect to smart meters, electric vehicle (EV) uptake, EV charging type, locations and user profile for example. These are difficult to model with conventional approaches, so the project proposes the creation of an energy-focused geospatial system that will enable the user to visualise overlays of multivariate spatially and temporally varying data, model and predict trends and correlations, infer across areas of sparse data collection, and model the effects of changes on the system such as varying supply, demand or infrastructure.

LAEPapps (led by Advanced Infrastructure Technology Ltd) – £150,000
The project aims to assess the value of a Local Area Energy Planning (LAEP) Big Data Analysis application that allows non-technical energy planners to rapidly model energy scenarios in close to real-time using a novel Common Analysis Framework. The application should build on the Common Data Architecture from recent data projects – accessing, integrating and exploring the datasets from those projects. It should also integrate open datasets that are currently too dissipated and difficult for planners to explore without expensive specialist support.

The aim is to reduce the cost of data acquisition and modelling by up to 50%. Data cleaning and aggregation currently costs £30,000 and full LAEP analysis costs £100,000. By maintaining this data in up-to-date and geospatially mapped formats accessible on web-based visualisation platforms we remove data-as-a-barrier to LAEP analysis. Better whole-system planning with full stakeholder engagement has been shown to reduce cost of capital by 7% saving the UK tax-payer £1.4billion annually in infrastructure expenditure.