Electricity pricing and market power: Evidence from the Hungarian balancing

energy market

László PaizsInstitute of Economics

Hungarian Academy of Sciences

Mannheim 4rd Energy Conference, May 7-8, 2015

2

Motivation

• Bids for decremental energy show that generators offer decremental energy with a significant markdown below marginal cost: – average bid: 5.7 EUR/MWh (in 2012)

• High market concentration:– average number of bidders in the daily auctions for

decremental energy : 2.3 (in 2012)• Reasons for high concentration:

– balancing market is national in scope (foreign generators cannot supply balancing power)

– not all domestic generators can or willing to provide balancing power

– some gas-fired generators are switched off due to weak market conditions

3

Empirical research on market power

• Previous research has focused on the electricity wholesale market, much less attention has been paid to the balancing market

• Niu (2005):– studies ERCOT (Texas balancing energy market)– predicts equilibrium prices in a linear SFE using estimated cost data– shows that price data fit the theoretical model quite well for upward balancing

• Sioshani and Oren (2006):– study ERCOT– use similar methodology as in Niu, but also individual behavior analyzed– show that SFE model produces a good prediction of bidding behavior of large

power plants only• Heim and Götz (2013):

– examine the drastic price increase in the German power reserve market– show that the two largest generators in pivotal position abused their market

power

4

Market design for the Hungarian balancing market

• Upward and downward reserve power/balancing energy are procured as separate products

• Procurement consists of two phases: – 1st : “capacity-selection” – 2nd : “capacity-ordering”

• Reserve capacity auction:– held once a year, simultaneous auction of 365 service periods (each of one

day-duration) → significant variation in the mix of suppliers across days– the TSO awards market maker contracts to successful bidders, who are

selected on the basis of their reserve capacity price offers– data released cover accepted bids for each day (identity of bidders,

amount of reserve capacity allocated to each bidder, accepted reserve capacity fee)

• Balancing energy auction– held daily, 24 separate sealed-bid auctions, one for each hour– participants:

• market makers (obligatory) • non-contracted parties (voluntary) – no capacity fee paid

– activation order on the basis of energy price bids– remunerated if called, corresponding to their energy price bids– data released cover the simple hourly average of all submitted energy

price bid, hourly max and min energy price bids

5

Providers of downward regulation

Providers of downward regulation in 2012 (contracted parties)

• Dominance of gas-fired plants are explained by:– the lack of hydroelectric power plants– the unwillingness of nuclear and lignite-fired based load plants to

provide downward reserve

6

Market environment for gas-fired generators in 2012

• General market conditions:– low electricity prices

• average hourly spot price: 51.5 EUR/MWh • in peak-load hours: 61.3 EUR/MWh

– high natural gas prices:• average price of natural gas for power plants: 42 EUR/MWh

• Consequences:– Operation of gas-fired units was not profitable in most

hours.– Gas-fired plants operated only in periods in which they

had a contract with the TSO to provide balancing power, allowing them to recover their loss in the capacity fee.

– In the balancing energy auctions, the TSO received bids only from market makers, as other (gas-fired) suppliers were not on line.

7

Concentration in the decremental energy market

Distribution of the numbers of bidders in the balancing

energy auctions

0

20

40

60

80100

120

140

160

180

1 2 3 4 5

Number of bidders (contracted parties)

Nu

mb

er o

f d

ays

• Available data cover only bids from contracted parties

• But, for reasons mentioned before, others (most probably) did not submit offers in most hours

• Daily auction markets for decremental energy is characterized by high concentration:– average number of bidders: 2.3– 1 bidder on 74 days– 2 bidders on 160 days

8

Supply-demand balance in the decremental energy market

Demand-supply balance in the decremental energy market

(2012)

020406080

100120140160

Jan

Mar

May Ju

lSep Nov

MW

Average amount of down regulatingcapacity reservedAverage hourly negative balancingenergy volumes

• The expected amount of negative balancing energy tends to be higher in the heating season

