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Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology, 2nd Edition

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 4734

Special Issue Editors

Dr. Yushuai Li

E-Mail Website
Guest Editor
Department of Computer Science, Aalborg University, 9220 Aalborg, Denmark
Interests: distributed optimization; machine learning; digital twin; energy internet; autonomous driving
Special Issues, Collections and Topics in MDPI journals
Dr. Ning Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Interests: distributed control; energy internet; energy management system; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jiayue Sun

E-Mail Website
Guest Editor
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: multiagent; consensus; adaptive dynamic programming; smart grid
Special Issues, Collections and Topics in MDPI journals
Dr. Hengyu Liu

E-Mail Website1 Website2
Guest Editor
Department of Computer Science, Aalborg University, 9220 Aalborg, Denmark
Interests: energy data management; spatio-temporal data management; education data management

Special Issue Information

Dear Colleagues,

Increasing pressure in the move toward the protection of resources and the environment has made research on renewable energies urgent. The integration of a high proportion of renewable energy resources into our energy systems will enable sustainable energy development and a low-carbon society. To this end, this Special Issue focuses on new architecture, methodologies and technologies for renewable and sustainable energy systems (RSESs). From the perspective of energy type, the deep integration of multiple-energy networks offers diversified energy utilisation forms, resulting in improved energy efficiency, enhanced system resilience and the increased utilisation of renewable energy. From the perspective of equipment, the electrification of energy devices (e.g., electric vehicle) can effectively reduce dependency on traditional fossil fuels, thus decreasing carbon emissions. From the perspective of cyber technology, advances in communication and big data analysis methods benefit the intelligent detection, coordination and management of energy systems.

There are many challenges that require further research and development on policy, architecture, modelling, planning, operation, optimisation and control of renewable and sustainable energy systems. This Special Issue aims to address and disseminate state-of-the-art research and opportunities regarding the applications of innovative solutions to achieve low-carbon and sustainable energy development. We welcome the submission of original papers with novel research contributions in all aspects of tools, models and methods of relevance and impact for RSESs.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • The design of energy market and policies for RSESs;
  • Energy management architecture and method for RSESs;
  • Smart planning, operation and control for RSESs;
  • Spatiotemporal data analytics for RSESs;
  • Digital twin for RSESs;
  • Power electronics for RSESs;
  • Stability analysis for RSESs;
  • New algorithms and convergence analysis for RSESs;
  • The applications of AI, IoT and 6G/5G technologies in RSESs.

We look forward to receiving your contributions.

Dr. Yushuai Li
Dr. Ning Zhang
Dr. Jiayue Sun
Dr. Hengyu Liu
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. Sustainability 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 2400 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
  • intelligent operation systems
  • multiple-energy systems
  • sustainable energy development

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Related Special Issue

Published Papers (4 papers)

