Evidence map›Paper›PMID 42608402›Full record

ReviewNature communications2026

Power distribution system blackstart restoration using renewable energy.

Wenlong Shi, Cong Bai, Zhaoyu Wang

Abstract readReview
In one paragraph

Review in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Wenlong ShiIowa State University, Department of Electrical and Computer Engineering, Ames, IA, USA.
Cong BaiIowa State University, Department of Electrical and Computer Engineering, Ames, IA, USA.
Zhaoyu WangIowa State University, Department of Electrical and Computer Engineering, Ames, IA, USA. wzy@iastate.edu.ORCID http://orcid.org/0000-0003-0656-0562

Funding

National Science Foundation (NSF) 2042314U.S. Department of Energy (DOE) DE-EE0011374U.S. Department of Energy (DOE) DE-OE0000986
6 · The paper itself

Abstract

Large-scale blackouts continue to expose the fragility of traditional top-down restoration, where end users remain de-energized until bulk generation and transmission are recovered. The rapid growth of inverter-based distributed energy resources (DERs) creates a credible alternative: bottom-up blackstart, in which grid-forming resources connected to the distribution system establish local voltage and frequency references, energize islands, and progressively extend and merge them after synchronization conditions are satisfied. However, feasibility at the grid edge is governed less by installed capacity than by deliverable inverter headroom under current and apparent power limits, low-inertia frequency dynamics, cold load pickup transients, protection admissibility, and imperfect situational awareness as topology changes. This Review synthesizes the operational foundations that define the bottom-up blackstart security envelope, and organizes the literature on multi-step, closed-loop decision workflows that co-optimize switching, stepwise load pickup, and DER dispatch decisions under renewable variability. We further survey uncertainty-aware formulations, information-adaptive rolling horizon and recourse perspectives, and learning-assisted surrogates that accelerate screening and combinatorial search while preserving physical feasibility guarantees. Finally, we connect standardization pathways to practical validation needs, and highlight open gaps in dynamic admissibility modeling, protection, observability, and real-time decision support required for reliable, scalable and autonomous bottom-up blackstart.

Identifiers

PMID42608402
PMCPMC13482465

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.