How greater flexibility can help UK deliver 50% renewables by 2030

electricity pylons

The National Infrastructure Commission (NIC) recently published its first National Infrastructure Assessment (NIA), setting out a strategy for the UK’s economic infrastructure from 2020 to 2050. A key focus is decarbonising the UK’s energy supply and the report recommends 50% of generation is supplied by renewable power by 2030, with the UK’s electricity supply almost entirely zero-carbon – thanks to nuclear and renewables – by 2050. But how can we integrate this level of renewables cost-effectively, and what do we do when the sun doesn’t shine, and the wind doesn’t blow? Wendel Hortop, Commercial Analyst at Open Energi, explores the role of flexibility in enabling the UK’s transition to a zero-carbon energy system.

What would such high levels of renewables mean for the energy system?

The UK is on track to power 50% of our electricity supply with renewable generation by 2030 but this level of renewables creates some very specific challenges. Solar and wind, which would form most of new renewable capacity, are highly inflexible – energy is only generated when the sun is shining, or wind is blowing. Despite increasingly accurate forecasting, this inflexibility introduces short-term (balancing electricity supply and demand within a given half-hour) and long-term (what to do when wind and/or solar output is low for hours or days at a time) challenges, and reduces the level of inertia on the grid, resulting in much quicker changes in system frequency – which must be managed to ensure power keeps flowing.

Flexibility can help to address these impacts cost-effectively – reducing total system spending by between £1-7bn per year – and enable the UK to integrate renewable generation at the scale required by the NIC assessment.

Flexibility can deliver significant cost reductions in in a high renewable system

Source: Open Energi
Source: Aurora Energy Research

 What role does flexibility have to play?

The majority of system balancing occurs through the energy market in response to energy prices visible over different timescales, of which the last resort is the imbalance price. Energy generators and suppliers forecast their half-hourly energy usage and provide this to National Grid, who then take action to correct any differences between forecast and actual energy usage. Anyone out of balance in a way which harms the system pays a penalty, whilst the opposite is also true – putting yourself in imbalance to benefit the system gets rewarded. The imbalance price (or System Price) is not known until afterwards so predicting and reacting to it allows energy users to help the grid and be rewarded; increasingly trading teams at big suppliers are looking to their customers to help manage this.

Open Energi are already responding to the imbalance price by flexing loads through signals from suppliers, such as Ørsted’s Renewable Balancing Reserve. Increased renewable generation on the grid will increase the likelihood of system imbalances, and the incentive to respond.

Flexible loads can respond in real-time to predicted system prices

Flexible loads can respond in real-time
Source: Open Energi

The wholesale market doesn’t balance all supply and demand so National Grid look to the suite of services they procure to do the rest. For example, frequency response services fine tune the system balance and provide a ‘first line of defence’ after large generation outages.

Demand flexibility is already an established tool in helping to balance frequency on the grid via the Firm Frequency Response market. Inertia levels falling means faster frequency response is needed. Lithium-ion batteries are perfect for delivering this, whilst some forms of demand flexibility can also respond at the required speed. National Grid is developing a Faster Acting Frequency Response product which will allow loads capable of responding quickly enough to participate and will procure a mix of assets capable of tracking frequency (such as batteries) and those capable of delivering large shifts in demand almost instantaneously (such as large industrial processes).

Longer term shortfalls in generation introduce a new challenge for flexibility

The more significant challenge is in longer periods of low wind and solar generation. Increased interconnection with Europe will help but demand flexibility can again play a key role.

Frequency response has tended to focus on energy flexibility within a half-hour period, however many processes have inherent energy storage of hours or even days. Water pumps, heating and CHPs are all assets which can shift demand over long periods. The signals to do so come from the market – low renewable generation leads to increased wholesale energy prices, and vice versa. As wholesale energy prices can be known a day ahead, a load can be optimised in advance to increase consumption when prices are lowest, and reduce consumption when prices are high.

Many flexible processes have hours or even days of energy storage
 

Many flexible loads have hours or even days of storage
Source: Open Energi

Advances in storage technology will also assist with this longer duration requirement for flexibility. Technologies such as vanadium flow batteries can provide over 4 hours of energy storage and can help balance sustained periods of low or high renewable generation as well as providing short-term frequency response and price arbitrage.

