1. Ancillary Services Market in
India
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2. Ancillary Services
 Voltage Support Service
 Regulation and Frequency Response Service
 Energy Imbalance Service
 Operating Reserve Service
 Black Start Capability Service
In each of the above it is important to specify
 Nature of Service
 When is the service required?
 Source of Service and Supplier Qualification
 Procurement Mechanism
 Charging Mechanism
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3. Regulation and Frequency Support Service
 Regulation and frequency response services are necessary for the
continuous balancing of resources (generation and Control Area
interchange) with load, and to assist in maintaining scheduled
Interconnection frequency at 50 Hz.
 The above frequency profile is obtained after considerable load
shedding by the states (illustrated in the Next Slide)
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4. Load Shedding during peak hours and off peak hours on
08.04.2013
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5. Considerations
 Regulation and Frequency support service is provided by
 Primary Control
 Secondary Control
 Tertiary Control
 The states already resort to load shedding, RLDCs are able to “observe”
curtailed demand
 Tertiary Control can be provided to manage deviations between the
“curtailed demand” and generation
 Can the Frequency Support Services operate to always match this
“curtailed demand” with generation obtained through tertiary frequency
control ancillary service?
 Or, do we need frequency triggers?
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6. Pre-requisites
 What quantum of ancillary services should be sought?
 Regulation Service requirements need to be determined consistent with reliability
standards to be set by NLDC/CEA and approved by CERC.
 The Regulation Service requirements need to include locational requirements
and consider transmission constraints
 Deviation is different in different states and there may be transmission constraints
between states
 What happens when
 Insufficient Regulation service is bid into the market
 Scheduled Resources are not available
 More than anticipated regulation service is required
 Regulation Service providers may receive Regulation Service signals directly
from the RLDC (even if they are located in the control area of the SLDC).
 Receiving regulation signals directly from the NLDC does not eliminate the need
to receive signals directly from the SLDC
 Regulation Response Rate needs to be established to qualify resources for
this service (can vary on hourly / seasonal basis)
 Are service allocation principles based only on costs (bids) or also
Response Rates?
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7. Mechanisms for Performance Tracking
If the resource providing FS service deviates from schedule –
payment mechanism needs to be linked to performance
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8. Procurement Mechanism
 Through Power Exchanges based on supply offers
 Offer parameters – price (Rs/MW), Response Rate, location, MW
 Joint management of Congestion and FS would require SCUC with network
 How is the demand curve determined?
 Demand curve would need to be considered inelastic initially (till we have
adequate supplies)
 Since procurement would be “location-based” (markets balkanized by
transmission constraints), state-specific considerations might be required
 Two Settlement
 Day Ahead
 Real Time (since capacity requirements and Response Rate requirements
may change close to real time)
 Regulation to prevent abuse of Market Power
 Price Cap / moving yardstick (discussed later)
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9. Charging Mechanism
 In NYISO, LSEs / Generators (who do not provide FS service and do not
follow RTD base points sufficiently accurately) in proportion of their
load/generation
 Alternatively, more efficient Shapley pricing and computationally more
efficient Aumann-Shapley pricing mechanisms could be used
 These have extensively been applied in allocation of transmission costs
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10. VOLTAGE SUPPORT SERVICE
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11. How do the ISOs / RTOs compensate the Generators for
Reactive Power?
 Most make capacity payments according to
compensate the allocated revenue requirements
 Some pay the “opportunity cost” of reactive power
when the generators need to back down real power
output
 Some impose penalties on generators for failing to
provide reactive power
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12. International Experience (Taken from Richard O’Neill)
 In England and Wales, a generator can accept a
 default payment of ~ $2.40/Mvarh leading or lagging, or
 it may offer contracts with a minimum term of one year.
 In Australian ISO, generators and synchronous condensers.
 receive an availability payment,
 an enabling payment when dispatched and
 opportunity costs from forgone sales of real power.
 In India, the regulator imposes a 10 paise/kVArh price on
reactive power when the 1.03 < voltage < .97
 In the Netherlands, generators are
 contracted are paid for reactive power capability
 no additional payment is made when it is supplied.
