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.

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 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.

The 4th industrial revolution: a smart power revolution?

Sainsbury's deliver demand side response from its stores UK wide

On the 8th September, James Heappey, Conservative MP for Wells took part in a House of Commons debate on the 4th Industrial Revolution.

In his speech he talked about the “smart energy revolution” that is underway in the UK today, and highlighted the pioneering work of two of Open Energi’s customers, Sainsbury’s and Aggregate Industries. Here’s what he had to say:

Speaking twice in 25 hours is a record for me, and I am grateful for the opportunity. I congratulate my hon. Friend Mr Mak, who has secured a worthwhile debate and opened it brilliantly. I apologise for being late, but I was working on the Energy and Climate Change Committee’s paper on renewable heat and transport targets, which will be released this evening. I commend it to the House: it is probably one of the most insightful Select Committee reports that Members will read all year. Indeed, all of our Committee’s reports are insightful.

In summing up yesterday’s debate, the Minister used some fantastic theatrical references, which I hope will become a tradition of his summing-up speeches. He has an encyclopaedic knowledge of the theatre, so we look forward to that. Today, I present, to use my own theatrical reference, the second part of my play in two parts, in which I will talk about the energy opportunities provided by the collision of emerging technologies and our existing energy infrastructure.

There is some dispute over whether this is the third or fourth industrial revolution. A book by Professor Jeremy Rifkin has become a bit of a bible for me, as I have sought to develop my thinking on how energy policy might evolve. He thinks that this is the third industrial revolution, but none the less it is an excellent read that very much pulls in the same direction as those who are advocating the fourth industrial revolution.

Ministers will already have looked in great detail at the National Infrastructure Commission’s “Smart Power” report, which is a fantastic publication setting out how we can harness all these wonderful technologies as we digitise the energy system. The reality, as the report observes, is that we could save £8 billion a year for the UK economy if we digitise our energy system and harness those technologies. That figure represents not just immediate savings on our energy bills, but gains in productivity.

Nicola Shaw, the head of National Grid, told the BBC “Today” programme last week that we are seeing

“a smart energy revolution across the country with consumption adjustments reflecting when energy is cheapest”.

The idea that we have to change our consumption habits to meet a changing energy market sounds like a nightmare to most people, but the reality is that we already have many of the technologies in our homes. Most major white goods manufacturers are producing smart appliances already: they are in our shops and, probably unknowingly, we already have them in our homes. Through the internet of things, they will all start to speak to one another to make sure that they operate at the most efficient and cost-effective time. They also report faults, so people will not have to carry on for years with a fridge that uses more power than it should, because it will already have flagged up its fault to whoever manufactured it. These are exciting times and the technologies already exist. It is not, in my view, going to be a case of opting into them, because manufacturers are building them as standard and they will increasingly do so.

The Government face a challenge in preparing our homes, businesses and society for the internet of things from an energy perspective, so I will give my thoughts on our system preparedness before moving on to examples of where we are already seeing the huge economic advantages.

As Ministers know only too well, the smart meter programme is the keystone in achieving the digitisation of our energy system, and I know that they will be keen to push on with that roll-out at best speed. Everything that we seek to do in bringing technological innovation into the energy space depends on those smart meters being in place to digitise the system. Similarly, on the way in which our grid is put together, we want all our generational capacity—from the smallest to the largest—to be able to speak in real time about what it is producing, so that we can have a more dynamic generation system. We also need to sort out the regulatory framework for storage, because at the moment people have, in effect, to pay for their energy twice: first when it is generated, and secondly when it is released from storage. Surely, that cannot continue for much longer.

We also have to make sure that our distribution networks—the substations in our communities—are capable of dealing with more dynamic demand and clustered demand, particularly overnight, when people might be taking advantage of cheap energy to charge cars, run the washing machine and tumble dryer, and heat immersion tanks. None of that will happen automatically without the Government paving the way. Thereafter, however, I am sure that these technologies will find their place in the market by themselves. They will make life better, and people will buy them as a result. The Government do not need to encourage people every year or so to change their mobile phone, because people just want to have the latest technology at their disposal. I am sure that that will be the case in this area if the Government create the right regulatory framework with energy policy.

I turn to storage. The price of storage has already come down from $3,000 per kWh to about $200 today, and it will come down even more quickly still. We saw over the summer reports about the Tesla Panasonic factory in Colorado, the construction of which is being accelerated quite rapidly given the increase in demand. These are exciting times, because storage is the key to flattening the energy supply curve and unlocking the real potential of renewables.

