The Impact of Battery Storage on Power Flow and Economy in an Automated Transactive Energy Market
Bibliographic record
Abstract
This article explores the use of battery energy storage in a transactive energy approach for a heavily solar-penetrated community. We hypothesize that the efficient market interactions between independently acting, fully automated agents (some equipped with battery energy storage) can result in both bill savings and improvements in power flow, without explicitly optimizing for power flow ahead of time. A test setting of nine typical residential prosumers and one heavily loaded prosumer is used. The heavily loaded prosumer initially experiences multiple undervoltage violations, and a 13.5 kWh battery is installed to alleviate the problem. Two profile-shaping strategies are compared. The first scenario uses greedy control that maximizes self-sufficiency, while the second scenario uses a local market to enable energy trading between participants and a rule-based trading and management agent for control. The results show that the first scenario made minimal improvements to power flow, but the second scenario eliminated all occurrences of under-voltage violations. Furthermore, the total amount of energy from the grid is reduced by 24.3%, and the amount of energy injected into the grid is reduced by 39.2%. This results in lower bills for every participant and a bill reduction of 16.7% for the community as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".