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
Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles
Energies 2024, 17(9), 2163; https://doi.org/10.3390/en17092163 (registering DOI) - 30 Apr 2024
Abstract
A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management
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A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 C, which is the highest operating temperature attained under the proposed strategy.
Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
Open AccessArticle
Analyzing Geospatial Cost Variability of Hybrid Solar–Gravity Storage System in High-Curtailment Suburban Areas
by
Soumya Basu, Tetsuhito Hoshino and Hideyuki Okumura
Energies 2024, 17(9), 2162; https://doi.org/10.3390/en17092162 (registering DOI) - 30 Apr 2024
Abstract
The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However,
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The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However, GES costs vary geospatially, specifically in decentralized suburban areas, due to the impact of urban socioeconomics. This study aims to find a mathematical approximation of a cost-optimized location for suburban Solar–GES hybrid systems in curtailment-prone areas. A multi-parameterization model mathematically programmed land, transmission, supply chain and excavation costs into geospatial matrix approximations for suburban areas of 2500 km2 in Fukuoka and Ibaraki in Japan. It was found that SPV-GES location-dependent costs were mainly affected by distance from the city’s economic center and flat plains in suburbs, and supply chain and transmission costs optimized the location-dependent cost for GES at a specific point. It was also found that flat terrains were more economical than mountainous terrains due to high GES supply chain costs. With GES found to be cost-competitive compared to other storage technologies in Japan, this study reveals that GES introduction benefits the LCOE of suburban, decentralized SPV when curtailment is >50% irrespective of terrain.
Full article
(This article belongs to the Topic Multiple Roads to Achieve Net-Zero Emissions by 2050)
Open AccessArticle
An Off-Site Power Purchase Agreement (PPA) as a Tool to Protect against Electricity Price Spikes: Developing a Framework for Risk Assessment and Mitigation
by
Karolina Kapral, Kobe Soetaert and Rui Castro
Energies 2024, 17(9), 2161; https://doi.org/10.3390/en17092161 (registering DOI) - 30 Apr 2024
Abstract
Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food
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Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food insecurity, business bankruptcies and recession. A well-known tool to protect energy consumers from energy price spikes, while at the same time contributing to the development of sustainable technologies, is Power Purchase Agreements. PPAs are long-term bilateral contracts for the purchase and sale of a certain amount of electricity, usually generated from renewable sources. The primary goal of this paper is to assess how the risk associated with PPAs has evolved between 2020 and 2023. It aims to examine whether, after the events in 2022, PPAs remain a robust solution that protects the off-taker from energy price spikes, ensures greater energy budget stability and enables savings. To achieve this, the probability of PPA prices being higher than market prices is evaluated, considering the changing market landscape. Furthermore, this paper intends to gain a thorough understanding of each risk related to PPAs and the best strategies for mitigating it, to maximize the protection of the off-taker.
Full article
(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)
Open AccessReview
Review of Fuel-Cell Electric Vehicles
by
Tingke Fang, Coleman Vairin, Annette von Jouanne, Emmanuel Agamloh and Alex Yokochi
Energies 2024, 17(9), 2160; https://doi.org/10.3390/en17092160 (registering DOI) - 30 Apr 2024
Abstract
This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology
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This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future.
