Cross-product Manipulation in Electricity Markets, Microstructure Models and Asymmetric Information.
|Prete, Chiara Lo
Electricity market design aims at defining rules and incentives leading to a workably competitive market whose outcome achieves a broad social benefit (Joskow and Schmalensee, 1983). In the United States, the reference framework for electricity market design is given by the model of bid-based, security-constrained economic dispatch with locational marginal prices (Hogan, 2010). (1) Organized electricity markets run by Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) are built around this model, and have a two-settlement structure with day-ahead and real-time coordinated auctions. (2) The commodity traded in each market is the quantity of power, in MWh, produced and consumed in real-time at a given location on the transmission network. A market auction on the day before actual power dispatch creates a financial obligation to buy or sell power for delivery in real-time. In contrast, the real-time market is a physical market where actual supply and demand of electricity are balanced continuously over the delivery day. In both auctions, the result is market clearing with locational marginal prices that, under competitive market conditions, reflect the short-run marginal cost of serving one incremental megawatt of load at each node (Schweppe et al., 1988). Sales and purchases cleared at the day-ahead price that are not converted into physical positions must be bought or sold back at the real-time price.
Due to the lack of economic energy storage, the existence of capacity and transmission constraints, and the small short-run price elasticity of demand, real-time physical markets are vulnerable to price manipulation. It is well known that power generators may exercise supplier market power by not bidding available capacity into the market ("physical withholding") or by raising offer prices above marginal cost of production ("economic withholding") (Newbery, 1995; Cardell et al., 1997; Borenstein et al., 2002; Joskow and Kahn, 2002; Harvey and Hogan, 2002; Wolak, 2003; Kim and Knittel, 2006; Puller, 2007; Sweeting, 2007; Lo Prete and Hobbs, 2015). Extensive monitoring and mitigation rules are in place to prevent this type of generator manipulation (Helman, 2006). In contrast, a policy concern raised by enforcement actions of the Federal Energy Regulatory Commission (FERC) focuses on price manipulation involving forward electricity markets and related financial positions: market participants may act against their economic interest in the day-ahead market to artificially move prices and benefit related positions in another market (FERC, 2017). For example, several actions brought by the FERC involved uneconomic (i.e., unprofitable) virtual transactions and alleged manipulation of day-ahead prices to benefit related financial transmission rights (FTRs). Cross-product manipulation cases are being litigated or resulted in multi-million dollar settlements where the accused may not admit to the behavior alleged. As a material case in point, an investigation culminating in 2012 alleged electricity market cross-product manipulation by Constellation Energy (138 FERC [paragraph] 61,168). A settlement resulted in $135 million in civil penalties and $110 million in disgorged profits, but with no agreement on the merits of the claim. (3)
By design, settlements approved by FERC do not disclose details regarding the underlying analysis conducted during the investigation and the market price impact of the manipulation. (4) Further, there is concern that FERC's Anti-Manipulation Rule for electricity markets (18 C.F.R. [section]lc.2), modeled on Rule 10b-5 of the U.S. Securities and Exchange Commission, is too narrowly focused on the notion of fraud (Pirrong, 2010; Hogan, 2014; Evans, 2015) and has been applied on a case-by-case basis, creating uncertainty as to what types of misconduct constitute manipulative behavior in practice (Scherman et al., 2014). Evans (2015) suggested that FERC adopt the rules of the Commodity Futures Trading Commission (CFTC) as a model to design a new, more flexible regulatory regime that encompasses all forms of potential manipulation in electricity markets.
A proposed framework for the analysis of electricity market manipulation appears in Ledgerwood and Carpenter (2012). The analytical outline breaks the process into several necessary components that would separate prohibited market manipulation from other possible interpretations of market actions. Central to this process would be an explicit model that characterizes the direction and magnitude of market responses, and links the alleged actions to the difference between observed prices and the counterfactual case. An immediate challenge is the lack of such a cross-product price manipulation model for the electricity market.
Absent control over real-time prices, how could a market participant affect day-ahead electricity prices over a sustained period using only financial positions from the forward market? "It is not possible for a market participant that has only a paper electricity position, and no generating assets, to distort prices merely by taking delivery on this position, because electricity must be consumed precisely when it is produced" (Pirrong, 2017). Something more is required than is found in the analysis of real-time market power withholding. Why are other participants unable to restore price convergence by profiting from arbitrage opportunities created by the alleged manipulation? Market monitoring and enforcement activities must distinguish between manipulative and efficient transactions. Yet, the theoretical foundations of day-ahead electricity price manipulation are neither obvious nor well developed. A theory (or theories) of day-ahead price manipulation would explain what market imperfections allow manipulation to be sustained over time, quantify its material effect on prices in a transparent way, and provide empirical implications that may be tested in the data to determine if actions were consistent with manipulation.
The focus is on equilibrium conditions that would support the alleged sustained manipulation. To illustrate, we construct an example of equilibrium manipulation under uncertainty and asymmetric information in the context of a modified Kumar and Seppi (1992) model for cash-settled financial transactions. The equilibrium manipulation strategy consists in incurring losses in one financial market in order to bias a market outcome and benefit related positions in another financial market. Empirical and welfare implications of the equilibrium are compared to those from three benchmark models to examine the impact of manipulation on market liquidity and performance, as well as its distributional effects. Finally, simulated equilibrium outcomes imposing futures position limits illustrate whether theoretical predictions are affected under more restrictive conditions that apply in electricity markets.
The remainder of the paper is organized as follows. Section 2 provides additional background and discusses why equilibrium models are useful and needed for the analysis of day-ahead price manipulation. Section 3 presents the modified Kumar and Seppi (1992) model of cross-product manipulation, adapted to the context of electric power markets. Section 4 derives empirical and welfare implications of the equilibrium and compares them to those from three benchmark equilibrium models. Section 5 describes our simulation results, while Section 6 offers concluding remarks and provides directions for future research. Analytical characterizations of equilibrium outcomes, empirical and welfare implications are presented in the Appendix.
(2.) POLICY CONTEXT
Organized electricity markets run by RTOs and ISOs are characterized by a two-settlement market structure with day-ahead and real-time coordinated auctions. In the presence of uncertainty, a sequential market structure helps improve the allocation of resources and risks (Anderson, 1984). The day-ahead market plays a pivotal role because it provides system operators with flexibility in planning the commitment of generation resources, as units may have ramping requirements and long lead times for starting up. Further, load-serving entities can manage risk by hedging their exposure to real-time prices through day-ahead purchases. In most organized electricity markets, about 95% of energy transactions are scheduled in the day-ahead market (FERC, 2015). As a result, its performance is critical to ensuring the efficient operation of electric systems, and closely monitored evaluating convergence between day-ahead and real-time prices.
In an efficient commodity market characterized by complete information, risk neutrality, no transaction costs and no market power, forward and spot contracts for delivery at the same time and location should transact at the same price, on average (Weber, 1983). Thus, day-ahead prices for delivery of power at a given hour and location should reflect participant price expectations for the following day, given the information available at the time bids were made in the day-ahead market. In general, locational day-ahead prices will be different from real-time prices on an hourly basis, due to factors like forced generation outages and load forecasting errors. However, day-ahead and expected real-time prices should not diverge systematically over long periods of time (i.e., monthly or annually). Empirical analyses of price differentials in organized electricity markets generally find evidence of a small positive forward premium, defined as the difference between average day-ahead and real-time prices (Pirrong and Jermakyan, 1999; Saravia, 2003; Longstaff and Wang, 2004; Borenstein et al., 2008; Hadsell, 2008; Bowden et al., 2009; Ito and Reguant, 2016).
When day-ahead prices are predictably higher or lower than expected real-time prices, arbitrage opportunities exist. Virtual transactions allow...
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