A new frontier in Dynamic Frequency – does it stack up?

National Grid ESO’s new dynamic frequency regulation services suite needs fast-responding assets, such as batteries, to enhance grid frequency stability. Operators will be able to pick and choose the services they provide. However, managing the state of charge ahead of time and keeping systems within warranty constraints by stacking these new products to achieve desirable utilisation and revenues adds complexity.

As the National Grid ESO procures Dynamic Regulation with a minimum unit size of 1MW, it could mean significant underutilisation of most existing systems below a 2-hour duration. So what does this mean for the state of charge (SoC), and how we can create the optimum ‘stack’?

In this blog, we explore a theoretical battery’s response to the new Dynamic Response (DR) and Dynamic Moderation (DM) services, modelling the expected impacts to the utilisation, cycling, and State of Charge (SoC) management.

An Increasing Appetite for Energy Storage

The UK’s electricity system is increasingly experiencing lower inertia, and there is a growing risk of more considerable and frequent generation losses. Last year the publication of the Future Energy Scenarios (FES) from National Grid ESO (NGESO) and the Government’s Smart Systems and Flexibility Plan 2021 outlined the need and role for flexibility in a net-zero UK energy system. As variable renewable power replaces conventional generation sources, flexibility not only unlocks increasing potential for renewables and ensures energy security but could also reduce system costs by between £30-70bn by 2050. At least 15GW of shorter duration assets (less than 4 hours) will be needed by 2050 to help balance the system. According to the FES, by 2030, 18GW of shorter duration assets could be built in the fastest credible decarbonisation scenario.  

New Frequency Services for a More Resilient, Lower Carbon System

In recent years we have seen a growing number of low-frequency events, where grid frequency has deviated by more than 0.3Hz (Figure 1). For example, on the 9th August 2019, we saw a blackout; 3% of system demand disconnected, and in London, 60% of the Tube network ground to a halt.

Figure 1. Number of lower than 49.7 Grid frequency events since 2014.

There are different strategies to combat dropping inertia:

  • procuring it directly from a conventional plant or synchronous generators;
  • reducing the size of the most significant single loss on the system (which reduces the risk of instability if that load is lost) or;
  • the procurement of faster frequency response.

Batteries enable the procurement of faster frequency response; they can react to significant frequency disturbances and restore stability in under a second by delivering ‘synthetic inertia’ to the system.

By April 2022, NGESO will have launched the full suite of new faster-acting frequency response services, eventually replacing all legacy frequency products, including Firm Frequency Response (FFR). 

Introduced in October 2020, Dynamic Containment (DC) was the first of these new services, and it now has over 850MW of participating volume. DC operates post-fault, providing a less than 1-second response to frequency deviations of greater than 0.2Hz. DC helps the system recover from sudden significant losses of generation or load.

In March and April 2022, we expect the soft launch of Dynamic Moderation (DM) and Dynamic Regulation (DR). Both DM and DR are pre-fault services. DR acts with a constant and proportional power response across the ‘operational’ frequency range and reaches full power at a +/- 0.2Hz frequency deviation. DM kicks in between +/- 0.1Hz-0.2Hz, giving an additional boost to stabilise frequency as it moves towards the edge of operational limits. Figure 2 compares the droop curves (the power response according to grid frequency) for all three services with the existing FFR product.

Graph showing Droop curves for Dynamic services (left) and FFR (right), showing the power response as % of nominal asset power across the frequency range in Hz.
Figure 2. Droop curves for Dynamic services (left) and FFR (right), showing the power response as % of nominal asset power across the frequency range in Hz.

The New Dynamic Services

Figure 2 shows that DR requires the highest comparative response due to its steep droop curve. We model the impact of performing this service for a 10MW system with infinite storage (i.e. with no energy limit to charging or discharging) using historic frequency data, accounting for efficiency losses. 

DR has twice the utilisation of FFR, at 10% (Table 1), and subsequently double the throughput (cumulative discharge energy). DM and DC have much lower utilisation. A 1-hour system would perform 0.5 and 0.12 cycles per day, respectively, compared to 2.4 for DR.

