Energies, Volume 17, Issue 9 (May-1 2024) – 224 articles

In this paper, a new axial-type magnetic gear with an auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AMGs, it has a higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of the proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterparts in RMGs, the AFS-AMG achieves a small size. Then, the geometrical parameters of the AFS-AMG are optimized to obtain better electromagnetic performance, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume. Full article

The fluctuating nature of renewable energies results in the need for sustainable storage technologies to defossilize the energy system without other negative consequences for humans and the environment. In this study, a pilot-scale trickle-bed reactor for biological methanation and various scale-up scenarios for 2024 and 2050 were investigated using life cycle assessment. A best- and worst-case scenario for technology development until 2050 was evolved using cross-consistency analysis and a morphological field, based on which the data for the ecological models were determined. The results show that the plant scale-up has a very positive effect on the ecological consequences of methanation. In the best-case scenario, the values are a factor of 23–780 lower than those of the actual plant today. A hot-spot analysis showed that electrolysis operation has an especially large impact on total emissions. The final Monte Carlo simulation shows that the technology is likely to achieve a low global warming potential with a median of 104.0 kg CO₂-eq/MWh CH₄ and thus can contribute to decarbonization. Full article

In order to reduce greenhouse gas emissions and achieve global decarbonisation, it is essential to find sustainable and renewable alternatives for electricity production. In this context, the development of distributed generation systems, with the use of thermodynamic and photovoltaic solar energy, wind energy and smart grids, is fundamental. ORC power plants are the most appropriate systems for low-grade thermal energy recovery and power conversion, combining solar energy with electricity production. The application of a micro-scale ORC plant, coupled with Parabolic Trough Collectors as a thermal source, can satisfy domestic user demand in terms of electrical and thermal power. In order to develop a micro-scale ORC plant, a commercial hermetic scroll compressor was tested as an expander with HFC-245fa working fluid. The tests required the construction of an experimental bench with monitoring and control sensors. The aim of this study is the description of the scroll performances to evaluate the application and develop optimization strategies. The maximum isentropic effectiveness is reached for an expansion ratio close to the volumetric expansion ratio of the scroll, and machine isentropic effectiveness presents small variations in a wide range of working conditions. The filling factor is always higher than one, due to leakage in the mechanical seals of the scroll or other inefficiencies. This study demonstrates that using a commercial scroll compressor as an expander within an ORC system represents a valid option for such applications, but it is necessary to improve the mechanical seals of the machine and utilize a dedicated control strategy to obtain the maximum isentropic effectiveness. Full article

This article proposes a new connection method of tapped inductors that works in the current source, which enables the current-mode power converter circuit to have a new topological relationship. Usually, in a switched-inductor circuit, a stable output multiple is obtained through the connection of the inductor and the switching devices. This is because the tapped point on the inductor varies, and the magnetomotive force (mmf) of inductance is adjusted. Thereby, the output current is controlled by the states of switching devices within a certain range. This optimized circuit structure can adjust the output current according to load changes in practical applications without changing the input power supply. The proposed method has been verified for its feasibility through detailed analysis and hardware work. The principal analysis based on the flux linkage and the PSIM simulation confirms that the theoretical circuit can be implemented. Finally, a hardware circuit is built to obtain real and feasible conclusions, and it is verified that the circuit can achieve a stable output and variable current within a specific range. The proposed work presents an alternative power conversion methodology using the active switching of mmf, and it is a stable and simple power conversion technique. Full article

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants. Full article

Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM₁₀. Routine air quality monitoring includes determining the PM₁₀ concentration as one of the basic measurements. At some air monitoring stations, the PM₁₀ measurement is supplemented by the simultaneous determination of the concentration of PM_2.5 as a finer fraction of suspended particles. Since the PM_2.5 fraction has a significant share in the PM₁₀ fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM_2.5 concentration using PM₁₀ concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM_2.5 concentrations. PM₁₀ concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM₁₀ predictor, the RMSE error values were in the range of 2.31–6.86 μg/m³. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m³. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM₁₀ concentration; therefore, the only condition to be met is monitoring the measurement of PM₁₀ concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM_2.5 concentrations at another station. Full article

This study aims to analyze the determinants that influence the consumers’ disposition to invest in microgrid technology in the United Arab Emirates (UAE). This research offers valuable insights for policymakers on investors’ motivations to develop strategies to foster microgrid technology adoption through end-user investments leading to a reduction in microgrid high capital cost. The study employed descriptive statistics, correlation, and regression analyses to analyze the responses of a sample of property owners to a quantitative survey. The study examines such variables as strategic alignment, profitability, digitization, renewable energy utilization, CO₂ emission reduction, and disaster recovery readiness. The data collected reveal a moderate level of understanding and cost-awareness of microgrid technology among the respondents, with a mean of 2.46 out of 5. Notably, the data highlight the significant influence of strategic alignment with the UAE’s national energy goals on the respondents’ inclination to invest in microgrids, with a strong positive correlation of 0.942 at the 0.01 level (two-tailed). By comparison, profitability and disaster recovery have a comparatively weaker correlation. Furthermore, based on the data collected during this study, it has been determined that there is a strong value added by the microgrid initiatives considering the UAE’s strategic direction and the positive influence of reduced CO₂. The regression models used were highly significant at F = 85.690. There is an acceptable level of multicollinearity with VIF values ranging from 1.087 to 2.155. UAE Strategy has low collinearity. UAE Strategy emerges as the only significant predictor of willingness to invest (p < 0.001) in the stepwise regression analysis. The analysis shows that villa and townhouse owners are willing to invest in community microgrid given that it is aligned with UAE strategy and leads to CO₂ emissions reduction. Full article

Recent increases in gas-fired power generation have engendered increased interdependencies between natural gas and power transmission systems. These interdependencies have amplified existing vulnerabilities in gas and power grids, where disruptions can require the curtailment of load in one or both systems. Although typically operated independently, coordination of these systems during severe disruptions can allow for targeted delivery to lifeline services, including gas delivery for residential heating and power delivery for critical facilities. To address the challenge of estimating maximum joint network capacities under such disruptions, we consider the task of determining feasible steady-state operating points for severely damaged systems while ensuring the maximal delivery of gas and power loads simultaneously, represented mathematically as the nonconvex joint Maximal Load Delivery (MLD) problem. To increase its tractability, we present a mixed-integer convex relaxation of the MLD problem. Then, to demonstrate the relaxation’s effectiveness in determining bounds on network capacities, exact and relaxed MLD formulations are compared across various multi-contingency scenarios on nine joint networks ranging in size from 25 to 1191 nodes. The relaxation-based methodology is observed to accurately and efficiently estimate the impacts of severe joint network disruptions, often converging to the relaxed MLD problem’s globally optimal solution within ten seconds. Full article

In Germany, there are over 32,000 schools, representing great potential for climate protection. On the one hand, this applies to educational work, as understanding the effects of climate change and measures to reduce GHG emissions is an important step to empower students with knowledge and skills. On the other hand, school buildings are often in bad condition, energy is wasted, and the possibilities for using renewable energies are hardly used. In our “Schools4Future” project, we enabled students and teachers to draw up their own CO₂ balances, identify weaknesses in the building, detect wasted electricity, and determine the potential for using renewable energies. Emissions from the school cafeteria, school trips, and paper consumption could also be identified. The fact that the data can be collected by the students themselves provides increased awareness of the contribution made to the climate balance by the various school areas. The most climate-friendly school emits 297 kg whilst the school with the highest emissions emits over one ton CO₂ per student and year. Our approach is suitable to qualify students in the sense of citizen science, carry out a scientific investigation, experience self-efficacy through one’s own actions, and engage politically regarding their concerns. Full article

This article presents a detailed study on the diagnosis of rotor faults in an Interior Permanent Magnet Machine based on a mathematical model. The authors provided a wide literature review, mentioning the fault diagnosis methods used for Permanent Magnet Machines. The research emphasizes the necessity of precise assumptions regarding winding construction to accurately analyze the additional harmonics appearing in rotor faults caused by electromotive force (EMF), i.e., rotor eccentricity and magnet damage. The article also discusses specific features appearing in the spectrum of air gap permeance functions and the impact of rotor eccentricity and magnet damage on PM flux density distribution and as a consequence on EMF stator windings. The novelty of the presented content is the analysis of induced EMFs for cases of the simultaneous occurrence of rotor eccentricity and PM damage. The findings of this study provide valuable insights for the diagnosis and understanding of internal asymmetries in Interior PM Machines. Full article

This article presents an analysis of the performance of a 14.04 kWp grid-connected photovoltaic (PV) installation consisting of monocrystalline silicon, polycrystalline silicon and bifacial glass–glass monocrystalline silicon modules. The photovoltaic system was mounted in Poland, a location characterized by temperate climate conditions. On the basis of the obtained results, the photovoltaic parameters were determined in accordance with the international standard. The annual energy yield of the entire system was 1033 kWh/kWp, and the performance ratio achieved was 83%. The highest average daily final yield was in the range of 4.0–4.5 kWh/kWp for each photovoltaic technology under investigation. In the cold part of the year, the efficiency of the photovoltaic modules was estimated to be 15%, and it was estimated to be 7% during the warm part of the year. Array capture losses accounted for around 0.75 kWh/kWp of energy loss per day, whereas the inverter efficiency was over 95% during the months that are beneficial for energy production. Full article

This study investigates the combustion of agricultural biomass rich in alkali elements in the fluidized bed. The experiments were performed with smashed corn cob in a 500 kW fluidized bed combustor which was designed for work under low bed temperatures (650–700 °C). During the experiments, the formed compounds from corn cob combustion were measured by sampling particulate matter, and mineral compositions were determined. Also, the temperature profile of the FBC was established. It was determined that the emissions of K and Na elements from the FBC increased from 4 to 7.3% and from 1.69 to 3%, respectively, changing the bed temperature from 650 to 700 °C. Though alkali emissions are reduced at a 650 °C bed temperature, CO emissions are higher by about 50% compared to the case of 700 °C. The addition of 3% of dolomite reduced the pollutant emissions and alkali emissions as well. Potassium content decreased by about 1% and 4%, respectively, at the bed temperatures of 650 °C and 700 °C. The NOx emissions were less than 300 mg/m³ and did not exceed the limit for medium plants regarding DIRECTIVE (EU) 2015/2193. During extended experiments lasting 8 h, no agglomeration of the fluidized bed was observed. Moreover, the proposed configuration of the FBC and its operational parameters prove suitable for facilitating the efficient combustion of agricultural biomass. Full article

This paper explores the impact of an increasing number of prosumers in electricity supply systems and investigates how market mechanisms can mitigate the negative effects. The Regional Energy Market Model simulates a supply system based on cellular structures, employing agent-based modeling to capture individual behaviors and simulate real market dynamics. This study includes various supply scenarios, such as a solely photovoltaic scenario and a technically diversified scenario with biogas-fueled combined heat and power units. For each scenario, fixed and flexible pricing scenarios are simulated to analyze their effects. The findings reveal that systems heavily reliant on photovoltaics experience negative effects at certain points due to seasonal limitations, while technically diversified supply scenarios demonstrate fewer drawbacks. Flexible pricing systems stimulate demand in a manner beneficial to the system, creating regional added value, and contributing to the balance between generation and consumption, depending on the supply scenario. However, the study underscores that economic incentives alone are insufficient for balancing generation and consumption. The results highlight the importance of exploring opportunities through the interplay of economic incentive mechanisms and technical possibilities. Full article

