Multi-Timescale Stochastic Electrical and Thermal Energy Management for Sustainable Communities with Wastewater Treatment Plants
Bibliographic record
Abstract
Renewable energy sources and energy storage systems have been considered promising solutions to improve the sustainability of the current society, where energy management is essential for ensuring the efficiency and reliability of energy systems. This paper investigates stochastic energy management of sustainable communities connected to smart distribution systems. The proposed sustainable community incorporates multiple renewable energy sources (RES), various energy storage devices, an innovative wastewater treatment plant, and a neighboring smart building. Considering the randomness of the electric load, wastewater flow, RES, and weather conditions, this optimal energy management problem is formulated based on the multi-timescale Markov decision process, where the objective is to minimize the total operating cost of the community while mitigating the impacts on the smart distribution system. The proposed energy management scheme is evaluated based on the IEEE 33-Bus Test Feeder, as well as real data of weather conditions, modeling for wastewater generation, photovoltaic (PV) and hydropower generation.
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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 | 0.001 |
| 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".