Table-showing-the-average-utilisation-and-average-throughput-for-Fast-Frequency-Response-and-dynamic-services
Table 1: Average Utilisation (%) and average throughput (MWh/MW) for FFR and new dynamic services. Calculated from historic Grid frequency.

Figure 3 shows the expected power response to a large low frequency event (0.3Hz deviation). DR and DM act to restore grid frequency faster than FFR. While DR and DM services reach their maximum response power levels at or below 49.8 Hz, the DC response stays below 50% of its contracted level.

Figure 2. Droop curves for Dynamic services (left) and FFR (right), showing the power response as % of nominal asset power across the frequency range in Hz.
Figure 3. Response to >0.3Hz low grid frequency deviation for all Dynamic Services and FFR. Here, negative power indicates discharging of a battery.

DC acts in reserve should an additional loss push frequency further away from 50Hz. Theoretically, grid frequency should become more robust to system trips even as inertia continues to fall with these three services acting in concert – vital for the UK’s security of supply. Depending on how much volume NGESO procure, we may therefore anticipate grid frequency to depart from what we see in historic frequency data. The utilisation and throughput of each service may then look different, but using historic frequency still gives us a reasonable idea of what to expect.

The Impact on State of Charge

With the higher utilisation of DR, SoC management becomes increasingly crucial for batteries, ensuring a ‘real’ battery of finite capacity can deliver the service and consider the service’s cost.

In Figure 4, the first plot shows how much the SoC would drift in each of 20 days for the same 10MW system. The bottom histogram shows the SoC drift over EFA blocks 1, 2 or 3 for a more considerable period of historical data. With no actions taken to rebalance the stored energy of a system providing 10MW of DR for 8 hours—or 2 EFA blocks. SoC would have drifted by more than +/-5MWh, 35% of the time.

This scenario suggests that a 1-hour battery would fully charge or discharge to empty after just 2 EFA blocks if it begins delivering the service at 50% SoC, ignoring availability requirements.

Graph-showing-the-state-of-charge-drift-of-a-battery-providing-Dynamic-Regulation
Graph-showing-the-state-of-charge-drift-of-a-battery-providing-Dynamic-Regulation_4Hr_8Hr_12Hr
Figure 4. State of charge drift of a battery providing Dynamic Regulation only.

Participants must provide the service continuously for the periods they have tendered for or risk penalties. Furthermore, NGESO has stipulated a 60-minute minimum energy requirement for energy-limited assets. That means even a 2-hour battery would need a rebalancing action within 8 hours (2 EFA blocks) 34% of the time, and within 12 hours (3 EFA blocks), 42% of the time. Alternatively, providers could de-rate the tendered volume (i.e. MW) offered, which would also be favourable to reduce cycling and preserve the system warranty.

Managing State of Charge

Baselining actions must be taken to manage SoC while a battery performs DR. We simulate a 10MW/10MWh battery, de-rated to 4MW, delivering DR over three days continuously. The SoC is managed with spare MW using an automated control algorithm that attempts to maintain a 4MWh of headroom and footroom, equivalent to a 60-minute energy requirement.

Following BM requirements, units performing DC baselining actions are planned 1-hour in advance. Under these conditions, the battery requires, on average, three rebalancing actions per day, with 3.8MWh of energy needed for baselining. Figure 5 shows simulated battery power and energy while responding to historical frequency deviations.

Graph-showing-modelled-Energy-management-with-stacked-dynamic-services-2MW-Dynamic-Containment-to-7MW-Dynamic-Regulation
Figure 5. Simulated State of Charge with a 10MW, 1-hour battery responding to Grid frequency with a Dynamic Regulation response curve and a de-rated 4MW DR tender. Automated baselining is applied to maintain system energy around 50%.

Given the ahead-of-time baselining requirement, the rebalancing algorithm fails and leads to 5 hours per day outside the 60-minute minimum energy requirement for a 4MW tender on a 10MWh asset. So a 1C system de-rated to 40% of its nameplate capacity–in effect becomes a 0.4C (or 2.5h) system that could still cycle over 400 times per year and would still not meet a 60-minute minimum availability requirement.