The real technological wizardry, however, is demand-side response. That may be a combination of words that many in the Chamber have not heard before, but it needs to be at the forefront of the way in which we discuss energy. Flattening the supply curve through the availability of storage deals with only half the problem; flattening the demand curve through demand-side management is equally important.

I have been hugely impressed as I have become enthused about DSR, and as I have gone around various companies that are delivering it, by the scale of the savings that it is bringing to businesses. Marriott hotels have signed up to a DSR contract that saves them hundreds of thousands of dollars a year. Workers at Aggregate Industries’ bitumen plants used to just turn up in the morning and fire up the boilers to get the bitumen tanks up to heat. They would operate over the course of the day, and then they would be switched off. Aggregate Industries now employs technologies that allow it to say, “Our tolerance is that we need to keep these tanks at a certain temperature, and provided that they are at that temperature, we can release energy back to the grid.” It does so, and it gets money for nothing as a result. By employing those technologies, it can sell back energy that it does not need, which it would otherwise just have paid for and wasted. That creates a huge saving.

Similarly, refrigeration is a massive cost for supermarkets and the food industry in general. Sainsbury’s has employed demand-side response, and the store in my constituency in Street, Somerset has released 20 kW of capacity back to the grid simply from DSR. That is extraordinary.

The other area that I want to touch on was the electrification of the transport system. I had to check very carefully with the Clerk of the Energy and Climate Change Committee about when I would find myself in contempt of Parliament, but I understand that if I draw on the evidence rather than on the report itself, it is fine. This is a hugely exciting opportunity for us to employ electric cars and electric haulage systems in the UK. The problem is that I am not sure that we yet have the infrastructure in place to support them, and I am not sure that we have the right fiscal structure to support them either.

I tried to buy an electric car over the summer, and sadly I found that their range was probably not quite enough to allow me to do my duties around my rural Somerset constituency. They are getting there, however, and we just need to incentivise the acceleration of the technology, so that we get beyond the 100-mile range to a range of 200 or 300 miles. If that happens, I think that people will, all of a sudden, go for electric cars quite quickly. All the incentives that the Government have in place—the £4,500 that they contribute towards the car and the contribution they make towards a charging point at the buyer’s home—are fantastic. The Government’s emphasis on establishing a charging infrastructure at motorway service stations and on main roads is also fantastic, but we really need to grow the infrastructure much more if people are to buy the cars and make the saving that we hope they will. The argument is that electric cars will make us more productive as well, particularly when we go beyond merely electric cars to electric autonomous cars, and we find that we can move around our towns and cities much more freely.

Interestingly, in the United States, Coca-Cola has employed hydrogen-electric hybrid vehicles for its entire fleet, and it has made a 20% reduction on its fuel costs. It made that huge saving by employing those technologies and electrifying its transport fleet, which is very exciting. We should look across at that and realise that this is not just something that people do if they are green and they want to be environmentally sensitive. It is something that an individual or a business can do if they want to reduce their operating costs—technology colliding with energy generation and energy consumption to make us more efficient and more cost-effective, and to make all our operating costs that bit cheaper.

Mr Deputy Speaker, you encouraged us to keep within 10 minutes, so I will summarise, rather than go into the many more examples that I am itching to provide. The bottom line is that, while we will focus very much on our digital infrastructure with broadband and 5G mobile phones and we will worry very much about the preparedness of our airports and air routes, as well as of our roads and rail, the energy infrastructure is just as important. In my view, alongside the broadband and mobile phone networks, the three sets of infrastructure of telecoms, broadband and energy will drive the fourth—or third—industrial revolution and allow us to harness all these fantastic technologies. We should seek to do so not just because we are seeking to arrest climate change, but because it is cost-effective, makes business sense, will increase productivity and, ultimately, will be great for our economy.

Access the full debate here.

How can machine learning create a smarter grid?

Dynamic Demand 2.0

Across the globe, energy systems are changing and creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, National Grid is facing shrinking margins, looming capacity shortages and unpredictable peaks and troughs in energy supply caused by increasing levels of renewable penetration.

At the recent Reinventing Energy Summit, Michael Bironneau, Head of Technology Development at Open Energi, explored how the same machine learning techniques that have let machines defeat chess and Go masters, can also be leveraged to orchestrate massive amounts of flexible demand-side capacity – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid; one that is cleaner, cheaper, more secure and more efficient.

