11 Nov 2022
James Hazzard

CCGTs: what’s the cost of managing transmission constraints?

Over the last decade, costs associated with managing constraints on the transmission system have increased dramatically - and are projected to reach £3b per year by 2028.

James and Robyn discuss the effects of locational transmission constraints on CCGTs.

Originally intended as a second-order adjustment to traded volumes of electricity, the system operator is now re-dispatching a major portion of the market via the Balancing Mechanism. This is a key issue National Grid ESO is attempting to address with REMA.

The increase in costs has been exaggerated by gas price hikes since October 2021 (an issue discussed in one of our latest articles), but the long-term trend is linked to rising balancing volumes motivated by constraint management.

In this article, we look at the impact of a constraint on an individual large generator, which sits behind a particularly constrained part of the network.

Jargon Buster

A load factor is a measure of how much of an asset’s total capacity is being utilized to import or export power during a given settlement period.

  • Planned Load Factor - load factor prior to any balancing actions, representing an asset’s planned delivery at gate closure.
  • Actual Load Factor - load factor after accounting for balancing actions, representing an asset’s actual delivery.

CCGTs in Great Britain

Figure 1 - Location of CCGTs in Great Britain (size proportional to capacity).

Combined cycle gas turbines (CCGTs) are a major source of flexible, non-renewable generation in the UK. They provide over 21 GW of capacity to the network (as per the Transmission Entry Capacity register) - this corresponds to half of peak national demand during winter. The locations of Great Britain’s major CCGT plants are shown above (figure 1).

Case Study: BMU actions on either side of a constraint boundary

Due to the presence of constraints, location can play a major role in the operation of an asset, including CCGTs. SSE-SP is a major transmission constraint boundary separating Northern Scotland from the rest of GB, shown below.

Here, we delve into how this constraint boundary influences the behavior of two CCGTs: Peterhead (1.2 GW) and South Humber Bank (0.8 GW). Peterhead sits behind the constraint in Northern Scotland, frequently competing with renewable wind power when exporting power southwards. South Humber Bank sits on the opposite side of the constraint to Peterhead, has a similar export capacity, and sits about as close to Peterhead as any other CCGT in GB.

Figure 2 - Peterhead (1.2 GW) and South Humber Bank (0.8 GW) are CCGTs operating on opposite sides of the SSE-SP constraint boundary.

On 26th January 2022, Scottish wind output was in the top 1% of days this year, averaging 4.4 GW. Consequently, the SSE-SP boundary was placed under significant strain, operating at 99% of its maximum capacity. In short, cheap Scottish wind flowed down to the demand centers south of the border.

Figure 3 - Southerly flows across the SSE-SP boundary between 25th and 28th January 2022.

This set of conditions resulted in opposing balancing actions being taken by Peterhead and South Humber Bank, due to their locations on opposing sides of the constraint.

Figure 2 (below) shows how these two plants were re-dispatched via the Balancing Mechanism on this particularly windy day.

Figure 4 - Planned versus actual generation of Peterhead and South Humber Bank CCGTs, 26th January 2022.
  • Peterhead submitted FPNs to deliver at near maximum capacity for much of the day, corresponding to a daily-average Planned Load Factor of 70%. It was bid down by a total of 17 GWh in the Balancing Mechanism, resulting in an average Actual Load Factor of 10%.
  • On the other hand, South Humber Bank’s Planned Load Factor was 0%, as it was not scheduled to export power. It was offered up to a peak of 85% of its capacity in the Balancing Mechanism, resulting in an average Actual Load Factor of 25%.
  • As a result of the balancing actions required to manage the transmission system across SSE-SP, National Grid ESO spent £7.5m in constraint costs relating to the boundary on 26th January alone.

Locational Influences on Long-Term Asset Behavior

We've seen how the presence of a transmission constraint can affect the day-to-day operation of assets. But how does location influence asset behavior over the long term?