Stacking Dynamic Services

National Grid ESO has stated that the three services can be stacked together (albeit not in the initial soft-launch phase). A single battery can deliver different services simultaneously but with each MW partitioned to provide a single service.

Since DR has high utilisation, we limit the DR tender to much less than the battery’s power rating. This prevents exceeding warranty limits on cycling and mitigates against the cost of a substantial SoC rebalancing regime, which may require peak-time charging to maintain availability. 

To monetise the remaining MWs, we stack lower utilisation services on top. Figure 6 below shows a low grid frequency event under two scenarios: the first with a complete 10MW DR response; the second with a stacked tender of 5MW DR and 5MW DC. In the latter case, during a frequency event below 49.8Hz, utilisation averages 60% of the total tendered volume during the period, rather than 100% for the DR response alone.

 Graph showing the resulting power response of Regulation service vs stacked Regulation and Containment services during a low-frequency event. NB negative power indicates discharging
Figure 6. Resulting power response of Regulation service vs stacked Regulation and Containment services during a low frequency event. N.B. negative power indicates discharging.

Towards an Optimal Stack

We can assess the ‘best’ stack for battery size by calculating utilisation and cycling for a range of tender stacking combinations. For example, figure 7 shows the utilisation and mean daily cycles when we combine DC and DR. The x-axis shows the share of DC, such that 10 represents 10MW of Containment and 0MW Regulation. 

If we must also operate below a “Warranty Cycles” level set to 400 cycles per year, a 6MW/4MW split is best for a 1-hour system (LHS of 7). If we must not exceed 5% utilisation, we also require 6MW/4MW. However, note that since a 1-hour system will likely not provide more than 5MW DR because of the energy requirement limits, the trend below 5 is purely theoretical.

The right plot shows how a 2-hour battery (10MW/20MWh) could significantly loosen constraints on delivering the Regulation service – reaching 8MW of Regulation to 2MW of Containment before reaching the Cycles limit. But, of course, the optimal split should also consider revenue potential. The revenue potential depends on the available auction prices, which are also influenced by the National Grid ESO requirements and, otherwise, the technical barriers to participation.

Graph-showing-the-utilisation-and-number-of-daily-cycles-for-a-10MW-battery-when-different-ratios-of-stacked-dynamic-services-are-selected
Figure 7. Utilisation and number of daily cycles for a 10MW battery when different ratios of stacked dynamic services are chosen.

A Real-World Stacking Strategy

We simulate a potential ‘real-world’ scenario, stacking DR and DC. We choose a 2-hour battery with an 8-2 tender split between DR and DC to meet the cycling constraint of approximately 1.1 cycles per day (400/year). However, reserving capacity as headroom for managing energy, we need to de-rate the tender to 7MW-2MW, preferring the lower-utilisation service. Services are presumed to be symmetrical (both high and low response). Our SoC management aims to keep the system close to 50% and maintain the minimum energy requirement.

The optimum solution compromises between minimising the number of actions taken to balance SoC, which could be expensive, and taking sufficient measures to limit the chances of violating minimum energy constraints and protecting the battery’s life through minimising time spent at extremely high or low energies. Figure 8 shows the modelled response over three days.

With a 2MW/7MW DC/DR split, for a 10MW 2-hour system, we see ca. 7.3% utilisation and 0.83 cycles per day. Balancing SoC requires a mean daily baseline volume of 6.2MWh. Despite the current cost uncertainties associated with providing grid services (due to recent high electricity prices), the simulated case would, if exposed to only cash-out prices, cost more than £3/MW/H.

Whilst achievable compared to current FFR prices, we also have to consider the penalty of higher utilisation and the opportunity cost of going elsewhere for traded revenue.

Graph-showing-modelled-Energy-management-with-stacked-dynamic-services-2MW-Dynamic-Containment-to-7MW-Dynamic-Regulation
Figure 8. Modelled Energy management with stacked dynamic services: 2MW Dynamic Containment to 7MW Dynamic Regulation. Mean daily cycles are under the target of 1.1.

In conclusion

Modelling the forecasted utilisation of 1C batteries with historical grid frequency can deliver DC and DM. However, the pre-fault DR service requires a significant MW de-rating or a longer duration battery to provide a sustained service energy requirement. Therefore, longer duration storage and higher cycling capability could offer good market commercialisation opportunities.

