The Multidimensional Value of Battery Storage

By Robyn Lucas, Head of Data Science, Open Energi

The energy landscape is undergoing an unprecedented change, which is accelerating as market barriers to distributed energy are dismantled. The last 12 months have seen standalone energy trading models emerge, access to the Balancing Mechanism widened and new platforms are promising to create new value streams from localised energy services.

There is now a huge variety of distributed energy assets capable of providing flexible capacity to the system – from energy storage, CHPs, electrolysers and Electric Vehicles, to more traditional demand-side response assets such as industrial pumps, boilers and chillers. What all these assets have in common is they need careful managing to deliver the most benefit with the least disruption.

Battery storage optimisation

For a battery storage system, the cost-benefit of every action has to be weighed in terms of battery degradation and lifetime, whilst continuously managing the state of charge to ensure system availability.

With multiple value streams to stack and optimise across timeframes – from day-ahead to real-time – getting the maximum price per hour of operation requires market insight, automated response, an understanding of the constraints of the battery and the site on which it sits, and an appreciation of the risks involved – with buy-in from all parties.

The limiting constraint on value is typically the number of cycles allowed by the warranty – usually around 400 cycles per year for a lithium ion system. This means that the battery can be completely charged up, and then discharged, just over once per day. Therefore, it is important to make this discharge at the right time to reap the largest rewards. It may, for instance, be more profitable to do two cycles on one day and none on another. Accurate forecasting and regular monitoring ensure the best £/kWh of throughput is achieved.

The necessity to stack multiple revenue streams to achieve an ROI that investors are comfortable with means considering these throughput limitations, akin to strike price setting, in a rapidly changing environment. Some revenue streams introduce a reasonably low utilisation, like Static Frequency Response. Meanwhile, others require higher utilisation. For example, throughput whilst tracking frequency in Dynamic Firm Frequency Response (FFR) accounts for around 1.3 cycles per day for a 1-hour system.

As more of the UK’s aging thermal fleet retire and the renewable generation increases, wholesale and imbalance markets are also expected to become more volatile, particularly when the grid is under stress. If a battery storage system is locked into a dynamic FFR contract during an extreme weather event, it may be unable to benefit from profitable price arbitrage opportunities. Balancing the seasonal risk of this against the reward of assured revenue from FFR needs to be decided between the asset manager, investor, and aggregator.

Seasonal volatility

The graph below shows the throughput, and benefit, over a one-year period for a 1.6 hour battery storage system, modelled for 2016 historical prices. The impact of seasonal price volatility is clear: most energy trading arbitrage opportunities occur over winter when prices are more volatile, so throughput will be high at this time. However, in summer the system can be used to provide reduced throughput capacity-based services to maximise the overall £/kW value.


Behind-the-meter models

Last November saw the unveiling of a 2MW battery installed by Pivot Power at Arsenal’s Emirates Stadium – the first behind-the-meter battery to be aimed primarily at wholesale energy trading – powered by the club’s Official Renewable Energy Partner Octopus Energy.

The system is fully automated and optimised by Open Energi’s Dynamic Demand 2.0 platform.

By using the battery to supply the stadium at the most expensive times of day, Arsenal reduces its electricity bill. At the same time, the system is generating revenue – split between Arsenal, Pivot Power and investor, Downing LLP – from energy arbitrage and imbalance opportunities. Crucially, with a limited number of dispatches, optimisation is about identifying the best opportunities.

To manage this, Open Energi assigns a cost to every MWh of throughput and a limit to the number of cycles for each part of the revenue stack. This ensures the optimum pay-off between throughput and revenues. Given the latest wholesale price forecasts and a full understanding of the other non-commodity costs involved, forecasts of the stadium demand (using Arsenal’s match schedule), and knowledge of the physical attributes of the battery system, Dynamic Demand 2.0 uses machine learning techniques to generate the most optimal profile for the system to follow. This is done at multiple timescales: day ahead, intraday, and real time. The simulations below are designed to illustrate how this process works in practice. Figure 1 shows the price signal, as known day ahead, and the resulting optimisation. This optimisation is then updated within the day, in response to a possible triad call: we deviate from the nominated schedule in order to make the most revenue from the possible triad, shown in Figure 2.

Figure 1: Day ahead price signal and resulting optimisation of battery. This is nominated to the supplier to purchase on wholesale market, day ahead.

Figure 2: Actual dispatch of battery according to day ahead schedule, with intra-day update due to a Triad call, where the price for one of the Triads is shown on a logarithmic axis. The actual Triad won’t be known until after the season.

The technology is helping to maximise benefit from assets on sites across the UK, not just premier league football clubs. Understanding the electricity contract for each of these sites is key to unlocking the most value. Figure 3 below shows one such industrial site which has co-located solar. The battery is charged up using excess solar power during the day, and is then used to take the site offline at the most expensive times. The industrial site operator saves money on their electricity bill as they reduce their imports from Grid, and the system also generates revenue by performing FFR.


Figure 3: Impact of battery on industrial site with co-located solar

Here, understanding the intricacies of the Power Purchase Agreements between the various parties involved has been crucial. Open Energi, acting as the aggregator, must have a full picture of the contracts between the solar system operator, the industrial site, and the asset manager. Will the system earn any revenues from export? Is the import and export held by the same supplier, or are they under providers and exposed to different terms and pricing? Are the parties fully aware of what markets and price components they are exposed to?

As more battery storage projects proceed on a merchant basis, creating innovative, multi-partner business models like this, in a sustainable, asset-centric way, will be vital to ensure momentum is maintained towards a low carbon, decentralised energy economy which reduces costs for consumers and maximises use of clean, cheap, renewable energy.


This blog was originally published at

Here’s to the end of coal. But the revolution in energy is just beginning.

This article was originally published on 18 June at Open Access Government

David Hill, Commercial Director, Open Energi talks about the necessary infrastructure required to achieve the government’s ambitious net-zero emissions target and whether the hype matches reality

At precisely 3.12pm on Friday 31st May, the UK went two weeks without burning a single lump of coal for the first time since the Industrial Revolution. Social media channels buzzed with the #coalfreefortnight as commentators across the energy policy spectrum chose to mark the occasion with a tweet.

We’ve come a long way since the ‘dark satanic mills’ of William Blake’s iconic poem. It is right that we celebrate milestones like coal-free fortnight. But consider this. It has taken the UK 130 years finally to turn its back on coal. With the spectre of climate change looming larger and more tangible by the day, we must ask ourselves whether we are moving fast enough and smartly enough to deliver the revolution in energy delivery required to satisfy future demand while protecting the planet?

Renewable energy, comprising wind, solar, and biomass generation hit 42GW in the UK last year, accounting for 33% of total capacity. Meanwhile, analysis by Carbon Brief suggests up to 50% of the UK’s electricity could come from renewable energy by 2025. Progress at this level is encouraging. But integrating this level of renewables cost-effectively is challenging. Unlike traditional thermal sources of power, turning wind and solar on and off according to demand is simply not possible. Sceptics are also quick to raise the question of what to do when the sun doesn’t shine, and the wind doesn’t blow.

A smart solution to a complex challenge

When dealing with intermittent supply, such as wind and solar power, flexible capacity management becomes a fundamental need and challenge. The goal is to reach a point where we no longer require polluting power stations to ‘fill in the gaps’ when wind turbines and solar powers aren’t generating.

Achieving this requires energy storage to act as a buffer, easing pressure on demand when supply is too low, or saving valuable energy from renewables on a very windy, or sunny day where too much energy is being produced.

Most people associate energy storage with batteries. Battery storage systems allow energy consumers to store low cost, renewable energy and deliver it back at times of peak demand. For example, if you have a battery storage system integrated with a solar farm, it is possible to use photovoltaic (PV) power to charge up the battery for free during the day, drawing on that power at peak times, or selling that power back to the grid.

Applications for energy storage projects in the UK have grown from 2MW in 2012, to over 6.8GW in 2018, according to trade body RenewableUK. Little wonder, as batteries are already proving their value in helping organisations save money on power while doing their bit for the planet. But in our pursuit of low-cost clean power, arguably the cheapest and cleanest type of energy storage comes from the pockets of flexibility in our demand for energy.

Finding the hidden pockets of flexibility

Demand-side flexibility involves getting energy consumer to change their energy consumption patterns based on certain market signals. A lot of us practice a version of this already at home. For example, we switch lights off when we’re not in a room, or we use the washing machine at times when energy is cheaper.

The same applies at an industrial scale. Think about a supermarket fridge, an industrial furnace, or a water pump that feeds 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.

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, then instead of paying wind farms to turn off we can ask equipment to use more now instead of later.

That sounds simple enough, but imagine trying to do this at scale, often with multiple assets on a single site, all with varying requirements. It would be virtually impossible to manage, and completely impossible to scale. Until now.

An automated approach to grid flexibility

Artificial intelligence and machine learning techniques are enabling us to keep an eye on multiple assets on a site, consider all the variables, from weather forecasting, through to pricing, and then make a series of intelligent, real-time decisions on how and when these assets should be using energy.

In the UK alone, we estimate there are 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.

Get low-cost, clean energy – with your car

Things become even more exciting when we add electric vehicles into the mix. Imagine if the charging points we used to charge our vehicles at home run on a system connected to technology which pinpoints the optimum time to charge the battery. Cars could help to absorb energy during periods of oversupply and to ease down demand during periods of undersupply. Your car would effectively become an energy storage unit that could help power your house with clean, low-cost energy.

Thanks to rapid advances in technology it is possible to envision a fully autonomous, self-balancing grid which delivers all the clean energy we need, incredibly cheaply. This is not decades away. Everything I have mentioned exists, is proven, and is scalable.

We’re forecasting up to 30GW of capacity in the UK by 2030, stemming from a mix of energy storage, combined heat and power units (CHPs), electrolysers and Electric Vehicles, through to more traditional demand-side response assets such as industrial pumps, boilers and chillers. Our technology already enables us to address all of these assets, unlocking potential cost-savings of up to £8 billion per year by 2030. When you begin to think about the opportunities this unlocks, it starts to put the long, slow demise of coal into perspective.