Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2012.00076 v1 pith:6Q7HDE5I submitted 2020-11-30 eess.SP cs.LG

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

classification eess.SP cs.LG
keywords marketbiddingconvergencestrategiescaliforniasubmittedbidscharacterize
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. STAR: Astrocyte-Inspired State-Augmented Repair for Supervised Memristive AI Hardware Systems

    cs.ET 2026-07 conditional novelty 5.0

    STAR restores accuracy of memristive neural networks after stuck-at faults by adding a repair nudge during equilibrium-propagation retraining that pulls layer states toward pre-fault class-conditional activation targets.

  2. Quantum error correction and biological error correction: A structural analogy between qubits and neurons

    physics.bio-ph 2026-07 conditional novelty 5.0

    A minimal three-neuron population model shares the generator structure and steady-state constraint-violation scaling of the three-qubit repetition code, supporting a structural QEC–neural analogy.