Value Of Wind Power Research Articles

Deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network for short-term wind power prediction

The dependence of wind power on the natural environment leads to volatility, which can cause hidden dangers to the safe and stable operation of the power grid. In this work, a deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected prediction network is proposed for the short-term prediction issue of wind power generation, and the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is compared with five algorithms including long short-term memory network and NasNet. The dataset was collected in Natal. The six algorithms employed predicted the value of wind power for the coming day. Among all, the deep learning-based GoogLeNet embedded no-pooling dimension fully-connected network achieved the optimal prediction results and evaluation metrics. The percentage reduction of each metric value from the second smallest long short-term memory network for the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is 27.0% for mean absolute error, 27.2% for mean absolute percentage error, 34.8% for mean squared error, 19.9% for root mean square error and 21.6% for symmetric mean absolute percentage error.

Machine Learning Method for Forecasting Wind Power Using Continuous Wind Speed Data

Among various nonconventional energy sources, wind energy is a noteworthy and suitable source with the ability to generate electricity continuously and sustainably. However, there are a number of drawbacks to wind energy, including high basic utilization costs, the static nature of wind farms, and the challenge of locating energy that is wind-efficient. regions. Using five machine learning methods, long-term wind power prediction was done in this study using daily wind speed data. We suggested an effective way to forecast wind power values using machine learning techniques. To demonstrate how machine learning algorithms, perform, we carried out a number of case studies. The outcomes demonstrated that long-term wind power values might be predicted using machine learning algorithms in relation to past wind speed data. Additionally, the consequences show that machine learning-based Models could be used in places other than those where they were taught. This study showed that, by employing a model of a base site, machine learning algorithms could be applied frequently prior to the development of wind plants in an undisclosed environmental region, provided that it makes sense.

Simulation study of jet trajectory based on Runge-Kutta method

Abstract To solve the problem of not being able to accurately spray to the designated target point due to wind and distance factors when firefighting at a long distance, this paper adopts the Runge-Kutta algorithm to simulate the trajectory of the fire cannon, and establishes the mathematical model of the trajectory of the jet under the ideal state and the trajectory of the jet under the influence of the wind. We carry out three-dimensional visualization research and compare the results of the simulation experiment with the real experimental data, and the range, height, and trajectory have a high degree of fit. Comparing the simulation results with the real experimental data, the range and height of the jet and the trajectory have a high degree of affinity, and adding the wind power value panel in the visualization interface can simulate the changes of the jet trajectory under different wind speeds and wind power and effectively simulate the trajectory of the jet under natural conditions.

Accelerating optimal scheduling prediction in power system: A multi-faceted GAN-assisted prediction framework

This study introduces a comprehensive framework aimed at enhancing power system optimality through a two-stage optimization process and the development of a deep-based model for optimal scheduling prediction (OSP). Initially, a Bidirectional Long Short-term Memory (Bi-LSTM) architecture is employed to accurately forecast wind power in the first stage. Subsequently, a convolutional Generative Adversarial Network (GAN) model utilizes these predicted wind power values to generate synthetic scenarios. These scenarios, based on the preceding 10 days’ wind power predictions, serve as inputs for the subsequent power system optimization stage. To streamline computational efficiency, the power system optimization is conducted via a two-stage model. The outputs from this process, alongside other pertinent parameters, are utilized to train the proposed deep-based OSP model. The efficacy of the proposed model in rapidly and reliably predicting optimal scheduling is evaluated using the 118-bus power system. Results indicate that the innovative approach demonstrates exceptional speed and precision in determining optimal scheduling for the power system. Specifically, the proposed OSP model accurately forecasts optimal dispatch for ten days ahead in a mere 0.38 s, with an error rate below 0.001. Furthermore, the model exhibits a 92 % correlation in predicting optimal dispatched wind power. Sensitivity analysis highlights that optimizing the arrangement of the proposed deep-based model using an automatic hyperparameter optimization software framework (OPTUNA) can significantly enhance performance accuracy, potentially by up to 24 %.

Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multi-energy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues

Interval reservoir computing: theory and case studies

The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.

Research on Collaborative Evaluation of Offshore Wind Power Value Chain in Guangxi-Based on BWM-FVIKOR Model

With the dynamic development of the offshore wind power industry and the global value chain, the traditional wind power industry is gradually turning to the offshore wind power value chain system that focuses on the value effect of the value chain, and it is of great significance to carry out value chain collaborative evaluation research. This paper puts forward the evaluation index system of Guangxi offshore wind power value chain from the comprehensive system dimension, key node dimension, subject contribution dimension and resource endowment dimension, builds the BWM-FVIKOR evaluation model, and proves the validity of the evaluation index and the feasibility of the evaluation model through example analysis, and this paper provides scientific and effective decision-making basis for the coordination of Guangxi offshore wind power value chain.

