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Energy storage is considered as an effective approach to deal with the power deviation that caused by the stochastic wind power forecast error. Because of the spatial-temporal correlation of forecast errors for wind farms, which locate close to each other and integrate into the same regional power grid, energy storage could be deployed effectively and economically. Therefore, this study proposes an optimisation method to size the capacity of energy storage system (ESS) considering the spatial-temporal correlation of forecast errors for multiple nearby wind farms. The copula theory based correlation model of high-dimensional forecast errors is built to capture the spatial-temporal characteristics of forecast error. Then, according to multiple scenarios technique, an optimal ESS sizing model is established to minimise the investment and operation costs of the ESS. Meanwhile, an operation strategy considering the trend of prediction and correlation of errors is used to implement rolling operation strategy of ESS. The case studies demonstrate the effectiveness of the new method and illustrate the significant impact of the correlation of forecast error on the capacity of ESS.

The basic data quality of automation equipments in substaion directly affects the accuracy of data analysis and rationality of decision-making. This paper proposes a method for checking key parameters of automation equipments basic data of intelligent substation based on multi-source data fusion. This method in this paper fuses multi-source data such as standard point tables of EMS, remote configuration description (RCD) files, and substation system configuration description (SCD) files, and can accurately point out the missing and wrong filling of automation equipment basic data of the automation basic data system, and realize the verification of multiple functions such as uniqueness, completeness and accuracy of key parameters of the system equipment. Compared with the manual verification, the examples show that this proposed method can save a lot of time and cost, and also provide support for promoting the improvement of the lean production management level.

Dispatching automation system and equipment is an important infrastructure to support the dispatching agency to perform its functions. Continuous evaluation of grid automation equipment is an important means to achieve closed-loop control of safety risks and equipment life management, and an effective measure to promote lean and standardized management. Through comprehensive analysis and evaluation of the technical level, operation status and existing problems of the station equipment of Shandong Power Grid Dispatching Automation, provide evidence for solving the technical and equipment problems in the production operation, preparation of equipment overhaul, the technical transformation plan and the special control measures. This paper establishes the status evaluation index system of station automation equipment, analyzes the typical defects and problems of each application equipment, and give overall assessment of the automation equipment of Shandong Power Grid Station. The paper is of great significance to ensure the safe and reliable operation of automation systems (equipment), strengthen the operation and maintenance skills of professional teams, improve the scientific decision-making level of automatic equipment selection at the station and terminal, share the advanced experience of operation management, and promote the healthy development of automation profession.

With the rapid development of electric vehicles (EVs), selecting site and determining capacity reasonably for charging facilities become more important and difficult. Therefore, in order to accommodate large-scale EV integrated, this paper proposes a charging facility planning model based on the mean clustering, which considers both the construction cost of public charging facilities and the interests of EV users. First, the number and the capacity of charging facilities are optimized by the sum of the time-consuming cost of the user and the charging facility construction cost. Second, the location of the charging facility is computed by the mean clustering method. Finally, the charging station service range is automatic division by the Voronoi diagram. The analysis of the case shows that the proposed model is feasible and reasonable for charging facilities planning.

The variable and intermittent nature of large-scale wind power integration makes day-ahead unit commitment (UC) decision-making difficult. This paper establishes a novel and effective UC model with wind power integration by optimizing the utilization of the forecast error and reserve decision. First, considering the temporal feature of the UC model, a time sequence segment-fitting method (TSFM) for the wind power forecast error is presented, in which the non-parametric fitting method is used to address the ‘fat-tail’ effect of error distribution. Second, according to the probability intervals of the forecast error and characteristic of the reserve, a new reserve decision method is proposed to define three classes of reserve strategies and optimize the capacity for each type of reserve. Third, a UC model with time-varying confidence levels is established by introducing conditional value at risk (CVaR) and chance-constrained programming (CCP), and is linked with the TSFM. This novel model can balance the costs of fuel, various reserves, load shedding risk, and wind curtailment risk, which can improve the economy of the power grid operation. Finally, an improved hybrid particle swarm optimization algorithm with a heuristic searching strategy is applied to solve this multivariate mixed integer non-linear programming problem. The simulation results verify the effectiveness and practicality of the model proposed.

