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

AuthorMirza, Faisal Mehmood
  1. 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; Musgens, 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 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:

  2. Are the import transmission bottlenecks exogenous in the Norwegian electricity market?

  3. 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.

  4. 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 gate-closure 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.

  5. 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).

    3.1 Identification of Market Power

    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.

    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;

    [P.sub.t] =[[alpha].sub.0] +[d.sup.a] +[[alpha].sub.1][Q.sub.t] +[[alpha].sub.2][Z.sub.t] +[e.sub.t] Competitive (1a)

    [P.sub.t] =[[alpha].sub.0] +[d.sup.a] +[d.sup.b] +[[alpha].sub.1][Q.sub.t] +[[alpha].sub.2][Z.sub.t] +[e.sub.t] Non-competitive (1b)

    where the parameter [[delta].sup.[alpha]] is the transformation of market conduct during competitive periods and the parameter [[delta].sup.b] denotes the changes in conduct when market is non-competitive and firms exercise market power. In this formulation, [[delta].sup.b] cannot be identified separately from [[alpha].sub.0] when estimating these equations, but [[delta].sup.b] (change in prices due to shift in conduct from competitive to non-competitive) carries information whether firms at the industry level exercised market power or not in the non-competitive situations. [P.sub.t], [Q.sub.t] and [Z.sub.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:

    [P.sub.t] = [[alpha].sub.0] + [[alpha].sub.1][Q.sub.t] + [[alpha].sub.2][Z.sub.t] + [d.sub.0][I.sub.t] + [t.sub.t] (2)

    where [I.sub.t] is a dummy variable indicating competitive ([I.sub.t] = 0) and non-competitive ([I.sub.t] = 1) periods. The shift in the supply due to changes in [I.sub.t] provides a direct assessment of the magnitude of price-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

    [mathematical expression not reproducible]

    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...

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