Clean and Low Carbon Energy, 2nd Edition

Topic Information

Dear Colleagues,

In recent years, the rapid development of the national economy has become inseparable from the contributions of traditional fossil energy such as coal, oil and natural gas. However, fossil energy has negative impacts such as environmental pollution, global warming and economic security. In this regard, it is important to break through the key bottlenecks of pollutants, carbon emissions and energy grade loss caused by the fossil energy utilization mode, and to build a clean and low-carbon energy utilization system based on solar energy, wind energy and geothermal energy, etc., to achieve the goal of environmental protection and energy technology revolution. Clean and low-carbon energy research has achieved major successes in the past decade and is expected to drive the development of other renewable energy sources. However, although significant progress has been made in clean and low-carbon energy in recent years, there are still major challenges in the implementation of new theories, new methods and new demands. From this perspective, this topic aims to contribute to the clean and low-carbon energy agenda by enhancing scientific and multidisciplinary work, aiming to improve knowledge and performance in harvesting clean and low-carbon energy. We strongly encourage papers providing innovative technological developments, reviews, case studies and analyses, as well as assessments and manuscripts targeting different disciplines, which are relevant to harvesting clean and low-carbon energy and its associated advances and challenges. The topic includes but is not limited to:

• Renewable energy resources and technologies;
• Renewable energy harvesting and conversion;
• Energy systems and efficiency improvement;
• Advanced energy technologies;
• Energy storage and applications;
• Energy and buildings;
• Energy use in industry;
• Energy and environment;
• Energy and nanotechnology;
• Energy mangement, policy and economics.

Prof. Dr. Dong Li
Prof. Dr. Fuqiang Wang
Prof. Dr. Zhonghao Rao
Prof. Dr. Chao Shen
Prof. Dr. Changyu Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
Clean Technologies cleantechnol	4.1	6.1	2019	33.5 Days	CHF 1600	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (10 papers)

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All Buildings Clean Technol. Energies Processes Sustainability

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23 pages, 639 KiB  

Open AccessArticle

Sustainable Investment Strategy: A Fuzzy Nonlinear Multi-Objective Programming for Taiwan’s Solar Photovoltaic Billboards

by Yu-Feng Lin

Sustainability 2025, 17(9), 3763; https://doi.org/10.3390/su17093763 - 22 Apr 2025

Viewed by 222

Abstract

In Taiwan, large advertising billboards on commercial buildings consume significant energy, exacerbating environmental challenges and straining sustainability efforts. This study explores the potential of rooftop solar photovoltaic systems (SPVS) to power these billboards, offering a dual solution for energy reduction and financial viability. [...] Read more.

In Taiwan, large advertising billboards on commercial buildings consume significant energy, exacerbating environmental challenges and straining sustainability efforts. This study explores the potential of rooftop solar photovoltaic systems (SPVS) to power these billboards, offering a dual solution for energy reduction and financial viability. Using a fuzzy nonlinear multi-objective programming approach, the research demonstrates that SPVS investments become profitable by the ninth year (0.7232% return), rising to 5.4463% by the twentieth year, while a 26-day reduction in construction time cuts carbon emissions by 223.11 kg. The innovative Revenue–Time–Cost–Quality–Carbon Emissions (RTCQCE) framework balances economic gains with environmental benefits, leveraging advertising revenue to fund SPVS. This model bridges a research gap by integrating financial and ecological factors, providing a practical tool for sustainable urban development in Taiwan. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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19 pages, 17668 KiB  

Open AccessArticle

A Pore-Scale Investigation of Oil Contaminant Remediation in Soil: A Comparative Study of Surfactant- and Polymer-Enhanced Flushing Agents

by Yu Pu, Erlong Yang, Di Wang and Shuqian Shen

Clean Technol. 2025, 7(1), 8; https://doi.org/10.3390/cleantechnol7010008 - 13 Jan 2025

Viewed by 844

Abstract

Pore-scale remediation investigation of oil-contaminated soil is important in several environmental and industrial applications, such as quick responses to sudden accidents. This work aims to investigate the oil pollutant removal process and optimize the oil-contaminated soil remediation performance at the pore scale to [...] Read more.

