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In a world with many GW of renewable generators on the network, when the sun shines, or the wind blows, the price of electricity crashes as a huge amount of (cheap, or free) solar and wind power floods onto the system simultaneously. This means the 'capture rate' for renewable assets - the average power price they achieve vs the overall average power price - is less than 100%. We see it today - it'll get worse over time. This is an issue for the business model of renewable generators.
In the Forecast, we have a half-hourly model for solar and wind generation and a half-hourly supply stack. See info on our renewable load factors.. So, at times of lots of renewable generation, demand intersects supply when clean, free power is abundant - and the clearing price is £0/MWh (in a post-subsidy regime). An example of this is below.
Generally, we see that the price crashes in the middle of the day due to solar generation (below, left), and similarly, when the wind generation is very high we get prices of £0/MWh.
Storage charges up when power is cheap, and discharges when it's expensive.
When you have a lot of storage on the system, it might discharge into the same periods, which changes the supply stack. With the addition of storage, there is suddenly a surplus of generation, and as a result, the price crashes. What used to be an expensive period is now a cheap one (and on the charging side, a more expensive one, though to a lesser extent, as charging is typically more spread out).
This means the opportunity for storage is lost - and the storage makes no (or much less) money.
In reality, high prices would get damped or suppressed as 'peaks' spread into neighboring periods.
In our storage model, we limit the amount of the storage fleet that can dispatch into an individual half-hour period. More information on this here. This acts to soften the price cannibalization.
Demand side response (DSR) is priced at £250/MWh, £500/MWh, and £1500/MWh. It sits near the top of the generation stack, with loss of load pricing at £6,000/MWh above it.
When there is a shortage of generation capacity (usually driven by low wind), a high price results as demand and generation intersect in this high-price region.
Transmission Loss Multipliers (TLMs) apply to transmission-connected assets.
Energy accounts are settled at the Notional Balancing Point (NBP). Energy is lost due to transmission losses from the point that it is generated to the Notional Balancing Point. For assets at lower voltages (such as those embedded in the distribution networks), the power has to travel through more of the network to get to the NBP - so the losses are higher.
They are regional (there are 14 zones corresponding to each Distribution Network or GSP group) and time-of-use dependent. There are two types of losses: Transmission Loss Multipliers (TLMs) and distribution level losses (D-loss). Typical values for these:
- TLMs ~ 1%
- D-Losses ~2%
They also depend on the voltage level of the asset in question. Usually, multipliers are higher overnight than during the peaks. They can be negative as well as positive. The TLM calculation attributes part of the transmission losses to generation and part to demand: there is a Generation/Demand split (currently, 45%).
Further explanation and example calculation for TLMs is given here.
Transmission-connected assets will be subject to TLMs only and not D-Losses.
Most batteries connected at the distribution level will be connected at EHV - and the D-Losses will be minor (less than the 2% quoted above).
Below is an example calculation of TLMs for a charge and discharge action in the summer in Northern Scotland. We import 100MWh/88% (to account for battery efficiency) at 4 am when the energy rate is £10/MWh. We export 100MW at 5 pm at a rate of £100/MWh.
|Energy Price £/MWh
|Average Transmission Loss
|-100MWh / 0.88 = -113.6MWh
These example prices are lifted from the National Grid ESO TLM guidance document
= Energy Tariff x (Metered Volume x TLM)
= Energy Tariff x Metered Volume x (1 +TLF + Gen Loss Adjustment)
= 100 x 100 x [1 + (-0.01471) + (–1 x 2% x 45%)]
= 100 x 100 x 0.97629
= Energy Tariff x (Metered Volume x TLM)
= Energy Tariff x Metered Volume x (1 +TLF + Demand Loss Adjustment)
= 10 x 113.6 x [1 + (-0.01471) + 1 x 2% x (1-45%)]
= 10 x 113.6 x 1.02571
Users should apply the relevant loss multiplier to their site to account for them.
TNUoS charges are calculated using metered volumes, not loss-adjusted volumes.
Using the above example, instead of
£100/MWh x 100MWh - £10/MWh x 113.6MWh = £8864 profit
£9762.90 - £1165.20 = £8597.70 profit
I.e. a difference of around 3% uplift.
Depending on the year the CfD has been awarded, payments stop after a certain number of consecutive hours of negative pricing.
Our CfD fleet is split into the early auctions (AR1) and later auctions. The AR1 fleet is priced at -£100/MWh - effectively, it never turns off. The later auctions are priced at £0/MWh.
You might expect, after many hours of negative pricing (above the cap of the auction round), assets turn off as they'd have to pay to generate.
We don't consider the previous half-hour's price when dispatching the subsequent half-hour. This means this is not dealt with in our model. We may therefore have more negative pricing than you might expect - as if these assets were to turn off, there would be less generation on the system (and the price would return to £0 or above).
However, there is uncertainty around how you'd turn off GW of wind or solar with a few hours notice and no major operational cost.
The volumes in the BM are set by modeling the amount of energy and system balancing the GB system operator must do to keep the grid balanced. We use our wind and demand forecast, along with the capacity of the transmission network in different areas of GB, to predict these. See here.
This determines the 'depth' of the Balancing Mechanism.
Next, we determine how much flexible generation will be on the system: the plants that can flex their output in response to BM actions, and where these will be. We allow batteries, pumped hydro, CCGTs to turn up, and these plus wind to turn down. We then estimate how much of the energy and system actions in a region (dictated by constraint boundaries) that batteries will be to provide, given this competition.
We limit the dispatch of batteries by their duration, day-ahead position, and cycling considerations.
Why aren't the revenues of batteries with a 4-hour duration double that of an equivalent 2-hour duration battery?
In wholesale, a 4-hour battery only makes double when high prices are sustained for 4 hours (and low prices for 4 hours), allowing the battery to fully discharge (and charge) during the most advantageous periods.
But, we rarely see such sustained high/low prices.
We might only see a price spike for 1 or 2 hours. A 1 or 2-hour duration battery can make the same revenues on such occasions as a 4-hour system.
How have we determined the demand forecast? E.g. in terms of future weather scenarios, electrification.
We take the annual peak and minimum demand from the FES, as well as overall TWh demand, and scale to a daily demand shape based on weekday/weekend and time of year using historic trends.
There is huge uncertainty on what shape future demand will take - the success and take-up of smart appliances, smart metering with flexible tariffs, I&C schemes for demand shifting, demand (and availability) for EV charging at peak, heat pumps, home batteries, climate change weather variations etc… Rather than conjecture what the relative take up of each of these elements which could change the demand shape in an unknown way, we use historic data to inform the future.
We put demand side response in the capacity stack at various prices which effectively shifts the demand shape according to price.
Inflation is dealt with in the model by using 2023 prices everywhere.
Our revenues use the same units as the latest FES which is real 2023 prices. FES also includes the following table for conversion to nominal prices. Therefore, our revenue outputs are also real.
When we optimize a battery, we co-optimize day-ahead & frequency response markets for the business case showing merchant & ancillary markets. That's because both of these are determined at the day-ahead. For the merchant-only scenario, we optimize against our forecast day-ahead power price. We then add an intraday uplift for both, depending on the availability for re-trading the day-ahead position.
The Balancing Mechanism uplift gets added on afterward, as in reality, it is done in real-time (as opposed to day-ahead). So, BM revenues depends on the site's availability for BM actions.
Excluding BM revenues, the merchant & ancillary case comes out higher.
Merchant only comes out higher once the BM is added in, because these scenarios have higher availability in the BM.
Our 2-cycle scenario shows average cycling of less than 2 cycles per day. Why?
Our cycling is limited to a maximum of 2 cycles per day (for example). It will only do this when economical - on many days, there aren't the price spreads to warrant the additional cycling. So, overall, the average comes out less than 2 per day.
The average spread that an asset captures are shown below, for a 2-cycle and 2-hour system.
We do have a 2.5 cycling scenario available in the run library, which gives an average cycling rate closer to 2.
We have no minimum threshold for cycling the battery in our optimisation, only require the action to be economical. This means any spread must at least compensate for efficiency losses in the charge/discharge action. Average revenues per cycle across the forecast horizon are shown below.
TNUoS rates are split into fixed and flexible costs; and are different for distribution and transmission-connected assets. An overview of the TNUoS charges is given here.
We assume no site is importing during a triad - as this can be an expensive time to charge. Any optimizer should avoid charging during those periods or price the risk of a triad period into any contracts they secure that may require charging.
Those connected at the distribution level are liable to get TNUoS triad fees for exporting - and these are shown in the regions where the fees are non-zero.
Those connected at the transmission level are exposed to the Wider Generation Tariff, which can be positive or negative. This is a fixed cost - and so cannot be optimized around. Generally it is low as the Annual Load Factor for storage is low.
At the moment, we don't show fixed network tariffs in the forecast line items. So, like other fixed network fees (like standing charges, RO, FIT tariffs, etc), we do not show TNUoS rates for transmission-connected assets.
