Market Power in the Norwegian Electricity Market: Are the Transmission Bottlenecks Truly Exogenous?

Abstract In this paper, we test whether producers in the southern Norway price zone utilize information on available transmission capacity to induce transmission congestion in their price zone to exercise market power or not. Endogeneity results for import congestion suggest that congestion is endogenous during late night and morning hours implying that producers in southern Norway restrict their output to induce transmission congestion into their price zone. We find an average markup of about 19.5 percent above the marginal cost during these hours. These results point that NordPool’s policy of making transmission capacity information public to ensure market transparency is not welfare maximizing as strategic producers can use this information to anticipate and induce transmission congestion into their price zone for driving prices away from the competitive levels.


INTRODUCTION
Since the liberalization of electricity markets around the world, studying the incentives for and estimating the extent of producers' market power have been on the research agenda (Borenstein et al., 1999, Borenstein et al., 2002;Mansur, 2008;Chao and Kim, 2007;Wolfram, 1999;Mu ¨sgens, 2006;Crampes and Moreaux, 2001;Garcia et al, 2001;Hansen, 2009;Bushnell, 2003).In particular, several studies have looked into the impact of transmission congestion on producers' exercise of market power (Joskow and Tirole, 2000;Borenstein et al., 2000;Bunn and Zachmann, 2006;Johnsen, 2001).Among these studies, particular focus has been placed on the impact of physical or financial transmission rights on the exercise of market power (Joskow and Tirole, 2000;Stoft, 1997;Bushnell, 1999).Quite a few other studies have looked at the impact of auction type and bidding rules on the pricing strategies of generators (Gilbert et al., 2002;Harvey and Hogan, 2000).Neuhoff (2003) argues that a system like that found in Scandinavia, where the system operators integrate the whole market and simultaneously clear the day-ahead market of several countries is helpful in mitigating the extent of market power being exercised.
Studies of the Norwegian electricity market that look into the impact of transmission congestion on market power find limited evidence that producers exploit transmission bottlenecks and exercise market power (Johnsen et al., 1999;Steen, 2005;Damsgaard et al., 2007;Mirza and 1.The capacity of the transmission lines can vary and depends upon a number of factors including temperature, wind speed, wind direction and the physical conditions of the network etc. 2. Details about congestion management practice in the Nordic market can be found in NordReg (2007) and NordEl (2008).Bergland, 2012).Many of the studies attribute the success of the NordPool electricity market in terms of limited market power to a sound market design that reduces the incentives for the exercise of market power and the of dilution of major domestic producers in the large NordPool market (Amundsen and Bergman, 2006;Bask et al., 2009).
The results from these studies can, however, be interpreted as the average markup of producers over time where the underlying models consider the events of binding capacity constraints as exogenous.This implies that the producers utilize a passive strategy with fixed markups over a period but invariant over time.Specifically, the issue that both the international and Norwegian studies fail to address empirically is the issue of "induced transmission congestion"."Induced transmission congestion" implies that by recognizing the limited import transmission capacity, the dominant firms in an importing zone might strategically reduce their output in order to induce congestion in their region and thereby providing them an opportunity for exercising market power with respect to any residual demand left unserved in the importing region.
In this scenario, transmission congestion in the system might not always be exogenous, rather the result of producers' strategy to exercise market power by endogenizing the transmission constraints.Given this background and using the insights from Porter (1983a), Porter (1983b), Green and Porter (1984), Lee and Porter (1984) and Bresnahan (1989), this study assesses the extent of market power in Norwegian electricity market and provides answers to the following questions: 1. Are the import transmission bottlenecks exogenous in the Norwegian electricity market?2. What is the extent of markups over the marginal cost under the events when producers act strategically and induce transmission congestion in the importing zone?
In the light of the empirical results, we further discuss the regulatory implications of NordPool's policy of announcing specific transmission capacities for the coming day-ahead market auction.
The remainder of the paper is organized as follows.Section 2 discusses the determination of day-ahead transmission capacity in the Norwegian electricity market; section 3 presents the theoretical foundations on how to identify market power, while section 4 delineates the empirical strategy.We present results in section 5 and discuss their implication in section 6, whereas section 7 concludes the paper.

DETERMINATION OF OPTIMAL TRANSMISSION CAPACITY IN THE NORWEGIAN ELECTRICITY MARKET
Electric power flows in transmission networks are governed by physical laws.The TSO is responsible for ensuring that injections and loads in a network are such that the resulting flows are within the physical limits of the network.Transmission lines in an electric network have limits; namely 1) the thermal capacity limit which is the maximum physical capacity of a particular line, and 2) stability limit which is imposed to ensure the operational security of the network. 1  The Nordic approach to congestion management involves three different tools; 2 1) market splitting, 2) counter trading and 3) reduction of cross-border transmission capacity.Market splitting is used in the day-ahead auction as a means to ensure that planned flows between price zones are within the preset capacity limits.Congestion inside a prize zone is handled by counter trading organized by the TSO and takes place after the day-ahead auction is completed.The TSOs can reduce the capacities of cross-border transmission lines in order to keep electricity flows in an area within thermal capacity limits and security margins, and maintain system stability.Due to these stability and thermal capacity requirements, transmission capacity can show significant variations between different hours of the day and the days of the week.The TSOs are obliged to make this information available to the market participants. 3 In particular, the Norwegian TSO, Statnett, assesses each day the physical conditions of the transmission grid, and anticipated injections and withdrawals from the network, and on that basis determines the need for reduced import/export capacities for any of the interconnections.The detailed reasoning behind these reductions is very seldom revealed.Capacity limit information is relayed to NordPool before the day-ahead auction, and is made available to everyone no later than 10AM to ensure market transparency.As the maximum physical capacity is known before the gateclosure at 12AM, the market participants can base their bids in the day-ahead market on the actual capacity constraints that will prevail the next day.This may increase the opportunity for them to act strategically by possibly inducing transmission congestion and exercise market power.

THEORETICAL BACKGROUND
There are a number of empirical methods available for assessment of market power and the choice of model depends in part on the availability of data.Recent research on market power in electricity markets employs the supply function equilibrium model that is based on access to bid data for individual firms (Green and Newbery, 1992;Baldick, Grant and Khan, 2004).However, detailed bid data is not available for participants in the NordPool market and we will base our analysis on aggregate market data and techniques from New Empirical Industrial Organization (Bresnahan, 1989).Green and Porter (1984) argue that non-competitive behavior in a particular industry structure can be assessed from the pattern of industry performance across different time periods.The distinctive behavior of firms under different regimes provides an opportunity for drawing inference about the presence or absence of non-competitive behavior using aggregated industry level data.According to Green and Porter (1984), looking for non-competitive behavior of the industry in situations when non-competitive behavior might plausibly occur, provides an important opportunity to find whether the firms exercised market power or not.

Identification of Market Power
Following Porter (1984) and Bresnahan (1989), when the conduct of industry changes between competitive and non-competitive outcomes over time, the aggregate supply curve 4 at the market level can be written as; 5. Mirza and Bergland (2012) employed Bresnahan-Lau framework to directly estimate the extent of market power being exercised in the Norwegian electricity markets under the regime of transmission line congestion.Current study goes one step further and tests whether the transmission congestion in the Norwegian electricity market is endogenous and if endogenous, what is the extent of market power being exercised under such a situation.
6.For detailed derivation of the demand curve, see Borenstein et al. (2000) and Mirza and Bergland (2011).
where the parameter is the transformation of market conduct during competitive periods and the a d parameter denotes the changes in conduct when market is non-competitive and firms exercise b d market power.In this formulation, cannot be identified separately from when estimating these a d α 0 equations, but (change in prices due to shift in conduct from competitive to non-competitive) b d carries information whether firms at the industry level exercised market power or not in the noncompetitive situations.P t , Q t and Z t represent prices, production and the other exogenous factors affecting prices respectively.Porter (1984) argues that if we have a dichotomous variable that provides some information about the periods wherein the firms in an industry behave non-competitively, we can directly make inference about the extent of markup above the marginal cost.The price setting equations ( 1a) and (1b) can be re-written as: where is a dummy variable indicating competitive ( ) and non-competitive ( ) periods.
The shift in the supply due to changes in provides a direct assessment of the magnitude of price-I t cost margins due to a shift in firm's conduct from competitive to the non-competitive.In the specific context of electricity markets, the binary variable 1 Import congestion I = t 0 Otherwise can provide an opportunity to measure changes in the price-cost margins when firms exercise market power under transmission congestion in the importing regions.Empirical evidence from Steen (2005) and Mirza and Bergland (2012) 5 suggests that producers in the Norwegian electricity market are better able to exercise market power when the transmission line adjoining different price zones in NordPool electricity market is congested.Optimal bidding behavior in an electricity wholesale market can be analyzed using the supply function equilibrium model (Baldick, Grant and Khan, 2004).The optimal bidding strategy for producer changes as the number of market participants changes, and will deviate from marginal cost bidding when there are few participants resulting in a shift in the observed supply schedule when a market area is import congested.
Price determination under alternative episodes of competitive and non-competitive conduct can be illustrated with the help of Figure 1.
The typical demand curve for a local electricity producer in an interconnected electricity market is characterized by a kinked shape and is denoted as in Figure 1. 6 The quantities denoted D 0 and represent the full import and export transmission capacity respectively for the do- local producers believe that by reducing their output by a quantity of , they can induce congestion R in their zone, they would do so to exercise market power over the residual demand.This is the case of induced transmission congestion and is represented by market equilibrium at E 1 and corresponding price P 1 , higher than the marginal cost of production.

Are the Strategic Decisions of a Hydro Producer Unconditional?
The earlier discussion suggests that congestion can be induced in the system in order to exercise market power.However, the strategic decisions by dominant producers in a hydro dominated market like Norway are not unconditional. 7There are a number of factors that play a pivotal role in deciding whether a hydro producer should act strategically or bid according to the marginal cost of production.
First, the total output of the hydro producers in electricity market for a typical year is predictable (determined by fixed reservoir size and the anticipated inflows).The only thing the hydro producers can do is to allocate their anticipated production across different hours of the day and between seasons of the year so as to maximize profits by equating price to the expected marginal revenue in the next period.The water value is not directly observable and depends on forecasts and beliefs about future prices that depend on future weather and demand/supply conditions.Any strategy that affects the production plans is likely to affect the water value hence the profits.To effectively exercise market power, hydro producers compare the profitability of their strategic actions to their overall profitability when they do not exercise market power.Restricting output to exercise market power also means forgone production at hand and relatively higher reservoir levels for future that put a downward pressure on the water value.They attempt to exercise market power only when they expect their markups to be higher than the value of foregone production and the resulting changes in water value.
Second, while submitting bids for the day-ahead market, producers in a price zone do not have perfect information if, in the outcome of the auction, their price zone will be net importer or a net exporter of electricity.And neither if it becomes a net importer, how much of the available transmission capacity will be actually utilized.They only have information about the available transmission capacity between different price zones of the NordPool market as determined by the respective TSOs before the deadline for submission of bids for the day-ahead market.
In these instances, to induce transmission congestion in their own price zone, local producers form expectations which are imperfect in nature.If they assign high probability to the event that they cannot induce transmission congestion into their zone, they bid according to the marginal cost of production.Conversely, if they predict with high probability that the transmission line will be congested or they can make the transmission line congested by shifting their bid curve, it creates incentives for them to increase their profits by temporarily bidding a price over and above their marginal cost of production.
Although local strategic producers do not have perfect information if their price zone will be net importer or net exporter of electricity, they have very good idea about the likely direction of the flow.The situation can also be viewed as an infinitely repeated game with imperfect knowledge and producers can make very good predictions if their strategies will bring about the expected outcomes.Evidence put forward by Løland et al. (2012) suggests that transmission congestion in the Nordic electricity market can be predicted efficiently.Using ARIMA and exponential smoothing models, they were able to predict the import congestion for NO1 price zone with correct prediction rates above 84 percent.
However, because the formed expectations are imperfect in nature, they also make prediction errors on account of a number of factors including the market design with implicit transmission capacity auctions and the stochastic nature of electricity demand.
This discussion suggests that hydro producers' strategy to induce transmission congestion into their price zone by shifting the supply curve is risky because they are not the perfect forecasters.They will adopt this strategy only if the expected net payoffs from their strategic actions are positive.It also implies that the transmission congestion might not always be endogenous, but rather exogenous and the result of an upward shift in the domestic demand curve.

Empirical Specification
Assume that the marginal cost function, or supply curve at the aggregate level, is linear in parameters and can be represented as: where represents price at time , denotes domestic production at time , represents exogenous factors affecting the water value and is a binary variable representing whether the transmission I t line at time is congested or not.To estimate the responses in percentage terms, the equation has t been estimated in double-log form.
In this specification, is the parameter of main concern.The magnitude and statistical d 0 significance of this parameter will determine if the market outcomes under transmission congestion are competitive or if the producers have exercised market power.The endogeneity of the parameter will indicate if the producers have induced transmission congestion in their price zone or if the transmission congestion is exogenous.The major factor that affects the marginal cost of production in hydro dominated electricity markets is the water value.The water reservoirs are filled during the summer and late autumn by melting of snow and the rainfall, and water is stored for electricity generation during the winter period.Depending upon the amount of snowfall during winter and precipitation during the early autumn, the producers adjust their production plans for the whole year in advance (see Figure 2).Any deviation in the amount of inflow from the long-term average in a particular period directly influences the electricity generation plans of producers.To capture these impacts, we have included the real time reservoir level, historical average of reservoir filling in that particular week and water inflow (as a ratio of southern Norway inflow to the Norwegian water inflow) in our supply specification.In the model, water variables have been lagged for five days as the water information is collected by the authorities on Monday, released Wednesday afternoon and becomes available for producers to be used in day-ahead bids for Friday.
Since electricity production follows seasonal fluctuations mainly caused by climatic conditions and directly translate into the water values, we have used sine and cosine functions in the supply equation to capture any additional annual cycle in the expectations forming the water value (Weron, 2006).Among the other shifters that affect the water value of hydro producers in Norway, we have included crude oil price and the price of tradable carbon emission certificate that directly affect the marginal cost of production from thermal electricity units and hence the expectations about power prices in the European Electricity Exchange.As oil and carbon prices on a particular day do not enter into the bids for spot market for that day, we have lagged them two days to capture the timing of the information.
The set of instruments used to identify and take out the impact of endogeneity in the supply curve can be divided into three groups.
First, from the domestic demand, we have included three variables that shift the domestic demand for electricity but do not affect the water value.These include, dummy variable for weekend, temperature in Oslo (heating degrees) and temperature squared.
Second, to take out the impact of what goes on elsewhere in the NordPool market on the domestic price, we have included wind production in Western Denmark because the cheap electricity generation through wind turbines in Western Denmark influences the market equilibrium through the shifts in Danish supply curve.
Third, to take care of the endogeneity in import transmission congestion, we have included import capacity, one day lag of import transmission congestion dummy to capture if transmission line was congested at the same time yesterday and seven day lag of import transmission congestion dummy to capture if transmission line was congested at the same time last week.All the variables used as instruments in the estimation are exogenous and do not belong to the domestic supply curve.Hence they fulfill the a priori criteria of being valid set of instruments.
We estimate the supply curve using the Generalized Method of Moments (GMM) technique.Heteroskedasticity and autocorrelation consistent standard errors (HAC) based upon a quadratic spectral kernel method with a lag of 7 time periods (days) have been used in order to make the statistical inference robust against potential serial correlation and heteroskedasticity (Hayashi, 2000).

Tests for Endogeneity of Transmission Bottlenecks
Testing for the endogeneity of transmission bottlenecks is a major objective of this paper and thus requires that the standard errors used for the tests are robust against any type of heteroskedasticity and serial correlations.To compute an efficient GMM estimator, a consistent estimate of variance-covariance matrix is required because the method used to estimate the matrix depends upon the time series properties of the population moment conditions.We have conducted endogeneity tests using both the Newey-West (Bartlett kernel function) and Andrew (1991)'s HAC estimator of variance-covariance matrix using quadratic spectral kernel function.Quadratic spectral kernel estimator of variance-covariance matrix uses asymptotic mean squares error (MSE) as optimality criterion and thus has the smallest asymptotic mean squares error (MSE) compared to other HAC estimators of this kind.
The GMM-C chi-squared test allows us to test for the orthogonality conditions in the model (Hayashi, 2000).The statistics is computed as the difference between GMM J-statistics of restricted model (using entire set of over-identification restrictions) versus the unrestricted model (using less restrictions by removing some instruments from the list).The C-test has a chi-squared distribution with degrees of freedom equal to the number of variables to be tested for endogeneity under the null hypothesis that the specific variables are exogenous.

Data and Descriptive Statistics
This section describes the variables included in the estimation of supply equation.The descriptive statistics of the variables are given in Table 1.This study employs hourly data for southern Norway from the period 31 st May 2004 to 20 th April 2008.The data on spot price, electricity production and import capacity, has been obtained from NordPool Spot (http:// www.nordpoolspot.com/).We use hourly temperature data in Oslo (Oslo Airport) as a proxy for the temperature in southern Norway.Temperature data are METAR observations for Oslo (OSL), and were obtained from the Norwegian Meteorological institute (http://eklima.met.no/).
We have used weekly data on reservoir level and inflow for southern Norway from the Norwegian Water Resource and Energy Directorate (http://www.nve.no/).We use hourly data on wind electricity production in West Denmark from Energinet (http://www.energinet.dk/).Data on daily international oil prices has been obtained from U.S. Energy Information Administration (http:// tonto.eia.doe.gov/).
To investigate the stationarity properties of the data, we apply augmented Dickey-Fuller (ADF) and Philips-Perron (PP) tests on all the variables.The results suggest that for all the variables except oil prices, the null hypothesis of the presence of unit root is rejected at 5% level of significance.

Supply Curve and Estimated Markup
Two stage GMM results of the supply equation are reported in Table A in the appendix.All the key variables in the supply equation carry the right theoretical signs and are statistically significant at the 5 percent level.Estimates suggest that other things remaining constant, higher rates of water inflows and higher real time reservoir levels put a downward pressure on electricity prices.On the contrary, a higher historical reservoir level increases the domestic prices of electricity.Increases in the price of crude oil and carbon emission permits increase the domestic price of electricity.F-test for the instrument relevance confirms that the instruments are not weak.Furthermore, the value of the test for over-identification is reasonably low and suggests that the instruments are exogenous and are not correlated with the error term at the 10 percent level for most of the hours.
The coefficient on the binary variable in the supply equation provides an estimate of the I t extent of market power exercised by hydro producers.In Figure 3, we plot the percentage of markup 8 during import congestion along with its statistical significance.We can observe a distinct pattern in the statistical significance of the markup parameter.It is significant at 5 percent during the late night and morning hours when southern Norway is generally a net importer of electricity and cheap electricity is imported from the connected price zones either until the prices equalize or the transmission capacity is fully exhausted.The relatively small size of the market during these hours provides an opportunity for strategic producers to reduce their output and thus induce transmission congestion in their price zone and earn markups above the marginal cost.
These results are further supported by the average trend in which import and export capacities are determined between southern Norway and the connected price zones across different hours of the week 9 (Figure 4).From midnight to 7.00 AM when southern Norway imports electricity most of the times, import capacity set by Statnett during these hours is always at its minimum compared to the rest of the hours in a day and the transmission lines have remained congested for about 25 percent of the times during these hours.During these hours when producers expect that with their strategic actions they can induce transmission congestion and the outcomes fall in line with their formed expectations, the coefficients on markup parameter are highly significant statis- tically and the extent of markups is higher.Prices during these hours have remained about 19.5 percent higher than the competitive levels.
However, when producers operate in the big market segment and export capacity is at its maximum levels compared to the other hours of the day, it is not optimal for them to induce congestion in their price zone but to expand their output and maximize revenues over the bigger market.Coefficient on our market power variable during these hours has remained statistically insignificant at 5 percent level.
This discussion asserts that not only the availability of cheap electricity, but also the suboptimal utilization of physical transmission capacity by the transmission system operator helps strategic producers to exercise market power.
These results are also in accordance with the theoretical underpinnings that the essential conditions for the hydro producers to exercise market power in a mixed hydro-thermal system depends on the existence of differences in price elasticity of demand between different time periods.Hydro producers produce more during the periods of high price elasticity and produce less during the periods with low price elasticities compared to the competitive outcomes.This is because of the fact that low price elasticity provides limited opportunities to the end users to shift their consumption to other time periods.

Endogeneity of Transmission Bottlenecks
The endogeneity results of the binary variable for import congestion, as presented in Table A in the appendix, are in line with the statistical significance of the estimated markups.The en-dogeneity results are also in accordance with the transmission capacities being determined in the Norwegian electricity market.Import transmission congestion is endogenous at the 5 percent level during the late night and morning hours, while it is exogenous during the rest of the hours of the day.These are mostly the hours when import capacity is at its minimum in the southern Norway price zone.These endogeneity results and the statistical significance of our markup parameter during these hours confirm our earlier conjecture that strategic producers change their bidding behavior to induce transmission congestion into their zone to drive prices away from the competitive levels.
Setting up of import capacities in this manner in the Norwegian electricity market could be the result of stability requirements of the national grid.Market clearing in the Norwegian electricity market requires the determination of equilibrium prices and the flows by simultaneously solving the demand and supply bids within a zone.The actual flows of electricity in a grid can substantially differ from the planned flows.Therefore, flows are distributed to the consumption nodes from the production nodes through the paths of least resistance.Probably, the current solutions in the Norwegian electricity market represent the existing realities of the grid situation to a smaller degree.Therefore, a detailed loop-flow model should be adopted to optimally allocate capacities in the market.This would reduce the probabilities of the import capacities to be used strategically.
Although the endogeneity results imply that on average, producers induce transmission congestion in NO1 price zone during the late night and early morning hours; however some of the congestion into the system might have had appeared due to the lack of available transmission capacity.Resultantly, all the transmission bottlenecks might not be strategically or artificially induced.In this paper, we do not disentangle these two types of bottlenecks nor do we estimate markup for these hours separately.This is an area that can be pursued for future research into this issue.

IMPLICATIONS OF THE RESULTS
These results have implications not only for overall efficiency of the market but also for the communication strategy of the NordPool market.
First, the markup is higher and statistically significant at 5 percent level mostly in those hours where the tests for endogeneity showed that the transmission congestion is endogenous.The extent of market power that we have found here is much higher than what is reported by other studies that consider the binding transmission capacity as an exogenous event (Hjalmarsson, 2000;Vassilpoulos, 2003;Steen, 2005;Bask et al., 2009;Mirza and Bergland, 2012).We can infer from here that market is not entirely competitive and producers drive prices away from the competitive levels when they can induce transmission congestion in their price zone.
Second, producers are somehow able to utilize the transmission capacity information extended by the transmission system operator (Statnett) at 10.00 AM in the morning, two hours before the deadline for submission of bids for the day-ahead market in making their production decisions.As the producers are aware of the maximum thermal capacity and the average trend in which transmission capacities between different price zones are determined, with the help of information on the available transmission capacity for the day-ahead market they are able to make informed guesses on how much reduction in their output will induce the transmission congestion in their price zone.With this information, they are also able to evaluate if this strategy will be profitable or not as they also forego some output that they could have otherwise produced.
Third, the allotted import capacity for southern Norway price zone during the midnight and early morning hours is at its lowest when compared to rest of the hours in a day.These are exactly the hours when cheaper electricity is imported from connected price zones and the import 10.Von der Fehr (2013) gives a critical assessment of European regulation aimed at increased transperancey and market performance.
lines remain congested more often during these hours.In a way, it artificially supports the domestic producers with higher prices at the expense of end users.
Fourth, it also suggests that although NordPool shares this information to ensure market transparency but in this case, this does not seem to ensure market efficiency.Producers are able to utilize this information to induce transmission congestion in their price zone to exercise market power.It also implies that more information might necessarily not result in welfare maximizing outcomes.The current practice of capacity announcements is in accordance with European regulation aimed at improving market efficiency through information disclosure and transparency.Our findings suggest that publishing this information may have the opposite effect. 10 A possible extension of this work is to explicitly model producers' expectations regarding transmission congestion on the extent of market power they exercise in the electricity market.Specifically, by making producers' expectations regarding transmission congestion conditional upon available information on the state of transmission network, it would be worthwhile to estimate the extent of markup if the outcomes emerge in line with their formed expectations and the cases when they make forecast errors.This will also help in evaluating the probabilities of their strategic actions to be successful.

CONCLUSION
In this paper, we attempt to explicitly analyze and test whether the electricity producers in the Norwegian electricity market induce transmission congestion into their zone by withholding their output to exercise market power.Employing hourly data from southern Norway from 31 st May 2004 to 20 th April 2008, endogeneity tests suggest that the binary variable for import congestion is endogenous during late night and morning hours.Using the insights from Porter (1984), GMM estimation of the supply equation confirms that producers in southern Norway have exercised market power during these induced transmission congestion hours and the average markup above marginal cost during these hours is about 19.5 percent.
The results imply that producers are well able to anticipate and use the available transmission capacity information extended by the Norwegian transmission system operator (Statnett) in their favor to induce transmission congestion into their price zone.It also suggests that NordPool's policy of sharing the available transmission capacity information to ensure market transparency is not welfare maximizing as it helps producers to drive price away from competitive equilibrium whenever possible.

Figure 1 :
Figure 1: Price Determination under Transmission Congestion

Figure 2 :
Figure 2: Reservoir Filling and Electricity Consumption in NO1 Price Zone

Figure 3 :
Figure 3: Estimated Markups During Induced Import Congestion

Figure 4 :
Figure 4: Patterns in Import and Export Capacity in Southern Norway Price Zone

Table A : Estimates of the Supply Equation Hour
9 Hour 10 Hour 11 Hour 12 Hour 13 Hour 14 Hour 15 Hour 16

Table A :
Estimates of the Supply EquationHour 17 Hour 18 Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 24