Economic analysis of hydrogen energy systems: A global perspective.

In the realm of renewable energy, the integration of wind power and hydrogen energy systems represents a promising avenue towards environmental sustainability. However, the development of cost-effective hydrogen energy storage solutions is crucial to fully realize the potential of hydrogen as a renewable energy source. By combining wind power generation with hydrogen storage, a comprehensive hydrogen energy system can be established. This study aims to devise a physiologically inspired optimization approach for designing a standalone wind power producer that incorporates a hydrogen energy system on a global scale. The optimization process considers both total cost and capacity loss to determine the optimal configuration for the system. The optimal setup for an off-grid solution involves the utilization of eight distinct types of compact horizontal-axis wind turbines. Additionally, a sensitivity analysis is conducted by varying component capital costs to assess their impact on overall cost and load loss. Simulation results indicate that at a 15 % loss, the cost of energy (COE) is $1.3772, while at 0 % loss, it stands at $1.6908. Capital expenses associated with wind turbines and hydrogen storage systems significantly contribute to the overall cost. Consequently, the wind turbine-hydrogen storage system emerges as the most cost-effective and reliable option due to its low cost of energy.

The utilization of renewable energy and the economic potential of offshore wind power supported by digital finance.

Colombia is well-positioned for the development of sustainable energy due to its abundance of natural resources, which include water, wind, and sun. Regulating the safe and sustainable use of offshore wind energy, which is considered non-conventional, is lacking in the nation, nonetheless. The development of offshore wind technology in Colombia shows potential to meet energy needs during dry hydrological conditions and El Niño/Southern Oscillation events when the hydroelectric system power supply is low. This study examines global initiatives that have encouraged nations to develop plans for cutting their CO2 emissions, stressing both their successes and shortcomings in putting offshore wind technology into practice. An examination of Colombia's renewable energy administrative framework finds a lack of data required to carry out offshore wind projects. Furthermore, a review of previous research on marine energy emphasizes how important it is to expand our knowledge of offshore wind generation. Although the majority of local renewable energy projects concentrate on terrestrial sources, an analysis of wind speed and wind power density in Colombia at different altitudes shows promising magnitudes and good trends.Digital finance plays a crucial role in this context by providing innovative funding mechanisms, enhancing financial accessibility, and reducing investment risks through improved financial technologies. These advancements support the mobilization of capital necessary for the development and expansion of offshore wind energy projects.As a result, the technical, economic, administrative, and legal data pertinent to renewable energy in Colombia is compiled in this study. It proposes to provide information to stakeholders involved in decision-making processes and promotes the possible installation of offshore wind farms in regions close to Colombia's Caribbean coast. Because of its plentiful resources, Colombia offers a great chance to implement offshore wind energy technology, which will lessen dependency on fossil fuels and provide a backup energy source in case of supply shortages. The integration of digital finance is key to unlocking the economic potential of these projects, ensuring sustainable and scalable energy solutions for the future.

Optimal design of hybrid green energy powered reduced switch converter based shunt active power filter using horse herd algorithm.

Renewable energy sources are playing a leading role in today's world. However, integrating these sources into the distribution network through power electronic devices can lead to power quality (PQ) challenges. This work addresses PQ issues by utilizing a shunt active power filter in combination with an Energy Storage System (ESS), a Wind Energy Generation System (WEGS), and a Solar Energy System. While most previous research has relied on complex methods like the synchronous reference frame (SRF) and active-reactive power (pq) approaches, this work proposes a simplified approach by using a neural network (NN) for generating reference signals, along with the design of a five-level reduced switch voltage source converter. The gain values of the proportional-integral controller (PIC), as well as the parameters for the shunt filter, boost, and buck-boost converters in the WEGS and ESS, are optimally selected using the horse herd optimization algorithm. Additionally, the weights and biases for the neural network (NN) are also determined using this method. The proposed system aims to achieve three key objectives: (1) stabilizing the voltage across the DC bus capacitor; (2) reducing total harmonic distortion (THD) and improving the power factor; and (3) ensuring superior performance under varying demand and PV irradiation conditions. The system's effectiveness is evaluated through three different testing scenarios, with results compared against those obtained using the genetic algorithm, biogeography-based optimization (BBO), as well as conventional SRF and pq methods with PIC. The results clearly demonstrate that the proposed method achieves THD values of 3.69%, 3.76%, and 4.0%, which are lower than those of the other techniques and well within IEEE standards. The method was developed using MATLAB/Simulink version 2022b.

Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

Quaternary Dielectric Triboelectric Nanogenerator for Self-Powered Electrochemical Systems.

The energy conversion efficiency of conventional binary dielectric triboelectric nanogenerators is not satisfactory due to the limitations of material selection and triboelectrification, which motivates the design of more efficient multicomponent structures to reveal the charge accumulation mechanism for improving the energy conversion efficiency. Herein, a rotating quaternary dielectric triboelectric nanogenerator (Q-TENG) is designed to construct a self-powered system integrating illumination, sensing, and electrochemical decolorization. Through the equivalent capacitance model, the mechanisms for charge generation, transfer, and accumulation in a Q-TENG are elucidated to achieve efficient matching of quaternary dielectric materials and high output performance. At a wind speed of 3.5 m s-1, the peak power density of the Q-TENG reaches 44.94 W m-2, setting a new record for a wind-driven TENG. A 5 ppm solution of methyl orange is completely degraded by the wind-driven Q-TENG in <6 h. This work not only guides the direction for constructing more efficient TENG systems but also promotes the practical development of self-powered electrochemical systems.

A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems.

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model's better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and long-term predictions.

This study elucidates the formulation and validation of a dynamic hybrid model for wind energy forecasting, with a particular emphasis on its capability for both short-term and long-term predictive accuracy. The model is predicated on the assimilation of time-series data from past wind energy generation and employs a triad of machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). Empirical data, harvested from a 2 MW grid-connected wind turbine, served as the basis for the training and validation phases. A comparative evaluation methodology was devised to scrutinize the performance of each constituent algorithm across a diverse array of metrics. This evaluation framework facilitated the identification of individual algorithmic limitations, which were subsequently mitigated through the implementation of a dynamic switching mechanism within the hybrid model. This innovative feature enables the model to adaptively select the most efficacious forecasting technique based on historical performance data. The hybrid model demonstrated superior forecasting accuracy in both, short-term energy forecasts at 15-min intervals over a day, and in broad, long-term. It recorded a Normalized Mean Absolute Error (NMAE) of 5.54 %, which is notably lower than the NMAE range of 5.65 %-9.22 % observed in other tested models, and significantly better than the average NMAE found in the literature, which spans from 6.73 % to 10.07 %. Such versatility renders it invaluable for grid operators and wind farm management, aiding in both operational and strategic planning. The study's findings not only contribute to the existing body of knowledge in renewable energy forecasting but also suggest the hybrid model's broader applicability in various other predictive analytics domains.

MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance.

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work.

Ameliorating a vertical axis wind turbine performance utilizing a time-varying force plasma actuator.

Controlling wind flow on vertical axis wind turbine blades is an effective technique for enhancing their performance. Modern equipment such as plasma actuators have gained significant attention for their ability to control, and improve the flow behavior in wind turbines. Previous studies have primarily focused on investigating plasma actuators with constant force. In this study, plasma actuators with varying forces over time were applied to the turbine blades. An unsteady 2D model was used to analyze the wind turbine. The sliding mesh model was employed to simulate rotor rotation, and the SST k - ω model was utilized for turbulence modeling. Initially, the performance of the clean turbine was examined. In the next step, the plasma actuators with different force waveforms were applied to the wind turbine blades, including constant, sine, cosine, positive ramp, negative ramp, pulse in the first half-cycle, and pulse in the last half-cycle waveforms. The results indicated that the cosine, and sinusoidal waveforms, led to the greatest improvement with 37.28% and 35.59% increase in the net energy produced by the turbine, respectively, compared to the baseline case.

Real time implementation of scaled droop control in hybrid microgrid with hydrogen storage for regulation of voltage and frequency.

