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

Tesla South Mimms Supercharger and PowerPack

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

At South Mimms Welcome Break Motorway Services, we have installed a 250kW/500kWh Powerpack alongside one of Tesla’s largest and busiest UK charging locations. The Supercharger site can charge up to 12 cars at one time, and since popular charging periods often coincide with peak periods of grid demand – between 4pm and 7pm, when electricity prices are at their highest – flexible solutions are needed to ease the strain on local grids and control electricity costs.

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

Open Energi own and operate the Powerpack, which is part of our portfolio of assets that help maintain the frequency of the grid. Combining batteries and electric vehicles makes vehicle charging part of the solution to integrating more renewables without affecting drivers, unlocking vital flexibility to help build a smarter, more sustainable system.

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

How demand flexibility can boost the benefit of a Corporate PPA

solar panels

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

The rise of corporate PPAs

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

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

How does a corporate PPA work?

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

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

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

Aligning supply and demand

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

Matching factory demand and renewable generation

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

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

Cutting costs with demand flexibility

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

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

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

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

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

How Artificial Intelligence is shaping the future of energy

Artificial Intelligence can unlock demand side flexibility for end users

Across the globe, energy systems are changing, creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, large fossil fuelled power stations are being replaced by increasing levels of widely distributed wind and solar generation. This renewable power is clean and free at the point of use but it cannot always be relied upon. To date National Grid has managed this intermittency by keeping polluting power stations online to make up the difference but Artificial Intelligence offers an alternative approach.

What’s needed is a smart grid which can integrate renewable energy efficiently at scale without having to keep polluting power stations online to manage intermittency. This requires energy storage to act as a buffer, reducing demand when supply is too low or increasing it when it is too high. Most people associate energy storage with batteries, but the cheapest and cleanest type of energy storage comes from flexibility in our demand for energy.

This demand-side flexibility takes advantage of thermal or pumped energy stored in everyday equipment and processes, from an office air-con unit, supermarket fridge or industrial furnace through to water pumped and stored in a local reservoir. The electricity consumption patterns of these types of devices are not necessarily time-critical. Provided they operate within certain parameters – such as room temperature or water levels – they can be flexible about when they use energy.

This means that when electricity demand outstrips supply, instead of ramping up a fossil fuelled power station, certain types of equipment can defer their electricity use temporarily. And if the wind blows and too much electricity is being supplied instead of paying wind farms to turn off we can ask equipment to use more now instead of later.

Making our demand for electricity “intelligent” in this way means we can provide vital capacity when and where it is most needed and pave the way for a cleaner, more affordable, and more secure energy system. The key lies in unlocking and using demand-side flexibility so that consumers are a) not impacted and b) appropriately rewarded.

At Open Energi, we’ve been exploring how artificial intelligence and machine learning techniques can be leveraged to orchestrate massive amounts of demand-side flexibility – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid.

We have spent the last 6 years working with some of the UK’s leading companies to manage their flexible demand in real-time and help balance electricity supply and demand UK-wide.  In this time, we have connected to over 3,500 assets at over 350 sites, operating invisibly deep with business processes, to enable equipment to switch on and off in response to fluctuations in supply and demand.

Already, we are well on the way to realising a smarter grid, but to unlock the full potential of demand-side flexibility, we need to adopt a portfolio level approach. Artifical intelligence and machine learning techniques are making this possible, enabling us to look across multiple assets on a customer site, and given all the operational parameters in place, make intelligent, real-time decisions to maximise their total flexibility and deliver the greatest value at any given moment in time.

For example, a supermarket may have solar panels on its roof and a battery installed on site, as well as flexibility inherent in its air-con and refrigeration systems. Using artificial intelligence and machine learning means we can find creative ways to reschedule the power consumption of many assets in synchrony, helping National Grid to balance the system while minimising the cost of consuming that power for energy users.

Lack of data is often an obstacle to progress but we collect between 10,000 and 25,000 messages per second relating to 30 different data points and perform tens of millions of switches per year. This data is forming the basis of a model which can look at a sequence of actions leading to the rescheduling of power consumption and make grid-scale predictions saying “this is what it would cost to take these actions”. The bleeding edge in deep reinforcement learning shows how, even with very large scale problems like this one, there are optimisation techniques we can use to minimise this cost beyond what traditional models would offer.

Artificial Intelligence model learning to control the electricity consumption of a portfolio of assets

Graph of AI model

More rapid progress could be made across the industry if energy companies made more anonymised half-hourly power data available. It would enable companies working on smart grid technologies to validate these ideas quickly and cheaply. In the same vein, it would be a major breakthrough for balancing electricity supply and demand if energy companies made available APIs for reporting and accessing flexibility; it would allow companies like Open Energi to unlock enormous amounts of demand-side flexibility and put it to good use balancing not just the grid but also helping to optimise the market positions of those same energy companies.

In the UK alone, we estimate there is 6 gigawatts of demand-side flexibility which can be shifted during the evening peak without affecting end users. Put into context, this is equivalent to roughly 10% of peak winter demand and larger than the expected output of the planned Hinkley Point C – the UK’s first new nuclear power station in generations.  Artificial Intelligence can help us to unlock this demand-side flexibility and build an electricity system fit for the future; one which cuts consumer bills, integrates renewable energy efficiently, and secures our energy supplies for generations to come.

Michael Bironneau is Technical Director at Open Energi. He graduated from Loughborough University in 2014 with a PhD in Mathematics and has been writing software since the age of 10.

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

Sunshine through tree tops - green energy

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

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

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

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

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

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

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

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

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

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

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

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

In the UK, Open Energi’s analysis suggests there is 6 gigawatts of peak demand which can be shifted for up to an hour without impacting end users. Put into context, this is equivalent to roughly 10% of peak winter demand and larger than the expected output of the planned Hinkley Point C — the UK’s first new nuclear power station in generations.

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

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

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

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.

Open Energi Harnessing the power of IoT for cleaner, more efficient and affordable energy

David Hill, Business Development Director at Open Energi speaks to theCUBE about how Open Energi is harnessing the benefits of connectivity to bring customers more efficient, more affordable and, ultimately, cleaner energy.

“We were an IoT company before we even knew what IoT was,” said David, discussing how Open Energi was founded pre-Hadoop. Becoming Hadoop customers was a “huge leap,” and Hortonworks Dataflow services are enabling much more cost-effective integration that has Open Energi extremely excited about the future.

David spoke to theCUBE whilst attending Hadoop Summit 2016, Dublin – http://bit.ly/1qIrgEN

Powering a Virtual Power Station With Big Data

Michael Bironneau, Data Scientist at Open Energi, discusses powering a virtual power station with big data.

At Open Energi in order to prove that we’ve delivered our Dynamic Demand service to National Grid and kept it running at optimum, we need to analyse large amounts of data relatively quickly. We’re also making our service smarter so that more assets will be able to participate in Dynamic Demand than before. This is where Big Data and Hortonworks Data Platform come in.

Big Data is a phrase that has been floating around companies like Google for the last two decades. It has never really had a precise definition, but when used casually it usually means that someone somewhere is running out of space for your data and/or computing power to analyse it, this can also mean that your data is so unorganised it is difficult if not impossible to analyse. Data is the most important asset when considering Dynamic Demand, it tells us when to flex certain assets, it proves we are providing a service and it allows us to better understand our portfolio.

Michael was speaking at the 2016 Hadoop Summit, Dublin – http://hadoopsummit.org/dublin/

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 Challenges of Device Management in Energy

The Challenges of Device Management in Energy

1248 CEO Pilgrim Beart chats with David Hill, Business Development Director at Open Energi.

P: “David, tell us a bit about what you’re trying to achieve at Open Energi”

D: Open Energi is deploying ‘Internet of Things’ software within electricity consuming assets and paving the way for a new energy system; a system that is cleaner, cheaper, more efficient and more secure than the system we have today.

Our software is able to measure and monitor machine and appliance behaviour in real-time and subtly adjust electricity consumption in response to signals from the market, preventing fossil fuelled power stations from coming online and maximising our use of renewable energy, without any impact on consumer living standards or business productivity.

Every appliance or machine that we connect to using our Dynamic Demand software is another step towards removing power stations from the electricity system all together – and the money that would have been paid to those power stations is instead paid to British businesses.

P: “That sounds like a classic IoT application to me – making better use of a resource, with benefit to all the parties concerned – and helping address climate change too. What stage have you’ve reached in this ambitious goal?”

D: To date we have signed over 25 customers and deployed our software within over 1230 individual assets across 275 sites. Appliances range from Heating, Ventilation and Refrigeration Appliances in Sainsbury’s, to Water Pumps and Waste Management Equipment in United Utilities. The total power of all the devices that Open Energi interacts with is about 45MW, of which we currently bid about 15MW – around 30% – into energy markets operated by National Grid.

30% represents what is typically flexible at any given moment in time. The remaining power is being used for its primary purpose, e.g. heating supermarkets or pumping water. It is only by being able to identify flexibility on a second by second basis that we are able to provide this service.

P:”And can you give us some idea of the sort of challenges that you’ve encountered as you’ve grown in scale?”

One of the key challenges we face is being able to interface with different industrial and commercial processes – ranging from water pumps to bitumen storage tanks to refrigeration systems.  These systems operate in very different ways and generate different kinds of data, so a key challenge for us is being able to collect and view this data in a coherent way, and then understand how all these different processes are consuming energy. 

Overcoming this problem is an essential part of how we can provide a consistent and reliable service to National Grid from many thousands of individual assets.  Today many aspects of our interface with each of these processes inevitably become bespoke, which then leads to challenges in how we maintain and operate the software on all of our devices, particularly as we scale up.

P: “Your growing need for device management rings very true for us – it’s a common story as a connected service starts to achieve scale. We encountered something similar at AlertMe as we scaled from 1,000 to 10,000 devices, which is why we decided to focus on that challenge at 1248. Can you put some more meat on the bones for us?”