Aggregation of assets such as these, diverse in both location and technology, will help to tackle longer periods by spreading the requirement for flexibility. Digitalised platforms that use artificial intelligence (AI), statistics and probability can schedule and manage asset behaviour to deliver the optimal amount of flexible capacity.

As we look to 2030, increased adoption of electric vehicles (EVs) will also come into play, either through smart charging or vehicle-to-grid (V2G) charging. In their latest Future Energy Scenarios report National Grid predict we could have over 10 million electric vehicles in 2030, and over 35 million in 2040 – a huge number of flexible, distributed assets.

Smart charging will allow EV charging to be modulated or staggered to avoid surges in consumption or shifted to times of day when demand is low, reducing the infrastructure required to support them. Aurora Energy Research estimate that smart charging can reduce the level of generating capacity required in 2050 by up to 22GW in a high renewables system. Meanwhile V2G charging introduces possibilities such as taking households off-grid during peak periods – Open Energi are part of the PowerLoop consortium exploring this and other potential V2G applications.

Smart charging significantly reduces the need for flexible generating capacity

Source: Aurora Energy Research
Source: Aurora Energy Research

Decarbonisation of heat will introduce new sources of flexibility

One common process with very high levels of inherent storage is heating; however the UK’s reliance on gas means potential flexibility which could be offered to the electricity system is currently limited. Looking forward the decarbonisation of heat therefore offers long-term opportunities, whether this comes through electrification or a transition to hydrogen and district heating.

Switching to heat pumps would introduce a large but flexible energy load into the system with significant storage potential. Coupled with smart meters and other advances in technology this could lead to a highly distributed source of flexibility for the grid, just as with the shift to electric vehicles.

Hydrogen powered heating – produced via electrolysis – is an energy-intensive but flexible process, which alongside district heating networks would likely lead to many more CHPs – which offer short and long term flexible capacity.

Technology will play an important role in delivering this flexibility

The NIA shows that flexibility has a key role to play in delivering or surpassing our carbon targets. As renewable generation increases significantly so will the need for flexibility. We already have many of the solutions we need – the real challenge is rolling these out at the required scale and speed.

This is where AI and cloud computing can come into their own. Aggregation of larger and larger portfolios of diverse loads will require the behaviour of each of these individual loads to be optimised and controlled in real-time in response to the requirements of the system. Meanwhile the move to smaller, distributed loads, including those on a domestic scale such as electric vehicles, will rely heavily on cloud computing with dispatch instructions delivered over the internet and loads communicating their behaviour with the platform and each other.

Ultimately these solutions can give rise to an autonomous, self-balancing grid which operates incredibly cheaply. Open Energi are leading this transition, connecting, aggregating and optimising distributed energy resources in real-time, to create a more sustainable energy future.

How EVs can help drive a more sustainable energy future

Tesla South Mimms Supercharger and PowerPack

Electric Vehicles (EVs) have taken off in 2017 with governments, manufacturers and industry queuing up to announce bold commitments, product launches and sales figures. Suddenly, EVs have shifted from being a future technology, to a technology of the here and now.

The next decade will be critical for EVs, and their accelerating deployment will have a significant impact on infrastructure systems and markets. A lot of attention has been given to ‘worst-case’ scenarios but smart charging technology means EVs can be managed to the benefit of the system, accelerating our transition to a sustainable energy future and supporting low carbon growth. New analysis by Open Energi suggests that EVs could provide over 11GW of flexible capacity to the UK’s energy system by 2030.

Rise of EVs

The next decade will be incredibly important for EVs, and their deployment has been strengthened by manufacturer commitment, government influence and price curves. Manufacturers including Volvo, Jaguar, and Volkswagen to name a few have made bold statements, claiming the electrification of their product lines and assigning large budgets for R&D. Global EV line-up will almost double by 2020, as the release of Chevy’s Bolt, Tesla’s Model 3 and Nissan’s new Leaf lead EVs into the mainstream.

Governments such as France and the UK have agreed to ban sales of diesel vehicles by 2040. Other countries have set aggressive sales targets, for example China, who has set a 7m target in its 2025 Auto Plan. And all want to become world leaders in EV technology. Here in the UK, BEIS has announced funding for battery and V2G technology development with further funding announced in the Autumn Budget.

Technology development and manufacturing scale-up continues to drive prices down. Battery prices, which account for around 50% of the cost of an EV, have fallen more than 75% since 2010 and are expected to continue to do so at about 7% year on year to 2030. Analysis from both UBS and BNEF claims price parity will be achieved in Europe, US and China sometime in the 2020s, repeatedly accelerating the next million of sales.

The first million takes the longest: length of time, in months, to reach electric vehicle sales milestones
The first million takes the longest: length of time, in months, to reach electric vehicle sales milestones

EVs and electricity demand

According to BNEF, in 2040 54% of global new car sales and 33% of the global fleet will be electric, with a demand of up to 1,800 TWh (5% of projected global power consumption). In the UK, National Grid suggests around 9 million EVs will be on the road by 2030[1]. This uptake in EVs will have a significant effect on our electricity system.

Source: National Grid Future Energy Scenarios 2017 (Two Degrees)
Source: National Grid Future Energy Scenarios 2017 (Two Degrees)

 

Source: Bloomberg New Energy Finance
Source: Bloomberg New Energy Finance

Although EV charging will cause an increase in overall electrical energy demand, the greater challenge lies in where, when and how this charging takes place. The overall electricity demand change will be a single-digit percentage increase but if all this energy is consumed at the same time of day, it could result in double digit percentage increases in peak power demand. This creates challenges for generation capacity and for local networks, who could be put under strain to meet these surges in power demand.

There has been a lot of attention given to the worst-case impact EVs could have on the system – but less analysis of the benefit they could bring as a flexible grid resource controlled by smart charging. At Open Energi, we have used a bottom up approach to quantify the flexibility EVs could offer the UK’s energy system, and the opportunities it could create.

Flexibility scenarios

Different charging scenarios were designed based on the charging speeds currently available and their granular flexibility was quantified (see below for a full description of the methodology). Then, the time at which each of these scenarios is likely to occur was evaluated. Finally, using EV fleet forecasts, volume was attributed to each scenario and a set of future flexibility profiles produced.

EV speed table

EV charging scenarios table

By 2020, with around 1.6 million EVs on the road, Open Energi’s analysis suggests there could exist between 200 – 550 MW of turn-up and between 400 and 1.3GW of turn-down flexibility to be unlocked from smart-charging. The available flexibility would change throughout the day depending on charging patterns and scenarios. In 2030, with 9 million EVs on the road, this rises to up to 3GW of turn-up and 8GW of turn-down flexibility respectively.

EV flex profile 2020 down

EV flex profile 2020 up EV flex profile table

Opportunities: smart charging for flexibility

Smart charging technology turns EVs from a threat to grid stability into an asset that can work for the benefit of the system. Optimal night-dispatch for example, can ensure all vehicles are charged by the time they’ll be used the next day without compromising their local network infrastructure. Cars could help to absorb energy during periods of oversupply, and to ease down demand during periods of undersupply. On an aggregate basis, they can help the system operator, National Grid, with its real-time balancing challenge, and provide much needed flexibility to support growing levels of renewable generation. Suppliers could work with charge point operators to balance their trading portfolios and manage imbalance risk, helping to lower costs for consumers.

Of course, smart charging can only happen with the consent of the driver, and drivers will only consent if their car is charged and ready to go when they need it. This means deploying artificial intelligence and data insight to automate charging without affecting user experience, so that the technology can learn and respond to changing patterns of consumer behaviour and deliver an uninterrupted driver experience. Getting this right is key to aligning the future of sustainable energy and transport.

Dago Cedillos is Strategy and Innovation Lead at Open Energi

Methodology

Open Energi’s methodology consists of a bottom up approach, looking at the different charging scenarios and quantifying the flexibility from each of them. The time at which each of these scenarios is likely to occur has been analysed. Finally, using EV fleet forecasts, based on National Grid Future Energy Scenario forecasts (2017, Two Degrees), we’ve attributed volume to each scenario and generated a flexibility profile.

Charging speeds

We formulated our charging scenarios based on the different charging speeds and the capabilities of each. Charging speeds are currently referred to as Slow, Fast and Rapid as set out below.

EV speed tableScenarios

Based on these speeds, we built some scenarios considering the use-cases. Slow charging is likely to be used at home, Fast charging in public spaces and Rapid in public spaces and forecourts. We assumed typical plug-in durations for these charging scenarios.

