Market-Oriented Energy Management of a Hybrid Wind-Battery Energy Storage System Via Model Predictive Control With Constraint Optimizer
Bibliographic record
Abstract
This paper presents a market-oriented energy management system (EMS) for a hybrid power system composed of a wind energy conversion system and a battery energy storage system (BESS). The EMS is designed as a real-time model predictive control (MPC) system. The EMS dispatches the BESS to achieve the maximum net profit from the deregulated electricity market. Furthermore, the EMS aims at expanding the BESS lifetime by applying typical and practical constraints in the MPC problem on both the daily number of cycles (DNC) and depth of discharge (DOD). The MPC constraint optimizer is designed to tune the lifetime constraints optimally. It guarantees the optimal economic profit by finding the optimal DNC and DOD to achieve the maximum market revenue from energy arbitrage with the minimal expended-life cost. The effectiveness of this work is verified by comparison with a conventional MPC used in previous works. Simulation is conducted using real wind power and market data in Alberta, Canada.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".