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In recent years, with the deepening of China's electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model. [ABSTRACT FROM AUTHOR]

For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid. [ABSTRACT FROM AUTHOR]

• Combined with the error reconstruction rate, an adaptive VMD technology is developed and used for data preprocessing. • Two mainstream deep learning methods (LSTM, DBN) are used as sub-series predictors, and the classic PSO algorithm is utilized to determine the number of neurons in the DBN network. • A novel hybrid prediction model based on the PSO-DBN nonlinear combination mechanism is proposed. Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods. [ABSTRACT FROM AUTHOR]

Nowadays, various wind power forecasting methods have been developed to improve wind power utilization. Most of these techniques are designed based on the mean square error (MSE) loss, which are very suitable for the assumption that the error distribution obeys the Gaussian distribution. However, there are many outliers in real wind power data due to many uncertain factors such as weather, temperature, and other random factors. Meanwhile, the highly nonlinear process of converting wind energy into wind power may changes the statistical characteristics of errors. Therefore, the prediction model established based on the traditional MSE loss may lead to unsatisfactory results. As a result, a robust short-term wind power hybrid forecasting model based on Long Short-term Memory (LSTM) neural network with Correntropy combining an improved variational mode decomposition (IVMD) and Sample Entropy (SE) is proposed in this work. The IVMD in which the parameter K in the IVMD is determined by the Maximum Correntropy Criterion (MCC) is used to decompose the original wind power data and the decomposed subseries is reconstructed by SE to improve the prediction efficiency. Then the MCC is also utilized to replace the MSE in the classic LSTM network to develop a novel robust hybrid model to forecast the wind power. Finally, four experiments were conducted using real data from two wind farms in China at different sampling intervals to evaluate the effectiveness of the proposed method, and the results show that proposed method is more effective than other traditional methods. • An adaptive VMD decomposition algorithm with MCC is proposed. • A new data preprocessing model is proposed by combining with the sample entropy. • The LSTM network with MCC loss is proposed in the forecasting process. • A novel IVMD-SE-MCC-LSTM model is proposed for short-term wind power forecasting. • Experiments are performed to evaluate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

• Analyzes factors voltage levels affecting and the extent of the impact. • Propose a method for determining the voltage sequence value, which takes good use of a variety of factors. • Evaluation of the recommended values are discussed in terms of five aspects. • Give recommended values and future urban distribution network structure in China cities. The future urban power system will come in the form of direct current (DC) distribution network. Compared with alternating current (AC) distribution network, DC distribution network has many advantages, but it is an area where has not yet made any practically extensive appearance, this area is still in research phase. DC distribution network plays a key role in the connection of power generation side and urban loads side, but unfortunately there is still no DC distribution voltage level standard in the world. Aiming at DC distribution network in China cities, this paper proposed a method to select reasonable DC voltage level sequence. To begin with, this method analyzes the factors voltage levels affecting and the extent of the impact and give the classification of internal and external factors. Furthermore, considering the constraint principle, impact of AC distribution network, the factors voltage levels affecting, the influence of DC loads and DC projects, gives a set of scientific voltage grade sequence value: 0.048/0.06/0.22/0.4/0.75/1.5/3/6/10/24/55/100 kV. Subsequently, the rationality of the recommended value is evaluated from five aspects of voltage supply capacity, network loss, and AC distribution network adaptability. Finally, from the aspect of load, this paper provide the outline of the future DC distribution network of China cities. The research results of this paper can provide innovative ideas for future dc distribution network. [ABSTRACT FROM AUTHOR]

The electricity consumption forecasting (ECF) technology plays a crucial role in the electricity market. The support vector regression (SVR) is a nonlinear prediction model that can be used for ECF. The electricity consumption (EC) data are usually nonlinear and non-Gaussian and present outliers. The traditional SVR with the mean-square error (MSE), however, is insensitive to outliers and cannot correctly represent the statistical information of errors in non-Gaussian situations. To address this problem, a novel robust forecasting method is developed in this work by using the mixture maximum correntropy criterion (MMCC). The MMCC, as a novel cost function of information theoretic, can be used to solve non-Gaussian signal processing; therefore, in the original SVR, the MSE is replaced by the MMCC to develop a novel robust SVR method (called MMCCSVR) for ECF. Besides, the factors influencing users' EC are investigated by a data statistical analysis method. We find that the historical temperature and historical EC are the main factors affecting future EC, and thus these two factors are used as the input in the proposed model. Finally, real EC data from a shopping mall in Guangzhou, China, are utilized to test the proposed ECF method. The forecasting results show that the proposed ECF method can effectively improve the accuracy of ECF compared with the traditional SVR and other forecasting algorithms. [ABSTRACT FROM AUTHOR]

• Four indexes are established which can reflect the characteristics comprehensive. • Cascading failure searching model is established based on non-cooperative game theory. • Immune quantum particle swarm optimization algorithm is used to solve the game model. To identify and assess the risk of cascading failure sequence in AC/DC hybrid power grid better, according to the grid topology structure and operation coupling characteristics, the four quantitative indexes of cascading failure sequence are established from the aspects of local link relationship with the topology structure, mutual influence between AC/DC systems, distribution characteristics of power flow operation and the support capacity of AC system to DC system. Considering the interaction of the established quantitative indexes, it is in line with the non-cooperative game problem. On this basis, a cascading failure sequence searching index game model is established to determine the weights of each quantitative index. For solving the optimal Nash equilibrium solutions and algorithm convergence of non-cooperative game model, the cooperative immune quantum particle swarm optimization algorithm is proposed. Considering the load loss of AC/DC hybrid power grid during the fault process, power flow off-limit severity index of risk assessment is carried out. Simulation results show that the established quantitative indexes and searching model of the cascading failure sequence in AC/DC hybrid power grid are more accurately. A realistic provincial AC/DC hybrid power grid in China is carried out to verify the practicability of the proposed method. [ABSTRACT FROM AUTHOR]