Towards net zero: is battery storage leading the way?

Volumes of Dynamic FFR by technology type since 2014. EFR and DC are provided by battery storage.

As the UK decarbonises and real time balancing of the electricity system becomes more challenging, battery energy storage will play a crucial role in maintaining a stable system. The UK’s Electricity System Operator, National Grid ESO, has the ambition of operating a zero-carbon electricity system by 2025. This growing requirement for robust real time balancing of the system has been the dominant revenue driver for battery storage projects over the last few years, via Firm Frequency Response. As the ESO begins a journey of reform in UK frequency regulation via new services such as Dynamic Containment, and 2025 draws closer, we look back at how the system has changed and the impact that batteries have already had, through the lens of grid frequency. 

The UK’s electricity system has been rapidly decarbonising over the last few years: renewables accounted for 19.1% of generation in 2014. In 2020, this figure stood at 44.1%. As we move towards a zero-carbon grid, a higher proportion of electricity comes from renewables with no rotating mass, and this has an impact on how the system is operated – not just in dealing with GW swings in wind generation within a few hours, but in the delicate balance of supply and demand over a matter of seconds. 

The spinning turbines of traditional power generation give rise to system ‘inertia’: similar to a bike wheel that keeps turning when you stop pedalling, inertia is an important part of the stability of a power system. Grid frequency is then the needle showing the stability of that system; when all things are equal, it is 50Hz. When they are not, blackouts can occur – frequency plummeted to 48.8Hz on August 9th 2019, leading to nationwide power cuts. 

Here, we look in some detail at grid frequency since 2014: at the trends in frequency ‘events’ (when frequency spikes or dips in response to an outage), and in the way the system recovers. Because we have many more renewables now, system inertia has decreased. We see that year on year, grid frequency is becoming more volatile (see Figure 1), and events are becoming longer. However, the rate of change of frequency (RoCoF) is becoming less severe (Figure 2).  

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So why would RoCoF, a key indicator of system stability, be getting ‘less’ bad with decreasing inertia? One explanation for this is in the new technologies which have been coming onto our system in recent years. National Grid ESO, the UK’s Electricity System Operator, procure an array of services to balance supply and demand in real time. One of the most important of these is dynamic firm frequency response (FFR), in which a plant moderates its output to help balance the system in real time, given the system frequency. It has become dominated by batteries, which, when operated well, can respond reliably and nearly-instantaneously to frequency events – and crucially for net-zero, cleanly.  

Figure 3 shows the volumes of frequency response from different technology types. Since 2014, batteries have gone from providing no FFR volume to now providing virtually all FFR volumes. This, alongside frequency regulation volumes delivered by batteries via Enhanced Frequency Response (EFR) tenders, and more recently the Dynamic Containment (DC) auctions, mean the UK now has close to 1GW of low carbon, ultra-fast battery storage providing real time frequency regulation to balance the electricity system. 

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Looking at two comparable frequency events, one from summer 2017 and one from summer 2019, we can corroborate this theory. There were similar conditions on these days – a similar national demand (INDO), wind outturn, and a sudden power loss equivalent to around 2% of total demand (Table 1). The event in 2019 is after the evening peak, when national demand is decreasing, while the one in 2017 is during the evening ramp. Figure 4 shows the frequency trace for these two events, on a common time axis. 

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Despite the slightly smaller loss in generation causing the 2017 frequency event, it was more severe – with a larger RoCoF and reaching a lower frequency: 49.57 vs 49.70 Hz. The post-event response overshoots and subsequently the frequency remains high for a couple of minutes, whereas the 2019 event returns to a frequency close to 50Hz. While time of day considerations may be at play, it is interesting to consider the volumes of frequency response on the system during both of these periods: see Figure 5. The earlier event had around 30% more dynamic FFR volume. Logic then says the system should stabilize more quickly to a comparable loss, but the opposite is true. However, there is a marked shift in the makeup of that volume; batteries in both FFR and EFR provide significantly more of the stack in 2019.

