Energy storage planning in distribution systems considering life-cycling-usage relationship
Bibliographic record
Abstract
Asset expansion planning in Distribution System is important and should be expanded to consider utility scale energy storage systems such as batteries, flywheels, compressed air, thermal, etc. Battery Energy Storage Systems (BESS) are maturing for utility scale applications and are considered in this thesis for asset planning exercise. Unlike other electromechanical assets such as generators, transformers, motors, feeders, distribution lines, etc., usage parameters such as number of storage cycles and depth of discharge have a dramatic nonlinear effect on the life of Battery Energy Storage Systems. Hence, in the optimal asset planning formulation of electric power distribution systems considering BESS, it is imperative to include their relationship between life in years, number of storage cycles and extent of usage in terms of depth of discharge. A new optimal asset expansion planning formulation and algorithm for distribution systems is developed and presented in this thesis that considers (1) new sources of energy supply, and (2) BESS, while modeling nonlinear relationship life-cycling-usage of BESS. The formulation aims to minimize annualized cost of the optimal expansion plan while satisfying forecasted demand and other distribution system service requirements. The proposed method in this thesis is then used to develop optimal expansion plans for a 6-bus synthetic system and an IEEE 33-bus distribution network. The results show the effect of considering the life-cycling-usage relationship of BESS on optimal asset expansion plans. Further, using sensitivity analysis, the effect of ratio off-peak load to peak load on total asset costs are analyzed and reported.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".