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Research

20 pages, 3257 KiB  
Article
A Reputation-Based Pricing Strategy for Distributed Diverse Entity Systems: Enhancing Market Efficiency Through Real-Time Reputation Updates
by Tong Li, Yuheng Li, Junpeng Gao, Benhua Qian and Hai Zhao
Sustainability 2024, 16(24), 11216; https://doi.org/10.3390/su162411216 - 20 Dec 2024
Viewed by 682
Abstract
Although existing studies address the reduction of default rates by adjusting electricity trading rankings based on reputation values, the mechanisms for penalizing electricity trading defaults remain incomplete. Therefore, this paper proposes a real-time reputation-based pricing method for distributed diverse entity systems to mitigate [...] Read more.
Although existing studies address the reduction of default rates by adjusting electricity trading rankings based on reputation values, the mechanisms for penalizing electricity trading defaults remain incomplete. Therefore, this paper proposes a real-time reputation-based pricing method for distributed diverse entity systems to mitigate electricity trading defaults. First, a reputation reward and penalty mechanism evaluates the trading behavior of diverse entities. Next, a ‘price-dominant, reputation-auxiliary’ pricing concept guides the process. Following this, a reputation-driven pricing strategy model for distributed adjustable resources allows for bid adjustments based on real-time market dynamics. Upon electricity trading completion, the reputation values of all entities are recalculated and disclosed, enabling entities to adjust future pricing and electricity trading quantities to optimize their profits. This method effectively reduces default rates while alleviating the impact of market electricity tradings on peak-to-valley fluctuations. Finally, simulations conducted on the MATLAB 2018b platform confirm the rationality and feasibility of the proposed real-time reputation-based pricing strategy within distributed diverse entity systems. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology, 2nd Edition)
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22 pages, 4195 KiB  
Article
Carbon Resilience of University Campuses in Response to Carbon Risks: Connotative Characteristics, Influencing Factors, and Optimization Strategies
by Yang Yang, Hao Gao, Feng Gao, Yawei Du and Parastoo Maleki
Sustainability 2024, 16(24), 11165; https://doi.org/10.3390/su162411165 - 19 Dec 2024
Cited by 2 | Viewed by 1171
Abstract
With the increasing and intensifying effects of global climate change and the rapid development of higher education, energy and resource consumption at university campuses has been rising drastically. This shift has been worsened by campuses’ expanded role in addressing extreme weather hazards and [...] Read more.
With the increasing and intensifying effects of global climate change and the rapid development of higher education, energy and resource consumption at university campuses has been rising drastically. This shift has been worsened by campuses’ expanded role in addressing extreme weather hazards and taking on additional cultural and community functions. This article carries out a comprehensive literature review of the low-carbon measures and resilient behaviors implemented on university campuses based on publications published in two major databases, the China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Results show that: (1) most existing studies only focus on campus carbon emission reduction from a single perspective, without considering the correlation between carbon emissions in different dimensions on campuses and without analyzing the causes of excessive campus carbon emissions from the perspective of the built environment; (2) current studies have not constructed an assessment system for campus carbon resilience and lack the tools and methods for assessment. After summarizing and analyzing, this study proposes the concept of campus “carbon resilience”, which refers to the ability of campuses to cope with the risks of disasters and uncertainties caused by excessive carbon emissions. The research framework of this study is divided into three parts: connotative characteristics, influencing factors, and optimization strategy. Following this framework, the concept and critical features of campus carbon resilience “carbon minus resilience”, “carbon saving resilience”, “carbon reduction resilience”, and “carbon sequestration resilience” are analyzed and outlined. Next, an integrated impact factor system for campus carbon resilience is proposed. This system incorporates aspects such as land utilization, building operation, landscape creation, and energy regeneration from the perspective of the built environment. Finally, with the core objective of effectively reducing the dynamic range of carbon emissions when dealing with critical disturbances and improving the adaptability and resilience of campuses to cope with excessive carbon emissions, this study proposes an optimization strategy of “setting development goals–establishing an evaluation system–proposing improvement strategies–dynamic feedback and adjustment” to provide ideas and theoretical guidance for responding to university campus carbon risk and planning carbon resilience. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology, 2nd Edition)
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14 pages, 1677 KiB  
Article
Research on Industrial CO2 Emission Intensity and Its Driving Mechanism Under China’s Dual Carbon Target
by Jinfang Sun, Wenkai Li, Kaixiang Zhu, Mengqi Zhang, Haihao Yu, Xiaoyu Wang and Guodong Liu
Sustainability 2024, 16(23), 10785; https://doi.org/10.3390/su162310785 - 9 Dec 2024
Viewed by 1261
Abstract
As global climate change becomes increasingly severe, industrial CO2 emissions have received increasing attention, but the impact factors and driving mechanisms of industrial CO2 emission intensity remain unclear. Based on panel data from 2010 to 2021 in Shandong Province, a key [...] Read more.
As global climate change becomes increasingly severe, industrial CO2 emissions have received increasing attention, but the impact factors and driving mechanisms of industrial CO2 emission intensity remain unclear. Based on panel data from 2010 to 2021 in Shandong Province, a key economic region in eastern China, the industrial CO2 emission intensity under China’s dual carbon target was analyzed using multivariate ordination methods. The results showed that (1) total CO2 emissions from industry are increasing annually, with an average growth rate of 3.74%, and electricity, coal, and coke are the primary sources of CO2 emissions. (2) Total CO2 emissions originated primarily from the heavy manufacturing, energy production, and high energy intensity industry categories, and the CO2 emission intensity of different types of energy increased by 21.24% from 2010 to 2021. (3) CO2 emission intensity is significantly positively correlated with the proportion of high energy intensive industry, energy consumption intensity, and investment intensity and significantly negatively correlated with gross industrial output. In addition, the effects of different types of energy on industrial CO2 emission intensity varied, and coal, coke, electricity, and diesel oil were significantly positively correlated with CO2 emission intensity. Therefore, to reduce the CO2 emission intensity of the industrial sector in the future and to achieve China’s dual carbon target, it is necessary to adjust and optimize the industrial and energy structure, strengthen technological progress and innovation, improve energy utilization efficiency, improve and implement relevant policies for industrial carbon reduction, and then ensure the sustainable development of the economy, society, and environment. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology, 2nd Edition)
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13 pages, 2148 KiB  
Article
False Data Injection Attacks Detection Based on Stacking and MIC-DCXGB
by Tong Li, Tian Xia, Haoming Zhang, Dongyang Liu, Hai Zhao and Zhuolin Liu
Sustainability 2024, 16(22), 9692; https://doi.org/10.3390/su16229692 - 7 Nov 2024
Viewed by 876
Abstract
With the integration of sustainable energy, the power grid has become increasingly information-intensive and complex. To address the issue of power grid cyber-physical systems being unable to operate securely and stably when systems suffer false data injection attacks, a two-stage detection method based [...] Read more.
With the integration of sustainable energy, the power grid has become increasingly information-intensive and complex. To address the issue of power grid cyber-physical systems being unable to operate securely and stably when systems suffer false data injection attacks, a two-stage detection method based on Stacking and Maximum Information Coefficient and Dual-layer Confidence Extreme Gradient Boosting (MIC-DCXGB) is proposed by the paper. Firstly, a Stacking classification model consisting of multiple heterogeneous learners detects anomalies in real-time measurement data samples to determine if false data are present. Secondly, the method incorporates the Maximum Information Coefficient (MIC) for feature selection, which non-linearly measures the correlation between data features and fairly removes redundant features by evaluating the amount of information one feature variable contains about another. This approach effectively tackles the high-dimensional redundancy problem commonly faced in false data injection attack detection. Then, the paper introduces a dual-layer confidence Extreme Gradient Boosting (XGBoost) tree with positive feedback information transmission to classify node states. By combining grid topology learning with label correlation, it selectively uses preceding label information to reduce errors in the predictions learned by subsequent classifiers, achieving precise localization of the attack positions. Finally, extensive simulations validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology, 2nd Edition)
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