Optimal real-time scheduling of battery operation using reinforcement learning
Bibliographic record
Abstract
Adoption of battery energy storage systems working with solar photovoltaic distributed systems for residential household applications strongly depends on their return on investment. Battery energy storage system (BESS) technology costs have been strongly decreasing during the last decade. However, such a tendency has to be supported by optimal BESS real-time operation strategies that adapt to the stochastic operation conditions (residential load, solar generation, and electricity prices) and minimize the customer's electric bill. This work presents a real-time adaptive BESS controller that implements a load-shifting strategy under time-of-use and feed-in-tariff (microFIT) regulatory incentives. The optimization of the battery operating strategy is carried out by a Q-learning algorithm and later encoded in a neural network that implements the optimal strategy at a fraction of the computation cost. Real residential demand and solar generation profiles during the summer and winter seasons in Edmonton, Canada, are utilized to train and test the controller. Two battery technologies, lithium-ion and vanadium redox flow, are simulated; real charge-discharge experimental data from an installed system was used. The proposed adaptive controller outperforms the optimal strategy, both during the summer and winter testing periods.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".