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Energy System Optimisation and Integration and the Sustainable Use of Renewable Energy

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 979

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton LU1 3JU, UK
Interests: net zero energy solutions; low-carbon propulsion systems; advanced combustion; solar-based energy generation; thermal energy storage solutions; low-carbon gas turbines, internal combustion engines; autonomous transport; hydrogen fuel cells; oxy-fuel combustion; carbon capture; predictive models for turbulent premixed combustion, with applications in gas turbines; IoT application in energy infrastructure
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Global College of Engineering and Technology (GCET), Muscat, Oman
Interests: ground source heat pump; pyrolysis; gasification and combustion; energy analysis and management; energy policy and economics; hydrogen energy; industrial safety analysis; ground heat exchanger; renewable energy

Special Issue Information

Dear Colleagues,

The transition to sustainable energy systems is imperative for addressing climate change, improving energy security, and enhancing efficiency, and this Special Issue will highlight cutting-edge research, novel methodologies, and technological advancements in optimising energy systems, integrating renewable energy, and promoting sustainable energy practices. Additionally, this Special Issue of Sustainability aims to provide a comprehensive platform for researchers, policymakers, and industry professionals to share insights and solutions that drive the future of energy systems.

The growing body of literature on energy system optimisation, integration, and sustainability highlights the importance of interdisciplinary approaches in terms of addressing global energy challenges. This Special Issue builds upon these foundational studies by encouraging novel contributions that extend existing methodologies and propose new frameworks for advancing renewable energy integration and sustainability.

Researchers and professionals are invited to submit original research articles, case studies, and review papers covering theoretical and practical aspects of energy system optimisation, renewable energy integration, and sustainability. Accepted papers will contribute to a growing body of knowledge and inform future research, policy, and practice.

Dr. Rohitha Weerasinghe
Dr. Sivasakthivel Thangavel
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 250 words) can be sent to the Editorial Office for assessment.

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

  • energy system optimisation
  • advanced modelling and simulation techniques for energy systems
  • optimisation algorithms for energy management and grid efficiency
  • AI and machine learning applications in energy optimisation
  • demand-side management and energy storage optimisation
  • integration of renewable energy sources
  • challenges in integrating solar, wind, hydro, and bioenergy
  • hybrid energy systems and microgrid technologies
  • smart grid solutions and digitalisation for seamless integration
  • policy and regulatory frameworks for renewable integration
  • sustainable use of renewable energy
  • lifecycle analysis and environmental impact assessment
  • sustainable energy policies and transition pathways
  • community-based renewable energy projects
  • the decarbonisation of industrial and transportation sectors

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Published Papers (1 paper)

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Research

37 pages, 6274 KB  
Article
Analysis and Prediction Evaluation of Provincial Carbon Emissions Under Multi-Model Fusion
by Ketong Liu, Hao Ren, Siyao Lu, Xuecheng Shang, Zheng Liu and Baofu Yu
Sustainability 2026, 18(5), 2545; https://doi.org/10.3390/su18052545 - 5 Mar 2026
Cited by 1 | Viewed by 481
Abstract
Against the backdrop of sustainable development and global climate governance, this study focuses on the evaluation and trend prediction of provincial carbon emission efficiency and constructs a multi-model integrated analytical framework featuring “data preprocessing—efficiency decomposition—dynamic forecasting—policy deduction”. First, economic, energy consumption and carbon [...] Read more.
Against the backdrop of sustainable development and global climate governance, this study focuses on the evaluation and trend prediction of provincial carbon emission efficiency and constructs a multi-model integrated analytical framework featuring “data preprocessing—efficiency decomposition—dynamic forecasting—policy deduction”. First, economic, energy consumption and carbon emission data for 30 provinces in China from 2009 to 2019 are collected. Data cleaning is performed through outlier identification and Lagrange interpolation, and a cross-regionally comparable quantification system is established based on a unified carbon emission standard, laying a foundation for subsequent analysis. Second, data envelopment analysis (DEA) is adopted to decompose carbon emission efficiency. It is found that approximately 23% of provinces lie on the technical efficiency frontier, with the average variance share of technical inefficiency being 0.62; 6% of provinces have the potential for scale expansion; and 10% suffer from diseconomies of scale, reflecting significant structural efficiency losses in regions concentrated with high-carbon industries. Third, the long short-term memory (LSTM) neural network is employed for dynamic forecasting and scenario simulation of carbon emissions by 2025. The model’s prediction error in 2019 is controlled within 8.7%. Simulation results show that when the share of clean energy rises to 35%, China’s national carbon emission growth rate can be reduced to 1.2% by 2025. However, multi-scenario sensitivity analysis indicates that the achievement of this target highly depends on policy enforcement intensity and power grid accommodation capacity. In addition, stochastic frontier analysis (SFA) reveals the heterogeneous contributions of different energy types to economic and social outputs. The consumption elasticities of electricity, liquefied petroleum gas and gasoline are significantly positive, whereas the negative elasticities of oil, fuel oil and coal deeply reflect the low energy utilization efficiency and rigid lock-in of high-carbon industries in some regions. Finally, combined with efficiency evaluation, trend prediction and mechanism analysis, differentiated emission reduction strategies are proposed for technologically backward provinces, scale-imbalanced provinces and clean energy base provinces, forming a complete closed loop from “efficiency diagnosis” to “future deduction” and then to “policy feedback”. This study breaks through the limitations of a single model. Through the coupling of parametric and non-parametric methods, as well as the integration of dynamic forecasting and scenario simulation, it effectively addresses issues such as data heterogeneity. It provides scientific support for local governments to formulate emission reduction policies and optimize energy structures, establishes a methodological foundation for industrial efficiency analysis and international carbon responsibility allocation research, and helps to promote regional clean, low-carbon, and sustainable development. Full article
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