UK Onshore Wind

Sunset for subsidies and a new day for batteries

Nearly all of the UK’s renewables generation was developed with the help of subsidies: feed-in-tariffs, the renewables obligation (RO) scheme and contract-for-difference auctions.

The gradual sunsetting of these incentives makes it a time of seismic change for the entire energy industry. That being said, it’s far from the apocalypse.

September’s energy auction, if anything, offered a glimpse of a future where advanced commercial and risk-mitigation solutions will become the best way to guard revenue streams, not public patronage.

The best place to observe this shift is in the case of onshore wind. These operators are vanguard for the end of subsidy, with schemes having been closed to new entrants for a few years. The first RO schemes for onshore windfarms will finish in 2027. It may not seem imminent, but the long-term investment required for renewables means that onshore operators should all be in advanced planning for future profitability.

 

The September CFD Auction turning point

The market was braced for big price movement well ahead of September’s contract-for-difference auction. It was still a surprise. Offshore wind went for under £40/MWH, 30% lower than the lower limit of 2017, and well under the government’s £49/MWH wholesale market price.

Far from a sign that onshore would never be able to compete with offshore peers, CfDs veering below wholesale prices only illustrated how close we have come to a level playing field – one where every operator needs every competitive advantage to succeed.

In the absence of subsidy, the challenge for onshore wind is maximising profits without offering too much of a discount on account of the unavoidable variability of its source.

 

Derisking the route to market

The two most straightforward routes to market for onshore wind are securing new long-term contracts outside of government auction or adopting an active next-day trading strategy, returning to the market every day.

An increasing number of corporations are looking to buy long-term power purchase agreements to secure a cleaner energy supply. This offers operators a welcome and predictable revenue stream, but at the cost of ‘paying’ the counterparty to take on the risk with discount prices.

The alternative is surrendering long-term security and deciding to ‘play the market’ with active day-ahead nomination. There are a number of options here: N2EX market, spot market or another balancing mechanisms. For those up to the challenge of a constantly changing supply and demand balance, the potential rewards are great. Unfortunately, so are the risks. Significant in-house expertise and attention is necessary to avoid one bad day wiping out a month’s worth of gains .

What both approaches share are returns which hinge on the risk of variability at the point of generation. This means any way of mitigating that risk will have a major impact on returns.

When bidding directly into the N2EX market, the operator must accept a variable day-ahead price for their forecasted wind generation, with any forecasting errors settled at a possibly lower, or even negative, price. While the mean day-ahead price will be higher than the mean price an energy supplier will be willing to offer in a PPA, the time-variance in the price leaves the operator at the mercy of wind forecasting errors – or simply untimely generation.

To make matters worse, high levels of forecasted national wind generation tend to lead to low prices.

A portfolio operator can mitigate forecasting risk by placing all wind farms under a single supply contract and nominating their aggregated volumes. This is because forecasting errors, while geographically correlated, will be lower on aggregate as positive and negative errors across the portfolio cancel out.

Managing day-ahead bidding, forecasting, and intra-day positions requires not only significant expertise but also robust IT systems. An energy optimisation platform with auto-bidding capabilities can do the heavy lifting cost-effectively, obviating the need to build this capability in-house.

To mitigate market risk, aggregated nominations are not enough, as geographical correlations in wind speed imply that times when wind speeds are forecasted to be high will also be times when N2EX prices will be low. It may not be possible to tame the wind, but what is possible is installing solutions that intelligently store energy and sell it at a time when prices are higher.

 

On-site batteries – to build or buy?

Wind operators who realise the value in installing (or upgrading) onsite batteries face yet another choice: install and manage the full operation of the new batteries, including the charge management, forecasting and market bidding, or – outsource it to a partner.

While most operators have highly technical teams, unquestionably the experts on the particular nuances of their own sites, a self-build strategy is still one where minor oversights or missed opportunities will rapidly erode ROI.

Take the deceptively simple task of choosing the right size of battery. Colocated batteries have the advantage of a shared grid connection point with wind generation on site, and a lower cost of installation due to easier access (compared to those out at sea). However, not every site will have the same amount of room before it hits its connection limit, or may have a wide range of forecasting error.

Making the most of each individual site, and avoiding wasted battery headroom or overflow energy spillage, requires careful battery selection.

Even with a wealth of site data, minor sizing errors will add up to significant loses in the long run. Lacking the size to conduct effective state of charge management, for example, significantly reduces the lifetime potential of each battery, and forces operators to either reinvest or seek external support after all.

Across larger portfolios, the benefits of a networked system of batteries is even greater. This is especially true for windfarms which have a greater potential for site-to-site variance than solar equivalents. With a connected system, the aggregation of risk and capacity means that the individual size (and cost) of each battery can be smaller, reducing overall cost. Larger portfolios allow for distributed risk, but also require more complicated systems to to apportion balancing between the available storage in the portfolio within the constraints of the battery systems’ warranties.

The most advanced management systems do more than simply manage a state of charge or capture overflowing electricity. Reducing variability and risk means also capturing every possible market access point, including accessing the ancillary services and capacity markets, and even the balancing market via a range of aggregators. Not only are these revenue streams decoupled from day-ahead market prices, diversifying market risk – they can more than double the value generated by the storage system.

Especially for larger portfolios, the potential ROI of an advanced management system far outweighs upfront costs. Forecasting day-ahead generation, managing charge levels and setting optimal nomination volumes for suppliers are all vital components of a long-term strategy to maximise return. Partnering with experts for both hardware and software is the most effective and rapid route to success.

In a year-long simulation using 2018 market prices, we found that a suitably sized battery storage system deployed on an on-shore wind farm running Open Energi’s DD2.0 optimisation software could annually generate £77.10 of value per kW of battery capacity (net of connection charges). The system helped buffer wind forecasting errors, reducing them by up to 75%, arbitrage day-ahead energy market price shape, and participate in ancillary services such as Firm Frequency Response.

At a portfolio level, the optimally sized batteries allowed the wind operator to take more risk with their PPA with day-ahead exposure, resulting in a 8% increase in portfolio turnover compared to a PPA with risk taken on by the supplier.

 

End to end optimisation

Wind operators don’t have the luxury of picking and choosing which areas they would most like to see revenue optimised. Every advantage is necessary to survive in a post-subsidy renewables energy market. A comprehensive solution, and an experienced partner to install and run it, offers the best and fastest route to future returns.

Open Energi is one of the UK’s longest standing providers of solutions to mitigate risk and improve market access for renewable operators. We have spent over a decade working to build solutions and platforms that help operators protect their revenue streams, ensure they begin delivering value fast. One of our most recently installations, at one of the UK’s largest battery sites, was taken from ‘contract to commission’ within a week.

The UK is rapidly approaching a time when renewables are competing directly – without government subsidy – through a mix of both long term and day-to-day trading through a range of markets. An onsite battery solution offers a commercially optimised route to success in the UK’s post-subsidy future with a high potential for capturing returns.

The Open Energi Podcast – Lessons from the 2019 Blackout

The blackout that struck the UK on the 9th of August 2019 was a once in a decade event: a lightning strike causing near simultaneous drops in output from both a conventional gas power plant and the UK’s largest offshore windfarm.

This led to widespread blackouts and travel chaos in London as passengers were left stranded on depowered trains. It also raised conversations about the systems necessary for the stability of the UK’s power grid into the mainstream.

Open Energi has been actively working for over ten years helping the UK’s National Grid deploy the technology and software necessary to underpin a reliable renewable power network.

Our first podcast features insight from Dr Robyn Lucas, Open Energi’s Head of Data Science and Sebastian Blake, Head of Markets and Policy, as they discuss what made this blackout so important as a moment to reflect on the evolution of the UK’s power infrastructure, including:

  • how systems designed to offer resilience to the grid responded (or failed to respond) to the blackout.
  • how assets – like batteries – are managed through software to react to both changes in frequency and market prices.
  • the most important lessons emerging from the blackout and subsequent investigations for everyone looking to help answer the UK’s future energy needs.

