A wind power probabilistic prediction method combines kernel density estimation and copula

In order to improve the computational efficiency of power system time series simulation, the redundancy of wind power and photovoltaic time series data should be reduced. At present, the time series data of wind-photovoltaic are aggregated separately, wind and pv correlation is not involved, reducing the accuracy and reliability of high-proportion renewable energy power system simulation. For this reason, This paper presents a aggregation method involving wind power and photovoltaic correlation. Based on wind and photovoltaic power historical data, firstly, based on kernel density estimation method and Copula theory, generate the wind-photovoltaic kernel density function and the joint probability density function, by sampling the joint probability density function to obtain N groups of typical daily curves. Take a provincial power grid as an example, a comparative analysis was made between the proposed wind-photovoltaic correlation aggregation method and the separate wind-photovoltaic aggregation method, The results show that the aggregation series can not only accurately reflect the characteristics of original wind-photovoltaic output power, but also maintain the correlation between wind-photovoltaic output power, which has certain engineering application value.

K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting

An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events

Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering

In recent years, new energy has achieved rapid development in the whole world. However, with the gradual increase in the installed capacity of new energy, because of the uncertainty and volatility of new energy output, new energy has brought many impacts and challenges to the operation of the power system. In order to guarantee the safe and stable operation of the power system, the power system has to provide sufficient flexibility to cope with the volatility and uncertainty of the new energy output. Improving the prediction error accuracy of new energy output can effectively reduce the uncertainty of new energy output, so as to reduce the demand for power system flexibility, reduce the operating cost, and improve the system operation reliability. This paper utilizes the data mining technology to establish the prediction error identification module. Based on the data feature extracted from the wind power prediction input data, the potential abnormal wind power prediction error can be identified, and the original wind power output prediction result can be corrected, so as to improve the predication accuracy of the wind power generation. At the same time, in the prediction and identification process, the wind speed prediction data and historical wind power output data of neighbouring regional wind farms will also be utilized to improve the prediction accuracy of wind farm output in this region. The predicated time scale is 12 hours ahead of time, and the predicted result is the wind power output of the next 12 hours (a predicted resolution of 1 hour, and a total of 12 predicted values).

Abstract: Home‐range estimators are commonly tested with simulated animal locational data in the laboratory before the estimators are used in practice. Although kernel density estimation (KDE) has performed well as a home‐range estimator for simulated data, several recent studies have reported its poor performance when used with data collected in the field. This difference may be because KDE and other home‐range estimators are generally tested with simulated point locations that follow known statistical distributions, such as bivariate normal mixtures, which may not represent well the space‐use patterns of all wildlife species. We used simulated animal locational data of 5 point pattern shapes that represent a range of wildlife utilization distributions to test 4 methods of home‐range estimation: 1) KDE with reference bandwidths, 2) KDE with least‐squares cross‐validation, 3) KDE with plug‐in bandwidths, and 4) minimum convex polygon (MCP). For the point patterns we simulated, MCP tended to produce more accurate area estimates than KDE methods. However, MCP estimates were markedly unstable, with bias varying widely with both sample size and point pattern shape. The KDE methods performed best for concave distributions, which are similar to bivariate normal mixtures, but still overestimated home ranges by about 40–50% even in the best cases. For convex, linear, perforated, and disjoint point patterns, KDE methods overestimated home‐range sizes by 50–300%, depending on sample size and method of bandwidth selection. These results indicate that KDE does not produce home‐range estimates that are as accurate as the literature suggests, and we recommend exploring other techniques of home‐range estimation.

Wind speed prediction is crucial for optimizing wind farm and power system operations. The magnitude, nonlinearity, and randomness of wind speed affect the stable operation of wind power systems. This paper proposes a wind speed combined prediction model based on a modified Gated Recurrent Unit (MGRU) and Long Short-Term Memory (LSTM). Firstly, an MGRU model is proposed by simplifying the GRU architecture into a single-gated structure, enhancing prediction accuracy while reducing computational complexity. Subsequently, the initial wind speed data and related features are fed into the MGRU and LSTM models respectively. Finally, the optimal weighted combination algorithm is utilized to determine the weight of the prediction results of the two models, and the final prediction value is obtained. The experimental results show that the MGRU-LSTM model has significantly improved the overall prediction performance with RMSE and MAPE of 0.6551, 0.0685, and time of 150.1s, respectively, which meets the wind speed prediction requirements of wind farms.

Power grid is developing towards the direction of intelligence. Nevertheless, the giant grid scale, complicated structure and external natural factors result in the potential of large-scaled power failure. Traditional black start power gives priority to hydropower. In areas with shortage of hydro power resources, suitable black start power is insufficient. The key of black start is able to realize the safe and stable operation of wind power and energy storage cogeneration system. Due to the combination of wind turbines and energy storage is a small system, energy storage, as a black start power source, starts the wind turbines together to supply power for the remote load. Therefore, it is necessary to run the balance and stable operation of a small system. In this paper, PSCAD simulation software is employed to conduct modeling of wind power and energy storage system, and the self-starting process of wind storage cogeneration system is analyzed by using resistance sensor as the load. The result verifies the wind power storage system's initiation process.

