How the rise of ‘Energy as a Service’ can power decarbonisation

open energi wind farm

Energy as a Service is the latest business model innovation to arrive in the energy supply industry. In short it is all about moving away from buying energy on a per unit (p/kWh) basis and moving towards a fixed fee per month within certain volume thresholds; akin to how we pay for mobile phone contracts. Energy as a Service has emerged off the back of disruption to the way we supply, consume and now ultimately buy energy, which has fundamentally changed energy market economics.

This disruption is the result of four major technology-driven trends:

  • Decarbonisation – The growth of energy supply from zero marginal cost renewable resources
  • Decentralisation – The growth in energy generated from smaller scale low carbon resources either on customer sites (Behind-the-Meter) or at the Distribution Level (Distributed Energy)
  • Digitisation – The ability to measure and monitor machine behaviour in real-time and automate how we use and supply energy
  • Democratisation – The rise in consumer participation, control and choice which is increasingly determining how energy is bought and used

Traditional per unit models work where the dominant cost in delivery of the product or service scales according to the volume used. This was true when the majority of power supplied came from sources that required a fuel input e.g. coal and gas. The more energy consumed the greater the proportional cost of buying and burning that fuel to generate more kWhs of power.  Other components which make up the total ‘at-the-meter’ price have also been charged on a per unit basis to ensure those who use more of the electricity network pay more for it; government taxes, utility profit margins and network charges (with some time-of-use element).

However, when you start to use zero marginal cost power the economics get flipped on their head. Renewable ‘fuel’ is free, so the dominant cost in consuming energy becomes the infrastructure needed to deliver it. Wind turbines, PV panels, transmission and distribution cables have low operational costs once built, so the initial capital expenditure is where the dominant cost lies.

Across Europe average wholesale prices now reflect wind and sun patterns more than the cost of coal and gas, and at periods of low demand and high renewable output we consistently see negative prices. Clearly change is needed as consuming more energy at these times is beneficial to the whole system but a per unit charging mechanism disincentivises users from doing that.

Enter, Energy as a Service. Already we are seeing a shift in network charging towards capacity-based charges instead of use-of-system charges. Wholesale prices are not far behind; the task becomes providing the flexibility to firm up renewable output. Thanks to the digital revolution described above this flexibility can come from consumers’ demand, cost-effectively tapping into flexibility inherent in distributed energy resources behind-the-meter.

Take a given offshore wind site, with known capacity factors of about 50%. It is possible to quantify the amount of flexible energy needed to ensure 99% of customer demand is met at all times. Using existing business assets means it is possible to take advantage of zero marginal cost flexibility in everyday processes (such as heating, cooling, pumping, battery storage and CHPs), avoid unnecessary infrastructure upgrades and minimise efficiency losses in transporting power. Once it is understood how much flexible power is needed to firm up the output of renewable generation the next task is what technologies do you use to meet that flexibility requirement.

Artificial intelligence-powered flexibility platforms – like Open Energi’s Dynamic Demand 2.0 technology – which can manage distributed energy resources in real-time, are critical. They can evaluate the amount of flexibility in existing power-consuming assets and processes – in addition to any battery storage and/or flexible generation (such as CHPs) – and map demand to supply. This then becomes a constant, real-time scheduling problem for the platform to manage; invisibly ramping processes up when wind is abundant and storing as much power as possible, or turning processes down to a stable minimum and discharging batteries or using a CHP when wind output is low.  If real-time scheduling isn’t maintained, the cost structure breaks down, so the reliability of these platforms is critical.

What is important to recognise here is that below a certain demand threshold the marginal cost of putting in place this service is the cost of operating the wind and the software required to schedule behind-the-meter flexibility. This is why Europe’s utilities are making huge investments and acquisitions in virtual power plant technology.

By doing so the costs of delivering energy become fixed and predictable and scale with size of connection instead of actual usage. Exactly like the mobile phone industry where the marginal cost of sending a packet of data is immaterial in comparison to network costs of all infrastructure.

For Open Energi Energy as a Service has always been the natural end-game in maximising the value of Demand Response. It shelters consumers from the continuously changing and complex incentives of the existing Demand Response markets, and instead offers a simple proposition: “By installing demand response software across a range of assets you can pay a lower fixed monthly fee for your energy”.

