A Data-Driven Framework for Quantifying Demand Response Participation Benefit of Industrial Consumers
Bibliographic record
Abstract
There is an increase in renewable energy sources connected to the electricity grid due to recent drives to achieve grid decarbonization milestones. However, such expansions cause grid balancing issues due to the renewable sources intermittency. Thus, grid operators introduced demand response(DR)schemes to mitigate this problem by controlling consumer load demands in exchange for incentives. Industries have enormous electricity demand making them ideal candidates for such programs. Nonetheless, non-intrusive demand load flexibility assessment for an industry's potential in DR programs remains a challenge. In this paper, a data-driven framework for quantifying the DR potential of an industrial consumer is proposed. The framework uses smart electricity meter data to identify operational patterns to derive a flexibility boundary that quantifies the flexibility in the industrial consumer's system. The framework also evaluates theDRparticipation scenario to quantify the net benefit of trading the identified flexibility. A Case-study has been carried out for two industrial consumers (i.e., an electronics factory and a poultry feed factory). Initial energy behavioral analysis indicates three different energy use patterns for the electronics factory and six energy use patterns for the poultry feed factory. Evaluating the operational flexibility boundary for the clusters, the framework found two feasible clusters with DR potentials for the electronics factory and three feasible clusters for the poultry feed factory. The cost-benefit analysis indicates a potential energy cost reduction in the region of$5\%-8\%$for passive participation and as much as$12\%-24\%$for active participation. The framework could be adopted to evaluate wide scale industrial consumers' flexibility potential.
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".