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
Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis
Energies 2024, 17(10), 2251; https://doi.org/10.3390/en17102251 (registering DOI) - 07 May 2024
Abstract
Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is
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Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.
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(This article belongs to the Section A1: Smart Grids and Microgrids)
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Modeling, Control and Diagnosis of Electrical Machines and Devices
by
Moussa Boukhnifer and Larbi Djilali
Energies 2024, 17(10), 2250; https://doi.org/10.3390/en17102250 (registering DOI) - 07 May 2024
Abstract
Nowadays, the increasing use of electrical machines and devices in more critical applications has driven the research in condition monitoring and fault tolerance [...]
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(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)
Open AccessArticle
RoUIE: A Method for Constructing Knowledge Graph of Power Equipment Based on Improved Universal Information Extraction
by
Zhenhao Ye, Donglian Qi, Hanlin Liu, Yunfeng Yan, Qihao Chen and Xiayu Liu
Energies 2024, 17(10), 2249; https://doi.org/10.3390/en17102249 - 07 May 2024
Abstract
The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from
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The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model’s extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model’s universal applicability.
Full article
(This article belongs to the Section C: Energy Economics and Policy)
Open AccessArticle
Active Fault-Locating Scheme for Hybrid Distribution Line Based on Mutation of Aerial-Mode Injected Pulse
by
Zhuang Jiang, Xiangjun Zeng, Feng Liu, Kun Yu, Lanxi Bi and Youpeng Wang
Energies 2024, 17(10), 2248; https://doi.org/10.3390/en17102248 - 07 May 2024
Abstract
Due to the overlap of initial traveling wave signals, the traveling wave propagation process in hybrid distribution lines is complicated to analyze. The most significant challenge posed by the traditional passive traveling wave-locating method for hybrid distribution lines lies in identifying the fault
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Due to the overlap of initial traveling wave signals, the traveling wave propagation process in hybrid distribution lines is complicated to analyze. The most significant challenge posed by the traditional passive traveling wave-locating method for hybrid distribution lines lies in identifying the fault section and distinguishing the reflected wave from the fault point or the hybrid connection points. Based on this approach, with the application of the aerial-mode component of the pulse signal generated at the fault point, a fault-section-identification and fault-locating scheme for hybrid distribution feeders with active pulse injection is proposed. When power in a line is cut after a single-to-line ground (SLG) fault occurs, the same pulse is injected into the three phases from the neutral point of the coupling capacitor bank to construct the zero-mode component, which propagates to the SLG fault three-phase asymmetrical point, producing an aerial-mode component that is reflected back to the first end of the line. With the application of the arrival time of an aerial-mode wavefront, it is simple to locate the SLG fault for arbitrary forms of hybrid lines. The simulation results confirm the feasibility of the fault-locating scheme under different feeders, different fault locations, and fault resistances. The results of the experiments confirm the high practical value of the proposed method.
Full article
(This article belongs to the Section F: Electrical Engineering)
Open AccessArticle
Environmental Design on Site-Specific Energy Solidary Communities around Public High Schools in the Metropolitan Area of Naples (Italy)
by
Roberto Bosco, Savino Giacobbe, Salvatore Losco, Louise Anna Mozingo and Renata Valente
Energies 2024, 17(10), 2247; https://doi.org/10.3390/en17102247 - 07 May 2024
Abstract
Renewable energy communities (RECs) around photovoltaic systems on public buildings are optimal solutions to counter energy poverty, ensuring all stakeholders access to cheap, reliable, sustainable, and modern energy systems. As the neighborhood is the minimum suitable unit for the implementation of highly sustainable
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Renewable energy communities (RECs) around photovoltaic systems on public buildings are optimal solutions to counter energy poverty, ensuring all stakeholders access to cheap, reliable, sustainable, and modern energy systems. As the neighborhood is the minimum suitable unit for the implementation of highly sustainable settlements, this article discusses the potential and criticality of RECs at this scale in southern Italy. Starting with the concept of RECs, this study presents a methodology to size sustainable urban communities around school buildings. It integrates practical energy indicators with those defining performance in vegetation and water management. The impact of these factors is analyzed to identify the ideal community size in terms of energy efficiency, economic value, and social cohesion. An interactive scorecard ranks high school sites suitable for transformation into community hubs, taking into consideration the scale of substation distribution. The findings provide empirically validated operational guidelines and best practices to support the transition to smart, efficient, and socially inclusive communities. At the urban scale, the analysis evaluates different urban morphologies, microclimates, characteristics and density of buildings, and population around each assumed community hub. The study provides valuable guidance to local designers, planners, and administrators for the implementation of sustainable technologies by preparing a map of potential RECs.
