Journal Description
Energies
Energies
is a peer-reviewed, open access journal of related scientific research, technology development, engineering policy, and management studies related to the general field of energy, from technologies of energy supply, conversion, dispatch, and final use to the physical and chemical processes behind such technologies. Energies is published semimonthly online by MDPI. The European Biomass Industry Association (EUBIA), Association of European Renewable Energy Research Centres (EUREC), Institute of Energy and Fuel Processing Technology (ITPE), International Society for Porous Media (InterPore), CYTED and others are affiliated with Energies and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, RePEc, Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: CiteScore - Q1 (Engineering (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.1 days after submission; acceptance to publication is undertaken in 3.3 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 41 topical sections.
- Testimonials: See what our editors and authors say about Energies.
- Companion journals for Energies include: Fuels, Gases, Nanoenergy Advances and Solar.
Impact Factor:
3.2 (2022);
5-Year Impact Factor:
3.3 (2022)
Latest Articles
Exploring Motion Stability of a Novel Semi-Submersible Platform for Offshore Wind Turbines
Energies 2024, 17(10), 2313; https://doi.org/10.3390/en17102313 (registering DOI) - 10 May 2024
Abstract
The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of
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The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.
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(This article belongs to the Topic Advances in Power Science and Technology)
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An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems
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Chao Tang, Yufeng Zhang, Fan Wu and Zhuo Tang
Energies 2024, 17(10), 2312; https://doi.org/10.3390/en17102312 (registering DOI) - 10 May 2024
Abstract
Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges
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Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.
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(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Energy and Environment Development)
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Research on Optimization of Financial Performance Evaluation of Energy Enterprises under the Background of Low-Carbon Economy
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Xiao Li, Hongxin Gao and Enyi Zhou
Energies 2024, 17(10), 2311; https://doi.org/10.3390/en17102311 (registering DOI) - 10 May 2024
Abstract
The development of human society and the production and operation activities of enterprises have brought about global warming, resulting in frequent natural disasters. It has become the consensus of all countries in the world to develop a green and low-carbon economy. Under this
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The development of human society and the production and operation activities of enterprises have brought about global warming, resulting in frequent natural disasters. It has become the consensus of all countries in the world to develop a green and low-carbon economy. Under this background, enterprises, as the main body of economic activities, especially energy industry enterprises, should optimize and upgrade the traditional production and operation mode with high pollution, high consumption, and low output to a high-efficiency and low-pollution mode, and pay attention to the co-ordinated development of economic benefits, social benefits, and ecological benefits. Financial performance evaluation indicators have become the main basis for senior leaders of energy industry enterprises to make decisions and evaluate the low-carbon economic benefits of enterprises. This paper constructs a set of financial evaluation index systems of energy industry enterprises under the background of a low-carbon economy from four dimensions: profitability, asset quality, debt risk, and business growth. The analytic hierarchy process (AHP) is used to measure the comprehensive contribution of financial indicators of low-carbon production and operation. The purpose of this study is to provide scientific financial management decisions for energy enterprises to reduce costs and increase the efficiency and low-carbon operation under the background of a low-carbon economy. The research results show that the comprehensive evaluation index system after the traditional financial evaluation index of energy industry enterprises is integrated with the evaluation index of a low-carbon economy can help enterprises make more correct and effective financial decisions in the process of survival, development, and growth, and, at the same time, the financial evaluation index of a low-carbon economy should pay more attention to the indicators with a higher comprehensive contribution, so as to effectively promote the low-carbon operation efficiency of enterprise production, management, and sales. Compared with other research results, this paper innovatively constructs a financial management decision-making index system for measuring the low-carbon operation of energy enterprises in theory, which has important value in guiding energy enterprises to reduce costs and increase the efficiency and low-carbon operation in practice.
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(This article belongs to the Special Issue Industrial Chain, Supply Chain and Value Chain in the Energy Industry: Impacts and Challenges in the Green and Low-Carbon Transition)
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Open AccessArticle
Energy Performance in Residential Buildings as a Property Market Efficiency Driver
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Marek Walacik and Aneta Chmielewska
Energies 2024, 17(10), 2310; https://doi.org/10.3390/en17102310 (registering DOI) - 10 May 2024
Abstract
Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and
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Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis.
