A new frontier in Dynamic Frequency – does it stack up?

National Grid ESO’s new dynamic frequency regulation services suite needs fast-responding assets, such as batteries, to enhance grid frequency stability. Operators will be able to pick and choose the services they provide. However, managing the state of charge ahead of time and keeping systems within warranty constraints by stacking these new products to achieve desirable utilisation and revenues adds complexity.

As the National Grid ESO procures Dynamic Regulation with a minimum unit size of 1MW, it could mean significant underutilisation of most existing systems below a 2-hour duration. So what does this mean for the state of charge (SoC), and how we can create the optimum ‘stack’?

In this blog, we explore a theoretical battery’s response to the new Dynamic Response (DR) and Dynamic Moderation (DM) services, modelling the expected impacts to the utilisation, cycling, and State of Charge (SoC) management.

An Increasing Appetite for Energy Storage

The UK’s electricity system is increasingly experiencing lower inertia, and there is a growing risk of more considerable and frequent generation losses. Last year the publication of the Future Energy Scenarios (FES) from National Grid ESO (NGESO) and the Government’s Smart Systems and Flexibility Plan 2021 outlined the need and role for flexibility in a net-zero UK energy system. As variable renewable power replaces conventional generation sources, flexibility not only unlocks increasing potential for renewables and ensures energy security but could also reduce system costs by between £30-70bn by 2050. At least 15GW of shorter duration assets (less than 4 hours) will be needed by 2050 to help balance the system. According to the FES, by 2030, 18GW of shorter duration assets could be built in the fastest credible decarbonisation scenario.  

New Frequency Services for a More Resilient, Lower Carbon System

In recent years we have seen a growing number of low-frequency events, where grid frequency has deviated by more than 0.3Hz (Figure 1). For example, on the 9th August 2019, we saw a blackout; 3% of system demand disconnected, and in London, 60% of the Tube network ground to a halt.

Figure 1. Number of lower than 49.7 Grid frequency events since 2014.

There are different strategies to combat dropping inertia:

  • procuring it directly from a conventional plant or synchronous generators;
  • reducing the size of the most significant single loss on the system (which reduces the risk of instability if that load is lost) or;
  • the procurement of faster frequency response.

Batteries enable the procurement of faster frequency response; they can react to significant frequency disturbances and restore stability in under a second by delivering ‘synthetic inertia’ to the system.

By April 2022, NGESO will have launched the full suite of new faster-acting frequency response services, eventually replacing all legacy frequency products, including Firm Frequency Response (FFR). 

Introduced in October 2020, Dynamic Containment (DC) was the first of these new services, and it now has over 850MW of participating volume. DC operates post-fault, providing a less than 1-second response to frequency deviations of greater than 0.2Hz. DC helps the system recover from sudden significant losses of generation or load.

In March and April 2022, we expect the soft launch of Dynamic Moderation (DM) and Dynamic Regulation (DR). Both DM and DR are pre-fault services. DR acts with a constant and proportional power response across the ‘operational’ frequency range and reaches full power at a +/- 0.2Hz frequency deviation. DM kicks in between +/- 0.1Hz-0.2Hz, giving an additional boost to stabilise frequency as it moves towards the edge of operational limits. Figure 2 compares the droop curves (the power response according to grid frequency) for all three services with the existing FFR product.

Graph showing Droop curves for Dynamic services (left) and FFR (right), showing the power response as % of nominal asset power across the frequency range in Hz.
Figure 2. Droop curves for Dynamic services (left) and FFR (right), showing the power response as % of nominal asset power across the frequency range in Hz.

The New Dynamic Services

Figure 2 shows that DR requires the highest comparative response due to its steep droop curve. We model the impact of performing this service for a 10MW system with infinite storage (i.e. with no energy limit to charging or discharging) using historic frequency data, accounting for efficiency losses. 

DR has twice the utilisation of FFR, at 10% (Table 1), and subsequently double the throughput (cumulative discharge energy). DM and DC have much lower utilisation. A 1-hour system would perform 0.5 and 0.12 cycles per day, respectively, compared to 2.4 for DR.

Table 1: Average Utilisation (%) and average throughput (MWh/MW) for FFR and new dynamic services. Calculated from historic Grid frequency.