For World Cities Day 2016, Michael talked to Nikita Johnson of Re:work about utilising data science in energy, creating a smarter grid, political challenges, and more.
What are the main transformative technologies that will help create a smarter grid?
A smarter grid is one where we can integrate renewable energy efficiently without having to keep polluting power stations online to manage intermittency. This requires energy storage to act as a buffer, reducing demand when supply is too low or increasing it when it is too high.

The cheapest and cleanest type of energy storage comes from flexibility in our demand for energy. Open Energi’s Dynamic Demand platform unlocks small amounts of stored energy from commercial and industrial processes – such as refrigerators, bitumen tanks and water pumps – and aggregates and optimises it second by second, creating a virtual battery.

How can machine learning be applied to help balance the grid?
The most transformative application of machine learning for grid balancing comes from unlocking and utilising flexibility in demand-side power consumption. Such algorithms can find creative ways to reschedule the power consumption of many demand and generation assets in synchrony to keep the grid in balance while helping to minimise the cost of consuming that power for energy users.

With sufficient data, a ML model can look at a sequence of actions leading to the rescheduling of power consumption and make grid-scale predictions saying “this is what it would cost to take these actions”. The bleeding edge in deep reinforcement learning shows how, even with very large scale problems like this one, there are optimisation techniques we can use to minimise this cost beyond what traditional models would offer.

What are the regulatory and political challenges to achieving a national smart grid in the UK?
Whatever your role in the vibrant menu of demand side innovations that are offered across Europe, a shared goal for serving consumers is advocating for the framework of flexibility adequacy at the energy system level. This opens so many possibilities – to facilitate Electric Vehicles, mitigate renewable intermittency, replace aging coal infrastructure, and realise a smart grid.

The key is market access. Currently, the UK market favours existing power generators to a disproportionate extent. To fully realise the potential of demand-side flexibility to help balance the grid, save energy and offer lower costs for consumers, we need a level playing field. Without it, there is a very real risk that we will lead ourselves into multi-decade contracts for power plants, paying for a system which is already over capacity and which has no incentive to get any smarter.

How can energy companies work with engineers and data scientists to achieve a more efficient energy system?
One obstacle that prevents many ideas from taking off is the lack of data to support them. If energy companies made more anonymised half-hourly power data available, data scientists and engineers working on new smart grid technologies would be able to validate these ideas quickly and cheaply. In the same vein, it would be a major breakthrough for grid balancing if energy companies made available APIs for reporting and accessing flexibility; it would allow companies like us to unlock enormous amounts of demand-side capacity and put them to good use balancing not just the grid but also helping to optimise the market positions of those same energy companies.

This post originally appeared on Re:work’s blog on the 31st October 2016.

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.

London’s spare gigawatt of power

London spare gigawatt of power

Lucy Symons, Policy Manager at Open Energi, explains how flexible demand could help power a sustainable future for London.

Projected population explosions in cities across the globe present urban planners with huge challenges. Between now and 2050, the number of Londoners alone is expected to increase from 8.6 million to 11.3 million, putting enormous pressure on energy infrastructure and requiring radical new solutions.

To meet the energy needs of 11.3 million Londoners in 2050, the Mayor is planning for a slew of new power plants as part of the enormous £1.3 trillion infrastructure spend earmarked in the London Infrastructure Plan. But there are alternative approaches to our current supply-side model that could deliver better value; we need to be original and also look at the demand-side opportunity.

Indeed, by taking a smarter, no-build approach to managing energy demand, London could shave off an eighth of the power currently used to keep the city’s lights on.

New modelling by Open Energi demonstrates that London has a whole gigawatt of ‘spare’ capacity in its current demand for energy: in-built flexibility that can be cheaply unlocked without the need to lay a single brick.

The challenge of matching supply with demand

London, like all mega cities, is still mostly fossil fuelled and this needs to change, fast. However, the rapid growth of renewable energy generation presents its own challenges, with spikes in electricity production that are often out of sync with times of high energy demand in homes and businesses; on a given day in winter, London’s energy demand peaks at 8GW between 4 and 7pm.

By contrast, at the height of summer, solar power supply follows the natural pattern of insolation- peaking at noon and in sharp decline by the late afternoon. Whatever the season, intermittency will be a persistent problem for balancing the London grid.

At present the generation infrastructure serving London is built to meet maximum possible demand. But with demand exceeding 7 gigawatts only 21% of the time, this is a hugely inefficient use of resources.

