Coupling of weather forecasts and smart grid-control of wastewater inlet to Kolding WWTP (Denmark)
Bibliographic record
Abstract
The increasing focus on renewable energy sources has caused many countries to initiate a shift to a more intelligent and flexible electricity system – the Smart Grid. This allows for the optimization of the electricity consumption according to the fluctuation in electricity prices. In this study four strategies for controlling the wastewater flow to Kolding Central wastewater treatment plant (WWTP) based on the Smart Grid concept are investigated. The control strategies use the storage volume in the pipe system upstream the WWTP to detain water during hours with high electricity prices, releasing the water when the price decreases. A lumped conceptual model was constructed based an existing highly detailed hydrodynamic model of the catchment. The conceptual model was used to assess the performance of the four control strategies, which were evaluated based on savings in operation cost and emitted CO2 equivalents. Weather forecasts were used to empty out the system prior to a rain event, ensuring that the control strategies did not lead to increases in combined sewer overflow. The largest savings obtained were 833 EUR/month and 3909 kg CO2 equivalents/month, which were achieved by only sending wastewater to the treatment plant during the six cheapest hours of the day. The savings achieved with the other control strategies were however in the ranges 65–300 EUR/month and 196–910 kg CO2 equivalents/month. These evaluations were generally done with limited storage space of just around 20 % of the daily wastewater flow and relatively simplistic control schemes. Larger savings would be anticipated with more complex control schemes utilizing larger storage volumes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".