Energies, Volume 16, Issue 13 (July-1 2023) – 403 articles

Renewable energy power generation systems such as photovoltaic and wind power have characteristics of intermittency and volatility, which can cause disturbances to the grid frequency. The battery system of electric vehicles (EVs) is a mobile energy storage system that can participate in bidirectional interaction with the power grid and support the frequency stability of the grid. Lithium iron phosphate (LiFePO₄) battery systems, with their advantages of high safety and long cycle life, are widely used in EVs and participate in frequency regulation (FR) services. Accurate assessment of the state of charge (SOC) and remaining available energy (RAE) status in LiFePO₄ batteries is crucial in formulating control strategies for battery systems. However, establishing an accurate voltage model for LiFePO₄ batteries is challenging due to the hysteresis of open circuit voltage and internal temperature changes, making it difficult to accurately assess their SOC and RAE. To accurately evaluate the SOC and RAE of LiFePO₄ batteries in dynamic FR working conditions, a thermal-electric-hysteresis coupled voltage model is built. Based on this model, closed-loop optimal SOC estimation is achieved using the extended Kalman filter algorithm to correct the initial value of SOC calculated by ampere-hour integration. Further, RAE is accurately estimated using a method based on future voltage prediction. The research results demonstrate that the thermal-electric-hysteresis coupling model exhibits high accuracy in simulating terminal voltage under a 48 h dynamic FR working condition, with a root mean square error (RMSE) of only 18.7 mV. The proposed state estimation strategy can accurately assess the state of LiFePO₄ batteries in dynamic FR working conditions, with an RMSE of 1.73% for SOC estimation and 2.13% for RAE estimation. This research has the potential to be applied in battery management systems to achieve an accurate assessment of battery state and provide support for the efficient and reliable operation of battery systems. Full article

The methodology and test results of a three-phase three-column transformer with a Dy connection group are presented in this paper. This study covers the dynamics of events that took place in the first period of the transient state caused by the energizing of the transformer under no-load conditions. The origin of inrush currents was analyzed. The influence of factors accompanying the switch-on and the impact of the model parameters on the distribution and maximum values of these currents was studied. In particular, the computational methods of taking into account the influence of residual magnetism in different columns of the transformer core, as well as the impact of the time instant determined in the voltage waveform at which the indicated voltage is supplied to a given transformer winding, were examined. The study was carried out using a nonlinear model constructed on the basis of classical modeling, in which hysteresis is not taken into account. Such a formulated model requires simplification, which is discussed in this paper. The model is described using a system of stiff nonlinear ordinary differential equations. In order to solve the stiff differential state equations set for the transient states of a three-phase transformer in a no-load condition, a Runge–Kutta method, namely the Radau IIA method, with ninth-order quadrature formulas was applied. All calculations were carried out using the authors’ own software, written in C#. A ready-made strategy for energizing a three-column three-phase transformer with a suitable pre-magnetization of its columns is given. Full article

The influences of V-shaped delta-wing baffles on the thermohydraulic performance characteristics in a round tube were experimentally tested. The V-shaped delta-wing baffles having a set number of wings (N = 4, 6, and 8) were comparatively tested. The V-shaped delta-wing baffles with various pitch ratios of P/D = 2.0, 2.5, and 3.0 were thoroughly fitted inside a tube. In the present work, the baffles were responsible for both the recirculation/reverse flow behind the solid baffle and the longitudinal vortex flow behind the V-shaped wing. The V-shaped winged baffles with N = 8 produced high heat transfer rates by promoting the development of reverse and vortex flows. These currents aid in fluid mixing between the two streams. Experimental results suggested that utilizing V-shaped delta-wing baffles having N = 4, 6, and 8 led to Nusselt number enhancement of up to 97–105.6%, 105.8–127.8% and 114.8–138.9%, respectively. When N was 8, the V-shaped wings baffles created additional multi vortex flows, which resulted in some fluid mixing between the vortex and the reverse flow. It was discovered that a greater turbulent intensity is imparted to the flow that was occurring between the V-shaped delta-wing baffles, which led to an increase in the rate of heat transfer when the pitch ratio was decreased. The increase in Nusselt number was up to 118.26–151.3% more than it was in a tube with the lowest pitch ratio (P/D = 2.0). It was also found that the baffles with N = 8 wings and P/D = 3.0 offered a maximum aerothermal performance factor (APF) of 1.01. Furthermore, the V-shaped delta-wing baffles have the potential for energy savings at low Re ≤ 6000, indicated by the APF beyond unity. Full article

