How can machine learning create a smarter grid?

Dynamic Demand 2.0

Across the globe, energy systems are changing and creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, National Grid is facing shrinking margins, looming capacity shortages and unpredictable peaks and troughs in energy supply caused by increasing levels of renewable penetration.

At the recent Reinventing Energy Summit, Michael Bironneau, Head of Technology Development at Open Energi, explored how the same machine learning techniques that have let machines defeat chess and Go masters, can also be leveraged to orchestrate massive amounts of flexible demand-side capacity – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid; one that is cleaner, cheaper, more secure and more efficient.

For World Cities Day 2016, Michael talked to Nikita Johnson of Re:work about utilising data science in energy, creating a smarter grid, political challenges, and more.
What are the main transformative technologies that will help create a smarter grid?
A smarter grid is one where we can integrate renewable energy efficiently without having to keep polluting power stations online to manage intermittency. This requires energy storage to act as a buffer, reducing demand when supply is too low or increasing it when it is too high.

The cheapest and cleanest type of energy storage comes from flexibility in our demand for energy. Open Energi’s Dynamic Demand platform unlocks small amounts of stored energy from commercial and industrial processes – such as refrigerators, bitumen tanks and water pumps – and aggregates and optimises it second by second, creating a virtual battery.

How can machine learning be applied to help balance the grid?
The most transformative application of machine learning for grid balancing comes from unlocking and utilising flexibility in demand-side power consumption. Such algorithms can find creative ways to reschedule the power consumption of many demand and generation assets in synchrony to keep the grid in balance while helping to minimise the cost of consuming that power for energy users.

With sufficient data, a ML model can look at a sequence of actions leading to the rescheduling of power consumption and make grid-scale predictions saying “this is what it would cost to take these actions”. The bleeding edge in deep reinforcement learning shows how, even with very large scale problems like this one, there are optimisation techniques we can use to minimise this cost beyond what traditional models would offer.

What are the regulatory and political challenges to achieving a national smart grid in the UK?
Whatever your role in the vibrant menu of demand side innovations that are offered across Europe, a shared goal for serving consumers is advocating for the framework of flexibility adequacy at the energy system level. This opens so many possibilities – to facilitate Electric Vehicles, mitigate renewable intermittency, replace aging coal infrastructure, and realise a smart grid.

The key is market access. Currently, the UK market favours existing power generators to a disproportionate extent. To fully realise the potential of demand-side flexibility to help balance the grid, save energy and offer lower costs for consumers, we need a level playing field. Without it, there is a very real risk that we will lead ourselves into multi-decade contracts for power plants, paying for a system which is already over capacity and which has no incentive to get any smarter.

How can energy companies work with engineers and data scientists to achieve a more efficient energy system?
One obstacle that prevents many ideas from taking off is the lack of data to support them. If energy companies made more anonymised half-hourly power data available, data scientists and engineers working on new smart grid technologies would be able to validate these ideas quickly and cheaply. In the same vein, it would be a major breakthrough for grid balancing if energy companies made available APIs for reporting and accessing flexibility; it would allow companies like us to unlock enormous amounts of demand-side capacity and put them to good use balancing not just the grid but also helping to optimise the market positions of those same energy companies.

This post originally appeared on Re:work’s blog on the 31st October 2016.

IoT technology meets UK’s energy grid needs faster than power stations

Aggregate Industries Moorcroft quarry

Research has found that Open Energ’s Dynamic Demand technology can meet the UK’s crucial grid balancing requirements faster than a conventional power station. The paper published as a result of ongoing collaborative research by Open Energi, National Grid and Cardiff University, titled Power System Frequency Response from the Control of Bitumen Tanks, looked at the feasibility of DSR providing a significant share of frequency balancing services.

Bitumen tanks (containing the glue that binds our roads together) equipped with Dynamic Demand were used in combination with National Grid’s model of the GB transmission system to investigate the capability of industrial heating loads to provide frequency response to the power system.

The conclusion is that Open Energi’s Dynamic Demand technology, deployed at scale, can contribute to grid frequency control in a manner similar to, and, crucially, faster than that provided by traditional peaking power generation. Field tests showed that full response could be provided in less than two seconds, as compared to 5 – 10 seconds for a thermal generator. Large scale deployment of Dynamic Demand will reduce the reliance on frequency-sensitive generators and ensure that the grid stays balanced in a cost-effective, sustainable and secure manner.

While a lot of focus has understandably been given to tight capacity margins between supply and demand, the real threat could come from generators being unable to respond within the required window to balance instantaneous shifts in supply and demand. With more renewables and decreased thermal generation, ‘inertia’ on the Grid will decrease, making frequency more unstable. Dynamic Demand can help to counteract this effect by providing faster response, helping to future proof the Grid.

The paper was published in the IEEE Transactions on Power Systems Journal. Download a copy here.