Full article
(This article belongs to the Special Issue Emerging Trends in Energy and Environmental Design Integrating New Services and Tools for Smart Cities and Smart Buildings)
Open AccessArticle
Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden
by
Pontus Netzell, Hussain Kazmi and Konstantinos Kyprianidis
Energies 2024, 17(10), 2246; https://doi.org/10.3390/en17102246 - 07 May 2024
Abstract
As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address
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As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.
Full article
(This article belongs to the Section A: Sustainable Energy)
Open AccessArticle
Uncertainty Quantification of Engineering Parameters for a Nuclear Reactor Loaded with Dispersed Fuel Particles
by
Yukun Li, Zhenping Chen, Chao Yang, Guocai Huang, Kekun Gao, Aikou Sun, Chengwei Liu and Zhiqiang Wu
Energies 2024, 17(10), 2245; https://doi.org/10.3390/en17102245 - 07 May 2024
Abstract
Owing to their high intrinsic safety, dispersed fuel particles are an important fuel pattern for fourth-generation nuclear reactors. Due to the unique cladding layers and the random dispersion characteristics, dispersed fuel particles significantly differ from pressurized water reactors regarding operation-induced uncertainty. This study
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Owing to their high intrinsic safety, dispersed fuel particles are an important fuel pattern for fourth-generation nuclear reactors. Due to the unique cladding layers and the random dispersion characteristics, dispersed fuel particles significantly differ from pressurized water reactors regarding operation-induced uncertainty. This study quantitatively analyzed overall uncertainty while considering a random distribution of dispersed fuel particles, material thickness, and fuel enrichment. The results demonstrated that, for all packing fractions, the uncertainty induced by the random dispersion of dispersed fuel particles was below 0.03%. For every packing fraction, the differences between the results obtained by the regular and the random distribution models increased, and then decreased, until reaching its maximum (1.297%) at 15%. Keff decreased as the radius of the UO2 kernel increased; Keff increased as the thickness of the cladding layer increased; the uncertainty of Keff was 1.003% when a random distribution of particles, material thickness, and fuel enrichment were taken into consideration; the uncertainty of the power distribution of reactor core assemblies was maximized (1.495%) at the edge of the reactor core. Quantitative analysis of uncertainty provides references for the optimization of design and safety margin analysis for reactors.
Full article
(This article belongs to the Special Issue New Advances and Novel Technologies in the Nuclear Industry)
Open AccessArticle
Numerical Study of Fluid Flow in a Gyroid-Shaped Heat Transfer Element
by
Martin Beer and Radim Rybár
Energies 2024, 17(10), 2244; https://doi.org/10.3390/en17102244 - 07 May 2024
Abstract
This paper deals with the design of porous geometry of a heat transfer element. The proposed geometry combines a gyroid triply periodic minimal surface with the recursive principle of geometric body creation. The designed geometry is based on an attempt to increase the
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This paper deals with the design of porous geometry of a heat transfer element. The proposed geometry combines a gyroid triply periodic minimal surface with the recursive principle of geometric body creation. The designed geometry is based on an attempt to increase the heat transfer surface while eliminating negative impacts on the fluid characteristics in the form of pressure loss or increase of the friction coefficient. The proposed geometry of the heat transfer element was compared with a pair of geometries based on the basic gyroid shape but with different channel size parameters. A numerical simulation was performed in Ansys Fluent 2020 R1 using the SST k-omega turbulence model for flow velocities in the range of 0.01 m.s−1 to 0.5 m.s−1, which covered a wide range of the Reynolds number and thus also flow forms in terms of the turbulence intensity. The presented results clearly show lower values of pressure loss and friction coefficient of the proposed geometry compared to the evaluated porous structures. Also, at the same time, they describe the factors positively influencing the mixing process of the liquid in the proposed element, which leads to an increase in the efficiency of the heat transfer process.
Full article
(This article belongs to the Topic Advanced Heat and Mass Transfer Technologies)
Open AccessArticle
Optimized Dynamic Vehicle-to-Vehicle Charging for Increased Profit
by
Shorooq Alaskar and Mohamed Younis
Energies 2024, 17(10), 2243; https://doi.org/10.3390/en17102243 - 07 May 2024
Abstract
Many challenges have arisen as a result of the rapid growth of the electric vehicles (EVs) market, due to the lack of charging infrastructure capable of handling such a large number of EVs. To alleviate power grid system overloads and reduce the cost
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Many challenges have arisen as a result of the rapid growth of the electric vehicles (EVs) market, due to the lack of charging infrastructure capable of handling such a large number of EVs. To alleviate power grid system overloads and reduce the cost of corresponding infrastructure deployments, a direct vehicle-to-vehicle (V2V) energy exchange strategy has become an emerging research topic. In this paper, we formulate the problem of V2V energy charging on a time–space network and develop a dynamic-programming solution methodology for efficiently finding the solution. The algorithm can pair and route the energy supplier (ES) and the requester (ER) in such a way that maximizes the supplier’s profit. Specifically, the ES is incentivized to rendezvous ERs at any encounter nodes in order to dispense the requested energy amount through platooning. Unlike existing V2V charging solutions, our approach involves charging while vehicles are in motion. We validate the effectiveness of our approach in maximizing the profit of the ES and reducing the incurred overhead on the ER in terms of increased trip time, distance, and energy consumption.
Full article
(This article belongs to the Section E: Electric Vehicles)
Open AccessArticle
Numerical Investigation of Interventions to Mitigate Heat Stress: A Case Study in Dubai
by
Talha Batuhan Korkut and Ahmed Rachid
Energies 2024, 17(10), 2242; https://doi.org/10.3390/en17102242 - 07 May 2024
Abstract
Urbanization and changes in microclimate have negative impacts on outdoor thermal comfort, making urban design more important. This study aims to improve outdoor thermal comfort in a local climate zone (LCZ) in Dubai using computational fluid dynamics (CFD) methods. This study evaluates cooling
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Urbanization and changes in microclimate have negative impacts on outdoor thermal comfort, making urban design more important. This study aims to improve outdoor thermal comfort in a local climate zone (LCZ) in Dubai using computational fluid dynamics (CFD) methods. This study evaluates cooling interventions, such as vegetation, architectural, and pavement material, using Reynolds-averaged Navier–Stokes (RANS) equations and the SIMPLE scheme. The results show that a combination of cooling interventions affects the average temperature between 4.44 °C and 6.14 °C. Light-colored ground material has a 5.4 °C cooling effect in the LCZ compared with dark-colored materials. The predicted mean vote (PMV) method is used to compare outdoor thermal comfort and the results show that thermal sensation in the LCZ improves from warm to slightly cool. Lastly, the most effective cooling interventions are, in order, shade structures, trees, chimneys, and bushes.
Full article
(This article belongs to the Special Issue Building Energy Audits-Diagnosis and Retrofitting towards Decarbonization and Sustainable Cities II)
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Open AccessArticle
Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers
by
Johnny Rengifo, Jordan Moreira, Fernando Vaca-Urbano and Manuel S. Alvarez-Alvarado
Energies 2024, 17(10), 2241; https://doi.org/10.3390/en17102241 - 07 May 2024
Abstract
Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate
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Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.
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(This article belongs to the Special Issue Applications of Machine Learning and Optimization in Energy Sectors)
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Spatial Graphene Structures with Potential for Hydrogen Storage
by
Krzysztof Jastrzębski, Marian Cłapa, Łukasz Kaczmarek, Witold Kaczorowski, Anna Sobczyk-Guzenda, Hieronim Szymanowski, Piotr Zawadzki and Piotr Kula
Energies 2024, 17(10), 2240; https://doi.org/10.3390/en17102240 - 07 May 2024
Abstract
Spatial graphene is a 3D structure of a 2D material that preserves its main features. Its production can be originated from the water solution of graphene oxide (GO). The main steps of the method include the crosslinking of flakes of graphene via treatment
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Spatial graphene is a 3D structure of a 2D material that preserves its main features. Its production can be originated from the water solution of graphene oxide (GO). The main steps of the method include the crosslinking of flakes of graphene via treatment with hydrazine, followed by the reduction of the pillared graphene oxide (pGO) with hydrogen overpressure at 700 °C, and further decoration with catalytic metal (palladium). Experimental research achieved the formation of reduced pillared graphene oxide (r:pGO), a porous material with a surface area equal to 340 m2/g. The transition from pGO to r:pGO was associated with a 10-fold increase in pore volume and the further reduction of remaining oxides after the action of hydrazine. The open porosity of this material seems ideal for potential applications in the energy industry (for hydrogen storage, in batteries, or in electrochemical and catalytic processes). The hydrogen sorption potential of the spatial graphene-based material decorated with 6 wt.% of palladium reached 0.36 wt.%, over 10 times more than that of pure metal. The potential of this material for industrial use requires further refining of the elaborated procedure, especially concerning the parameters of substrate materials.
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(This article belongs to the Special Issue Research on Advanced Energy Materials for Meeting Global Energy Challenges)
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Open AccessArticle
Technical and Economic Aspects of Environmentally Sustainable Investment in Terms of the EU Taxonomy
by
Józef Ciuła, Agnieszka Generowicz, Aneta Oleksy-Gębczyk, Anna Gronba-Chyła, Iwona Wiewiórska, Paweł Kwaśnicki, Piotr Herbut and Viktor Koval
Energies 2024, 17(10), 2239; https://doi.org/10.3390/en17102239 - 07 May 2024
Abstract
Removing impurities that occur in landfill gas, from sulphur and silicon compounds, is crucial for the energetic use of biogas in a cogeneration unit for energy purposes. The aim of this study was to analyse the shaped activated carbon, examining its structure and
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Removing impurities that occur in landfill gas, from sulphur and silicon compounds, is crucial for the energetic use of biogas in a cogeneration unit for energy purposes. The aim of this study was to analyse the shaped activated carbon, examining its structure and elemental composition as part of the biogas purification. The qualitative study of the purified landfill gas performed in this study showed a significant overshoot of hydrogen sulphide at 304.1 ppm with respect to the gas engine manufacturer’s requirements, while the calculated hydrogen sulphide reduction efficiency was 24.58%. Examination of the surface of the spent carbon and its pores with a scanning microscope revealed a high level of clotting by sulphur compounds, which prevents proper reduction of this compound and reduces the efficiency of the treatment plant. Replacement of the activated carbon bed with a new one showed a hydrogen sulphide value of 7.5 ppm in the purified gas and a calculated reduction efficiency of 97.9%. The results of the study confirmed that continuous monitoring of the quality of the purified gas is necessary to control the adsorption properties of the activated carbon and can be used for the operation of gas engines in cogeneration units. The landfill gas treatment method described in this paper constitutes an environmentally sustainable project within the meaning of the EU regulation on the establishment of a framework to promote and facilitate this type of investment in terms of its financing and operation. The topic of the work fits into three key areas of broad research and implementation activities. The first, technological, is the transition to a low-carbon, sustainable and resource-efficient closed-loop economy; the second, environmental, pollution prevention and control. The third area is economics and finance in terms of making financial products available designed to reduce climate change and reporting on these activities.
