摘要 :
The importance of wind power as a renewable and cost-efficient power generation technology is growing globally. The impact of wind power on the existing power system, land use, and others over time has been widely studied. Such wi...
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The importance of wind power as a renewable and cost-efficient power generation technology is growing globally. The impact of wind power on the existing power system, land use, and others over time has been widely studied. Such wind integration studies, especially when they are designed as retrospective bottom-up studies, rely on detailed wind turbine data, including the geographic locations, hub height, and dates of commission. Given the frequency of gaps present in these data sets, basic concepts have been developed to cope with missing data points. In this paper, multiple advanced algorithms were compared with respect to their ability to complete such data sets. One focus was on the selection of predictor variables to analyze the impact of different completion techniques depending on the specific gaps in the data set. A sample application using a German data set indicated that random forests are particularly well suited to the problem at hand.
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The increasing penetration of wind power brings great uncertainties into power systems, which poses challenges to system planning and operation. This paper proposes a novel probabilistic load flow (PLF) method based on clustering ...
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The increasing penetration of wind power brings great uncertainties into power systems, which poses challenges to system planning and operation. This paper proposes a novel probabilistic load flow (PLF) method based on clustering technique to handle large fluctuations from large-scale wind power integration. The traditional cumulant method (CM) for PLF is based on the linearization of load flow equations around the operating point, therefore resulting in significant errors when input random variables have large fluctuations. In the proposed method, the samples of wind power and loads are first generated by the inverse Nataf transformation and then clustered using an improved K-means algorithm to obtain input variable samples with small variances in each cluster. With such pre-processing, the cumulant method can be applied within each cluster to calculate cumulants of output random variables with improved accuracy. The results obtained in each cluster are combined according to the law of total probability to calculate the final cumulants of output random variables for the whole samples. The proposed method is validated on modified IEEE 9-bus and 118-bus test systems with additional wind farms. Compared with the traditional CM, 2m+1 point estimate method (PEM), Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS) based MCS, the proposed method can achieve a better performance with consideration of both computational efficiency and accuracy.
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Targeting the problem of the power grid facing greater risks with the connection of large-scale wind power, a method for power grid state analysis using big data is proposed. First, based on the big data, the wind power matrix and...
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Targeting the problem of the power grid facing greater risks with the connection of large-scale wind power, a method for power grid state analysis using big data is proposed. First, based on the big data, the wind power matrix and the branch power matrix are each constructed. Second, for the wind energy matrix, the eigenvalue index in the complex domain and the spectral density index in the real domain are constructed based on the circular law and the M-P law, respectively, to describe the variation of wind energy. Then, based on the concept of entropy and the M-P law, the index for describing the variation of the branch power is constructed. Finally, in order to analyze the real-time status of the grid connected to large-scale wind power, the proposed index is combined with the sliding time window. The simulation results based on the enhanced IEEE-33 bus system show that the proposed method can perform real-time analysis on the grid state of large-scale wind power connection from different perspectives, and its sensitivity is good.
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摘要 :
No study in the literature considers both randomness and fuzziness simultaneously, which actually coexist as the penetration of renewable energy in power system increases. In order to handle these two kinds of uncertain features s...
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No study in the literature considers both randomness and fuzziness simultaneously, which actually coexist as the penetration of renewable energy in power system increases. In order to handle these two kinds of uncertain features simultaneously, a novel random fuzzy power flow (RFPF) calculation method for a distribution network based on random fuzzy theory is presented here. Firstly, the random fuzzy models of wind and photovoltaic (PV) generation, and loads are set up for the first time according to their features of randomness and fuzziness. Then, a two-fold random fuzzy simulation is conducted to obtain the results of the RFPF calculations; the random simulation stage is based on the 2
m
+ 1 scheme of the point estimate method. Finally, the proposed method is applied to two test systems. The results show that the proposed method is feasible and effective in identifying important areas in the power system affected by distribution generation and loads with these two uncertainties.
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The key variables in the development and operation of wind and solar power systems are wind speed and solar radiation. The prediction of solar and wind energy parameters is important to alleviate the effects of power generation fl...