We can generate a greater return from batteries while keeping cycling within a ‘standard’ warranty by stacking high and low utilisation services. Modelling the delivery of this type of stacked service with historic grid frequency demonstrates a trade-off between efficient state of charge management and cost minimisation. Imposing operational constraints such as ahead-of-time rebalancing notification for a high utilisation service presents a challenge even for 2-hour systems. Taking baselining actions ahead of time and offering multiple services at once is operationally complex within system warranty constraints.

Furthermore, if National Grid ESO is to procure these services (particularly DR) with a minimum unit size of 1MW, it could mean significant underutilisation of battery capacity at the sub-10MW level. Ultimately, the prices of these services should reflect this complexity and potential inefficiency. Given the difference in utilisation and technical requirements, it will be fascinating to see where price levels transpire between the three services.

Unlocking the investment necessary to meet the ambitious storage goals of the Smart Systems and Flexibility Plan 2021 requires confidence in the revenue streams for these assets. The existing ‘stack’ of revenue streams open to storage assets can be uncertain and high risk, and barriers to entry remain. Economies of scale, volume caps, licencing, and changes to demand charges (namely the Targeted Charging Review) favour large standalone storage assets. More planning relaxation will further incentivise the building of systems greater than 50MWh.

These new markets will undoubtedly form an essential part of the flexibility revenue stack and should provide investors with confidence in storage revenues’ long-term security. Even without operational examples for Dynamic Regulation and Dynamic Moderation, as with Dynamic Containment, battery storage appears favourable to deliver these services at the lowest cost.

However, with wholesale market volatility, trading opportunities elsewhere may yet present a more attractive option for battery operators looking to maximise revenue stacking if the cost-of-service provision, combined with high technical barriers in grid services, outstrip the benefits.

To find out more about our products and services, email us; info@openenergi.com.

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:

 

 

 

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 https://www.current-news.co.uk/blogs/throughput-vs-revenues-making-the-most-from-battery-storage

Revolutionary Vehicle-To-Grid Technology Launching in the UK: The Smallest and Lightest Residential V2G Charge Point in the Country

Following the news in February 2018 of the PowerLoop consortium formation, which is part of a three-year, £7m project part-funded by the UK Government via InnovateUK, Open Energi is excited to see the next project stemming from this group come to life. On Friday, Octopus Electric Vehicles and Wallbox announced their partnership to bring Wallbox’s revolutionary vehicle-to-grid (V2G) technology, which is when an electric vehicle releases energy through your home and out into the local grid, to the UK. In partnership with Octopus Energy, Open Energi will lead on developing a bespoke V2G aggregation platform and will work alongside UK Power Networks to integrate domestic V2G into their flexibility services.

This will be the smallest and lightest residential V2G charge point in the country, helping the consortium’s objective to support the grid, reduce costs and deliver a more sustainable future. This step forward in technology has been achieved by using a silicon carbide inverter to switch power from DC (used by electric cars) to AC (used in homes and by the grid) at a rate much faster than previously possible. The new technology allows Wallbox to reduce the size and weight of the inverter, a component which traditionally meant V2G charge points were much heavier and larger than existing one-way charge points (where energy only flows from the grid to charge the car).

Our analysis suggests EVs could deliver over 11GW of flexible capacity to the UK’s energy system and conservatively forecasts the market providing about £1bn a year in consumer benefits by 2030. Initiatives like the PowerLoop consortium with Octopus Energy are playing a vital role in unlocking that potential at scale. As part of the consortium we have been able to draw on our extensive experience connecting, aggregating and optimising industrial equipment, battery storage and generation assets on a second-by-second basis, to demonstrate how these principles can be applied to domestic smart charging infrastructure.

V2G technology allows drivers to earn money from plugging in their electric cars. V2G discharges excess electricity from their car through their home and out to the grid at times when there is peak demand on energy networks, and recharges the car when energy demand (and energy prices) is low. The renewable energy will be based on the current 12 month fixed energy tariff provided by energy supplier Octopus Energy.

