Optimal Size and Multi-objective Control of Battery Energy Storages in Distribution System with High Penetration of Distributed PV Generators
Bibliographic record
Abstract
High penetration of PV into distribution system brings about side effects, such as local voltage rise because of light local loads and increase of power loss in the network. The application of Battery Energy Storages (BES) may be one of possible solutions to mitigate such side effects of PV systems. This paper presents a multi-objective optimal design and control method of BES used in distribution system with high penetration of distributed PV generators, such as smoothing the output power of PV generators, minimizing the loss in the network and peak shaving and valley filling by considering the capability of line connected with PVs, and an economic optimal model of BES sizing with maximum net income is proposed. Taking a real distribution system of 35kV with 60MW of PVs in total in China as an example, simulations are performed to test the proposed methods. The simulation results show that by the proposed optimal designed method and multi-objective control strategy has better economic performances than single objective control while the voltage of key nodes can be kept within the limits with minimized capacity of BES. The method proposed in this paper is beneficial to promote the high penetration of PV generators into power system.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".