Listen here:

 

 

 

How greater flexibility can help UK deliver 50% renewables by 2030

electricity pylons

The National Infrastructure Commission (NIC) recently published its first National Infrastructure Assessment (NIA), setting out a strategy for the UK’s economic infrastructure from 2020 to 2050. A key focus is decarbonising the UK’s energy supply and the report recommends 50% of generation is supplied by renewable power by 2030, with the UK’s electricity supply almost entirely zero-carbon – thanks to nuclear and renewables – by 2050. But how can we integrate this level of renewables cost-effectively, and what do we do when the sun doesn’t shine, and the wind doesn’t blow? Wendel Hortop, Commercial Analyst at Open Energi, explores the role of flexibility in enabling the UK’s transition to a zero-carbon energy system.

What would such high levels of renewables mean for the energy system?

The UK is on track to power 50% of our electricity supply with renewable generation by 2030 but this level of renewables creates some very specific challenges. Solar and wind, which would form most of new renewable capacity, are highly inflexible – energy is only generated when the sun is shining, or wind is blowing. Despite increasingly accurate forecasting, this inflexibility introduces short-term (balancing electricity supply and demand within a given half-hour) and long-term (what to do when wind and/or solar output is low for hours or days at a time) challenges, and reduces the level of inertia on the grid, resulting in much quicker changes in system frequency – which must be managed to ensure power keeps flowing.

Flexibility can help to address these impacts cost-effectively – reducing total system spending by between £1-7bn per year – and enable the UK to integrate renewable generation at the scale required by the NIC assessment.

Flexibility can deliver significant cost reductions in in a high renewable system

Source: Open Energi
Source: Aurora Energy Research

 What role does flexibility have to play?

The majority of system balancing occurs through the energy market in response to energy prices visible over different timescales, of which the last resort is the imbalance price. Energy generators and suppliers forecast their half-hourly energy usage and provide this to National Grid, who then take action to correct any differences between forecast and actual energy usage. Anyone out of balance in a way which harms the system pays a penalty, whilst the opposite is also true – putting yourself in imbalance to benefit the system gets rewarded. The imbalance price (or System Price) is not known until afterwards so predicting and reacting to it allows energy users to help the grid and be rewarded; increasingly trading teams at big suppliers are looking to their customers to help manage this.

Open Energi are already responding to the imbalance price by flexing loads through signals from suppliers, such as Ørsted’s Renewable Balancing Reserve. Increased renewable generation on the grid will increase the likelihood of system imbalances, and the incentive to respond.

Flexible loads can respond in real-time to predicted system prices

Flexible loads can respond in real-time
Source: Open Energi

The wholesale market doesn’t balance all supply and demand so National Grid look to the suite of services they procure to do the rest. For example, frequency response services fine tune the system balance and provide a ‘first line of defence’ after large generation outages.

Demand flexibility is already an established tool in helping to balance frequency on the grid via the Firm Frequency Response market. Inertia levels falling means faster frequency response is needed. Lithium-ion batteries are perfect for delivering this, whilst some forms of demand flexibility can also respond at the required speed. National Grid is developing a Faster Acting Frequency Response product which will allow loads capable of responding quickly enough to participate and will procure a mix of assets capable of tracking frequency (such as batteries) and those capable of delivering large shifts in demand almost instantaneously (such as large industrial processes).

Longer term shortfalls in generation introduce a new challenge for flexibility

The more significant challenge is in longer periods of low wind and solar generation. Increased interconnection with Europe will help but demand flexibility can again play a key role.

Frequency response has tended to focus on energy flexibility within a half-hour period, however many processes have inherent energy storage of hours or even days. Water pumps, heating and CHPs are all assets which can shift demand over long periods. The signals to do so come from the market – low renewable generation leads to increased wholesale energy prices, and vice versa. As wholesale energy prices can be known a day ahead, a load can be optimised in advance to increase consumption when prices are lowest, and reduce consumption when prices are high.

