High Wind Power Research Articles

Two-stage Day-ahead Multi-step Prediction of Wind Power Considering Time-series Information Interaction

With the large-scale development of wind power, high penetration wind power grid connection poses serious challenges to the safe and stable operation of the power system. However, the current accuracy of wind power forecasting is facing bottlenecks due to the limitations of Numerical Weather Prediction (NWP) data. Therefore, this article proposes a two-stage day-ahead multi-step wind power prediction (WPP) scheme that considers temporal information interaction. In the first stage, the next day prediction of wind power is based on historical power and 0∼24 hours NWP data. Then, an embedded deep decomposition module is used to extract predictable components and multi-scale information fusion is performed. In the second stage, the result of day-ahead WPP is obtained based on the extracted predictable components and combined with 24∼48 hours of NWP data. The wind farms in Jilin and Inner Mongolia of China are used to experimental analysis. The results show that the scheme proposed in the article has a better prediction effect compared with other schemes in the paper, which can effectively improve the multi-step prediction accuracy of day-ahead wind power.

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.

Accurate parameter identification method for coupled sub/super-synchronous oscillations for high penetration wind power systems

As the penetration of renewable energy increases to a large scale and power electronic devices become widespread, power systems are becoming prone to synchronous oscillations (SO). This event has a major impact on the stability of the power grid. The recent research has been mainly concentrated on identifying the parameters of sub-synchronous oscillation. Sub/Super synchronous oscillations (Sub/Sup-SO) simultaneously occur, increasing the difficulty in accurately identify the parameters of SO. This work presents a novel method for parameter identification that effectively handles the Sub/Sup-SO components by utilizing the Rife-Vincent window and discrete Fourier transform (DFT) simultaneously. To mitigate the impact of spectral leakage and the fence effect of DFT, we integrate the tri-spectral interpolation algorithm with the Rife–Vincent window. We use the instantaneous data of the phasor measurement unit (PMU) to identify Sub/Sup-SO-related parameters (Sub/Sup-SO damping ratio, frequency, amplitude and phase). First, the spectrum of the Sub/Sup-SO signals is analyzed after incorporating the Rife-Vincent window, and the characteristics of the Sub/Sup-SO signal are determined. Then, the signal spectrum is identified using a three-point interpolation algorithm, and the damping ratio, amplitude, frequency, and phase of the Sub/Sup-SO signals are obtained. In addition, we consider the identification accuracy of the algorithm under various complex conditions, such as the effect of Sub/Sup-SO parameter variations on parameter identification in the presence of a non-nominal frequency and noise. The proposed algorithm accurately identifies the parameters of multiple Sub/Sup-SO components and two Sub-SO components that are in close proximity. Testing with synthetic and real data demonstrates that the proposed algorithm outperforms existing methods in terms of identification accuracy, identification bandwidth, and adaptability.

What does clean energy attract? Evidence from the transfer structure of energy-intensive projects in China

The transfer of energy-intensive projects to areas abundant in clean energy resources holds promise for optimizing the energy structure. After obtaining specific information on transferred projects of listed energy-intensive firms through search or phone contact, this paper innovatively constructs a three-dimensional panel containing 1,859,220 observations to empirically examine the attraction of clean energy to energy-intensive projects under the carbon intensity constraint policy (CICP). Our findings reveal that: (1) the CICP promotes the transfer of gray energy-intensive projects, leading to the transfer structure trending towards graying. (2) Furthermore, we observe a positive correlation between the proportion of thermal power in transfer-out areas and the outflow of gray energy-intensive projects. Conversely, areas with higher wind power and photovoltaics attract a greater inflow of gray energy-intensive projects. (3) Notably, clean energy predominantly attracts the inflow of gray projects rather than green projects. Our findings provide micro evidence and valuable insights into the transfer of energy-intensive industries, informing the design and implementation of future environmental policies for policymakers and stakeholders.

A multi-objective two-stage stochastic unit commitment model for wind and battery-integrated power systems

This paper presents a mathematical programming framework for modeling the operations of power systems with high wind power penetration and BESS, accounting for wind and power demand uncertainties. For this, we introduce a multi-objective two-stage stochastic unit commitment model aimed at constructing on/off schedules that balance operating costs and CO2 emissions. Under the realistic limitations imposed by BESS high variable operating cost, we focus on assessing and analyzing the operational impact of BESS for two different integration schemes: wind farm-coupled and stand-alone. The proposed method of solution consists in an augmented ϵ-constrained algorithm with an integrated normalized euclidean distance-based bisection routine to produce more equispaced Pareto optimal solutions. We applied the proposed framework to a modified version of the New England IEEE-39 bus test system for three disjoint cases of analysis: without BESS integration, with BESS integrated into a wind farm, and with a stand-alone BESS integrated into the grid. The results demonstrate the efficacy of the proposed framework and show substantial benefits in terms of CO2 emissions reductions, particularly in the stand-alone configuration, which, although limited in extent of use, promotes the direct injection of wind power into the grid, the decommitment of polluting thermal units, enhance the use of free or low emissions technologies, and contributes as a provider of spinning reserves.