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(This article belongs to the Section G: Energy and Buildings)
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High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine
by
Wei Du, Shi-Tao Peng, Pei-Sen Wu and Ming-Lang Tseng
Energies 2024, 17(10), 2309; https://doi.org/10.3390/en17102309 (registering DOI) - 10 May 2024
Abstract
Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced
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Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.
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(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Brief Analysis of the Location and Determination of Maximum Capacity of Distributed Generation in Electrical Systems Considering Demand Scenarios in Ecuador
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Roger David De la Cruz, Luis Fernando Tipán and Cristian Cristobal Cuji
Energies 2024, 17(10), 2308; https://doi.org/10.3390/en17102308 (registering DOI) - 10 May 2024
Abstract
This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the
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This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.
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(This article belongs to the Section F1: Electrical Power System)
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Optimizing Nanofluid Hybrid Solar Collectors through Artificial Intelligence Models
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Safae Margoum, Bekkay Hajji, Stefano Aneli, Giuseppe Marco Tina and Antonio Gagliano
Energies 2024, 17(10), 2307; https://doi.org/10.3390/en17102307 (registering DOI) - 10 May 2024
Abstract
This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy
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This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R2) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R2 values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R2 values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study’s implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems.
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(This article belongs to the Special Issue Advanced Solar Technologies and Thermal Energy Storage)
Open AccessReview
Bridging Geo-Data and Natural Gas Pipeline Design Standards: A Systematic Review of BIM-GIS Integration for Natural Gas Pipeline Asset Management
by
Selcuk Demir and Tahsin Yomralioglu
Energies 2024, 17(10), 2306; https://doi.org/10.3390/en17102306 (registering DOI) - 10 May 2024
Abstract
In today’s world, effective management and the use of spatial data are of great importance in many sectors. Industries such as land management, asset management, and infrastructure management are areas where spatial data are heavily utilized. Advanced technologies such as Geographic Information Systems
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In today’s world, effective management and the use of spatial data are of great importance in many sectors. Industries such as land management, asset management, and infrastructure management are areas where spatial data are heavily utilized. Advanced technologies such as Geographic Information Systems (GISs) and Building Information Modeling (BIM) are used in the processes of collecting, analyzing, and managing geographically enabled data (geo-data). These technologies enable the effective processing of large datasets, improve decision-making processes based on geographic information, and facilitate more efficient collaboration across sectors. This study conducts an in-depth examination of the existing literature on asset management, infrastructure management, and BIM-GIS integration using bibliometric analysis and systematic literature review methods. Bibliometric analysis is employed to determine statistical values such as current research trends, frequently cited authors, most used keywords, and country performances in the relevant field. This study’s results highlight future research trends and significant gaps in the areas of asset management, infrastructure management, natural gas pipelines, and BIM-GIS integration. In particular, this study demonstrates the critical importance of asset management and BIM-GIS integration for sustainable infrastructure design, construction, and management. In this context, attention is drawn to the importance of data standardization, digitization, systematic integration, and contemporary land management requirements.
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(This article belongs to the Section H: Geo-Energy)
Open AccessReview
Wireless Power Transfer for Unmanned Underwater Vehicles: Technologies, Challenges and Applications
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Iñigo Martínez de Alegría, Iñigo Rozas Holgado, Edorta Ibarra, Eider Robles and José Luís Martín
Energies 2024, 17(10), 2305; https://doi.org/10.3390/en17102305 (registering DOI) - 10 May 2024
Abstract
Unmanned underwater vehicles (UUVs) are key technologies to conduct preventive inspection and maintenance tasks in offshore renewable energy plants. Making such vehicles autonomous would lead to benefits such as improved availability, cost reduction and carbon emission minimization. However, some technological aspects, including the
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Unmanned underwater vehicles (UUVs) are key technologies to conduct preventive inspection and maintenance tasks in offshore renewable energy plants. Making such vehicles autonomous would lead to benefits such as improved availability, cost reduction and carbon emission minimization. However, some technological aspects, including the powering of these devices, remain with a long way to go. In this context, underwater wireless power transfer (UWPT) solutions have potential to overcome UUV powering drawbacks. Considering the relevance of this topic for offshore renewable plants, this work aims to provide a comprehensive summary of the state of the art regarding UPWT technologies. A technology intelligence study is conducted by means of a bibliographical survey. Regarding underwater wireless power transfer, the main methods are reviewed, and it is concluded that inductive wireless power transfer (IWPT) technologies have the most potential. These inductive systems are described, and their challenges in underwater environments are presented. A review of the underwater IWPT experiments and applications is conducted, and innovative solutions are listed. Achieving efficient and reliable UWPT technologies is not trivial, but significant progress is identified. Generally, the latest solutions exhibit efficiencies between 88% and 93% in laboratory settings, with power ratings reaching up to 1–3 kW. Based on the assessment, a power transfer within the range of 1 kW appears to be feasible and may be sufficient to operate small UUVs. However, work-class UUVs require at least a tenfold power increase. Thus, although UPWT has advanced significantly, further research is required to industrially establish these technologies.
