ArticleScientific reports2025
Dynamic modeling and adaptive variable constraint optimization for integrated energy systems based on double-loop framework.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Integrated energy systems (IESs) provide support for improving energy utilization and have become an inevitable choice for the energy revolution. However, because the dynamic characteristics of different energy conversion devices vary considerably, the system operation exhibits source-load energy deviation, which affects the optimal performance of IESs. This paper proposes a dynamic optimization dispatch strategy based on double-loop optimization framework for IESs. Firstly, a nonlinear difference equation model is developed to characterize dynamic response capacity of different devices, and the optimization dispatch model is established. Next, a double-loop optimization framework is proposed to separate the devices with different dynamic response capacity and time scales. Then, the nonlinear difference equation is transformed into an algebraic equation by power time-domain integration, and an adaptive variable constraint particle swarm optimization (AVCPSO) algorithm is proposed to solve the dispatch plan, which achieves constraints updating by continuous feedback of the device state. Finally, an IES case study is conducted to perform optimization analysis, the simulation results show that the optimized strategy can reduce the daily operation cost by 6.25% and carbon emission by 2.90%, increase the source-load matching by 3.06%, and reduce total power consumption by 29.36% and peak-valley load difference by 19.97% to power grid.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.