Optimal Ensemble Control of Loads in Distribution Grids with Network\n Constraints
Bibliographic record
Abstract
Flexible loads, e.g. thermostatically controlled loads (TCLs), are\ntechnically feasible to participate in demand response (DR) programs. On the\nother hand, there is a number of challenges that need to be resolved before it\ncan be implemented in practice en masse. First, individual TCLs must be\naggregated and operated in sync to scale DR benefits. Second, the uncertainty\nof TCLs needs to be accounted for. Third, exercising the flexibility of TCLs\nneeds to be coordinated with distribution system operations to avoid\nunnecessary power losses and compliance with power flow and voltage limits.\nThis paper addresses these challenges. We propose a network-constrained,\nopen-loop, stochastic optimal control formulation. The first part of this\nformulation represents ensembles of collocated TCLs modelled by an aggregated\nMarkov Process (MP), where each MP state is associated with a given power\nconsumption or production level. The second part extends MPs to a multi-period\ndistribution power flow optimization. In this optimization, the control of TCL\nensembles is regulated by transition probability matrices and physically\nenabled by local active and reactive power controls at TCL locations. The\noptimization is solved with a Spatio-Temporal Dual Decomposition (ST-D2)\nalgorithm. The performance of the proposed formulation and algorithm is\ndemonstrated on the IEEE 33-bus distribution model.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".