For more information on the PowerLoop bundle, register on the Octopus EV website at www.octopusev.com/powerloop.

Innovative research project aims to support greater local integration of Solar PV

solar panels

Increasing levels of solar PV are having a growing impact on the operation of the low voltage (LV) network. The need for new grid connections has impacted project viability and in some areas of the country Distribution Network Operators (DNOs) have been forced to limit new solar integration. However, new technologies are introducing ways to make smarter use of the abundant free energy provided by the sun and deliver new revenue streams, without the need for costly infrastructure upgrades.

Funded by Innovate UK, this innovative research project aims to support greater solar PV integration, by forecasting solar output in near-time with better accuracy, and enabling generation to interact dynamically with demand.

In the South West of England, where these challenges are particularly acute due to a constrained network, Meniscus Systems, BRE National Solar Centre, Cornwall Council and Open Energi are collaborating to create short-interval (every 5 minutes), location-specific solar intensity and power predictions that will improve local grid operation, optimise the performance of solar farms and enable operators to participate in Demand Side Response (DSR) schemes to maximise revenue, with or without energy storage.

Cornwall has the fewest grid interconnections with the largest solar PV installed capacity – over 475MW of large-scale (1MW+) solar farms – leading to network operating problems. Resulting constraints imposed by the DNO make it harder to connect large scale renewable generation. The ability to better predict and manage the performance of solar PV on the LV network is an important step towards the creation of local energy markets, and will help to ensure that Cornwall’s residents, communities and local economy benefit from the low carbon energy transition.

The project will make use of:

  • Real-time and historic satellite based imagery to predict solar intensity for any location at intervals of 5 minutes on an hour ahead basis.
  • Historic and near real-time PV data from the Cornwall Council solar farm at Cornwall Airport Newquay (CAN) to test and demonstrate the system and explore the role of on-site battery storage.
  • Open Energi’s expertise to deliver accurate, real-time PV-based DSR solutions to DNOs and owner/operators of solar farms to more efficiently manage local networks.

Accurately modelling the commercial benefits of solar PV and battery storage will be an important aspect of the project. If predicted solar generation is higher than the export limit of the site, a battery can be charged instead of curtailing generation, discharged to grid during a later period of high demand, and in the meantime the battery can be employed for DSR. For a site with no installed storage, generation can be curtailed at times when the network is constrained in response to DSR signals, such as Demand Turn-Up. Accurate predictions allow the DNO or Transmission System Operator (National Grid) to efficiently manage their network

With the UK’s solar capacity forecast to rise to 15.7GW by 2020 – from just over 9.3GW at present – using advanced technology to more efficiently integrate and optimise solar PV sites is vital to create a more sustainable energy future. Due for completion in early 2019, this project aims to pave the way for the smarter use of solar PV via peer-to-peer energy markets that benefit local communities, delivering a smarter, more flexible energy system across the UK.

The lead Project Team comprise:

  • Meniscus Systems – Project Lead and delivery of solar intensity predictions in a form that will allow integration with the DSR market.
  • Cornwall Council – owner/operator of solar farm which will be used to test and demonstrate the system.
  • BRE National Solar Centre – responsible for ensuring the system meets the requirements of the PV industry and validating the system’s performance.
  • Open Energi – DSR aggregator responsible for identifying DSR revenue opportunities and systems needed to deliver this capability.

For more details, please get in touch.

Robyn Lucas is Head of Data Science, Open Energi

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.

Faster Frequency Response: A Cost-effective Solution to Future System Balancing

open energi wind farm

Creating a sustainable energy future will take decades and the pace of technological development will lead to ideas and solutions that no one has even thought of yet. This innovation will come from the next generation of energy leaders, who are already conducting vital research at universities across the globe.

 Over the last year, we’re delighted to have been supporting Yifu Ding, who is studying for an MSc in Sustainable Energy Futures at Imperial College. Yifu has been assessing the value of faster frequency response times in power systems, and Open Energi’s Dagoberto Cedillos has been one of her supervisors. Yifu’s project was recognized as the Best MSc Research Project in the cohort, and we’re pleased to share a post from Yifu about her work.
Y_Ding_Headshot

What is System Inertia?