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The fault recovery of the system appears much better in 2019, suggesting all of these batteries are really improving our response to unplanned plant outages. Returning to the bicycle analogy, it’s much easier to stop a bike from falling over if you catch it just as it starts to topple rather than just before it hits the ground. Batteries are so much faster as compared to the gas, pumped storage and hydro plants which dominated just a few years ago, and it means the system operator needs less volumes to provide the “same” service, so it’s more efficient. Batteries are also very happy performing these low utilisation services – we see very low levels of cell degradation in battery systems performing FFR over long periods of time, and it can be easily stacked with other services. 

All this is great news for net-zero. Leaps and strides in energy storage technology over the last few years, alongside the platforms which operate them, mean we can integrate far more intermittent renewable generation into our electricity mix – whilst ensuring the system remains robust and secure, crucial in our highly electrified society. As we build more wind and more solar, the importance of battery storage technologies in operating a decarbonised, digitalised, democratised and decentralised system will continue to grow. And, not just in frequency regulation but across the board of balancing requirements. 

Written By Grecia Monsalve

Scarcity pricing in the Balancing Mechanism

The last couple of weeks saw some power stations make some bumper profits as cold weather drove scarcity pricing. The greatest profits were made by some large gas (CCGT) plant in the balancing mechanism on Friday 9th and Wednesday 13th January, earning up £4,000/MWh (around 100 times the normal cost of power), so what was it that enabled these plants to earn large revenues compared to other assets? 

The first thing to bear in mind is that the Balancing Mechanism (BM) is not a single market with a well-defined supply and demand but a marketplace for several system needs. The BM is used to correct supply demand imbalances that naturally occur (eg from demand forecasting errors) but crucially also to manage constraints, voltage, frequency, inertia, reserve (and more) in real time (sometimes augmenting specific tendered markets for these services). 

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If we examine the accepted actions by the System Operator (NGESO) on the afternoon Wednesday 13th after the event we can see this immediately given the large volume of both Bids and Offers accepted. If the only purpose was to correct the overall system imbalance it would be very inefficient to pay one plant £4000/MWh to turn up while turning others down at £0/MWh.

 Bids and Offers accepted 1700-1730 on Wednesday 13th. Source: LCP Enact.

These actions only start to make sense once we consider other requirements which the BM is used for, namely Reserve. Operating Reserve is needed to ensure sufficient backup from sudden impacts (like an interconnector tripping off or wind generation dropping off) and therefore GWs of back-up power is required to be available and able to respond in minutes. Reserve can be created by the System Operator through tenders ahead of time (STOR & Fast Reserve), bilateral contracts (SpinGen) and by creating headroom in the BM.

Headroom refers to difference between current output level and the Maximum Export Level (MEL) on thermal plant, the amount they can ramp up to and hold indefinitely. CCGTs take hours to start from zero ouput, but once operating above a Stable Export Level (SEL) they can change output in minutes and so contribute to Operating Reserve.

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A CCGT brought on to provide Headroom. Source BM Reports.

This is where we see the perverse incentive of being a large, slow moving beast sometimes emerges in the BM. Faster (traditionally smaller) plant like OCGTs, batteries and pumped storage can ramp up in a timescale of minutes (or faster) from a standing start which means they are naturally providing Reserve simply by offering volume into the BM. Whereas CCGTs must be turned on to SEL (and paid for this) to create Reserve.

Put yourself in the shoes of a Control Room engineer as the evening peak approaches: if you are looking at the resource available and think you might come up short clearly the best thing is to start up the slower plants in preparation for this. But if no incident does happen or the demand turns out lower than expected then the faster assets will likely not have been used during this period, whereas the CCGTs will have been renumerated heavily from the start-up instruction. 

This is what happened on Wednesday where each of four CCGTs were ramped up from zero to their Stable Export Limit (SEL), ie the least amount NGESO could procure, to create as much Headroom as possible. However, it turned out the system was long over the peak and with no incidents occurring meant many OCGTs received no instructions, despite offering much cheaper volume into the BM (it should be noted that size also plays a role here, the CCGTs offer much more volume). 

