Power Responsive success stories: South Mimms battery storage and EV charging

At South Mimms Motorway Services, Open Energi own and operate a 250kW/500kWh Powerpack alongside one of Tesla’s largest and busiest UK charging locations. The project, which is one of the first of its kind globally, was selected as a demand side flexibility success story and showcased by National Grid at their 2018 Power Responsive summer reception.

The Supercharger site can charge up to 12 cars at one time, and since popular charging periods often coincide with peak periods of grid demand – between 4pm and 7pm, when electricity prices are at their highest – flexible solutions are needed to ease the strain on local grids and control electricity costs.

Integrating a Powerpack at the location has meant that during peak periods, vehicles can charge from Powerpack instead of drawing power from the grid. Throughout the remainder of the day, the Powerpack system charges from and discharges to the grid, providing a Firm Frequency Response (FFR) service to National Grid and earning revenue for balancing grid electricity supply and demand on a second-by-second basis.

Combining batteries and electric vehicles makes vehicle charging part of the solution to integrating more renewables without affecting drivers, unlocking vital flexibility to help build a smarter, more sustainable system.

Robyn Lucas, Head of Data Science at Open Energi explained “[the battery] provides a source of flexibility to what is otherwise a very inflexible demand. We do frequency response for most of the time, and over the peak period we use the battery to charge the car up, rather than them charging from the grid.

“Open Energi hope to repeat this blueprint with multiple other stationary storage assets next to EV charging stations. Having stationary storage assets used in this way allows both transport and electricity networks to be decarbonised and allows for greater renewable penetration.”

Power Responsive success stories: Aggregate Industries

National Grid’s Summer Reception 2018 profiled Aggregate Industries’ pioneering partnership with Open Energi as an example of real life achievements to unlock demand side flexibility and the innovation and collaboration within the industry.

Aggregate Industries is the first business to deploy Open Energi’s artificial intelligence-powered flexibility platform, Dynamic Demand 2.0, to deliver electricity cost savings of 10%.

40 bitumen tanks at ten Aggregate Industries’ sites UK-wide have already been connected to the platform, which uses artificial intelligence to automatically optimise their daily electricity use in response to a variety of signals, including wholesale electricity prices, peak price charges, fluctuations in grid frequency, and system imbalance prices.

Aggregate Industries is accessing the imbalance market via Renewable Balancing Reserve (RBR), a product offered by its renewable electricity supplier, Ørsted. RBR enables Aggregate Industries to tap into the financial benefits of participating in the imbalance market, by reducing its demand at certain times.

Over time Aggregate Industries plans to expand its use of Dynamic Demand 2.0 to 48 asphalt plants UK-wide – representing up to 4.5MW of demand flexibility. It is also exploring its wider portfolio of assets and processes to identify where further benefits may lie.

Talking to National Grid, Richard Eaton, Energy Manager at Aggregate Industries explained: “What we’re doing now is rolling out Open Energi’s Dynamic Demand 2.0 platform, where what we do is we flex our assets, not only to calls from National Grid, but also now to calls from Ørsted under their Renewable Balancing Reserve.

“The artificial intelligence within Dynamic Demand 2.0 is helping us to optimise our bitumen tanks leading to a predicted 10-15% reduction in the operating costs of those assets.”

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.

Why precision matters for Triad prediction

United Utilities Davyhulme

National Grid recently confirmed the Triad dates for Winter 2017-18. As most businesses know all too well, consuming electricity during a Triad is extremely costly, so accurately predicting and avoiding these three half-hour peak periods is vital. Open Energi’s machine learning approach correctly predicted the specific half-hour Settlement Period (SP) for all three Triad days in 2017-18. Wouter Kimman, Data Scientist at Open Energi, looks back at what happened this year, the growing need for precision forecasting, and what the future may hold.

Triad season 2017-18

The demand patterns and Triad results seen in 2017-18 demonstrate the strength of the Triad incentive. But, as more businesses shift their demand making the ‘peak’ flatter, identifying the right SP is becoming more of a challenge.

The 2017-18 Triads all occurred on a Monday, but with a different SP for each, spanning 35 (5-5.30pm), 36 (5.30-6pm) and 37 (6-6.30pm) by order of date; see Figure 1. The 26 February was the latest Triad to date and only the second time it has fallen in SP 37. Given that historically, 85% of Triads have fallen within SP 35, this represents quite a shift.

 

Triads 2017-18
Figure 1: Triads for the winter of 2017-2018 as published by National Grid

 

The trend for falling overall electricity demand continued, with all three Triad days below 50GW. Comparing the daily peak figures to previous winters (Figure 2) we see the spread between the extremes (lowest and highest daily peak) was also reduced, with peak demand more concentrated around the average 45 GW.

 

Triads 2017-18 demand
Figure 2: Peak demand figures comparing all winters since 2011. Each dot represents a day, and box plots represent the minimum, first quartile, median, third quartile, and maximum of their distribution in demand.

However, quite possibly due to Triad avoidance techniques, the timings of daily peaks were spread out across the evening period, including many more during SP 34. Comparing days with peak demand greater than 46 GW in the last 2 winters to previous years in Figure 3, this past winter had a profoundly different pattern; even compared to last winter, shown on the left.

 

Shift in Settlement Periods
Figure 3: The shift in the SP with the highest national demand from previous winters to this winter. A much broader spread is seen this year, evidence of wide spread Triad avoidance strategies?

Another clear example of the impact of Triad avoidance was provided by the Beast from the East, which gave rise to an exceptional late and severe cold spell. This year’s largest demand peak actually fell on 1 March, just outside the Triad season. The demand profile for that day (Figure 4) shows a very distinct peak, much more characteristic of days of low demand, outside the winter. In contrast, days with high demand within the Triad season had a flatter peak, as seen in Figure 4. As more behind-the-meter flexibility comes on-line the impact of Triad avoidance will continue to be seen on the national daily demand profile, making predictions ever more difficult.

 

Daily national demand profile
Figure 4: Daily national demand profile of various days throughout the 2017-2018 winter, and the 1st March. A very different profile is seen outside of Triad season.

 

Triad Predictions

These shifts in the national daily demand show how successful businesses have been at avoiding Triad periods, aided by increasingly sophisticated strategies. Triad management can now be automated and optimised according to a site’s specific energy profile (and the company’s risk appetite). Using Open Energi’s Dynamic Demand 2.0, our cutting edge machine learning approach enables companies to precisely target a specific SP, minimising calls, disruption, and manual intervention.

As many of our customer’s operations are not able to switch off for long periods of time without disruption (e.g. bitumen tanks that need occasional heating to stay within temperature setpoints, or sewage treatment plants that have a continuous usage pattern) there is considerable value in precisely identifying the 30-minute window in which they should reduce demand. Knowing exactly when a Triad might start allows us to manage equipment to avoid impacting operational performance; Triads can be successfully avoided without allowing processes to violate their permitted control parameter ranges.

For batteries of limited storage duration, this can be even more significant as they can export to the grid during peak-prices. Depending on their capacity, it can be vital to issue a dispatch at the right time, ensuring the system has sufficient state of charge to realise maximum revenue.

To minimise the number of unnecessary calls, Open Energi updates the prediction during the day, given the latest information. An initial prediction gives a good indication of the likelihood of a Triad occuring, and over the season gives rise to around 20 warnings. During the day we then update our prediction, exploiting available real-time data. This allows us to cut the total number of Triad calls in half, while accounting for the uncertainties involved. By using the latest machine learning techniques, and real-time automated dispatch, over the course of a year the total number of Triad calls can be thus be reduced; disruption is reduced while value is maintained.

 

Figure 5: Open Energi’s Triad Settlement Period prediction on the three Triad days
Figure 5: Open Energi’s Triad Settlement Period prediction on the three Triad days


O
utlook for Triads

The electricity system must be able to meet peak demand; enough investment in the network must be provided in order that it can deliver the peak amount of electricity to homes and businesses. Triads are the current way that the transmission network is paid for. At present, there is a significant advantage to businesses in reducing their demand at peak, and as a result, the system peak has reduced. In theory at least (while the price signal exists), less copper is required.