In recent years, grid-connected wind power has developed rapidly, however, it presents a significant challenges to power system dispatch due to the high volatility and randomness of wind power. In order to meet the requirements of power grid planning, wind power forecasting system not only needs to provide forecast value, but also to make a reasonable assessment of the risks contained in the forecast. In this paper, firstly, the sequential time clustering support vector machine is applied to analyze the deterministic prediction of wind power. Then, the method of kernel density estimation and the selection of optimal bandwidth are used to establish the probability density function for the relative error, the mainly method of solving the optimal bandwidth is to develop a higher order small term in minimum mean integrated square error, through it, the prediction interval of wind power which satisfies the certain confidence probability is obtained. Finally, compared with recursive method and slide bandwidth method in terms of prediction interval coverage probability and normalized average width, the proposed method has higher precision and is more conducive to the development of power system dispatching decision-making and system reliability evaluation.

Research on output distribution modeling of photovoltaic modules based on kernel density estimation method and its application in anomaly identification

This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabilistic approaches have been introduced to assess the impact of RESs on the power system. A probabilistic approach requires a wind power scenario (WPS), and the WPS is generated from the WPPM. Previously, WPPM was generated using a parametric density estimation, and it had limitations in reflecting the characteristics of wind power data (WPD) due to a boundary bias problem. The paper proposes a WPPM generated using the KDE, which is a non-parametric method. Additionally, the paper proposes a reflection method correcting for the boundary bias problem caused by the double-bounded characteristic of the WPD and the multi-kernel function KDE minimizing the effect of tied values. Six bandwidth selectors are used to calculate the bandwidth for the KDE, and one is selected by analyzing the correlation between the normalized WPD and the calculated bandwidth. The results were validated by generating WPPMs with WPDs in six regions of the Republic of Korea, and it was confirmed that the accuracy and goodness-of-fit are improved when the proposed method is used.

Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy business and control of wind power generation requires prediction of wind speed (WS) from few seconds to different time steps in advance. To deal with prediction shortcomings, various WS prediction methods have been used. Predictive data mining offers variety of methods for WS predictions where artificial neural network (ANN) is one of the reliable and accurate methods. It is observed from the result of this study that ANN gives better accuracy in comparison conventional model. The accuracy of WS prediction models is found to be dependent on input parameters and architecture type algorithms utilized. So the selection of most relevant input parameters is important research area in WS predicton field. The objective of the paper is twofold: first extensive review of ANN for wind power and WS prediction is carried out. Discussion and analysis of feature selection using Relief Algorithm (RA) in WS prediction are considered for different Indian sites. RA identify atmospheric pressure, solar radiation and relative humidity are relevant input variables. Based on relevant input variables Cascade ANN model is developed and prediction accuracy is evaluated. It is found that root mean square error (RMSE) for comparison between predicted and measured WS for training and testing wind speed are found to be 1.44 m/s and 1.49 m/s respectively. The developed cascade ANN model can be used to predict wind speed for sites where there are not WS measuring instruments are installed in India.

At present, the proportion of new energy sources such as wind power and photovoltaic in the power system is gradually increasing, flexibility has gradually become one of the important evaluation criteria for the safe and stable operation of current and future power systems. As a power system measure of its ability to respond to uncertain events, reasonable quantification and analysis power system’s flexibility is a prerequisite. This paper, quantified the power system flexibility under two time scales of 15min and 1h, and then two flexibility evaluation indicators are proposed for ramp flexibility gap and peak-shaving flexibility gap. Finally, according to the actual data of a regional power grid, different time scales are selected, and the flexibility quantitative model of this paper is applied by random production simulation technology, and the flexibility of the power grid is quantified and analyzed, which verifies the effectiveness of the proposed model and puts forward prevention suggestions for the period of insufficient flexibility.

Kernel density estimation (KDE) is one of the most widely used nonparametric density estimation methods. The fact that it is a memory-based method, i.e., it uses the entire training data set for prediction, makes it unsuitable for most current big data applications. Several strategies, such as tree-based or hashing-based estimators, have been proposed to improve the efficiency of the kernel density estimation method. The novel density kernel density estimation method (DMKDE) uses density matrices, a quantum mechanical formalism, and random Fourier features, an explicit kernel approximation, to produce density estimates. This method has its roots in the KDE and can be considered as an approximation method, without its memory-based restriction. In this paper, we systematically evaluate the novel DMKDE algorithm and compare it with other state-of-the-art fast procedures for approximating the kernel density estimation method on different synthetic data sets. Our experimental results show that DMKDE is on par with its competitors for computing density estimates and advantages are shown when performed on high-dimensional data. We have made all the code available as an open source software repository.

Renewable sources such as solar PV and wind are stochastic in nature, hence their integration with emerging isolated microgrid (MG) is challenging especially with regards to stability issues. An accurate prediction model of wind and solar sources is necessary to analyze the uncertainty in MG system and to encourage the reliable participation of wind and solar power in the energy market. The advancement in deep learning methods has made it possible to develop a multi-step forecasting model unlike shallow neural networks (SNNs). The time series forecasting using SNN and Recurrent Neural Network (RNN) suffers from the problem of vanishing/exploding gradient while training. To eliminate this problem the long short-term memory (LSTM) RNN has been used in this study for wind speed and solar irradiance prediction. The forecasted solar and wind power is applied to analyze the load frequency behavior and the response of nonrenewable sources for sudden rise and fall in load power demand and PI controller is used to mitigate frequency deviation to ensure the stability of the MG power system.