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Record W1613154166 · doi:10.1109/pesgm.2015.7286405

Smart distribution system operations with price-responsive and controllable loads

2015· article· en· W1613154166 on OpenAlexaff
Isha Sharma, Kankar Bhattacharya, Claudio A. Cañizares

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControllabilityLoad managementScheduling (production processes)Demand responseComputer scienceMinificationLoad profileSmart gridLoad regulationMathematical optimizationEngineeringElectrical engineeringVoltageElectricityMathematics

Abstract

fetched live from OpenAlex

This paper presents a new modeling framework for analysis of impact and scheduling of price-responsive as well as controllable loads in a three-phase unbalanced distribution system. The price-responsive loads are assumed to be linearly or exponentially dependent on price, i.e., demand reduces as price increases and vice versa. The effect of such uncontrolled price-responsive loads on the distribution feeder is studied as customers seek to reduce their energy cost. Secondly, a novel constant energy load model, which is controllable by the local distribution company (LDC), is proposed in this paper. A controllable load is one that can be scheduled by the LDC through remote signals, demand response programs, or customer-end home energy management systems. Minimization of cost of energy drawn by LDC, feeder losses, and customers cost pertaining to the controllable component of the load are considered as objectives from the LDCs and customers' perspective. The effect of a peak demand constraint on the controllability of the load is further examined. The proposed models are tested on two feeders: 1) the IEEE 13-node test feeder; and 2) a practical LDC feeder system. Detailed studies examine the operational aspects of price-responsive and controllable loads on the overall system. It is observed that the LDC controlled load model results in a more uniform system load profile, and that with a reduction in the peak demand cap, the energy drawn decreases, consequently reducing feeder losses and LDC's and customers' costs.

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 score0.654
Threshold uncertainty score0.294

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 head0.007
GPT teacher head0.171
Teacher spread0.164 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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