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Market Participation of Energy Storage Systems for Frequency Regulation Service: A Bi-level Model

2019· article· en· W2991122571 on OpenAlexaff
Yanan Sun, Vincent W. S. Wong, Lutz Lampe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKarush–Kuhn–Tucker conditionsBiddingMathematical optimizationOperator (biology)RevenueMarket clearingComputer scienceElectric power systemStrong dualityService (business)Linear programmingOptimization problemOperations researchEconomicsEngineeringPower (physics)MicroeconomicsMathematicsFinance

Abstract

fetched live from OpenAlex

This paper examines the prospect of using the energy storage systems (ESSs) in the distribution network for frequency regulation service under the two-settlement market mechanism. A bi-level problem is formulated to determine the bidding strategy for the ESS which provides regulation service for the system operator in the day-ahead and real-time markets, where the upper-level problem maximizes the ESS' revenue from frequency regulation and the lower-level problem models the system operator's market clearing. The problem is rendered applicable for the ESSs in the distribution network by addressing the power flow constraints. The uncertainty associated with other competitive ESSs and the system frequency deviations are incorporated by using scenarios for possible realizations. The formulated problem is transformed to a mixed-integer linear program by replacing the lower-level problem with the Karush-Kuhn-Tucker (KKT) optimality conditions and tackling the nonconvexity in the objective function based on strong duality. Case studies are carried out on an IEEE 37-bus test feeder by using market data from California Independent System Operator (CAISO). The results demonstrate that the ESS can increase its revenue from frequency regulation by using our proposed method to determine the bidding strategy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.204
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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