Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.7
3.3
Journals
Sustainability
Special Issues
AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and...
sustainability-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations

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

Deadline for manuscript submissions: 12 November 2026 | Viewed by 5644

Special Issue Editors

Dr. Ning Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Interests: integrated energy system modelling and operation control; application of artificial intelligence in energy internet; distributed control based on multi-intelligence; safe and economic operation of smart distribution network; integrated energy system and smart energy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yan Juan

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei, China
Interests: integration of artificial intelligence and smart grid technologies; power system source-load forecasting
Dr. Chao Ning

E-Mail Website
Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Interests: AI-driven optimization under uncertainty; green electricity-hydrogen-chemical systems engineering
Dr. Yumeng Song

E-Mail Website
Guest Editor
Department of Computer Science, Aarhus University, Aarhus, Denmark
Interests: machine learning digital twin energy application; AI-driven spatio-temporal analytics for carbon reduction

Special Issue Information

Dear Colleagues,

The dual imperatives of climate urgency and the energy transition demand transformative approaches to decarbonize global energy systems, and the integration of AI-driven methodologies with multi-energy networks has emerged as a pivotal pathway toward achieving carbon neutrality while maintaining energy resilience. This Special Issue focuses on paradigm-shifting innovations spanning intelligent system design, advanced computational frameworks, and cross-domain technological synergies that redefine sustainable energy ecosystems.

From a systems engineering perspective, AI-driven digital twins enable the dynamic co-optimization of interconnected energy vectors to increase renewable energy penetration through predictive grid adaptation and multi-timescale flexibility management. From a device perspective, machine learning-enhanced power electronics and intelligent energy storage systems fundamentally change the operational boundaries of distributed energy resources, enabling unprecedented efficiencies in resource-constrained environments. From a cyberspace perspective, IoT architectures and edge computing facilitate the convergence of the cyber and the physical, creating self-organizing energy networks with distributed autonomy.

While the existing literature has established foundational models for renewable integration, critical gaps persist in addressing the complexity of AI-embedded energy systems. This Special Issue aims to bridge these gaps by curating cutting-edge research that advances both theoretical frameworks and practical implementations. We seek original papers with novel contributions and impacts on computational efficiency and model accuracy, system-wide decarbonization and localized energy justice, and technological breakthroughs and regulatory adaptation.

We invite original research and comprehensive reviews exploring, but not limited to:

  • AI-based charging demand forecasting and dispatch optimization for electric vehicles;
  • Research on intelligent decision-making mechanisms for multi-energy collaborative optimization in integrated energy systems;
  • Applications of reinforcement learning in the coordinated control of distributed energy resources;
  • AI-enhanced stability analysis for modern power systems;
  • The exploration of AI-driven intelligent decision-making for dynamic pricing and demand response in electricity markets;
  • Multi-modal digital twin modeling for integrated energy systems;
  • Applications of deep learning in energy systems;
  • The AI-driven design of energy market mechanisms;
  • AI-based model construction and the co-scheduling of virtual power plants.

Dr. Ning Zhang
Prof. Dr. Yan Juan
Dr. Chao Ning
Dr. Yumeng Song
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

  • AI-driven optimization
  • reinforcement learning
  • integrated energy systems
  • deep learning
  • digital twin modeling
  • virtual power plants

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 (6 papers)

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

Research

23 pages, 376 KB  
Article
The Green Side of the Machine: Industrial Robots and Corporate Energy Efficiency in China
by Ze Chen and Yuxuan Wang
Sustainability 2026, 18(3), 1193; https://doi.org/10.3390/su18031193 - 24 Jan 2026
Viewed by 481
Abstract
In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning [...] Read more.
In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning the period 2012–2023, the research assesses the relationship between exposure to industrial robots and corporate energy efficiency metrics. The empirical analysis demonstrates that greater exposure to industry-level robotization substantially boosts corporate energy performance, verifying that intelligent modernization and green transition can be mutually reinforcing. This positive effect is particularly pronounced among superstar firms, in more competitive industries, and for capital-intensive enterprises. Mechanism analysis reveals that, first, robotization processes generate a scale effect that effectively dilutes the fixed energy consumption per unit of product. Second, the diffusion of robots intensifies market competition, creating a competition effect that compels all firms within the industry to optimize costs and management with a focus on energy conservation. This study demonstrates that enhancing human capital within organizations significantly amplifies the beneficial impact of robotic integration on energy efficiency metrics. By providing empirical data from an emerging market context, this research not only elucidates the role of industrial robots but also offers policy-relevant insights for developed economies navigating the concurrent challenges of industrial modernization and environmental sustainability. Full article
(This article belongs to the Special Issue AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations)
►▼ Show Figures