As London’s population grows, predicting electricity demand will be increasingly difficult. The GLA has forecast four scenarios, with demand in 2050 deviating from the 2015 baseline by as much as 30%. And this presents a major planning challenge.

Energy production local to demand

One approach is to throw more capacity at the problem, building London’s energy infrastructure for a theoretical peak that could be as much as 60% too high by 2050. Indeed, the Greater London Authority is already planning for local generation to meet 25% of London’s needs by 2025. Estimated total capital costs for this range from £50 billion to £100 billion.

While local generation undoubtedly has an important role to play, building 119MW of co-generation units requires space, which is already at a premium in London, and continues our reliance on carbon-emitting gas in a city struggling with air pollution.

And the challenge of building out clean supply-side alternatives is clear when looking at GLA projections for wind power for 2050, which depend on technological developments that will allow for small, decentralised turbines to be running right across the capital.

Flexibility local to demand

It’s a well reported fact that electricity margins are tighter than they have been for years and, as populations continue to grow, the need to balance energy supply and demand in order to mitigate the risk of power blackouts will be more important than ever. Grid agility and flexibility has traditionally been provided by building new supply assets, but a smarter approach can be found on the demand-side.

Demand response technology is, at its core, an intelligent approach to energy that enables aggregators to harness flexibility in our demand for energy to build a smart, affordable and secure new energy economy. True DSR technology invisibly increases, decreases or shifts users’ electricity consumption, enabling businesses and consumers to save on total energy costs and reduce their carbon footprints while at the same time enabling National Grid to keep capacity margins in check.

Using over 5 years of data from working with businesses and National Grid to deliver demand response from all kinds of equipment –  including heating and ventilation systems, fridges and water pumps – right across the UK, Open Energi has modelled London’s industrial and commercial energy use to reveal an estimated 1040 MW of flexible demand that could be invisibly shifted to provide capacity when it is most needed.

This gigawatt of flexibility is electricity currently being put to use in powering London’s homes and workplaces between 4 and 7pm – with over half used in retail, commerce and light industry.

Harnessing this flexible power – a sizable slice of London’s 8GW winter peak demand – is not a technology problem. Right now, Open Energi’s Dynamic Demand technology is connected to 3000+ machines, invisibly and automatically reducing, increasing or delaying power demand, depending on available supply. Given that the bulk of London’s flexibility comes from non-domestic sites (large commercial buildings, retail and industry), using Dynamic Demand to unlock this 654 MW of flexibility could be the cleanest and most cost effective way to provide the power for London to operate, businesses to grow and its inhabitants to lead healthy lives.

As a direct alternative to building new power plants, Demand side response is an efficient way to optimise the existing generation infrastructure- shifting 1GW out of the peak would save the need to build a new mega power plant, equivalent to the size of Barking Power station.

From where we stand, powering London is a data-driven problem. The answer lies in decrypting patterns of flexible demand.

Analysis conducted by Remi Boulineau, remi.boulineau@openenergi.com

 

The Business Case for Flexibility

The Business Case for Flexibility

The move to a low carbon economy coupled with rapid advances in technology and innovation are transforming electricity supply and demand. Grid agility and flexibility are essential as we move away from models of centrally dispatched generation and incorporate more intermittent renewable energy generation onto the system.

This flexibility can be provided in a variety of forms, from demand side response (DSR) and energy storage to new build gas generation. However, there is a clear merit order emerging in terms of both carbon and consumer cost of these offerings, and to enable this merit order to play out requires a technology-agnostic approach to the energy system, free of subsidies and long-term contracts that prevent these solutions from competing on an equal footing.

The National Infrastructure Commission’s Smart Power report signifies the concrete shift in thinking needed to unleash flexibility and shore up energy security for the UK. The conditions are right for innovation, and innovation is about being able to run systems effectively at tighter margins with no impact on reliability or risk through storage and invisible, automated and no-build DSR.

Demand response technology is, at its core, an intelligent approach to energy that enables aggregators to harness flexibility in our demand for energy to build a smart, affordable and secure new energy economy. True DSR technology invisibly increases, decreases or shifts users’ electricity consumption, enabling businesses and consumers to save on total energy costs and reduce their carbon footprints, while at the same time enabling National Grid to keep capacity margins in check. Although in its infancy, the UK’s demand side response market is a reality, delivering flexibility today.