Figure 3 shows the expected power response to a large low frequency event (0.3Hz deviation). DR and DM act to restore grid frequency faster than FFR. While DR and DM services reach their maximum response power levels at or below 49.8 Hz, the DC response stays below 50% of its contracted level.

Figure 2. Droop curves for Dynamic services (left) and FFR (right), showing the power response as % of nominal asset power across the frequency range in Hz.
Figure 3. Response to >0.3Hz low grid frequency deviation for all Dynamic Services and FFR. Here, negative power indicates discharging of a battery.

DC acts in reserve should an additional loss push frequency further away from 50Hz. Theoretically, grid frequency should become more robust to system trips even as inertia continues to fall with these three services acting in concert – vital for the UK’s security of supply. Depending on how much volume NGESO procure, we may therefore anticipate grid frequency to depart from what we see in historic frequency data. The utilisation and throughput of each service may then look different, but using historic frequency still gives us a reasonable idea of what to expect.

The Impact on State of Charge

With the higher utilisation of DR, SoC management becomes increasingly crucial for batteries, ensuring a ‘real’ battery of finite capacity can deliver the service and consider the service’s cost.

In Figure 4, the first plot shows how much the SoC would drift in each of 20 days for the same 10MW system. The bottom histogram shows the SoC drift over EFA blocks 1, 2 or 3 for a more considerable period of historical data. With no actions taken to rebalance the stored energy of a system providing 10MW of DR for 8 hours—or 2 EFA blocks. SoC would have drifted by more than +/-5MWh, 35% of the time.

This scenario suggests that a 1-hour battery would fully charge or discharge to empty after just 2 EFA blocks if it begins delivering the service at 50% SoC, ignoring availability requirements.

Figure 4. State of charge drift of a battery providing Dynamic Regulation only.

Participants must provide the service continuously for the periods they have tendered for or risk penalties. Furthermore, NGESO has stipulated a 60-minute minimum energy requirement for energy-limited assets. That means even a 2-hour battery would need a rebalancing action within 8 hours (2 EFA blocks) 34% of the time, and within 12 hours (3 EFA blocks), 42% of the time. Alternatively, providers could de-rate the tendered volume (i.e. MW) offered, which would also be favourable to reduce cycling and preserve the system warranty.

Managing State of Charge

Baselining actions must be taken to manage SoC while a battery performs DR. We simulate a 10MW/10MWh battery, de-rated to 4MW, delivering DR over three days continuously. The SoC is managed with spare MW using an automated control algorithm that attempts to maintain a 4MWh of headroom and footroom, equivalent to a 60-minute energy requirement.

Following BM requirements, units performing DC baselining actions are planned 1-hour in advance. Under these conditions, the battery requires, on average, three rebalancing actions per day, with 3.8MWh of energy needed for baselining. Figure 5 shows simulated battery power and energy while responding to historical frequency deviations.

Figure 5. Simulated State of Charge with a 10MW, 1-hour battery responding to Grid frequency with a Dynamic Regulation response curve and a de-rated 4MW DR tender. Automated baselining is applied to maintain system energy around 50%.

Given the ahead-of-time baselining requirement, the rebalancing algorithm fails and leads to 5 hours per day outside the 60-minute minimum energy requirement for a 4MW tender on a 10MWh asset. So a 1C system de-rated to 40% of its nameplate capacity–in effect becomes a 0.4C (or 2.5h) system that could still cycle over 400 times per year and would still not meet a 60-minute minimum availability requirement.

Stacking Dynamic Services

National Grid ESO has stated that the three services can be stacked together (albeit not in the initial soft-launch phase). A single battery can deliver different services simultaneously but with each MW partitioned to provide a single service.

Since DR has high utilisation, we limit the DR tender to much less than the battery’s power rating. This prevents exceeding warranty limits on cycling and mitigates against the cost of a substantial SoC rebalancing regime, which may require peak-time charging to maintain availability. 

To monetise the remaining MWs, we stack lower utilisation services on top. Figure 6 below shows a low grid frequency event under two scenarios: the first with a complete 10MW DR response; the second with a stacked tender of 5MW DR and 5MW DC. In the latter case, during a frequency event below 49.8Hz, utilisation averages 60% of the total tendered volume during the period, rather than 100% for the DR response alone.

 Graph showing the resulting power response of Regulation service vs stacked Regulation and Containment services during a low-frequency event. NB negative power indicates discharging
Figure 6. Resulting power response of Regulation service vs stacked Regulation and Containment services during a low frequency event. N.B. negative power indicates discharging.

Towards an Optimal Stack

We can assess the ‘best’ stack for battery size by calculating utilisation and cycling for a range of tender stacking combinations. For example, figure 7 shows the utilisation and mean daily cycles when we combine DC and DR. The x-axis shows the share of DC, such that 10 represents 10MW of Containment and 0MW Regulation. 

If we must also operate below a “Warranty Cycles” level set to 400 cycles per year, a 6MW/4MW split is best for a 1-hour system (LHS of 7). If we must not exceed 5% utilisation, we also require 6MW/4MW. However, note that since a 1-hour system will likely not provide more than 5MW DR because of the energy requirement limits, the trend below 5 is purely theoretical.

The right plot shows how a 2-hour battery (10MW/20MWh) could significantly loosen constraints on delivering the Regulation service – reaching 8MW of Regulation to 2MW of Containment before reaching the Cycles limit. But, of course, the optimal split should also consider revenue potential. The revenue potential depends on the available auction prices, which are also influenced by the National Grid ESO requirements and, otherwise, the technical barriers to participation.

Figure 7. Utilisation and number of daily cycles for a 10MW battery when different ratios of stacked dynamic services are chosen.

A Real-World Stacking Strategy

We simulate a potential ‘real-world’ scenario, stacking DR and DC. We choose a 2-hour battery with an 8-2 tender split between DR and DC to meet the cycling constraint of approximately 1.1 cycles per day (400/year). However, reserving capacity as headroom for managing energy, we need to de-rate the tender to 7MW-2MW, preferring the lower-utilisation service. Services are presumed to be symmetrical (both high and low response). Our SoC management aims to keep the system close to 50% and maintain the minimum energy requirement.

The optimum solution compromises between minimising the number of actions taken to balance SoC, which could be expensive, and taking sufficient measures to limit the chances of violating minimum energy constraints and protecting the battery’s life through minimising time spent at extremely high or low energies. Figure 8 shows the modelled response over three days.

With a 2MW/7MW DC/DR split, for a 10MW 2-hour system, we see ca. 7.3% utilisation and 0.83 cycles per day. Balancing SoC requires a mean daily baseline volume of 6.2MWh. Despite the current cost uncertainties associated with providing grid services (due to recent high electricity prices), the simulated case would, if exposed to only cash-out prices, cost more than £3/MW/H.

Whilst achievable compared to current FFR prices, we also have to consider the penalty of higher utilisation and the opportunity cost of going elsewhere for traded revenue.

Figure 8. Modelled Energy management with stacked dynamic services: 2MW Dynamic Containment to 7MW Dynamic Regulation. Mean daily cycles are under the target of 1.1.

In conclusion

Modelling the forecasted utilisation of 1C batteries with historical grid frequency can deliver DC and DM. However, the pre-fault DR service requires a significant MW de-rating or a longer duration battery to provide a sustained service energy requirement. Therefore, longer duration storage and higher cycling capability could offer good market commercialisation opportunities.

We can generate a greater return from batteries while keeping cycling within a ‘standard’ warranty by stacking high and low utilisation services. Modelling the delivery of this type of stacked service with historic grid frequency demonstrates a trade-off between efficient state of charge management and cost minimisation. Imposing operational constraints such as ahead-of-time rebalancing notification for a high utilisation service presents a challenge even for 2-hour systems. Taking baselining actions ahead of time and offering multiple services at once is operationally complex within system warranty constraints.

Furthermore, if National Grid ESO is to procure these services (particularly DR) with a minimum unit size of 1MW, it could mean significant underutilisation of battery capacity at the sub-10MW level. Ultimately, the prices of these services should reflect this complexity and potential inefficiency. Given the difference in utilisation and technical requirements, it will be fascinating to see where price levels transpire between the three services.

Unlocking the investment necessary to meet the ambitious storage goals of the Smart Systems and Flexibility Plan 2021 requires confidence in the revenue streams for these assets. The existing ‘stack’ of revenue streams open to storage assets can be uncertain and high risk, and barriers to entry remain. Economies of scale, volume caps, licencing, and changes to demand charges (namely the Targeted Charging Review) favour large standalone storage assets. More planning relaxation will further incentivise the building of systems greater than 50MWh.