To meet the 2050 EU decarbonization goals, there is a need for new and innovative ideas to increase energy efficiency, which includes reducing the energy consumption of buildings and increasing the use of on-site renewable energy sources. One possible solution for achieving efficient thermal energy transition in the building sector is to assign new functionalities to the building envelope. The building envelope can function as a thermal energy storage system, which can help compensate for irregularities in solar energy availability. This can be accomplished by utilizing phase change materials as the energy storage medium in the building envelope. In this paper, two phase change materials with different melting temperatures of 21 °C and 28 °C are compared for their application in a dynamic solar building envelope. Both experimental and numerical studies were conducted within the scope of this study. The laboratory testing involved simulating the conditions of the four seasons through steady-state and dynamic experiments. The performance of the phase change materials was evaluated using a small-scale PASLINK test stand that imitates indoor and outdoor conditions. A numerical model of a small-scale building envelope was created using data from laboratory tests. The purpose of this model was to investigate how the tested phase change materials perform under different climate conditions. The experimental findings show that RT21HC is better at storing thermal energy in the PCM and releasing it into the indoor area than RT28HC. On the other hand, the numerical simulation results demonstrate that RT28HC has an advantage in terms of thermal storage capacity in climates found in Southern Europe, as it prevents overheating of the room. Full article

This research paper aims to investigate the optimal design of grading rings for high-voltage polymeric insulators in an actual power transmission system, with a focus on improving the electrical representation of the insulator strings. One such subsidiary accessory commonly used with porcelain and polymer insulator strings is the grading ring, which is employed to improve the electric field and voltage distribution surrounding the insulator string. The efficiency of insulator strings can be enhanced by grading rings, as they facilitate a more linear potential division along the strings. The design parameters of grading rings significantly influence their performance on insulator strings. In this study, we examine the optimal design of the grading rings of high-voltage polymer insulators, since no uniform design methodology has been developed for high-voltage polymer insulators, and their optimization is currently the subject of many research studies. The electric field on an outdoor polymeric insulator is examined using finite element method (FEM) software and COMSOL Multi-Physics program. A 2D model is utilized to simulate a 220 kV polymeric insulator. The effectiveness of high-voltage polymeric insulators greatly depends on the dimensions and locations of the grading rings. Therefore, the impacts of the radius of the grading ring and that of its tube and the tube’s vertical position are thoroughly investigated, under dry and humid conditions. To achieve this objective, a search algorithm is employed to adjust the dimensions and locations of the grading ring. The optimization approach in this study is based on determining the maximum electric field across the insulator surface, while ensuring that it remains below the corona initiation level. Full article

This article proposes an approach and develops an appropriate method of applying linear non-equilibrium thermodynamics to analyze energy processes, in particular using the example of the wind energy conversion system (WECS) with a directly connected vertical axis wind turbine (VAWT) and vector-controlled permanent magnet synchronous generator (PMSG). The main steps of the proposed approach are the description of the component subsystems as universal linear or linearized energy converters (ECs), which are characterized by several dimensionless parameters, the main one of which is the degree of coupling between their input and output. According to their value, as well as justified efficiency criteria, the optimal operating points of each ECs can be easily found. Such an approach makes it possible to abstract from physical laws of a different nature and equally assess the work of each of the subsystems. The next step is a connection of the received ECs. As shown in the paper, for the most common cascade connection of ECs, there are the best conditions for their connection, under which the newly formed equivalent EC can have maximum efficiency. This opens up an opportunity to analyze the influence of already real parameters of cascaded interconnected subsystems on the quality of their connection and justify specific solutions that would not have been seen without this approach. For example, in this study, from all parameters of the PMSG, only the selection of the optimal rated inductance of the armature winding made it possible to improve the quality of the connection of the PMSG with a specific VAWT and approximate the efficiency of the entire WECS to the maximum possible, especially in medium and high winds. Full article