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(This article belongs to the Topic Transportation Electrification Key Applications: Battery Storage System, DC/DC Converter, Wireless Charging, Sensors)
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A Comparative Analysis of Distributor and Rotor Single Regulation Strategies for Low Head Mini Hydraulic Turbines
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Dario Barsi, Francesca Satta, Marina Ubaldi and Pietro Zunino
Energies 2024, 17(10), 2304; https://doi.org/10.3390/en17102304 (registering DOI) - 10 May 2024
Abstract
Tubular axial turbines (TATs) play a crucial role in mini and micro hydropower setups that require simplified yet reliable solutions. In very low head scenarios, single regulation in TATs is common, due to economic impracticality of the sophisticated mechanisms involved in the conjugate
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Tubular axial turbines (TATs) play a crucial role in mini and micro hydropower setups that require simplified yet reliable solutions. In very low head scenarios, single regulation in TATs is common, due to economic impracticality of the sophisticated mechanisms involved in the conjugate distributor–rotor regulation typical of the Kaplan turbines. Distributor or rotor single regulation strategies offer operation flexibility, each with distinct advantages and disadvantages. Stator regulation is simpler, while rotor regulation is more complex but offers potential efficiency gains. The present paper analyzes energy losses associated with these regulation strategies using two approaches: 1D mean line turbomachinery equations and 3D Computational Fluid Dynamics (CFD). The 1D mean line approach is used for understanding the conceptual fluid dynamic aspects involved in the two different regulation approaches, thereby identifying the loss-generation mechanisms in off-design operation. Fully 3D CFD simulations allow for quantifying and deeply explaining the differences in the hydraulic efficiencies of the two regulation strategies. Attention is focused on the two main loss contributions: residual tangential kinetic energy at the rotor outlet and entropy generation. Rotor regulation, even if more complex, provides better results than distributor regulation in terms of both effectiveness (larger flow rate sensitivity to stagger angle variation) and turbine operating efficiency (lower off-design losses).
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(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)
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Techno-Economic and Environmental Analysis of the Integration of PV Systems into Hybrid Vessels
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Lewis McAllister and Haibin Wang
Energies 2024, 17(10), 2303; https://doi.org/10.3390/en17102303 (registering DOI) - 10 May 2024
Abstract
Solar energy is one type of clean energy resource, and currently the IMO, EU and UK are targeting net zero carbon emissions by 2050. This paper delves into the integration of photovoltaic (PV) systems into hybrid vessels in order to meet their strategies
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Solar energy is one type of clean energy resource, and currently the IMO, EU and UK are targeting net zero carbon emissions by 2050. This paper delves into the integration of photovoltaic (PV) systems into hybrid vessels in order to meet their strategies and targets. The technical challenges that come with designing such systems as well as their economic and environmental impacts are examined. By optimizing the usage of harnessed solar energy, we discover the operational strategy that provides maximal benefits through day-to-day savings as well as over the 25 year lifespan of solar panels. It demonstrates impressive economic viability, with cost savings of up to GBP 4.55 per day and a payoff period as short as 9 years. It also displays a modest emission reduction of up to 8.002 kg of CO2, which serves as proof for a pathway to greener practices in the maritime industry. This report highlights the operational flexibility that a hybrid vessel possesses once paired with a PV system through the ability to withstand regulatory and market changes. Also, when looking ahead, further adoption of PV technology creates opportunities for innovation in adopting renewable energy solutions in maritime transportation.
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(This article belongs to the Section B: Energy and Environment)
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Open AccessArticle
A Numerical Simulation and Experimental Study of Fluidization Characteristics of a Bubbling Fluidized Bed in Biomass Gasification
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Na Gao, Kang Zhu, Shiwen Fang, Lisheng Deng, Yan Lin, Zhen Huang, Jun Li and Hongyu Huang
Energies 2024, 17(10), 2302; https://doi.org/10.3390/en17102302 (registering DOI) - 10 May 2024
Abstract
Traditional fossil energy sources still dominate the world energy structure. And fully utilizing biomass is a viable approach for energy transition. A bubbling fluidized bed has better heat and mass transfer, while particle agglomeration limits the development of its industrial application. In this
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Traditional fossil energy sources still dominate the world energy structure. And fully utilizing biomass is a viable approach for energy transition. A bubbling fluidized bed has better heat and mass transfer, while particle agglomeration limits the development of its industrial application. In this paper, two-phase flow characteristics of a bubbling fluidized bed are investigated by combining numerical simulations and fluidized bed gasification experiments. Numerical simulations found that the bed fluidization height reached twice the initial fluidization height at the 0.054 m initial fluidization height with uniform particle distribution. Fluidized bed gasification experiments found that syngas yield increased with increasing temperature. The carbon conversion efficiency reached 79.3% and the effective gas production was 0.64 m3/kg at 850 °C. In addition, when the water vapor concentration reached 15%, the carbon conversion efficiency and effective gas production reached the maximum values of 86.01% and 0.81 m3/kg, respectively.
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(This article belongs to the Section I3: Energy Chemistry)
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Can China’s Regional Industrial Chain Innovation and Reform Policy Make the Impossible Triangle of Energy Attainable? A Causal Inference Study on the Effect of Improving Industrial Chain Resilience
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Tianyu Lu and Hongyu Li
Energies 2024, 17(10), 2301; https://doi.org/10.3390/en17102301 (registering DOI) - 10 May 2024
Abstract
This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can
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This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China’s impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path “innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy” has significantly positive direct and indirect effects in both the treatment group and the control group. However, “innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector’s impossible triangle coupling coordination degree” and “innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector’s impossible triangle coupling coordination degree” have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group.
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(This article belongs to the Section C: Energy Economics and Policy)
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Open AccessArticle
Modeling of a Solar Thermal Plant to Produce Hot Water and Steam for a Brewery Factory
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Kalo G. Traslosheros-Zavala, Ivett Zavala-Guillén, Alexis Acuña-Ramírez, Manuel Cervantes-Astorga, Daniel Sauceda-Carvajal and Francisco J. Carranza-Chávez
Energies 2024, 17(10), 2300; https://doi.org/10.3390/en17102300 - 10 May 2024
Abstract
The environmental impact caused by the intensive exploitation of fossil fuels to generate heat and electricity has already reached a critical level. Also, as the industrial sector is the largest energy consumer, mainly in the form of heat, it has then become compulsive
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The environmental impact caused by the intensive exploitation of fossil fuels to generate heat and electricity has already reached a critical level. Also, as the industrial sector is the largest energy consumer, mainly in the form of heat, it has then become compulsive to implement the use of renewable solar heat in industrial processes, such as those found in the food processing and beverages industries, which do not require high temperatures. Consequently, this study examines the viability of supplying heat as hot water at 80 °C and saturated steam at 160 °C to a medium-sized brewery factory through a hybrid solar plant composed of flat plate and parabolic trough collectors and sensible thermal energy storage. The study was conducted numerically using the meteorological conditions of a city different from that where the factory is located because it benefits from higher insolation levels. The mean annual solar fractions achieved were 49.9% for hot water production and 37.3% for steam generation, at a levelized cost of heat of 0.032 USD/kWh, which can be considered competitive if compared against the values reported in other similar solar projects. Also, the decrease in fossil fuel consumption allowed an annual reduction of 252 tons of carbon dioxide emissions.
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(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Open AccessArticle
Arguments for a Community-Based Approach to Geothermal Energy Development
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Katarzyna A. Kurek, Johan van Ophem and Jacek Strojny
Energies 2024, 17(10), 2299; https://doi.org/10.3390/en17102299 - 10 May 2024
Abstract
This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. We derive it from the integral characteristics of geothermal energy next to the shift in the energy
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This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. We derive it from the integral characteristics of geothermal energy next to the shift in the energy transition policies to focus on managing green resources locally. This study presents arguments for a framework that approaches geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characteristics of geothermal exploitation beyond its geological conditions. It aims to conceptualise a community-based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. This induces the internalisation of labour and technology in the local economic system, a specific condition for local geothermal projects where a community remains a prime beneficiary. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.
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(This article belongs to the Section C: Energy Economics and Policy)
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Open AccessArticle
Practical Experiments with a Ready-Made Strategy for Energizing a Suitable Pre-Magnetized Three-Column Three-Phase Dy Transformer in Unloaded State for Inrush Current Computations
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Marian Łukaniszyn, Łukasz Majka, Bernard Baron, Marcin Sowa, Krzysztof Tomczewski and Krzysztof Wróbel
Energies 2024, 17(10), 2298; https://doi.org/10.3390/en17102298 - 10 May 2024
Abstract
This article presents the results of an experimental verification of three-phase Dy transformer dynamics under no-load conditions. This study is motivated by previous ferroresonance analyses where the occurrence of inrush currents has been observed. The measurements covered all available electrical quantities in a
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This article presents the results of an experimental verification of three-phase Dy transformer dynamics under no-load conditions. This study is motivated by previous ferroresonance analyses where the occurrence of inrush currents has been observed. The measurements covered all available electrical quantities in a transient state (12 measured and 3 additionally computed waveforms) during the device’s start-up under no-load conditions, as well as in a long-term steady state. A detailed analytical analysis is carried out for the obtained comprehensive set of measurement results. As a result of the conducted research, the mathematical model of the pre-magnetized three-phase Dy transformer is modified. Particular attention is paid to the issue of residual magnetism of the transformer core and its consideration in further research. The original strategy for energizing a three-column three-phase Dy transformer with a suitable pre-magnetization of its columns and original control switching system with a given/set value of the initial phase in the supply voltage is put to the test. The evolution of the induced inrush phenomenon up to the quasi-steady state under given (forced) conditions is documented (currents, voltages and the dynamics of changes taking place in the core (hysteresis loops)). This article represents a continuation of ongoing work on the study of transient states (dynamics of transformer inrush currents). At present, the Dy three-phase transformer is analyzed because of the requirements of industrial operators.
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(This article belongs to the Section F1: Electrical Power System)
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Open AccessArticle
Piezoelectric Sensors Pressed by Human Footsteps for Energy Harvesting
by
Kyrillos K. Selim, Idris H. Smaili, Hossam M. Yehia, M. M. R. Ahmed and Demyana A. Saleeb
Energies 2024, 17(10), 2297; https://doi.org/10.3390/en17102297 - 10 May 2024
Abstract
Human footsteps are a sustainable energy source that is derived from kinetic energy. As a result, in this study, piezoelectric sensors placed beneath floor tiles were excited by human footsteps to provide practical electrical energy. A simple rectifying circuit with a filter was
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Human footsteps are a sustainable energy source that is derived from kinetic energy. As a result, in this study, piezoelectric sensors placed beneath floor tiles were excited by human footsteps to provide practical electrical energy. A simple rectifying circuit with a filter was used to capture electrical power. The floor tile is 455 mm in length and 405 mm in width. Two light-emitted diodes were lit up as the actual load by utilising electrical energy obtained from the kinetic energy generated by human footsteps. The greatest attainable power that could be extracted from the suggested floor tile was 249.6 milliwatts, with an approximate cost of $10.2.
