The UK’s net-zero strategy aims to ”fully decarbonise our power system by 2035”.1 Achieving this ambitious target requires a sea change in the way the energy industry works. The Government is critical to making this change happen, but it’s also worth noting that stakeholders are demanding change at an unprecedented rate. The current energy cost crisis adds urgency to these stakeholder demands. Meanwhile, incentives to decarbonise go well beyond environmental factors, with most now benefiting the bottom line.
Hard work is required to make these changes and meet this goal, and much of this involves data. Data provides a path to unlock the energy transition and forms a key driver for the six key energy trends below. This gives a whole new meaning to the phrase “data is the new oil”, coined by Clive Humby in 2006.
Six key energy trends
These six trends highlight how the production and transmission of energy are changing. The current surge in energy costs throws a bright light on the relationship between decarbonisation and cost. Change is particularly imperative for large, old, capital-intensive assets, for example oil and gas facilities, processing plants, power generation and distribution facilities. Energy and infrastructure businesses are working on several fronts to reduce their carbon emissions and meet the goals of the green energy transition.
- Oil and gas companies are reducing the carbon impact of their current exploration and production activities. They are applying their subsurface expertise to carbon capture and storage, and geothermal energy sources. Their substantial expertise in heavyweight asset finance and development is being applied to large-scale wind and solar farm development.
- Electricity distributors are re-engineering their grids to accommodate the decentralisation of energy sources, widespread increases in demand and substantial changes to both supply and demand profiles.
- Energy distribution networks are working with consumers on energy consumption efficiencies, for example, smart tariffs and smart meters.
- Asset owners are building ‘digital twins’ to help model asset behaviour, prioritise investment in development and maintenance, and support operations.
- Big energy consumers are applying circular processes, including repurposing waste as input to other processes, rebalancing thermal flows and reusing materials.
Can big data save us?
Asset owners are working to improve asset performance using their project planning and management know-how, backed by data and analytics. At the same time, new capabilities are emerging from the fields of information and data management.
- New, rich data sources provide insights into the distributed activities that give rise to energy demand.
- Open data sources give energy producers and distributors the power to predict demand and optimise energy provision.
- Cloud-based data architecture enables data to be assembled in new ways. For example, combining old data on capital asset performance with new data on consumer behaviour that can help manage demand.
- Sophisticated physical asset modelling and ‘digital twins’ enable operations and maintenance resources to be more efficiently applied.
- New applications, such as robotic process automation, hold the potential to simplify and streamline bureaucratic processes on old assets operating in high hazard environments.
- New analytics tools enable operators to analyse asset performance across a wide range of technical and commercial databases, and take decisions in real time based on a better understanding of the short and long-term impact on asset performance.
- Low-code and no-code applications put power back into the hands of engineers and operators looking for marginal gains in efficiency and performance.
These new capabilities have high leverage for sustainability because energy assets typically have an inherently high environmental impact. Incremental gains in the energy supply chain are big gains for the climate, but we have to act fast. Grabbing these data-driven opportunities and turning them into practical outcomes has to be a priority.
Overcoming the hurdles to act now
Energy companies typically manage large, capital-intensive assets, often built over many generations, which can’t be easily replaced by clean, green facilities. Change needs to be radical, but it must be carefully planned.
With rapid advances in data science, artificial intelligence and machine learning, energy companies have been scrambling to find ways to apply technology to address the challenges facing the sector. The opportunities are there, but applying them in practice is another matter. Many data science experts cut their teeth in consumer-facing sectors, where the explosion of new data sources provides a rich playground for fresh thinking. Old, capital-intensive assets, however, present a different problem and a different set of opportunities.
Every energy asset has some existing databases of asset integrity and performance – typically relational databases, sometimes within or linked to enterprise resource planning systems. These are often based on legacy systems that are hard to completely replace while the asset is still being used. In addition, knowledge of these assets is often held by senior staff members, many of whom are approaching retirement.
New data sources have emerged, coming from sophisticated sensors, smart systems and IoT devices. New platforms, systems and architectures now allow data to be combined in new ways, bringing insights from analytics and tools. Yet the data pioneers that bring these tools have trouble connecting with the legacy databases and systems that hold the core asset information.
WHAT’S HOLDING THE INDUSTRY BACK?
◦ Legacy databases can’t support new demand
◦ New methods from consumer sectors don’t work on large and old assets
◦ Asset knowledge held by more experienced, senior individuals is not captured in databases
◦ It’s hard to produce usable applications that combine old and new data sources
◦ Technology acquisitions are hard to integrate (people and processes as well as technology)
Now the going is getting tough for asset owners. Many have brought in – or even bought out – tech start-ups with promising applications, only to find the promise difficult to deliver on.
There’s a lot of work to do on legacy databases beforehand to get them in the right shape to bring in new analytics. New data and analytics initiatives quickly become bogged down in problems with old database structure issues and data cleansing.
New technology holds great potential to transform energy efficiency and asset productivity to achieve step changes in sustainability and bring down the cost of energy. Realising that promise can, however, be harder than it looks.
SO, WHERE DO WE START?
Focus on five potential areas.
- Develop a comprehensive data architecture. Where does it come from? How is it used? How can it be presented in a way that drives reduction in consumption, loss and waste? Focus on material and energy use, rather than the technology.
- Map governance processes. How is data gathered, ingested, maintained, cleaned, stored, processed and reviewed?
- Identify data management accountabilities and responsibilities. Accountabilities should mirror the responsibilities for managing the physical assets. Key accountabilities lie with engineering and operations functions rather than IT. Data management and sustainable development should be built into responsibilities across the organisation, not left to separate HSE and IT functions.
- Develop a culture of respect for data. It should be treated as an asset in itself, in much the same way as the physical assets.
- Challenge line functions to develop outcome-based ideas and proposals for better leverage of data to reduce consumption and waste. Engineers should be asking ‘how can we reduce downtime, reduce failure rates and improve spare availability?’. Don’t start by asking ‘What do we do with all this data?’
Tackling climate change is too urgent to be left to generational change processes. We can’t wait for every old plant to reach the end of its design life and be replaced; we have to work with what we have. New data-based technologies hold the key. We need to be agile and creative to take advantage of these technologies, accelerate implementation and achieve the big changes in efficiency that are required.