Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Challenges and Research Trends of Integrated Zero-Carbon Power Plant
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Challenges and Research Trends of Integrated Zero-Carbon Power Plant

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 357

Special Issue Editors

Dr. Sijie Chen

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: power market; power system optimization; energy blockchain
Dr. Ran Li

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: power system forecasting and decision-making; power economics; transport electrification

Special Issue Information

Dear Colleagues,

The global transition to sustainable energy systems has intensified the demand for integrated zero-carbon power plants, which aim to provide reliable energy while minimizing their environmental impact. This Special Issue, titled "Challenges and Research Trends of Integrated Zero-Carbon Power Plant", invites contributions that explore the technological, economic, and operational aspects of developing and implementing such power plants and supply systems. The focus is on research that addresses the integration of renewable energy sources, energy storage systems, smart grid, and demand-side technologies to achieve efficient and reliable zero-carbon energy production and distribution.

This Special Issue aims to serve as a comprehensive resource for academics, engineers, and policymakers working towards the development of integrated zero-carbon power systems. It will provide insights into current challenges, emerging trends, and innovative solutions that can accelerate the global shift towards sustainable energy production.

Scope and Themes

  1. Renewable Energy Integration: Advanced methods for harmonizing intermittent renewables (e.g., solar, wind) using grid stability, including hybrid energy storage systems and demand-side management.
  2. Carbon Capture and Utilization (CCUS): Novel materials, processes, and system-level strategies for integrating CCUS into power plants.
  3. Smart Grid and Digitalization: Advanced smart grid technologies, such as AI-driven optimization, IoT-enabled monitoring, and blockchain applications, for energy trading and transparency.
  4. Industrial Energy Efficiency and Demand Responses: Innovations in industrial and commercial load management and demand responses to reduce carbon footprints, especially in energy-intensive sectors.
  5. AI-Driven Innovations in Energy Systems: The application of artificial intelligence (AI) for predictive maintenance, energy forecasting, load disaggregation, and system optimization. Techniques such as deep learning, reinforcement learning, and LLM are of particular interest.

Dr. Sijie Chen
Dr. Ran Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • renewable energy integration
  • carbon capture and utilization (CCUS)
  • smart grid and digitalization
  • industrial energy efficiency and demand responses
  • AI-driven innovations in energy systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 4319 KiB  
Article
Hybrid Transformer–Convolutional Neural Network Approach for Non-Intrusive Load Analysis in Industrial Processes
by Gengsheng He, Yu Huang, Ying Zhang, Yuanzhe Zhu, Yuan Leng, Nan Shang, Jincan Zeng and Zengxin Pu
Energies 2025, 18(10), 2464; https://doi.org/10.3390/en18102464 - 11 May 2025
Viewed by 254
Abstract
With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without [...] Read more.
With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without extensive hardware requirements. However, existing NILM methods primarily tailored for residential applications struggle to capture complex inter-device correlations and production-dependent load dynamics prevalent in industrial environments, such as cement plants. This paper proposes a novel sequence-to-sequence-based non-intrusive load disaggregation method that integrates Convolutional Neural Networks (CNN) and Transformer architectures, specifically addressing the challenges of multi-device load disaggregation in industrial settings. An innovative time–application attention mechanism was integrated to effectively model long-term temporal dependencies and the collaborative operational relationships between industrial devices. Additionally, global constraints—including consistency, smoothness, and sparsity—were introduced into the loss function to ensure power conservation, reduce noise, and achieve precise zero-power predictions for inactive equipment. The proposed method was validated on real-world power consumption data collected from a cement production facility. Experimental results indicate that the proposed method significantly outperforms traditional NILM approaches with average improvements of 4.98%, 3.70%, and 4.38% in terms of accuracy, recall, and F1-score, respectively. These findings underscore its superior robustness in noisy conditions and under device fault conditions, further affirming its applicability and potential for deployment in industrial settings. Full article
(This article belongs to the Special Issue Challenges and Research Trends of Integrated Zero-Carbon Power Plant)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop