Tackling the photovoltaic integration challenge in the distribution network with deferrable load
Bibliographic record
Abstract
In recent years, there is an increasing deployment of photovoltaic (PV) units and energy storage systems (ESSs) in households. When the energy generated by PV units is greater than the aggregate load of households and the capacity of ESS, there will be a reverse energy flow from households to the substation. When the reverse energy flow exceeds a certain threshold, it will cause a voltage rise problem, which is a challenge for PV units to be effectively integrated with the distribution network. In this paper, we propose an energy consumption scheduling algorithm, which shifts the deferrable load (e.g., washing machines, dryers) from peak hours (e.g., 7 pm - 10 pm) to high solar radiation hours (e.g., 10 am - 2 pm) in order to jointly shave the peak load and reduce the reverse energy flow. We formulate the energy consumption scheduling problem as a stochastic optimization problem to capture the uncertainty of the amount of PV output power. The objective of our algorithm is to minimize the electricity bill for the household users which have PV units and ESS installed. We use inclining block rate (IBR) pricing and time of use (TOU) pricing to encourage users to shift their load. Numerical results show that our proposed algorithm can avoid the voltage rise problem and reduce the peak-to-average ratio (PAR) in the aggregate load.
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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".