 In Sweden reactive power is supplied by generators on a
mandatory basis, and there is no compensation.
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13. Spot Markets for Reactive Power
FERC recognizes that real time prices could be determined
in the market through auctions.
 the reactive power prices could either be calculated directly or
 derived from the implicit opportunity costs associated with real
power prices and supplier’s real power energy bids.
 The mechanics of price determination in each of these
approaches is:
 Under the direct pricing approach, reactive power sellers would
submit price bids for supplying specific amounts of reactive
power and the reactive power price would be the highest
accepted price bid.
 Under the derived approach, reactive power suppliers would
submit price bids for supplying real power as well as information
indicating the trade-off between supplying various amounts of
real and reactive power
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14. Market Power in Reactive Power Markets
 Simulation and experimentation are needed to understand the effects
of alternative auction market designs before such a spot market is
implemented.
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15. Objectives of Simulations
The objectives of the simulations done (on CIGRE 32 Bus
ssystem) are two-fold:
 To formulate and simulate strategic behavior of players (System
Operator (SO) and the GenCos),
 The results are intended to suggest mechanisms for addressing
market power concerns of the regulator.
 To study the “potency” of price cap regulation in alleviating
abuse of market power, and suggest an alternative regulatory
mechanism.
 A comparison of the price cap regulation and the proposed
alternative regulatory mechanism is drawn in terms of their
respective abilities to produce “production efficiency” and
“allocative efficiency” at the same level as a pure competitive
benchmark case.
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16. The Analysis
 What is the impact of strategic behaviour of players in the
reactive power market on reactive power dispatch?
 How do the strategically behaving GenCos respond to price-
cap regulation?
 Does ownership of a generator / synchronous condenser by
the Public Sector (Government owned) help to mitigate
market power?
 Does the suggested regulatory mechanism induce
efficiency?
16
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17. The Game: Formulation
 Multi-leader follower game
 Multiple dominant players – GenCos
 One follower – the SO
 Response of SO is constrained to be identical for each leader
 GenCos bid different quantities of reactive power at different
prices to maximize profits
 Supply function competition
 SO dispatches the system given these bids so as to minimize the
cost of reactive power procurement
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18. Reactive Power Management Scheme
 Day ahead real power markets clear first. The reactive
power market clears in real time.
 The generators know that they can be called upon to
generate reactive power, which might require them to
change their real power dispatch.
 This may alter their expected cash flow in the real
power market. Hence they bid a supply curve for
reactive power.
 The system operator (SO) minimizes the cost of
procurement of reactive power and dispatches
reactive power subject to the security constraints
 All reactive power suppliers (generators) in a
geographic area get the same price
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19. Solution Method (1)
 The SO’s KKT conditions are parameterized in strategic
variables k
 The KKT conditions of the SO’s problem are concatenated with
the constraints of the GenCo’s problem
 Each GenCo’s problem is then a Mathematical Problem with
Complementarity Constraints (MPCC)
 The Equilibrium problem among the above MPCCs represents a
“generalized Nash game” and it could have zero or multiple
Nash equilibria
 Since SO’s problem is non-convex, the solution to the KKT
conditions may lead to a saddle point or a local maximum…
practical way to overcome this is to try with different initial point
Cont…
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20. Solution Method (2)
 GenCos are assumed to compete with each other in
terms of their Supply Functions
 Hence to find Nash Equilibrium (Equilibria), KKT
conditions of all the GenCos need to be solved
simultaneously
 This is actually a non square Non-linear
Complementarity Problem
 This makes these problems harder to solve as
compared to standard Nash game
Cont…
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21. Solution Methods (3)
 Leyffer and Munson (2005) have proposed a NLP
formulation which aims to avoid this difficulty by
minimizing the complementarity constraints
 The constraints do not include any complementarity
conditions
 It is shown in Leyffer and Munson that local solution to
above problem with the objective function value = 0, is a
strongly stationary point of the multi-leader follower game
 In all the cases presented in this paper, the value of the
objective function was less than 10-9
 This compares well with the only other similar model (Bautista,
Anjos, Vanelli, IEEE Trans. on Power Systems 2007), where the
objective function value reported is 10-4
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22. How do the GenCos and the System Operator respond to the
reactive power management scheme proposed above?
