Online Adaptive Real-Time Optimal Dispatch of Privately Owned Energy Storage Systems Using Public-Domain Electricity Market Prices
Bibliographic record
Abstract
This paper aims to evaluate and improve the usefulness of publicly available electricity market prices for real-time optimal dispatching (RTOD) of a privately owned energy storage system (ESS) in a competitive electricity market. The RTOD algorithm seeks to maximize the revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day-ahead market. The pre-dispatch prices, issued by the Ontario independent electricity system operator, and the corresponding ex-post hourly Ontario energy prices are employed as the forecast and the actual prices. A compressed-air ESS is sized and employed for evaluations due to its lower capital expenditure and its ability to be positively influenced by the availability of waste heat. First, the conventional RTOD algorithm is developed by formulating a mixed integer linear programming problem. It is demonstrated that the forecast inaccuracy of publicly available market prices significantly reduces the ESS revenue. Then, a new adaptive algorithm is proposed and evaluated which adapts the objective function of the optimization problem online based on historical market prices available before real-time. The outcomes reveal that the proposed adaptive RTOD can significantly increase the ESS revenue compared to the conventional algorithm as well as the back-casting method proposed in prior studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".