Balancing Efficiency and Longevity in Community Energy Storage Systems Using Predictive Scheduling
Bibliographic record
Abstract
Community energy storage systems must balance equitable energy sharing among prosumers with long-term battery health. While forecast-driven allocation strategies improve fairness and operational efficiency of such systems, their impact on battery degradation remains underexplored. This study integrates supply-demand forecasting with a comprehensive battery aging model to examine the trade-offs between system performance and asset longevity in community storage applications. An extended power-law degradation model is used to capture the combined effects of state-of-charge variability, C-rate fluctuations, and thermal conditions on capacity fade mechanisms. To address these dynamics, a multi-objective optimization framework with special ordered sets linearization is proposed, balancing degradation minimization with renewable self-consumption maximization under adaptive power constraints. Validation using a 10-year dataset of five residential prosumers sharing a 20 kWh system shows that forecast-driven control enhances utilization while reducing capacity retention from 93.88% to 85.45% due to intensified cycling. The proposed degradation-aware optimization mitigates this penalty, retaining 91.01% capacity—representing a 6.5% improvement over the base forecast approach—while preserving efficiency gains. Results highlight that intelligent state-of-charge management with adaptive power limiting can reduce stress-induced aging while maintaining predictive scheduling advantages, particularly during periods of renewable energy surpluses when aggressive charging strategies become acceptable from a degradation perspective. The proposed framework demonstrates that sustainability and equity in community energy systems need not be mutually exclusive objectives.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".