The clarity and certainty offered by Energy as a Service makes it easy to structure simple, long-term financing solutions for different technologies – e.g. solar PV, energy storage, CHP – and allows businesses to concentrate on what they do best.  All the complexities of power procurement and demand response markets are removed in place of a known fixed fee per month that ensures reliable, clean and affordable energy. 

David Hill, Commercial Director, Open Energi

This blog was originally posted on Current News.

How Artificial Intelligence is shaping the future of energy

Artificial Intelligence can unlock demand side flexibility for end users

Across the globe, energy systems are changing, creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, large fossil fuelled power stations are being replaced by increasing levels of widely distributed wind and solar generation. This renewable power is clean and free at the point of use but it cannot always be relied upon. To date National Grid has managed this intermittency by keeping polluting power stations online to make up the difference but Artificial Intelligence offers an alternative approach.

What’s needed is a smart grid which can integrate renewable energy efficiently at scale 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. Most people associate energy storage with batteries, but the cheapest and cleanest type of energy storage comes from flexibility in our demand for energy.

This demand-side flexibility takes advantage of thermal or pumped energy stored in everyday equipment and processes, from an office air-con unit, supermarket fridge or industrial furnace through to water pumped and stored in a local reservoir. The electricity consumption patterns of these types of devices are not necessarily time-critical. Provided they operate within certain parameters – such as room temperature or water levels – they can be flexible about when they use energy.

This means that when electricity demand outstrips supply, instead of ramping up a fossil fuelled power station, certain types of equipment can defer their electricity use temporarily. And if the wind blows and too much electricity is being supplied instead of paying wind farms to turn off we can ask equipment to use more now instead of later.

Making our demand for electricity “intelligent” in this way means we can provide vital capacity when and where it is most needed and pave the way for a cleaner, more affordable, and more secure energy system. The key lies in unlocking and using demand-side flexibility so that consumers are a) not impacted and b) appropriately rewarded.

At Open Energi, we’ve been exploring how artificial intelligence and machine learning techniques can be leveraged to orchestrate massive amounts of demand-side flexibility – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid.

We have spent the last 6 years working with some of the UK’s leading companies to manage their flexible demand in real-time and help balance electricity supply and demand UK-wide.  In this time, we have connected to over 3,500 assets at over 350 sites, operating invisibly deep with business processes, to enable equipment to switch on and off in response to fluctuations in supply and demand.

Already, we are well on the way to realising a smarter grid, but to unlock the full potential of demand-side flexibility, we need to adopt a portfolio level approach. Artifical intelligence and machine learning techniques are making this possible, enabling us to look across multiple assets on a customer site, and given all the operational parameters in place, make intelligent, real-time decisions to maximise their total flexibility and deliver the greatest value at any given moment in time.

For example, a supermarket may have solar panels on its roof and a battery installed on site, as well as flexibility inherent in its air-con and refrigeration systems. Using artificial intelligence and machine learning means we can find creative ways to reschedule the power consumption of many assets in synchrony, helping National Grid to balance the system while minimising the cost of consuming that power for energy users.

Lack of data is often an obstacle to progress but we collect between 10,000 and 25,000 messages per second relating to 30 different data points and perform tens of millions of switches per year. This data is forming the basis of a model which 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.

Artificial Intelligence model learning to control the electricity consumption of a portfolio of assets

Graph of AI model

More rapid progress could be made across the industry if energy companies made more anonymised half-hourly power data available. It would enable companies working on smart grid technologies to validate these ideas quickly and cheaply. In the same vein, it would be a major breakthrough for balancing electricity supply and demand if energy companies made available APIs for reporting and accessing flexibility; it would allow companies like Open Energi to unlock enormous amounts of demand-side flexibility and put it to good use balancing not just the grid but also helping to optimise the market positions of those same energy companies.

In the UK alone, we estimate there is 6 gigawatts of demand-side flexibility which can be shifted during the evening peak without affecting end users. Put into context, this is equivalent to roughly 10% of peak winter demand and larger than the expected output of the planned Hinkley Point C – the UK’s first new nuclear power station in generations.  Artificial Intelligence can help us to unlock this demand-side flexibility and build an electricity system fit for the future; one which cuts consumer bills, integrates renewable energy efficiently, and secures our energy supplies for generations to come.

Michael Bironneau is Technical Director at Open Energi. He graduated from Loughborough University in 2014 with a PhD in Mathematics and has been writing software since the age of 10.

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.