Study on Primary Frequency Regulation Reserve Configuration of High Wind Power System Considering Governor Limiting Links

With the large-scale use of wind turbines, the ability of the power system to resist frequency fluctuations has been greatly weakened, making the contradiction between frequency regulation supply and demand in the power system increasingly prominent. In order to ensure the frequency security of the power system, this paper conducts research on the primary frequency regulation (PR) backup configuration problem of a power system containing a high proportion of wind power. First, according to the dynamic properties of the speed regulator, a system frequency response model considering the limiting link is established. And the system frequency response model is transformed into a time-domain analytical function of PR reserve capacity and frequency maximum value. On this basis, a PR backup configuration model of a power system containing a high proportion of wind power is constructed with the optimization goal of minimizing system operating costs and taking into account the limiting link constraints. It is proposed to use the L-shape algorithm to decompose the model into main problems and sub-problems, which effectively reduces the solution complexity of the model. Finally, the correctness and effectiveness of this model are verified based on the improved IEEE 39-bus system.

Green finance, green technology innovation, and wind power development in China: Evidence from spatial quantile model

Renewable energy is an inevitable choice to address global climate change and energy shortages. Wind power production has already ranked second in renewable energy production and has enormous development prospects. However, wind power companies generally face funding shortages. How to effectively leverage the positive role of the financial sector in wind power development is a topic worthy of research. Using a novel spatial quantile model, this article investigates the impact of green finance on wind power development and its impact mechanism. The results shows that green finance has a driving effect on the provinces with low- and middle-wind power production, such as Shanghai, Tianjin, Zhejiang, and Shaanxi. However, there is a negative spatial correlation between wind power development in the 10th–25th, and 25th–50th group provinces, such as Qinghai, Sichuan, and Henan. In addition, the impact of wind power prices on the wind power in all provinces is negative, since wind power prices are still on the high side. Green technology innovation and foreign direct investment are two important intermediate mechanisms that green finance affects wind power development. Mechanism analysis displays that foreign direct investment contributes the most to the wind power in the 25th–50th and 50th–75th group provinces, such as Jiangsu, Liaoning, and Guangdong. However, green technology innovation has the smallest contribution to the provinces with high-wind power production, such as Shandong, Shanxi, and Inner Mongolia. Therefore, policy makers should consider the spatial effects and inter- provincial differences in wind power development.

A Refined Wind Power Forecasting Method with High Temporal Resolution Based on Light Convolutional Neural Network Architecture

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks.

Tube-Based Distributed MPC for Load Frequency Control of Power System With High Wind Power Penetration

Tube-Based Distributed MPC for Load Frequency Control of Power System With High Wind Power Penetration

Which wind turbine types are needed in a cost‐optimal renewable energy system?

AbstractPrevious research has indicated that wind power plants can be designed to have less‐variable power generation, thereby mitigating the drop in economic value that typically occurs at high wind power penetration rates. This study investigates the competitiveness of adapted turbine design and the interplay with other flexibility measures, such as batteries and hydrogen storage, for managing variations. The analysis covers seven turbine designs for onshore and offshore wind generation, with different specific power ratings and hub heights. Various flexibility measures (batteries, hydrogen storage and transmission expansion) are included in the optimization of investment and dispatch of the electricity system of northern Europe. Three driving forces for turbine design selection are identified: (1) lowest cost of electricity generation; (2) annual wind production per land area and (3) improved generation profile of wind power. The results show that in regions with good wind resources and limited availability of variation management, it is cost‐efficient to reduce the variability of wind power production by adapting the turbine design. This remains the case when variation management is available in the form of batteries, hydrogen storage and transmission system expansion. Moreover, it is more cost‐effective to improve variability by changing the specific power rating rather than the turbine hub height.