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(This article belongs to the Special Issue Analysis of Energy Efficiency: Perspectives and Policies towards Sustainable Development)
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Open AccessArticle
Efficiency of Photosynthetic Microbial Fuel Cells (pMFC) Depending on the Type of Microorganisms Inhabiting the Cathode Chamber
by
Marcin Zieliński, Paulina Rusanowska, Magda Dudek, Adam Starowicz, Łukasz Barczak and Marcin Dębowski
Energies 2024, 17(10), 2296; https://doi.org/10.3390/en17102296 - 10 May 2024
Abstract
Photosynthetic microbial fuel cells (pMFCs) are hybrid systems that enable simultaneous wastewater treatment under anaerobic conditions and the generation of electricity by utilizing the potential difference in the anaerobic anode chamber and the oxygenated cathode chamber. Dairy wastewater with a concentration of 2000
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Photosynthetic microbial fuel cells (pMFCs) are hybrid systems that enable simultaneous wastewater treatment under anaerobic conditions and the generation of electricity by utilizing the potential difference in the anaerobic anode chamber and the oxygenated cathode chamber. Dairy wastewater with a concentration of 2000 mg COD/L was treated in the anode of a batch pMFC. In the cathode chamber, Chlorella vulgaris or Arthrospira platensis was cultivated in synthetic medium, and next in diluted effluent from the anode chamber. The highest power density of 91 mW/m2 was generated by the pMFC with the cultivation of Arthrospira platensis. Higher values of dissolved oxygen remained during the dark phase in the cathodic medium with Arthrospira platensis cultivation than with Chlorella vulgaris. This depletion of oxygen significantly decreased voltage generation, which during the light phase increased again to the maximum values. The COD removal achieved in the anodic chamber was 87%. The efficiency of nitrogen removal in the cathode chamber during the cultivation of Arthrospira platensis and Chlorella vulgaris was about 78% and 69%, respectively. The efficiency of phosphorus removal in the cathode chamber with the cultivation of Arthrospira plantensis and Chlorella vulgaris was 58% and 43%, respectively. This study has shown that the introduction of Arthrospira platensis into the cathode chamber is more effective than that of Chlorella vulgaris.
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(This article belongs to the Section B: Energy and Environment)
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Open AccessArticle
An Open-Source Supervisory Control and Data Acquisition Architecture for Photovoltaic System Monitoring Using ESP32, Banana Pi M4, and Node-RED
by
Wei He, Mirza Jabbar Aziz Baig and Mohammad Tariq Iqbal
Energies 2024, 17(10), 2295; https://doi.org/10.3390/en17102295 - 10 May 2024
Abstract
To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for
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To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper.
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(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Open AccessReview
A Review of the Structure–Property Relationship of Nickel Phosphides in Hydrogen Production
by
Linyuan Chen and Xian-Kui Wei
Energies 2024, 17(10), 2294; https://doi.org/10.3390/en17102294 - 10 May 2024
Abstract
Hydrogen, one of the most promising forms of new energy sources, due to its high energy density, low emissions, and potential to decarbonize various sectors, has attracted significant research attention. It is known that electrocatalytic hydrogen production is one of the most widely
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Hydrogen, one of the most promising forms of new energy sources, due to its high energy density, low emissions, and potential to decarbonize various sectors, has attracted significant research attention. It is known that electrocatalytic hydrogen production is one of the most widely investigated research directions due to its high efficiency in the conversion of electricity to H2 gas. However, given the limited reserves and high cost of precious metals, the search for non-precious metal-based catalysts has been widely explored, for example, transition metal phosphides, oxides, and sulfides. Despite this interest, a detailed survey unveils that the surface and internal structures of the alternative catalysts, including their surface reconstruction, composition, and electronic structure, are poorly studied. As a result, a disconnection in the structure–property relationship severely hinders the rational design of efficient and reliable non-precious metal-based catalysts. In this review, by focusing on Ni5P4, a bifunctional catalyst for water splitting, we systematically summarize the material motifs pertaining to the different synthetic methods, surface characteristics, and hydrolysis properties. It is believed that a cascaded correlation may provide insights toward understanding the fundamental catalytic mechanism and design of robust alternative catalysts for hydrogen production.
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(This article belongs to the Section A5: Hydrogen Energy)
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