بازده بازار حقوق انتقال مالی در مزایده های دوره ای هماهنگ شده مرکزی

Electricity market design in the United States is increasingly dominated by locational marginal pricing (LMP) of energy and transmission. LMP markets are typically coupled with periodic auctions of financial transmission rights (FTRs) to hedge transmission price risks. While LMP designs offer considerable advantages, forward price discovery in these markets requires participants to form efficient expectations on spot congestion price differences. In this paper, we examine trends in the efficiency of one of the early LMP markets, the New York Independent System Operator (NYISO), analyzing a panel data set of over 9000 contracts over a six-year period beginning September 2000. We show that NYISO FTR markets were inefficient in their early years, but that market participants learned to predict forward prices and thus efficiency improved for FTRs not solely within the New York City/Long Island sub-region. FTRs within this sub-region, which has a number of special characteristics, remain relatively inefficient.

The design of effective market mechanisms for electric power has presented significant challenges for economists. Central to the problem of electricity market design is the allocation of scarce transmission capacity. Because electricity cannot be easily stored, generation and demand for power on the grid must be kept within a close tolerance at all times. Moreover, the production and transmission of alternating current (AC) power is subject to a number of intertemporal and spatial constraints, given existing thermal power plant technology and the limitations of power flows on the grid (Schweppe et al., 1988). The constraints make it difficult to define tradable property rights for transmission. This difficulty has led economists to instead create markets for Financial Transmission Rights (FTRs). Our goal in this paper is to examine the efficiency of the New York Independent System Operator (NYISO) market for FTRs, one of the earliest FTR markets to be established. The paper proceeds as follows. We first describe the various approaches that have been used to solve the problem of pricing transmission capacity and then describe the approach used in the NYISO market. In Section 2, we describe the market design problem and review the prior literature. In Section 3, we describe the econometric approach to testing hypotheses about market efficiency, risk premia, and learning. We also describe the data set used to estimate the model coefficients. Section 4 presents results. We conclude in Section 5 and offer suggestions for further research. 1.1. Explicit and implicit pricing of transmission capacity In the developed world, the pricing of transmission capacity tends to incorporate either an explicit or an implicit framework. In European countries, the explicit auctioning of transmission capacity, separate from energy markets, is common. However, coordination of these auctions across regions can be problematic. A completely decentralized set of explicit auctions in an interconnected grid, for example, is unlikely to be fully efficient because the transmission capacity available across one path of the grid may depend on flows elsewhere on the grid. Even if these problems are addressed, separate pricing of energy and transmission (which in many circumstances have strong substitution and complementary characteristics) can undermine efficiency in the underlying local energy commodity markets. Thus, it makes sense that the European Union is fixing increasing attention on the implicit pricing of transmission capacity, following trends in market design in the United States (Duthaler and Finger, 2009). Under an implicit market coupling approach, energy and transmission are priced together, in a single market mechanism that recognizes the interrelationships between the energy and transmission markets and clears both simultaneously. Within this implicit pricing context, transmission rights are most commonly defined as financial transmission rights or FTRs. An FTR does not provide any physical right to flow power across a transmission interface, but rather provides a hedge against the locational price differences created under an implicit market coupling approach. Because the FTR is a purely financial contract, the transmission system operator (TSO) continues to control all transmission capacity and can allocate it efficiently in real-time to meet load. The locational marginal pricing (LMP) model that is used in the Northeastern and Midwestern United States and is scheduled to be introduced into Texas and California, is an extension of the implicit market coupling approach. The essence of the LMP approach is that all operational decisions are made by the grid operator and that power produced and consumed is traded at the locational spot power price. All significant transmission constraints are reflected in the unit commitment and dispatch models run by the TSO and are reflected in locational prices. Thus, at every major point on the grid, an array of different spot power prices is used for settling energy transactions. The New York Independent System Operator (NYISO), along with Pennsylvania, New Jersey, Maryland (PJM), was one of the first LMP markets in the United States, having conducted periodic FTR auctions since November 1999. The NYISO markets calculate day-ahead and real-time LMPs at numerous points across New York State's power grid, which has a complex interconnected topology. For purposes of financial settlement, the NYISO markets implemented a system by which generators are considered to generate at their bus, while loads are considered to consume in a load zone. The NYISO grid is divided into 11 load zones–labeled “A” to “K” as shown in Fig. 1 below–plus 4 import zones that are used to price imports and exports to and from neighboring U.S. and Canadian markets. Prices are denoted in dollars per megawatt-hour. For example, a generator who produces 100 MW for an hour at a specific node x within Zone A will be paid 100 times the node x price for that hour while a load at a specific node z of 10 MW in Zone J will pay 10 times the local price for that hour. The centrally coordinated spot market effectively clears all nodal markets simultaneously, taking account of loads, generation, and transmission constraints. 1.2. The structure of financial transmission rights markets in New York Although this spot market system is effective at addressing the realities of power flow on an interconnected grid, on its own it poses substantial financial risks for both generators and users of power. Congestion prices are highly volatile, as Fig. 2 illustrates. This example shows the hourly congestion charge (per MWh) in each hour for a hypothetical bilateral transaction between the West Zone (Zone A) and New York City (Zone J) for one day in early July 2008. Given the magnitude and volatility of congestion prices in an LMP market, some method is needed to hedge the price risks posed by spot power prices that vary from location to location and by hour. In response to this problem, Hogan (1992) proposed a system of financial hedging contracts designed to mitigate the component of this risk associated with congestion. These financial hedging contracts–fundamentally similar to financial swaps–pay the owner of the congestion contract the quantity (in MW) times the congestion price difference between a specified Point of Injection (PoI) and Point of Withdrawal (PoW) for each hour in the term of the contract. These FTRs (which are called transmission congestion contracts or TCCs in the NYISO) play the role that ordinary point-to-point transmission rights play in physical market designs, although in this case they act solely as financial swaps and have no direct effect on system operations. For example, a monthly FTR might be defined with a PoI of Albany and a PoW of New York City. For each hour in the month, the FTR holder is paid the difference between the NYC and Albany congestion prices. The LMPs in New York also include a locationally specific marginal loss component. This element of the LMP is not included in the TCC structure and is not considered in the present analysis. FTR payments over an hour (or longer periods) can be negative — an FTR is an obligation to pay the sum of congestion price differences even if this sum is negative. Hogan has shown that the merchandizing surplus obtained by the grid operator in the energy market is sufficient to fund a full set of FTRs that reflect the maximum physical simultaneous transfer capability of the transmission grid (Hogan, 1992). If the TSO therefore auctions or allocates no more FTRs (in megawatt terms) than it can reliably sell as physical transmission rights, its energy transaction surplus in the day-ahead market will be sufficient to pay the aggregate congestion price differences to FTR holders. This result has had a substantial impact on modern electricity market design and underlies the FTR auctions that occur in the NYISO market and elsewhere in the U.S. NYISO has conducted periodic FTR auctions since November 1999. Market participants include generators, transmission owners, and marketers, including financial participants such as investment banks. In New York, FTRs are sold for varying durations — 1 month, 6 months, or 1 year. As described above, a one-month FTR is the right to hourly differences between congestion prices at two specified locations for the period of a calendar month. Since the FTR is defined as an obligation, and not an option, it may have a negative value, in which case a reverse auction is used to allocate it. Both positive and negative FTRs are allocated in the same auction. An auction of FTRs covering a month is conducted early in the preceding month, so that a FTR covering the month of November, for example, will be auctioned in early October. We focus on the monthly FTRs, which by design are never overlapping in coverage, in order to avoid statistical issues associated with overlapping observations.