Many flexible processes have hours or even days of energy storage
 

Many flexible loads have hours or even days of storage
Source: Open Energi

Advances in storage technology will also assist with this longer duration requirement for flexibility. Technologies such as vanadium flow batteries can provide over 4 hours of energy storage and can help balance sustained periods of low or high renewable generation as well as providing short-term frequency response and price arbitrage.

Aggregation of assets such as these, diverse in both location and technology, will help to tackle longer periods by spreading the requirement for flexibility. Digitalised platforms that use artificial intelligence (AI), statistics and probability can schedule and manage asset behaviour to deliver the optimal amount of flexible capacity.

As we look to 2030, increased adoption of electric vehicles (EVs) will also come into play, either through smart charging or vehicle-to-grid (V2G) charging. In their latest Future Energy Scenarios report National Grid predict we could have over 10 million electric vehicles in 2030, and over 35 million in 2040 – a huge number of flexible, distributed assets.

Smart charging will allow EV charging to be modulated or staggered to avoid surges in consumption or shifted to times of day when demand is low, reducing the infrastructure required to support them. Aurora Energy Research estimate that smart charging can reduce the level of generating capacity required in 2050 by up to 22GW in a high renewables system. Meanwhile V2G charging introduces possibilities such as taking households off-grid during peak periods – Open Energi are part of the PowerLoop consortium exploring this and other potential V2G applications.

Smart charging significantly reduces the need for flexible generating capacity

Source: Aurora Energy Research
Source: Aurora Energy Research

Decarbonisation of heat will introduce new sources of flexibility

One common process with very high levels of inherent storage is heating; however the UK’s reliance on gas means potential flexibility which could be offered to the electricity system is currently limited. Looking forward the decarbonisation of heat therefore offers long-term opportunities, whether this comes through electrification or a transition to hydrogen and district heating.

Switching to heat pumps would introduce a large but flexible energy load into the system with significant storage potential. Coupled with smart meters and other advances in technology this could lead to a highly distributed source of flexibility for the grid, just as with the shift to electric vehicles.

Hydrogen powered heating – produced via electrolysis – is an energy-intensive but flexible process, which alongside district heating networks would likely lead to many more CHPs – which offer short and long term flexible capacity.

Technology will play an important role in delivering this flexibility

The NIA shows that flexibility has a key role to play in delivering or surpassing our carbon targets. As renewable generation increases significantly so will the need for flexibility. We already have many of the solutions we need – the real challenge is rolling these out at the required scale and speed.

This is where AI and cloud computing can come into their own. Aggregation of larger and larger portfolios of diverse loads will require the behaviour of each of these individual loads to be optimised and controlled in real-time in response to the requirements of the system. Meanwhile the move to smaller, distributed loads, including those on a domestic scale such as electric vehicles, will rely heavily on cloud computing with dispatch instructions delivered over the internet and loads communicating their behaviour with the platform and each other.

Ultimately these solutions can give rise to an autonomous, self-balancing grid which operates incredibly cheaply. Open Energi are leading this transition, connecting, aggregating and optimising distributed energy resources in real-time, to create a more sustainable energy future.

How the rise of ‘Energy as a Service’ can power decarbonisation

open energi wind farm

Energy as a Service is the latest business model innovation to arrive in the energy supply industry. In short it is all about moving away from buying energy on a per unit (p/kWh) basis and moving towards a fixed fee per month within certain volume thresholds; akin to how we pay for mobile phone contracts. Energy as a Service has emerged off the back of disruption to the way we supply, consume and now ultimately buy energy, which has fundamentally changed energy market economics.

This disruption is the result of four major technology-driven trends:

  • Decarbonisation – The growth of energy supply from zero marginal cost renewable resources
  • Decentralisation – The growth in energy generated from smaller scale low carbon resources either on customer sites (Behind-the-Meter) or at the Distribution Level (Distributed Energy)
  • Digitisation – The ability to measure and monitor machine behaviour in real-time and automate how we use and supply energy
  • Democratisation – The rise in consumer participation, control and choice which is increasingly determining how energy is bought and used

Traditional per unit models work where the dominant cost in delivery of the product or service scales according to the volume used. This was true when the majority of power supplied came from sources that required a fuel input e.g. coal and gas. The more energy consumed the greater the proportional cost of buying and burning that fuel to generate more kWhs of power.  Other components which make up the total ‘at-the-meter’ price have also been charged on a per unit basis to ensure those who use more of the electricity network pay more for it; government taxes, utility profit margins and network charges (with some time-of-use element).

However, when you start to use zero marginal cost power the economics get flipped on their head. Renewable ‘fuel’ is free, so the dominant cost in consuming energy becomes the infrastructure needed to deliver it. Wind turbines, PV panels, transmission and distribution cables have low operational costs once built, so the initial capital expenditure is where the dominant cost lies.

Across Europe average wholesale prices now reflect wind and sun patterns more than the cost of coal and gas, and at periods of low demand and high renewable output we consistently see negative prices. Clearly change is needed as consuming more energy at these times is beneficial to the whole system but a per unit charging mechanism disincentivises users from doing that.

Enter, Energy as a Service. Already we are seeing a shift in network charging towards capacity-based charges instead of use-of-system charges. Wholesale prices are not far behind; the task becomes providing the flexibility to firm up renewable output. Thanks to the digital revolution described above this flexibility can come from consumers’ demand, cost-effectively tapping into flexibility inherent in distributed energy resources behind-the-meter.

Take a given offshore wind site, with known capacity factors of about 50%. It is possible to quantify the amount of flexible energy needed to ensure 99% of customer demand is met at all times. Using existing business assets means it is possible to take advantage of zero marginal cost flexibility in everyday processes (such as heating, cooling, pumping, battery storage and CHPs), avoid unnecessary infrastructure upgrades and minimise efficiency losses in transporting power. Once it is understood how much flexible power is needed to firm up the output of renewable generation the next task is what technologies do you use to meet that flexibility requirement.

Artificial intelligence-powered flexibility platforms – like Open Energi’s Dynamic Demand 2.0 technology – which can manage distributed energy resources in real-time, are critical. They can evaluate the amount of flexibility in existing power-consuming assets and processes – in addition to any battery storage and/or flexible generation (such as CHPs) – and map demand to supply. This then becomes a constant, real-time scheduling problem for the platform to manage; invisibly ramping processes up when wind is abundant and storing as much power as possible, or turning processes down to a stable minimum and discharging batteries or using a CHP when wind output is low.  If real-time scheduling isn’t maintained, the cost structure breaks down, so the reliability of these platforms is critical.

What is important to recognise here is that below a certain demand threshold the marginal cost of putting in place this service is the cost of operating the wind and the software required to schedule behind-the-meter flexibility. This is why Europe’s utilities are making huge investments and acquisitions in virtual power plant technology.

By doing so the costs of delivering energy become fixed and predictable and scale with size of connection instead of actual usage. Exactly like the mobile phone industry where the marginal cost of sending a packet of data is immaterial in comparison to network costs of all infrastructure.

For Open Energi Energy as a Service has always been the natural end-game in maximising the value of Demand Response. It shelters consumers from the continuously changing and complex incentives of the existing Demand Response markets, and instead offers a simple proposition: “By installing demand response software across a range of assets you can pay a lower fixed monthly fee for your energy”.

The clarity and certainty offered by Energy as a Service makes it easy to structure simple, long-term financing solutions for different technologies – e.g. solar PV, energy storage, CHP – and allows businesses to concentrate on what they do best.  All the complexities of power procurement and demand response markets are removed in place of a known fixed fee per month that ensures reliable, clean and affordable energy. 

David Hill, Commercial Director, Open Energi

This blog was originally posted on Current News.

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

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

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

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

 

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

 

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

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

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

Transforming the network

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

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

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

Grid Frequency and FFR

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

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

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

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

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

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

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

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

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

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

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

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

By Wouter Kimman, Data Scientist, Open Energi

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

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.