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 Accepted Offers from CCGTs to SEL 1700-1730 13/01/21. Source LCP Enact.

Offers from OCGTs not accepted 1700-1730 13/01/21. Source LCP Enact.

Offers from OCGTs not accepted 1700-1730 13/01/21. Source LCP Enact.

The problem is because in a utilisation payment only market the insurance value provided by fast responding assets isn’t valued if they aren’t called into action. NGESO are taking the right steps to remedy the issue by reintroducing STOR at Day Ahead (which allows providers to value in tight system conditions to their tenders) and investigating new methodologies like the BM Reserve Trial; both of which feature availability payments for those best able to provide Reserve. These are part of a wider package of Reserve reform which is fundamentally about the strategic shift to managing the system with predominantly duration limited resource, like batteries, instead of traditional notions of Headroom (and Footroom).

But this still doesn’t answer why the CCGTs were able to command such high prices. The answer is of course scarcity, cold days with little wind can create tight system conditions and we saw this reflected in the Day Ahead wholesale price at 1700-1800 (£1500/MWh on the N2EX). These CCGTs made the calculation they could earn more by not self-dispatching against the peak hour-long wholesale price and instead Grid would need to bring them online and pay high prices for an entire six hour run. It’s a gamble which doesn’t always pay off but did here, with a single unit at West Burton earning £3.78m from the BM that day compared to £0.53m if it had made the same run against wholesale.

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West Burton Unit 3 profits in the BM compared to wholesale. Source BM Reports.

The wholesale market and Balancing Mechanism are linked of course by the imbalance price and it is the SIP calculation formula which drives these trading decisions between the wholesale market and the BM. The SIP didn’t clear at high levels on Wednesday afternoon because ultimately there wasn’t a shortage of energy so the large costs of balancing on this day, shown below, are put down as Reserve costs and so instead pass through via BSUOS. Interestingly SIP did reach £990 earlier at 1pm that day when similar actions were being taken but the system was short (also on the 8th Jan when it cleared at £400/MWh). Whether this is the SIP calculation working well, or a sign it is broken, however is for another post.

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Balancing costs on week starting 11th Jan. Source NGESO.

Securing Digital Distributed Energy Infrastructure

Open Energi Engineer

The Internet of Things (IoT), a term used to denote the digital infrastructure where any digitalised asset, no matter how small, can connect to an invisible mesh of other assets through the internet, has recently become synonymous with security breaches and exploitation. This is true not just in a domestic setting, where flaws in Samsung’s ‘Smart Home’ let hackers unlock doors and set off fire alarms[1], but also in industrial IoT systems, where hackers were able to change the levels of chemicals being used to treat tap water[2]. While security breaches in websites are common, with credit card details frequently stolen, breaches to industrial systems connected to the internet are fewer and more recent, as such systems were previously isolated from public networks.

In the world of Demand Side Response (DSR), security is one of the priorities of most asset owners. DSR assets have a core purpose other than helping to balance the energy system, so a DSR provider must be able to demonstrate that they will never prevent the safe and correct operation of the asset, be that treating wastewater in a sewage treatment facility or refrigerating food in a supermarket. This condition must hold even in the presence of bugs in the DSR provider’s software, and even if their own systems are penetrated by hackers. The usual practices of data encryption, strong access controls and network segregation only provide part of the answer, as they still don’t guarantee safe operation under all possible failure modes.

This condition may seem unreasonable, but in critical systems development it is crucial. A nuclear facility operating normally is run through a software system, but all safety-critical checks are duplicated in hardware interlocks that take over should the software fail in any way[3]. These interlocks are immutable and thus immune to hacking or software bugs. In space missions, NASA has since the Challenger and Columbia incidents started to use consensus of multiple software systems developed by several independent teams to control rocket operation to eliminate the possibility that a single bug could affect the mission[4].