In a stable power system operating with a fixed nominal frequency (50 Hz in the UK) electricity supplies must closely match loads on a continuous, second-by-second basis. This is especially difficult during some special cases such as the power pick-ups after big football games or a royal wedding.

Undoubtedly, achieving such a real-time balance is not a simple thing, but there are many approaches. Large power systems have an inherent property which provides the quickest response for contingencies. In a conventional power plant like coal, gas and even nuclear, electricity is generated by a turbine, basically a large spinning mass of metal. The inertia stored in these rotating turbines provides an energy store which automatically stabilises the system and insulates it from sudden shocks. In an event of a generation outage or surge in demand, inertial energy is released which prevents the frequency from falling. Equally the inverse happens in the case of a sharp increase in electricity supply or decrease in demand.

After that, the system operator begins to manipulate power assets through an array of automated measures already in place (like different frequency response products) and by sending out manual notifications. In response to these, large-scale power stations adjust their outputs. Hydroelectric reservoirs release or pump water. Aggregators control loads or battery assets they manage to provide a response.

Challenges for System Balancing

In light of the decarbonisation trend, great changes have been undertaken in the UK power system. Old methods relying on fossil-fueled power plants to balance the system are challenged and we need to explore new options.

As a rule of thumb, we are losing the system inertia. According to the National Grid System Operator Framework (SOF) 2016, approximately 70% of the UK system inertia is provided by thermal power plants. Unfortunately, the rapidly increasing volume of renewable generation units with power electronics interfaces, including solar PV and wind turbines, are not synchronized with the Grid. Therefore they don’t contribute to the system inertia.

Fig1a synchronous coupling

Figure 1: Generators contributing (or not) to the system inertia (From National Grid SOF 2016)
Figure 1: Generators contributing (or not) to the system inertia (From National Grid SOF 2016)

In our research, we considered ‘Gone Green’ and ‘Steady State’ scenarios from National Grid Future Energy Scenarios (FES) 2017, to compare and contrast what could happen in the near-term future. We found out that the inertia of the UK system will fall from 198 GVAs in 2015 to 132 -155 GVAs by 2025, as large numbers of thermal power plants are closed to meet carbon reduction targets.

Figure 2: The future scenarios considered in this research according to National Grid FES 2017
Figure 2: The future scenarios considered in this research according to National Grid FES 2017

Why Faster Frequency Response?

From this point of view, our power system will become more ‘erratic’ than before due to lack of this self-stabilization property. To counter this we could use more Frequency Response (FR) services, or perhaps something else?

We can envisage a power system with a stable frequency as a large tank with a stable level of water. The current inlet and outlet represent the generation and demand respectively. If a sudden imbalance occurs between inlet and outlet, we need to respond quickly in case the water level becomes too low or overflows.

In this fashion, one of the effective solutions is delivering faster-acting response. In July 2016, National Grid launched and tendered a sub-second FR service called Enhanced Frequency Response (EFR). Currently it is provided by batteries which can respond fast enough to provide a similar level of security to the inertial response from conventional power generators.

Value of Enhanced Frequency Responses

A few statistics from our research and other documents give you an idea of the exact economic benefit from delivering this new FR service.

By developing an optimization mathematical model to simulate power generation, dispatch and balancing in a row, we estimated the economic benefits of EFR will reach £564 to £992 per kW by 2020. National Grid has already contracted 201 MW of EFR, therefore the total economic benefit is estimated to be up to £200 million. This result conforms to the estimation published by National Grid on 26 Aug 2016.

Figure 3: A screenshot of the daily power generation and dispatch outcomes from the optimization model.
Figure 3: A screenshot of the daily power generation and dispatch outcomes from the optimization model.

But this isn’t the whole story. Although the fast-acting FR service demonstrates many advantages, there are still obstacles when it comes to the implementation.  For the system operator, an issue which might arise is how to determine the optimal mix of those FR products. Otherwise some of them could be undersubscribed or oversubscribed as mentioned in System Needs and Product Strategy (SNAP) report from National Grid.