While businesses are focused on avoiding Triad costs, Ofgem is increasingly concerned that their success is creating an unfair charging system, where those least able to afford the cost of the network (i.e. less well-off domestic users) end up paying more than their fair share. Consequently, Ofgem is in the middle of a network charging review expected to result in changes to the Triad system from April 2020. Its intention is to create a fairer charging structure where large, non-domestic users cannot avoid paying their fair share of network costs.

In the meantime, Winter 2018-19 will see two charging reforms coming into effect; updates to Distribution use of System (DUoS) charging and the start of embedded benefit reform. DUoS charges are being ‘flattened out’ across the day, while Triad payments for exporting from distributed generation will reduce gradually from £47/kW to £3.22/kW over the next three years.

As the policy landscape continues to shift – and new markets emerge – we expect the task of managing behind-the-meter flexibility to deliver value becoming an increasingly intricate exercise. The ability to manage demand and generation assets in real-time, according to different site characteristics and constraints, will be crucial to choosing the right course of action to maximise client value.

By Wouter Kimman, Data Scientist, Open Energi

Using demand flexibility to reduce supplier imbalance risk

Bitumen tanks

At Open Energi, we are teaming up with energy suppliers and their customers to help make the most of the flexibility in their energy consumption. Using smart demand flexibility to sustainably balance the system, we can mitigate the risk of volatile prices and help reduce rising system charges.

The balancing act

Electricity can’t be stored efficiently or cheaply at scale, so electricity suppliers must balance the energy that they produce themselves or procure from third parties with the energy that their customers use. This means, ahead of time, forecasting how much electricity is going to be generated, forecasting customer demand, and taking any actions to balance them out: buying or selling additional electricity as required.

Any imbalance between generation and demand can result in suppliers facing costly charges from National Grid, who are forced to act in real time to balance the system. Some of the balancing actions that National Grid takes to ensure the lights stay on are expensive and polluting, and lead to gross inefficiencies in the system. During periods when the system is short (insufficient generation / high demand) it might call on a thermal power station to increase its output. Similarly, when the system is long (too much generation / low demand), a thermal power station could be asked to decrease output.

For the flexible energy generators of the UK – namely CCGTs – to be able to respond to these calls, they are run at < 100% of their maximum capacity. The inefficiencies here are twofold. The plants are not run optimally – they use more fuel and produce more carbon per MWh of electricity produced – and, more power stations are required to meet the nation’s electricity requirements. Balancing actions, by their nature, are also taken very close to real time, often outside of the market, which pushes prices up.

An alternative to balancing on the generation-side is to do it on the demand-side: instead of increasing or decreasing the output of a power station, decrease or increase the demand of electricity users. By enabling flexibility behind the meter, for example using battery storage alongside inherent process flexibility, demand-side response can provide an efficient and economical (roughly an order of magnitude cheaper than more traditional methods1) way to balance the system.

Rising system prices

National Grid recovers the cost of balancing from suppliers and generators through Balancing Services use of System (BSUoS) charges, which are passed onto the consumer. A large part of these charges are driven by the imbalance, or system price, which quantifies the cost of balancing energy of the system per half hour period by asking power stations to turn up or down. High prices usually occur when system margins are small; when there is a lack of surplus generation that can be called on. Similarly, low, or even negative prices can occur when there is a surplus of generation. This typically happens during periods of low demand, when solar power is at a maximum – for example on a sunny weekend day.

In the last 6 months or so we have seen the highest and most volatile system prices ever. They peaked at over £1500/MWh in November 2016, compared to an average cost of about £40/MWh over the last year. This peak was caused by a combination of factors. Much of the UK’s aged coal fleet was placed in Supplemental Balancing Reserve (SBR) to be called upon only as a last resort. Then, maintenance to the French nuclear fleet (causing the UK to export rather than import power through the French interconnector) coincided with maintenance to some UK gas peaking plants and low wind speeds, creating a situation where the system got very, very short. When one generator pushes prices up, and these high prices get accepted by National Grid, other generators are likely to follow suit to maximize their profits. For suppliers, this means that an imbalance of a few MW over a few half hours at the wrong time can suddenly become very, very expensive.

Figure 1 shows how system prices have risen since January 2016. With BSUoS similarly rising, suppliers can no longer afford to be complacent with their self-balancing.