This paper presents an analysis of FTR market returns and efficiency in the NYISO market, based on a much larger sample of contracts than has been previously considered. The models are estimated using OLS regression and errors are corrected for heteroskedasticity using robust regression to eliminate some of the biases inherent in simple OLS estimates. The results have important implications for FTR market design and for the implementation of LMP power markets. Even for sophisticated market participants, the ability to form efficient price expectations was quite limited in the early days of the NYISO FTR markets, circa 2000–2001. This suggests that participants may also have had trouble pricing energy forward contracts over this early period, a problem that may have extended to other regional LMP-based U.S. markets. Since forward market allocative efficiency depends on the ability of participants to form expectations of prices, designers looking to improve this process may need to consider what information is needed by participants as markets open. For contracts outside New York City/Long Island, however, the market efficiency results are somewhat more reassuring for market designers. New York State FTR markets that are not solely within zones J and K–positive and negative–appear to have become rapidly more efficient, and, although standard tests of market efficiency are not met, the expected transaction profits are relatively small. The persistence of higher expected transactions profits within the New York City/Long Island market may be explained by a number of hypotheses, not all of which can be tested given our current data. In general, we would not in general expect risk premia to be a plausible candidate for explaining the observed positive transaction profits. As explained earlier, the volatility of transmission constraint shadow prices should not have significant correlation with aggregate market returns. Within the context of traditional finance theory, expected profits on futures positions can only be rationalized by a premium for bearing systematic risk or by carrying costs. Thus we consider alternate explanations. First, under the conjecture of Deng et al. (2004), non-zero transaction profits may persist in FTR markets due to inherent auction design characteristics unless bid volumes reach substantial levels. At present, our data set does not include FTR auction bid data (e.g., the number of bids for FTRs submitted — we have only clearing FTR prices and quantities) that would allow the Deng model to be tested directly. To the extent that these data are available, the ratio of the quantity of bids submitted in the FTR auction to the quantity of FTRs allocated could be used as an explanatory variable in the econometric model. It is of course possible that both learning and quantity effects are important to different degrees, and an appropriate econometric specification should allow the importance of these two explanatory models to be measured and compared. Finally, we note that similar data are available for other FTR markets, including PJM, New England and the Midwest ISO. Each of these markets started after the NYISO FTR began in 1999, so there may be evidence for learning across markets, as many of the same firms participated in each of these markets. Second, it may be that the special characteristics of the New York City/Long Island market have slowed learning so that positive profits are still seen after a number of years. Hence, even with our fairly long data series, it might be rash to conclude that the realized positive profits imply positive expected profits. As an inspection of the spot price data shows, congestion charges, particularly charges in the New York City/Long Island market, are very sensitive to large infrequent shocks to the supply and demand for power and inputs. Because these shocks are large and infrequent, a given realized history of shocks may, even over a fairly long time series, not mirror expectations. This problem with identifying the expected gains from realized gains is a special case of the “peso problem” that has been extensively analyzed in the financial asset pricing literature.7 Thus, it is possible that the positive transactions profits in the NYC/LI market that we observe were simply the result of a realized series of shocks. This would include, for example, Hurricane Katrina, which occurred in the sample period and had a very large impact on natural gas prices, which were largely unexpected ex ante. As NYC/LI congestion prices are highly dependent on realized gas prices (much more than prices outside this region), some fraction of transaction profits may be due to unpredictable effects which have not yet moved to an equilibrium level indistinguishable from zero. This randomness of events might explain the positive and persistent profits on positive contracts within New York City/Long Island, which an even longer time series might eventually explain away under a conventional learning hypothesis. Finally, there may be participation and informational costs unique to the NYC/LI market that have prevented transaction profits from being eliminated within our study period. Within smaller, dense regions of the grid (such as within the NYC/LI region as seen in Group 3 and 4 FTRs), congestion is affected significantly by specific and oft-changing characteristics of the grid itself, rather than by broader supply–demand characteristics (which FTR market participants can model relatively easily). For example, within New York City (a region of the power grid unrivaled in the United States for density of power consumption and complexity of the network topology), small constraints in the grid require constant re-dispatch of plants to meet voltage, thermal, and other constraints affecting the flow of power on the local grid. These constraints have a much more random nature, as they are primarily affected by line and equipment failures, etc. Modeling the effect on power flows and congestion of even a single line outage in the NYC network is highly complex, so perhaps it is unsurprising that market participants have found it less easy to form efficient expectations of congestion prices within New York City and Long Island. The underlying engineering problem is significantly more difficult and the effect on congestion prices within a single month may be easily dominated by transmission outages and maintenance about which FTR market participants have little or no advance information. As a supplemental component of this hypothesis, we also note that costs of analyzing detailed intra-zonal constraints (such as those within New York City and Long Island) may be much higher than forecasting “macro” congestion across the major NYISO constraints (as seen in Group 1 and 2 FTRs for the most part). The former requires a highly accurate model of the NYISO transmission grid, complex AC load flow models, and highly trained engineers to interpret the model results, and even then the outputs are subject to considerable uncertainty. Relatively few firms are staffed to conduct this analysis or are likely to see any positive net benefit from doing so, given the smaller scale of the intra-zonal Group 3 and 4 markets. For most FTR market participants in NYISO, the benefits of forecasting these congestion prices more accurately (and hence bringing forward FTR prices more on line with spot outcomes) may simply not be worth the additional investment and costs required.