Introducing Energy hubs (EHs) is a beneficial strategy for incorporating quickly expanding renewable energies. However, the stochastic nature of renewable energy sources (RESs) and fluctuating energy demand have produced a number of difficulties, including unstable voltage/frequency, challenging energy management, and difficult network interaction. Additionally, the changing in response time of electrical and heat demands will make control challenging. This paper proposes a distributed control system for use with dynamic EHs. The RESs and loads present in the multi-carrier system cause the dynamics considering here. In order to optimise system performance, this research suggests a distributed model predictive control strategy that considers expected behaviour and operational restrictions. The strategy's potential is demonstrated via simulations in which the proposed scheme is applied to a benchmark system.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557847PMC
http://dx.doi.org/10.1038/s41598-024-78314-5DOI Listing

Publication Analysis

Top Keywords

distributed model
8
model predictive
8
predictive control
8
energy
5
coordinated distributed
4
control
4
control multi
4
multi energy
4
energy carrier
4
carrier systems
4

Similar Publications

Background: The integration of traditional Chinese medicine (TCM) into emergency health systems in China serves as a model for global policy development and refining the inclusion of traditional medicine in health emergencies.

Methods: This study investigated 13 public health emergency policies related to TCM released by the Chinese central government from 2003-2023. A PMC(Policy Modeling Consistency) index model was developed combining ROSTCM text mining analysis software.

View Article and Find Full Text PDF

Limited restoration of T cell subset distribution and immune function in older people living with HIV-1 receiving HAART.

Immun Ageing

January 2025

State Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Bioactive Peptides of Yunnan Province, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.

Background: Older people living with HIV-1 (PLWH) experience a dual burden from the combined effects of aging and HIV-1 infection, resulting in significant immune dysfunction. Despite receiving HAART, immune reconstitution is not fully optimized. The objective of this study was to investigate the impact of aging and HAART on T cell subsets and function in PLWH across different age groups, thereby providing novel insights into the prognosis of older PLWH.

View Article and Find Full Text PDF

Background: Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the generation of structured and actionable data, supporting complex tasks like cohort identification and the analysis of clinical records. To accomplish those tasks, we introduce a deep learning-based and lexicon-based named entity recognition (NER) tool for texts in Spanish.

View Article and Find Full Text PDF

Diagnostic value of the MZXBTCH scoring system for acute complex appendicitis.

Sci Rep

January 2025

Department of Gastrointestinal Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Changping District, Beijing, 102218, China.

The objective of this study was to develop a novel scoring model, assess its diagnostic value for complex appendicitis, and compare it with existing scoring systems. A total of 1,241 patients with acute appendicitis were included, comprising 868 patients in the modeling group (mean age, 35.6 ± 14.

View Article and Find Full Text PDF

The delivery of accurate diagnoses is crucial in healthcare and represents the gateway to appropriate and timely treatment. Although recent large language models (LLMs) have demonstrated impressive capabilities in few-shot or zero-shot learning, their effectiveness in clinical diagnosis remains unproven. Here we present MedFound, a generalist medical language model with 176 billion parameters, pre-trained on a large-scale corpus derived from diverse medical text and real-world clinical records.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!