This paper, for the first time, investigates the operation and impact of convergence bids (CBs) during blackouts . First, the amount of load shedding in the real-time market (RTM) is modeled as a function of the amount of the cleared CBs in the day-ahead market (DAM). The sign of the slope of this function is proposed as a metric to determine if a CB exacerbates or heals the power outages. Next, a series of mathematical theorems are developed to characterize this new metric under different network conditions. It is proved that, when there is no congestion in the DAM, the metric is always greater than or equal to zero. When there is congestion in the DAM, the metric can be positive or negative. Using numerical case studies, we show that, this metric in fact most often is positive. Therefore, supply CBs almost always hurt the system during blackouts while demand CBs almost always help the system. Furthermore, the impact of load shedding on the profit of CBs is also analyzed. It is shown that, load shedding usually creates advantage for supply CBs and disadvantage for demand CBs in their profit. The implications of these results are discussed. We also analyze the real-world market data from the California Independent System Operator (ISO) during the blackouts in August 2020. It is shown that, the decision by the California ISO to suspend CBs during this event matches the mathematical and numerical results in this paper.
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A Data-Driven Study to Discover, Characterize, and Classify Convergence Bidding Strategies in California ISO Energy Market
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 and 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 any of the strategic bidding methods that currently exist in the literature.
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- Award ID(s):
- 1711944
- NSF-PAR ID:
- 10283179
- Date Published:
- Journal Name:
- IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISGT49243.2021.9372191
