Decentralized Optimal Home Energy Management in Active Distribution Networks
Bibliographic record
Abstract
Modern power systems are faced with rising loads and uncertain power flows owing to increased penetration of renewable energy sources (RES), introduction of time-of-use pricing and dynamic user-priorities. As a result, utilities witness fluctuating load profiles with higher peak and average loads. This adversely affects distribution network assets such as transformers, leading to lifetime deterioration and early replacement requirements before end of their rated operating life. Decentralized control of loads, RES and energy storage at user-end has the potential to effectively mitigate uncertainties arising in residential buildings. Accordingly, this paper aims to develop an optimal home energy management systems (HEMS) to coordinate optimal dispatch of multiple resources, through effective handling of real-time fluctuations in RES output and load, thus, ensuring deterministic load profiles at the distribution transformers. The proposed HEMS has been developed as a mixed integer non-linear programming (MINLP) model which minimizes residential consumption of grid electricity while maximizing user-preferences through optimal dispatch and priority-based disaggregated load control. A mathematical formulation for appliance priority index has been developed to facilitate comfort-constraint HEMS. For quantification of impact-evaluation of proposed scheme on user satisfaction and grid-reliability, two performance indices have been developed. Results demonstrate effectiveness of proposed HEMS in mitigating uncertainties, while managing asset utilization to improve operational life and minimize losses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".