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Gross margin and components to provide regulation FCAS

This page derives the gross margin for providing regulation services.  The value components as well as key variables in the value components are visible in the Financial reports in FCAS-pays.

A derivation

Assuming FCAS bids are not a function of energy bids (cooptimisation or stranded not withstanding) then a unit's gross margin equation for raise and lower services respectively follow.  

Raise Regulation Gross Margin (divide by 12 for a 5 minute dispatch interval) =   
Raise regulation revenue+ ∆ Electricity spot revenue– Causer pays– ∆ Fuel used– Trip Risk (usually negligible)– other terms
Raise enabled × RaiseRegRRP+ Utilisation1 × Raise enabled × RRP – RaiseregRRP × Total Requirement × Causer pays participant factor– Fuel cost × Utilisation × Raise enabled – Dynamic costs2– Cost of trip × ∆ Probability of trip


Lower Regulation Gross Margin (divide by 12 for a 5 minute dispatch interval) =   
Lower regulation revenue– ∆ Electricity spot revenue– Causer pays+ ∆ Fuel used– Trip Risk (usually negligible)– other terms
Lower enabled × LowerRegRRP– Utilisation × Lower enabled × RRP – LowerregRRP × Total Requirement × Causer pays participant factor+ Fuel cost × Utilisation × Lower enabled – Dynamic costs– Cost of trip × ∆ Probability of trip

Optimising gross margin is the objective of bidding into the spot market.  Generally only the value of regulation revenue is considered however gross margin is a function of a number of other value components.  

The change in the Electricity spot revenue is a component since it varies as a function of regulation enablement.  This is due to a unit's response to AGC (or other control systems) that results in an change in electricity generated and hence electricity revenue.  Note that this occurs even though there is no change to the energy bid.

1 Utilisation is defined as the proportion of a regulation service enablement that is actually utilised - that is to say the actual change in generator output caused by instructions to provide regulation FCAS.  This is a number between 0 and 1.

Causer pays is the participants liability of the total cost of regulation services to the market (or regional component of the market).  The portion of the total cost is represented by the participant's causer pays participation factor.   

The change in the fuel used follows the same logic that electricity spot revenue changes.

2 Dynamic costs are derived from the change in the efficiency curve of the generator caused by the rate of change in generator output.  Given an equal generation of energy, a unit with changing output will consume more fuel than a unit with steady output.   

The cost of trip risk represents the cost of a trip multiplied by the change in the probability of a trip.  The derivation of this value is very specific to the unit and often negligible.  I shall provide two cases to consider:  first, providing regulation FCAS will increase wear on the unit.  Power engineers have advised me that any change in wear of a units components is not material enough to change an outage schedule nor the maintenance programme therefore no obvious cost can be directly derived from the impact on maintenance.  However they advised that the reason for maintenance is partly to improve unit reliability and therefore the impact on value should be assessed on the increase in the probability of a unit trip.  The second case is that providing FCAS will increase the risk of a trip due to vibration issues, this risk is likely to be only material for some hydro units.   

Dynamic and trip costs are always negative.    


Mathematics and Optimisation

This section symbolises the equations shown in "a derivation" and shows the terms as a function of utilisation and enablement.  Regulation price is the main term to consider since it is a function of enablement due to price elasticity. 

Round brackets use the convention of x = x(y).  That is to say for any particular point in time, x is a function of (y).  Square brackets will replace normal brackets so that 2[7 - 3] = 8.  I will symbolise the lower regulation service.  The same follows for the raise service except the sign on the change in spot revenue and the fuel used is opposite.


Lower Regulation Gross Margin (divide by 12 for a 5 minute dispatch interval) =   
ML = EL PL(EL) – UL EL N – PL(EL) QL K  +  F UL EL  –   D(EL, UL)  – C T(EL, UL)

Lower regulation revenue

– ∆ Electricity spot revenue

– Causer pays

+ ∆ Fuel used

– Trip Risk (usually negligible)

– other terms

Lower enabled × LowerRegRRP

EL                    × PL(EL

– Utilisation × Lower enabled × RRP 

– UL            × EL                         × N 

– LowerregRRP × Total Requirement × Causer pays participant factor

– PL(EL)             × QL                          × K

+ Fuel cost × Utilisation × Lower enabled – Dynamic costs

+ F             × UL            × EL                    –  D(EL, UL)

– Cost of trip × ∆ Probability of trip

– C                × T(EL, UL)



For simplicity I shall drop the Dynamic and Trip risk costs.  However it is worth noting that both the first and second derivative of the dynamic and trip risk cost with respect to enablement are negative (at least that is my claim).  

Removing the subscripts to denote raise and lower service, re-arranging the equations and dropping the dynamic and trip risk costs we get:


Raise Regulation Gross Margin
ML = P(E) × [E – QK]   +   E × U × [N – F] 

Optimisation single unit

Example Chart and Simplified Gross Margin Equation 

The chart plots an example of gross margin (and the components) for a single unit (or station) against the control variable, the enablement volume (which is controlled by the lower regulation bid).

Lower Regulation Gross Margin
ML = E P(E) – U E N – P(E) Q K  +  F U E
ML = P(E) [E – Q K]   +   E U [F – N] 

Chart inputs

  • E is the control variable shown as the x-axis. 
  • ML is the dependent variable shown as the y-axis.
  • P(E) varies between $10 and $20 (to account for some price elasticity).  Shown as thin purple line on chart.
  • U = 25%
  • N = $250 /MWhr
  • Q = 150 MW
  • K = 2%
  • F = $20 /MWhr

In this example the foregone energy revenue (in the spot market) dominates the components of the gross margin.  In this scenario the lower regulation bid volume is considerably under priced.  

Optimising lower regulation bids for two units in different regions (keeping lower enablement volume constant)

The purpose of this example is to show the change in value that can be gained using a different regulation pricing structure.  This example preserves the enablement levels of a portfolio.  For this example assume the following market conditions;

  • N (RRP) in Region1, N1 = $272  /MWhr
  • N in Region2, N2 = $50 /MWhr
  • F (Fuel cost) for Unit 1 (in Region 1), F1  = $20 /MWhr
  • F (Fuel cost) for Unit 2 (in Region 2), F2  = $30 /MWhr
  • U (Utilisation) = 25%
  • Regulation Lower price is the same in both regions




Change in lower gross margin by moving 1MW of enablement volume from unit1/region1 to unit2/region2 per hour

Use the lower regulation gross margin equation for each unit:  ML = E P(E) – U E N – P(E) Q K  +  F U E

For this example P(E)1 = P(E)2  and the sum of E is constant, therefore P(E) [E – Q K]  is constant.  When comparing the change in gross

margin these terms cancel out hence we can ignore these terms.  

Moving 1MW of enablement of unit 1/region 1 to unit 2/region 2 we get:

∆ ML    =    1* U [F2 – N2]     –    1* U [F1 – N1

            =    25% *  [ $30 – $50 ]     –     25% *  [ $20 – $272 ]          

            =    – $5  –    [ – $63 ]                                  

            =   $58 /MWhr of enablement   

If this was the benefit for one year then without impacting the market in any way earnings would be improved by over $1/2 million per year.     

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