As the DSR industry progresses towards standardisation and common best practice guidelines, a key safety requirement must be safe operation of the asset under any failure mode. Open Energi on-site controllers are always supplemented by independent hardware or software interlocks that cannot be modified by us, creating an orthogonal layer of control required to operate critical assets. For example, on asphalt sites, we supplement our own controls with hardware interlocks to disable our control should the temperature of the tank increase beyond a safe limit. On water sites, we augment our controls with independently developed PLC code that checks that the asset is still within its control parameters and disables our control immediately if not. This dual layer of security means that even if our systems are compromised by an attacker, the DSR assets will continue to operate safely.

Michael Bironneau is Technical Director at Open Energi.

[1]     Wired Magazine. May 2016. ‘Flaws in Samsung Smart home let hackers unlock doors and set off fire alarms’.

[2]     The Register. March 2016. ‘Water treatment plant hacked, chemical mix changed for tap supplies’.

[3]     L.J. Jardine and M.M. Moshkov. Nuclear Materials Safety Management, Vol 2. 1998. See eg. p.151.

[4]     Organizational Learning at NASA: The Challenger and Columbia Incidents. 1989. J. Mahler. See eg. p.63.

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.

VIDEO: Optimising data architectures for IoT & Cloud

Tech image

Rapid data growth from a wide range of new data sources is significantly outpacing organizations’ abilities to manage data with existing systems. Today’s data architectures and IT budgets are straining under the pressure. In response, the center of gravity in the data architecture is shifting from structured transactional systems to cloud based modern data architectures and applications; with Hadoop at it’s core.

Join this live and on-demand video panel – featuring Open Energi’s Head of Technical Development, Michael Bironneau – as they discuss how the landscape is changing and offer insights into how organizations are successfully navigating this shift to capture new business opportunities while driving cost out.

UK demand side flexibility mapped

United Kingdom Map - London's spare GW of power

Open Energi  has mapped the UK’s demand side flexibility to reveal 6GW of peak-shifting potential, and 750MW of dynamic flexibility available for real-time grid balancing.

Demand-side response is at its core an optimisation of electricity usage in order to increase the stability of an energy network. The additional flexibility provided by adequate adjustments of energy consumption has major advantages within the context of an energy infrastructure designed to meet occasional peak demands. It represents an already-existing, cheap, sustainable and efficient alternative to building additional generation capacity that is used infrequently.

Flexibility can be defined in different ways, and several of these definitions can also overlap. First we will investigate the peak-shifting flexibility, which we define as the potential for shifting electricity usage for one hour outside of the peak demand of a given winter day. Currently, this is typically a time period where extra generation capacity is needed to ensure Grid stability.

The estimation of the potential peak-shifting flexibility for the GB Grid was obtained by cross-referencing publicly available annual energy consumption datasets with flexibility profiles for domestic and non-domestic users. Open Energi successively manages assets for DSR in the I&C sector, and has developed a large insight knowledge of the associated loads’ flexibility. The installation costs in this sector are around £50,000/MW, which makes it a target of choice for an immediately available and cheap source of flexibility.

While tapping into domestic flexibility might reveal to be slightly more difficult and expensive than for large energy users, we accounted for this sector in order to give a complete sense of the potential size of the flexibility in the country[1].

The outcome of this analysis reveals that the GB Grid has a peak-shifting potential flexibility of 6 GW, split almost evenly between domestic (3.2 GW) and non-domestic users (2.8 GW). The flexibility results, normalised per area unit in order to identify geographical zones with high flexibility potential, were mapped at a Local Authority level. Unsurprisingly, peak-shifting flexibility correlates with areas of significant electricity usage, namely big cities such as London and areas where energy-intensive industries are present.

This highlights the fact that the development of demand response, along with the improvement of the global energy efficiency in large cities, is a key factor in improving the resilience of the local utility system to cope with peak demand. The ability to shift demand temporally also presents the advantage of being much easier and cost-effective for implementation in urban areas compared to additional generation techniques, such as embedded generation and fuel substitution.