Research by Open Energi, National Grid and Cardiff University published in October 2015 illustrates that smart demand side response technology can already meet the UK’s crucial grid balancing requirements faster than a conventional power station. Added to this, using new build gas to provide flexibility in a renewables-based system is counter-intuitive. DSR technologies are already working for the UK, providing flexibility to the UK grid at a far cheaper cost per MW than both batteries and gas.
This is precisely why National Grid has established its Power Responsive campaign as a framework for turning debate into action with a practical platform to galvanise businesses, suppliers, policy makers and others to seize the opportunity to shape the growth of demand side response collaboratively, and deliver it at scale by 2020.

It’s a well reported fact that electricity margins are tighter than they have been for a number of years, as illustrated by the NISM National Grid issued in late 2015. Knee jerk reactions to this are to incentivise infrastructure investment in power stations with long-term contracts, but this is inefficient and costly.

The £18 billion Hinkley Point project is a case in point. Looking at future demand curves, once the plant is up and running, there will be periods when its supply exceeds demand for power across the whole of the UK. The UK should capitalise on smart options for delivering flexibility which can be delivered faster and more cheaply than traditional infrastructure projects. Behind the meter solutions are much more empowering to consumers.

The conditions are right for innovation, and innovation is about being able to run systems effectively at tighter margins with no impact on reliability or risk. This is possible through storage and DSR. In this ‘year of innovation’, disruptors must be able to implement their solutions on a free-market basis, without guarantees and subsidies for certain technologies that block competition. To achieve flexibility goals, government must be technology agnostic.

US regional transmission organisation PJM provides a useful case study, with its real-time and near-term energy markets that incentivise the best and cheapest technology at any given time. PJM’s approach has seen a proliferation in innovative flexibility solutions accompanied by falling costs for customers. According to ABB, two thirds of the 62MW of storage deployed in the US in 2014 was located in PJM territory . Market intervention is not necessary for energy system innovation to flourish. In fact, PJM shows that the opposite is true.

National Grid is already on the case with its Enhanced Frequency Response auction, which has seen 63 generators, energy storage companies and DSR aggregators pre-qualify to bid for contracts that will make it easier to manage the system. Demand side response is part of a wider energy market picture that must focus on flexibility and achieving the lowest cost for consumers. If just 5 per cent of peak demand was met with flexible power, the response would be equivalent to the generation of a new nuclear power station, without the huge costs to consumers.

Government needs to recognise that gas sits at the bottom of the flexibility merit order. Storage will undoubtedly play an important role, but Rudd’s pledge to explore long term storage incentives to get battery market moving are anti-competitive, not to mention unnecessarily costly for consumers.

DSR technology is already working today – not only to reduce electricity load at peak times, but also to increase load when demand is low and support National Grid’s second-by-second frequency balancing needs. And this is happening at both national and local scales.

2016 must be the year of flexibility and, to achieve this, we need consolidated markets that are technology agnostic. An energy department that styles itself as pro-innovation must send clear signals to innovators that it doesn’t pick winners.

David Hill, Business Development Director, Open Energi

Webinar: Using Big Data To Enable Smarter Energy

Data Centre Image ISO9001

February 24th, 2016 at 10am GMT/ 11am CET

Source: Hortonworks

Open Energi are presenting a live webinar on the 24th February 10am GMT, it’ll also be available and on-demand immediately below.

Energy wasted through excess capacity is one of the world’s most challenging problems especially in developed countries where energy doesn’t come cheap. Meet Open Energi, an organization working with businesses in the UK to harness excess capacity from their equipment and aggregating it to create a virtual power station to better meet demand. Join this webinar to learn how Open Energi uses data to fuel its virtual power station enabling them to provide cheaper and efficient uses of energy across the UK powered by the Hortonworks Data Platform.

Topics covered:

Exploiting Excess Energy Capacity
Building a Virtual Power Station
Details of the Big Data platform driving the future of energy consumption

Mapping Britain’s Heat Storage Potential

Bitumen tanks

Chris Kimmett, Commercial Manager, Open Energi

The energy system is undergoing a huge transformation away from centralised generation to small-scale, distributed power. National Grid’s Future Energy Scenarios (FES) models indicate that by 2020, small-scale, distributed generation will represent a third of total capacity in the UK and, as a result, speed of response to changes in energy supply and demand will be more important than ever.

And it is not only the increase in distributed generation that will prove challenging for the UK grid. The coal-fired Ferrybridge, Longannet, Fiddler’s Ferry and Rugeley are all expected to come offline this year, and with gas power stations procured under the Capacity Market now in doubt, the cushion between supply and demand is smaller than ever.

A new source of flexibility is urgently required, and storage to provide this flexibility will be an increasingly essential part of a responsive, secure and sustainable energy future for the UK.

Energy storage is commonly understood to mean batteries and pumped hydro systems. While both are valuable, current costs, installation times, and issues around recycling and decommissioning are all prohibitive to wider deployment. But storage exists in a number of forms, including through demand side response (DSR), which takes advantage of latent heat in energy-intensive equipment and devices to create new flexibility for the grid.
If too much energy is supplied at any given time, it doesn’t have to be stored in a battery: instead, Internet of Things (IOT) based forms of demand response can adjust the consumption of energy-intensive devices to make use of power when it is available. In instances when there is not enough power, demand can be deferred rather than drawing from a battery to supplement supply.

This smart DSR approach is ideally suited to heating and cooling assets that have the characteristics of stored energy devices.  By harnessing existing everyday equipment, from fridges to furnaces, and invisibly switching them on or off for a few minutes at a time, energy demand can be adjusted to meet available supply in real-time, creating a distributed storage technology.

Take the asphalt plants which manage the complete asphalt production process for road construction as an example. Liquid bitumen for road surfacing is stored in large, well-insulated tanks, and a heater maintains the temperature of the bitumen between a low set point (typically 150 degrees C) and a high set point (typically 180 degrees C).

These tanks have “thermal inertia”, meaning the amount of energy they use can be adjusted and the temperature of the bitumen won’t be immediately affected: Bitumen tanks can be switched off for an hour and the temperature may only fall by between 0.5-15 degrees C.

Using demand response technology, bitumen tanks can deliver a full response to National Grid within two seconds (quicker than traditional thermal generation) and for up to 30 minutes, provided they are within their set-points. The average duration of Open Energi’s switch requests to bitumen tanks is just 3.3 minutes.

Cooling systems such as supermarket refrigeration also provide a distributed storage network that can help to balance UK-wide electricity supply and demand in real-time.

Open Energi estimates that if Dynamic Demand was deployed in the commercial refrigeration assets of the five largest retailers in the UK, it could meet approximately 6% of the UK’s total 1.8GW requirement for Frequency Response, roughly equivalent to 100 MW. This would generate revenues of up to £10 million a year for the asset owners and reduce UK CO2 emissions by around 227,600 tonnes a year.

Other latent heat storage assets include: heating, ventilation, air conditioning, and hot water boilers in commercial property; electric induction furnaces, ovens and melting pots in foundries and metal processing sites; and heaters and aerators at water processing sites.

Because these devices have already been built, it is possible to aggregate the stored thermal energy they contain and build a virtual power station at a fraction of the cost of building a grid scale battery or new generation capacity. The capital cost of building a new peaking power station can be up to £5 million per MW and battery systems in the region of £0.5 million-£1.8 million per MW. A MW of demand response, on the other hand, costs around £200,000 to aggregate.

DSR, coupled with on-site generation and energy storage technologies means that the energy market is no longer a linear value chain driven by fossil fuel production but is becoming decentralised and bi-directional; creating a new energy economy where energy consumers can both take and provide service back to the grid and generate revenue.

To realise the full potential of DSR technology we now need to further understand where the potential for flexibility, including latent heat, lies across the UK’s entire electricity network: assessing both regions and sectors.

In the same way that traditional energy commodities like oil, gas and coal are mapped by geologists to identify resource rich areas, a flexibility mapping process will enable demand response aggregators to identify the DSR ‘hot spots’. This in turn will give business, industry and policy makers the confidence to invest in DSR technology ahead of building additional spinning reserve, and the certainty they need to plan for a future where flexible Demand Response plays an integral role in delivering a secure and resilient energy system.

By using land data from regional authorities, for instance the GLA for London, the industry can develop a better understanding of where the flexibility potential lies, whether that be in heavy industry, commercial buildings or residential areas.

Open Energi is working to map flexible demand in the UK from the bottom up, asset by asset, sector by sector, to model the capacity in the market and demonstrate how much generation can be displaced.

Increasing flexibility on the grid has historically meant building more generation, but latent heat in energy intensive equipment presents a hugely valuable opportunity. And through mapping, this opportunity can be realised at scale for the UK.