Stakeholders in the balancing markets, such as electricity storage operators, can make themselves invaluable by providing such a service. However, we should note that it is designed to be fulfilled continuously, meaning it’s unlikely to be delivered in combination with other network services. In this case, the operator can only obtain the single revenue from the asset, risky from an investor perspective. Providing such a service is technically challenging since it requires a sophisticated state of charge (SoC) control to meet the service specifications and manage battery throughput.

As we look into the future balancing markets, fast-acting FR services indeed provide a cost-effective solution towards the low-carbon power system. Planned streamlining of  procurement mechanisms and ongoing technology development will help to fully unlock its potential.

 

The future of flexibility: making the most of behind-the-meter storage

Camborne Energy Storage

The slowdown in the Firm Frequency Response (FFR) market, where prices are falling and volumes have been capped – in large part thanks to the decreasing cost of battery storage – has been a big talking point of late.

National Grid has now procured much of the low response it needs for the next two years, and average prices of accepted bids have decreased 20% over the last 7 months. National Grid has also announced proposed changes intended to rationalise and reform the current suite of balancing services it procures, including a streamlined FFR-type product.

These rapidly changing market conditions may leave some battery companies going back to the drawing board and asking how to guarantee income over a long period and satisfy investors that it is worth investing hundreds of thousands in the latest battery storage system, without the relatively high revenues that providing FFR has historically guaranteed?

Fig: The distribution of FFR accepted tenders for the period January - October 2017. Source: National Grid
Fig: The distribution of FFR accepted tenders for the period January – October 2017. Source: National Grid

But it is possible to turn to other markets for flexibility. FFR will remain a part of the revenue stream for batteries, and for the fast-acting Demand Side Response (DSR) that makes up most of Open Energi’s FFR portfolio; but being a bit smarter about where, and how, you employ the flexibility at hand can open up a wealth of other revenue streams. These, stacked up, provide a stronger business case than FFR alone.

Open Energi’s new platform, Dynamic Demand 2.0, is designed with exactly this in mind. By connecting to distributed energy assets which have inherent flexibility in their electricity consumption or generation, including industrial equipment, electric vehicle charging stations, and of course battery storage systems, it enables businesses to dispatch this flexibility in the most valuable place at any one time.

Tailored to the particular operational or site constraints of an asset, balancing services, energy trading, the capacity market, network constraint management, peak price management such as DUoS red-band and TRIAD avoidance are all opened up as stacked revenue streams, as well as operational energy efficiencies automated with machine learning.

For behind-the-meter batteries, this means considering any on-site generation or demand, the import and export limits of the site, the warranty particulars of the battery, and evaluating all the potential revenue streams of the asset. By taking a holistic view of the energy market, using state of the art machine learning techniques and cloud based technology, we remotely operate the asset to maximise its return on investment: FFR is just one slice of the cake.

In many ways, a battery is the perfect asset for flexibility. It has a defined storage capacity; it can discharge power up to a maximum well-defined rate; and it has a known state of charge (SoC) at any one time. However, to operate a battery across multiple markets, careful management of the SoC of the system is necessary.

While batteries very naturally perform frequency response, charging up when frequency is high – removing excess electricity from the grid, and discharging when frequency is low – when more electricity is required, the operation is not quite that simple. To have the capacity, or availability, to both charge up and discharge in line with grid frequency, the ideal SoC of a battery is 50%.

Periods of high or low grid frequency can rapidly take the SoC of the system far from 50%, and batteries have an inherent efficiency (around 90% in the best systems): active state of charge management is required to maintain the availability for frequency response.

The figure below shows the same 500kW, 800kWh battery over the same 52-hour period, with and without state of charge management. The availability to fully charge or discharge at the capacity of the battery for 30 mins (equivalent to a frequency 50.5Hz or 49.5Hz for half an hour) – is quickly impeded on when no SoC management is present, and SoC will eventually tail off to zero due to the non-perfect efficiency of the battery. Battery throughput, defined as the cumulative discharge of energy through the battery, is also higher without SoC management, eating into that allowed by the warranty.

Fig: The state of charge (%, top), availability for FFR (kW, middle) and throughput (kWh, bottom) of a 500kW, 800kWh system are shown across 2 days of frequency data. With no SoC management in action, state of charge is routinely out of the dotted lines which signify ½ hour of storage capacity being available to charge and discharge at full power; here, state of charge is low. As a result, low availability for FFR is diminished. Throughput of the battery, defined as the cumulative sum of the battery discharge, is also higher when no SoC management is used.
Fig: The state of charge (%, top), availability for FFR (kW, middle) and throughput (kWh, bottom) of a 500kW, 800kWh system are shown across 2 days of frequency data. With no SoC management in action, state of charge is routinely out of the dotted lines which signify ½ hour of storage capacity being available to charge and discharge at full power; here, state of charge is low. As a result, low availability for FFR is diminished. Throughput of the battery, defined as the cumulative sum of the battery discharge, is also higher when no SoC management is used.

When introducing revenue streams in addition to FFR, as we do with Dynamic Demand 2.0, battery operation gets rather more complex. For a behind-the-meter battery, electricity bills can be significantly reduced by discharging over peak periods to either avoid importing electricity when it is most expensive (if the site has demand), or exporting to grid and taking advantage of the various network charging revenue streams (DUoS, TRIAD, CM Levy).

Electricity generated earlier in the day, from on-site PV generation, or stored when it was cheap to import, can be exported to grid at peak times. Energy arbitrage on the wholesale market: charging up when the N2EX price is low and discharging when it is high, or hedging against pre-bought volumes, can give a day-ahead price signal, as in the figure below; playing on the intraday market can similarly offer additional uplift. If the asset owner is exposed to the Balancing Mechanism through their supplier, short notice calls to discharge in line with a large system imbalance offers another revenue stream.

Battery Storage Energy Arbitrage
Fig: Energy arbitrage using the N2EX day-ahead market is shown for a 60kW, 300kWh system over one day. The stored capacity of the battery is varied to minimise the cost of electricity over the day, such that the battery charges when electricity is cheap, and discharges when it is more expensive. By layering on additional price signals closer to time and re-optimising the power profile, additional revenues from energy arbitrage across a range of markets can be achieved.

These requests to charge and discharge in line with ahead-of-time and real-time price signals must be dispatched depending on the import and export constraints of the site and of the capacity of the battery. It needs enough stored energy to be able to discharge for the duration of the price spike, while maintaining the availability to do FFR in accordance with any pre-contracted volumes required by National Grid, and such that recovering the SoC afterwards does not cost more than earnings during the call itself. It also needs to do so without overly increasing the throughput of the battery such that the valuable warranty is violated.

Open Energi has experience of doing this with assets already in operation behind-the-meter. At South Mimms Welcome Break services, we are managing a Tesla Powerpack alongside an EV charging station, to displace site demand from EV charging throughout the day, while providing FFR, and providing energy savings by doing a full discharge of the battery during peak periods.

In a Somerset field, we operate a battery integrated with a solar farm, using PV generation to charge up the battery for free during the day, discharging at peak times, and managing the SoC so that FFR availability is maximised the rest of the time. By stacking these additional revenue streams alongside FFR we increase the income of the asset, and with our Dynamic Demand 2.0 platform, will be able to extend these services across a host of other assets and services.

This includes managing fleets of battery storage systems, where the optimal control strategy goes beyond revenue stacking each asset individually. A central intelligence that is aware of the state of each battery can determine how hard to work different assets to maximise revenue or minimise the number of charge-discharge cycles performed, extending each asset’s life. Dynamic Demand 2.0 can combine revenue stacking with fleet management strategies to meet these objectives.

As we look to the future of energy, it is clear that behind-the-meter battery storage has a huge role to play in creating a more sustainable system. As renewable energy becomes more prevalent, value is emerging in new places, and the key to optimising this value, without negatively impacting battery performance, is technology.

Robyn Lucas is Head of Data Science at Open Energi

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

Tesla South Mimms Supercharger and PowerPack

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

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

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

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

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

How demand flexibility can boost the benefit of a Corporate PPA

solar panels

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

The rise of corporate PPAs

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

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

How does a corporate PPA work?

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

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

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

Aligning supply and demand

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

Matching factory demand and renewable generation

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

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

Cutting costs with demand flexibility

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

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

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

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

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