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(This article belongs to the Special Issue New Challenges in Waste-to-Energy and Bioenergy Systems)
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Open AccessArticle
Optimal Planning of PV Sources and D-STATCOM Devices with Network Reconfiguration Employing Modified Ant Lion Optimizer
by
Sujatha B. C., Usha A. and Geetha R. S.
Energies 2024, 17(10), 2238; https://doi.org/10.3390/en17102238 - 07 May 2024
Abstract
This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the
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This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the majority of the research is based on constant power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the DSTATCOM device’s optimal size and position. The effect of the constant power (CP) and polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration is examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm is verified on 118-node test systems. The investigation is carried out for planning network upgrading to a high-voltage distribution system (HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited (BESCOM) radial distribution scheme. The simulated results obtained with this method are validated with the BAT algorithm and techniques available in the literature. It is observed that in the IEEE 118-bus system, via the simultaneous placement and sizing of PV sources considering a 25% penetration level and DSTATCOM devices during network reconfiguration, the total power loss reduction is 41.47% and 42.98% for the constant power model and ZIP with the load growth model. For the 317-bus system, the total power loss reduction observed for 11 kV is 49.77% and 59.34% for the constant power model and ZIP model with load growth. Similarly, for the 22 kV system, the power loss reduction observed is 51.69% and 55.75% for the constant power model and ZIP with the load growth model.
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(This article belongs to the Section F2: Distributed Energy System)
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Effects of Removing Energy Subsidies and Implementing Carbon Taxes on Urban, Rural and Gender Welfare: Evidence from Mexico
by
Jorge Alberto Rosas Flores, David Morillón Gálvez and Rodolfo Silva
Energies 2024, 17(9), 2237; https://doi.org/10.3390/en17092237 - 06 May 2024
Abstract
The demand for different energy goods and services is a fundamental component in a country’s economic structure for development. Understanding it is vital in designing economic policies, such as taxes, that can improve the welfare of the population. A comprehension of the distributional
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The demand for different energy goods and services is a fundamental component in a country’s economic structure for development. Understanding it is vital in designing economic policies, such as taxes, that can improve the welfare of the population. A comprehension of the distributional effects of elasticities and the application of them to simulate household responses to price changes, as well as a calculation of the welfare impacts on poor and rich households in Mexico, should inform policy design. This paper uses the Household Income and Expenditure Survey (ENIGH) from 1996 to 2018 to estimate the demand of Mexican households for fuels, specifically electricity, liquefied petroleum gas, and gasoline. A Quasi Ideal Quadratic Demand System (QUAIDS) is employed to analyse the effects of removing energy subsidies and introducing a carbon tax. The results indicate that welfare losses would be regressive concerning electricity price increases, while changes in gasoline prices would be progressive. Redistributing the tax revenues accrued by removing energy subsidies and imposing the carbon tax would have more progressive effects on the economy of Mexican households, with welfare gains of up to 350% for the poorest households in the case of electricity consumption taxes.
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(This article belongs to the Special Issue Economic Analysis and Policies in the Energy Sector)
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Numerical Investigation of a Two-Phase Ejector Operation Taking into Account Steam Condensation with the Presence of CO2
by
Tomasz Kuś and Paweł Madejski
Energies 2024, 17(9), 2236; https://doi.org/10.3390/en17092236 - 06 May 2024
Abstract
The application of a two-phase ejector allows for the mixing of liquid and gas and provides effective heat transfer between phases. The aim of the study is a numerical investigation of the performance of a water-driven, condensing two-phase ejector. The research was performed
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The application of a two-phase ejector allows for the mixing of liquid and gas and provides effective heat transfer between phases. The aim of the study is a numerical investigation of the performance of a water-driven, condensing two-phase ejector. The research was performed using CFD methods, which can provide an opportunity to analyze this complex phenomenon in 2D or 3D. The 2D axisymmetric model was developed using CFD software Siemens StarCCM+ 2022.1.1. The Reynolds-Averaged Navier–Stokes (RANS) approach with the Realisable k-ε turbulence model was applied. The multiphase flow was calculated using the mixture model. The boiling/condensation model, where the condensation rate is limited by thermal diffusion, was applied to take into account direct contact condensation. Based on the mass balance calculations and developed pressure and steam volume fraction distributions, the ejector performance was analyzed for various boundary conditions. The influence of the suction pressure (range between 0.812 and 0.90) and the steam mass flow rate (range between 10 g/s and 25 g/s) is presented to investigate the steam condensation phenomenon inside the ejector condenser. The provided mixture of inert gas (CO2) with steam (H2O) in the ejector condenser was investigated also. The weakening of the steam condensation process by adding CO2 gas was observed, but it is still possible to achieve effective condensation despite the presence of inert gas.
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(This article belongs to the Special Issue Advances in Numerical Modeling of Multiphase Flow and Heat Transfer)
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Open AccessArticle
Comprehensive Dynamic Interaction Studies in Inverter-Penetrated Power Systems
by
Fujian Li and Jin Ma
Energies 2024, 17(9), 2235; https://doi.org/10.3390/en17092235 - 06 May 2024
Abstract
In a renewable-energy-penetrated power system (RPPS), inverter-based resources (IBRs) pose serious challenges to power system stability due to their completely different dynamic characteristics compared with conventional generators; thus, it is necessary to study the dynamic interactions between IBRs and power systems. Although many
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In a renewable-energy-penetrated power system (RPPS), inverter-based resources (IBRs) pose serious challenges to power system stability due to their completely different dynamic characteristics compared with conventional generators; thus, it is necessary to study the dynamic interactions between IBRs and power systems. Although many research efforts have been dedicated to this topic from both power electronics and power system researchers, some research from the power electronics field treats the external power system as a voltage source with an impedance, therefore ignoring the dynamic characteristics of a power system, while most of the research from the power system field applies simulation-based methods, for which it is difficult to directly interpret the interaction mechanism of IBRs and external system dynamics. Thus, none of these studies can explore the accurate dynamic interaction mechanism between IBRs and power systems, leading to performance degradation of IBR-integrated power systems. Our study takes into account the dynamic characteristics of both IBRs and the external power system, resulting in the development of a new open-loop transfer function for RPPSs. Based on this formulation, it is observed that under certain operating conditions, the dynamic interactions between the inverter and the power system help enhance IBR-penetrated power system stability compared with the case for which the external power system is controlled as a voltage source. The study also reveals how the inverter (phase-locked loop, control parameters, etc.), external power system (network strength) and penetration ratio in an IBR-penetrated power system affect the dynamic interactions between IBRs and the external power system using the proposed quantified interaction indices.
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(This article belongs to the Section F: Electrical Engineering)
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Optimal Scheduling Strategy for Distribution Network with Mobile Energy Storage System and Offline Control PVs to Minimize the Solar Energy Curtailment
by
San Kim and Jinyeong Lee
Energies 2024, 17(9), 2234; https://doi.org/10.3390/en17092234 - 06 May 2024
Abstract
As offline control photovoltaic (PV) plants are not equipped with online communication and remote control systems, they cannot adjust their power in real-time. Therefore, in a distribution network saturated with offline control PVs, the distribution system operator (DSO) should schedule the distributed energy
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As offline control photovoltaic (PV) plants are not equipped with online communication and remote control systems, they cannot adjust their power in real-time. Therefore, in a distribution network saturated with offline control PVs, the distribution system operator (DSO) should schedule the distributed energy resources (DERs) considering the uncertainty of renewable energy to prevent curtailment due to overvoltage. This paper presents a day-ahead network operation strategy using a mobile energy storage system (MESS) and offline control PVs to minimize power curtailment. The MESS model efficiently considers the transportation time and power loss of the MESS, and models various operating modes, such as the charging, discharging, idle, and moving modes. The optimization problem is formulated based on mixed-integer linear programming (MILP) considering the spatial and temporal operation constraints of MESSs and is performed using chanced constrained optimal power flow (CC-OPF). The upper limits for offline control PVs are set based on the probabilistic approach, thus mitigating overvoltage due to forecasting errors. The proposed operation strategy was tested in the IEEE 33-node distribution system coupled with a 15-node transportation system. The test results show the effectiveness of the proposed method for minimizing curtailment in offline control PVs.
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(This article belongs to the Special Issue Optimal Planning and Operation in RES-Rich Power Systems under Electricity and Carbon Emission Market Environment)
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Open AccessArticle
Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency
by
Serhii Vladov, Ruslan Yakovliev, Maryna Bulakh and Victoria Vysotska
Energies 2024, 17(9), 2233; https://doi.org/10.3390/en17092233 - 06 May 2024
Abstract
The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct
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The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct propagation neural network with linear neurons in the output layer for training in which the scale conjugate gradient algorithm is modified by introducing a moment coefficient into the analytical expression. This modification helps in calculating new model parameters to avoid falling into a local minimum. The dependence of the energy released during helicopter turboshaft engine compressor rotation on the gas-generator rotor r.p.m. was obtained. This enables the determination of the optimal gas-generator rotor r.p.m. region for a specific type of helicopter turboshaft engine. The optimal ratio of energy consumption and compressor operating efficiency is achieved, thereby ensuring helicopter turboshaft engines’ optimal performance and reliability. Experimental data support the high efficiency of using a three-layer feed-forward neural network with linear neurons in the output layer, trained using a modified scale conjugate gradient algorithm, for approximating parameters of helicopter turboshaft engines compared to the analogues. Specifically, this method better predicts the relations between the energy release during compressor rotation and gas-generator rotor r.p.m. The efficiency coefficient of the proposed method was 0.994, which exceeded that of the closest analogue (0.914) by 1.09 times.
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(This article belongs to the Section I2: Energy and Combustion Science)
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Open AccessArticle
Ventilation Methods for Improving the Indoor Air Quality and Energy Efficiency of Multi-Family Buildings in Central Europe
by
Joanna Ferdyn-Grygierek and Krzysztof Grygierek
Energies 2024, 17(9), 2232; https://doi.org/10.3390/en17092232 - 06 May 2024
Abstract
In Poland and other countries in Central Europe, residential buildings from the second half of the 20th century dominate, which have recently undergone deep thermomodernisation. Research on the retrofitting of residential buildings has focused mainly on energy efficiency, with only a few studies
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In Poland and other countries in Central Europe, residential buildings from the second half of the 20th century dominate, which have recently undergone deep thermomodernisation. Research on the retrofitting of residential buildings has focused mainly on energy efficiency, with only a few studies on indoor air quality. The aim of this study was to present a comparative analysis of the impact of five ventilation scenarios (three natural and two mechanical) on CO2 concentration and energy demand for heating and ventilation in residential spaces of a multi-family building located in Poland. The analyses were based on the results of building performance co-simulation using the EnergyPlus and CONTAM programs carried out under dynamic conditions with a 5 min time step for the entire heating season. The calculations took into account the instantaneous occupancy variability of twenty apartments. In the buildings equipped with new tight windows, the natural ventilation system provided extremely low air exchange (on average 0.1 h−1) and poor indoor air quality (average CO2 concentration at the level of 2500 ppm). Opening windows to ventilate the rooms generated a multiple increase (up to 8 times) in heating demand during these periods, but average CO2 concentration was on the level of 930 ppm. The use of mechanical ventilation was profitable both in terms of energy savings (at the level of 50%) and improvement in the indoor air.
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(This article belongs to the Special Issue Thermal Comfort, Environment Quality and Energy Consumption)
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