A Competitive Techno-Economic Framework for Distribution Network Expansion Under Deep Electrification Considering Flexible Resources
Bibliographic record
Abstract
Deep electrification generates unprecedented power demand growth, profoundly affecting distribution networks. Existing models typically overlook the competitive integration of flexible resources in favor of short-term timeframes, incremental demand increases, and conventional infrastructure investments. To address this gap, this paper proposes a long-term, competitive techno-economic model that optimizes investments. It leverages competitive market mechanisms, allowing local generation, energy storage systems (ESS), and network reinforcements to compete directly. Two case studies-a simplified 4-bus network and a modified IEEE 123-node system-uniquely capture and show cost savings for scenarios up to $8 \%$ by optimally selecting ESS and shunt capacitors over more expensive renewable energy sources or conventional system reinforcements. The model demonstrates the potential benefits of deploying flexible resources, particularly ESS, to manage deep electrification and support net-zero targets. This planning method gives distribution system operators (DSOs) a useful tool for practical decision-making to economically reach targets of deep electrification and net-zero goals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".