Figure 5 - Monthly average load factor for Peterhead, 2022.
  • The graph above shows us that Peterhead has consistently been turned down in 2022. In every month so far this year, Actual Load Factor lags behind Planned Load Factor.
  • This translates into a net annual turn-down of 1400 GWh (33% reduction versus planned export volume), versus a net annual turn-up of 1300 GWh (2% increase versus planned export volume) for the rest of the CCGT fleet combined (see Figure 6 below).
  • Compared to the CCGT fleet average, Peterhead’s Planned Load Factor was above average in every month between March and September, showing that it expects to export a greater proportion of its total capacity than the average gas generator on a monthly basis. However, it only actually managed to do so in 2 months so far this year (July and August).

Below, we visualize the total balancing volumes of each CCGT asset in 2022. This equals the total difference in volume between an asset’s planned and actual export over the course of the year.

Figure 6 - Total balancing volumes ‘net out’ across the CCGT fleet in 2022.
  • The net turn-down of Peterhead is almost entirely canceled out by the net turn-up of the rest of the CCGT fleet. This makes sense, since CCGTs are a key source of flexible generation, making it likely that this asset class will be turned up in the Balancing Mechanism to ensure ample capacity when a constrained asset such as Peterhead is turned down.
  • The scale of net turn-down experienced by Peterhead (1400 GWh) is over 26 times more than Salted Unit 3, which is the asset with the next largest net turn-down (50 GWh). This highlights just how uniquely constrained the Scotland-England border is compared to the rest of the transmission system.

REMA: Locational pricing to remedy constraint costs?

REMA is a hot topic right now (read up on the state of play here), with the idea of locational marginal pricing a key point of discussion. The position that National Grid ESO is minded to take is that in a market with locational pricing, the relationship between regional supply, demand, and constraints would drive electricity prices.

Figure 7 - In a nodal system, electricity price could vary by Grid Supply Point (or node), displayed here.
  • For example, high wind generation in Northern Scotland would drive down prices in this area due to low regional demand, and constraints limiting the export of power elsewhere.
  • Through a locational price signal, generators such as Peterhead may be incentivised to minimize their planned output during windy times. This would negate the need for National Grid ESO to re-dispatch the asset in the Balancing Mechanism.
  • National Grid ESO views the implementation of locational pricing as an important aspect of achieving net zero carbon emissions, since a cleaner fuel mix is encouraged during times of high renewable generation.
  • In order to recover costs, assets such as Peterhead may need to increase their outputs during times when intermittent generation is lower, and consequently, regional power prices are higher.
  • This could be challenging due to operational factors such as the ability to accurately predict wind levels.
  • However, if viable, it would result in a more efficient market, helping to smooth out the supply of electricity to this constrained region of the grid, and thus provide more stability at a reduced cost to the consumer. Crucially, it would also mean a lower carbon cost, as the plant should need to ramp up and down less frequently.

The themes drawn from this case study exemplify why REMA is looking so heavily into locational pricing, namely, that this type of system could result in a greener grid at a lower cost to the consumer. The elephant in the room, however, is whether our Net Zero goals can be achieved purely by market transformation, without the corresponding investment needed to provide grid infrastructure transformation.

So, what have we learned?

  • CCGTs are a key source of flexible generation to the grid, providing in excess of 21 GW in capacity.
  • Assets in constrained regions are bid down in the Balancing Mechanism at times when the constraint is under significant pressure.
  • For example, Peterhead is a uniquely positioned CCGT, as it lies behind a key constraint in Northern Scotland, limiting its ability to export power towards demand in the South.
  • Peterhead’s competition with renewable generation on windy days causes it to experience large bid volumes in the Balancing Mechanism, which are largely canceled out by a net turn-up from the rest of the CCGT fleet.
  • Locational marginal pricing may present a more efficient market mechanism for solving constraint-related issues, providing the necessary conditions for a decarbonized grid in the future. National Grid ESO is consulting on this topic as part of the REMA proposals.