The incorporation of renewable energy resources (RERs) into smart city through hybrid microgrid (HMG) offers a sustainable solution for clean energy. The HMG architecture also involves linking the AC-microgrid and DC-microgrid through bidirectional interconnection converters (ICC). This HMG combines AC sources like wind-DFIG with DC sources such as solar PV and solid oxide fuel cell (SOFC), supported by battery energy storage systems (BESS) and hydrogen storage units (HSU). The HSU can generate and store hydrogen during RER surplus. This stored hydrogen can be further employed for production of electrical power along with numerous other applications. The HSU is emerged as a competent tool which can be utilised alone/in combination with BESS to enhance the system reliability. Harvesting power from clean and green sources requires its optimal operation and control while feeding to the existing grid. The existing strategies of controlling ICC are complex and not efficient; hence, a novel intelligent scaled droop control structure (SDCS) is proposed, utilizing frequency, DC voltage, and active power. The SDCS regulate voltage and frequency in both islanded mode (IM) and grid connected mode (GCM) of HMG. Experimental validation demonstrates its simplicity and effectiveness, making it suitable for smart city environments, ensuring uninterrupted power for critical loads with improved air quality.

Enhanced grid integration through advanced predictive control of a permanent magnet synchronous generator - Superconducting magnetic energy storage wind energy system.

In this study, the use of an Unscented Kalman Filter as an indicator in predictive current control (PCC) for a wind energy conversion system (WECS) that employs a permanent magnetic synchronous generator (PMSG) and a superconducting magnetic energy storage (SMES) system connected to the main power grid is presented. The suggested UKF indication in the hybrid WECS-SMES arrangement is in charge of estimating vital metrics such as stator currents, electromagnetic torque, rotor angle, and rotor angular speed. To optimize control strategies, PCCs use these projected properties rather than direct observations. To control the unpredictable wind energy's nature, SMES must be regulated to minimize fluctuations in the DC-link voltage and power output to the main grid. Fractional order-PI (FOPI) controllers are used in a novel control structure for the SMES system to regulate the output power and DC-link voltage. An artificial bee colony optimization approach is employed to optimize the FOPI controllers. Three commonly utilized indicators, including sliding-mode, EKF, and Luenberger, were evaluated using "MATLAB" to evaluate the performance of the UKF estimate. Assessment criteria such as mean absolute percentage error and root mean squared error were used to gauge the accuracy of the estimates. Simulation findings showed the efficiency of fractional order-PI controllers for SMES and the proposed UKF indication for predictive current control, especially in the presence of measurement noise and over a variety of wind speeds. An improvement in estimation accuracy of up to 99.9 % was demonstrated by the UKF indicator. Moreover, the stability of the suggested UKF-based PCC control for the hybrid WECS-SMES combination was confirmed using Lyapunov stability criteria."

Control approaches of power electronic converter interfacing grid-tied PMSG-VSWT system: A comprehensive review.

The growing interest in wind power technology is motivating researchers and decision-makers to focus on maximizing wind energy extraction and enhancing the quality of power integrated into the grid. Over the past decades, significant advancements have been made in Wind Energy Conversion Systems (WECS), such as moving to variable speed wind turbines (VSWT), using various generator types, and interfacing with many power electronic converter topologies. Recently, the majority of wind turbine industries have adopted the VSWT, which is based on the permanent magnet synchronous generator (PMSG) and incorporates a fully controlled power electronic converter (FCPEC) topology due to its notable features of full controllability, ultimately enhancing the efficiency and power quality of the WECS. This paper presents a concise overview of the PMSG-VSWT system and comprehensively reviews the most recent control approaches developed for the FCPEC that play a crucial role in the operation and performance of the PMSG-VSWT system. The paper begins with a comprehensive review of the Maximum Power Extraction Algorithms (MPEA) used in the PMSG-VSWT system, as reported in esteemed research articles over recent years. It investigates the fundamental concepts of each MPEA, examining their advantages and disadvantages, providing critical comparisons, highlighting related work, and discussing the advancements achieved in this field. Subsequently, the paper reviews the prevalent control schemes for the Grid-Side Inverter and Machine-Side Rectifier (GSI/MSR) in the FCPEC. It covers common control approaches such as vector control, direct control, sliding mode control, and model productive control, including modern and intelligent techniques. Additionally, the paper details recent improvements and approaches adopted to address challenges in these common schemes, involving optimizing algorithms and adaptive techniques. The paper provides essential insights into trends, improvements, and challenges in the domain and acts as a crucial reference for researchers working with PMSG-VSWT systems.

Robust control of a wind energy conversion system: FPGA real-time implementation.

This study employs an FPGA board to implement a robust control technique for wind energy conversion systems (WECS). This approach facilitates extensive testing and validation of the control system across diverse wind conditions, utilizing the FPGA's parallel processing capabilities and advanced control algorithms. This method ensures robustness against nonlinearities and system uncertainties. FPGA-in-the-loop (FIL) testing provides precise and effective simulation results, enabling rapid prototyping and iterative modifications of control algorithms. The effectiveness of the robust control strategy is confirmed by FIL findings, demonstrating significant improvements in WECS stability and efficiency. Furthermore, the study highlights the strategy's potential to enhance WECS reliability and efficiency in real-world applications.

Assessment of wind energy resource in the western region of Somaliland.

As the population of Somaliland continues to grow rapidly, the demand for electricity is anticipated to rise exponentially over the next few decades. The provision of reliable and cost-effective electricity service is at the core of the economic and social development of Somaliland. Wind energy might offer a sustainable solution to the exceptionally high electricity prices. In this study, a techno-economic assessment of the wind energy potential in some parts of the western region of Somaliland is performed. Measured data of wind speed and wind direction for three sites around the capital city of Hargeisa are utilized to characterize the resource using Weibull distribution functions. Technical and economic performances of several commercial wind turbines are examined. Out of the three sites, Xumba Weyne stands out as the most favorable site for wind energy harnessing with average annual power and energy densities at 80 m hub height of 317 kW/m2 and 2782 kWh/m2, respectively. Wind turbines installed in Xumba Weyne yielded the lowest levelized cost of electricity (LCOE) of not more than 0.07 $/kWh, shortest payback times (i.e., less than 7.2 years) with minimum return on investment (ROI) of approximately 150%.

Addressing ocean planning challenges in a highly crowded sea space: a case study for the regional sea of Catalonia (Western Mediterranean).

Background: This study performs an exploratory analysis of current-future sustainability challenges for ocean planning for the regional seas of Catalonia located in the Western Mediterranean (Spain). Methods: To address the challenges we develop an Maritime Spatial Planning (MSP)-oriented geodatabase of maritime activities and deploy three spatial models: 1) an analysis of regional contribution to the 30% protection commitment with Biodiversity Strategy 2030; 2) a spatial Maritime Use Conflict (MUC) analysis to address current and future maritime activities interactions and 3) the StressorGenerator QGIS application to locate current and anticipate future sea areas of highest anthropogenic stress. Results & Conclusions: Results show that the i) study area is one of the most protected sea areas in the Mediterranean (44-51% of sea space protected); ii) anthropogenic stressors are highest in 1-4 nautical miles coastal areas, where maritime activities agglomerate, in the Gulf of Roses and Gulf of Saint Jordi. iii) According to the available datasets commercial fishery is causing highest conflict score inside protected areas. Potential new aquaculture sites are causing highest conflict in Internal Waters and the high potential areas for energy cause comparably low to negligible spatial conflicts with other uses. We discuss the added value of performing regional MSP exercises and define five challenges for regional ocean sustainability, namely: Marine protection beyond percentage, offshore wind energy: a new space demand, crowded coastal areas, multi-level governance of the regional sea and MSP knowledge gaps.

Boosting cuckoo optimization algorithm via Gaussian mixture model for optimal power flow problem in a hybrid power system with solar and wind renewable energies.

This paper presents a novel approach, the Gaussian Mixture Method-enhanced Cuckoo Optimization Algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, emissions, network loss, and voltage deviation. GMMCOA integrates the strengths of COA and GMM while mitigating their individual limitations. While COA offers robust search capabilities, it suffers from initial parameter dependency and the risk of getting trapped in local optima. Conversely, GMM delivers high-speed performance but requires guidance to identify the best solution. By combining these methods, GMMCOA achieves an intelligent approach characterized by reduced parameter dependence and enhanced convergence speed. The effectiveness of GMMCOA is demonstrated through extensive testing on both the IEEE 30-bus and the large-scale 118-bus test systems. Notably, for the 118-bus test system, GMMCOA achieved a minimum cost of $129,534.7529 per hour and $103,382.9225 per hour in cases with and without the consideration of renewable energies, respectively, surpassing outcomes produced by alternative algorithms. Furthermore, the proposed method is benchmarked against the CEC 2017 test functions. Comparative analysis with state-of-the-art algorithms, under consistent conditions, highlights the superior performance of GMMCOA across various optimization functions. Remarkably, GMMCOA consistently outperforms its competitors, as evidenced by simulation results and Friedman examination outcomes. With its remarkable performance across diverse functions, GMMCOA emerges as the preferred choice for solving optimization problems, emphasizing its potential for real-world applications.

Enhancement of ANN-based wind power forecasting by modification of surface roughness parameterization over complex terrain.

Wind energy plays an important role in the sustainable energy transition towards a low-carbon society. Proper assessment of wind energy resources and accurate wind energy prediction are essential prerequisites for balancing electricity supply and demand. However, these remain challenging, especially for onshore wind farms over complex terrains, owing to the interplay between surface heterogeneities and intermittent turbulent flows in the planetary boundary layer. This study aimed to improve wind characteristic assessment and medium-term wind power forecasts over complex hilly terrain using a numerical weather prediction (NWP) model. The NWP model reproduced the wind speed distribution, duration, and spatio-temporal variabilities of the observed hub-height wind speed at 24 wind turbines in onshore wind farms when incorporating more realistic surface roughness effects, such as the subgrid-scale topography, roughness sublayer, and canopy height. This study also emphasizes the good features for machine learning that represent heterogeneities in the surface roughness elements in the atmospheric model. We showed that medium-term forecasting using the NWP model output and a simple artificial neural network (ANN) improved day-ahead wind power forecasts by 14% in terms of annual normalized mean absolute error. Our results suggest that better parameterizations of surface friction in atmospheric models are important for wind power forecasting and resource assessment using NWP models, especially when combined with machine learning techniques, and shed light on onshore wind power forecasting and wind energy assessment in mountainous regions.

A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy Assessment.

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators.

Charge generation by passive plant leaf motion at low wind speeds: design and collective behavior of plant-hybrid energy harvesters.

Energy harvesting techniques can exploit even subtle passive motion like that of plant leaves in wind as a consequence of contact electrification of the leaf surface. The effect is strongly enhanced by artificial materials installed as 'artificial leaves' on the natural leaves creating a recurring mechanical contact and separation. However, this requires a controlled mechanical interaction between the biological and the artificial component during the complex wind motion. Here, we build and test four artificial leaf designs with varying flexibility and degrees of freedom across the blade operating onNerium oleanderplants. We evaluate the apparent contact area (up to 10 cm2per leaf), the leaves' motion, together with the generated voltage, current and charge in low wind speeds of up to 3.3 m s-1and less. Single artificial leaves produced over 75 V and 1µA current peaks. Softer artificial leaves increase the contact area accessible for energy conversion, but a balance between softer and stiffer elements in the artificial blade is optimal to increase the frequency of contact-separation motion (here up to 10 Hz) for energy conversion also below 3.3 m s-1. Moreover, we tested how multiple leaves operating collectively during continuous wind energy harvesting over several days achieve a root mean square power of ∼6µW and are capable to transfer ∼80µC every 30-40 min to power a wireless temperature and humidity sensor autonomously and recurrently. The results experimentally reveal design strategies for energy harvesters providing autonomous micro power sources in plant ecosystems for example for sensing in precision agriculture and remote environmental monitoring.

A Blade-Type Triboelectric-Electromagnetic Hybrid Generator with Double Frequency Up-Conversion Mechanism for Harvesting Breeze Wind Energy.

Triboelectric nanogenerators (TENGs) have garnered substantial attention in breeze wind energy harvesting. However, how to improve the output performance and reduce friction and wear remain challenging. To this end, a blade-type triboelectric-electromagnetic hybrid generator (BT-TEHG) with a double frequency up-conversion (DFUC) mechanism is proposed. The DFUC mechanism enables the TENG to output a high-frequency response that is 15.9 to 300 times higher than the excitation frequency of 10 to 200 rpm. Coupled with the collisions between tribomaterials, a higher surface charge density and better generating performance are achieved. The magnetization direction and dimensional parameters of the BT-TEHG were optimized, and its generating characteristics under varying rotational speeds and electrical boundary conditions were studied. At wind speeds of 2.2 and 10 m/s, the BT-TEHG can generate, respectively, power of 1.30 and 19.01 mW. Further experimentation demonstrates its capacity to charge capacitors, light up light emitting diodes (LEDs), and power wireless temperature and humidity sensors. The demonstrations show that the BT-TEHG has great potential applications in self-powered wireless sensor networks (WSNs) for environmental monitoring of intelligent agriculture.