Full article
(This article belongs to the Section E: Electric Vehicles)
Open AccessArticle
A New Method for the Techno-Economic Analysis and the Identification of Expansion Strategies of Neutral-Temperature District Heating and Cooling Systems
by
Selva Calixto, Marco Cozzini, Roberto Fedrizzi and Giampaolo Manzolini
Energies 2024, 17(9), 2159; https://doi.org/10.3390/en17092159 (registering DOI) - 30 Apr 2024
Abstract
Neutral-temperature district heating and cooling (NT-DHC) is a recent concept in the district heating sector. The current literature does not directly address the ability to create comprehensive master plans for NT-DHC systems and reliably model their performance. This research presents a new approach
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Neutral-temperature district heating and cooling (NT-DHC) is a recent concept in the district heating sector. The current literature does not directly address the ability to create comprehensive master plans for NT-DHC systems and reliably model their performance. This research presents a new approach for the evaluation and planning of NT-DHC systems. The methodology involves the use of a knapsack optimization algorithm to perform a comprehensive analysis of the conditions that make the NT-DHC solution competitive against individual heating and cooling technologies. The algorithm determines the optimal combination of potential extensions that maximizes overall economic value. The results of a case study, which was conducted in Italy, show that NT-DHC is more suitable in dense urban areas, while air-to-water heat pumps are better suited for low heat density zones. This methodology aims to reduce the risks associated with energy demand and provide more certainty about which areas a network can expand into to be competitive. It is targeted at energy planners, utilities experts, energy engineers, and district heating experts who require assistance and guidance in the planning and early stages of designing a NT-DHC system. This method might enable pre-feasibility studies and preliminary design to determine the opportunities and limitations of a system of this kind from an economic and technological perspective.
Full article
(This article belongs to the Topic District Heating and Cooling Systems)
Open AccessArticle
Decision Process for Identifying Appropriate Devices for Power Transfer between Voltage Levels in Distribution Grids
by
Nassipkul Dyussembekova, Reiner Schütt, Ingmar Leiße and Bente Ralfs
Energies 2024, 17(9), 2158; https://doi.org/10.3390/en17092158 (registering DOI) - 30 Apr 2024
Abstract
During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids
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During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered.
Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
Open AccessArticle
Optimised Congestion Management Using Curative Measures in Combined AC/DC Systems with Flexible AC Transmission Systems
by
Denis Mende and Lutz Hofmann
Energies 2024, 17(9), 2157; https://doi.org/10.3390/en17092157 (registering DOI) - 30 Apr 2024
Abstract
Due to the increasing demand for transport of electrical energy, measures for power flow control, congestion management, and higher utilisation of the existing grid play a decisive role in the transformation of the power system. Hence, enormous efforts must be undertaken using measures
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Due to the increasing demand for transport of electrical energy, measures for power flow control, congestion management, and higher utilisation of the existing grid play a decisive role in the transformation of the power system. Hence, enormous efforts must be undertaken using measures of congestion management. Modelling and integration of corresponding measures in optimisation tools to support grid and system operation and therewith reduce the resulting efforts become more important. This is especially true because of the high intermittency and decentralisation of renewable generation leading to increased complexity of the power system, higher loading of assets, and a growing need for control over flexible alternating current transmission systems (FACTS) and high-voltage direct current (HVDC) converters. This work therefore describes the implementation of optimised congestion management in an A Mathematical Programming Language (AMPL)-based nonlinear optimisation problem. AMPL is an effective tool to deal with highly complex problems of optimisation and scheduling. Therefore, the modelling of assets and flexibilities for power flow control in AC/DC systems in combination with an innovative grid operation strategy using predefined curative measures for the optimised use of the existing grid is introduced. The nonlinear mathematical optimisation aims at the optimal cost selection of flexibility measures. The application of the optimisation technique in a combined AC/DC system shows the optimal preventive and curative use of measures in operational congestion management. Simulation results prove that, by using predefined curative measures, the volume of cost-intensive preventive measures can significantly be reduced, especially in association with power flow control.
Full article
(This article belongs to the Section F1: Electrical Power System)
Open AccessArticle
Optimal Scheduling of Off-Site Industrial Production in the Context of Distributed Photovoltaics
by
Sizhe Xie, Yao Li and Peng Wang
Energies 2024, 17(9), 2156; https://doi.org/10.3390/en17092156 (registering DOI) - 30 Apr 2024
Abstract
A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational
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A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China’s power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.
Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
Open AccessArticle
Laboratory Tests of Electrical Parameters of the Start-Up Process of Single-Cylinder Diesel Engines
by
Jacek Caban, Jarosław Seńko and Piotr Ignaciuk
Energies 2024, 17(9), 2155; https://doi.org/10.3390/en17092155 (registering DOI) - 30 Apr 2024
Abstract
Despite continuous work on new power systems for vehicles, machines, and devices, the combustion engine is still the dominant system. The operation of the combustion engine is initiated during the starting process using starting devices. The most common starting system used is the
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Despite continuous work on new power systems for vehicles, machines, and devices, the combustion engine is still the dominant system. The operation of the combustion engine is initiated during the starting process using starting devices. The most common starting system used is the electric starter. The starting process of an internal combustion engine depends on the following factors: the technical condition of the starting system, technical condition of the engine, battery charge level, lubricating properties, engine standstill time, engine and ambient temperature, type of fuel, etc. This article presents the results of laboratory tests of the electrical parameters of the starting process of a single-cylinder compression–ignition engine with variable fuel injection parameters and ambient temperature conditions. It was confirmed that for the increased fuel dose FD2, higher values of the measured electrical parameters (Imax, Pmax, and Pmed) were obtained compared to the series of tests with the nominal fuel dose. Knowledge of the values of the electrical parameters of the starting process is important not only for the user (vehicle driver, agricultural machinery operator, etc.), but above all for designers of modern starting systems for combustion engines and service personnel. The obtained results of testing the electrical parameters of the combustion engine during start-up may be helpful in designing new drive systems supported by a compression–ignition combustion engine.
Full article
(This article belongs to the Special Issue Internal Combustion Engine: Research and Application—2nd Edition)
Open AccessArticle
Health State Assessment of Lithium-Ion Batteries Based on Multi-Health Feature Fusion and Improved Informer Modeling
by
Jun He, Xinyu Liu, Wentao Huang, Bohan Zhang, Zuoming Zhang, Zirui Shao and Zimu Mao
Energies 2024, 17(9), 2154; https://doi.org/10.3390/en17092154 (registering DOI) - 30 Apr 2024
Abstract
Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this
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Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%.
Full article
(This article belongs to the Section D: Energy Storage and Application)
Open AccessArticle
Investigation of the Combustion Properties of Ethylene in Porous Materials Using Numerical Simulations
by
Linyu Tu, Siyu Ding, Shefeng Li, Haitao Zhang and Wei Feng
Energies 2024, 17(9), 2153; https://doi.org/10.3390/en17092153 (registering DOI) - 30 Apr 2024
Abstract
As industrial modernization advances rapidly, the need for energy becomes increasingly urgent. This paper aims to enhance the current burner design by optimizing the combustion calorific value, minimizing pollutant emissions, and validating the accuracy of the burner model using experimental data from previous
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As industrial modernization advances rapidly, the need for energy becomes increasingly urgent. This paper aims to enhance the current burner design by optimizing the combustion calorific value, minimizing pollutant emissions, and validating the accuracy of the burner model using experimental data from previous studies. The enhanced porous medium burner model is used to investigate the burner’s combustion and pollutant emission characteristics at various flow rates, equivalence ratios, combustion orifice sizes, and porosity of porous media. In comparison with the previous model, the combustion traits during ethylene combustion and the emission properties of pollutants under various operational circumstances have been enhanced with the enhanced porous medium burner model. The maximum temperature of ethylene combustion in the enhanced model is 174 k higher than that before the improvement, and the CO emissions are reduced by 31.9%. It is believed that the findings will serve as a guide for the practical implementation of porous media combustion devices.
Full article
(This article belongs to the Section I2: Energy and Combustion Science)
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Open AccessArticle
Design and Experimental Tests of a Four-Way Valve with the Determination of Flow Characteristics for Building Central Heating Installations Using Solid Modeling
by
Mariusz Niekurzak and Jerzy Mikulik
Energies 2024, 17(9), 2152; https://doi.org/10.3390/en17092152 (registering DOI) - 30 Apr 2024
Abstract
The article presents the design of a four-way valve, implemented in SolidWorks software (SOLIDWORKS® i 3DEXPERIENCE® Works Simulation) and used for central heating installations in buildings. The project was carried out in order to examine the innovative design of the medium
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The article presents the design of a four-way valve, implemented in SolidWorks software (SOLIDWORKS® i 3DEXPERIENCE® Works Simulation) and used for central heating installations in buildings. The project was carried out in order to examine the innovative design of the medium mixing mechanism and to conduct strength and FMEA analysis. The innovative solutions proposed by the authors in this work will allow valves of this type to meet stringent environmental standards. These standards are currently being introduced for this type of structural element of machine parts as part of the energy transformation of buildings. Potential failures occurring in individual elements of the four-way valve were also tested using Failure mode and effects analysis. In addition, strength tests were performed in SolidWorks software using static analysis, and optimization tests were performed on the refrigerant in terms of its impact on the environment. The characteristics of the tested materials in the valve design show that the best materials are brass and stainless steel. Brass has a Poisson’s ratio of 0.33, a tensile strength of 478.4 MPa and a yield strength of 239.7 MPa. In turn, stainless steel is characterized by the following parameters: Poisson’s ratio of 0.27, tensile strength of 685 MPa and yield strength of 292 MPa. The designed valve reduces energy consumption by 30% through a properly designed medium flow with the appropriate selection of materials. Moreover, the design reduces the thickness of the contaminant layer by 0.17 mm, with a capacity factor of −2.50% and an evaporator Δp of 3.10% (53 kPa). The performed research provides knowledge on the subject selection of appropriate material, a description of the potential failures of the structural elements of the designed four-way valve and methods of counteracting these failures. The article presents the optimization role of the tested component in the context of sustainable development.
Full article
(This article belongs to the Special Issue Latest Research on Heat and Mass Transfer in Buildings)
Open AccessArticle
Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs
by
Sonny Irawan, Dennis Delali Kwesi Wayo, Alfrendo Satyanaga and Jong Kim
Energies 2024, 17(9), 2151; https://doi.org/10.3390/en17092151 (registering DOI) - 30 Apr 2024
Abstract
Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the
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Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. Hence, this paper examines the use of a global genetic algorithm (GA) to optimize the placement of wells in complex reservoirs, rather than relying on gradient-based (GB) methods. This is because GB approaches are influenced by the solution’s surface gradient and may only reach local optima, as opposed to global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. The explicit focus of this study was to examine the impact of various initial well placement distributions, the number of random solution sizes and the crossover rate on cumulative oil production, the optimization of the synthetic reservoir model created by CMG Builder, CMOST, and IMEX indicated that using a greater number of random solutions led to an increase in cumulative oil production. Despite the successful optimization, more generations are required to reach the optimal solution, while the application of GA on our synthetic model has proven efficient for well placement; however, different optimization algorithms such as the improved particle swarm (PSO) and grey wolf optimization (GWO) algorithms could be used to redefine well-placement optimization in CMG.
Full article
(This article belongs to the Special Issue New Insights into Reservoir 3D Modeling and Simulation)
Open AccessArticle
Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives
by
Ajithanjaya Kumar Mijar Kanakabettu, Rajkiran Ballal Irvathoor, Sanath Saralaya, Sathyendra Bhat Jodumutt and Athokpam Bikramjit Singh
Energies 2024, 17(9), 2150; https://doi.org/10.3390/en17092150 (registering DOI) - 30 Apr 2024
Abstract
This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based
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This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor’s speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive.
Full article
(This article belongs to the Section F: Electrical Engineering)
Open AccessArticle
Low-Salinity Waterflooding for EOR in Field A of Western Offshore Basin: A Pilot Study Analysis with Laboratory and Simulation Studies—Early Observations
by
Vivek Raj Srivastava, Hemanta K. Sarma and Sharad Kumar Gupta
Energies 2024, 17(9), 2149; https://doi.org/10.3390/en17092149 - 30 Apr 2024
Abstract
Carbonate reservoirs hold vast oil reserves, but their complex properties make traditional enhanced oil recovery (EOR) methods challenging. This study explores the application of low-salinity water flooding (LSWF) as a novel EOR method for India’s largest offshore carbonate oil field. Conventional EOR techniques
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Carbonate reservoirs hold vast oil reserves, but their complex properties make traditional enhanced oil recovery (EOR) methods challenging. This study explores the application of low-salinity water flooding (LSWF) as a novel EOR method for India’s largest offshore carbonate oil field. Conventional EOR techniques were deemed unsuitable due to reservoir heterogeneity, pressure decline, high temperature, and the offshore location. Favorable factors for LSWF included successful seawater flooding history, medium-weight crude oil, and existing infrastructure. Following core flooding experiments demonstrating a 6–16% increase in oil recovery, a multi-pronged evaluation process was implemented. Single-well chemical tracer tests (SWCTT) and reservoir simulations confirmed the potential of LSWF. A specific target area was chosen based on reservoir characteristics, production data, and available facilities. Simulations predicted a 1.5% incremental oil recovery using diluted seawater (25% salinity) at 30% pore volume injection. After a positive techno-economic analysis, the first offshore LSWF project in India was completed within 3 years. Initial monitoring results are encouraging. This study highlights the successful journey of LSWF from concept to field deployment in a challenging carbonate reservoir, showcasing its potential for revitalizing such fields. Furthermore, this work provides valuable data relevant to Indian offshore environments, where factors like salinity, mineralogy, and crude oil composition pose unique challenges compared to other LSWF applications. These detailed data fill a critical gap in the existing literature.
Full article
(This article belongs to the Section I1: Fuel)
Open AccessArticle
Classification of Smart and Sustainable Urban Mobility
by
Aleksandra Gulc and Klaudia Budna
Energies 2024, 17(9), 2148; https://doi.org/10.3390/en17092148 - 30 Apr 2024
Abstract
The main purpose of this article is to identify and classify smart and sustainable urban mobility solutions in the context of three narratives for sustainable mobility: electromobility, collective transport 2.0 and low-mobility societies. The research process of this study included the following methods:
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The main purpose of this article is to identify and classify smart and sustainable urban mobility solutions in the context of three narratives for sustainable mobility: electromobility, collective transport 2.0 and low-mobility societies. The research process of this study included the following methods: review of the literature, namely, strategic European documents, sustainable urban mobility plans in chosen cities and scientific publications addressing smart and sustainable mobility, case studies of selected cities in Europe and technology mapping to visualize the study results. The main result of this study is the review of various smart and sustainable urban solutions (SSUM) and their classification within the three narratives of electromobility, collective transport 2.0 and low-mobility societies. This article expands the concept of the three narratives for sustainable mobility with the aspect of smart mobility enriched with the variety of example solutions, case studies and good practices within mobility strategies in European cities. The study results can be useful for different stakeholders engaged in developing and introducing the sustainable mobility strategies in cities. Based on the catalogue of SSUM solutions, presented case studies and good practices, they may gain the necessary knowledge, consider the possible initiatives towards green transformation in cities and finally adjust them to the citizens’ need.
Full article
(This article belongs to the Special Issue Advances in Energy Transition in Transport and Logistics in Modern, Low-Emission, Smart and Sustainable Cities)
Open AccessArticle
Definition of Regulatory Targets for Electricity Default Rate in Brazil: Proposition of a Fuzzy Inference-Based Model
by
Nivia Maria Celestino, Rodrigo Calili, Daniel Louzada and Maria Fatima Almeida
Energies 2024, 17(9), 2147; https://doi.org/10.3390/en17092147 - 30 Apr 2024
Abstract
The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge
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The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility.