The most practical way of storing hydrogen gas for fuel cell vehicles is to use a composite overwrapped pressure vessel. Depending on the driving distance range and power requirement of the vehicles, there can be various operational pressure and volume capacity of the tanks, ranging from passenger vehicles to heavy-duty trucks. The current commercial hydrogen storage method for vehicles involves storing compressed hydrogen gas in high-pressure tanks at pressures of 700 bar for passenger vehicles and 350 bar to 700 bar for heavy-duty trucks. In particular, hydrogen is stored in rapidly refillable onboard tanks, meeting the driving range needs of heavy-duty applications, such as regional and line-haul trucking. One of the most important factors for fuel cell vehicles to be successful is their cost-effectiveness. So, in this review, the cost analysis including the process analysis, raw materials, and manufacturing processes is reviewed. It aims to contribute to the optimization of both the cost and performance of compressed hydrogen storage tanks for various applications. Full article

Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to three cryptocurrencies, taking into account their respective levels of “dirty” energy consumption from 16 May 2018 to 15 May 2023. The purpose is to determine whether the eventual increase in correlation resulting from the events of 2020 and 2022 leads to volatility spillovers between clean energy indexes and cryptocurrencies categorized as “dirty” due to their energy-intensive mining and transaction procedures. The level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen’s methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the t-test of the heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of this study’s results are significant for investment strategies, and this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policy makers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market. Full article

The latest progress in the design of the water-cooled lithium–lead (WCLL) tritium extraction and removal (TER) system for the European DEMO tokamak reactor is presented. The implementation and optimization of the conceptual design of the TER system are performed in order to manage the tritium concentration in the LiPb and ancillary systems, to control the LiPb chemistry, to remove accumulated corrosion and activated products (in particular, the helium generated in the BB), to store the LiPb, to empty the BB segments, to shield the equipment due to LiPb activation, and to accommodate possible overpressure of the LiPb. The LiPb volumes in the inboard (IB) and outboard (OB) modules of the BB are separately managed due to the different pressure drops and required mass flow rates in the different plasma operational phases. Therefore, the tritium extraction is managed by 6 LiPb loops: 4 loops for the OB segments and 2 loops for the IB segments. Each one is a closed loop with forced circulation of the liquid metal through the TER and the other ancillary systems. The design presents the new CAD drawings and the integration of the TEU into the tokamak building, designed on the basis of an experimental characterization carried out for the permeator against vacuum (PAV) and gas–liquid contactor (GLC) technologies, the two most promising technologies for tritium extraction from liquid metal. Full article

Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model’s accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models. Full article

The potential energy recovery of hydraulic excavators is very significant for improving energy efficiency and reducing pollutant emissions. However, the more common solutions for potential energy recovery require more energy conversion processes before these potential energies can be reused, which adds to the complexity and high cost of the system. To tackle the above challenges, we proposed a novel energy recovery system for hydraulic hybrid excavators based on the digital pump with an energy recovery function. The new system could operate in three different modes: pump, energy recovery, and direct reuse. Based on the descriptions of the working principle of the digital pump and the whole energy recovery system, the mathematical models of the digital pump, the excavator arm cylinder, and the accumulator were established and the AMESim simulation model (combining mechanics, hydraulics, and electrics) was developed. The dynamic characteristics of the energy recovery system were studied under no-load and full-load conditions. The simulation results showed that this scheme could achieve 86% energy recovery when the boom was lowered and reused the recovered energy directly when raised, which could decrease the system input energy by 78.1%. This paper can provide an optimized solution for construction machinery or off-road vehicles and presents a reference for the research on digital hydraulics. Full article

Due to the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade conditions (PSCs). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPTs) may become locked in one of the LPs, significantly reducing the PV system’s generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms’ key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA’s convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA’s superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time was reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, the best initialization of search agents, and the optimal design of the dc–dc converter are introduced for optimal MPPT performance. Full article