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangements. By employing a linear additive model to solve a multi-criteria decision model, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare and installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in MATLAB R2023b, with results validated through simulations in DIALux evo 12.0. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions. Full article

Energy production from coal combustion is responsible for nearly 40% of global CO₂ emissions including SO_x and NO_x. This study aims to produce solid biomass fuels from oil-palm residues by torrefaction, having a high heating value (HHV) equivalent to fossil coals. The experiments were designed using Design Expert version 13 software to optimize the conditions affecting the fuel characteristics of the torrefied products. The statistical analysis suggested that the optimal conditions to achieve a high HHV and fixed carbon content while retaining the mass yield of biomass mainly depended on the temperature and torrefying time, while the size played a less important role in affecting the properties. The optimal conditions were observed to be at 283 °C (120 min) for EFBs, 301 °C (111 min) for PF, and 285 °C (120 min) for PKSs. The maximum HHV of 5229, 5969, and 5265 kcal/kg were achieved for the torrefied EFBs, PF, and PKSs, respectively. The energy efficiency of torrefied biomass was increased to 1.25–1.35. Ecoefficiency analysis suggested that torrefaction should be carried out at high temperatures with a short torrefying time. This low-cost bio-circular torrefied biomass showed promising fuel characteristics that could be potentially used as an alternative to coals. Full article

With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for “sweet spots” prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R² values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment. Full article

The combined heating method of seat heating and air conditioning (A/C) was applied in the passenger compartment under different experiment conditions, using thermocouples to continuously measure the wall surfaces and air temperatures in the passenger compartment and the passengers’ skin temperatures of 17 segments. Meanwhile, a subjective evaluation questionnaire survey was conducted using a nine-point evaluation scale on the local and overall thermal sensation and thermal comfort of the passengers, and the data from the questionnaire were analyzed with the ANOVA method. The results showed that the use of the heating pad directly affected the changes in human skin temperature, which in turn affected the local and overall thermal sensation and thermal comfort. For the two thermally stimulated segments of the back and under the thighs, the skin temperature of the back was higher than that of the thighs. Using the heating pad resulted in a rapid increase in the mean skin temperature in the early period of the experiment. Thermal sensation of the back and under-thighs shifted rapidly towards the hot zone in the first 10 min, and then settled around +3, with even more significant differences between the groups. Thermal sensations in non-thermally stimulated segments changed in relation to their position on the heating pad, with slower changes in those at the “distal” end of the body, the head and the feet. Continued use of the heating pads at lower ambient temperatures maintained overall thermal comfort at a neutral level in the range of 0–1, whereas at higher ambient temperatures there was a gradual deterioration of local and overall thermal comfort. Full article

Simplified methods are often used for load estimations during the initial design of the foundations of offshore wind turbines (OWTs). However, the reliability of simplified methods for designing different OWTs needs to be studied. This paper provides a comparative study to evaluate the reliability of simplified approaches. The foundation loads are calculated for OWTs at the mudline level using a simplified approach and OpenFAST simulations and compared. Three OWTs, NREL 5 MW, DTU 10 MW, and IEA 15 MW, are used as reference models. An Extreme Turbulence Model wind load at a rated wind speed, combined with a 50-year Extreme Wave Height (EWH) and Extreme Operating Gust (EOG) wind load and a 1-year maximum wave height are used as the load combinations in this study. In addition, the extreme loads are calculated using both approaches for various metocean data from five different wind farms. Further, the pile penetration lengths calculated using the mudline loads via two methods are compared. The results show that the simplified method provides conservative results for the estimated loads compared to the OpenFAST results, where the extent of conservativism is studied. For example, the bending moment and shear force at the mudline using the simplified approach are 23% to 69% and 32% to 53% higher compared to the OpenFAST results, respectively. In addition, the results show that the simplified approach can be effectively used during the initial phases of monopile foundation design by using factors such as 1.5 and 2 for the shear force and bending moment, respectively. Full article

Aiming at the problem that silicon-controlled rectifiers (SCR) and pulse width modulation (PWM) rectifiers cannot balance high power levels, high hydrogen production efficiency, and high grid connected quality in the current research on rectifier power supplies for electrolytic hydrogen production, a new hybrid rectifier topology based on a modular multilevel converter (MMC) is proposed. The hybrid topology integrates a silicon-controlled rectifier (SCR) with an auxiliary power converter, wherein the SCR is designated as the primary power source for electrolytic hydrogen production. The auxiliary converter employs a cascaded modular multilevel converter (MMC) and an input-series-output-parallel (ISOP) phase-shifted full-bridge (PSFB) arrangement. This configuration allows the auxiliary converter to effectively mitigate AC-side harmonics and minimize DC-side ripple, concurrently transmitting a small amount of power. The effectiveness of the hybrid rectifier in achieving low ripple and harmonic distortion outputs was substantiated through hardware-in-the-loop experiments. Notably, the hybrid topology is characterized by its enhanced electric-to-hydrogen conversion efficiency, elevated power density, cost efficiency, and improved grid compatibility. Full article

The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This paper presents a comprehensive review of the state-of-the-art research and developments concerning the flexibility in the operation of low-carbon integrated gas and electricity networks (IGENs) as part of the whole system approach. Methods and solutions to provide and improve flexibility in the mentioned systems are studied and categorized. Flexibility is the system’s ability to deal with changes and uncertainties in the network while maintaining an acceptable level of reliability. The presented review underscores the significance of this convergence in facilitating demand-side management, renewable energy integration, and overall system resilience. By highlighting the technical, economic, and regulatory aspects of such integration, this paper aims to guide researchers, policymakers, and industry stakeholders toward effective decision-making and the formulation of comprehensive strategies that align with the decarbonization of energy systems. Full article

In this article, we analyze the correlation between GDP/capita variation, primary and renewable energy consumption and greenhouse gas emissions on the one hand, and green taxes on the other. Green taxes are the main instruments used to limit activities that have a negative impact on the environment. These consist of taxes paid by producers and/or consumers for any activity that generates pollution. The results of dynamic regressions, validated by the applied robustness tests, indicate a significant and positive correlation between primary energy consumption and total environmental taxes, respectively energy taxes. At the same time, this shows that variation in GDP/capita significantly and positively influences transport taxes and pollution taxes. In contrast, net greenhouse gas emissions and the supply, transformation and consumption of renewable sources and waste do not significantly influence the total green taxes and their components. This finding is useful to both academic research and government policies for the realistic substantiation of the levels of green tax revenues and for establishing appropriate measures meant to reduce CO₂ emissions. Full article

The flexible mode of operation of coal-fired units can accommodate large-scale renewable power integration into the grid, providing more grid capacity. The flexibility transformation of coal-fired units in thermal power plants can be achieved through main steam extraction and reheated steam extraction. A 300 MW subcritical unit, 600 MW subcritical unit and 660 MW ultra-supercritical unit with six flexible operation modes were chosen as the research model to investigate the thermal adaptability for flexible operation. The results show that from the perspective of the source of steam extraction, the main steam extraction scheme is suitable for the flexible adjustment of peak load capacity, and the reheated extraction scheme is suitable for the flexible operation of low load and high thermal efficiency. Moreover, from the perspective of thermal performance adaptability, the 600 MW unit has a wider load regulation capacity than the 300 MW and 660 MW units, and is suitable as the peak shaving unit. This work can provide theoretical guidance for different types of coal-fired units in choosing flexible operation schemes. Full article

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability. Full article

In this study, a perovskite-based mixed cation/anion ultraviolet photodetector with an added halide material is fabricated using perovskite combined with an ABX_3 structure. Mixed cation/anion perovskite thin films of MAPbI₃/FABr are manufactured through a relatively simple solution process and employed as light-absorption layers. In the produced thin film, SnO₂–sodium dodecylbenzenesulfonate acts as an electron transport layer and spiro-OMeTAD acts as a hole injection layer. Compared to a single cation/anion perovskite, the fabricated device exhibits phase stability and optoelectronic properties, and demonstrates a responsivity of 72.2 mA/W and a detectability of 4.67 × 10¹³ Jones. In addition, the films show an external quantum efficiency of 56%. This suggests that mixed cation/anion films can replace single cation/anion perovskite films. Thus, photodetectors based on lead halides that can be applied in various fields have recently been manufactured. Full article

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes. Full article

This study aims to explore additional fluids beneficial for coastal thermal energy converter (CTEC) operation. Ammonia’s thermodynamic properties, characterized by higher condensation temperatures and pressures, demand significantly elevated operating pressures, resulting in a substantial energy load for efficient operation. Thus, exploring alternatives such as R134a becomes crucial, particularly considering its potential as a better working fluid for power generation in a Rankine cycle. The research methodology involves employing computational fluid dynamics (CFD) simulations alongside experimental investigations to examine the performance of an axial turbine concept under different working fluids. The results obtained indicate that R134a is the most appropriate working fluid for an axial turbine within a CTEC, outperforming ammonia, thereby implying significantly better operational efficiency. Full article

Thermochemical energy storage (TCES) is one of the key technologies facilitating the integration of renewable energy sources and mitigating the climate crisis. Recently, Li₄SiO₄ has been reported to be a promising heat carrier material for TCES applications, owing to its moderate operation temperature and stability. During the synthetic processes, the properties of the Si source used directly influence the performance of derived Li₄SiO₄ materials; however, the internal relations and effects are not yet clear. Hence, in this work, six kinds of SiO₂ sources with different phases, morphology, particle size, and surface area were selected to synthesize a Li₄SiO₄-based TCES heat carrier. The physicochemical properties of the SiO₂ and the corresponding derived Li₄SiO₄ were characterized, and the comprehensive performance (e.g., heat storage/releasing capacity, rate, and cyclic stability) of the Li₄SiO₄ samples was systematically tested. It was found that the silica microspheres (SPs), which possess an amorphous phase, uniform micro-scale structure, and small particle size, could generate Li₄SiO₄ TCES materials with a highest initial capacity of 777.7 kJ/kg at 720 °C/900 °C under pure CO₂. As a result, the SP-L showed an excellent cumulative heat storage amount of 5.84 MJ/kg within 10 heat-releasing/storage cycles, which was nearly 1.5 times greater than the value of Li₄SiO₄ derived from commonly used silicon dioxide. Furthermore, the effects of the utilized Si source on the performance of as-prepared Li₄SiO₄ and corresponding mechanisms were discussed, which offers guidance for the future selection of Si sources to produce high-performance Li₄SiO₄-based TCES heat carriers. Full article

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software. Full article

Electric ship propulsion is considered one of the most promising alternatives to conventional combustion systems. Its goal is to reduce the carbon footprint and increase a ship’s maneuverability, operational safety, and reliability. The high requirements for ship propulsion make permanent magnet synchronous motors (PMSMs) an attractive solution due to their characteristics. This paper discusses the control problem of a PMSM based on the input–output feedback linearization method combined with the optimal and adaptive control techniques. The method presented here integrates the parameter tuning process with the optimal design of the baseline controller. Since the load disturbances are treated as an additional unknown parameter, there is no need to introduce an integral action to deal with the resulting steady-state error. An important feature of the designed controller is the so-called energetic optimization of the system; i.e., in addition to the aforementioned adaptive and optimal controller, it has a feature of ensuring zero reactive power consumed by the system. The performed simulations of the machine speed stabilization process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental (load) disturbances. Besides achieving high-quality control, an essential feature of this approach is the elimination of the tuning problem. Full article