Optimal Reactive Power Dispatch problem integrating probabilistic model of wind power for uncertainties in load using JAYA algorithm

The optimal reactive power dispatch (ORPD) is a method for optimising power system engineering to reduce power loss and voltage deviation. This work addresses the ORPD problem considering load uncertainty, where load is varied in ten different instances. The optimal conditions have been determined for the system with minimum power loss and minimum total voltage deviations for different loading instances. The problem has also been integrated with the probabilistic nature of wind power, calculated using the Weibull Probability Distribution Function (WPDF). The aim is to obtain the optimal values of wind power using the WPDF for two separate cases: overestimation and underestimation, integrate these power individually, and minimise both the power loss and voltage deviation for the ten different loading scenarios. This hybrid wind power integrated ORPD problem, considering load uncertainty, has been tested on the IEEE 30 and IEEE 57 bus systems using the JAYA algorithm and other optimisation techniques. This tuning-parameter-free JAYA algorithm has obtained the best solutions for power loss of 1.8641 MW and TVD of 0.123 p.u. for the overestimated wind power integration at the IEEE 30 bus system at 100% loading compared to the other algorithms. It also acquired the best power loss and TVD solutions of 1.9144 MW and 0.1245 p.u., respectively, for the underestimated wind power at the same bus system for 100% loading with the highest convergence rate. The overall study establishes the JAYA algorithm as a superior optimising technique for solving this hybrid ORPD problem compared to the other techniques.

Tracking-dispatch of a combined wind-storage system based on model predictive control and two-layer fuzzy control strategy

To maximize improving the tracking wind power output plan and the service life of energy storage systems (ESS), a control strategy is proposed for ESS to track wind power planning output based on model prediction and two-layer fuzzy control. First, based on model predictive control, a model with deviations of grid-connected power from the planned output and the minimum deviation of the remaining capacity of the ESS from the ideal value is established as the target. Then, when the grid-connected power exceeds the allowable deviation band of tracking, the weight coefficients in the objective function are adjusted by introducing the first layer of fuzzy control rules, combining the state of charge (SOC) of the ESS with the dynamic tracking demand of the planned value of wind power. When the grid-connected power is within the tracking allowable deviation band, the second layer of fuzzy control rules is used to correct the charging and discharging power of the ESS to improve its ability to track the future planned deviation while not crossing the limit. By repeatedly correcting the charging and discharging power of the ESS, its safe operation and the multitasking execution of the wind power plan output tracking target are ensured. Finally, taking actual data from a wind farm as an example, tests on a simulation platform of a combined wind-storage power generation system verify the feasibility and superiority of the proposed control strategy.

Optimized ensemble model for wind power forecasting using hybrid whale and dipper-throated optimization algorithms

Introduction: Power generated by the wind is a viable renewable energy option. Forecasting wind power generation is particularly important for easing supply and demand imbalances in the smart grid. However, the biggest challenge with wind power is that it is unpredictable due to its intermittent and sporadic nature. The purpose of this research is to propose a reliable ensemble model that can predict future wind power generation.Methods: The proposed ensemble model comprises three reliable regression models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM models. To boost the performance of the proposed ensemble model, the outputs of each model are optimally weighted to form the final prediction output. The ensemble models’ weights are optimized in terms of a newly developed optimization algorithm based on the whale optimization algorithm and the dipper-throated optimization algorithm. On the other hand, the proposed optimization algorithm is converted to binary to be used in feature selection to boost the prediction results further. The proposed optimized ensemble model is tested in terms of a dataset publicly available on Kaggle.Results and discussion: The results of the proposed model are compared to the other six optimization algorithms to prove the superiority of the proposed optimization algorithm. In addition, statistical tests are performed to highlight the proposed approach’s performance and effectiveness in predicting future wind power values. The results are evaluated using a set of criteria such as root mean square error (RMSE), mean absolute error (MAE), and R2. The proposed approach could achieve the following results: RMSE = 0.0022, MAE = 0.0003, and R2 = 0.9999, which outperform those results achieved by other methods.

Predictive maintenance of wind turbines based on digital twin technology

Predictive maintenance of wind turbines plays a crucial part in directing power grid dispatching and maintaining power grid security. In this paper, a way of ultra-short term wind power prediction relied on digital twin technology is proposed, which realizes actual time and accurate wind power prediction by building a digital model. Firstly, BP neural network (back propagation neutral network) was used to predict the wind power, and the initial predicted value of wind power was obtained. Then the meteorological data was substituted into the historical meteorological database to find similar meteorological conditions data, and the BP neural network predicted value was weighted to get the final digital twin predicted value. The simulation results indicate that this method can effectively enhance prediction accuracy of wind power.

Validity of wind power for heavy winter demand in eastern Japan power systems

On a day with unusually low temperatures in March 2022, there was a risk of a shortage in the power supplied by the eastern Japan (Tohoku and Tokyo) power systems owing to the spiked demand. Mass-installed photovoltaic power systems cannot be considered as winter supply capacity because they generate almost no electricity during such heavy demand in winter. Conversely, wind power, which is currently installed in much less capacity, is promising in these areas owing to the high output due to the East Asian winter monsoon. This study clarifies that, in the Tohoku power system, the winter capacity values of wind power are sufficiently high and higher than the annual capacity factors. The study concluded that increasing offshore wind farms along the Tohoku area can effectively supply part of the tight winter electricity demand that needs to be fulfilled by power systems in eastern Japan. Moreover, it increases the share of renewable energy sources.

A two-stage robust optimization model for a virtual power plant considering responsiveness-based electric vehicle aggregation

With the goal of pursuing carbon neutrality, this study is aimed to investigate effectively managing distributed renewable energy. Considering the uncertainty of wind power (WP), photovoltaic power (PV), and load, a two-stage robust optimization model for virtual power plant (VPP) is proposed, with a focus on calculating the available capacity of electric vehicle (EV) aggregated virtual energy storage (VES). Time–space distribution characteristics of EVs and the responsiveness of EV users are analyzed and the available capacity calculation model is constructed. In the first stage, the bidding strategy and unit combination of the VPP are determined based on the predicted values of WP, PV, and load. Combining with the pre-decided decision from the first stage, the output of each unit is determined by considering an uncertainty set in the second stage, for achieving the best operational benefit under worst-case scenarios. Robust control parameters are introduced into the model and used to adjust conservativeness. The model is solved by applying the C&CG algorithm. Additionally, a carbon trading mechanism is leveraged to balance the system economy and environmental friendliness. The results show that the optimal scheduling solution helps to reduce fluctuations caused by uncertainties, balancing the economic benefits and operational risks of the VPP. Besides, guidance is provided for VPP operators to formulate reasonable incentives for EV users and the construction of energy storage systems. When the carbon trading price of the VPP proposed in the paper is set at 160 ¥/ton, economic and environmental sustainability can be effectively balanced.

Ultra-short-term wind power prediction based on double decomposition and LSSVM

To reduce the influence of the random fluctuation on wind power prediction, a new ultra-short-term wind power prediction model, based on wavelet decomposition (WD), variational mode decomposition (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method is based on the double decomposition and LSSVM, where the wind power sequence is decomposed by WD into low- and high-frequency components, which are further decomposed by VMD to obtain many modal components with tendency and periodicity. Multiple LSSVM prediction models are then established with historical wind power data and weather data as the inputs to obtain the predicted values of the multiple modal components. The final predicted values of wind power are achieved by data fusion of outputs of these LSSVM models. The experimental results show that the MAPE (mean absolute percentage error) of the combined prediction model is 4.66%, which is the best compared with nine benchmark models. This demonstrates the high performance of the proposed WD-VMD-LSSVM model for short-term prediction of wind power.

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and data preprocessing were performed to the data. The dataset used in this study includes 4 features and 50530 instances. To accurately predict the wind power values, we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization (SFS-PSO) to optimize the parameters of LSTM network. Five evaluation criteria were utilized to estimate the efficiency of the regression models, namely, mean absolute error (MAE), Nash Sutcliffe Efficiency (NSE), mean square error (MSE), coefficient of determination (R2), root mean squared error (RMSE). The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99% in predicting the wind power values.

Research on Wind Power Energy Storage Joint Optimization Operation under the Double Detailed Rules Assessment Taking into Account the Benefits of Green Certificate

Due to the uncertainty of wind power outputs, there is a large deviation between the actual output and the planned output during large-scale grid connections. In this paper, the green power value of wind power is considered and the green certificate income is taken into account. Based on China’s double-rule assessment system, the maximum net income of the wind farm is used as the target and the optimization model is established with the maximum income of the wind-energy storage consortium as the objective function at 96 points on a typical day. The particle swarm optimization algorithm with improved weight and learning factor is adopted to solve the model and the energy storage output characteristics are analyzed. At the same time, the effect of changing the deviation coefficient and the penalty factor on the combined income is compared. The example results show that the wind storage consortium improves the stability of output, effectively reduces the double-rule assessment cost, and increases the green certificate income of the new energy station, which verifies the validity and correctness of the proposed method.