Figure 1

18 pages, 3635 KB  
Article
Multi-Agent Reinforcement Learning for Sustainable Integration of Heterogeneous Resources in a Double-Sided Auction Market with Power Balance Incentive Mechanism
by Jian Huang, Ming Yang, Li Wang, Mingxing Mei, Jianfang Ye, Kejia Liu and Yaolong Bo
Sustainability 2026, 18(1), 141; https://doi.org/10.3390/su18010141 - 22 Dec 2025
Viewed by 671
Abstract
Traditional electricity market bidding typically focuses on unilateral structures, where independent energy storage units and flexible loads act merely as price takers. This reduces bidding motivation and weakens the balancing capability of regional power systems, thereby limiting the large-scale utilization of renewable energy. [...] Read more.
Traditional electricity market bidding typically focuses on unilateral structures, where independent energy storage units and flexible loads act merely as price takers. This reduces bidding motivation and weakens the balancing capability of regional power systems, thereby limiting the large-scale utilization of renewable energy. To address these challenges and support sustainable power system operation, this paper proposes a double-sided auction market strategy for heterogeneous multi-resource (HMR) participation based on multi-agent reinforcement learning (MARL). The framework explicitly considers the heterogeneous bidding and quantity reporting behaviors of renewable generation, flexible demand, and energy storage. An improved incentive mechanism is introduced to enhance real-time system power balance, thereby enabling higher renewable energy integration and reducing curtailment. To efficiently solve the market-clearing problem, an improved Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is employed, along with a temporal-difference (TD) error-based prioritized experience replay mechanism to strengthen exploration. Case studies validate the effectiveness of the proposed approach in guiding heterogeneous resources toward cooperative bidding behaviors, improving market efficiency, and reinforcing the sustainable and resilient operation of future power systems. Full article
(This article belongs to the Special Issue AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations)
►▼ Show Figures

Figure 1

24 pages, 1262 KB  
Article
A Novel Hybrid Framework for Short-Term Carbon Emissions Forecasting in China: Aggregate and Sectoral Perspectives
by Lijie Guo and Guiqiong Xu
Sustainability 2025, 17(22), 10206; https://doi.org/10.3390/su172210206 - 14 Nov 2025
Cited by 1 | Viewed by 841
Abstract
Accurate forecasting of carbon emissions is not only essential for addressing the challenges of climate governance but also provides timely support for dynamic carbon quota adjustments and emergency emission reduction decisions. In this study, we take China’s daily carbon emission data from 2021 [...] Read more.
Accurate forecasting of carbon emissions is not only essential for addressing the challenges of climate governance but also provides timely support for dynamic carbon quota adjustments and emergency emission reduction decisions. In this study, we take China’s daily carbon emission data from 2021 to 2024 as the research objects and propose a novel forecasting framework called STL-wLSTM-SVR based on seasonal-trend decomposition with Loess (STL), long short-term memory network (LSTM) and support vector regression (SVR). First, the original carbon emission sequence is decomposed via STL into seasonal, trend and residual components. Subsequently, LSTM is employed to predict the seasonal and trend components with hyper-parameters optimized by whale optimization algorithm (WOA), and SVR is used to predict the residual component with parameters optimized through grid search method. Then, the final results are obtained by accumulating the forecasted values of the three subsequences. The experimental results illustrate that the STL-wLSTM-SVR model achieved a high-precision forecast for China’s total daily carbon emissions (RMSE of 0.1129, MAPE of 0.28%, MAE of 0.0851) and demonstrated remarkable adaptability for five major sectors—from navigating the high volatility of ground transport (MAPE of 0.36%) to effectively handling the dramatic post-pandemic structural break in aviation (MAPE of 0.72%). These findings assess the effectiveness of the hybrid forecasting framework and provide a valuable methodological reference for similar prediction tasks, such as sector-specific pollutant emissions and regional energy consumption. Full article
(This article belongs to the Special Issue AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations)
►▼ Show Figures

Figure 1

20 pages, 1016 KB  
Article
Low-Carbon Economic Dispatch of Integrated Energy Systems for Electricity, Gas, and Heat Based on Deep Reinforcement Learning
by Xiaojuan Lu, Yaohui Zhang, Duojin Fan, Jiawei Wei and Xiaoying Yu
Sustainability 2025, 17(20), 9040; https://doi.org/10.3390/su17209040 - 13 Oct 2025
Viewed by 1019
Abstract
Under the background of “dual-carbon”, the development of energy internet is an inevitable trend for China’s low-carbon energy transition. This paper proposes a hydrogen-coupled electrothermal integrated energy system (HCEH-IES) operation mode and optimizes the source-side structure of the system from the level of [...] Read more.
Under the background of “dual-carbon”, the development of energy internet is an inevitable trend for China’s low-carbon energy transition. This paper proposes a hydrogen-coupled electrothermal integrated energy system (HCEH-IES) operation mode and optimizes the source-side structure of the system from the level of carbon trading policy combined with low-carbon technology, taps the carbon reduction potential, and improves the renewable energy consumption rate and system decarbonization level; in addition, for the operation optimization problem of this electric–gas–heat integrated energy system, a flexible energy system based on electric–gas–heat is proposed. Furthermore, to address the operation optimization problem of the HCEH-IES, a deep reinforcement learning method based on Soft Actor–Critic (SAC) is proposed. This method can adaptively learn control strategies through interactions between the intelligent agent and the energy system, enabling continuous action control of the multi-energy flow system while solving the uncertainties associated with source-load fluctuations from wind power, photovoltaics, and multi-energy loads. Finally, historical data are used to train the intelligent body and compare the scheduling strategies obtained by SAC and DDPG algorithms. The results show that the SAC-based algorithm has better economics, is close to the CPLEX day-ahead optimal scheduling method, and is more suitable for solving the dynamic optimal scheduling problem of integrated energy systems in real scenarios. Full article
(This article belongs to the Special Issue AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations)
►▼ Show Figures

