Optimal Asset Expansion in Distribution Networks Considering Battery Nonlinear Characteristics Expansion optimale des actifs dans les réseaux de distribution en tenant compte des caractéristiques non linéaires des batteries
Bibliographic record
Abstract
Asset expansion planning in distribution systems is vital and should be extended to consider utility-scale energy storage systems such as batteries. Unlike other assets, usage parameters such as number of storage cycles and depth of discharge (DOD) have a dramatic nonlinear effect on the lifetime of battery energy storage systems (BESSs). Hence, it is imperative to include the relationship between lifetime, number of storage cycles, and DOD of BESS in the optimal asset planning formulation. This paper presents a new formulation and solution for the optimization problem of asset expansion planning in power distribution systems. The research considers adding new BESS units to existing distribution grids. The nonlinear life-cycling-usage relationship of BESS has been modeled for the first time in the context of asset expansion planning in power systems. The formulation aims at minimizing the annualized cost of the expansion plan while satisfying forecasted demand and other distribution system requirements. The methodology is used to optimally plan for the expansion of 6-bus and 33-bus distribution networks. The results show the effect of considering the life-cycling-usage relationship of BESS on optimal asset expansion plans including the optimal size and capacity of the assets. In addition, the impact of the ratio of off-peak load to peak load on total asset cost is analyzed and reported. It is shown that an annual cost saving of 51.79% is possible via the proposed approach. Findings of this paper will capture the attention of planning and asset management departments of electric distribution utilities.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".