Scarcity pricing in the Balancing Mechanism

The last couple of weeks saw some power stations make some bumper profits as cold weather drove scarcity pricing. The greatest profits were made by some large gas (CCGT) plant in the balancing mechanism on Friday 9th and Wednesday 13th January, earning up £4,000/MWh (around 100 times the normal cost of power), so what was it that enabled these plants to earn large revenues compared to other assets? 

The first thing to bear in mind is that the Balancing Mechanism (BM) is not a single market with a well-defined supply and demand but a marketplace for several system needs. The BM is used to correct supply demand imbalances that naturally occur (eg from demand forecasting errors) but crucially also to manage constraints, voltage, frequency, inertia, reserve (and more) in real time (sometimes augmenting specific tendered markets for these services). 

No alt text provided for this image

If we examine the accepted actions by the System Operator (NGESO) on the afternoon Wednesday 13th after the event we can see this immediately given the large volume of both Bids and Offers accepted. If the only purpose was to correct the overall system imbalance it would be very inefficient to pay one plant £4000/MWh to turn up while turning others down at £0/MWh.

 Bids and Offers accepted 1700-1730 on Wednesday 13th. Source: LCP Enact.

These actions only start to make sense once we consider other requirements which the BM is used for, namely Reserve. Operating Reserve is needed to ensure sufficient backup from sudden impacts (like an interconnector tripping off or wind generation dropping off) and therefore GWs of back-up power is required to be available and able to respond in minutes. Reserve can be created by the System Operator through tenders ahead of time (STOR & Fast Reserve), bilateral contracts (SpinGen) and by creating headroom in the BM.

Headroom refers to difference between current output level and the Maximum Export Level (MEL) on thermal plant, the amount they can ramp up to and hold indefinitely. CCGTs take hours to start from zero ouput, but once operating above a Stable Export Level (SEL) they can change output in minutes and so contribute to Operating Reserve.

No alt text provided for this image

A CCGT brought on to provide Headroom. Source BM Reports.

This is where we see the perverse incentive of being a large, slow moving beast sometimes emerges in the BM. Faster (traditionally smaller) plant like OCGTs, batteries and pumped storage can ramp up in a timescale of minutes (or faster) from a standing start which means they are naturally providing Reserve simply by offering volume into the BM. Whereas CCGTs must be turned on to SEL (and paid for this) to create Reserve.

Put yourself in the shoes of a Control Room engineer as the evening peak approaches: if you are looking at the resource available and think you might come up short clearly the best thing is to start up the slower plants in preparation for this. But if no incident does happen or the demand turns out lower than expected then the faster assets will likely not have been used during this period, whereas the CCGTs will have been renumerated heavily from the start-up instruction. 

This is what happened on Wednesday where each of four CCGTs were ramped up from zero to their Stable Export Limit (SEL), ie the least amount NGESO could procure, to create as much Headroom as possible. However, it turned out the system was long over the peak and with no incidents occurring meant many OCGTs received no instructions, despite offering much cheaper volume into the BM (it should be noted that size also plays a role here, the CCGTs offer much more volume). 

No alt text provided for this image

 Accepted Offers from CCGTs to SEL 1700-1730 13/01/21. Source LCP Enact.

Offers from OCGTs not accepted 1700-1730 13/01/21. Source LCP Enact.

Offers from OCGTs not accepted 1700-1730 13/01/21. Source LCP Enact.

The problem is because in a utilisation payment only market the insurance value provided by fast responding assets isn’t valued if they aren’t called into action. NGESO are taking the right steps to remedy the issue by reintroducing STOR at Day Ahead (which allows providers to value in tight system conditions to their tenders) and investigating new methodologies like the BM Reserve Trial; both of which feature availability payments for those best able to provide Reserve. These are part of a wider package of Reserve reform which is fundamentally about the strategic shift to managing the system with predominantly duration limited resource, like batteries, instead of traditional notions of Headroom (and Footroom).

But this still doesn’t answer why the CCGTs were able to command such high prices. The answer is of course scarcity, cold days with little wind can create tight system conditions and we saw this reflected in the Day Ahead wholesale price at 1700-1800 (£1500/MWh on the N2EX). These CCGTs made the calculation they could earn more by not self-dispatching against the peak hour-long wholesale price and instead Grid would need to bring them online and pay high prices for an entire six hour run. It’s a gamble which doesn’t always pay off but did here, with a single unit at West Burton earning £3.78m from the BM that day compared to £0.53m if it had made the same run against wholesale.

