Virtual Bidding and Financial Transmission Rights: An Equilibrium Model for Cross-Product Manipulation in Electricity Markets

Prete, Chiara Lo; Guo, Nongchao; Shanbhag, Uday V.

doi:10.1109/tpwrs.2018.2875624

Cited by 38 publications

(8 citation statements)

References 31 publications

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“…However, a CB is not a physical bid; it is purely financial. A supply (demand) 1 CB is a bid to sell (buy) energy in DAM and buy (sell) the same amount of energy in RTM without any obligation to produce (consume) energy [4], [17]- [21]. If a supply (demand) CB is cleared in DAM, then the bidder is credited (charged) at the DAM price and charged (credited) at the RTM price.…”

Section: Overview Of the Real-world California Iso Market Data A Back...mentioning

confidence: 99%

A Data-Driven Study to Discover, Characterize, and Classify Convergence Bidding Strategies in California ISO Energy Market

Samani

Mohsenian‐Rad

2021

2021 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Convergence bidding has been adopted in recent years by most Independent System Operators (ISOs) in the United States as a relatively new market mechanism to enhance market efficiency. Convergence bidding affects many aspects of the operation of the electricity markets and there is currently a gap in the literature on understanding how the market participants strategically select their convergence bids in practice.To address this open problem, in this paper, we study three years of real-world market data from the California ISO energy market. First, we provide a data-driven overview of all submitted convergence bids (CBs) and analyze the performance of each individual convergence bidder based on the number of their submitted CBs, the number of locations that they placed the CBs, the percentage of submitted supply or demand CBs, the amount of cleared CBs, and their gained profit or loss. Next, we scrutinize the bidding strategies of the 13 largest market players that account for 75% of all CBs in the California ISO market. We identify quantitative features to characterize and distinguish their different convergence bidding strategies. This analysis results in revealing three different classes of CB strategies that are used in practice. We identify the differences between these strategic bidding classes and compare their advantages and disadvantages. We also explain how some of the most active market participants are using bidding strategies that do not match any of the strategic bidding methods that currently exist in the literature.

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Section: Overview Of the Real-world California Iso Market Data A Back...mentioning

confidence: 99%

A Data-Driven Study to Discover, Characterize, and Classify Convergence Bidding Strategies in California ISO Energy Market

Samani

Mohsenian‐Rad

2021

2021 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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“…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. The concept of cyber attacks in wholesale electricity markets with virtual bidding activities is analyzed in [34].…”

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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“…In theory, the introduction of virtual bidding increases market efficiency and reduces price spreads between DA LMP and RT LMP [7], [12]- [14]. However, market manipulation [15], [16] and inappropriate market designs such as modeling discrepancies [12] and virtual bidding on the interties [17] can lead to inefficient market solutions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis

Li¹,

Yu²,

Wang³

2021

Preprint

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This paper develops a machine learning-driven portfolio optimization framework for virtual bidding in electricity markets considering both risk constraint and price sensitivity. The algorithmic trading strategy is developed from the perspective of a proprietary trading firm to maximize profit. A recurrent neural network-based Locational Marginal Price (LMP) spread forecast model is developed by leveraging the interhour dependencies of the market clearing algorithm. The LMP spread sensitivity with respect to net virtual bids is modeled as a monotonic function with the proposed constrained gradient boosting tree. We leverage the proposed algorithmic virtual bid trading strategy to evaluate both the profitability of the virtual bid portfolio and the efficiency of U.S. wholesale electricity markets. The comprehensive empirical analysis on PJM, ISO-NE, and CAISO indicates that the proposed virtual bid portfolio optimization strategy considering the price sensitivity explicitly outperforms the one that neglects the price sensitivity. The Sharpe ratio of virtual bid portfolios for all three electricity markets are much higher than that of the S&P 500 index. It was also shown that the efficiency of CAISO's two-settlement system is lower than that of PJM and ISO-NE.

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Virtual Bidding and Financial Transmission Rights: An Equilibrium Model for Cross-Product Manipulation in Electricity Markets

Cited by 38 publications

References 31 publications

A Data-Driven Study to Discover, Characterize, and Classify Convergence Bidding Strategies in California ISO Energy Market

A Data-Driven Study to Discover, Characterize, and Classify Convergence Bidding Strategies in California ISO Energy Market

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

Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis

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