These new markets will undoubtedly form an essential part of the flexibility revenue stack and should provide investors with confidence in storage revenues’ long-term security. Even without operational examples for Dynamic Regulation and Dynamic Moderation, as with Dynamic Containment, battery storage appears favourable to deliver these services at the lowest cost.

However, with wholesale market volatility, trading opportunities elsewhere may yet present a more attractive option for battery operators looking to maximise revenue stacking if the cost-of-service provision, combined with high technical barriers in grid services, outstrip the benefits.

To find out more about our products and services, email us; info@openenergi.com.

Utility Week: Anglian Water partners for solar and energy storage project

Anglian Water has partnered with redT and Open Energi to have energy storage facilities installed alongside solar panels at one of its water treatment works.

The water company has purchased a 60kW/300kWh redT energy storage machine to install alongside a 450kWp solar PV system. This will enable it to store excess solar generated during the day and use it at other times, to reduce the site’s reliance on the grid.

As the largest power consumer in the East of England, reducing reliance on “volatile grid electricity” will help optimise a £77 million energy bill, which is one of the company’s “most significant” operational costs.

Read the full article.

edie: Anglian Water to boost onsite generation with AI-powered energy storage technology

Water utility Anglian Water is set to install an energy storage machine controlled by Artificial Intelligence (AI) technology at one of its water treatment facilities, in a move it claims will increase the site’s solar generation by 80%.

The 60kW/300kWh storage device, designed by energy storage firm redT, will be set up at the company’s ‘pathfinder’ site in Norfolk to bolster the performance of its existing photovoltaic (PV) array from 248kWp to 450kWp.

The machine, which can store enough energy to power the facility for at least five hours, will enable Anglian Water to store surplus power generated by the array for use within its own operations. Meanwhile, it will use AI software to provide real-time balancing and energy flexibility services. The machine is expected to have a lifespan of 25 years.

Called Dynamic Demand 2.0 and designed by Open Energi, the AI software will optimise the site’s energy consumption and stack multiple demand-side value streams, enabling Anglian Water to take advantage of wholesale energy price arbitrage. In total, the installation is expected to halve the site’s electricity bills by 2050.

Read the full article.

Future of Utilities: Smart Energy 2018 – 20/21.11.18

Future of Utilities: Smart Energy is set to bring together 300+ attendees for two days of collaboration discussing energy storage, supply and smart grid developments.

Featuring technology-driven content about how to make energy retail smarter, and systems more flexible, Smart Energy will showcase the experiences of a wider range of energy companies than ever before. 

Open Energi’s Commercial Director David Hill will join a panel session to explore the business case for storage and different approaches from across the value chain.

Date: 20th-21st November 2018

Panel: 14.35, 20th November

Location: The Tower Hotel, Guoman – London

Speaker: David Hill, Commercial Director

Further information is available from the event website.


Our Head of Data Science, Dr Robyn Lucas, is joining a panel on Energy Storage at this year’s conference to share our views on the business case for behind-the-meter battery storage.
Date: 2-3 May
Location: SEC Glasgow
Session: Smarter energy storage at the local level
Topic: Beyond FFR: making the business case for BTM battery storage
Time: 15.00-16.30, 3 May
Further information is available from the event website.

Water Industry Energy Conference 2018

Water industry energy conference

Open Energi is delighted to be exhibiting at Water Industry Energy Conference 2018. Now in its 5th year, the conference will explore how water companies can identify commercial opportunities in energy.

Finding innovative ways to optimise existing assets, better utilise renewable technologies and generate energy from new sources are key to cutting costs and driving efficiency, and therefore essential to a successful business strategy.

This established event will analyse the business benefits of DSR, storage and biofuels, as well as behaviour change strategies and regulatory frameworks. We look forward to seeing you there.

Date: 12th June 2018

Location: Birmingham


Further information is available from the conference website.

How demand flexibility can boost the benefit of a Corporate PPA

solar panels

More and more companies are turning to corporate PPAs as a way to power their business sustainably and manage their long-term energy costs. Using demand flexibility to help align patterns of supply and demand can boost the benefits all round, as Open Energi’s Commercial Analyst, Dago Cedillos, explains.

The rise of corporate PPAs

The increasing cost competitiveness of renewables and the desire from many businesses to strengthen their sustainability credentials has led to the rise in popularity of the corporate PPA. Through a corporate Power Purchase Agreement (PPA), a company agrees to purchase the energy produced by a renewable project(s). This helps businesses to meet their sustainability goals whilst enabling them to hedge against future energy prices and even bring down the cost of their current energy bill.

Renewable developers have turned to corporate PPAs as a means to enable the delivery of their pipelines. With the removal of subsidies such as the Feed-in Tariffs (FiTs) here in the UK, PPAs can help developers  finance and develop projects by securing long-term energy sale contracts which guarantee revenue for a substantial part of the project lifetime.

How does a corporate PPA work?

A corporate PPA is a contract between a renewable power producer and a corporate, agreeing to supply a specified volume of electricity at an agreed price. It is usually structured to last for 10 years or more, considerably longer than an energy supply tariff which tend to be for one to three years.

There’s no need for the corporate and the renewable project to be located near one another – they could be next door to each other or located on opposite sides of the country.

Of course a company’s demand will not always match a project’s generation. To manage this disparity companies have to go through a licensed supplier who will trade and settle in the market the surplus energy they do not use and/or the additional energy they may require, guaranteeing power delivery and assuming responsibility for issuing the corporate’s electricity. Suppliers take a fee or a premium for administration and taking the risk of balancing the residual of the renewable generation and the company’s electricity demand.

Aligning supply and demand

For example: let’s say a factory with demand profile X (blue line) agrees a PPA with a small solar farm with generation profile Y (grey line). The factory effectively consumes energy generated by the solar farm represented by shaded area A. The area B represents the additional energy that must be bought by the supplier to meet the factory’s demand, whilst the area C represents the surplus renewable energy that is sold to another party as the site’s demand has already been met.

Matching factory demand and renewable generation

The cost of this residual balancing will be affected by market dynamics and the premium charged by the supplier for managing this process.

The overall business benefit of a PPA will be determined by a number of factors, including the demand profile of the site, generation profile of the asset, market prices and the structure of the agreement with the supplier. But the more responsive a corporate’s demand can be to these factors, the better positioned they will be to maximise the benefits of a PPA.

Cutting costs with demand flexibility

This is where demand side response (DSR) and energy storage come in; shifting demand to more closely match the project’s renewable generation profile could maximise the effective consumption of this energy real-time and result in lower residual balancing. This would mean having to buy less energy during the shortage periods, which might be more expensive than that offered by the PPA, and selling back less energy during the surplus periods. Additionally, it could help decrease the imbalance risk of the supplier and make the case for a lower fee or premium.

Demand flexibility and corporate PPAsIt could also present arbitrage opportunities for the business. By shifting consumption away from peak times to cheaper periods, surplus energy from the PPA can be sold on at a high rate, while avoiding punishing network and capacity market charges which occur at the same time. Flexibility could even be used to respond to instantaneous market opportunities, such as high system prices occurring with mismatch in supply and demand, much in the way the trading team of a supplier would do today with large generators.

Optimising a PPA with demand flexibilityThe value of this balancing achieved through flexibility with storage and DSR will vary across hours, days and seasons according to changing market conditions and patterns of supply and demand. What’s needed is technology that can evaluate these parameters in real-time, and optimise a business’ demand accordingly. This is where Open Energi comes in. We’re using our advanced technology, data-driven insight and experience of invisibly managing demand flexibility to help corporates make the most of their PPA.

Our solutions not only help to balance the grid, but can also balance demand real-time against PPA generation. This means businesses can make better use of cheap, renewable energy when it’s there, lower costs for suppliers, and ultimately bring their own energy bills down.

Dago Cedillos is a Commercial Analyst at Open Energi, where he focuses on innovative methods and business models to enable a more flexible energy system. Prior to Open Energi, Dago was part of a clean-tech startup working on a novel carbon-negative electricity generation technology. Dago has an MSc in Sustainable Energy Futures from Imperial College London, and has published a paper on investment strategies for decarbonisation and decentralized energy systems.

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 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.

Making a success of batteries

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The surge in interest in battery storage projects has highlighted a fundamental change in the energy market, as commercially viable systems become progressively more available. We explore critical success factors, from choosing the right battery to managing state of charge.

The deployment of physical energy storage assets can broadly be separated into two project categories. The first kind of project consists of grid-scale assets in “front of the meter”, which are usually implemented by industry partners on large grid connections. The second type is “behind the meter” batteries which provide an added layer of flexibility to energy consumption patterns of sites already connected to the electricity network – and offer tremendous potential to unlock previously inaccessible revenue streams for industrial and commercial customers.

Both project types require different approaches to select the best battery type and optimise operational strategy and performance over time.

Selecting the optimal battery operating strategy

Battery flexibility has the ability to unlock several non-mutually exclusive revenue streams. For example, a battery can be used to reduce site demand (for “behind the meter” projects), or export to Grid (for “front of the meter” opportunities) during peak price periods, reducing costs associated with wholesale, Duos, Triads and Capacity Market levy charges. Outside periods of peak tariffs, batteries can participate in the frequency response market and earn a revenue from National Grid for helping to dynamically balance electricity supply and demand.

The characteristics of Battery Energy Storage Systems (BESS) differ widely between manufacturers, with important factors to consider including capital and operating costs, power rating, energy storage capacity, energy density, cell chemistry, operating temperature, round-trip efficiency, self-discharge, degradation profile and tolerance to various depth of discharge. All these parameters have an influence on the economic viability of the project, so it is important to select the appropriate technical solution for a given project.

Once the different parameters are known, the determination of the most economical operating strategy becomes an optimisation problem in response to an aggregated electricity price signal and a potential frequency response revenue, under several constraints such as the battery technical characteristics and the site operational constraints (existing demand/generation on site if any, and import and export capacity).

The operating strategy might change over time, for example because one component of the price signal has changed, or if there is a new opportunity for flexibility that is more financially viable than current revenue streams. In that case the optimisation process will be performed again and the operating strategy modified accordingly.

Battery State of Charge profile
State of charge profile of a BESS doing peak price avoidance from 4PM to 7PM and participating in the frequency response market the rest of the time. The energy stored in the system is maximised before 4PM in order to optimise arbitrage revenues.

Choosing the right battery

The next crucial decision is choosing a battery that is optimal for a given project and operating strategy. The goal here is to select the battery that will be commercially viable under the constraints of a given project. For a “front of the meter” BESS the main factors driving the battery characteristics are the Authorised Supply Capacity (ASC) for importing and exporting, the capital and operational costs and the electricity tariffs for import and export.

There are additional parameters for a “behind the meter” battery. As most of these projects are implemented in sites with no or a small export capacity, the battery would respond to a low frequency event by discharging power into the site, reducing its overall energy consumption. It is therefore crucial to forecast the demand on site to choose the optimal battery size and tender an accurate power availability in the frequency response market.

The same approach can be used for generating sites (like wind or solar farms) where there must be sufficient potential for export in addition to the generating activity on site. The potential energy savings are also dependent on the demand and the site constraints, which might in return drive the optimal power/energy ratio of the BESS.

Managing battery state of charge and maintaining performance

Once installed, the challenge is to manage batteries while ensuring high performance following the operating strategy selected. A requirement of entering the frequency response market is to be able to provide the power tendered for 30 minutes at a time, which highlights the need for a performant state of charge management.

There is an inherent efficiency in BESS, with average efficiency ranging from 75% to 90 % for conventional systems. When used in the frequency response market, successive cycles of charge and discharge will progressively cause a net discharge of the battery, and ultimately cause the battery to be fully discharged if no corrective actions are taken. Similarly, if several large high frequency events happen in close succession, a frequency-responsive BESS might reach a high state of charge at which it will not be able to respond to high frequency events anymore.

Battery charge management graph
State of charge of a 1MW/2MW.h frequency responsive battery. An appropriate state of charge management helps keep the energy stored in the battery at an optimal level over time.

A control strategy should ensure that the battery state of charge always stays within appropriate boundaries in order to meet its contracted obligations at any given point in time. It should also ensure that the total throughput of the battery (which is the cumulative sum of discharge processes over time) is minimised while in operation. A reduced throughput decreases the wear and tear of the battery, enhancing the BESS lifetime.

At Open Energi we are working with several customers to successfully operate batteries in the frequency response market, optimising their operating profile to maximise revenues, applying designed state of charge management techniques, while limiting the degradation of the battery lifetime to the lowest value possible.