Optimal scheduling of energy storage to mitigate power quality issues in power systems
Bibliographic record
Abstract
In this paper, a new optimal scheduling algorithm is proposed to enhance the economic feasibility of integrating energy storage systems (ESSs) in power grids. In addition to the profit gained by ESS owners from arbitrage, ESSs can contribute to mitigate power quality (PQ) issues in power systems. Toward that end, using a mixed-integer linear programming optimization problem, an adaptive reserve margin is created in the ESS reservoir; this enables an ESS to accept and follow the PQ external signals received from the grid operator. A new index is proposed to measure the ESS contribution to mitigation of PQ issues. Due to its contribution to mitigate PQ issues, the ESS owner is financially compensated by the grid operator. This financial benefit can be added to the regular profit resulted from exploiting arbitrage, thereby increasing the profitability of investment in ESSs. Numerical studies are conducted using actual data adopted from Ontario's electricity market.
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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".