Combined MIMO Deep Learning Method for ACOPF with High Wind Power Integration

The higher penetration of renewable energy sources in current and future power grids requires effective optimization models to solve economic dispatch (ED) and optimal power flow (OPF) problems. Data-driven optimization models have shown promising results compared to classical algorithms because they can address complex and computationally demanding problems and obtain the most cost-effective solution for dispatching generators. This study compares the forecast performance of selected data-driven models using the modified IEEE 39 benchmark system with high penetration of wind power generation. The active and reactive power load data of each bus are generated using Monte Carlo simulations, and synthetic wind power data are generated by utilizing a physical wind turbine model and wind speed samples withdrawn from a Weibull distribution. The objective is to design and evaluate an enhanced deep learning approach for the nonlinear, nonconvex alternating current optimal power flow (ACOPF) problem. The study attempts to establish relationships between loads, generators, and bus outcomes, utilizing a multiple-input, multiple-output (MIMO) workflow. Specifically, the study compares the forecast error reduction of convolutional neural networks (CNNs), deep feed-forward neural networks (DFFNNs), combined/hybrid CNN-DFFNN models, and the transfer learning (TL) approach. The results indicate that the proposed combined model outperforms the CNN, hybrid CNN-DFFNN, and TL models by a small margin and the DFFNN by a large margin.

Optimal scheduling of integrated energy systems considering wind power uncertainty

Abstract To reduce the impact of high wind power penetration rate on integrated energy systems, a wind power scenario generation method based on polynomial regression is proposed and an optimization scheduling model is constructed accordingly. First, a probability distribution model for wind power is constructed. Then, scenario generation is performed through Latin hypercube sampling and typical scenarios are obtained using the synchronous back propagation elimination method. Finally, the optimal output of each piece of equipment in the system is obtained with the lowest overall cost as the optimization objective. The simulation results validate the effectiveness and low-carbon nature of this method, while reducing costs.

A Direct Torque Control Method for CSC Based PMSG Wind Energy Conversion Systems

The Current Source Converter (CSC) technology is well suited for high power wind energy applications. Because it drives generally features like simple converter structure, smooth power injection at the grid side and excellent short circuit protection. The main drawback lays its dynamic torque response. An improved Direct Torque Control (DTC) method is proposed in this paper to overcome this drawback. Using DTC method for Permanent Magnet Synchronous Generators (PMSG) in a wind energy system has some advantages such as, simple control system and topology, ease of tracing Maximum available power from the wind and flux weakening control in high wind speeds. The simulation results demonstrate the effectiveness of the proposed control system, good dynamic response and acceptable MPPT capability.

Assessment of High Wind Power Permeability Power System Frequency Regulation

Global wind power has expanded quickly in recent years to help with the energy issue and environmental degradation. The large-scale integration of wind power has become a hot topic for academic research in recent years due to the potential to improve the stability of the power system through the application of wind power participation in power grid frequency control. This study reviews the research on power system frequency regulation approach with high wind power permeability and classes the frequency control difficulties of wind power integration. A review and analysis are conducted on the energy storage system that is involved in the frequency management of the power system with high wind permeability.

Energy storage system coordinated with phase-shifting transformer and dynamic rating equipment for optimal operation of wind-rich smart power networks

Emergence of flexibility devices into smart power systems can assist the power system operators in making effective and economical decisions for the power system scheduling. These devices include energy storage system (ESS), phase-shifting transformer (PST), dynamic transformer rating (DTR), and dynamic line rating (DLR). In this paper, an approach is proposed for optimal day-ahead scheduling of power system using coordinated operation of ESS, PST, DTR, and DLR units under high wind power penetration situation. The aim is to minimize operational, load shedding, and wind power curtailment costs by regarding all the problem constraints. All of the model's relations as well as AC power flow equations have been convexified in order to construct a mixed-integer quadratically-constrained programming (MIQCP) model whose solution will obtain global optimum results. The developed model is implemented in GAMS and applied to the IEEE 24-bus system under various case studies, and the obtained results are investigated thoroughly. By coordinated operation of the employed flexibility devices, the loading of transformers and lines reach 159 % and 246 % compared to their nominal rating without violation of temperature limitations. Through releasing the capacity of lines and transformers, the total scheduling cost is reduced by about 52 % compared to traditional operation. Also, the whole energy of wind farms is utilized without any curtailment or constraints violation.

Advanced Fault Ride-Through Operation Strategy Based on Model Predictive Control for High Power Wind Turbine

Advanced Fault Ride-Through Operation Strategy Based on Model Predictive Control for High Power Wind Turbine

Large-Scale BESS for Damping Frequency Oscillations of Power Systems with High Wind Power Penetration

With the high penetration of renewable energy into power grids, frequency stability and oscillation have become big concerns due to the reduced system inertia. The application of the Battery Energy Storage System (BESS) is considered one of the options to deal with frequency stability and oscillation. This paper presents a strategy to size, locate, and operate the BESS within the power grid and, therefore, investigate how sizing capacity is related to renewable energy penetration levels. This paper proposes an identification method to determine the best location of the BESS using the Prony method based on system oscillation analysis, which is easy to implement based on measurements while actual physical system models are not required. The proposed methods for BESS size and location are applied using MATLAB/Simulink simulation software (version: R2023a) on the Kundur 2-area 11-bus test system with different renewable energy penetration levels, and the effectiveness of the applied method in enhancing frequency stability is illustrated in the study cases. The case studies showed a significant improvement in steady-state frequency deviation, frequency nadir, and Rate of Change of Frequency (ROCOF) after implementing BESS at the selected bus. The integration of BESS can help to avoid Under-frequency Load Shedding (UFLS) by proper selections of size, location, and operating strategy of the BESS within the power grid.

