Wind Power Generation Research Articles

A piezoelectric-electromagnetic hybrid energy harvester for rotational motion driven by magnetic repulsion

Harvesting energy from rotational motion is an efficient and widely used technology that provides power support for various electronic devices and systems by converting mechanical kinetic energy into electrical energy. This energy harvesting method shows great potential and advantages in industrial automation, transportation, wind power generation, and smart homes. This study proposes a piezoelectric-electromagnetic hybrid energy harvester (HEH) for rotational motion driven by magnetic repulsion. HEH comprises of two parts: a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMH). HEH utilizes the magnetic drive reciprocating motion to enhance the energy collection efficiency of PEH and EMH. Additionally, by adding the magnetic effect to the ends of bimorph piezoelectric sheets, the spectrum of energy harvesting is expanded. Its parameters are analyzed using theoretical analysis and simulation, and an experimental testbed is established to explore the influence of HEH output performance. The results indicate that the output power reaches its maximum when there are 2 circular magnets on the rotor, the gap distance of magnets is 15 mm, and 2 mass blocks at the end of the bimorph piezoelectric sheet. The PEH and EMH outputs are 173.36 V and 4.81 V, respectively. The maximum output power of HEH is 53.45 mW. The power density can reach 6.818 mW cm−3. Compared with PEH and EMH, the output performance is improved by 46.94% and 174.95%, respectively. When the rotation speed is 500 r min−1, HEH can effortlessly light up 80 LEDs. The experimental results all demonstrate the potential of HEH to power low-power sensors.

Modeling of Brazilian Wind Power Generation Capacity: A Multivariate Analysis with Neural Networks

Over the last twenty years, Brazil has experienced significant changes in its electric energy matrix, notably with the expansion of wind and solar energy sources. In the early 2000s, hydroelectric plants with a capacity of 70 gigawatts (GW) were responsible for generating 90% of the country's electricity, while solar and wind sources contributed merely 1%. Today, the scenario has evolved, with wind and solar energy comprising approximately 25% of the national generation, indicating a departure from the previous hydro-dominated matrix. This research aims to apply specific types of neural networks to multivariate data to forecast Brazil's wind power generation capacity, providing insight into the potential for future energy strategies. This research contributes as a as a decision support tool contemplating relevant information for analysts, researchers, and participants in the renewable energy market. The monetary volume destined to import key components of the wind, considering specific time lags, was utilized to forecast the Installed capacity of the wind farms in Brazil. The Toda-Yamamoto Causality Tests revealed unidirectional Granger causality in the sense of the Import variable for the Brazilian Installed Capacity variable for wind energy production. Another result obtained was the finding that the variable that matters most in the multivariate configuration of the Neural Network is the variation in Installed Capacity in megawatts, followed by the import volume of wind energy components, which exhibited critical lags at 3 and 6 months.

How wind forecast updates are balanced in coupled European intraday markets

AbstractThe market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future.

Influence of Wind Power Generation on the Steel Structure Design of Green Buildings

Wind power generation in renewable energy is the current research focus, but the relationship between wind power generation and green buildings is the basis for ensuring the stable operation of wind power generation, so it has become the current research focus. I propose a composite method based on the heat energy limit theory to study the relationship between wind power generation and the steel structure of green buildings, find the weak points of the steel structure in time, and ensure the continuous operation of wind power generation. The simulation results show that there is a negative correlation between wind power generation and green steel structure, and there is a close relationship between the design characteristics of the steel structure, the material and the installation method of wind power generation. The composite method proposed in this paper can monitor the operation effect of power generation at noon in time, and the accuracy of heat energy monitoring of steel structure can reach 80~90%, and the whole monitoring time is less than 15 seconds, which shows that the composite method proposed by us can provide support for the design and analysis of wind power generation and green steel structure buildings, and has theoretical feasibility. Therefore, strengthening the relationship between wind power generation and green steel structure design is the technical condition for the operation and implementation of steel structure, and it is also the guarantee of wind power generation.

Integration of Price Data in Wind Power Sales Systems Using Association Rule Mining Combined with Deep Learning Algorithms

With the growing attention of the global population towards renewable energy sources such as wind power derived from natural resources such as Solar, Geothermal, and Wind energy, there is a growing need for genuine estimation of wind energy. Wind energy is important in the supply of electricity in the global energy markets because of its enormous importance in the delivery of renewable energy. As such, it is crucial for accurate appreciation of the power of the wind to ably respond to issues that relate to the trading of power while at the same time addressing issues to do with planning, scheduling and strategic positioning of wind power generation. Therefore, the present work aims to develop a new model known as the Association Rule with DL-based Wind Power Generation and Price Prediction (ARDL-WPGPP). It utilizes two datasets: An Energy dataset includes various columns like tie, wind onshore forecast, and price actual, whereas the weather features dataset includes only wind speed. These datasets are combined to create a dataset where each transaction represents 2 hours of wind generation. A data mining approach is employed to uncover hidden patterns, rules, concepts, and correlations within these datasets, operating on various types of data including quantitative, textual, and multimedia formats. To efficiently extract rules from the dataset, an improved Apriori algorithm is introduced. Subsequently, the generated rules incorporating wind speed are passed into an improved LSTM model, which learns by comparing the label data. The price actual value serves as the label data, assigning labels to data points based on their actual price value. High price actual values indicate high wind power prices, while low values indicate low wind power prices.

Dynamic Stability Enhancement of Wind Power Generation with Static VAR Compensator using Multiobjective Optimization Algorithms

Dynamic Stability Enhancement of Wind Power Generation with Static VAR Compensator using Multiobjective Optimization Algorithms

Chronological DC transmission expansion planning considering new energy and load uncertainties

Abstract The integration of renewable energy into the grid and the expansion of new loads have significantly increased the uncertainty of the power system, which has brought many new challenges to power system planning. Hence, it is necessary to establish a transmission expansion planning model that adapts to future development to improve the economic benefits of power grid planning. This paper proposes a chronological DC transmission expansion planning method for new energy sources, such as wind and solar energy, considering the time characteristics of source loads. First, the method establishes a chronological DC transmission expansion planning model by simulating long-term grid operations. Among them, the objective function aims to minimize the sum of investment costs and power generation operating costs related costs. Secondly, to simulate unit operation, a loop optimization modeling method is used to optimize the model structure. Finally, simulation analysis is conducted using the improved Garver’s 6 bus system under random planning scenarios as a basis for comparison. By comparing various scenarios, it is concluded that the proposed chronological DC transmission expansion planning can provide a more economical solution than non-chronological planning. This helps ensure that the system operates cost-effectively under uncertain source and load scenarios while enhancing the integration capabilities of wind and solar power generation.

The optimization and dispatch of regional multi-energy complementary power system in green certificate Trading - Carbon emission trading

Abstract This proposal of the “double carbon” goal underscores the pressing need to reduce carbon emissions. To address this issue, the utilization of an optimal scheduling model for an integrated multi-energy complementary system, comprising wind, solar storage, and thermal power generation, is proposed. Two renewable energy generating units, wind and photovoltaic power, cooperate with thermal power units to generate electricity to meet urban base load requirements. Energy storage units compensate for the instability of power generation from renewable energy units. To significantly decrease system carbon emissions, a green certificate trading-carbon emission trading system is introduced. The carbon emission reduction facilitated by the green certificate can offset a portion of the carbon emissions, and the quantities of tradable green certificates and carbon emissions in their respective markets are adjusted accordingly. This model aims to maximize the system’s comprehensive operating earnings while considering the low-carbon economy aspect. The proposed model undergoes simulation analysis, and the results demonstrate its reasonableness and efficacy in terms of carbon reduction and economic viability.

Simulation analysis of overvoltage caused by grounded short circuit in the collection system of far-offshore wind farms

Abstract As the distance from the shore of offshore wind farms increases, MMC-HVDC technology is gradually gaining attention in its grid connection schemes. A broadband electromagnetic transient model of offshore wind farms with a total capacity of 800 MW connected to the grid via a flexible direct current transmission system is developed. After three types of grounded short circuit faults occur at different fault positions within the power collection system, the characteristics of overvoltage are obtained and they are analyzed on the basis of considering the low voltage ride-through capability of the direct-drive permanent magnet wind power generator. Combining the two factors of range and amplitude, the results show that the overvoltage phenomenon is the most serious when a two-phase grounded short circuit occurs at the converging busbar, which can be the focus of the study when setting up overvoltage protection.

The effect assessment of wind turbine capacity on offshore wind power generation potential in guangdong

Abstract The installed capacity of offshore wind power directly impacts the number of turbines and the total electricity generation. This study uses wind speed and offshore wind power planning data, and Guangdong Province is a case study. Three scenarios are set (7.5 MW, 9.5 MW, and 15 MW) to explore the influence of turbine installed capacity on electricity generation. The results indicate that when the turbine capacity is 9.5 MW, it generates the highest electricity, which accounts for approximately 162718.9 GWh per year.

Small signal stability analysis of a wind power system integrated with a multi-band supplementary damping controller

Abstract With the integration of wind turbines represented by doubly-fed induction generators (DFIG) into the power system, the inertia level of the system decreases sharply, and small disturbances can easily cause low-frequency oscillation (LFO) in the system. In this paper, we propose a multi-band supplementary damping controller (MBSDC) directly applied to the active power control link of the rotor-side converter in the wind power generation system. We establish a linearized model for DFIG integrated with the power system, calculate eigenvalues, and screen out multiple electromechanical oscillation modes to be suppressed. Based on the calculation results, an MBSDC is designed to suppress LFO, and we propose a parameter-setting algorithm to enhance the suppression effect. Taking the New England 10-generator 39-bus system containing DFIG as an example for simulation verification, the results show that the MBSDC and parameter setting algorithm designed in this paper can effectively suppress LFO.

Wind power generation forecasting based on multi‐model fusion via blending ensemble learning architecture

AbstractBecause of the intermittency and randomness of wind power generation, constructing an accurate wind power generation forecasting model is of great necessity for stable operation and optimal scheduling of modern power systems. Considering the unsatisfied performance of the single learner model and the diverse learning abilities of different machine learning algorithms, XGBoost model, KNN algorithm, SVM algorithm, and CNN‐BiLSTM‐Attention neural network are integrated via blending ensemble architecture to construct the multi‐model fusion short‐term wind power forecasting model. Pearson correlation analysis is applied to reveal the interrelation between meteorological factors and wind power. Additionally, the training samples of base learners are reconstructed for ensuring all data can be utilized. The advantages of each learner are combined co‐ordinately via blending ensemble learning framework. Prediction results of ensemble learning model and single learner model are compared in the same scenario. Simulation results indicate that the ensemble learning model can effectively extract potential features of input information and realize higher prediction accuracy.

Using enhanced Variational Modal Decomposition and Dung Beetle Optimization Algorithm optimization-kernel Extreme Learning Machine model to forecast short-term wind power

With the widespread use of clean energy, the forecasting of wind power has become increasingly significant. Extracting time series from sophisticated wind power data and thus improving the prediction accuracy of wind power generation has become the key to short-term forecasting. This paper proposes a Kernel Extreme Learning Machine (KELM) prediction model based on Crested Porcupine Optimizer (CPO), Variational Modal Decomposition (VMD) with Improved Dung Beetle Optimization Algorithm (QHDBO). Firstly, by analyzing the correlation between the variables in the wind power data, the CPO algorithm is used to optimize the modal decomposition number and the penalty parameter of the VMD and extract the time-series information of the wind power series, and smooth it to obtain a number of sub-sequences with strong regularity. Then, the KELM prediction model is constructed for different subsequences, and the kernel parameters and regularization coefficients of KELM are optimized using the QHDBO algorithm. Finally, the predicted values of different subsequences are reconstructed to obtain the final prediction results. The simulation results show that the CVMD-QHDBO-KELM model can effectively improve the wind power prediction performance, and the MAPE values are all below 9 %, and most of the RE values are below 10 %.

Korean Offshore Wind Electrical Power Generation: Current Status and Prospects

Offshore wind power generation is recognized as an environmentally friendly and sustainable energy source, rapidly advancing with governmental support in many countries such as Denmark, China, Germany, and across Europe due to its high energy stability, potential, and economic benefits. South Korea has set a target of 17.7GW for wind power generation through its Renewable Energy 3020 Implementation Plan, aiming to supply 12GW of offshore wind power by 2030. However, Korea faces slower development compared to overseas counterparts due to various obstacles including lack of community acceptance, low business feasibility, complex approval processes, and insufficient supply chain. This study identifies fundamental reasons behind Korea's sluggish growth in offshore wind power generation and proposes the following solutions through comparison with leading international cases. 1) Strengthening positive communication with residents by clearly articulating the value and benefits of offshore wind power projects to address community acceptance issues. 2) Resolving low business feasibility issues through the establishment of government-led incentive programs and support measures. 3) Streamlining approval processes to mitigate delays and cancellations in project implementation. 4) Expanding domestic market size and enhancing efficient power interconnection to address supply chain deficiencies.

Coordinated voltage control for large-scale wind farms with ESS and SVG based on MPC considering wake effect

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink.

A Study of Development of Cylindrical Linear Generator for Bladeless Wind Power Generation System

A Study of Development of Cylindrical Linear Generator for Bladeless Wind Power Generation System

Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series

The inherent randomness and volatility of wind power generation present significant challenges to the reliable and secure operation of the power system. Therefore, it is crucial to have interpretable wind power forecasting (WPF) to ensure seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving WPF performance and ignore the interpretability of the model, resulting in ambiguous forecasting. In this paper, the interpretable feature-temporal transformer (IFTT) for short-term wind power forecasting with multivariate time series is presented. The model uses an encoder-decoder architecture to effectively integrate historical information and future prior information from multiple variables. The designed decoupled feature-temporal self-attention (DFTA) module and variable attention network (VAN) effectively realize the interpretability of temporal information and multi-variable inputs while extracting important features. The Auxiliary Forecasting Network (AFN) plays a key role in providing pseudo-future wind speed predictions, which serve as an essential input for the model's decoder, and enhancing forecasting accuracy through multi-task learning. Experimental results on multiple datasets in different geographical locations show that the proposed algorithm is superior to various advanced methods. Besides, the interpretability of the IFTT model offers valuable insights for ensuring the safety of wind power utilization and supporting informed risk decision-making.

Toward a physics-based model of power coefficient in horizontal-axis wind turbine

Global energy is in a major shift from energy using fossil sources to renewable energy using more sustainable energy sources. The wind is the clean and inexhaustible one of renewable energy resources that is available in most parts of the world. In order for wind power generation to meet more ambitious targets around the world, it is necessary to understand all the physics behind process of wind generation. The most interesting of this process is the power coefficient which involves the real characteristics of the wind turbine as a function of the generated power. Up to now, the power coefficient does not yet have a general form but are fitted as mathematical functions from manufacturer data measured from popular wind turbines in the world. In this paper, we develop an analytical physics-based method to estimate the power coefficient general for various wind generation systems of horizontal-axis wind turbines (HAWT). Instead of 12 parameters in semi-experimental exponential formula , our power coefficient uses only 5 parameters with their physical meanings. By varying these five parameters we also obtain the power coefficients fitted from experimental data.

Long-term wind power forecasting with series decomposition and spatio-temporal graph neural network

ABSTRACT The stable operation of the power grid requires accurate predictions of wind power generation and the stabilization of its fluctuations through the integration of other energy sources. An increasing number of deep learning methods are now being employed in the field. However, due to the instability of wind power data, existing methods struggle to uncover deep spatiotemporal dependencies. We propose a novel method named SD-STGNN (Series Decomposition and Spatio-Temporal Graph Neural Network). SD-STGNN first decomposes unstable wind power data into seasonal and trend components. For the seasonal data reflecting short-term fluctuation patterns, we employ a Gated Temporal Convolutional Network and Graph Convolutional Network to capture spatiotemporal relationships. Additionally, for trend data reflecting long-term fluctuation patterns, we introduce a Temporal-Feature Enhancement module, utilizing Multi-Layer Perceptrons to extract deep information along both temporal and feature dimensions. Extensive experiments were conducted on the SDWPF public dataset. Compared to existing state-of-the-art baseline methods, our proposed SD-STGNN model achieves a notable average reduction in Mean Absolute Error by approximately 6.26%, in Root Mean Squared Error by 7.55%, and in Mean Absolute Percentage Error by 2.65%. Additionally, there is an average improvement of about 4.74% in the coefficient of determination.

Droop control-based fast frequency support of wind power generation integrated grid-forming VSC-HVDC system

Droop control-based fast frequency support of wind power generation integrated grid-forming VSC-HVDC system