The manufacturing industry is the pillar industry of China’s economy and a major carbon emitter, and its carbon emission reduction efforts directly determine whether the country’s carbon emission reduction target can be successfully met. In the context of the goals of the carbon peak and carbon neutrality policy, we examine the impact of manufacturing structure optimization on carbon emissions from 2003 to 2020 through a spatial econometric model, taking the old industrial centers in Northeast China as an example. We then apply a machine learning model to simulate manufacturing carbon emissions during the carbon peak stage and identify the optimal path for carbon emission reduction, which is important for promoting manufacturing carbon emission reduction in Northeast China. Since the goal of low-carbon economic development has gradually replaced the goal of maximizing economic efficiency in recent years, manufacturing structure optimization has come to focus on energy saving and emission reduction. Therefore, we define manufacturing structure optimization from the dual perspective of technology and energy consumption to broaden the existing research perspective. The results show the following: (1) The overall trend in manufacturing structure optimization in Northeast China is steadily improving, and the level of manufacturing structure optimization from the technology perspective is higher than that from the energy consumption perspective. (2) Manufacturing structure optimization and manufacturing carbon emissions in Northeast China both show a positive spatial correlation. Manufacturing structure optimization in Northeast China can effectively promote carbon emission reduction, and it also has a spatial spillover effect. (3) The carbon emission reduction effect of manufacturing structure optimization from the energy consumption perspective is better than that from the technology perspective, and the carbon emission reduction effect under the institutional innovation scenario is better than that under the baseline scenario and the technological innovation scenario. Focusing on manufacturing structure optimization from both technology and energy consumption perspectives, as well as continuously improving technological innovation and institutional innovation, can help to achieve manufacturing carbon emission reduction in Northeast China. Full article

In order to effectively combat the effects of global warming, all sectors must actively reduce greenhouse gas emissions in a sustainable and substantial manner. Sector coupling has emerged as a critical technology that can integrate energy systems and address the temporal imbalances created by intermittent renewable energy sources. Despite its potential, current sector coupling capabilities remain underutilized, and energy modeling approaches face challenges in understanding the intricacies of sector coupling and in selecting appropriate modeling tools. This paper presents a comprehensive review of sector coupling technologies and their role in the energy transition, with a specific focus on the integration of electricity, heat/cooling, and transportation, as well as the importance of hydrogen in sector coupling. Additionally, we conducted an analysis of 27 sector coupling models based on renewable energy sources, with the goal of aiding deciders in identifying the most appropriate model for their specific modeling needs. Finally, the paper highlights the importance of sector coupling in achieving climate protection goals, while emphasizing the need for technological openness and market-driven conditions to ensure economically efficient implementation. Full article

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R² of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R² value of 0.94 and a MAPE value of 4.8%. Full article

Soiling of photovoltaic (PV) modules is a major issue due to its critical impact on PV performance and reliability, especially in the desert and arid regions such as the state of Qatar. Soiling frequently results in a severe reduction in PV power generation, which drastically affects the economical profitability of the PV plant, and therefore, must be mitigated. The most common way of mitigating PV soiling is surface cleaning. However, the latter could consequently increase the associated operation and maintenance (O&M) cost of the PV site. However, previous studies indicated that even if the best-optimized cleaning schemes are used, the actual global solar-power production can still be reduced by about 4%, which is associated with at least EUR 5 billion in annual revenue losses worldwide. This loss is expected to reach a conservative value of EUR 7 billion in 2023. Accordingly, investigating the interplayed physics phenomena related to the various soiling processes, the site-specific O&M costs, along with a techno-economical assessment of state-of-the-art soiling mitigation strategies (including innovative anti-soiling coating materials) is of paramount importance. The goal of this comprehensive report is to provide the solar community at large, and those focusing on the desert environment in particular, with real field measurements that provide key findings and challenges in addressing soiling research obtained from multiyear testing at the Outdoor Test Facility (OTF) field station, located in the desert environment of the city of Doha, in the state of Qatar. Full article

This article offers a comprehensive analysis of the global gas and liquefied natural gas (LNG) markets, discussing increasing demand, market volatility, supply and demand dynamics, and the implications of the Paris Agreement on natural gas demand. It emphasizes the potential impacts of decarbonization policies on the LNG market, including changes in energy composition, reduced LNG demand, increased costs, and the need for industry adaptation. The article also examines the future outlook, investment needs, and implications for global gas and LNG markets, highlighting the continued uptake of gas in heavy-duty transport and the importance of investment to avoid supply–demand gaps. Overall, the analysis provides insights into the complex dynamics and challenges facing the global gas and LNG markets in the context of energy transition and climate change mitigation efforts. Full article

