AIMS Energy

In order to obtain high energy efficiency in a concentrated solar thermal power plant, more and more high concentration ratio to solar radiation are applied to collect high temperature thermal energy in modern solar power technologies. This incurs the need of a heat transfer fluid being able to work at more and more high temperatures to carry the heat from solar concentrators to a power plant. To develop the third generation heat transfer fluids targeting at a high working temperature at least 800 ℃, a research team from University of Arizona, Georgia Institute of Technology, and Arizona State University proposed to use eutectic halide salts mixtures in order to obtain the desired properties of low melting point, low vapor pressure, great stability at temperatures at least 800 ℃, low corrosion, and favorable thermal and transport properties. In this paper, a survey of the available thermal and transport properties of single and eutectic mixture of several key halide salts is conducted, providing information of great significance to researchers for heat transfer fluid development.

Chao-Jen Li

Peiwen Li

Kai Wang

Edgar Emir Molina

AIMS energy

Survey of Properties of Key Single and Mixture Halide Salts for Potential Application as High Temperature Heat Transfer Fluids for Concentrated Solar Thermal Power Systems

潜在高温传热流体用关键单组分和混合卤化物盐性质综述——适用于聚光太阳能热发电系统

&lt;p&gt;A false data injection attack (FDIA) indicates that attackers mislead system decisions by inputting false or tampered data into the system, which seriously threatens the security of power cyber-physical systems. Considering the scarcity of FDIA attack samples, the traditional FDIA detection models based on neural networks are always limited in their detection capabilities due to imbalanced training samples. To address this problem, this paper proposes an efficient FDIA attack sample generation method by an adversarial attention-diffusion model. The proposed scheme consists of a diffusion model and a GAN model with an attention mechanism (ATTGAN). First, the forward diffusion of the diffusion model was used to add noise to the real data while injecting the attack vector. Then, the ATTGAN model was trained to effectively focus on the information of power grid measurements and topological nodes, while weakening irrelevant information. In the reverse diffusion process, the trained ATTGAN model was combined to predict the noise, and it was further iterated forward step by step and denoised in this process. Finally, a large number of efficient FDIA attack samples can were generated. Extensive experiments have been carried out on IEEE 14, IEEE 39, and IEEE 118 bus systems. The experimental results indicate that the generated attack samples outperform existing state-of-the-art schemes in terms of evasion detection capability, robustness, and attack strength.&lt;/p&gt;

Kunzhan Li

Fengyong Li

Baonan Wang

Meijing Shan

False data injection attack sample generation using an adversarial attention-diffusion model in smart grids

基于对抗注意力扩散模型的智能电网虚假数据注入攻击样本生成

&lt;p&gt;The Sine Cosine Algorithm (SCA) excels in local search capabilities for solving real optimization problems. However, its strong local search ability and rotational invariance often lead to convergence at local optima. In this paper, we introduce a hybrid single-objective optimization algorithm, the Improved Sine Cosine Algorithm, and the Population-Based Incremental Learning Algorithm (ISCAPBIL). First, the Improved Sine Cosine Algorithm (ISCA) is developed by incorporating the hyperbolic sinusoidal cosine function, which dynamically interferes with individual positions to enhance optimization accuracy. Additionally, the Levy flight function is embedded within ISCA to improve its exploratory capabilities. The combination of ISCA and PBIL leverages their respective strengths, with ISCA performing local searches and PBIL handling global searches. This integration achieves a dynamic balance between global and local search processes. Our experimental results demonstrated that ISCAPBIL effectively avoided local optima, significantly improving solution accuracy compared to other algorithm variants. Moreover, when applied to the economic load scheduling problem in power systems, ISCAPBIL exhibited superior optimization efficiency and potential for practical application. The Economic Load Dispatch (ELD) problem is a core optimization task in power systems that aims to minimize generation costs while satisfying demand balance and various operational constraints. However, ELD is often formulated as a complex nonlinear optimization problem, influenced by high dimensionality and constraints, making it challenging for traditional methods to achieve efficient solutions. To address these challenges, we proposed a hybrid algorithm combining the improved Sine Cosine Algorithm (SCA) and Population Incremental Learning (PIL). By leveraging the strengths of both techniques, the proposed algorithm achieved a balance between global exploration and local exploitation. The algorithm was applied to several benchmark ELD problems, and the results demonstrated its superiority in terms of convergence speed and solution quality compared to other methods.&lt;/p&gt;

Aoshuang Ye

Yichao Li

Dong Xu

Zhiwei Wu

Guohua Chen

Junjie Tang

Zhiyuan Zhu

A hybrid algorithm based on improved sine cosine algorithm and population incremental learning and its application to economic load dispatch in power systems

基于改进正弦余弦算法和种群增量学习的混合算法及其在电力系统经济负荷调度中的应用

The energy management of Hybrid Electric Vehicles (HEV) has been the subject of a great scientific effort in recent years. Moreover, in HEV the power must be managed in real time within system constraints. The proposed approach based on a fuzzy controller, uses different set of rules depending on different phases present in a mission profile. The challenge is to compute offline these rules and to manage online a decision method to switch optimally from one rule to the other depending on the power demand. From the proposed segmentation/prediction of the requested power profile to follow, derives a switch condition between three different rules in order to decrease the fuel consumption instead of applying a unique rule computed globally on a given profile. This strategy drives the fuel cell (FC) to operate at the points of best performance. It has been verified that if this method is applied online on an unknown profile, the consumption obtained is almost optimal.

Ahmed Neffati

Amira Marzouki

Local energy management in hybrid electrical vehicle via Fuzzy rules system

基于模糊规则系统的混合动力电动车本地能量管理

&lt;p&gt;Renewable energy and electric vehicles are used globally for reducing fuel dependency and carbon footprints. Here, I explore the complex field of "Sustainable Energy Technologies for Emerging Renewable Energy and Electric Vehicles" and examined the most recent advancements, obstacles, and ideas that are promoting the effective use of renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicles (EVs). I focused on the integration of renewable energy (RE), energy storage (ES), and EVs into modern sustainable power grids. With the recent advances in power generation technologies, the fluctuations in generation and electrical demand are common in hybrid power systems. This special issue was aimed at addressing the economic challenge of integrating RESs, ES, and EVs to improve the resilience and flexibility of grid operations. The widespread adoption of intermittent RESs and EVs makes it difficult to maintain a consistent power supply and significantly impacts grid stability. To address this challenge, ESSs are emerging as a potential solution. With the right investments and policies in place, RE, ES, and EVs can play a significant role in creating a more sustainable energy landscape worldwide.&lt;/p&gt;

Surender Reddy Salkuti

Sustainable energy technologies for emerging renewable energy and electric vehicles

新兴可再生能源和电动汽车的可持续能源技术

&lt;p&gt;Integrating an increasing number of distributed energy resources into medium-voltage and low-voltage radial distribution networks is causing significant shifts in power flow and fault current distribution. These changes introduce new challenges for power system protection coordination. We present an adaptive protection coordination strategy designed to address these challenges. The proposed approach involved tracking the connectivity of the system structure to establish a relay numbering sequence, which served as a tracking route. These routes were further categorized into main feeder and branch paths based on the system topology. The strategy to optimize the operation time of overcurrent relays involved adjusting the time multiplier setting (TMS) and pickup current setting (PCS) for each relay, focusing on improving relay coordination. The coordination problem was formulated to minimize the total operation time of both primary and backup relays while adhering to coordination time interval (CTI) constraints. A refined immune algorithm, augmented with an auto-tuning reproductive mechanism, was proposed to determine the optimal time multiplier settings and pickup current settings parameters along the tracking route. We used a 16-bus actual distribution network and the IEEE 37 Bus system with distributed generators to evaluate the effectiveness of the proposed adaptive protection coordination. The results demonstrated that the proposed algorithm significantly reduced overall operation time and mitigated the impact on protection coordination settings following the integrations. Furthermore, a comparative analysis with other metaheuristic algorithms highlighted the superior efficiency and performance of the proposed approach.&lt;/p&gt;

Tung-Sheng Zhan

Yih-Der Lee

Jheng-Lun Jiang

Optimal OCR coordination in a high penetration distribution power system using a refined immune algorithm with an auto-tuning reproductive mechanism

使用具有自动调谐再生机制的改进免疫算法在高渗透分布式电力系统中实现最佳OCR协调

&lt;p&gt;With China's economic growth, energy-economy-environment (3E) issues in rural areas have become more prominent. As key areas for energy consumption and environmental protection, analyzing the influencing factors of the 3E system in rural areas is crucial. We constructed and analyzed the rural 3E model using clustering and examined the relationship between scientific and technological talent, the economic level of rural residents, and 3E development through principal component regression. The findings showed: (1) Overall improvement in 3E development, with coastal and non-coastal areas showing significant differences; (2) room for energy improvement, rapid economic growth, and more balanced environmental development in some regions; (3) scientific and technological talent and economic level significantly impacted 3E development; and (4) in coastal areas, talent development had a greater impact, while in non-coastal areas, both talent and economic development had comparable effects. Moreover, 1-unit increase in talent development in coastal areas boosted 3E development by 0.12 units, while in non-coastal areas, talent and economic level increased 3E development by 0.04 and 0.06 units, respectively.&lt;/p&gt;

Shi Yin

Yuan Yuan

Integrated assessment and influencing factor analysis of energy-economy-environment system in rural China

中国农村能源-经济-环境系统综合评估及影响因素分析

Abbreviation
Journal Impact	1.64
Quartiles(Global)	ENERGY & FUELS(Q4)

ISSN	2333-8326, 2333-8334
h-index	23

Publication Information	Publisher: AIMS Press，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2016，Proportion of original research papers: 68.52%，Self Citation Rate:11.10%， Gold OA Rate: 99.43%
Journal Fees	$500Details
Average review cycle	网友分享经验：12 Weeks

AIMS Energy

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Survey of Properties of Key Single and Mixture Halide Salts for Potential Application as High Temperature Heat Transfer Fluids for Concentrated Solar Thermal Power Systems

False data injection attack sample generation using an adversarial attention-diffusion model in smart grids

A hybrid algorithm based on improved sine cosine algorithm and population incremental learning and its application to economic load dispatch in power systems

Local energy management in hybrid electrical vehicle via Fuzzy rules system

Sustainable energy technologies for emerging renewable energy and electric vehicles

False data injection attack sample generation using an adversarial attention-diffusion model in smart grids

A hybrid algorithm based on improved sine cosine algorithm and population incremental learning and its application to economic load dispatch in power systems

Sustainable energy technologies for emerging renewable energy and electric vehicles

Optimal OCR coordination in a high penetration distribution power system using a refined immune algorithm with an auto-tuning reproductive mechanism

Integrated assessment and influencing factor analysis of energy-economy-environment system in rural China