Pore-scale remediation investigation of oil-contaminated soil is important in several environmental and industrial applications, such as quick responses to sudden accidents. This work aims to investigate the oil pollutant removal process and optimize the oil-contaminated soil remediation performance at the pore scale to find the underlying mechanisms for oil removal from soil. The conservative forms of the phase-field model and the non-Newtonian power-law fluid model are employed to track the moving interface between two immiscible phases, and oil pollutant flushing removal process from soil pores is investigated. The effects of viscosity, interfacial tension, wettability, and flushing velocity on pore-scale oil pollutant removal regularity are explored. Then, the oil pollutant removal effects of two flushing agents (surfactant system and surfactant–polymer system) are compared using an oil content prediction curve based on UV-Visible transmittance. The results show that the optimal removal efficiency is obtained for a weak water-wetting system with a contact angle of 60° due to the stronger two-phase fluid interaction, deeper penetration, and more effective entrainment flow. On the basis of the dimensionless analysis, a relatively larger flushing velocity, resulting in a higher capillary number (Ca) in a certain range, can achieve rapid and efficient oil removal. In addition, an appropriately low interfacial tension, rather than ultra-low interfacial intension, contributes to strengthening the oil removal behavior. A reasonably high viscosity ratio (M) with a weak water-wetting state plays synergetic roles in the process of oil removal from the contaminated soil. In addition, the flushing agent combined with a surfactant and polymer can remarkably enhance the oil removal efficiency compared to the sole use of the surfactant, achieving a 2.5-fold increase in oil removal efficiency. This work provides new insights into the often-overlooked roles of the pore scale in fluid dynamics behind the remediation of oil-contaminated soil via flushing agent injection, which is of fundamental importance to the development of effective response strategies for soil contamination. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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31 pages, 40231 KiB  

Open AccessArticle

A Surrogate Model-Based Optimization Approach for Geothermal Well-Doublet Placement Using a Regularized LSTM-CNN Model and Grey Wolf Optimizer

by Fengyu Li, Xia Guo, Xiaofei Qi, Bo Feng, Jie Liu, Yunpeng Xie and Yumeng Gu

Sustainability 2025, 17(1), 266; https://doi.org/10.3390/su17010266 - 2 Jan 2025

Cited by 1 | Viewed by 1124

Abstract

The placement of a well doublet plays a significant role in geothermal resource sustainable production. The normal well placement optimization method of numerical simulation-based faces a higher computational load with the increasing precision demand. This study proposes a surrogate model-based optimization approach that [...] Read more.

The placement of a well doublet plays a significant role in geothermal resource sustainable production. The normal well placement optimization method of numerical simulation-based faces a higher computational load with the increasing precision demand. This study proposes a surrogate model-based optimization approach that searches the economically optimal injection well location using the Grey Wolf Optimizer (GWO). The surrogate models trained by the novel Multi-layer Regularized Long Short-Term Memory–Convolution Neural Network concatenation model (MR LSTM-CNN) will relieve the computation load and save the simulation time during the simulation–optimization process. The results showed that surrogate models in a homogenous reservoir and heterogenous reservoir can predict the pressure–temperature evolution time series with the accuracy of 99.80% and 94.03%. Additionally, the optimization result fitted the real economic cost distribution in both reservoir situations. Further comparison figured out that the regularization and convolution process help the Long Short-Term Memory neural network (LSTM) perform better overall than random forest. And GWO owned faster search speed and higher optimization quality than a widely used Genetic Algorithm (GA). The surrogate model-based approach shows the good performance of MR LSTM-CNN and the feasibility in the well placement optimization of GWO, which provides a reliable reference for future study and engineering practice. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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23 pages, 7308 KiB  

Open AccessArticle

Reforming Natural Gas for CO₂ Pre-Combustion Capture in Trinary Cycle Power Plant

by Nikolay Rogalev, Andrey Rogalev, Vladimir Kindra, Olga Zlyvko and Dmitriy Kovalev

Energies 2024, 17(22), 5544; https://doi.org/10.3390/en17225544 - 6 Nov 2024

Cited by 1 | Viewed by 1082

Abstract

Today, most of the world’s electric energy is generated by burning hydrocarbon fuels, which causes significant emissions of harmful substances into the atmosphere by thermal power plants. In world practice, flue gas cleaning systems for removing nitrogen oxides, sulfur, and ash are successfully [...] Read more.

Today, most of the world’s electric energy is generated by burning hydrocarbon fuels, which causes significant emissions of harmful substances into the atmosphere by thermal power plants. In world practice, flue gas cleaning systems for removing nitrogen oxides, sulfur, and ash are successfully used at power facilities but reducing carbon dioxide emissions at thermal power plants is still difficult for technical and economic reasons. Thus, the introduction of carbon dioxide capture systems at modern power plants is accompanied by a decrease in net efficiency by 8–12%, which determines the high relevance of developing methods for increasing the energy efficiency of modern environmentally friendly power units. This paper presents the results of the development and study of the process flow charts of binary and trinary combined-cycle gas turbines with minimal emissions of harmful substances into the atmosphere. This research revealed that the net efficiency rate of a binary CCGT with integrated post-combustion technology capture is 39.10%; for a binary CCGT with integrated pre-combustion technology capture it is 40.26%; a trinary CCGT with integrated post-combustion technology capture is 40.35%; and for a trinary combined-cycle gas turbine with integrated pre-combustion technology capture it is 41.62%. The highest efficiency of a trinary CCGT with integrated pre-combustion technology capture is due to a reduction in the energy costs for carbon dioxide capture by 5.67 MW—compared to combined-cycle plants with integrated post-combustion technology capture—as well as an increase in the efficiency of the steam–water circuit of the combined-cycle plant by 3.09% relative to binary cycles. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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22 pages, 9839 KiB  

Open AccessArticle

Laboratory Experimental Investigation on the Structural Optimization of a Novel Coupled Energy Tunnel

by Jiwei Wen, Pengshuai Zhang, Zhe Xiong, Falin Guo, Huilin Qiao, Jiale Feng, Yachen Ma, Yao Li and Minchuan Gan

Buildings 2024, 14(11), 3333; https://doi.org/10.3390/buildings14113333 - 22 Oct 2024

Viewed by 1027

Abstract

Freezing damage to tunnels in cold regions has long posed a threat to the safe operation of high-speed trains and other means of transportation. Finding a reasonable and effective solution to this problem, while also considering green, low-carbon, energy-saving, and environmental protection measures, [...] Read more.

Freezing damage to tunnels in cold regions has long posed a threat to the safe operation of high-speed trains and other means of transportation. Finding a reasonable and effective solution to this problem, while also considering green, low-carbon, energy-saving, and environmental protection measures, has garnered widespread attention. Herein, the concept of a novel coupled energy tunnel is proposed, which combines the technologies of an air curtain and ground source heat pump (GSHP). The aim is to effectively address the issue of freezing damage in tunnels located in cold regions, while ensuring traffic safety. First, the multifunctional experimental apparatus for testing the anti-freezing and insulation performance of a coupled energy tunnel was independently designed and developed for laboratory experiments. Second, single-factor experiments and orthogonal experiments are conducted, and the influences of five key factors (i.e., the air outlet hole diameter, air outlet hole spacing, circulating water temperature of the GSHP, wind speed at the tunnel model entrance, and airflow jet angle) on the internal temperature field of the tunnel model are discussed. Third, combined with range analysis and variance analysis, the ranking of importance for each key factor and the optimal scheme of the coupled energy tunnel are obtained as follows: wind speed at the tunnel model entrance D > circulating water temperature of GSHP C > airflow jet angle E > air outlet hole spacing B > air outlet hole diameter A, and the optimal scheme is A₂B₁C₄D₁E₂, i.e., the air outlet hole diameter is 3 mm, the air outlet hole spacing is 10 mm, the circulating water temperature of GSHP is 50 °C, the wind speed at the tunnel model entrance is 1.5 m/s and the airflow jet angle is 45°. In conclusion, the research achievements presented in this paper can offer a new perspective for the structural design of tunnels in cold regions. Additionally, they contribute to the early achievement of a carbon dioxide emissions peak and carbon neutrality, and provide some valuable and scientific references for both innovators and practitioners. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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15 pages, 31592 KiB  

Open AccessArticle

A Stability Control Method to Maintain Synchronization Stability of Wind Generation under Weak Grid

by Minhai Wu, Jun Zeng, Gengning Ying, Jidong Xu, Shuangfei Yang, Yuebin Zhou and Junfeng Liu

Energies 2024, 17(17), 4450; https://doi.org/10.3390/en17174450 - 5 Sep 2024

Viewed by 825

Abstract

When wind generation systems operate under weak grid conditions, synchronization stability issues may arise, restricting the wind farms’ power transfer capacity. This paper aims to address these challenges on the grid side. Firstly, a clear exposition of the coupling mechanism between the grid-connected [...] Read more.

When wind generation systems operate under weak grid conditions, synchronization stability issues may arise, restricting the wind farms’ power transfer capacity. This paper aims to address these challenges on the grid side. Firstly, a clear exposition of the coupling mechanism between the grid-connected inverters (GCI) of wind generations and the weak grid is provided. Then, an equivalent parallel compensation method integrated into the PLL to enhance synchronization stability is proposed. The method changes the reference of the PLL and equivalently parallels the virtual resistance with the grid impedance, which alters the strength of the grid. It reshapes the inverter qq-axis impedance at the impedance level. And the proper design of the virtual resistance will enhance the system’s stability without compromising the dynamic performance of PLL. In addition, the proposed method is robust to the parameter changes of the grid-connected system and the grid impedance measurement error. Experimental results are presented to validate the effectiveness of the compensation method. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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35 pages, 3152 KiB  

Open AccessReview

Deep Learning Models for PV Power Forecasting: Review

by Junfeng Yu, Xiaodong Li, Lei Yang, Linze Li, Zhichao Huang, Keyan Shen, Xu Yang, Xu Yang, Zhikang Xu, Dongying Zhang and Shuai Du

Energies 2024, 17(16), 3973; https://doi.org/10.3390/en17163973 - 10 Aug 2024

Cited by 6 | Viewed by 3516

Abstract

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning [...] Read more.