Figure 1

23 pages, 2295 KB  
Article
A Two-Stage Sustainable Optimal Scheduling Strategy for Multi-Contract Collaborative Distributed Resource Aggregators
by Lei Su, Wanli Feng, Cao Kan, Mingjiang Wei, Rui Su, Pan Yu and Ning Zhang
Sustainability 2025, 17(15), 6767; https://doi.org/10.3390/su17156767 - 25 Jul 2025
Cited by 1 | Viewed by 996
Abstract
To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for [...] Read more.
To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for distributed resource aggregators. A phased multi-contract collaborative scheduling model oriented toward sustainable development is proposed. Through intelligent algorithms, the model dynamically optimises decisions across the day-ahead and intraday phases: During the day-ahead scheduling phase, intelligent algorithms predict load demand and energy output, and combine with elastic performance-based response contracts to construct a user-side electricity consumption behaviour intelligent control model. Under the premise of ensuring user comfort, the model generates a 24 h scheduling plan with the objectives of minimising operational costs and efficiently integrating renewable energy. In the intraday scheduling phase, a rolling optimisation mechanism is used to activate energy storage capacity contracts and dynamic frequency stability contracts in real time based on day-ahead prediction deviations. This efficiently coordinates the intelligent frequency regulation strategies of energy storage devices and electric vehicle aggregators to quickly mitigate power fluctuations and achieve coordinated control of primary and secondary frequency regulation. Case study results indicate that the intelligent optimisation-driven multi-contract scheduling model significantly improves system operational efficiency and stability, reduces system operational costs by 30.49%, and decreases power purchase fluctuations by 12.41%, providing a feasible path for constructing a low-carbon, resilient grid under high renewable energy penetration. Full article
(This article belongs to the Special Issue AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations)
►▼ Show Figures

Figure 1

21 pages, 3435 KB  
Article
A Dynamic Inertia Control Method for a New Energy Station Based on a DC-Driven Synchronous Generator and Photovoltaic Power Station Coordination
by Libin Yang, Wanpeng Zhou, Chunlai Li, Shuo Liu and Yuyan Qiu
Sustainability 2025, 17(11), 4892; https://doi.org/10.3390/su17114892 - 26 May 2025
Cited by 1 | Viewed by 898
Abstract
The inertia control ability of photovoltaic power stations is weak. This leads to the problem that photovoltaic power stations cannot provide effective physical inertia support in the grid-connected system. In this paper, a photovoltaic power station controlled by a synchronous generator and virtual [...] Read more.
The inertia control ability of photovoltaic power stations is weak. This leads to the problem that photovoltaic power stations cannot provide effective physical inertia support in the grid-connected system. In this paper, a photovoltaic power station controlled by a synchronous generator and virtual synchronous power generation is taken as the research object. A station-level dynamic inertia control model with synchronous machine and inverter control parameters coordinated is established. Firstly, the weakening of system inertia after a high-proportion photovoltaic grid connection is analyzed. Inertia compensation analysis based on an MW-level synchronous unit is carried out. According to the principle of virtual synchronous control of inverter, the virtual inertia control method and physical mechanism of a grid-connected inverter in a photovoltaic station are studied. Secondly, the inertia characteristics of the DC side of the grid-connected inverter are analyzed. The cooperative inertia control method of the photovoltaic grid-connected inverter and synchronous machine is established. Then, the influence of inertia on the system frequency is studied. The frequency optimization of the grid-connected parameter optimization of a photovoltaic station based on inertia control is carried out. Finally, aiming at the grid-connected control parameters, the inertia control parameter setting method of the photovoltaic station is carried out. The neural network predictive control model is established. At the same time, the grid-connected control model of the MW-level synchronous machine is embedded. The control system has the inertia characteristics of the synchronous generator and the fast-response dynamic characteristics of the power inverter. Full article
(This article belongs to the Special Issue AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop