Evidence map›Paper›PMID 40592958›Full record

ArticleScientific reports2025

Multi-objective stochastic model optimal operation of smart microgrids coalition with penetration renewable energy resources with demand responses.

Ali Abdolahzadeh, Amir Hassannia, Farhoud Mousavizadeh, Mohammad Tolou Askari

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Microgrids as a Tool for Energy Self-Sufficiency.Sensors (Basel, Switzerland) · 2025
    Review
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.

Ali AbdolahzadehDepartment of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran.
Amir HassanniaFaculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran. amir.hassannia@shahroodut.ac.ir.
Farhoud MousavizadehDepartment of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran.
Mohammad Tolou AskariDepartment of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid transformation of energy systems necessitates innovative approaches to ensure cost-effective, reliable, and environmentally sustainable operation. This paper presents a novel multi-objective stochastic optimization model for the optimal operation of a coalition of interconnected smart microgrids, integrating renewable energy resources, demand response mechanisms, and electric vehicles (EVs) under uncertainty. The proposed framework simultaneously minimizes operational costs and environmental emissions while enhancing energy balance through demand-side management, dynamic energy trading, and EV-based vehicle-to-grid (V2G) services. A key innovation of this study is the development of an advanced hybrid solution methodology, combining the ε-constraint method for multi-objective optimization with Benders decomposition for computational efficiency. This approach enables the scalable solution of large-scale, scenario-based optimization problems while maintaining accuracy. The proposed model accounts for uncertain renewable generation and fluctuating energy demand, leveraging real-time flexibility through demand-side adjustments and bidirectional EV charging. Extensive case studies on a coalition of five interconnected microgrids highlight the advantages of coordinated energy management. Results demonstrate that cooperation among microgrids yields significant benefits compared to independent operation, including up to 22.7% reduction in total operational costs, 75% utilization of renewable energy resources, and a 31.1% decrease in carbon emissions. The integration of EV-based storage and demand response strategies further enhances system resilience and economic performance by dynamically adjusting energy consumption patterns and mitigating the intermittency of renewables. The findings underscore the potential of smart microgrid coalitions in reducing dependency on fossil fuels, improving grid stability, and creating economically viable, sustainable energy ecosystems. By incorporating a novel hybrid optimization technique and addressing the computational challenges of large-scale energy systems, this research provides a practical and scalable framework for future smart grid development, supporting a cleaner and more efficient energy transition.

Indexed as

Benders decompositionDemand responseElectric vehiclesMulti-objective optimizationRenewable energy integrationSmart microgridsStochastic optimization

Identifiers

PMID40592958
PMCPMC12218328

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LicenceCC BY-NC-ND
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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.