109 reads in the past 30 days
Review of commercial nuclear fusion projectsJune 2023
·
1,851 Reads
·
31 Citations
Published by Frontiers
Online ISSN: 2296-598X
Disciplines: Energy and Fuels
109 reads in the past 30 days
Review of commercial nuclear fusion projectsJune 2023
·
1,851 Reads
·
31 Citations
89 reads in the past 30 days
State of charge estimation of lithium-ion battery based on extended Kalman filter algorithmMay 2023
·
1,042 Reads
·
65 Citations
79 reads in the past 30 days
Unique safety features and licensing requirements of the NuScale small modular reactorApril 2023
·
980 Reads
·
7 Citations
71 reads in the past 30 days
Solvent-free NMC electrodes for Li-ion batteries: unravelling the microstructure and formation of the PTFE nano-fibril networkJanuary 2024
·
1,047 Reads
·
7 Citations
56 reads in the past 30 days
Insights into Fischer–Tropsch catalysis: current perspectives, mechanisms, and emerging trends in energy researchApril 2024
·
211 Reads
·
6 Citations
Frontiers in Energy Research is a multidisciplinary journal that explores sustainable developments and technological advances in all fields of energy research to help produce reliable and affordable energy sources.
Led by Field Chief Editor Prof Uwe Schröder (University of Greifswald, Germany), Frontiers in Energy Research welcomes submissions in all areas of energy research which facilitate and support sustainable innovation and long-term solutions. Topics include, but are not limited to:
In particular, the journal welcomes submissions which support and advance the UN’s Sustainable Development Goal (SDG), notably SDG#7: access to affordable, reliable, sustainable and modern energy for all.
Manuscripts relating to the consumption of fossil fuels and non-sustainable technologies are not suitable for publication in this journal.
Frontiers in Energy Research is committed to advancing developments in the field of energy research by allowing unrestricted access to articles, and communicating scientific knowledge to researchers and the public alike, to enable the scientific breakthroughs of the future.
February 2025
Introduction Wind turbine generators (WTGs) and electric vehicles (EVs) are used as source-side and load-side resources, respectively. And the uncertainties of WTGs output and EVs charging load seriously affect the frequency stability of the power system. For the wind speed prediction error and the uncertainty of EV off-grid time, a cooperative frequency modulation (FM) strategy is proposed. Methods Firstly, the wind speed interval is divided based on the operating characteristics of WTGs and the load reduction rate. On the basis, a load reduction operation strategy based on rotor speed control and pitch Angle control is proposed to enable WTGs to have bidirectional FM capability. The adjustable capacity of WTGs is determined based on the wind speed prediction error and the operation strategy of load reduction; Secondly, based on the controllable domain model of an EV considering the off-grid time uncertainty, the adjustable capacity of EV clusters is determined by state grouping of the state of charge (SOC) according to its charging urgency. By defining EV FM capability parameters and charging urgency parameters, the EV priority list for FM is determined and the power allocation strategy is proposed; Then, based on the urgency of EV charging and the economy of WTGs load reduction operation, a cooperative FM task assignment strategy is proposed. Results Simulations demonstrate the strategy enhances FM capability and improves FM effect by 6.05% compared to fixed-proportion task allocation. It strengthens frequency stability by leveraging complementary strengths. Discussion Consideration of wind speed prediction errors can improve WTG adjustable capacity estimation, boosting FM accuracy; Coordinated task allocation minimizes WTGs intermittent FM output impacts, ensuring stable grid frequency. This dual-source-load approach offers a robust solution for modern power systems with high renewable penetration.
February 2025
·
2 Reads
As renewable energy sources are extensively incorporated into electrical grids, the necessity for enhanced flexibility and stability within the power system has significantly grown., Demand Response (DR) has attracted widespread attention as an effective load management tool. This study delves into the master-slave game theory-based demand response strategy integrated with renewable energy, aiming to optimize the interaction mechanism between grid operators and users participating in demand response through a game-theoretic framework, thereby enhancing the system’s economic efficiency and reliability. In this research, we first constructed a power system model that includes renewable energy sources such as wind and solar power. A master-slave game theory-based demand response strategy framework was proposed, where the energy suppliers act as the leader by setting demand response policies, while energy operators who act as followers decide their consumption behaviors to maximize their own interests. The strategy allows participants to adjust their strategies based on real-time market information and changes in renewable energy output so as to realize optimal scheduling of demand response resources. Through theoretical analysis and simulation experiments, the results illustrate that the demand response strategy affects the respective revenues of energy operators and energy suppliers by dispatching electricity purchases in four different modes. The effectiveness of the demand response strategy was verified in reducing operational costs of the grid, enhancing the system’s adaptability to fluctuations in renewable energy, and encouraging active user participation in demand response. In summary, the master-slave game theory-based demand response strategy for renewable energy integration proposed in this study not only promotes the economic and efficient operation of the power grid but also provides important theoretical support and technical references for the development of future smart grids.
January 2025
·
1 Read
In the context of “dual carbon”, in order to promote the consumption of renewable energy and improve energy utilization efficiency, a low-carbon economic dispatch model of an integrated energy system containing carbon capture power plants and multiple utilization of hydrogen energy is proposed. First, introduce liquid storage tanks to transform traditional carbon capture power plants, and at the same time build a multi-functional hydrogen utilization structure including two-stage power-to-gas, hydrogen fuel cells, hydrogen storage tanks, and hydrogen-doped cogeneration to fully exploit hydrogen. It can utilize the potential of collaborative operation with carbon capture power plants; on this basis, consider the transferability and substitutability characteristics of electric heating gas load, and construct an electric heating gas comprehensive demand response model; secondly, consider the mutual recognition relationship between carbon quotas and green certificates, Propose a green certificate-carbon trading mechanism; finally establish an integrated energy system with the optimization goal of minimizing the sum of energy purchase cost, demand response compensation cost, wind curtailment cost, carbon storage cost, carbon purchase cost, carbon trading cost and green certificate trading compensation. Optimize scheduling model. The results show that the proposed model can effectively reduce the total system cost and carbon emissions, improve clean energy consumption and energy utilization, and has significant economical and low-carbon properties.
January 2025
Ocean thermal energy conversion is a new energy technology that utilizes the temperature difference conditions of seawater to generate electricity. This paper focuses on the closed cycle of ocean thermal energy conversion using ammonia as the working fluid. Based on pressure energy utilization devices such as pressure exchangers, hydraulic turbines, and ejectors, three methods are proposed to recover and utilize the pressure energy of the lean ammonia solution, in order to improve the thermal efficiency and systematic performance. By analyzing and comparing the performance of the cycle, it can be concluded that all three methods can achieve a utilization of pressure energy, and under the same conditions, the highest thermal efficiency of the ejector cycle is 5.71%.
January 2025
·
21 Reads
Energy efficiency has been identified as a way of addressing the need to reduce climate impact from fossil fuels. Furthermore, the ongoing twin transition may provide better and more energy-efficient control of buildings with systems such as building management systems (BMS). However, there appear to be barriers to investments in functional digital tools, as there are for other energy-efficient technologies for buildings. This paper is based on a questionnaire study with technology providers, decision makers and users of building management systems. The questionnaire included questions regarding barriers, drivers, and motivations for investments in BMS. Improved energy efficiency was found to be an important motivation for investments in BMS for users and decision makers, but the technology providers elevated more easy work as important. The main driver for investments in BMS was related to reduced energy costs, while for the decision makers, financial barriers such as risks and hidden cost were ranked highest. An important knowledge barrier was found as knowledge is needed for decisions about investments, use of BMS and decisions regarding IT security, such as handling of data. A key conclusion is the need for a facilitator, as knowledge is needed for decisions about BMS investments and for its use. On a broader scale, the paper argues for the need to include facilitators as a core part of future policies within the twin transition.
January 2025
·
2 Reads
The integration of a distributed generator (DG) into the distribution network alters the topology structure and power flow distribution, subsequently causing changes in network loss. Moreover, existing distribution network optimization methods face high computational complexity, low efficiency, and susceptibility to local optima. This article proposes a scenario generation method using a generative adversarial network (GAN) to handle the uncertainty associated with DGs and constructs a two-layer optimization model for the distribution network. The upper layer model determines the installation location and capacity of distributed power and energy storage systems with the lowest economic cost. The lower layer model establishes an optimization model, including wind, solar, and storage, with active power network loss and voltage deviation as objective functions. Both layers are solved using the Improved Whale Optimization algorithm (IWOA). Then, the IEEE-33 node distribution system was taken as a simulation example to verify the effectiveness and superiority of the proposed model and algorithm.
January 2025
·
12 Reads
As renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differences in user electricity consumption behavior, resulting in reduced accuracy. To address this, we propose an optimized short-term load forecasting method based on time and weather-fused features using a ConvLSTM-3D neural network. The Prophet algorithm is first employed to decompose historical electricity load data, extracting feature components related to time variables. Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. A case study using real-world data validates the proposed method, demonstrating significant improvements in forecasting accuracy.
January 2025
·
16 Reads
This paper presents a distributed consensus-based voltage and frequency control (VFC) strategy for isolated microgrids with distributed energy resources (DERs) and induction motor loads. The proposed controller coordinates the DERs to regulate microgrid frequency and voltage while mitigating fault-induced delayed voltage recovery (FIDVR), a phenomenon where system voltage remains depressed for several seconds after fault clearance due to induction motor stalling. The VFC loop adjusts DER voltage setpoints based on frequency deviation and voltage level to regulate voltage and mitigate FIDVR events, while the active power control loop maintains frequency stability by coordinating active power sharing among DERs and compensating for the constant power load behavior of stalled induction motors. A proximity-based reactive power support prioritization and a distributed voltage estimator enhance the controller’s response to FIDVR events. Coordination between the VFC and active power control loops is achieved through adaptive gain adjustment and a voltage recovery coordination term. Simulation results demonstrate the effectiveness of the proposed controller in maintaining microgrid stability, ensuring fast voltage recovery, and providing robust performance under various operating conditions, including communication delays and different fault durations.
January 2025
·
16 Reads
Developing an accurate mathematical model for parameter extraction in photovoltaic modules is a crucial endeavor in optimizing photovoltaic energy systems. This study seeks to assess and compare various analytical and numerical methods for extracting the primary five parameters of photovoltaic modules. Specifically, six established approaches based on a single diode model (SDM) are employed, including the methods introduced by Khan et al., Blas et al., Phang et al., Vika, Cubas et al., and Almonacid et al. The performance of these approaches is evaluated and compared under standard test conditions (STC) with a focus on maximum power point current and voltage. The analytical and numerical methods demonstrate their precision in predicting photocurrent-voltage (I-V) and power-voltage (U-V) curves, with the exception of the Almonacid et al. method, which tends to underestimate the I-V curve at the module’s maximum power. Among these methods, the Phang et al. approach stands out, displaying a strong agreement between experimental data and the predicted curve. This is evidenced by the lower values of root mean square error (RMSE), mean bias error (MBE), normalized RMSE (NRMSE), mean absolute percentage error (MAPE), and absolute error (AE). These findings underscore the high quality of results obtained through the Phang et al. method.
January 2025
·
4 Reads
When the Grid-Following (GFL) and the Grid-Forming (GFM) converters are hybrid-connected to the grid, they are coupled through the grid impedance. During grid faults, the transient characteristics of the two converters become more complex due to this coupling. If one of the converters experiences stability issues, it affects the other, making fault ride-through challenging. A mathematical model for the hybrid grid-connected system of the two converters is first established to analyze the existence conditions of the equilibrium point. Using the phase-plane method, the mutual influence mechanism during faults is revealed. Subsequently, a method to adjust the GFM phase angle based on the degree of voltage sag is proposed, which also improves the phase-locked loop (PLL) of the GFL. The influence of GFL current injection is considered to limit the GFM fault current, thereby achieving hybrid fault ride-through control. Finally, the simulation verifies the effectiveness of the proposed control strategy. The results show that the proposed method can adjust the phase angle to support the grid, ensuring that the GFM outputs more reactive current within the maximum allowable current range. Meanwhile, the GFL injects current according to grid guidelines, effectively preventing overcurrent and phase angle instability of the converters.
January 2025
·
11 Reads
In this paper, the effect of electric vehicles (EVs) on load frequency control (LFC) in the context of a deregulated market within an asymmetric three-area system featuring a novel combination of hybrid power plants is presented. The paper discusses load frequency control within a deregulated market in an unequal three-area system using a new combination of hybrid power plants. All the areas have one renewable energy source and a thermal power plant (TPP), and each area incorporates electric vehicles. Area 1 contains a combination of a wind turbine system (WTS) and thermal, Area 2 has a geothermal power plant (GTPP) and thermal, and Area 3 has a biogas power plant (BPP) and thermal. This proposed system is investigated. Conventional PID, PI, and I controllers are used because they are simple, cheap, and easily available. Their performance is observed and compared. The controller parameters undergo optimization by applying an innovative optimization method called the Mine Blasting algorithm, which utilizes an integral square error (ISE)-based fitness function. The analysis is done under bilateral and contract violation cases with and without generation rate constraints. Moreover, the state of charge (SoC) estimation concept under a deregulated environment and the significance of EVs in the proposed system, especially in the case of contract violation, is presented.
January 2025
·
3 Reads
Lithium-ion batteries (LIBs) are integral to modern technology, yet their reliance on flammable liquid electrolytes poses significant safety challenges, especially in electric vehicles and large-scale energy storage systems. This paper presents the development of flame-retardant electrolytes utilizing the Define-Measure-Analyze-Design-Optimize-Verify (DMADOV) methodology to enhance both safety and performance of LIBs. The study initiates by defining the correlation between the properties of organic solvents and electrochemical stability, focusing on the overcharging risks that can induce thermal runaway. Through systematic measurement and analysis of candidate components, critical factors influencing the quality of flame-retardant electrolytes are identified. The design phase prioritizes the establishment of solid electrolyte interface (SEI) conditions for γ-butyrolactone (γ-BL), ensuring the electrolyte’s performance and stability in LIBs. The optimization phase further refines the SEI formation conditions to address performance challenges identified during initial design, incorporating related manufacturing processes. The final verification phase confirms the alignment of the flame-retardant electrolyte composition with optimized SEI conditions, establishing a viable electrolyte range for practical applications. The study demonstrates that the use of γ-BL markedly reduces the explosion risk due to overcharging. The final verification phase confirms the alignment of the flame-retardant electrolyte composition with optimized SEI conditions, establishing a viable electrolyte range for practical applications. Significantly, this study emphasizes the importance of robust SEI design in developing flame-retardant electrolytes with high-flash-point organic solvents like γ-BL, supported by validation experiments on patented technology. These advancements not only enhance the safety profile of LIBs but also demonstrate the potential for improved battery performance, paving the way for broader applications in energy storage solutions.
January 2025
·
5 Reads
In the process of integrating distributed energy, photovoltaic (PV) power generation systems encounter issues of intermittency and volatility, posing significant challenges to the stability of the power grid. Numerous studies have explored various control strategies to address these challenges, including droop control, virtual synchronous generator (VSG) control, and others. However, existing methods often struggle to provide sufficient inertia and damping support to the power system, particularly under dynamic conditions. This paper aims to address these limitations by introducing an adaptive inertia control method based on an improved active power loop in a PV-storage system. This method aims to optimize the impact and instability phenomena that occur during the integration of distributed PV, reduce system fluctuations, decrease the overshoot of oscillations, and enhance the dynamic performance of the system. Firstly, the mathematical models and control methods of photovoltaic cells and batteries are introduced. Secondly, the control principle of the traditional VSG is explained. Then, the adaptive inertia algorithm is incorporated into the active power loop of the VSG control, and an adaptive inertia control method based on the improved active power loop is proposed. Finally, the effectiveness of the proposed method is verified through simulations.
January 2025
Accurate load forecasting plays a crucial role in the effective planning, operation, and management of modern power systems. In this study, a novel approach to load time series situational prediction is proposed, which integrates spatial correlations of heterogeneous load resources through the application of Random Matrix Theory (RMT) with a Multi-Task Learning (MTL) framework based on Gated Recurrent Units (GRU). RMT is utilized to capture the complex, high-dimensional statistical relationships among various load profiles, enabling a deeper understanding of the underlying data patterns that traditional methods may overlook. The GRU-based MTL framework is employed to exploit these spatiotemporal correlations, allowing for the sharing of essential features across multiple tasks, which in turn enhances the accuracy and robustness of load predictions. This approach was validated using real-world data, demonstrating notable improvements in prediction accuracy when compared to single-task learning models. The results indicate that this method effectively captures complex relationships within the data, leading to more accurate load forecasting. This enhanced predictive capability is expected to contribute significantly to improving demand-side management, reducing the risks of grid overloading, and supporting the integration of renewable energy sources, thereby fostering the overall sustainability and resilience of power systems.
January 2025
·
10 Reads
This paper proposes a short-term wind and photovoltaic power forecasting framework considering time-frequency decomposition based on bidirectional long short-term memory networks. First, the seasonal and trend decomposition using loess is applied to the original wind and photovoltaic data for time domain decomposition, obtaining trend, seasonal, and residual components. Then, the residual component undergoes variational mode decomposition to further extract features of different frequencies. Next, the maximum information coefficient is used to select features, which is highly correlated with wind and photovoltaic power as input features to the prediction model. Finally, the selected features are input into bidirectional long short-term memory networks for training and prediction. Experimental validation using actual data from a photovoltaic station and a wind power station in Hebei Province, China from July to August 2023, which shows that the proposed method achieves high accuracy and reliability in photovoltaic and wind power output prediction. The proposed time-frequency decomposition with the smallest root mean square error of 0.92 and mean absolute error of 0.58 in photovoltaic prediction, at the same time, the smallest root mean square error of 67.5 and mean absolute error of 48.16 in wind power prediction, significantly outperforming other power prediction methods.
January 2025
·
1 Read
The detection and recognition of foreign objects on coal conveyor belts play a crucial role in coal production. This article proposes a foreign object detection method for coal conveyor belts based on EfficientNetv2. Since MBConv and Fused-MBConv structures in EfficientNetv2 employ information compression and fusion strategies, which may lead to the loss of important information and affect the integrity of feature extraction, a hard shuffle attention (Hard-SA) mechanism is utilized to enhance the focus on important features and improve the representation ability of coal conveyor belts image features. To address the potential gradient disappearance issue during the backpropagation process of the network, an elastic exponential linear unit (EELU) activation function is introduced. Additionally, since the cross-entropy loss function may not be flexible enough to handle complex data distributions and may fail to fit the non-linear relationships between data well, a Polyloss function is adopted. Polyloss can better adapt to the complex data distribution and task requirements of coal mine images. The experimental results show that the proposed method achieves an accuracy of 93.02%, which is 2.39% higher than that of EfficientNetv2. It also outperforms some other state-of-the-art (SOTA) models and can effectively complete the detection of foreign objects on coal conveyor belts.
January 2025
·
4 Reads
In the context of global climate change, the multi-city interconnected power system offers the potential for low-carbon and efficient energy utilization, addressing the challenge of ensuring safety, stability, and reduced carbon emissions while meeting diverse demands. This study proposes a security region-based method to evaluate the power supply guarantee capability of such systems, employing a collaborative support framework to characterize the low-carbon feasible space of each city system. A multi-dimensional piecewise linear approximation method and model transformation were applied to construct a scheduling and transformation model for the provincial power grid. The proposed approach enhances power supply security, achieving a 7.21% reduction in system operating costs and a 24.7% decrease in carbon emissions. These findings highlight the effectiveness of the security region approach in balancing safety, efficiency, and environmental objectives, providing a scalable solution for interconnected power grids.
January 2025
·
1 Read
With the rapid development of integrated energy systems (IES), the extensive integration of dis-tributed energy and the increasing coupling of multiple energy systems need higher requirements for the coordinated control methods of IESs. This paper proposes a distributed emergency control method for integrated energy systems of industrial parks based on the alternating direction multiplier method (ADMM). Firstly, an optimization scheduling model is established for the integrated energy system in industrial parks. On the basis of minimizing operating costs, the model takes into account the operational constraints of each energy equipment. Secondly, considering the shortage of energy supply, in order to achieve energy transfer between users, a distributed optimization scheduling model for IES of the industrial park is established based on the typical energy structure of users. And a distributed optimization scheduling algorithm for the comprehensive energy system of the industrial park based on ADMM is proposed. Finally, the proposed emergency control method is verified under typical fault scenarios.
January 2025
·
35 Reads
This review focuses on the potential of carbon-based hybrid nanofluids to enhance the performance of solar thermal energy systems. Solar thermal technology is pivotal in transitioning towards renewable energy sources, offering sustainable alternatives to conventional fossil fuels. However, traditional heat transfer fluids (HTFs) often exhibit limitations in thermal conductivity (TC), which hinders the overall efficiency of solar collectors. The introduction of nanofluids, particularly hybrid nanofluids that combine two or more types of nanoparticles, has emerged as a promising solution to address these challenges. Among various nanomaterials, carbon-based materials such as graphene and multi-walled carbon nanotubes (CNTs) have garnered significant attention due to their exceptional thermal properties. This review critically analyses the thermal and rheological characteristics of carbon-based hybrid nanofluids and their effects on solar thermal applications, including flat-plate collectors and parabolic trough collectors. The unique synergy achieved by integrating carbon-based nanoparticles with metallic nanoparticles results in improved TC, enhanced heat transfer rates, and greater stability compared to single-component nanofluids. Despite the notable advantages, challenges such as increased viscosity and the need for long-term stability under operational conditions remain pertinent. Future research directions should prioritize optimizing nanoparticle concentrations, exploring cost-effective alternatives, and investigating the long-term performance of hybrid nanofluids in dynamic environments. The findings of this review underscore the transformative potential of carbon-based hybrid nanofluids in improving the efficiency and effectiveness of solar thermal systems, thus supporting the broader adoption of renewable energy technologies. This exploration is essential for advancing solar thermal applications and addressing the ongoing challenges of energy sustainability and efficiency in the face of growing global energy demands.
January 2025
·
6 Reads
To enable the online strength assessment of distribution systems integrated with Distributed Energy Resources (DERs), a novel hybrid model and data-driven approach is proposed. Based on the IEC-60909 standard, a new short-circuit calculation method is developed, allowing inverter-based DERs (IBDERs) to be represented as either voltage or current sources with controllable internal impedance. This method also accounts for the impact of distant generators by introducing a site-dependent Short Circuit Ratio (SCR) index to evaluate system strength. An adaptive sampling strategy is employed to generate synthetic data for real-time assessment. To predict the strength of distribution systems under various conditions, a rectified linear unit (ReLU) neural network is trained and further reformulated as a mixed-integer linear programming (MILP) problem to verify its robustness and input stability. The proposed method is validated through case studies on modified IEEE-33 and IEEE-69 bus systems, demonstrating its effectiveness regarding the varying operating conditions within the system.
January 2025
·
28 Reads
Environmental challenges, such as climate change and resource depletion, are driving the search for alternative energy sources like wind energy. This study explores the ecological effects of installing wind turbines in Małopolska, Poland. The goals are to find suitable wind power locations, analyse the impact of distance-to-building criteria, and assess carbon emission reduction. The study was carried out in two stages: identification of possible sites for wind farms, taking into account the two criteria of distance from residential buildings of 700 m and 500 m, and estimation of potential carbon emission reductions. Results show optimal locations in north and south of Małopolska. Reducing the distance criteria doubles suitable areas, potentially decreasing Poland’s annual carbon emissions by 0.44%–1.03% and generating up to 1.49 TWh of wind energy, comparable to combined heat and power (CHP) plants in the region.
January 2025
·
12 Reads
Floating offshore wind turbines (FOWTs) often operate under turbulent wind conditions. However, to enhance computational efficiency, steady wind is sometimes used as an alternative to turbulent wind, potentially resulting in conservative estimates. Assessing FOWT motion and fatigue performance under both steady and turbulent wind conditions is therefore crucial. This study focuses on an enhanced steel semi-submersible FOWT, adapted from the LIFES50+ OO-Star concrete design. The FOWT is modeled using OPENFAST software under various load scenarios, including steady and turbulent winds with irregular waves, for time-domain analysis. The results reveal that the FOWT experiences reduce motion, tension response, blade root loads, and tower-top loads under steady winds combined with irregular waves, compared to turbulent winds with irregular waves. The blade root and tower top loads are lower under steady winds with irregular waves, indicating that steady wind analysis may yield unfavorable outcomes for FOWTs. The findings in this study offer valuable theoretical insights and technical support for the design and evaluation of FOWTs.
January 2025
·
13 Reads
This study investigated the influence of site location, tilt angle, and solar orientation on Ethiopia’s photovoltaic (PV) module performance. We determined optimal tilt angles for different time scales and locations across the country by analyzing global horizontal radiation data and employing various decomposition and transposition models. Results showed that optimal tilt angles increase with latitude, ranging from 0° to 47.9° monthly and from 14.1° to 21.5° annually. Seasonal optimal tilt angles were found to be 29.2°, 21.65°, 12.34°, and 8.8° for winter, autumn, spring, and summer, respectively. Additionally, the study compared the performance of PV modules with different tracking mechanisms. Dual/full-axis tracking yielded the highest energy gain (44.89%), while NS tracking resulted in a significant loss (28.46%). This research provides valuable insights for optimizing Ethiopia’s PV system design and installation, aiding in accurate energy assessment and forecasting.
January 2025
·
49 Reads
Microgrid-equipped electric vehicle charging stations offer economical and sustainable power sources. In addition to supporting eco-friendly mobility, the technology lowers grid dependency and improves energy reliability. The manuscript introduces a hybrid technique for efficient electric vehicle (EV) charging integrating the Dollmaker Optimization algorithm (DOA) and spatial Bayesian neural network (SBNN). This method optimizes the joint operation of photovoltaic (PV), wind turbines (WTs), supercapacitors (SCs), and battery energy storage systems (BESSs) in microgrids to enhance EV charging station efficiency, reliability, and power quality while reducing grid outages. The SBNN predicts EV load demand for improved efficiency and reliability, while DOA manages microgrid (MG) fluctuations to ensure seamless EV charging. The MG system features a four-phase inductor coupled interleaved boost converter (FP-ICIBC) and a fractional-order proportional-integral-derivative (FOPID) controller for optimal power management. An evaluation in MATLAB compares DOA–SBNN with existing approaches, demonstrating its effectiveness in enhancing EV charging performance. The proposed method outperforms all current techniques, including the Multi swarm Optimization (MSO), the Multi-Objective Gray Wolf Optimizer (MOGWO), and the Modified Multi-objective Salp Swarm Optimization algorithm (MMOSSA). The results show that the energy efficiency of the recommended approach is 19.19%, 26.15%, and 32.57% higher than the three current techniques, respectively, and that of total harmonic distortion (THD) is 19.09%, 25.85%, and 31.17% lower than those three techniques, respectively.
January 2025
·
6 Reads
To address the technical challenge of rapid and reliable interruption of DC faults in offshore wind power DC collection and transmission systems, which are critical for large-scale renewable energy integration, this paper proposes an integrated multi-port DC circuit breaker (DCCB) with bus fault clearing capability based on a dual H-bridge configuration. By extending a pair of bridge arms in the H-bridge to connect to the DC bus and employing diodes in the load commutation switches (LCSs) to form the second H-bridge, the proposed DCCB not only achieves conventional line fault clearing but also has the ability to interrupt bus faults. A five-terminal offshore wind power DC transmission system simulation model was built in PSCAD/EMTDC to verify the performance of the DCCB under various operating conditions. The results demonstrate that the proposed multi-port DCCB can protect multiple DC lines effectively under different conditions.
Journal Impact Factor™
CiteScore™