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23. The three cases
 Case A: Competitive Setting
 Case B: Oligopolistic Setting: Supply Function Equilibrium
(SFE) with Price Cap
 Case C: SFE with a Price Cap and Government-owned
GenCo
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24. 6 Bus Example: Case A
2 3
Q=0.539
6
Q=0.449
5
Q=0.749
1
= $0.399 pu
4 Payment for Reactive
Power = 0.693 $/hr
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25. 6 Bus Example: Case B
GenCo 1 owns generators at Nodes 1 and 3, GenCo 2 owns generator at
Node 2
2 3
Q=0.539
6
Q=0.449
5
Q=0.749
1 1 = 9.759 $/hr
2 = 3.403 $/hr
= $7.981 pu
4 Payment for Reactive
All Values in pu
Power = 13.855 $/hr
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26. 6 Bus Example: Case C
GenCo 1 owns generators at Nodes 1 and 3, GenCo 2 owns generator at
Node 2, SO puts up a 100 MVAr Synchronous Condenser at Node 1
2 3
Q=0.067 k = 20.00
k = 12.11
6
Q=0.093
k = 2.12 5
Q=0.883
1 1= 0.532 $/hr
Q=1.00 2= 1.000 $/hr
K=2 = $1.000 pu
4 Payment for Reactive
All values in pu
Power = 2.043 $/hr
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27. Nordic 32-Bus System
 Similar results are obtained here
 However, in the earlier cases the placement of
the SO-Owned generator lead to a deviation in
the voltage profile and real power generation
from Case A
 The placement on Bus no. 4072 in this case was
such that not only were the voltages closer to those
in Case A
 But real power dispatch and voltage angles remained
unchanged
Why is this result important?
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28. Production Efficiency and Allocative Efficiency
 The real power dispatch is 11331.1 MW in Case A, 11330.2 MW in Case
B and 11330.9 MW in the Case C
 Also the state variables (V and ) are very close to the competitive case.
 The effectiveness of a regulatory mechanism is to be measured in terms
of its ability to mimic conditions of pure competition.
 Hence it is demonstrated that prudent application of the alternative
regulatory mechanism leads to the same production efficiency as the
competitive case.
 The allocative efficiency is however compromised and leads the GenCos
to charge a higher price than that under competitive conditions (Case A).
 The outcome of the alternative mechanism (Case C) is however shown
to be better than the uniform price cap mechanism (Case B) in terms of
both production and allocative efficiency.
‘prudent’ here is used in terms of the selection of the optimal site and
capacity of Government-owned generator
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29. Conclusions
 The Simulation investigates the problem of market power in real
time spot reactive power markets
 We model the equilibrium which emerges from the strategic
interaction between GenCos using the supply function equilibrium
framework.
 When applied prudently, the proposed regulatory mechanism is
shown to incentivize the competing GenCos to lower their bids
and hence reduce the procurement cost of reactive power.
 This mechanism of regulation is non-intrusive and yet is shown to
mimic the outcome of a competitive market better than a plain
price cap regulation.
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30. THANK YOU
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31. The SO’s Problem
Maximize
 Q fi
J=
f i
Subject to
 Pfi  PDi   Vi V j Gij cos(i   j )  Bij sin(i   j )  i
N
iP
f j 1
 
N
Q
f
fi
 QDi   Vi V j Gij sin(i   j )  Bij cos(i   j ) i
j 1
iQ
Vi  Vi min i  imin Vi  Vi max i  imax
Q fi  Qmin i f  fi
min Q fi  Q max
fi i f  fi
max
fi
 Pfi   Pfi
min
i f  min
fi Pfi  Pfi
max
i f  max
fi
k fi (a fi  b fiQ fi )   i f  fi
  0
31
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32. The GenCo’s Problem
Max. R f   Q fi   (a fi  b fiQ fi )Q fi
i i
Subject to
k fi  k min
fi  0 i  min
fi
k max
fi  k fi  0  max
fi
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33. Solution Method …Contd
 Let K f  k fi , fi  H f 
denote the bidding strategy of
the generating company f.
 The KKT conditions of SO are parameterized in
 
terms of K f , f  1, 2,...., F
 The KKT conditions are necessary for optimality of
the ISO’s optimization problem.
 Since the SO’s problem is not convex, the solution to
the KKT conditions may lead to a saddle point or a
local maximum.
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34.  Let y denote the vector of all decision variables and
lagrange multipliers of the ISO’s problem
P , Q , V ,  ,  , 
 ,  i ,  i ,  fi ,  fi 

Q P min max min max
  y , y 
O 1
y
fi fi i i i i
,  ,  ,  , 
 

min max
fi fi fi
y  Pfi , Q fi ,Vi ,i ,  , 
o
i
Q
i
P

y  
1
i
min
, i
max
, min
fi , max
fi ,  fi ,  min
fi , max
fi , 
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35.  Let the following represent SO’s Equality, Inequality and
Complementarity KKT conditions
z ( K1 , K 2 , K3 ,..., K F , y)  0
E
I
z ( K1 , K2 , K3 ,..., K F , y)  0
z ( K1 , K2 , K3 ,..., K F , y)  0
C
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36.  The GenCo f’s problem now is
Max. R f   Q fi   (a fi  b fiQ fi )Q fi
Subject to
i i
k fi  k min  0 i  min
fi fi
k max  k fi  0i  max
fi fi
E
z ( K1 , K2 , K3 ,..., K F , y)  0 wE (dual vector )
f
I
z ( K1 , K2 , K3 ,..., K F , y)  0 wIf (dual vector )
C
z ( K1 , K2 , K3 ,..., K F , y)  0 wC (dual vector )
f
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37. Minimize C pen   Subject to
 R f z I z 
T R f z I I z E E z C C
I
z
E C
 o .w f  o .w  o .w  0
 1  1 .w f  1 .wE  1 .wC  y1 
 y y y y  y o
y y y
R f z I I z E E z C C
T  1 .w f  1 .w  1 .w  0
 z ( K , K , K ,..., K , y)  wI 
I
y 1
y y y

 1 2 3 F  f
 R f z f
I
z f
E
z f
C
 .w f  .w f  .w f   f   f 0
I E C min max
K f K f K f K f
 min .( K f  K min ) 
f f
K f  K min  0
f
K max  K f  0
 max
f .( K max
f  Kf ) f
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38. Two-Bus Example: Case A
Q11  0.881 pu Q22  0.528 pu
GenCo 1 GenCo 2
1 2
System
Flow1 to Flow2 to Marginal Price:
PD=0.7, 2: 0.181 1: - PD=0.7,
QD=0.7 0.172 pu QD=0.7 0.533*0.881 =
pu
$0.469 pu
1 2
Generator Nodes
Pmin 0.450 0.850
Pmax 0.550 0.950
Qmax 1.000 1.000
bfi 0.533 0.889
afi 0 0
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39. Two-Bus Example: Case B
Q11  0.881 pu Q22  0.528 pu GenCo 2
GenCo 1 2
1
PD=0.7, PD=0.7,
QD=0.7 QD=0.7
Dispatch Remains the same, however, the GenCos jack up their
bids to the maximum possible level (k = 20)
The System Marginal Price ($/pu) = 9.393
If either GenCo deviates from this outcome they lose their profits
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40. Two-Bus Example: Case C
Q11  0.365 pu Q22  0.112 pu
GenCo 2
Qso,1  1.000 pu
GenCo 1 2
1
PD=0.7, PD=0.7,
QD=0.7 QD=0.7
k11  5.477 k22  10.738
kso,1  2.000
Uniform System Marginal Price = $1.066 pu
Solution does not change with initial values
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