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(This article belongs to the Section C: Energy Economics and Policy)
Open AccessArticle
An Improved Single-Phase Multiple DC Source Inverter Topology for Distributed Energy System Applications
by
Mohd Faraz Ahmad, M. Saad Bin Arif, Uvais Mustafa, Mohamed Abdelrahem, Jose Rodriguez and Shahrin Md. Ayob
Energies 2024, 17(9), 2146; https://doi.org/10.3390/en17092146 - 30 Apr 2024
Abstract
This work presents an improved structure of a single-phase muti-input multilevel inverter (MIMLI) for distributed energy resources, which is capable of producing a nine-level output in symmetric mode and 21 levels in asymmetrical mode. The topology uses four DC sources and ten switches,
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This work presents an improved structure of a single-phase muti-input multilevel inverter (MIMLI) for distributed energy resources, which is capable of producing a nine-level output in symmetric mode and 21 levels in asymmetrical mode. The topology uses four DC sources and ten switches, with four switches being bidirectional and the remaining unidirectional. The operation of the circuit is analyzed in an asymmetrical mode, and switching signals are accomplished using the Nearest Level Control (NLC) PWM technique. Depending on the value of the DC sources used, the number of levels can vary. In this work, different DC source algorithms were also proposed, and the analysis of the inverter has been carried out considering the algorithms producing the maximum number of levels. The inverter was simulated in MATLAB/Simulink under steady state and dynamic conditions, achieving a 3.89% THD in output. The thermal analysis was conducted using PLECS software 4.1.2 to assess losses and efficiency. A laboratory prototype of the proposed topology was developed and tested, confirming its performance through simulation results and proving it economically viable for medium- and high-power applications.
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(This article belongs to the Special Issue Electro-Thermal Modeling, Aging and Lifetime Estimation of Power Electronics Circuits)
Open AccessArticle
A Method for State of Charge and State of Health Estimation of LithiumBatteries Based on an Adaptive Weighting Unscented Kalman Filter
by
Fengyuan Fang, Caiqing Ma and Yan Ji
Energies 2024, 17(9), 2145; https://doi.org/10.3390/en17092145 - 30 Apr 2024
Abstract
This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC
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This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC estimation, an adaptive weighting unscented Kalman filter algorithm is proposed to deal with the nonlinear update problem of the mean and covariance, which can substantially improve the estimation accuracy of the internal state of the lithium battery. Compared with fixed weights in the traditional unscented Kalman filtering algorithm, this algorithm adaptively adjusts the weights according to the state and measured values to improve the state estimation update accuracy. Finally, according to simulations, the errors of this algorithm are all lower than 1.63 %, which confirms the effectiveness of this algorithm.
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(This article belongs to the Section D2: Electrochem: Batteries, Fuel Cells, Capacitors)
Open AccessArticle
Assessing the Theoretical, Minimal Intervention Potential of Floating Solar in Greece: A Policy-Oriented Planning Exercise on Lentic Water Systems of the Greek Mainland
by
Despoina Athanasiou and Dimitrios Zafirakis
Energies 2024, 17(9), 2144; https://doi.org/10.3390/en17092144 - 30 Apr 2024
Abstract
According to the recent revision of the Greek National Energy and Climate Plan, the country sets out to accomplish an ambitious target concerning the integration of renewables in the local electricity mix during the ongoing decade, at the levels of 80% by 2030.
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According to the recent revision of the Greek National Energy and Climate Plan, the country sets out to accomplish an ambitious target concerning the integration of renewables in the local electricity mix during the ongoing decade, at the levels of 80% by 2030. This implies the need to more than double the existing wind and PV capacity at the national level, which in turn introduces numerous challenges. Amongst them, spatial planning for new RES installations seems to be the most demanding, with the adoption of novel technological solutions in the field of RES potentially holding a key role. New technologies, like offshore wind and floating solar, are gradually gaining maturity and may offer such an alternative, challenged at the same time however by the need to entail minimum disruption for local ecosystems. To that end, we currently assess the theoretical potential of floating PVs for lentic water systems of the Greek mainland. We do so by looking into 53 different lentic water systems across the Greek territory that meet the constraint of 1 km2 minimum surface area, and we proceed with the estimation of the relevant floating PV capacity per system under the application of a minimal intervention approach, assuming PV coverage of 1% over the total lentic water system area. In this context, our findings indicate a maximum, aggregate theoretical capacity that could exceed 2 GWp at the national level, with the respective annual energy yield reaching approximately 4 TWh or, equivalently, >6% of the country’s anticipated annual electricity consumption in 2030. Finally, our results extend further, offering a regional level analysis and a set of policy directions and considerations on the development of floating solar in Greece, while also designating the energy merits of floating PVs against similar, land-based installations.
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(This article belongs to the Special Issue Floating PV Systems On and Offshore)
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