 

Suppliers must manage their imbalance to mitigate the risk of volatile system prices
Figure 1: System price over the last 15 months, for periods when the system has been short (insufficient generation) and long (insufficient demand). Prices have increased compared to the mean over the period for both cases

Thus, suppliers are increasingly looking to protect themselves against the risk of coming up short. This is particularly true of renewable generators: you can’t make the wind blow harder at the same time as customer demand peaks (whereas you can burn more gas). Rather than buying in more conventional ‘brown’ (rather than ’green’) generation to make up any gaps at the last minute, or paying the imbalance price on any shortfall, an alternative is to use the inherent flexibility in connected customer loads to alter your demand, and better align with the power being generated by the wind. Instead of flexing the generation, flex the demand.

Flexing electricity consumption

Here at Open Energi, we are using our experience with Dynamic Frequency Response to flex the energy usage of large industrial & commercial consumers to balance the books of their renewable supplier. By intelligently talking to equipment which has energy stored in its processes we can shift electricity consumption without affecting the operation of a customer’s site. For example, the stored energy in a bitumen tank means we can delay heating it for an hour with very little impact on its temperature. Given notice by a supplier that they are short in the next hour and so require a reduction in demand, or, they think system prices will be high, we can delay turning on the tank’s heater until after the price spike.

Figure 2 shows a typical bitumen tank. The blue line shows the tank under ‘normal’ operation and the orange line shows the tank under Open Energi control. Following a request from the supplier (given approximately 30 minutes before hand) to reduce demand at 11am, we can delay switching the tank on, without affecting its operational parameters (the temperature always remains within set limits). We then allow the tank to switch on and heat up after the price spike, shifting its power consumption.

Demand flexibility can help suppliers to manage their imbalance risk
Figure 2: Flexing the power consumption of a single bitumen tank, such that it’s temperature always remains within predefined limits

Do this across a portfolio of tanks, and you make a sizeable reduction in the supplier’s demand during periods when they would otherwise be short: see Figure 3. The energy is recovered later, and, given the energy storage in any one asset, this definition of ‘later’ can be flexible.

Open Energi is working with businesses and their suppliers to manage imbalance risk using demand flexibility
Figure 3: Resulting shift in electricity consumption when flex energy across a portfolio of bitumen tanks

Suppliers save money by avoiding costly imbalance prices and mitigate the risk of price volatility, while managing renewable intermittency and reducing the need for brown generation. By partnering with innovative suppliers who create a market for such flexibility in an open and accessible manner, businesses can use technology to deliver smart demand side flexibility, in real time, with no impact on their operations, while saving money on their electricity bills. This kind of smart, digitized demand side flexibility is crucial to building the decentralized, decarbonized energy system of the future.

1Open Energi analysis

Robyn Lucas is a Data Scientist at Open Energi. She works on demand side flexibility in the UK electricity network; modelling, forecasting and optimizing the usage and performance of a variety electrical loads and enabling customers to intelligently control their electricity consumption. Prior to Open Energi she worked for a technology consultancy, helping clients make the best use of their data. Robyn graduated from Imperial College London in 2015 with a PhD in Physics, during which she worked on one of the experiments at the CERN LHC.

 

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.

Ashden Awards 2016: Open Energi Case Study

To keep the lights on, National Grid has to keep electricity demand and supply exactly in balance, and when faults occur a rapid response is needed – within two seconds! Traditionally this was provided by gas and coal power plants running below full power, so they can adjust output quickly, but this is inefficient, expensive and increases CO2 emissions. Open Energi has developed an alternative – cutting-edge software which can automatically switch energy-hungry equipment on or off when required, without disrupting business operations.

Large energy users like water companies identify which items of equipment are not time-sensitive in their operation and this equipment can then increase or decrease its consumption within agreed parameters to provide a rapid response service to National Grid.

Ever ready: will batteries power up in 2016?

Open Energi Banner ADE

David Hill, Business Development Director, Open Energi

Open Energi tends to extol the virtues of Demand Side Response as a solution to the energy storage challenge.  It provides a no-build, sharing economy approach which is cheap, sustainable, scalable and secure.

By harnessing flexible demand and tapping into the thermal inertia of bitumen tanks or the pumped energy stored in a reservoir for example, we have created a distributed storage network able to provide flexible capacity to the grid in real-time without any impact on our customers.

But flexibility comes in many forms, and as the cost of energy storage systems tumble, it looks like 2016 might be the year when commercial batteries become a viable part of the UK’s electricity infrastructure, with recent analysis suggesting they could deliver 1.6GW of capacity by 2020, up from just 24MW today.

The price of energy storage systems is expected to fall sharply over the next three decades, with Bloomberg New Energy Finance predicting the average cost of residential energy storage systems will fall from $1,600 per KWh in 2015 to below $1,000 per KWh in 2020, and $260 per KWh in 2040.

As costs have fallen we have seen increasing interest from industrial and commercial customers keen to explore the benefits of installing batteries on-site and looking at systems capable of meeting 50%-100% of their peak demand – depending on their connection agreement (although it is worth noting an export licence is not a prerequisite).

In addition to providing security in the event of power outages, battery systems can help companies to reduce their demand during peak price periods, enabling them to seamlessly slash the astronomical costs – and forecasting difficulties – associated with Triads, and minimise their DUoS Red Band charges.

When they aren’t supporting peak price avoidance – which may be only 10% of the time – batteries can help to balance the grid – earning revenue for participating in National Grid’s frequency response markets. For example, discharging power to the system if the frequency drops below 50 Hertz and charging when the frequency rises above 50 Hertz.

National Grid’s new Enhanced Frequency Response market has been developed with battery systems in mind – requiring full response within 1 second – but isn’t expected to be up and running for a year or more.

In the meantime battery systems can generate significant revenues today via National Grid’s Dynamic Firm Frequency Response market, tendering alongside loads from companies like Sainsbury’s, United Utilities and Aggregate Industries, to help balance the grid, 24/7, 365 days a year.  And in the longer term the opportunity exists for companies to trade their batteries’ capacity in wholesale electricity markets.

With these saving and revenue opportunities in mind, we’re now at a point where battery systems can be installed behind-the-meter and deliver a ROI within 3-5 years for industrial and commercial sites. The ROI will be subject to certain factors, such as geographic location, connection size and of course the cost of the battery system itself, but these figures would have been unthinkable only a few years ago.

There are important technical factors to consider, including both the battery sizing in terms of its kW power rating and kWhr energy storage capacity, and also the underlying battery chemistry.  By taking into account the physical location of the battery along with models of different markets that it will operate in, it is possible to narrow down to the most appropriate technical parameters.  Another consideration is the gradual effect of wear and tear on the battery with continuous usage.  By analysing these effects it is possible to reduce some of the uncertainty around battery lifecycles (likely to be in the region of 10 years) and get better predictions of the likely revenue in each year of operation.

But whilst a payback of 5 years seems reasonable from an energy infrastructure perspective (where 15-20 years is more typical) for most companies used to a ROI within 2-3 years on energy projects it is not easy financing battery systems.

Some larger, capital rich companies may have the appetite and money to finance these projects themselves, but the majority of the companies we are talking to are keen to take these assets off balance sheet and finance installations via banks and other investors under third party ownership.

In these circumstances, managing the performance of battery systems – so that they meet their warranty and their lifecycle is maximised – whilst optimising their potential as a flexible resource able to cut energy costs, earn revenue and deliver a vital uninterruptible power supply  during outages will be key to their commercial success and scale of deployment.

Demand Response: how much is your flexible demand worth?

David Hill Headshot

Our energy system is changing and this is creating new opportunities for businesses to turn their energy use into an asset. By harnessing flexibility  in your electricity consumption, demand response can provide a new source of income and help build a cleaner, more secure and affordable energy system. Open Energi’s Business Development Director, David Hill explores how energy and technology are converging and putting you in charge of transforming our energy future.

Is demand response the Airbnb of energy?

In recent years we’ve learned to find spare capacity in all kinds of places, not least our homes. Imagine if we could do the same with our energy use? David Hill explains why Demand Response could be the Airbnb of the energy market.

The Internet of Things is enabling us to exploit tiny kilowatts of flexible capacity from everyday equipment to build a virtual power station and sell the aggregated energy back to National Grid.