In this work, N/O codoped porous carbons (NOPCs) were derived from corn silk accompanied by Na₂CO₃ activation. The porous structures and surface chemical features of as-prepared carbon materials were tailored by adjusting the Na₂CO₃ mass ratio. After activation, the optimized sample (NOPC1) with abundant ultramicropores and pyrrolic N displays an enhanced CO₂ adsorption capacity of 3.15 mmol g⁻¹ and 1.95 mmol g⁻¹ at 273 K and 298 K at 1 bar, respectively. Moreover, this sample also exhibited high IAST selectivity (16.9) and Henry’s law selectivity (15.6) for CO₂/N₂ at 298 K as well as moderate heat adsorption. Significantly, the joint effect between ultramicropore structure and pyrrolic N content was found to govern the CO₂ adsorption performance of NOPCs samples. Full article

In this study, five laboratories, namely, BRML (Romania), TUBITAK UME (Turkey), IMBIH (Bosnia and Herzegovina), BAM (Germany), and DTI (Denmark), developed and validated analytical procedures by ICP-MS, ICP-OES, MWP-AES, WD-XRF, and ID-MS for the determination of inorganic impurities in solid and liquid biofuels, established the budget of uncertainties, and developed the method for determining the amount of ash in the measurement range 0–1.2% with absolute repeatability less than 0.1% and absolute reproducibility of 0.2% (according to EN ISO 18122). In order to create homogeneous certified reference materials, improved methodologies for the measurement and characterization of solid and liquid biofuels were developed. Thus, information regarding the precision, accuracy, and bias of the method, and identifying the factors that intervened in the measurement of uncertainty were experimentally determined, supplementing the information from the existing standards in the field. Full article

Small-scale wind turbines play an important role in distributed generation since customers can use their houses, farms, and business to produce electric energy. The development of the power electronics system that processes the electric energy from small-scale wind turbines is a concern due to cost, simplicity, efficiency, and performance trade-offs. This paper presents the results of applying a three-phase phase-modular single-ended primary-inductance converter rectifier to processing the energy of a small-scale wind turbine system. The rectifier was designed according to the specifications of a commercial small-scale wind turbine system and tested in an emulator workbench, providing experimental data on the operation of the rectifier in this application. The rectifier can process the energy of a non-sinusoidal three-phase system since the permanent magnet synchronous generator has trapezoidal waveforms. The results show that the rectifier has the advantages of (i) using the inductance of the generator as the input filter inductor of the rectifier, (ii) providing input currents with the same shape as the voltages and in phase without the use of a current control system, (iii) simplicity of control of the DC output voltage and PWM modulation, and (iv) phase-modular characteristics that allow operating with phase fault without any additional control techniques. Due to the operation in discontinuous conduction mode, low efficiency in high power and/or low input voltage specifications are disadvantages. Full article

Carbon dioxide (CO₂) dissolution is the secondary trapping mechanism enhancing the long-term security of CO₂ in confined geological formations. CO₂ injected into a randomly multilayered formation will preferentially migrate along high permeability layers, increasing CO₂ dissolution efficiency. In this study, sequential Gaussian simulation is adopted to construct the stratified saline formations, and two-phase flow based on MRST is established to illustrate the spatial mobility and distribution of CO₂ migration. The results show that gravity index $G$ and permeability heterogeneity ${\sigma }_Y^2$ are the two predominant factors controlling the spatial mobility and distribution of CO₂ transports. The CO₂ migration shows a totally different spatial mobility under different gravity index and heterogeneity. When the permeability discrepancy is relatively larger, CO₂ preferentially migrates along the horizontal layer without accompanying the vertical migration. For the formation controlled by gravity index, CO₂ migration is governed by supercritical gaseous characteristics. For the medium gravity index, the upward and lateral flow characteristics of the CO₂ plume is determined by gravity index and heterogeneity. When the gravity index is smaller, permeability heterogeneity is the key factor influencing CO₂ plume characteristics. Permeability heterogeneity is the decisive factor in determining final CO₂ dissolution efficiency. This investigation of CO₂ mobility in randomly multilayered reservoirs provides an effective reference for CO₂ storage. Full article