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning models for PV power forecasting, concentrating on comparisons of the features, advantages, and limitations of different model architectures. First, we analyze the commonly used datasets for PV power forecasting. Additionally, we provide an overview of mainstream deep learning model architectures, including multilayer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN), and graph neural networks (GNN), and explain their fundamental principles and technical features. Moreover, we systematically organize the research progress of deep learning models based on different architectures for PV power forecasting. This study indicates that different deep learning model architectures have their own advantages in PV power forecasting. MLP models have strong nonlinear fitting capabilities, RNN models can capture long-term dependencies, CNN models can automatically extract local features, and GNN models have unique advantages for modeling spatiotemporal characteristics. This manuscript provides a comprehensive research survey for PV power forecasting using deep learning models, helping researchers and practitioners to gain a deeper understanding of the current applications, challenges, and opportunities of deep learning technology in this area. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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19 pages, 2890 KiB  

Open AccessArticle

A Novel Multi-Timescale Optimal Scheduling Model for a Power–Gas Mutual Transformation Virtual Power Plant with Power-to-Gas Conversion and Comprehensive Demand Response

by Shuo Yin, Yang He, Zhiheng Li, Senmao Li, Peng Wang and Ziyi Chen

Energies 2024, 17(15), 3805; https://doi.org/10.3390/en17153805 - 2 Aug 2024

Cited by 1 | Viewed by 1014

Abstract

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty [...] Read more.

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day’s internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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6 pages, 1217 KiB  

Open AccessPerspective

Plasma-Assisted One-Step Direct Methanol Conversion to Ethylene Glycol and Hydrogen: Process Intensification

by Olumide Bolarinwa Ayodele

Energies 2024, 17(13), 3216; https://doi.org/10.3390/en17133216 - 29 Jun 2024

Viewed by 1281

Abstract

This perspective reports a process intensification strategy that converts methanol into ethylene glycol (MeOH-2-EG) in a single step to circumvent multi-step naphtha cracking into ethylene followed by ethylene epoxidation to ethylene oxide (EO) and the subsequent hydrolysis of EO to ethylene glycol (EG). [...] Read more.

This perspective reports a process intensification strategy that converts methanol into ethylene glycol (MeOH-2-EG) in a single step to circumvent multi-step naphtha cracking into ethylene followed by ethylene epoxidation to ethylene oxide (EO) and the subsequent hydrolysis of EO to ethylene glycol (EG). Due to the thermodynamic restriction for the direct MeOH-2-EG, plasma-assisted catalysis was introduced, and platinum group metals were identified as prospective transition metal catalysts that can achieve the formation of strong metal hydride bonds and guarantee the controlled C–C coupling of two plasma-activated hydroxymethyl radicals (*CH₂OH) from methanol, both of which are essential for the single-step MeOH-2-EG. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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19 pages, 7820 KiB  

Open AccessArticle

Numerical Simulation Research on Combustion and Emission Characteristics of Diesel/Ammonia Dual-Fuel Low-Speed Marine Engine

by Qinran Wu, Xingyu Liang, Zhijie Zhu, Lei Cui and Teng Liu

Energies 2024, 17(12), 2960; https://doi.org/10.3390/en17122960 - 16 Jun 2024

Cited by 4 | Viewed by 1788

Abstract

Amid increasingly stringent global environmental regulations, marine engines are undergoing an essential transition from conventional fossil fuels to alternative fuels to meet escalating regulatory requirements. This study evaluates the effects of injection pressure, the timing of ammonia injection, and the pre-injection of ammonia [...] Read more.

Amid increasingly stringent global environmental regulations, marine engines are undergoing an essential transition from conventional fossil fuels to alternative fuels to meet escalating regulatory requirements. This study evaluates the effects of injection pressure, the timing of ammonia injection, and the pre-injection of ammonia on combustion and emissions, aiming to identify optimal operational parameters for low-speed marine engines. A three-dimensional model of a large-bore, low-speed marine engine in a high-pressure diffusion mode was developed based on computational fluid dynamics (CFD). Simulations were conducted under 25%, 50%, 75% and 100% loads with a high ammonia energy substitution rate of 95%. The results indicate that, compared to traditional pure diesel operation, adjusting the injection pressure and the ammonia injection timing, along with employing appropriate pre-injection strategies, significantly enhances in-cylinder pressure and temperature, improves thermal efficiency, and reduces specific fuel consumption. Additionally, the dual-fuel strategy using diesel and ammonia effectively reduces nitrogen oxide emissions by up to 37.5% and carbon dioxide emissions by 93.7%. Full article

(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)

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