A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction

Accurate wind power forecast is critical to the efficient and safe running of power systems. A hybrid model that combines complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), random forest (RF), improved reptile search algorithm (IRSA), bidirectional long short-term memory (BiLSTM) network and extreme learning machine (ELM) is proposed for wind power prediction in this paper. Firstly, the CEEMD decomposes the non-stationary original wind power sequence into comparatively stationary modal components, and sample entropy aggregation is used to decrease the computational complexity. Secondly, redundant features are further eliminated through random forest feature selection. Thirdly, the BiLSTM model and the ELM model are applied to forecast high and low frequency components, respectively. IRSA is used to optimize the model's parameters. Finally, the predicted value of each component is summed to arrive at the final predicted value of wind power. By comparing with ten other models, the results show that the dual-scale ensemble model of BiLSTM and ELM can obtain better prediction accuracy. The RMSE of the model proposed in this study is reduced by more than 10% compared with other benchmark models, which demonstrates that the proposed model can better fit the wind power data and achieve better prediction results.

Prediction method of wind farm power generation capacity based on feature clustering and correlation analysis

• A wind power forecasting method based on deep sparse autoencoder is proposed. • An improved fuzzy C-means clustering algorithm is proposed. • Combining IFCM with PCC optimizes the input data for model training. • A DSAE model is constructed to effectively improve the prediction accuracy. Accurate wind power forecasting can help power systems achieve economical operation and dispatch management. This paper proposes a short-term wind power forecasting method based on feature clustering and correlation analysis, improving forecasting accuracy through data feature clustering, variable correlation analysis, and building forecasting models. More specifically, the improved fuzzy C-means (IFCM) algorithm is used to cluster the wind power dataset; Pearson correlation coefficient (PCC) is used to explore the correlation between meteorology and wind power variables; Based on the deep sparse auto-encoder (DSAE) model, the predicted value of short-term wind power is obtained. Combined with the actual wind power data, the effectiveness and superiority of the proposed method are verified by comparison.

Method of Quartile for determination of Weibull parameters and assessment of wind potential

Weibull Distribution is the most widely used distribution in wind power assessment. Two parameters Weibull distribution is commonly used for wind distribution modeling. The wind turbine converts wind energy into electrical energy. According to Betz law, No wind turbine can convert more than 59% of the available wind energy into electrical energy. The available method to find the parameters, e.g., Empirical Method (EM), Method of Moment (MoM), Energy Pattern Factor Method (EPFM), Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), measure an overestimated value of wind power. An attempt has been made to develop a new method to evaluate Weibull parameters to measure wind potential close to the actual one. The new methods depend on the Quartiles of the wind distribution, Method of Quartile. Wind speed data for twelve months, January to December of 2016, for the cities Hyderabad, Karachi, and Quetta is used in this study. The new method results are compared with the five methods of Weibull parameter determination, EM, MoM, EPFM, MLM, and MMLM. The new method’s average wind speed is closer to the actual average wind speed than those measured by other methods. The Root Mean Square Error (RMSE), Mean Absolute Error (MABE), and chi-square statistic calculated by all methods are close. The Akaike Information Criterion (AIC) model selection criterion was used for each method and month. It is found that the AIC values for every month and every city are the lowest for MoQ. It also suggests that the new method, MoQ, is the best among the existing method. Keywords: Weibull Distribution, Method of Quartile, Method of Moments, Maximum Likelihood, Empirical Method, Energy Pattern Factor Method

Forecasting ultra‐short‐term wind power by multiview gated recurrent unit neural network

AbstractWind power generation prediction plays an important role in the safety and economic operation of the power system. There are many parameters recorded in wind farm data, such as wind power, wind speed, wind direction, and so on. Traditional wind power prediction modeling methods lack the mining of these parameter data and fail to make good use of some potential physical information. To address this challenge, this paper proposes a multiview neural network learning framework to predict wind power. One is the data attribute view of wind power, which uses the historical data feature of the wind power itself to learn the future wind power feature. The other is the physical attribute view of wind power, which uses the physical attribute features associated with the wind power definition to learn the future features. Then all the learned features are jointly fused to predict the future wind power values. In addition, an uncertain factor is proposed and computed, which is inspired by the wind power formula and usually associated with internal and external environment perturbation. All time series features are input into the gated recurrent unit neural network to form a hybrid neural network framework for wind power prediction. Experimental results under the measured condition and the standard condition of wind farms demonstrate the effectiveness of the proposed method.