Churn, or non-physical trading, is quantified via our intraday uplift: detailed here. There is also some churn within our Balancing Mechanism revenues, as positions are re-traded via accepted bids or offers. A graph showing this behavior is here.
We do a historical analysis of the uplift due to re-optimizing the day-ahead position into intraday markets, assuming perfect foresight of intraday RPD HH prices (find these on our API). We limit cycling, duration, efficiency etc, according to each scenario, and then apply the uplift to day-ahead revenues going forward within the forecast. There is a smaller uplift for assets providing frequency response, as they have more constraints on how much power they can charge or discharge at any time.
This shift of day-ahead to intraday position consists of churn, or non-physical trading. Systems are still delivering physical positions when it is profitable to do so.
We use NREL's data on CAPEX and OPEX of storage systems to calculate IRR on battery revenues within the forecast model, using the revenues we project. NREL put 2024 battery prices CAPEX at $720/kW for a 2-hour system, which drops ~$30/kW a year to 2030, when the annual decline in price slows. Using these figures, we find the 25-year IRR stable at around 10-12% for the forecast horizon. In our central scenario we have 50GW of storage by 2050, which is probably more aggressive than other providers in the market - but with the trend of falling wholesale prices and more and more intermittent generation coming online, we find the markets are deep enough to take this much short duration storage.
Looking at the sensitivity of modeled price to grid-scale battery storage, we find daily power price spreads fall <3% with each additional 10GW of storage in the model at 2040.
We also perform a similar IRR calculation for other generation types within the model (gas, nuclear, wind, biomass, solar, etc.) to ensure the commercial model of all the generator types makes sense, including any subsidy mechanism like a CfD.
Some new 'non-firm' grid connections are being offered, which have some level of curtailment. This means the transmission network operator, or the distribution network operator, could reduce (or curtail) the amount of power imported or exported through the site at any one time. It can also be referred to as 'Active Network Management'. This could be to ease pressure on the local network.
To model the impact of curtailment on battery revenues, we would need to understand the level of curtailment the site is likely to face. So, the number of hours that restrictions could be placed, and what those restrictions look like - if all exports are set to 0, or all imports, or maybe exports limited to 50% over solar peak hours in the summer months, for example.
Our dispatch model can show what the impact on revenues will be for such curtailments, under a custom run. Please contact the team so we can understand what this could look like for your site.
There is a bit more info on active network management here.
One cycle is defined as the energy throughput of a battery relative to the nameplate or starting capacity of the site.
For a site that was 100MWh in capacity on day 1 of its life, one cycle is the same throughput throughout its lifetime: 100MWh. Throughput is defined as discharge energy.
So, after 7,000 cycles, that site will still discharge 100MWh to perform its 7001st cycle, even if it's now degraded down to 70% of that initial capacity and can only store 70MWh now.
Cycling is defined like this to be aligned with battery warranties.
Cycles in the forecast spreadsheets consider battery actions within the day-ahead and frequency markets. The cycling is then topped up to the maximum number of cycles permitted per day in the Balancing Mechanism (when it is profitable to cycle here).
With more and more subsidy-free wind at the back end of the forecast horizon, we find are fewer double-price spikes within a day, and more and more zero-priced days. This means that over the forecast horizon, we tend to see less cycling in the day ahead markets.
Yes, they are.
Our dispatch model works out when to charge and when to discharge. The revenue in wholesale markets we present is the net of this position.
In our ancillary modelling, we account for the energy cost (or revenue) of importing power to be able to discharge (or charge) in response to low (or high) grid frequency. This reflects the cost of the state of charge management, and is shown in wholesale costs. Given the requirements of the particular service, we limit the state of charge in both upper and lower directions. In addition, we take into account the cost of charging in response to high grid frequency, and discharging when grid frequency is low, using historic data as an input to the expected throughput of each service.
We do not include Distribution Use of System (DUoS) charges in our revenue forecasts. This is because the majority of battery sites are connected at EHV on the distrbution networks. DUoS rates are very close to 0 for these sites.
We do not model fixed costs in our revenue forecast - like fixed DUoS or standing charges. Batteries are exempt from most of these costs.
The demand we show is domestic only, so excluding any demand from interconnectors, energy storage, and curtailment of renewables.
In 2050 we have 55GW of energy storage (battery, pumped, and other) and 22GW of interconnection. These represent a significant demand - energy is lost in charging each of the storage types due to efficiencies, and interconnectors represent a demand when we export power to the continent.
Updated 1 day ago