No alt text provided for this image

West Burton Unit 3 profits in the BM compared to wholesale. Source BM Reports.

The wholesale market and Balancing Mechanism are linked of course by the imbalance price and it is the SIP calculation formula which drives these trading decisions between the wholesale market and the BM. The SIP didn’t clear at high levels on Wednesday afternoon because ultimately there wasn’t a shortage of energy so the large costs of balancing on this day, shown below, are put down as Reserve costs and so instead pass through via BSUOS. Interestingly SIP did reach £990 earlier at 1pm that day when similar actions were being taken but the system was short (also on the 8th Jan when it cleared at £400/MWh). Whether this is the SIP calculation working well, or a sign it is broken, however is for another post.

No alt text provided for this image

Balancing costs on week starting 11th Jan. Source NGESO.

Manual vs Automated Trading

In essence, optimising flexible assets in traded energy markets means trying to maximise (or minimise) the captured price for whatever energy can be sold (or bought) by the device in question: a gas power station, battery storage or just a single electric vehicle. In practice, it is often a highly complex exercise requiring processing and a combination of information from two distinct sources: the asset characteristics and market intel.

This cross-optimisation involves continuous calculation over different time horizons, as market opportunities (like Day Ahead auctions) and asset limits (such as ramp rates restrictions) must be planned against. A classic example would be a trader managing a gas power plant; assessing changing market conditions and working closely with plant operators to understand variable physical parameters, such as efficiency at different power outputs or the energy required to start up the generator.

This information must then be considered when selling energy into the power markets, and that involves a careful trade-off between physical and price considerations. For example, when low demand causes low prices overnight, plants must choose between shutting down in the evening then starting again in the morning or running through the night, selling power at a loss – whichever is the more economical.


Adapting to new technologies

However as storage, demand response and hydrogen production become the dominant tools for balancing the grid, this is also driving change in the methods for optimising assets. Novel technologies are being deployed at the megawatt or kilowatt level, not gigawatt, meaning many more assets will be involved in making up the required level of balancing capacity.

Each device has its own features and characteristics, such as power capacity or response time, as well as dynamically changing parameters impacting optimisation, eg state of charge (SoC) or energy recovery period. So it is clear when dealing with thousands of individual assets the complexity of the problem scales greatly.

Even for larger Front-of-the-Meter batteries, warranties are becoming increasingly complex to manage in real time; specifying rest periods or dynamically limiting depth of discharge, as opposed to simply warrantying a certain number of cycles per year. Also, many projects will be co-located with variable renewable power to exploit the benefits of a shared connection, effectively giving a dynamically varying export connection to factor into optimisation.


Pros and cons of automation

In this world, automation is fast becoming essential. Allowing the principles of continuous asset optimisation can be applied at a scale far below what would be economical for a human trader.

Open Energi has been trading fully algorithmically in the Day Ahead markets for over a year and has some key learnings.

On the face of it picking the highest and lowest hour in the day is simple (and fairly predictable). However, to maximise revenue you must also respond to more real time signals which occur within day (eg Triad), which alters your SoC from the planned schedule. This creates a problem when submitting bids in advance for the trading day before the current day has ended, as you do not yet know what SoC the battery will be at at the start of the trading day.

Automated fixes are able to easily correct for misalignments to get round this; however, doing so in the most economical fashion is harder. And the problem is exacerbated when dealing with more dynamically changing markets like the intraday continuous, which require thinking between different trading horizons.


Manual plus automated trading – a winning team

Overall, traders still have the upper hand on algorithms in areas like price formation, especially during extreme events like 4th March, and the best solutions will be ones that exploit the strengths of both. This is the principle of our solution Dynamic Demand 2.0 Trader, where full automation capabilities will perform the heavy lifting but oversight from Erova Energy’s 24hr trading desk provides the manual oversight and possibly intervention if greater opportunity is identified.

Ultimately, combining the strengths of power traders and algorithms provides the best optimisation. And ensuring each is capable of running independently provides the built-in resilience that proves its worth as COVID-19 pushes systems to their limits.


For a free consultation about automated trading, call +44 (0)20 3051 0600

Solar plus storage – The benefits of co-location

The UK will need 30GW of storage to meet our climate goals, according to Imperial College. That’s 10 times the current capacity. There are many questions this raises, such as ‘what is the appropriate technology mix?’, but if we consider the form predominantly being developed now (lithium-ion batteries of under or around two hours duration), one of the main questions is where best to situate this?

So far the vast majority of volume has been deployed as front-of-the-meter installations – a standalone site with a dedicated connection for the battery alone. However, given that connections can be costly or administratively difficult to obtain, a more cost-effective solution would be co-location.

In this blog we will dig into some of the advantages of co-location of batteries with solar farms (PV installations with a dedicated connection). We will leave the current policy/regulatory framework to the side and focus on the fundamental benefits.


The case for co-location

When new generation connects to the grid, it has to pay for the new copper in the ground. This isn’t cheap. For a battery (short duration, lithium-ion) the connection typically represents 10-20% of the total capital expenditure – and can be much higher, depending on the conditions of the local network (eg if you are unlucky enough to be the marginal connection requiring a sub-station to be upgraded).

If the battery was able to share an existing connection with a solar farm of the same size, it could save the 10-20% of upfront cost; it would still need to buy an import connection, which the solar farm doesn’t need, to ensure the battery can draw from the grid to charge, but the economy would be significant.

In the UK, solar PV (without tracking) has an average capacity factor of 10-15%. This means that for the vast majority of the time the connection is an expensive sunk cost, lying there unused. By sharing it with storage, the return on investment from the connection increases several times over.


Why solar and storage are perfect bedfellows

Sharing one connection becomes a problem when both energy sources are producing at the same time. Export from one resource will constrain the ability of the other to export. With solar and battery storage, we find that this is rarely the case; ie both assets are very seldom trying to use the connection to export at the same time [see fig 1].

This is due to the price cannibalisation of renewables – where prices are lowered at times of high renewable generation because they produce at zero marginal cost, and so will sell power at any price above zero. There is sufficient solar power in the UK (>12GW) that during the middle of the day market prices will drop lower.

If we are performing price arbitrage with the battery, it is obvious that we would not be looking to discharge at these times when prices are low. We want the highest prices – usually over the evening peak. In fact, we have found that when optimising the battery against Day Ahead prices, a conflict occurs with the solar generation less than 1% of the time.

Figure 1: Generally, the battery looks to discharge at times of high prices which does not align with solar output

What this means is that we can effectively trade the two assets separately from each other, rather than as one. This has great benefits because solar predictions are best made at portfolio level, taking advantage of geographical dispersion to neutralise stochastic cloud impacts on production. Therefore, the solar can be traded as a portfolio, while battery activity can be scheduled separately.

This also highlights one major advantage of pairing batteries with solar rather than wind. Solar has very high diurnal periodicity, so we can guarantee there would be no production over the most lucrative peak time (4-7pm, especially in winter). The same cannot be said for wind; so if it a wind farm was generating during this peak period there could be large entailed opportunity cost for the battery.

Given that the battery is the more controllable resource, and solar produces at zero cost, the battery should accommodate the solar generation and plan activity around it.


The need for advanced optimisation

Pairing solar with batteries, then, looks like an obvious and straightforward win. However, there is more nuance.

While Day Ahead prices will rarely incentivise discharging the battery in the middle of the day, there are often later market opportunities that do; for example, if a large generator trips off the network to create a shortage. The most advanced battery trading strategies will cycle far more than once per day by looking to optimise at each time horizon, to stack value across the day.

Given that cloud cover could have a strong impact on PV output, there will still be many instances where a solar farm has the connection unutilised in the middle of the day [fig 2].

Figure 2 – Clouds drive large short term volatility in production

To take advantage of this we need reliable, real time monitoring of the solar farm (we can only dispatch the battery and make use of the connection if we can be certain the solar PV isn’t) and dynamic responsive controls to ensure solar output is tracked closely (to prevent overloading the connection point).

Dynamic Demand 2.0 Trader is powered by our fully automated platform, needed to support this level of sophistication. Control logic is held both locally on the battery, to ensure responsiveness to changing site conditions, and centrally in the cloud, enabling responsiveness to live price opportunities provided by Erova Energy’s advanced market insight.

With this level of automated control and trading insight, pairing solar with storage represents a cost-effective solution for storage location and a potentially lucrative way forward for energy investment.


For a free consultation about co-location and your energy investment, call +44 (0) 20 3051 0600

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: