Using Virtual Bids to Manipulate the Value of Financial Transmission Rights

Ledgerwood, Shaun D.; Pfeifenberger, Johannes

doi:10.1016/j.tej.2013.09.014

Cited by 38 publications

(9 citation statements)

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“…The impact of convergence bidding on the efficiency of the California ISO market and impact on price convergence is studied in [19] and also in [24]. There are also a few papers in the literature that are concerned with the potential to manipulate the wholesale electricity market by using CBs [30]- [36]. In [31], an equilibrium model is developed to study the cross-product manipulation in financial transmission right and two-settlement energy markets.…”

Section: Literature Reviewmentioning

confidence: 99%

A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants' Performance: A Case of California ISO

Samani,

Kohansal,

Mohsenian-Rad

2021

Preprint

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Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world electricity markets. We address this open problem with focus on the electricity market that is operated by the California Independent System Operator (ISO). In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world California ISO market data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common realworld strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the advantages and disadvantages of the existing real-world strategies in order to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy selection algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used.

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Section: Literature Reviewmentioning

confidence: 99%

A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants' Performance: A Case of California ISO

Samani,

Kohansal,

Mohsenian-Rad

2021

Preprint

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“…Constraint (9) provides the credit constraints for the UTC bidding capacities, which guarantees that the total positive risk exposure of all the time periods and paths does not exceed the total credit available of the financial participant for any scenario w. The , , and , , in constraint (9) are the parameters related to the credit constraints that are calculated by using (5) and (6), respectively. Constraint (10) provides the lower and upper bounds of the UTC bidding capacity on each path, which are specified to avoid the potential unbounded solutions when solving the optimization problem.…”

Section: Stochastic Optimization Model For Generating Utc Bidding Curvesmentioning

confidence: 99%

“…However, the authors of [8] found out that if a virtual bidder cannot forecast market outcomes accurately, its virtual transactions might decrease the total social welfare of the electricity market. In [9], it was addressed that virtual transactions can be used by FTR holders to manipulate the electricity prices and increase the value of their FTRs. The authors of [10] established an equilibrium model to investigate the characteristics of the FTR holders with virtual transactions.…”

Section: Introductionmentioning

confidence: 99%

“…In [14] and [15], stochastic virtual transaction models were developed for a financial participant and a solar power producer, respectively, where the electricity prices were characterized using autoregressive integrated moving average (ARIMA) models. In [16], the authors proposed a data-driven [4], [6], [7], [9], [11] and [12] INC offer and DEC bid Deterministic [5], [8], [10], and [13]- [16] INC offer and DEC bid Stochastic [2] and [3] INC offer, DEC bid and UTC transaction Deterministic virtual bidding strategy by using an online learning algorithm, where the Sharpe ratio was used to measure its performance. The literature on virtual bidding were summarized in Table I, and it is shown most of the existing research work just focus on the INC offers and DECs used at the nodes, while the UTC transactions used on the paths have not been studied in detail.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Up to Congestion Bidding Strategy in the Nodal Electricity Markets Considering Risk Management

Xiao¹,

Chen²

2020

IEEE Access

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Up to congestion (UTC) is a type of financial product available in the nodal electricity markets of the United States, based on which a financial participant can earn profits by utilizing the different congestion and loss components of the electricity prices in the day-ahead (DA) and real-time (RT) markets. This paper proposes the UTC bidding strategy by using stochastic optimization technique, where the uncertain electricity prices on the UTC transaction paths are represented via scenario sets. In the established stochastic model, the total expected profit and the Conditional Value at Risk (CVaR) of the UTC bidding strategy are maximized simultaneously considering risk management, where the risk preference of financial participant is characterized by using a risk aversion parameter. By solving the proposed stochastic model, non-increasing DA UTC bidding curves can be generated for all the time periods of the next operating day, where the credit requirements for UTC transactions are taken into account in detail. Finally, to verify the effectiveness of the proposed strategy, case studies are carried out based on the historical data and trading policies of the Pennsylvania-New Jersey-Maryland (PJM) electricity market, and the UTC bidding strategies generated by different models are analyzed. The numeral results indicate that, compared to the deterministic UTC bidding strategy, the proposed stochastic strategy can bring much higher expected profit and lower potential risks for the financial participant. Moreover, by adjusting the risk aversion parameter in the proposed model, the risks can be managed efficiently according to the financial participant's preference. INDEX TERMS Electricity market, financial participant, risk management, stochastic optimization, up to congestion I. NOTATIONS A. INDEXES AND SETS B. VARIABLES , , UTC bidding capacity on path d in time period t for scenario w. Auxiliary variable used to compute the CVaR. Auxiliary variable used to compute the Conditional Value at Risk (CVaR) for scenario w. , , Profit of the UTC bid used on path d in time period t for scenario w. , , Risk exposure of the UTC bid used on path d in time period t for scenario w.

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“…CTS performance may be influenced by potential uneconomic bidding that aims to benefit financial positions whose value is tied to CTS outcomes, such as FTRs. Price manipulation that involves uneconomic virtual transactions has emerged as a central policy concern for FERC, as shown by several high-profile enforcement cases that ended in multi-million dollar settlements (see Ledgerwood and Pfeifenberger (2013)). Here, we investigate the CTS performance when any subgroup of market participants hold FTR positions.…”

Section: Interactions With Financial Transmission Rights (Ftrs)mentioning

confidence: 99%

Coordinated Transaction Scheduling in Multi-Area Electricity Markets: Equilibrium and Learning

Ndrio¹,

Bose²,

Tong³

et al. 2020

Preprint

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Tie-line scheduling in multi-area power systems in the US largely proceeds through a market-based mechanism called Coordinated Transaction Scheduling (CTS). We analyze this market mechanism through a game-theoretic lens. Our analysis characterizes the effect of market liquidity, market participants' forecasts about inter-area price spreads, transactions fees and interaction of CTS markets with financial transmission rights. Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from equilibrium analysis.

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Using Virtual Bids to Manipulate the Value of Financial Transmission Rights

Cited by 38 publications

References 0 publications

A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants' Performance: A Case of California ISO

A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants' Performance: A Case of California ISO

Stochastic Up to Congestion Bidding Strategy in the Nodal Electricity Markets Considering Risk Management

Coordinated Transaction Scheduling in Multi-Area Electricity Markets: Equilibrium and Learning

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