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.

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.

UK demand side flexibility mapped

United Kingdom Map - London's spare GW of power

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

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

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

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

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

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

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

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

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

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

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

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

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

Advancing hydrogen fuel cell electric vehicles across Europe

H2ME refuelling station for fuel cell electric vehicles

Hydrogen Mobility Europe (H2ME) is an ambitious multi-country, multi-partner project to demonstrate that hydrogen can support Europe’s future transport demands. The €170 million demonstration project is co-funded with €67 million from the Fuel Cells and Hydrogen Joint Undertaking (FCH JU), a public-private partnership supporting fuel cell and hydrogen energy technologies in Europe.

H2ME is the largest-scale project of its kind, and aims to perform large-scale market tests of hydrogen refuelling infrastructure; deploy passenger and commercial fuel cell electric vehicles operated in real-world customer applications; and demonstrate the system benefits generated by using electrolytic hydrogen solutions in grid operations.

Open Energi is participating in the project to test the ability of electrolysers at Hydrogen Refuelling Stations to help balance electricity grids in real-time. The expectation is that the electrolysers could provide incredibly fast response and compete with the fastest forms of grid balancing available today.

In February this year, H2ME announced that it had deployed its first 100 fuel cell electric vehicles (FCEVs) on the road in Germany, France and the UK, to address the actions required to make the hydrogen mobility sector truly ready for market. Sixty of Symbio’s Renault Kangoo ZE-H2 range-extended fuel cell vans have been deployed in the UK and France, supporting the development of a network of hydrogen refuelling stations in those markets. Powered by a compact 5 kW fuel cell module, coupled with a hydrogen storage unit and medium-size automotive battery pack, Symbio’s range-extender kit doubles the range of Renault’s electric-only Kangoo ZE model to 320 Km.

In addition, Daimler has deployed 40 B-Class F-CELL vehicles under H2ME in Germany. Thanks to the 700-bar, high-pressure fuel-tank system, the car has a long operating range of around 400 kilometres and can be refuelled in less than three minutes. The vehicle’s electric motor develops an output of 100 kW and, with a torque of 290 Nm, the car combines local emission-free mobility with day-to-day suitability and good performance figures.

The new vehicles offer the potential of a new generation of fuel-efficient, zero-emission vehicles. In the coming years, the H2ME project will deploy partners’ next-generation FCEVs, including: Symbio’s next-generation FC RE-EV (Fuel Cell Range Extender Electric Vehicle) vans and Symbio Fuel Cell range-extended trucks; Honda’s second-generation FCEV; and Daimler’s next-generation Mercedes-Benz GLC F-CELL, which includes the additional energy source of a large lithium-ion battery and will feature external charging by plug-in technology for the first time.

In total, more than 1,400 FCEVs will be deployed as part of the H2ME project throughout the UK, France, Germany, the Netherlands and Scandinavia. The aim is to increase the number of FCEVs operating on Europe’s roads to build on the strong networks of hydrogen refuelling stations created by H2ME and other initiatives across the EU.

IoT technology meets UK’s energy grid needs faster than power stations

Aggregate Industries Moorcroft quarry

Research has found that Open Energ’s Dynamic Demand technology can meet the UK’s crucial grid balancing requirements faster than a conventional power station. The paper published as a result of ongoing collaborative research by Open Energi, National Grid and Cardiff University, titled Power System Frequency Response from the Control of Bitumen Tanks, looked at the feasibility of DSR providing a significant share of frequency balancing services.

Bitumen tanks (containing the glue that binds our roads together) equipped with Dynamic Demand were used in combination with National Grid’s model of the GB transmission system to investigate the capability of industrial heating loads to provide frequency response to the power system.

The conclusion is that Open Energi’s Dynamic Demand technology, deployed at scale, can contribute to grid frequency control in a manner similar to, and, crucially, faster than that provided by traditional peaking power generation. Field tests showed that full response could be provided in less than two seconds, as compared to 5 – 10 seconds for a thermal generator. Large scale deployment of Dynamic Demand will reduce the reliance on frequency-sensitive generators and ensure that the grid stays balanced in a cost-effective, sustainable and secure manner.

While a lot of focus has understandably been given to tight capacity margins between supply and demand, the real threat could come from generators being unable to respond within the required window to balance instantaneous shifts in supply and demand. With more renewables and decreased thermal generation, ‘inertia’ on the Grid will decrease, making frequency more unstable. Dynamic Demand can help to counteract this effect by providing faster response, helping to future proof the Grid.

The paper was published in the IEEE Transactions on Power Systems Journal. Download a copy here.