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The key variables in the development and operation of wind and solar power systems are wind speed and solar radiation. The prediction of solar and wind energy parameters is important to alleviate the effects of power generation fluctuations. Consequently, it is essential to predict renewable energy sources like solar radiation and wind speed precisely. An artificial intelligence-based random forest method is recommended in this paper to estimate wind speed and solar radiation. The number of decision trees in the random forest model is suggested to be optimised using a novel coot algorithm (CA), and the effectiveness of the CA is evaluated to that of the currently used particle swarm optimisation (PSO) method. The best forecasting data are used in this work to develop a dynamic Microgrid (MG) in MATLAB/SIMULINK. A novel binary CA is proposed to control the MG to minimize the cost. The effect of the energy storage system is also investigated during the simulation of the MG.
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As wind farms are commonly installed in areas with abundant wind resources, spatial dependence of wind speed among nearby wind farms should be considered when modeling a power system with large-scale wind power. In this paper, a n...
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As wind farms are commonly installed in areas with abundant wind resources, spatial dependence of wind speed among nearby wind farms should be considered when modeling a power system with large-scale wind power. In this paper, a novel bivariate non-parametric copula, and a bivariate diffusive kernel (BDK) copula are proposed to formulate the dependence between random variables. BDK copula is then applied to higher dimension using the pair-copula method and is named as pair diffusive kernel (PDK) copula, offering flexibility to formulate the complicated dependent structure of multiple random variables. Also, a quasi-Monte Carlo method is elaborated in the sampling procedure based on the combination of the Sobol sequence and the Rosen-blatt transformation of the PDK copula, to generate correlated wind speed samples. The proposed method is applied to solve probabilistic optimal power flow (POPF) problems. The effectiveness of the BDK copula is validated in copula definitions. Then, three different data sets are used in various goodness-of-fit tests to verify the superior performance of the PDK copula, which facilitates in formulating the dependence structure of wind speeds at different wind farms. Furthermore, samples obtained from the PDK copula are used to solve POPF problems, which are modeled on three modified IEEE 57-bus power systems. Compared to the Gaussian, T, and parametric-pair copulas, the results obtained from the PDK copula are superior in formulating the complicated dependence, thus solving POPF problems.
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摘要 :
Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, th...
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Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multistage decision-making problems, e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short-term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long-term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi-MW wind farm over a period of more than 2 years.
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Effects of the fluctuation inherent in wind speed are studied by a probabilistic method. The random variation in wind speed is responsible for random behavior in output power and internal voltage of a wind power generator. In case...
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Effects of the fluctuation inherent in wind speed are studied by a probabilistic method. The random variation in wind speed is responsible for random behavior in output power and internal voltage of a wind power generator. In case of fault occurrence at the instant of high internal voltage, the resultant short-circuit current will be big, and vice versa. The DC component is also affected. According to the study, 2.4% and 1.3% increase of short-circuit current in AC and DC components are observed respectively in a large variation case. This implies that the wind speed variation should be considered for accurate short-circuit study.
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In order to improve the forecast accuracy of wind power, an Improved Long Short Term Memory (ILSTM) network structure is proposed. Firstly, Variational Mode Decomposition (VMD) method is adopted to decompose wind power signal to t...
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In order to improve the forecast accuracy of wind power, an Improved Long Short Term Memory (ILSTM) network structure is proposed. Firstly, Variational Mode Decomposition (VMD) method is adopted to decompose wind power signal to the long-term component, the fluctuation component and the random component, which are used as the input of forecast model. Then a parameter was defined and added to the memory cell to suppress the random component to long term memory of neural network. To provide a pass for the current random component, the output gate was modified accordingly. Compared with the traditional Long Short Term Memory (LSTM), the improved LSTM can reduce the impact of random component on the patterns in long term memory cells, while maintain the current random component in the short term memory of network. As a result, the learning for the real patterns of wind power is strengthened, avoiding over-fitting and achieve a better generalized forecast model. Finally, the performance of the forecast method proposed in this paper is tested by using the wind power data from the Belgian ELIA website.
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This paper proposes a new distributionally robust optimization (DRO) framework for unit commitment under uncertain wind power. The proposed framework minimizes the worst-case expected total cost over an ambiguity set of possible p...
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This paper proposes a new distributionally robust optimization (DRO) framework for unit commitment under uncertain wind power. The proposed framework minimizes the worst-case expected total cost over an ambiguity set of possible probability distributions. Unlike the other DRO models that typically exploits variance and covariance data of random variables, this framework uses a distributional interpretation of uncertainty sets to construct the ambiguity set, and it can be solved as an equivalent problem that resembles a conventional two-stage robust linear program. Case studies demonstrate that the proposed model may effectively capture ambiguous distribution information and improve system performance.
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