Anglian Water has partnered with redT and Open Energi to have energy storage facilities installed alongside solar panels at one of its water treatment works.
The water company has purchased a 60kW/300kWh redT energy storage machine to install alongside a 450kWp solar PV system. This will enable it to store excess solar generated during the day and use it at other times, to reduce the site’s reliance on the grid.
As the largest power consumer in the East of England, reducing reliance on “volatile grid electricity” will help optimise a £77 million energy bill, which is one of the company’s “most significant” operational costs.
Water utility Anglian Water is set to install an energy storage machine controlled by Artificial Intelligence (AI) technology at one of its water treatment facilities, in a move it claims will increase the site’s solar generation by 80%.
The 60kW/300kWh storage device, designed by energy storage firm redT, will be set up at the company’s ‘pathfinder’ site in Norfolk to bolster the performance of its existing photovoltaic (PV) array from 248kWp to 450kWp.
The machine, which can store enough energy to power the facility for at least five hours, will enable Anglian Water to store surplus power generated by the array for use within its own operations. Meanwhile, it will use AI software to provide real-time balancing and energy flexibility services. The machine is expected to have a lifespan of 25 years.
Called Dynamic Demand 2.0 and designed by Open Energi, the AI software will optimise the site’s energy consumption and stack multiple demand-side value streams, enabling Anglian Water to take advantage of wholesale energy price arbitrage. In total, the installation is expected to halve the site’s electricity bills by 2050.
Future of Utilities: Smart Energy is set to bring together 300+ attendees for two days of collaboration discussing energy storage, supply and smart grid developments.
Featuring technology-driven content about how to make energy retail smarter, and systems more flexible, Smart Energy will showcase the experiences of a wider range of energy companies than ever before.
Open Energi’s Commercial Director David Hill will join a panel session to explore the business case for storage and different approaches from across the value chain.
Held annually in Scotland, All-Energy connects the UK’s renewable energy community, bringing energy suppliers, developers, investors, technology developers and policy makers together to conduct business, network and learn.
Our Head of Data Science, Dr Robyn Lucas, is joining a panel on Energy Storage at this year’s conference to share our views on the business case for behind-the-meter battery storage.
Date: 2-3 May
Location: SEC Glasgow
Session: Smarter energy storage at the local level
Topic: Beyond FFR: making the business case for BTM battery storage
Open Energi is delighted to be exhibiting at Water Industry Energy Conference 2018. Now in its 5th year, the conference will explore how water companies can identify commercial opportunities in energy.
Finding innovative ways to optimise existing assets, better utilise renewable technologies and generate energy from new sources are key to cutting costs and driving efficiency, and therefore essential to a successful business strategy.
This established event will analyse the business benefits of DSR, storage and biofuels, as well as behaviour change strategies and regulatory frameworks. We look forward to seeing you there.
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.
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.
It 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.
The 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.
At Open Energi, we are teaming up with energy suppliers and their customers to help make the most of the flexibility in their energy consumption. Using smart demand flexibility to sustainably balance the system, we can mitigate the risk of volatile prices and help reduce rising system charges.
The balancing act
Electricity can’t be stored efficiently or cheaply at scale, so electricity suppliers must balance the energy that they produce themselves or procure from third parties with the energy that their customers use. This means, ahead of time, forecasting how much electricity is going to be generated, forecasting customer demand, and taking any actions to balance them out: buying or selling additional electricity as required.
Any imbalance between generation and demand can result in suppliers facing costly charges from National Grid, who are forced to act in real time to balance the system. Some of the balancing actions that National Grid takes to ensure the lights stay on are expensive and polluting, and lead to gross inefficiencies in the system. During periods when the system is short (insufficient generation / high demand) it might call on a thermal power station to increase its output. Similarly, when the system is long (too much generation / low demand), a thermal power station could be asked to decrease output.
For the flexible energy generators of the UK – namely CCGTs – to be able to respond to these calls, they are run at < 100% of their maximum capacity. The inefficiencies here are twofold. The plants are not run optimally – they use more fuel and produce more carbon per MWh of electricity produced – and, more power stations are required to meet the nation’s electricity requirements. Balancing actions, by their nature, are also taken very close to real time, often outside of the market, which pushes prices up.
An alternative to balancing on the generation-side is to do it on the demand-side: instead of increasing or decreasing the output of a power station, decrease or increase the demand of electricity users. By enabling flexibility behind the meter, for example using battery storage alongside inherent process flexibility, demand-side response can provide an efficient and economical (roughly an order of magnitude cheaper than more traditional methods1) way to balance the system.
Rising system prices
National Grid recovers the cost of balancing from suppliers and generators through Balancing Services use of System (BSUoS) charges, which are passed onto the consumer. A large part of these charges are driven by the imbalance, or system price, which quantifies the cost of balancing energy of the system per half hour period by asking power stations to turn up or down. High prices usually occur when system margins are small; when there is a lack of surplus generation that can be called on. Similarly, low, or even negative prices can occur when there is a surplus of generation. This typically happens during periods of low demand, when solar power is at a maximum – for example on a sunny weekend day.
In the last 6 months or so we have seen the highest and most volatile system prices ever. They peaked at over £1500/MWh in November 2016, compared to an average cost of about £40/MWh over the last year. This peak was caused by a combination of factors. Much of the UK’s aged coal fleet was placed in Supplemental Balancing Reserve (SBR) to be called upon only as a last resort. Then, maintenance to the French nuclear fleet (causing the UK to export rather than import power through the French interconnector) coincided with maintenance to some UK gas peaking plants and low wind speeds, creating a situation where the system got very, very short. When one generator pushes prices up, and these high prices get accepted by National Grid, other generators are likely to follow suit to maximize their profits. For suppliers, this means that an imbalance of a few MW over a few half hours at the wrong time can suddenly become very, very expensive.
Figure 1 shows how system prices have risen since January 2016. With BSUoS similarly rising, suppliers can no longer afford to be complacent with their self-balancing.
Thus, suppliers are increasingly looking to protect themselves against the risk of coming up short. This is particularly true of renewable generators: you can’t make the wind blow harder at the same time as customer demand peaks (whereas you can burn more gas). Rather than buying in more conventional ‘brown’ (rather than ’green’) generation to make up any gaps at the last minute, or paying the imbalance price on any shortfall, an alternative is to use the inherent flexibility in connected customer loads to alter your demand, and better align with the power being generated by the wind. Instead of flexing the generation, flex the demand.
Flexing electricity consumption
Here at Open Energi, we are using our experience with Dynamic Frequency Response to flex the energy usage of large industrial & commercial consumers to balance the books of their renewable supplier. By intelligently talking to equipment which has energy stored in its processes we can shift electricity consumption without affecting the operation of a customer’s site. For example, the stored energy in a bitumen tank means we can delay heating it for an hour with very little impact on its temperature. Given notice by a supplier that they are short in the next hour and so require a reduction in demand, or, they think system prices will be high, we can delay turning on the tank’s heater until after the price spike.
Figure 2 shows a typical bitumen tank. The blue line shows the tank under ‘normal’ operation and the orange line shows the tank under Open Energi control. Following a request from the supplier (given approximately 30 minutes before hand) to reduce demand at 11am, we can delay switching the tank on, without affecting its operational parameters (the temperature always remains within set limits). We then allow the tank to switch on and heat up after the price spike, shifting its power consumption.
Do this across a portfolio of tanks, and you make a sizeable reduction in the supplier’s demand during periods when they would otherwise be short: see Figure 3. The energy is recovered later, and, given the energy storage in any one asset, this definition of ‘later’ can be flexible.
Suppliers save money by avoiding costly imbalance prices and mitigate the risk of price volatility, while managing renewable intermittency and reducing the need for brown generation. By partnering with innovative suppliers who create a market for such flexibility in an open and accessible manner, businesses can use technology to deliver smart demand side flexibility, in real time, with no impact on their operations, while saving money on their electricity bills. This kind of smart, digitized demand side flexibility is crucial to building the decentralized, decarbonized energy system of the future.
1Open Energi analysis
Robyn Lucas is a Data Scientist at Open Energi. She works on demand side flexibility in the UK electricity network; modelling, forecasting and optimizing the usage and performance of a variety electrical loads and enabling customers to intelligently control their electricity consumption. Prior to Open Energi she worked for a technology consultancy, helping clients make the best use of their data. Robyn graduated from Imperial College London in 2015 with a PhD in Physics, during which she worked on one of the experiments at the CERN LHC.
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
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 surge in interest in battery storage projects has highlighted a fundamental change in the energy market, as commercially viable systems become progressively more available.We explore critical success factors, from choosing the right battery to managing state of charge.
The deployment of physical energy storage assets can broadly be separated into two project categories. The first kind of project consists of grid-scale assets in “front of the meter”, which are usually implemented by industry partners on large grid connections. The second type is “behind the meter” batteries which provide an added layer of flexibility to energy consumption patterns of sites already connected to the electricity network – and offer tremendous potential to unlock previously inaccessible revenue streams for industrial and commercial customers.
Both project types require different approaches to select the best battery type and optimise operational strategy and performance over time.
Selecting the optimal battery operating strategy
Battery flexibility has the ability to unlock several non-mutually exclusive revenue streams. For example, a battery can be used to reduce site demand (for “behind the meter” projects), or export to Grid (for “front of the meter” opportunities) during peak price periods, reducing costs associated with wholesale, Duos, Triads and Capacity Market levy charges. Outside periods of peak tariffs, batteries can participate in the frequency response market and earn a revenue from National Grid for helping to dynamically balance electricity supply and demand.
The characteristics of Battery Energy Storage Systems (BESS) differ widely between manufacturers, with important factors to consider including capital and operating costs, power rating, energy storage capacity, energy density, cell chemistry, operating temperature, round-trip efficiency, self-discharge, degradation profile and tolerance to various depth of discharge. All these parameters have an influence on the economic viability of the project, so it is important to select the appropriate technical solution for a given project.
Once the different parameters are known, the determination of the most economical operating strategy becomes an optimisation problem in response to an aggregated electricity price signal and a potential frequency response revenue, under several constraints such as the battery technical characteristics and the site operational constraints (existing demand/generation on site if any, and import and export capacity).
The operating strategy might change over time, for example because one component of the price signal has changed, or if there is a new opportunity for flexibility that is more financially viable than current revenue streams. In that case the optimisation process will be performed again and the operating strategy modified accordingly.
Choosing the right battery
The next crucial decision is choosing a battery that is optimal for a given project and operating strategy. The goal here is to select the battery that will be commercially viable under the constraints of a given project. For a “front of the meter” BESS the main factors driving the battery characteristics are the Authorised Supply Capacity (ASC) for importing and exporting, the capital and operational costs and the electricity tariffs for import and export.
There are additional parameters for a “behind the meter” battery. As most of these projects are implemented in sites with no or a small export capacity, the battery would respond to a low frequency event by discharging power into the site, reducing its overall energy consumption. It is therefore crucial to forecast the demand on site to choose the optimal battery size and tender an accurate power availability in the frequency response market.
The same approach can be used for generating sites (like wind or solar farms) where there must be sufficient potential for export in addition to the generating activity on site. The potential energy savings are also dependent on the demand and the site constraints, which might in return drive the optimal power/energy ratio of the BESS.
Managing battery state of charge and maintaining performance
Once installed, the challenge is to manage batteries while ensuring high performance following the operating strategy selected. A requirement of entering the frequency response market is to be able to provide the power tendered for 30 minutes at a time, which highlights the need for a performant state of charge management.
There is an inherent efficiency in BESS, with average efficiency ranging from 75% to 90 % for conventional systems. When used in the frequency response market, successive cycles of charge and discharge will progressively cause a net discharge of the battery, and ultimately cause the battery to be fully discharged if no corrective actions are taken. Similarly, if several large high frequency events happen in close succession, a frequency-responsive BESS might reach a high state of charge at which it will not be able to respond to high frequency events anymore.
A control strategy should ensure that the battery state of charge always stays within appropriate boundaries in order to meet its contracted obligations at any given point in time. It should also ensure that the total throughput of the battery (which is the cumulative sum of discharge processes over time) is minimised while in operation. A reduced throughput decreases the wear and tear of the battery, enhancing the BESS lifetime.
At Open Energi we are working with several customers to successfully operate batteries in the frequency response market, optimising their operating profile to maximise revenues, applying designed state of charge management techniques, while limiting the degradation of the battery lifetime to the lowest value possible.
Blackout Britain is a headline which has become increasingly common over recent years. Many argue that decades of under investment in generation infrastructure has left the margin between demand and supply in the UK desperately short, raising the possibility of network outages at times of high power demand. Given the blame that would be landed at the Government’s feet were the lights to go out, energy security has been given top priority over the other facets of the energy trilemma; decarbonisation and affordability.
The Government’s solution to this was to devise the Capacity Market as a mechanism to encourage investment in new power plants, with yearly auctions for participants who can provide capacity over the winter peak. Crucially, auctions are held four years in advance of the capacity ‘go live’ date, to guarantee revenue and give investors the confidence they need to build new power stations.
There are, however, major flaws in the thinking behind such an approach. There is much evidence to suggest that the UK is in fact well supplied with power station capacity, that building more stations is unnecessary and that running the system more efficiently on tighter margins is a good thing. And by ensuring there is sufficient power plant capacity to meet the instance of highest demand in the year other potentially greater threats to security of supply are being ignored.
The graph below shows the frequency of the UK grid, which is the primary indicator of the system stability. The network is in balance when the frequency is hovering around the 50Hz mark, however any significant variation either side is a sign of a serious imbalance between generation and demand and could result in a potential shutdown of the network. This isn’t a distant threat: whole towns had to be shut off as an emergency measure in 2008 when grid frequency dropped to 48.8Hz.
In this case, we can see what happed to the frequency when a large supply source – an interconnector between the UK and France – failed, leading to more power being drawn by consumers than was being supplied to the grid. To counteract the resulting frequency drop and avoid a system shut down, a series of automatic measures kicked into action, including turning up thermal power plants (coal and gas) and sending water reserves cascading through turbines of hydroelectric plants.
More recently on the 9th May 2016 there were 37 significant failures across 27 different coal and gas plants as well as the France interconnector; with each one disrupting frequency and testing the grid’s resilience. At one point in the day National Grid issued a warning that insufficient spare capacity would be available in an hour’s time. This is too short notice for a thermal plant to start up (which takes around four hours) so not something the Capacity Market would have helped with.
National’s Grid’s Head of Commercial Operation Cathy McClay has said managing the grid frequency is becoming an increasing headache for our island system. However, the technologies traditionally used to respond in these situations look increasingly unfit for the role. The best new candidate is demand side flexibility – in the form of batteries and demand side response – which offers numerous benefits.
Energy storage and demand side response offer five core advantages over traditional solutions
Speed of response: Demand side response and batteries can deliver their full power in under 1 second from receiving a request from the network. By comparison thermal plants and hydroelectric generators need around 10 seconds. As the interconnector example shows, this difference is crucial for avoiding a potential network shutdown and will be needed more and more due to continued reductions in system inertia.
Decentralisation: Demand side response and batteries are distributed technologies meaning a required level of response can be made up from aggregating together many smaller sites. We have seen how relying on large centralised technologies (like the undersea link to France) poses increased risk to system stability as they represent significant single points of failure. Thermal power stations fail on a daily basis so individual plants cannot be relied upon for response; whereas with distributed technologies this risk is shared across many assets; if one fails the whole service is not compromised.
No need for spinning reserve: Traditional providers are only able to achieve the 10 seconds or so when starting from an already running position, hence the generators must be operating at some partial output to provide the quick response. This impacts fuel efficiency by around 10-20%, greatly increasing costs and CO2
Flexibility: The network can only absorb as much power as there is demand, so at times of low demand, National Grid must turn down clean and zero marginal cost power from renewable sources like wind to accommodate the thermal generators which must be kept running for frequency response. Demand side response and batteries overcome this problem.
Low carbon: By maximising the use of demand side response and energy storage technologies, the UK will be able to achieve further growth in renewable generation; while reducing its reliance on interconnectors and its exposure to volatile gas prices.
The high capacity fossil fuel plants which have historically been used to respond to the demands of the grid are increasingly unfit for purpose in a modern electricity network, yet the Capacity Market fails to encourage the development or implementation of smarter, cleaner and decentralised solutions which would provide a more efficient means of addressing both our energy security and other elements of the trilemma.
Neglecting these alternative solutions via the Capacity Market will undermine exactly the thing Government is trying to advance: security of supply. National Grid should be applauded for its efforts to implement change through its Power Responsive campaign – designed to encourage demand side participation in the balancing markets – but many policy makers remain locked into the old paradigm of an archaic industry; no doubt weighed down by the stranglehold of well-established energy incumbency (better known as the Big Six).
For these parties, using distributed assets to balance the system still represents a significant departure from the orthodoxy of constructing and operating a few large centralised assets like Hinkley Point C, which will deliver 7% of all UK electricity when completed.
To achieve a real paradigm shift towards a secure, affordable and low carbon economy, we don’t even need to find new solutions. Distributed and demand side technologies are ready to deliver; we now need to change the supply-focused mind set of our policy makers and operators.
By Sebastian Blake, Commercial Analyst, Open Energi