Due to the inherent stochastic and intermittent nature of wind generation, it is very difficult to sharply improve the wind power forecasting accuracy. The sizing of energy storage system (ESS) for wind farm can effectively reduce the uncertainty caused by the inevitable forecast error of wind power. However, optimal sizing of ESS is a multi-period decision-making problem and it is the key point that exactly captures the variation magnitude and speed of forecast error. This paper proposes a method to establish the multivariate joint cumulative distribution function (JCDF) of hourly day-ahead short-term multi-period forecast errors using Normal/t copula. Based on the proposed JCDF and multiple scenarios technique, a model of optimal sizing of ESS is proposed in which the temporal correlation relationship between different time period forecast errors and probability distribution of each time period forecast error are both considered. The simulation results verify the effectiveness of the proposed methods and show that if the temporal correlation of wind power short-term forecast error is ignored, the rated energy and power capacity of sizing of ESS will be significantly misestimated.

The wide application of ultra high voltage(UHV) transmission lines solves the problem of unbalanced distribution between energy and consumption. However, high risk is also generated as the long distance connection between areas. A new emergency control of generation shedding scheme is proposed, which takes into account both the selection principle of generation shedding locations and the boundary amount of shedding. The shedding method is based on simulation of fault scenarios which is easy to be realized to obtain the optimal scheme of emergency control of power grid. The new method has also been analyzed in a simulation based on an actual UHV project of China.

With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The simulation results show that: (1) the autocorrelation function of the one-step ahead forecast errors of support vector machine shows more significant tailing than those of the autoregressive and persistence models and the correlation relationships of the multi-step ahead forecast errors of support vector machine do significantly exist, but in the case of the autoregressive and persistence models, they do not; (2) the proposed combined models have significantly enhanced the short-term wind power forecasting accuracy in the three forecasting modes. In particular, the one-step ahead forecasting accuracies of combined models show little difference; for the continuous multi-step ahead forecasting, the improvements of the proposed combined models compared with a certain individual model increase with increasing prediction steps.

The causes of uncertainty in wind farm power generation are not yet fully understood. A method for the scale division of wind power based on the Hilbert–Huang transform (HHT) and Hurst analysis is proposed in this paper, which allows the various multi-scale chaotic characteristics of wind power to be investigated to reveal further information about the dynamic behavior of wind power. First, the time–frequency characteristics of wind power are analyzed using the HHT, and then Hurst analysis is applied to analyze the stochastic/persistent characteristics of the different time–frequency components. Second, based on their fractal structures, the components are superposed and reconstructed into three series, which are defined as the Micro-, Meso- and Macro-scale subsequences. Finally, indices related to the statistical and behavioral characteristics of the subsequences are calculated and used to analyze their nonlinear dynamic behavior. The data collected from a wind farm of Hebei Province, China, are selected for case studies. The simulation results reveal that (1) although the time–frequency components can be decomposed, the different fractal structures of the signal are also derived from the original series; (2) the three scale subsequences all present chaotic characteristics and each of them exhibits its own unique properties. The Micro-scale subsequence shows strong randomness and contributes the least to the overall fluctuations; the Macro-scale subsequence is the steadiest and exhibits the most significant tendency; the Meso-scale subsequence which possesses the greatest variance contribution rate and the maximum largest Lyapunov exponent, is the dominant factor driving the fluctuation and dynamic behavior of wind power; (3) the short-term predictions of these three subsequences based on extreme learning machine (ELM) and least-squares support vector machine (LSSVM) models have validated the above analysis results, which show that the number of steps of look-ahead predictability have pursued an ordinal trend in term of the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and the prediction error contribution rate of the Meso-scale subsequence is the maximum. Furthermore, the short-term wind power forecasting of 6-step-ahead based on the multi-scale analysis is performed by EMD-LSSVM + ELM and the normalized Mean Absolute Error (nMAE) and normalized Root Mean Square Error (nRMSE) have been decreased by 49.45% and 44.30% compared with those of LSSVM, and 37.96% and 27.12% compared with those of EMD-LSSVM, respectively.

Proportional resonant (PR) controllers/repetitive controllers (RC) based circulating current suppressing strategies in the stationary frame are widely used in MMC because of their simpleness and effectiveness. Unfortunately, they cannot be directly used in MMC-HVDC systems since they can result in dc-line voltage ripples and thus deteriorate system performance and stability under unbalanced grid conditions. This paper studies the problem in detail and then proposes corresponding improved circulating current suppressing strategies for MMC-HVDC, in which newly designed zero-sequence circulating current controller is employed. The proposed strategies can effectively eliminate both circulating currents and dc-line voltage ripples for MMC-HVDC systems under balanced and unbalanced grid conditions. Hence, they greatly enhance the fault ride-through capability of MMC-HVDC systems without adding any control cost or the number of controllers. The theoretical analysis and the proposed strategies are both verified by an established MMC-HVDC simulation model in MATLAB/Simulink.