• The TSO tends to reserve more down-regulating capacity in the winter months

• No clear seasonal pattern in supply-demand balance:– the expected supply-

demand balance seems to be constant throughout the year

9

Theoretical framework

• Discriminatory multi-unit auction model (Fabra et al., 2006)

• Setup:– two single-capacity firms with asymmetric capacities and costs – each supplier makes one bid, constrained by the market reserve

price (set by the auctioneer)– demand is perfectly inelastic and stochastic

• Main results:– demand thresholds that distinguish between low-demand

realization and high demand realizations– unique pure Nash-equilibrium under low-demand realizations

• both firms bid the marginal cost of the less efficient supplier

– unique mixed Nash-equilibrium under high demand realizations:• with identical costs, the low-capacity supplier bids more aggressively • price competition is more intense when there are more bidders,

capacities are more symmetrically distributed and the reserve price is lower

10

The choice of the theoretical framework

• The discriminatory auction model considered in Fabra et al. (2006) captures the most important features of the auction mechanism applied in the Hungarian balancing energy market:– suppliers are allowed to submit one price per unit – the price in the offer must not be lower than the price-floor

specified in the market maker contract• The market structure modeled in Fabra et al. (2006) is very

representative of the Hungarian market for decremental energy:– high market concentration– only two firms were bidding for decremental energy on 160 days

in 2012– supply of negative balancing energy is perfectly inelastic and

stochastic

11

Comparative static results for the duopoly model in Fabra et al. (2006): the effect of capacity asymmetry on expected average bid price

• Capacity asymmetry (adjusted for cost differences) reduces the intensity of competition

1 2

1 1 2 2

( ) & ( )

if 1 ( ) ( )

E b E b

k P c k P c

1 2

1 2

k k

P c P c

Note: Teta = demand level

12

Comparative static results for the duopoly model in Fabra et al. (2006): the effect of capacity asymmetry on price dispersion

• Capacity asymmetry has a non-monotonic effect on price variance:– Increasing capacity asymmetry

leads to an initial increasing and an eventual drop of the price variance

Note: E[abs(b1-b2)] = E[max(b1;b2)] -E[min(b1;b2)]

Note: Teta = demand level

13

Hypotheses for the empirical analysis

• The number of bidders has a positive impact on the average decremental energy price (calculated as a simple average of all submitted hourly price bids).

• A more equal distribution of capacities among bidders has a positive impact on the average decremental energy price.

• An increase in capacity asymmetry initially increases and then decreases the price dispersion (measured by the difference between the highest and lowest price bids).

14

Regression model of the average price of decremental energy

asymt t t t t tB HHI D X Y

6

1 1

1 1 :

6

tn

t thih it

B bn

: bid of player in hour in day

: number of players in day thi

t

b i h t

n t

: group dummies (each representing the same group of bidders) tD

: measure of capacity asymmetry asymtHHI

: general control variables (i.e. spot gas price)tX

: control variables that apply to CHP plants only (i.e. outside temperature)tY

2

2 21

1

( 1 )1 1

( )

N

iNN asymi

ii

s NHHI s N N HHI HHI

N N N

average bid over all bidders and over all hours between 0AM and 6AM in day t

15

Model (1) (2) (3) (4)Method: OLS OLS WLS WLSC -2.608* -11.075*** -2.761*** -12.455***

(1.454) (2.428) (0.877) (1.363)HHIasym -2.820* -2.904** -3.024** -2.211**

(1.615) (1.310) (1.235) (1.094)D_TwoBuyers1 1.627** 1.677*** -0.289 0.502

(0.638) (0.516) (0.574) (0.515)D_TwoBuyers2 0.498 0.526 0.510 0.071

(0.525) (0.544) (0.536) (0.475)D_TwoBuyers3 1.310*** 1.377*** 1.158*** 1.238***

(0.423) (0.418) (0.380) (0.335)D_ThreeBuyers1 2.923*** 3.091*** 1.693*** 2.577***

(0.307) (0.299) (0.314) (0.295)D_ThreeBuyers2 1.667*** 2.050*** 1.110*** 1.901***

(0.374) (0.352) (0.416) (0.378)D_FourBuyers1 1.796*** 1.815*** 0.611* 1.201***

(0.284) (0.271) (0.313) (0.285)D_FourBuyers2 -0.346 -0.381 -1.780*** -1.093***

(0.278) (0.268) (0.320) (0.293)D_FourBuyers3 0.671*** 1.601*** -0.362 1.138***

(0.125) (0.243) (0.455) (0.437)TTF 0.417** 0.192 0.674*** 0.392***

(0.182) (0.120) (0.098) (0.092)Outside temperature 0.088** 0.108** -0.105*** 0.042

(0.039) (0.043) (0.029) (0.031)Total regulating capacity 0.073*** 0.077***

(0.018) (0.009)AR(1) 0.712*** 0.484***

(0.068) (0.072)Observations: 268 268 269 269R-squared: 0.86 0.87 0.80 0.85

Dependent variable:

daily average of decremental energy price bids (HUF/kWh)

heteroskedastic-consistent standard errors are in parentheses for OLS models;number of bidders is used as weights for WLS models;significant at 10%, ** significant at 5%, *** significant at 1%

6

1 1

1 1

6

tn

t thih it

B bn

16

Regression model of price dispersion

max min1 2( ) asym asym asym

t t t t t t t t tB B HHI HHI HHI D X Y

6max

1 21

1( ; ;...; ) : the average maximum bid in hours 1-6 in day

6 tt th th thnh

B Max b b b t

: bid of player in hour in day

: number of players in day thi

t

b i h t

n t

6min

1 21

1( ; ;...; ) : the average minimum bid in hours 1-6 in day

6 tt th th thnh

B Min b b b t

17

Eq Name: (1) (2) (3) (4)Method: OLS OLS WLS WLSC -0.261 5.727 -0.872 2.664

(6.033) (5.707) (3.265) (3.234)HHIasym -3.265 21.222*** -2.427 22.855***

(2.639) (7.549) (2.619) (6.016)HHIasym*HHIasym -63.491*** -66.473***

(16.662) (14.364)D_TwoBuyers1 2.410** 0.919 -0.360 -1.190

(1.075) (1.320) (1.232) (1.199)D_TwoBuyers2 0.032 0.254 -0.389 -0.313

(1.100) (1.243) (1.139) (1.096)D_TwoBuyers3 0.519 -0.591 0.264 -1.140

(0.911) (1.046) (0.803) (0.830)D_ThreeBuyers1 0.892 -0.814 -0.200 -1.832**

(0.637) (0.923) (0.707) (0.766)D_ThreeBuyers2 0.500 -1.826 -0.257 -2.529**

(0.763) (1.137) (0.905) (1.000)D_FourBuyers1 2.786*** 2.393*** 1.113 0.901

(0.592) (0.658) (0.681) (0.657)D_FourBuyers2 1.886*** 1.297* 0.485 0.312

(0.616) (0.691) (0.702) (0.677)D_FourBuyers3 0.865 0.221 -0.059 -0.352

(0.537) (0.593) (1.047) (1.009)TTF 0.523** 0.342 0.298 0.186

(0.265) (0.230) (0.221) (0.214)Outside temperature 0.191** 0.009 -0.067 -0.166**

(0.080) (0.090) (0.074) (0.075)Total regulating capacity -0.017 -0.043 0.017 0.001

(0.042) (0.039) (0.021) (0.021)AR(1) 0.513*** 0.443***

(0.091) (0.085)Observations: 268 268 269 269R-squared: 0.50 0.54 0.36 0.41

Dependent variable:the difference between the maximum and minimum

decremental energy price bids (HUF/kWh):

heteroskedastic-consistent standard errors are in parentheses for OLS models;number of bidders is used as weights for WLS models;significant at 10%, ** significant at 5%, *** significant at 1%

6

1 21

6

1 21

1( ; ;...; )

6

1( ; ;...; )

6

t

t

th th thnh

th th thnh

Max b b b

Min b b b

Imply that price dispersion is increasing at HHIasym levels below 0.17 and decreasing at HHIasym levels of 0.17 and higher

Thank you for your attention!paizs.laszlo@krtk.mta.hu