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.

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

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

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

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

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

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

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

Power Responsive success stories: Aggregate Industries

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

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

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

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

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

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

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

What National Grid’s latest forecasts mean for EV flexibility

EV smart charging

Last week National Grid published its 2018 Future Energy ScenariosMost notably, this year’s scenarios forecast there could be as many as 36 million electric vehicles (EVs) on UK roads by 2040, almost double the number suggested a year ago.

Accelerating EV uptake will increase overall electricity demand – with EVs accounting for 7.5% of total electricity demand by 2040 – but the impact of EVs can be managed and controlled thanks to smart charging and vehicle-to-grid (V2G) technology, which means EVs can be turned into a flexible asset which works for the benefit of the system.

The report recognises this – modelling the impact of V2G technology for the first time – and highlights the role of EVs in helping to manage peaks and troughs in demand and provide stored energy to support growing levels of renewable generation.

 

 

EV electricity consumption 2018

Open Energi have updated our modelling of EV flexibility to reflect National Grid’s latest forecasts. By 2030, with up to 11 million EVs on the road, our analysis suggests there could exist between 1.1–3.7GW of turn-up and between 2.5-8.5GW 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 2040, with 36 million EVs on the road, this rises to up to 12.6GW of turn-up and 29.7GW of turn-down flexibility respectively.   Our current analysis does not include V2G so these calculations will eventually be higher depending on the level of V2G penetration achieved.

 

EV flexibility 2018

 

EV Flexibility turn down profile 2020

 

EV flexibility profile 2020 turn-up

Open Energi is working to make these figures a reality.

We are part of the PowerLoop consortium, a 3-year, £7 million project backed by Innovate UK to develop the UK’s first large-scale domestic V2G trial. The consortium includes Octopus Energy, Octopus Electric Vehicles, UK Power Networks, ChargePoint Services, Energy Saving Trust and Navigant.

Open Energi is leading on developing a bespoke V2G aggregation platform and is working closely with UK Power Networks to integrate domestic V2G into their flexibility services.  Together, we aim to demonstrate the benefits of using domestic V2G to support the grid and reduce costs for drivers.

In parallel, we’re working with businesses to develop EV charging and fleet management strategies that deliver valuable savings and income and support companies’ wider energy management and sustainability goals. Our Dynamic Demand 2.0 platform means EVs can be controlled and optimised alongside other energy assets – including on-site generation and storage – to ensure vehicles are charged and ready when needed, site constraints are managed, and value is maximised.

With the right technology in place, we can manage the impact of EVs on the electricity system, create the foundations for mass adoption and align sustainable energy and transport needs for the future.

For the full methodology behind our EV flexibility calculations, click here.

Dagoberto Cedillos, Strategy and Innovation Lead, Open Energi

Why precision matters for Triad prediction

United Utilities Davyhulme

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

Triad season 2017-18

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

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

 

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

 

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

 

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

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

 

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

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

 

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

 

Triad Predictions

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

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

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

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

 

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


O
utlook for Triads

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

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

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

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

By Wouter Kimman, Data Scientist, Open Energi

2017 in review: breakthrough tech and tumbling renewable records demand greater flexibility

2017 was a year of dramatic change in the UK electricity market. Overall, total UK electricity consumption fell 2.8% compared to the previous year: 264 TWh compared to 272 TWh in 2016[1]. This follows the long-term trend of decreasing peak and total yearly energy use, while the proportion of renewable generation continued to rise: 2017 smashed 13 clean energy records, low carbon generation exceeded fossil fuels, and the resulting trend for negative prices (as recent as last week in Germany thanks to high wind) looks set to continue.

Last year also saw a fall in the strike price for new offshore wind power to £57.50/MWh. Considering the government’s guaranteed price for Hinkley Point C is £92.50/MWh, it highlights just how competitive renewables, and particularly offshore wind, now are. However, the system must be able to cope with the intermittency that all this cheap, carbon-free power brings.

Figure 1 shows the huge variation in demand over the year: from peaks of nearly 50GW on winter evenings, to troughs of around 17GW on summer nights. Figure 2 shows the average daily profile of consumption. In 2017, the prize for peak demand goes to January 26th, which came in at 49.76 GW at 6pm. Compare this to the profile of June 11th, the day that the UK used the least energy: at 5am, it was 16.57 GW. This swing of over 30 GW presents many challenges for the system operator as more and more of the generation becomes intermittent and demand patterns shift: there is value in being flexible with one’s electricity consumption.

 

Figure 1. Daily demand over the year, and smoothed trend over the year.
Figure 1. Daily demand over the year, and smoothed trend over the year.

 

Figure 2. Peak and lowest demand of 2017, compared to the average daily profile.
Figure 2. Peak and lowest demand of 2017, compared to the average daily profile.

Historically, our electricity system has been built to cope with the peaks; and paying for this network accounts for around 30% of your electricity bill (and rising). What if, by being a bit smarter about when we use our electricity, we could flatten the swing out a little? Or better still, align it to renewable generation?

This is where demand flexibility comes in, empowering consumers and playing a vital role in providing the responsiveness needed to cope with huge swings in renewable generation as it makes up more and more of the UK’s generation mix.

Transforming the network

2017 will be known as the break-through year of batteries and electric vehicles (EVs). With the dramatic fall in battery prices we’ve seen a rush of parties buying up battery capacity, hoping to profit from what were lucrative flexibility markets. National Grid have seen batteries flooding into the Firm Frequency Response (FFR) market, attempting to secure profitable long-term contracts to satisfy investors. Market dynamics mean this is increasingly challenging. In a rapidly changing marketplace, a variety of revenue streams must be considered. Battery operation must encapsulate multiple markets to insure against future movements and maximise profits, while ensuring safe and careful operation of the asset such that state of charge, warranty, and connection limits are respected, an area where Open Energi has significant expertise.

EV take-up is accelerating more quickly than many estimated – UK sales of EVs and plug-in hybrids were up 27% in 2017 – and the need for managing this additional demand in a smart, automated way is crucial to alleviate strain on local networks. We have explored the enormous potential of EVs to provide flexible grid capacity and are working with a consortium to deliver the UK’s first domestic V2G trial.

While in the short term, as the big electricity players try to keep up with the changing needs of the system, flexibility markets present a degree of uncertainty, the long term need for demand-side response (DSR) and frequency regulation cannot be underestimated.

Grid Frequency and FFR

As the System Operator, National Grid must maintain a stable grid frequency of 50Hz. Generation and demand on the system must be balanced on a second-by-second basis to ensure power suppliers are maintained. Traditional thermal plant operates with physically rotating turbines, which carry physical inertia and act to stabilise the frequency. With the increase in generation from non-inertial sources (e.g. wind turbines, which don’t carry inertia in the same way, and PV cells), this stability is reduced. Larger deviations in frequency can result in the event of a power station, or interconnector trip, for example.

During 2017, the largest low frequency event (demand greater than supply) occurred on 13th July, when it dropped to 49.57Hz. Given that National Grid’s mandate is to keep it within 0.5Hz of 50Hz, this was rather close! Figure 3 shows the period, and we see a sudden drop in frequency which typically indicates the trip of a significant generator. In this case, the fault was at the French interconnector. Here, what usually functions to improve energy continuity and smoothen geographical variations in supply was the culprit for the biggest second-by-second imbalance in 2017!

The largest high frequency event (supply greater than demand), during which frequency reached 50.41Hz, occurred at the end of October, was much more gradual and seems to have been due to a combination of several effects. Demand typically drops quite steeply this late in the day, so large CCGT plants are reducing their output and on this occasion a sudden drop in wind-generation seemed to have been over-compensated by pumped storage.

Figure 3. Lowest and Highest frequency extremes in 2017.
Figure 3. Lowest and Highest frequency extremes in 2017.

As well as these relatively rare large frequency events, there are excursions that can last for several hours. Figure 4 shows two periods where the frequency deviated from 50 Hz. In general, the average frequency is 50Hz, and therefore any response to frequency regulation averages out to zero. However, over these medium-term time periods the average frequency is not 50Hz. For flexible assets like batteries, that are dynamically responding to correct grid frequency during such periods (performing FFR) the state of charge is affected.

For this reason, the state of charge of the battery must be actively, and automatically, managed – so that optimal state of charge is quickly recovered after such events. The battery is then able to continue to perform FFR, or other services such as peak price avoidance or price arbitrage in wholesale markets. The state of charge (bottom panels in Figure 4) can also have strict warranty limits set by the manufacturer.

Figure 4: Extended frequency events and impact on battery state of charge
Figure 4: Extended frequency events and impact on battery state of charge

Interestingly, 2017 saw an increase in both the number of frequency events (usually defined as frequency excursions larger than 0.2Hz away from 50Hz), and frequency mileage (defined as the cumulative deviation of the grid frequency away from 50 Hz), shown in Figure 5, particularly during the spring and autumn.

Could this be due to the large, somewhat unknown amount of PV on the system? It is distributed, meaning National Grid see PV generation as a fall in demand; they also have no control over it (unlike most other generation). PV efficiency is high in cold weather, so perhaps unexpectedly high and erratic solar generation on cold, sunny days in the Spring and Autumn led to a more unstable system this year, compared to 2016.

Figure 5. The grid has experienced more mileage and more events in 2016 than 2017, especially in March and October. Frequency “event” here is defined as a deviation of 0.1 Hz around 50Hz.
Figure 5. The grid has experienced more mileage and more events in 2016 than 2017, especially in March and October. Frequency “event” here is defined as a deviation of 0.1 Hz around 50Hz.

Figure 5. The grid has experienced more mileage and more events in 2016 than 2017, especially in March and October. Frequency “event” here is defined as a deviation of 0.1 Hz around 50Hz.

The rise of distributed generation, accelerating EV uptake, and plunging battery storage costs, are all driving a rapid transformation in the UK’s electricity system.  Managing these changes requires new approaches.  Demand-side response technologies, like Open Energi’s Dynamic Demand 2.0 platform, mean patterns of demand can be shifted in a completely carbon neutral way; enabling electricity to be consumed when it’s being generated: as the wind blows, or the sun shines. Rather than inefficiently changing the output of a gas fired power station to meet demand, we can make smart changes in demand up and down the country to meet generation, deliver local flexibility, and put consumers in control of their energy bills: delivering completely invisible, completely automated, intelligent DSR which paves the way for a more sustainable energy future.

By Wouter Kimman, Data Scientist, Open Energi

[1] For demand here and throughout this post we use INDO values as reported by ELEXON Ltd.

Faster Frequency Response: A Cost-effective Solution to Future System Balancing

open energi wind farm

Creating a sustainable energy future will take decades and the pace of technological development will lead to ideas and solutions that no one has even thought of yet. This innovation will come from the next generation of energy leaders, who are already conducting vital research at universities across the globe.

 Over the last year, we’re delighted to have been supporting Yifu Ding, who is studying for an MSc in Sustainable Energy Futures at Imperial College. Yifu has been assessing the value of faster frequency response times in power systems, and Open Energi’s Dagoberto Cedillos has been one of her supervisors. Yifu’s project was recognized as the Best MSc Research Project in the cohort, and we’re pleased to share a post from Yifu about her work.
Y_Ding_Headshot

What is System Inertia?

In a stable power system operating with a fixed nominal frequency (50 Hz in the UK) electricity supplies must closely match loads on a continuous, second-by-second basis. This is especially difficult during some special cases such as the power pick-ups after big football games or a royal wedding.

Undoubtedly, achieving such a real-time balance is not a simple thing, but there are many approaches. Large power systems have an inherent property which provides the quickest response for contingencies. In a conventional power plant like coal, gas and even nuclear, electricity is generated by a turbine, basically a large spinning mass of metal. The inertia stored in these rotating turbines provides an energy store which automatically stabilises the system and insulates it from sudden shocks. In an event of a generation outage or surge in demand, inertial energy is released which prevents the frequency from falling. Equally the inverse happens in the case of a sharp increase in electricity supply or decrease in demand.

After that, the system operator begins to manipulate power assets through an array of automated measures already in place (like different frequency response products) and by sending out manual notifications. In response to these, large-scale power stations adjust their outputs. Hydroelectric reservoirs release or pump water. Aggregators control loads or battery assets they manage to provide a response.

Challenges for System Balancing

In light of the decarbonisation trend, great changes have been undertaken in the UK power system. Old methods relying on fossil-fueled power plants to balance the system are challenged and we need to explore new options.

As a rule of thumb, we are losing the system inertia. According to the National Grid System Operator Framework (SOF) 2016, approximately 70% of the UK system inertia is provided by thermal power plants. Unfortunately, the rapidly increasing volume of renewable generation units with power electronics interfaces, including solar PV and wind turbines, are not synchronized with the Grid. Therefore they don’t contribute to the system inertia.

Fig1a synchronous coupling

Figure 1: Generators contributing (or not) to the system inertia (From National Grid SOF 2016)
Figure 1: Generators contributing (or not) to the system inertia (From National Grid SOF 2016)

In our research, we considered ‘Gone Green’ and ‘Steady State’ scenarios from National Grid Future Energy Scenarios (FES) 2017, to compare and contrast what could happen in the near-term future. We found out that the inertia of the UK system will fall from 198 GVAs in 2015 to 132 -155 GVAs by 2025, as large numbers of thermal power plants are closed to meet carbon reduction targets.

Figure 2: The future scenarios considered in this research according to National Grid FES 2017
Figure 2: The future scenarios considered in this research according to National Grid FES 2017

Why Faster Frequency Response?

From this point of view, our power system will become more ‘erratic’ than before due to lack of this self-stabilization property. To counter this we could use more Frequency Response (FR) services, or perhaps something else?

We can envisage a power system with a stable frequency as a large tank with a stable level of water. The current inlet and outlet represent the generation and demand respectively. If a sudden imbalance occurs between inlet and outlet, we need to respond quickly in case the water level becomes too low or overflows.

In this fashion, one of the effective solutions is delivering faster-acting response. In July 2016, National Grid launched and tendered a sub-second FR service called Enhanced Frequency Response (EFR). Currently it is provided by batteries which can respond fast enough to provide a similar level of security to the inertial response from conventional power generators.

Value of Enhanced Frequency Responses

A few statistics from our research and other documents give you an idea of the exact economic benefit from delivering this new FR service.

By developing an optimization mathematical model to simulate power generation, dispatch and balancing in a row, we estimated the economic benefits of EFR will reach £564 to £992 per kW by 2020. National Grid has already contracted 201 MW of EFR, therefore the total economic benefit is estimated to be up to £200 million. This result conforms to the estimation published by National Grid on 26 Aug 2016.

Figure 3: A screenshot of the daily power generation and dispatch outcomes from the optimization model.
Figure 3: A screenshot of the daily power generation and dispatch outcomes from the optimization model.

But this isn’t the whole story. Although the fast-acting FR service demonstrates many advantages, there are still obstacles when it comes to the implementation.  For the system operator, an issue which might arise is how to determine the optimal mix of those FR products. Otherwise some of them could be undersubscribed or oversubscribed as mentioned in System Needs and Product Strategy (SNAP) report from National Grid.

Stakeholders in the balancing markets, such as electricity storage operators, can make themselves invaluable by providing such a service. However, we should note that it is designed to be fulfilled continuously, meaning it’s unlikely to be delivered in combination with other network services. In this case, the operator can only obtain the single revenue from the asset, risky from an investor perspective. Providing such a service is technically challenging since it requires a sophisticated state of charge (SoC) control to meet the service specifications and manage battery throughput.

As we look into the future balancing markets, fast-acting FR services indeed provide a cost-effective solution towards the low-carbon power system. Planned streamlining of  procurement mechanisms and ongoing technology development will help to fully unlock its potential.

 

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.