There is a second form of flexibility that can be used to ensure the reliability of an energy network that we will refer to as dynamic flexibility. It consists in a real-time adjustment of power consumption in response to frequency deviation. This frequency regulation activity is a long-lasting opportunity to ensure Grid stability and reliability, and represents a needed enabler to the smooth integration of growing renewables generation sources such as wind and solar.

Our analysis shows that around 750 MW of dynamic flexibility in the non-domestic sector can be unlocked to participate in dynamic frequency regulation activities. This flexibility arises from assets whose power consumption can be shifted, without any consequence for the end user, in order to help balance the Grid at a dynamic scale.

It is important to note that dynamic and peak-shifting flexibilities are not mutually exclusive: an eligible asset fitted with the appropriate equipment can shift its power consumption for either usage. In the following we assume that on a given winter weekday peak-shifting flexibility is used for displacing demand away from the two hours peak (typically 17h.00 to 19.00) into the two subsequent hours, while dynamic flexibility is used during the 20 other hours. We calculated that on a given winter day the potential CO2 savings represents 1560t CO2e per day for peak-shifting flexibility and 3900t CO2e per day for dynamic flexibility.

If we extrapolate the potential CO2 savings of the 750 MW dynamic flexibility operating annually 24h per day this increases to 4860t CO2e per day, and we obtain a figure of around 1.7 million tonnes of C02e saved per year.

Unlocking flexibility means we can build fewer peaking plants, integrate more renewable generation and mitigate the effects of intermittency. It therefore offers major advantages in terms of cost and network reliability and sustainability. Open Energi‘s technology is able to access this flexibility by dynamically and invisibly shifting energy consumption patterns.

[1] In order to extrapolate the total latent flexibility in the GB Grid, we assumed electricity users that have similar annual energy consumption have comparable flexibility; and contribution to peak demand is correlated to the annual consumption of electricity.

Open Energi Harnessing the power of IoT for cleaner, more efficient and affordable energy

David Hill, Business Development Director at Open Energi speaks to theCUBE about how Open Energi is harnessing the benefits of connectivity to bring customers more efficient, more affordable and, ultimately, cleaner energy.

“We were an IoT company before we even knew what IoT was,” said David, discussing how Open Energi was founded pre-Hadoop. Becoming Hadoop customers was a “huge leap,” and Hortonworks Dataflow services are enabling much more cost-effective integration that has Open Energi extremely excited about the future.

David spoke to theCUBE whilst attending Hadoop Summit 2016, Dublin –

Powering a Virtual Power Station With Big Data

Michael Bironneau, Data Scientist at Open Energi, discusses powering a virtual power station with big data.

At Open Energi in order to prove that we’ve delivered our Dynamic Demand service to National Grid and kept it running at optimum, we need to analyse large amounts of data relatively quickly. We’re also making our service smarter so that more assets will be able to participate in Dynamic Demand than before. This is where Big Data and Hortonworks Data Platform come in.

Big Data is a phrase that has been floating around companies like Google for the last two decades. It has never really had a precise definition, but when used casually it usually means that someone somewhere is running out of space for your data and/or computing power to analyse it, this can also mean that your data is so unorganised it is difficult if not impossible to analyse. Data is the most important asset when considering Dynamic Demand, it tells us when to flex certain assets, it proves we are providing a service and it allows us to better understand our portfolio.

Michael was speaking at the 2016 Hadoop Summit, Dublin –

London’s spare gigawatt of power

London spare gigawatt of power

Lucy Symons, Policy Manager at Open Energi, explains how flexible demand could help power a sustainable future for London.

Projected population explosions in cities across the globe present urban planners with huge challenges. Between now and 2050, the number of Londoners alone is expected to increase from 8.6 million to 11.3 million, putting enormous pressure on energy infrastructure and requiring radical new solutions.

To meet the energy needs of 11.3 million Londoners in 2050, the Mayor is planning for a slew of new power plants as part of the enormous £1.3 trillion infrastructure spend earmarked in the London Infrastructure Plan. But there are alternative approaches to our current supply-side model that could deliver better value; we need to be original and also look at the demand-side opportunity.

Indeed, by taking a smarter, no-build approach to managing energy demand, London could shave off an eighth of the power currently used to keep the city’s lights on.

New modelling by Open Energi demonstrates that London has a whole gigawatt of ‘spare’ capacity in its current demand for energy: in-built flexibility that can be cheaply unlocked without the need to lay a single brick.

The challenge of matching supply with demand

London, like all mega cities, is still mostly fossil fuelled and this needs to change, fast. However, the rapid growth of renewable energy generation presents its own challenges, with spikes in electricity production that are often out of sync with times of high energy demand in homes and businesses; on a given day in winter, London’s energy demand peaks at 8GW between 4 and 7pm.

By contrast, at the height of summer, solar power supply follows the natural pattern of insolation- peaking at noon and in sharp decline by the late afternoon. Whatever the season, intermittency will be a persistent problem for balancing the London grid.

At present the generation infrastructure serving London is built to meet maximum possible demand. But with demand exceeding 7 gigawatts only 21% of the time, this is a hugely inefficient use of resources.

As London’s population grows, predicting electricity demand will be increasingly difficult. The GLA has forecast four scenarios, with demand in 2050 deviating from the 2015 baseline by as much as 30%. And this presents a major planning challenge.

Energy production local to demand

One approach is to throw more capacity at the problem, building London’s energy infrastructure for a theoretical peak that could be as much as 60% too high by 2050. Indeed, the Greater London Authority is already planning for local generation to meet 25% of London’s needs by 2025. Estimated total capital costs for this range from £50 billion to £100 billion.

While local generation undoubtedly has an important role to play, building 119MW of co-generation units requires space, which is already at a premium in London, and continues our reliance on carbon-emitting gas in a city struggling with air pollution.

And the challenge of building out clean supply-side alternatives is clear when looking at GLA projections for wind power for 2050, which depend on technological developments that will allow for small, decentralised turbines to be running right across the capital.

Flexibility local to demand

It’s a well reported fact that electricity margins are tighter than they have been for years and, as populations continue to grow, the need to balance energy supply and demand in order to mitigate the risk of power blackouts will be more important than ever. Grid agility and flexibility has traditionally been provided by building new supply assets, but a smarter approach can be found on the demand-side.

Demand response technology is, at its core, an intelligent approach to energy that enables aggregators to harness flexibility in our demand for energy to build a smart, affordable and secure new energy economy. True DSR technology invisibly increases, decreases or shifts users’ electricity consumption, enabling businesses and consumers to save on total energy costs and reduce their carbon footprints while at the same time enabling National Grid to keep capacity margins in check.

Using over 5 years of data from working with businesses and National Grid to deliver demand response from all kinds of equipment –  including heating and ventilation systems, fridges and water pumps – right across the UK, Open Energi has modelled London’s industrial and commercial energy use to reveal an estimated 1040 MW of flexible demand that could be invisibly shifted to provide capacity when it is most needed.

This gigawatt of flexibility is electricity currently being put to use in powering London’s homes and workplaces between 4 and 7pm – with over half used in retail, commerce and light industry.

Harnessing this flexible power – a sizable slice of London’s 8GW winter peak demand – is not a technology problem. Right now, Open Energi’s Dynamic Demand technology is connected to 3000+ machines, invisibly and automatically reducing, increasing or delaying power demand, depending on available supply. Given that the bulk of London’s flexibility comes from non-domestic sites (large commercial buildings, retail and industry), using Dynamic Demand to unlock this 654 MW of flexibility could be the cleanest and most cost effective way to provide the power for London to operate, businesses to grow and its inhabitants to lead healthy lives.

As a direct alternative to building new power plants, Demand side response is an efficient way to optimise the existing generation infrastructure- shifting 1GW out of the peak would save the need to build a new mega power plant, equivalent to the size of Barking Power station.

From where we stand, powering London is a data-driven problem. The answer lies in decrypting patterns of flexible demand.

Analysis conducted by Remi Boulineau,