Predictive energy management and control for renewable energy plus battery energy storage systems
Bibliographic record
Abstract
Renewable energy (RE) is one of the solutions to rising energy demands and growing environmental concerns. However, due to the intrinsic intermittency of RE resources, generated power is irregular and the supplied energy is intermittent. Intermittency renders RE systems non-dispatchable and can cause energy surplus and shortage. RE surplus can translate into curtailment and shortage can cause supply and demand issues. Curtailment wastes RE and supply and demand issues result in loss of load compromising service quality and system reliability. Battery energy storage system (BESS) is the widely accepted solution to mitigate the negative impacts of intermittency. However, this solution has relied on the conventional energy management and control (EMC) techniques that: 1) cause curtailment, 2) cause supply and demand issues, 3) cannot exploit BESS potential, 4) use RE passively (if and when available), and (5) are suitable only for readily dispatchable generation systems. This work proposes predictive EMC (PEMC) over conventional EMC (CEMC) to predictively perform EMC of RE systems (photovoltaic (PV) and wind) plus BESS (RE-BESS). PEMC predictively optimizes resources, makes control decisions and manages RE system operations based on the present and future (forecasted) load (or commitments) and RE potential over 24 hours horizon. PEMC 1) minimizes curtailment (maximize RE proportions), 2) minimizes supply and demand issues, 3) exploits BESS potential, 4) uses RE proactively (instead of operating on the mercy of weather), 5) compensates for forecast errors, and 6) maximizes savings (or revenue).
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".