A key area for us is being able to simplify how we connect and exchange information with so many different control systems and processes.  Standards could play a key part here – both in terms of how we connect to other devices and what information we exchange.  This would ease the process of connecting and maintaining devices as we scale up and allow us to focus on getting the maximum value out of the energy flexibility that we’re tapping into.

For example, in the future we believe that all appliances with the ability to provide flexibility to the electricity market will come with those principles built in off-the-shelf, so that an air conditioning controller designed to maintain room temperature is every bit as used to responding to electricity market signals as it is to temperature, likewise with water pumps and bitumen tanks.

Device management is very important when trying to operate service based on very large numbers of connected devices; we can only understand and quantify energy flexibility if we have an accurate understanding of which devices are online, and whether they are functioning correctly. Moving from the world we are in now, to the future I just outlined, will need collaboration with companies like 1248.

P: “David, thanks so much for sharing this with us.”

This blog post was originally featured on www.1248.io

The IOT technology meeting the UK’s grid balancing needs faster than a power station

Tech image

Chris Kimmett, Commercial Manager, Open Energi

It’s a well reported fact that electricity margins are tighter than they have been for a number of years and, as we move towards winter, talk will increasingly turn to the need to balance energy supply and demand in order to mitigate the risk of power black outs in the UK.

Almost all of the UK’s grid balancing has traditionally been done by coal and gas. But the EU’s Large Combustion Plant Directive has limited running hours at a number of plants and in the past twelve months both Longannet and Eggborough power stations, which currently provide around 5% of the UK’s capacity, have announced they will be closing their doors in 2016.

Add to this the solar and wind explosion – by the end of 2015 experts predict the UK will have 10GW of solar power, a benchmark most thought wouldn’t be reached until 2020, and by 2020 National Grid’s Future Energy Scenarios indicate that small-scale, distributed generation will represent a third of total capacity in the UK. This considered, we see that tomorrow’s electricity landscape will look very different to that of today.

The transformation of the energy system away from centralised generation to small-scale, distributed power means that speed of response to changes in energy supply and demand will be more important than ever.

Indeed, while most people are focusing on the tight capacity margin between supply and demand, the real blackout threat could come from generators being unable to respond within the required window to balance instantaneous shifts on the grid.

For more than 12 months, energy data analysts at EnAppSys have been monitoring grid frequency and analysing large deviations which, if not managed, can lead to instability. EnAppSys’ director Paul Verrill says that while we need to ensure the system has sufficient supply to meet demand, the real risk of blackouts could come from this second issue that often falls under the radar: a lack of capacity able to deliver additional power within the required timeframe.

Grid agility and flexibility will be essential as we move away from models of centrally dispatched generation, and National Grid, via its Power Responsive campaign, has already asserted that demand side response (DSR) will play an increasingly vital role in building a resilient, sustainable and affordable electricity system for the future.

This is especially pertinent given the results of new research by Open Energi, National Grid and Cardiff University, which suggests that smart demand side response (DSR) technology can meet the UK’s crucial grid balancing requirements faster than a conventional power station.

The latest research paper, which forms part of the ongoing collaborative research programme between Open Energi, National Grid and Cardiff University, titled Power System Frequency Response from the Control of Bitumen Tanks, looks at the feasibility of DSR to provide a significant share of frequency balancing services.

To test the scale of the opportunity for industrial heating loads to provide frequency response to the power system, bitumen tanks (which contain the glue that binds our roads together) equipped with Dynamic Demand technology were tested in combination with National Grid’s model of the GB transmission system.

Dynamic Demand deployed at scale is able to contribute to the grid frequency control in a manner similar to, and, crucially, faster than that provided by traditional peaking power generation – not to mention more cleanly and cheaply.

Field tests showed that full response could be provided in less than two seconds, as compared to 5 – 10 seconds for a thermal generator. Large scale deployment of Dynamic Demand will reduce the reliance on frequency-sensitive generators and ensure that the grid stays balanced in a cost-effective, sustainable and secure manner.

The research simulations help to shape National Grid’s understanding of DSR as a replacement for frequency-sensitive generation and will be used when they are planning their requirements for grid network operation in the future – with huge impacts on the future of our energy mix.

When launching Power Responsive, National Grid CEO Steve Holliday said: “The move to a low carbon economy coupled with rapid advances in technology and innovation are transforming our electricity supply. But supply is only half the story. The challenge now is to exploit new opportunities to radically evolve our energy system by changing the way we use electricity.”

And this is why the research findings are so significant.

With more renewables and decreased thermal generation, ‘inertia’ on the Grid will decrease, making frequency more unstable. To counteract this effect we need faster response, so by rolling out Dynamic Demand today we are future proofing the Grid.

With their new Power Responsive campaign, National Grid has recognised the need for a new source of flexibility and have stated they are committed to scaling the smart DSR industry.

Demand side response is intelligent energy usage. By knowing when to increase, decrease or shift their electricity consumption, businesses and consumers will save on total energy costs and can reduce their carbon footprints. It is the smart way to create new and efficient patterns of demand.