Clean and Low Carbon Energy, 2nd Volume

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	17.2 Days	CHF 2600	Submit
Clean Technologies cleantechnol	4.0	6.1	2019	30 Days	CHF 1600	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Processes processes	2.8	5.1	2013	14.4 Days	CHF 2400	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit

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Published Papers (5 papers)

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All Buildings Clean Technol. Energies Processes Sustainability

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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 265

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 Volume)

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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

Viewed by 977

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 Volume)

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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

Viewed by 384

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 Volume)

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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 610

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 Volume)

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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 1 | Viewed by 855

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 Volume)

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