EV charging scenarios table

Main assumptions

Considering the charging scenarios, calculations were performed on the turn-up and turn-down capabilities of each. An important element of this analysis, the average daily energy requirement per vehicle, was based on the following assumptions:

  • Average daily miles travelled per vehicle: 20.54 (based on UK National Transport Survey’s VMT)
  • A conservative assumption of 20kWh/100km (the Chevy bolt can travel 238 miles on a 60kWh battery)

EV electricity demand table
This leads to the figures in table (above), which align closely with National Grid’s Future Energy Scenarios 2017 when using their fleet forecasts.

Extracting flexibility

Different likely situations were built for each scenario, using 7kWh as a simple rule of thumb of what an EV would require as charge per day. For example, for the ‘Long’ scenario: using a 3kW (B) slow charger, energy to be charged (A) was evaluated for the different likely situations (J). Potential turn-up (F) and turn-down (H) was defined and saturation/underperformance parameters (G & I) were introduced for this flexibility. That is, to charge (A) using speed (B), there would only be (I) hours of turn-down flexibility (H) in an optimal case before underperformance (i.e. not fully charging the vehicle). This was repeated across all scenarios using the range of charging speeds, plug-in durations and rates of charge eligible for each to quantify flexibility.

Energy to charge table
The average flexibility potential for each possibility was calculated as a kW value, as the product of (F) & (G) and (H) & (I) divided by plug-in time (D). This was the estimated average kW value of flexibility for a vehicle under the option in the scenario. Max, mid and min flexibility values were defined for each scenario based on the options calculated per scenario.

Flexibility profiles

Having the average flexibility per vehicle for each scenario, this was then converted into a flexibility profile considering the following assumptions:

  • Long scenario (home charging) likely to take place during the night.
  • Medium scenario (workplace charging) likely to take place during office hours.
  • Short scenario (shopping/dining) likely to take place during early morning, lunch and after office hours.
  • Ultra-short scenario (forecourts) likely to take place during early morning, lunch and after office hours.

Time of day tableAttributing vehicle volume to each scenario was then performed as follows. Data from the Department of Transport[2] indicates that approximately 50-55% of households owning a vehicle have access to off-street parking. Open Energi assumed the following share of vehicles per scenario[3]. Further work needs to be carried out to define how this share will evolve over time with the development of charging technology.

Share 2020 table
The aggregate flexibility for each hour which defines the profile was then calculated using the flexibility per vehicle and scenario, the scenario schedules, and the number of vehicles in each scenario and for each time period (2017, 2020, 2030 and 2040).

[1] National Grid Future Energy Scenarios 2017 (Two Degrees)

[2] Department of Transport survey: http://webarchive.nationalarchives.gov.uk/20111006052633/http:/dft.gov.uk/pgr/statistics/datatablespublications/trsnstatsatt/parking.html

[3] Open Energi identified a gap in data available to define these shares with accuracy, these will have to be reviewed over time.

The future of flexibility: making the most of behind-the-meter storage

Camborne Energy Storage

The slowdown in the Firm Frequency Response (FFR) market, where prices are falling and volumes have been capped – in large part thanks to the decreasing cost of battery storage – has been a big talking point of late.

National Grid has now procured much of the low response it needs for the next two years, and average prices of accepted bids have decreased 20% over the last 7 months. National Grid has also announced proposed changes intended to rationalise and reform the current suite of balancing services it procures, including a streamlined FFR-type product.

These rapidly changing market conditions may leave some battery companies going back to the drawing board and asking how to guarantee income over a long period and satisfy investors that it is worth investing hundreds of thousands in the latest battery storage system, without the relatively high revenues that providing FFR has historically guaranteed?

Fig: The distribution of FFR accepted tenders for the period January - October 2017. Source: National Grid
Fig: The distribution of FFR accepted tenders for the period January – October 2017. Source: National Grid

But it is possible to turn to other markets for flexibility. FFR will remain a part of the revenue stream for batteries, and for the fast-acting Demand Side Response (DSR) that makes up most of Open Energi’s FFR portfolio; but being a bit smarter about where, and how, you employ the flexibility at hand can open up a wealth of other revenue streams. These, stacked up, provide a stronger business case than FFR alone.

Open Energi’s new platform, Dynamic Demand 2.0, is designed with exactly this in mind. By connecting to distributed energy assets which have inherent flexibility in their electricity consumption or generation, including industrial equipment, electric vehicle charging stations, and of course battery storage systems, it enables businesses to dispatch this flexibility in the most valuable place at any one time.

Tailored to the particular operational or site constraints of an asset, balancing services, energy trading, the capacity market, network constraint management, peak price management such as DUoS red-band and TRIAD avoidance are all opened up as stacked revenue streams, as well as operational energy efficiencies automated with machine learning.

For behind-the-meter batteries, this means considering any on-site generation or demand, the import and export limits of the site, the warranty particulars of the battery, and evaluating all the potential revenue streams of the asset. By taking a holistic view of the energy market, using state of the art machine learning techniques and cloud based technology, we remotely operate the asset to maximise its return on investment: FFR is just one slice of the cake.

In many ways, a battery is the perfect asset for flexibility. It has a defined storage capacity; it can discharge power up to a maximum well-defined rate; and it has a known state of charge (SoC) at any one time. However, to operate a battery across multiple markets, careful management of the SoC of the system is necessary.

While batteries very naturally perform frequency response, charging up when frequency is high – removing excess electricity from the grid, and discharging when frequency is low – when more electricity is required, the operation is not quite that simple. To have the capacity, or availability, to both charge up and discharge in line with grid frequency, the ideal SoC of a battery is 50%.

Periods of high or low grid frequency can rapidly take the SoC of the system far from 50%, and batteries have an inherent efficiency (around 90% in the best systems): active state of charge management is required to maintain the availability for frequency response.

The figure below shows the same 500kW, 800kWh battery over the same 52-hour period, with and without state of charge management. The availability to fully charge or discharge at the capacity of the battery for 30 mins (equivalent to a frequency 50.5Hz or 49.5Hz for half an hour) – is quickly impeded on when no SoC management is present, and SoC will eventually tail off to zero due to the non-perfect efficiency of the battery. Battery throughput, defined as the cumulative discharge of energy through the battery, is also higher without SoC management, eating into that allowed by the warranty.

Fig: The state of charge (%, top), availability for FFR (kW, middle) and throughput (kWh, bottom) of a 500kW, 800kWh system are shown across 2 days of frequency data. With no SoC management in action, state of charge is routinely out of the dotted lines which signify ½ hour of storage capacity being available to charge and discharge at full power; here, state of charge is low. As a result, low availability for FFR is diminished. Throughput of the battery, defined as the cumulative sum of the battery discharge, is also higher when no SoC management is used.
Fig: The state of charge (%, top), availability for FFR (kW, middle) and throughput (kWh, bottom) of a 500kW, 800kWh system are shown across 2 days of frequency data. With no SoC management in action, state of charge is routinely out of the dotted lines which signify ½ hour of storage capacity being available to charge and discharge at full power; here, state of charge is low. As a result, low availability for FFR is diminished. Throughput of the battery, defined as the cumulative sum of the battery discharge, is also higher when no SoC management is used.

When introducing revenue streams in addition to FFR, as we do with Dynamic Demand 2.0, battery operation gets rather more complex. For a behind-the-meter battery, electricity bills can be significantly reduced by discharging over peak periods to either avoid importing electricity when it is most expensive (if the site has demand), or exporting to grid and taking advantage of the various network charging revenue streams (DUoS, TRIAD, CM Levy).

Electricity generated earlier in the day, from on-site PV generation, or stored when it was cheap to import, can be exported to grid at peak times. Energy arbitrage on the wholesale market: charging up when the N2EX price is low and discharging when it is high, or hedging against pre-bought volumes, can give a day-ahead price signal, as in the figure below; playing on the intraday market can similarly offer additional uplift. If the asset owner is exposed to the Balancing Mechanism through their supplier, short notice calls to discharge in line with a large system imbalance offers another revenue stream.

Battery Storage Energy Arbitrage
Fig: Energy arbitrage using the N2EX day-ahead market is shown for a 60kW, 300kWh system over one day. The stored capacity of the battery is varied to minimise the cost of electricity over the day, such that the battery charges when electricity is cheap, and discharges when it is more expensive. By layering on additional price signals closer to time and re-optimising the power profile, additional revenues from energy arbitrage across a range of markets can be achieved.

These requests to charge and discharge in line with ahead-of-time and real-time price signals must be dispatched depending on the import and export constraints of the site and of the capacity of the battery. It needs enough stored energy to be able to discharge for the duration of the price spike, while maintaining the availability to do FFR in accordance with any pre-contracted volumes required by National Grid, and such that recovering the SoC afterwards does not cost more than earnings during the call itself. It also needs to do so without overly increasing the throughput of the battery such that the valuable warranty is violated.

Open Energi has experience of doing this with assets already in operation behind-the-meter. At South Mimms Welcome Break services, we are managing a Tesla Powerpack alongside an EV charging station, to displace site demand from EV charging throughout the day, while providing FFR, and providing energy savings by doing a full discharge of the battery during peak periods.

In a Somerset field, we operate a battery integrated with a solar farm, using PV generation to charge up the battery for free during the day, discharging at peak times, and managing the SoC so that FFR availability is maximised the rest of the time. By stacking these additional revenue streams alongside FFR we increase the income of the asset, and with our Dynamic Demand 2.0 platform, will be able to extend these services across a host of other assets and services.

This includes managing fleets of battery storage systems, where the optimal control strategy goes beyond revenue stacking each asset individually. A central intelligence that is aware of the state of each battery can determine how hard to work different assets to maximise revenue or minimise the number of charge-discharge cycles performed, extending each asset’s life. Dynamic Demand 2.0 can combine revenue stacking with fleet management strategies to meet these objectives.

As we look to the future of energy, it is clear that behind-the-meter battery storage has a huge role to play in creating a more sustainable system. As renewable energy becomes more prevalent, value is emerging in new places, and the key to optimising this value, without negatively impacting battery performance, is technology.

Robyn Lucas is Head of Data Science at Open Energi

Battery storage project a ‘blueprint’ for EV charging infrastructure globally

Tesla South Mimms Supercharger and PowerPack

Pairing batteries with EV charging stations can help to align sustainable transport and energy needs for the future.

At South Mimms Welcome Break Motorway Services, we have installed a 250kW/500kWh Powerpack alongside one of Tesla’s largest and busiest UK charging locations. 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.

Open Energi own and operate the Powerpack, which is part of our portfolio of assets that help maintain the frequency of the grid. 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.

The project at South Mimms Welcome Break Motorway Services provides a blueprint for the development of electric vehicle charging infrastructure globally. Moreover, by reducing National Grid’s reliance on fossil fuelled power stations as a means of balancing electricity supply and demand, the Powerpack helps to reduce UK CO2 emissions by approximately 1,138 tonnes per year.

Daring to imagine an energy future that’s unlike our past

Future Energy System

Two years ago, when we walked into a pub in Old Street to chat with Forum for the Future about creating a virtual power station, we had no idea we’d end up convening a project to challenge the fundamentals of how our energy system works – called the Living Grid.

Our early conversations were all about reducing our reliance on fossil fuelled generators, and proving the potential of demand-side management to revolutionise our energy grid.  But it turns out, when you mix inspiring conversations with businesses, universities, government bodies, local authorities, community groups, start-up technology providers, industry associations and everyone in between with a standing-room-only presentation from Michael Pawlyn, you end up with an initiative that questions every aspect of how our energy system is designed.

Why limit the conversation to the role of sophisticated, demand-side technologies. If our goal is an energy system powered by renewable energy rather than fossil fuels, we must look at the capabilities, interactions and design of every aspect of the system and question whether it has to be this way, or whether there is a better, alternative approach – and we must unleash our imaginations to do it.  The Living Grid has become a community of people and organisations who are deliberately challenging the beliefs we hold about our energy future, to open up solutions to us that are simply not possible using the same thinking that created our fossil-fuel powered grid.

“The system of nature, of which man is a part, tends to be self-balancing, self-adjusting, self-cleansing.  Not so with technology.”  E.F. Schumacher

The Living Grid is provoking us to look beyond carbon reductions at the mismatch between our human energy system and the wider, living energy system that’s evolved here over the last 3.8 billion years of life.   Rather than cycle solar energy through a closed-loop, cooperative system – as other life forms do around here – our linear, centralised grid releases energy into the atmosphere by burning fossil fuels, wasting it rather than recapturing it. ‘What it would take to make our alien energy system part and parcel of nature again? 

As founding technology partner, Open Energi is proud to have helped bring the Living Grid into existence with our customers Aggregate Industries, Sainsbury’s, Tarmac and United Utilities.  As the Living Grid grows, we’re pleased that other organisations who share its vision, such as Smartest Energy, are getting involved to usher-in the next stage of development. This next next phase is starting with a global, online conversation to reimagine our legacy grid.  Are you curious about how nature would design our energy system?  Join the debate here.

The Living Grid is a project convened by Forum for the Future.  To find out more about the Living Grid and how you could get involved please contact: g.adams@forumforthefuture.org or h.hauf@forumforthefuture.org

 

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.

Can a sharing economy approach to energy deliver a more sustainable future?

Sunshine through tree tops - green energy

As global demand for electricity grows, are there alternatives to building more power stations which make smarter use of existing infrastructure? And in an industry renowned for high levels of consumer mistrust, could an Airbnb of energy finally deliver a consumer-centric energy market?

Technology is shaping our lives like never before, making our world smarter, more efficient and more connected. In the last decade, it has fuelled an explosion of sharing economy business models — adopted by the likes of Uber, Airbnb and Zipcar — who in just a few short years have revolutionised established industries. But can a sharing economy approach help to tackle one of man-kind’s greatest challenges and deliver clean, affordable and secure energy to all?

Sharing economies are a consumer-led phenomenon which work by exploiting excess capacity or inefficiencies in existing systems for mutual benefit. Take Airbnb for example. The wasted asset is your property and the excess capacity is the space you are not using. By creating a user-friendly platform and giving homeowners the security they need Airbnb have built the biggest hotel chain in the world, surpassing the Intercontinental Group in less than four years. They have achieved this because they haven’t needed to construct a single thing.

So how could this apply to the energy industry? As global demand for electricity grows, are there alternatives to building more power stations which make smarter use of existing infrastructure? And in an industry renowned for high levels of consumer mistrust, could an Airbnb of energy finally deliver a consumer-centric energy market?

Since the world’s first power station was built in 1882 the global energy system has worked on the basis that supply must follow demand. Consumers — businesses and households — have been passive users of power, paying to use what they want when they want, whilst electricity supply has adapted to ensure the lights stay on. This has created inefficient systems built for periods of peak demand — in the UK this is typically between 4–7pm on a cold winter evening — which most of the time are massively underused.

But this is no longer the case. Today, our ability to connect and control anything from anywhere means we can manage our demand for electricity in previously unimaginable ways, and consumers are emerging as a driving force for change.

By connecting everyday equipment to a smart platform (just as you might upload your property to Airbnb), it’s now possible for consumers to take advantage of small amounts of “flexible demand” in their existing assets and processes — be it a fridge, a water pump, or an office air con unit — and sell it to organisations tasked with keeping the lights on — like National Grid.

Applying artificial intelligence and machine learning to govern when and for how long assets may respond gives consumers confidence their equipment’s performance will not be affected, and in return for sharing their “flexible demand”, they benefit from cost savings or direct payments.

This sharing economy approach relies on the power of tech and our ability to orchestrate many thousands of consumer devices at scale. Any one piece of equipment can only make small changes to the timing of its electricity consumption — e.g. delaying when a fridge motor comes on for a few minutes during a spike in electricity demand at the end of a football match — but collectively, the impact is transformational.

It means that when electricity demand is greater than supply, we don’t need to fire up fossil-fuelled power stations. Instead, we can reduce demand by asking non-time critical assets to power down for a short while.

If the wind is blowing and too much electricity is being supplied, we don’t need to let this clean, abundant power go to waste, but can ask equipment to shift its demand and make use of this power as it is available.

And we don’t need to keep building more power stations to meet occasional peaks in demand. Instead, we can distribute demand more intelligently throughout the day, reducing the size of these peaks and making better use of existing capacity.

In the UK, Open Energi’s analysis suggests there is 6 gigawatts of peak demand which can be shifted for up to an hour without impacting 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.

This doesn’t make it easy. Unlike other sharing economy success stories, energy is a public good. The need for incredibly robust solutions means the barriers to entry are high. But, if we can get it right, the prize is enormous; a cleaner, cheaper, more secure energy system which gives consumers control of how, when, and from where they consume their energy.

Businesses have already recognised the power they hold and the benefits it can bring, with the likes of Sainsbury’s, Tarmac, United Utilities and Aggregate Industries adopting the tech and demonstrating what’s possible. Households look set to follow, but wherever the flexibility comes from, it’s clear that consumers and the environment will benefit from a sharing economy approach to energy.

David Hill is strategy director of Open Energi. He is an expert on electricity markets and demand-side flexibility, including demand-side response and energy storage. He joined Open Energi in 2010 after completing an MSc Energy, Trade & Finance at Cass Business School.