The increasing penetration of wind energy in electric power systems leads to a great demand for flexible resources to regulate power fluctuations. This paper focuses on investigating the impacts of the operational flexibility of hydropower generation systems on reducing wind curtailment and load shedding in a hybrid hydro–wind power system. Considering timescale variabilities of wind power, the upward and downward regulation capabilities of hydro flexibility under sub-hour and hour dispatch scales are estimated. Based on developed flexible indicators, the ultimate access ratio of wind power penetration into the power system is obtained by using the estimated probability of insufficient regulation reserves. All these analyses are carried out under the wet and dry periods to better understand their differences with the hydro flexibility. The method and obtained results provide important guidance for the stable and high-efficiency operation of hybrid power systems. Full article

The summer-dominated Mediterranean island of Cyprus is often considered in the contexts of beach tourism, sunny weather, and different types of business economic activities and services. In terms of its climatic conditions, extreme heat and mild winters characterise the island; yet, recent evidence has shown that winter poses a significant threat to public health. Its excess winter mortality is amongst the highest in Europe and there is an increased risk of energy-poverty-related mortality compared to total mortality. This study is an extension of previous research, with the objective of further scrutinizing the shift observed between urban and rural energy poverty mortality in the time of a severe nationwide financial crisis. Mortality and temperature data for the period of 2008–2018, as well as macroeconomic indicators, were investigated through a linear regression analysis. The results indicated that the declining economic situation of the island severely hit rural areas, with a significant increase in energy-poverty-related mortality, while urban areas were more resilient to this. There are three existing challenges linked to energy poverty: low incomes, high energy prices, and poor building energy efficiency. In Cyprus, all three coincide and are aggravated in times of crisis, creating conditions of extreme vulnerability for populations already in a disadvantaged position. This study’s motivation was to highlight the intense vulnerability associated with crises in Cyprus, and its outcomes call for higher levels of support at such times, especially when it comes to rural populations. Full article

Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions. Full article

This paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the Micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the Micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the Micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems. Full article

Vietnam’s economy has been growing rapidly in the last 20 years, leading to significant increases in energy consumption as well as in carbon emissions. Most electricity is consumed by loads of industry and construction due to the country’s socio-economic development strategy. An energy saving strategy cannot be achieved if the industry factories lack energy consumption data. The installation of energy monitoring systems can help to improve energy efficiency by supplying daily, monthly, and yearly energy consumption reports. Moreover, major energy-consuming enterprises in Vietnam must implement solutions for energy-efficient use as prescribed in the Law on Energy Efficient Use. Therefore, this study aimed to determine the impact of an energy monitoring system as an improvement solution for energy efficiency in a typical major energy-consuming enterprise in Vietnam. The study’s results, after six months, show that the total saved electricity after installing the power monitoring system was 191,923 kWh. The company saved approximately 19.584 USD and reduced emission to the environment by 139 tons of CO₂. In addition, the return on investment time of power monitoring systems is about 14 months, while the annual energy costs of the factory can be reduced by about 9.62% per year. Therefore, power monitoring systems should be promoted in factories with different scales to control energy wastage in the domestic industry field. Full article

A Kite-based Airborne Wind Energy Conversion System (KAWECS) works by harnessing the kinetic energy from the wind and converting it into electric power. The study of the dynamics of KAWECS is fundamental in researching and developing a commercial-scale KAWECS. Testing an actual KAWECS in a location with suitable wind conditions is only sometimes a trusted method for conducting research. A KAWECS emulator was developed based on a Permanent Magnet Synchronous Machine (PMSM) drive coupled with a generator to mimic the kite’s behaviour in wind conditions. Using MATLAB-SIMULINK, three different power ratings of 1 kW, 10 kW, and 100 kW systems were designed with a kite surface area of 2.5 m${}^2$, 14 m${}^2$, and 60 m${}^2$, respectively. The reel-out speed of the tether, tether force, traction power, drum speed, and drum torque were analysed for a wind speed range of 2 m/s to 12.25 m/s. The satellite wind speed data at 10 m and 50 m above ground with field data of the kite’s figure-of-eight trajectories were used to emulate the kite’s characteristics. The results of this study will promote the use of KAWECS, which can provide reliable and seamless energy flow, enriching wind energy exploitation under various installation environments. Full article

