Evidence map›Paper›PMID 41188506›Full record

ArticleScientific reports2025

Grid aware electrification for decarbonising port logistics based on a case study from Sweden.

Sankar Mangalath Ramasan, Jagruti Thakur, Sivapriya M Bhagavathy, Björn Laumert

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

4 authors.

Sankar Mangalath RamasanDepartment of Energy Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Jagruti ThakurDepartment of Energy Technology, KTH Royal Institute of Technology, Stockholm, Sweden. jrthakur@kth.se.
Sivapriya M BhagavathyPNDC, University of Strathclyde, Glasgow, UK.
Björn LaumertDepartment of Energy Technology, KTH Royal Institute of Technology, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrification is emerging as a key strategy for decarbonisation of shore-side energy demand at ports. However, this electrification, particularly involving electric shore-side vehicles (ESVs), has a significant impact on the local electricity grid. A key research gap pertains to the specific challenges of ESV load integration into the grid and the effectiveness of mitigation strategies like smart charging and renewable energy integration at the operational level within ports. This study directly addresses this gap through several key contributions: firstly, by quantifying the impact of ESV loads on a localised port electricity grid; secondly, by introducing and evaluating smart charging strategies coupled with solar photovoltaic (PV) integration; and thirdly, by providing practical insights derived from a real-world case study at the port of Oskarshamn. Key findings include: (i) an impact analysis demonstrating that unmanaged ('dumb') ESV charging imposes the highest stress on the local grid, necessitating costly immediate upgrades; (ii) the demonstration that optimized charging significantly reduces grid stress, effectively deferring the need for substantial infrastructure investment; and (iii) the confirmation that solar PV integration further aids in managing peak loads and enhancing overall grid stability and energy independence. These results underscore the efficacy of smart charging and renewable integration in managing ESV loads and improving grid resilience. Furthermore, the study highlights potential pathways for future energy efficiency enhancements and even the possibility of energy export within port systems.

Identifiers

PMID41188506
PMCPMC12586423

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Registered trials

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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.