Study on the synergistic regulation strategy of load range and electrolysis efficiency of 250 kW alkaline electrolysis system under high-dynamic operation conditions

Alkaline water electrolysis (AWE) has the highest technological maturity among all the water electrolysis technologies for hydrogen production, however, reducing the minimum load boundary and improving the electrolysis efficiency are the technical challenges of the AWE system that still exist and urgently require optimization. The minimum load is primarily limited by the hydrogen to oxygen (HTO) from cross-diaphragm transfer and lye mixing, with HTO above 2.0% being a significant safety risk. Reducing the lye flow rate and pressure are effective while two of the few ways by regulating the operating parameters to improve the HTO thus extend the minimum load boundary, but will worsen electrolysis efficiency. Therefore, this study proposes a synergistic regulation strategy of pressure and lye flow rate: maximizing pressure and lye flow rate during high load period to ensure high electrolysis efficiency; adjusting lye flow rate and pressure during medium load period to ensure HTO≤2.0% and maximize the electrolysis efficiency; and reducing lye flow rate and pressure to a low level during the low load period to broaden the minimum load so as to improve overall efficiency of AWE system when loading with fluctuant green electric. This work elaborates the HTO routes, influencing factors and parameter optimization mechanism by building a system-level steady-state and dynamic gas purity model. The optimal combination curve of pressure and lye flow rate is obtained and its control effect on performance parameters, in terms of minimum load, system energy consumption, energy utilization, electrolysis efficiency and so on, is compared and verified in high dynamic wind and photovoltaic (PV) power scenarios. Finally, the optimal wind & PV power ratios are explored based on the optimal operation curve, which will provide a reference for the future large-scale development of hydrogen production scenarios direct-coupled with wind and PV power. The minimum load is extended from 42.0% in the lye flow rate alone control to 21.2% in the pressure alone control and finally to 15.6% in the lye flow rate and pressure synergistic control method. In the absence of electrical replenishment, wind and PV energy utilization efficiency can reach up to 98.3% and 95.6%, respectively.

Energy Storage Operation Analysis of High-proportion Wind Power System Based on Optimization Model

Energy storage is a valid way to ensure the actual-time power equilibrium of renewable energy systems. However, owing to the comparatively high cost of accumulation energy, the use of energy storage to equilibrium the system power will add to the system run cost, and the economy and dependability of the power system will be influenced. Therefore, in this paper, a wind-thermal-storage joint optimization model considering load-side demand response and carbon capture integrated cost is established for different wind power installed capacities, and the PSO algorithm and CPLEX solver are selected to solve the model. The power balance change and energy storage configuration of the system are compared and analyzed under the condition that the lowest cost of power generation operation is the goal function, which provides some guidance for energy storage collocation of the wind energy system and reliable power supply of the system. The results of the instance show that the improvement model introduced in this paper can validly solve the power balance issue of the high ratio wind power system with energy storage, and ensure the safe and reliable functioning of the power system while reducing the power generation cost of the system.

High and low frequency wind power prediction based on Transformer and BiGRU-Attention

An accurate and reliable wind power prediction model has important significance for the operation of power systems and large-scale grid connection. This paper proposes a hybrid deep learning model, CEEMDAN-SE-TR-BiGRU-Attention, for high and low frequency wind power prediction by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), Transformer (TR) and bidirectional gated recurrent unit with attention mechanism (BiGRU-Attention). Firstly, the CEEMDAN decomposes the original wind power sequence into multiple sub-modes and a residual, and the sample entropy of each sub-sequence is calculated by restructuring the sequence, which can effectively alleviate the impact of the original non-stationary series on the accuracy and computational complexity. Next, the reconstructed sequences are further divided into high and low frequency sequences according to the sample entropy value of the original sequence. The Transformer and BiGRU-Attention models are respectively applied to the prediction of high frequency and low frequency sequences according to the characteristics of each sequence. Finally, the predicted values of all components are superimposed to obtain the final prediction results. Experiments are carried out on four datasets with different seasons, and different models are compared to illustrate the effectiveness and superiority of the proposed model. The experimental results show that the proposed model achieves better prediction accuracy.