Oligopsony Analysis in the Italian Electricity Market. Preliminary Results

Transcription

1 Oligopsony Analysis in the Italian Electricity Market. Preliminary Results Simona Bigerna and Carlo Andrea Bollino 37th IAEE International Conference New York City, NY, USA, June 15-18, 2014

2 The aim of the paper estimate the oligopsonistic market power inverse of the hourly residual supply elasticity Italian day ahead electricity market ex ante individual bids in This is a novelty in the literature, as previous studies have used ex post market data.

3 Outline Introduction Model Dataset Results Conclusions

4 Introduction In literature there is analysis of oligopolistic market power constructing a residual demand curve using offer bid data with assumption of Cournot oligopoly market e.g. Wolak, F.A. (2003), Measuring Unilateral Market Power in Wholesale Electricity Markets: the California Market, American Economic Review 93 (2): Bollino, C.A. and Polinori, P. (2012), Market strategies and market structures in the Italian wholesale electricity market, in: Electricity Markets and reforms in Europe (M. Uvalic ed.), Milano: Franco Angeli.

5 Introduction There are several potential reasons for the existence of oligopsonistic behavior in IPEX. The first reason is to contrast generators exercise of market power on the supply side, i.e. generators attempt to distort the equilibrium price upward when there is high competition on the demand side, e.g. during peak hours. The second reason is that there exists vertical integration in electricity markets. The third reason is that the Government may order to a State owned company to somehow favor final consumers.

6 Introduction In the literature oligopsony market estimation has been usually conducted using ex post market data Shortcoming: that input supply slope and demand output slope cannot be separated from conjectural elasticities Simultaneity between supply and demand renders difficult to estimate separately the competitive behavior effect from the input supply high elasticity effect Our is the first attempt in the literature to analyze oligopsony market power in the day-ahead electricity market. We use IPEX data published by the Italian Market Operator ( Gestore mercato elettrico, GME). There are 221 listed operators of which 46 have been actively bidding on the demand side in the period The main 5 operators account on average for more than half of total market demand

7 The model D j (q) the demand function, i.e. his/her WTP for the electricity quantity q j. DO jh (p) the others demand, quantity that all other market participants except j are willing to demand in the market SR jh (p) the residual supply realization for different states of the world that can occur in a given hour. Call these h, for h = 1, 2, 3. SR jh (p) = [Q h -DO jh (p)] Marginal input cost MIC jh (p) equated to WTP, as shown by points E 1, E 2, E 3. Strategy bid curve SB j (p), realization of the profit maximizing strategy of consumer j facing different states of the world.

8 Figure 1. Profit maximizing bid strategy SB j (p) for consumer j

9 The model (1) max π j = zx j -pq j where x j = f j (q j ) is the production function; p= p(q) is the aggregate supply function of electricity in the wholesale market; Q=[q j +DO j ] is the sum of the demand of consumer j and the demand of all other participants except j. Thus, the residual supply for consumer j is SR j (q)=p(q-do j ).

10 The model The first order conditions for consumer j are: (2) π j / q j = z f j / q j - SR j / q j = 0 (3) π j / q j = z f j / q j [p+ p/ q j q j ] = 0 which can be rearranged as: (4) [(z f j / q j )-p]/p = (1/ε SRj ) where ε SRj denotes the elasticity of the residual supply facing consumer j inverse of this elasticity is a Lerner-type measure of the oligopsonist mark down over the WTP, i.e. a measure of unilateral market power of consumer j.

11 The model Conjectural variation of market quantity Q with respect to q j expressed by consumer j (following the classical approach by Appelbaum 1982): (5) π j / q j = z f/ q j - p/ Q Qp/ q j = 0 which can be rearranged as: (6) [(z f/ q j )-p]/p = [(s j /ε S ) θ j ] where ε S = Q/ p p/q is market supply elasticity s j = q j /Q is consumer j market share θ j =(1+ DO j p/ q j ) is his/her conjectural variation term denoting the potential degree of collusion: equal to 1 for Cournot DO j p/ q j =0 equal to 0 for competition DO j p/ q j =-1

12 Dataset demand bids data in the day-ahead market: January 2005 to September 2011 (GME, IPEX) discarding year 2004 data: demand bids was not operational in each month there are about 1.5 million records, reporting the type of market, bids of quantity and price, status of individual bid as accepted or rejected, awarded prices, accepted quantity, zone, trader ID For each hour: price range in 30 percentiles (.01 /MWh /MWh) aggregate individual bids cumulative demand functions for each consumer and market supply function balanced dataset of 720 (24 30) consumer demand observation for each day. Consumers may bid only the quantity without price indication - lumped these bids expressing inelastic behavior in a residual group (further details are in Bigerna and Bollino 2014).

13 Dataset price range around equilibrium USMP extreme values P u above and P b below compute the residual supply arc elasticity for each consumer j according to the usual formula (suppressing hour subscript for clarity): (7) ε SRj = [SR j (P u ) -SR j (P b )]/[P u -P b ] [P u + P b ]/[SR j (P u ) + SR j (P b )]

14 Fig. 1 -Hourly profile of electricity demand in Italy annual average the daily profile is bimodal with a peak in the morning hours around 11:00-12:00 and in the Summer a second peak around 19:00-20:00

15 Fig. 2 -Hourly profile of electricity demand in Italy annual average the daily profile is bimodal with a peak in the morning hours around 11:00-12:00 and in the Winter the second peak is much less evident around 17:00

16 Electricity prices in Italy monthly average -euro /MWh Electricity priceshave been structurally higherthan in the rest of Europe and have followed quite closely oil and gas price trends Not surprising: generation capacity is highly skewed toward hydrocarbons, which cover almost ¾ of total generation Notice: the difference between minimum and maximum price is quite large (20-25 euro/mwh, euro/mwh with an exceptional value of 378 euro/mwh in July 2006)

17 Main results average equilibrium price increased from 66 euro/mwh in 2010 to 71 euro/mwh in 2011 and that peak hours price is about 26% higher than average price in 2010 but only 21% in Notice also that equilibrium quantity during peak hours is about 55% higher than average quantity in 2010 and Therefore these data confirm that peak hours are to be taken with caution as an indicator of tight market conditions in Italy.

18 Table 1 Market prices, quantities and consumers market shares - Years euro/mwh and MWH Years Equilibrium Market price and quantity ALL HOUR S PEAK HOUR S PRICE QUANTITY PRICE MIN AVERAGE MAX MIN AVERAGE MAX QUANTITY Consumers MARKET SHARES ACTIVE H. MARKET SHARES MIN AVERAGE MAX MIN AVERAGE MAX ACTIVE H

19 Main results market power with values around values are all significantly different from zero at 1% confidence level Average values for the 24 hours of the day tend to be higher during late morning hours, 10:00-12:00 and late evening hours 20:00 22:00 In 2010 these values are around.05 In 2011 are between.08 and.10 Confirmation that oligopsonistic market power occurs more intensely when there is relatively abundant RES supply.

20 Table 2 - Estimated oligopsony L index for main consumers - year 2010 (inverse residual supply elasticity) YEAR 2010 ANNUAL AVERAGE CONSUMERS AVERAGE HOURLY AVERAGE HOUR

21 Table 3 - Estimated oligopsony L index for main consumers - year 2011 (inverse residual supply elasticity) YEAR 2011 ANNUAL AVERAGE CONSUMERS AVERAGE HOUR HOURLY AVERAGE

22 Main results compute conduct parameter θ j assuming that the Lerner-type index based on inverse residual supply elasticity is an accurate measure of each consumer mark down estimates for the market supply elasticity ε S conjectural variation term θ j

23 Main results ε S is on average around 1.24 in 2010 and.85 in 2011 less elastic during nightly hours and more elastic during business hours conjectural variation annual average is inversely correlated with consumer importance (close to zero for consumers 1 and 2 but closer to 1 for consumers 3, 4 and 5 in both years 2010 and 2011 hourly values are around 1 or above during morning and late evening hours and around otherwise there exists definitely some oligopsony market power some individual behavior supports the Cournot model and rules out collusion

24 Table 4 - Estimated inverse market supply elasticity and conjectural variations for main consumers hourly averages - year 2010 YEAR 2010 ε S ANNUAL AVERAGE CONSUMERS HOURLY AVERAGE HOUR , θ j

25 Table 5 - Estimated inverse market supply elasticity and conjectural variations for main consumers hourly averages - year 2011 YEAR 2011 ε S ANNUAL AVERAGE CONSUMERS HOURLY AVERAGE HOUR θ j

26 Conclusion we are the first to use individual bid data to test for oligopsony market power in the electricity market our empirical results cannot be directly compared with the previous literature. average mark down of about 3.5% -5.5% significant range between 3% and 10%, depending on the hours thus, oligopsony market power is influenced by the hourly day cycle stronger when there is injection of significant quantities of RES (wind and PV) Policy implication: fostering more competition on the demand side could yield correct equilibrium prices and help to solve the issue ofdisplacement by RES of thermal plants during peak hours