A business model is a “formula” for generating value in a company, and is considered a conceptual object that is part of a company’s intangible resources. It is a company’s unique recipe for sales, cost-effectiveness in operational terms as well as investment, and the financing of operations, both in the short and long term. Due to new challenges, such as sustainable development, faced by enterprises, as well as the new ways of creating and delivering value, such as the closed-loop economy, new concepts of business models are emerging. Presently, there are many different forms of decomposition of a company’s assets that will contribute to the process of creating more sustainable business models to ensure the achievement of cohesion in the financial, environmental and social areas. The purpose of this paper is to present the theoretical assumptions and practical solutions in the field of creating sustainable business models for enterprises by decomposing assets and changing their way of functioning to increase efficiency for stakeholders. The applied research method is based on statistical analysis, with the main focus on the analysis of the correlation between the prices of shares of a parent company and the prices of shares of a company separated from the existing structures. Full article

The study evaluates the geothermal energy potential of two depleted oil and gas reservoirs representing two different lithostratigraphic formations—the carbonate formation of the Visean age from the basement of the Carpathian Flysch and the Rotliegend sandstone formation from the Eastern part of the Foresudetic Monocline, Poland. Advanced modeling techniques were employed to analyze the studied formations’ heat, storage, and transport properties. The obtained results were then used to calculate the heat in place (HIP) and evaluate the recoverable heat (Hrec) for both water and CO₂ as working fluids, considering a geothermal system lifetime of 50 years. The petrophysical parameters and Hrec were subsequently utilized in the generalized c-means (GFCM) clustering analysis, which helped to identify plays with the greatest geothermal potential within the studied formations. The central block emerged as the most promising area for the studied carbonate formation with Hrec values of ~1.12 and 0.26 MW when H₂O and CO₂ were used as working fluids, respectively. The central block has three wells that can be easily adapted for geothermal production. The area, however, may require permeability enhancement techniques to increase reservoir permeability. Two prospective zones were determined for the analyzed Rotliegend sandstone formation: one in the NW region and the other in the SE region. In the NW region, the estimated Hrec was 23.16 MW and 4.36 MW, while in the SE region, it was 19.76 MW and 3.51 MW, using H₂O and CO₂ as working fluids, respectively. Both areas have high porosity and permeability, providing good storage and transport properties for the working fluid, and abundant wells that can be configured for multiple injection-production systems. When comparing the efficiency of geothermal systems, the water-driven system in the Visean carbonate formation turned out to be over four times more efficient than the CO₂-driven one. Furthermore, in the case of the Rotliegend sandstone formation, it was possible to access over five times more heat using water-driven system. Full article

This scientific paper highlights the critical significance of energy in driving sustainable development and explores the transformative potential of Artificial Intelligence (AI) tools in shaping the future of energy systems. As the world faces mounting challenges in meeting growing energy demands while minimizing environmental impact, there is a pressing need for innovative solutions that can optimize energy generation, distribution, and consumption. AI tools, with their ability to analyse vast amounts of data and make intelligent decisions, have emerged as a promising avenue for advancing energy systems towards greater efficiency, reliability, and sustainability. This paper underscores the importance of energy in sustainable development and investigates how AI tools can catalyse the next phase of human civilization. This paper presents a comprehensive review of the Collaborative Energy Optimization Platform (CEOP), an innovative model that utilizes AI algorithms in an integrated manner. The review of the CEOP model is based on an in-depth analysis of existing literature, research papers, and industry reports. The methodology encompasses a systematic review of the model’s key features, including collaboration, data-sharing, and AI algorithm integration. The conducted research demonstrates the effectiveness of applying MCDM methods, specifically fuzzy AHP and TOPSIS, in evaluating and ranking the performance of five Collaborative Energy Optimization Platforms (CEOP models) across 20 sub-criteria. The findings emphasize the need for a comprehensive and holistic approach in assessing AI-based energy optimization systems. The research provides valuable insights for decision-makers and researchers in the field, fostering the development and implementation of more efficient and sustainable AI-powered energy systems. Full article