Extreme natural disasters such as typhoons and earthquakes will easily destroy operating equipment and leading to large-scale fault of the power grid and power outages, so there is an urgent need for effective measures to assist the dispatching of complex power grid in order to prevent and deal with the faults caused by disasters. In this paper, the current coordinated control technology of generation-grid-load three sides after the fault of the high-voltage grid is introduced. In addition, according to the calculation of connectivity of weak links between and in stations, the resilient power grid construction technology which include network strengthening strategies for the hot standby lines operation and the bus tie switches closing with their technical implementation methods are proposed in detail. Finally, case studies on the high-voltage grid model of A province power grid prove that the strategy can effectively reduce the loss of power grid fault and provide reasonable auxiliary decision-making for power grid dispatching work.

With the large-scale access of new energy, the power grid presents the characteristics of strong DC system and weak AC system. When commutation failure occurs in HVDC transmission system, transient overvoltage will appear in weak sending end system. Since STATCOM responds to the change of grid voltage through the command of control link, the lag of its control link will lead to the enhancement of transient overvoltage by STATCOM. Firstly, in view of this phenomenon, this paper analyzes the generation mechanism of AC transient overvoltage in weak sending end system under commutation failure. Secondly, by analyzing the frequency response characteristics, theoretical derivation of STATCOM response delay time is given. Finally, an optimization strategy based on damped switching is proposed to suppress STATCOM’s increasing effect on overvoltage response, and the effectiveness of the optimization strategy is verified by simulation. This work is supported by National Natural Science Foundation of China (No. 51277066) and State Grid Corporation of China Research Program (No. 5102-201956301A-0-0-00).

Electric energy substitution has become an important trend and key path for the development of energy transition. The rapid and accurate estimation of energy saving is conductive to the promotion of electric energy substitution projects. To make full use of a small amount of the operation data during the electric energy adjustment period to achieve the purpose of rapid energy-saving estimation, this paper proposes an online unit energy-saving estimation method based on transfer learning. In this method, we firstly use regression algorithm to train a large amount of base period samples to obtain a base period energy consumption model. Secondly, we use the regression algorithm based on transfer learning to train a large amount of base period samples and a small amount of adjustment period samples together, and use different weight updating strategies to iteratively adjust the weights of the based period samples and the adjustment period samples to obtain an adjustment period energy consumption model. Finally, the normalization method is used to obtain the energy consumption difference under the reference conditions, that is, the unit energy-saving amount. In this paper, the simulation analysis of electric energy substitution in the drying field proves the effectiveness of the proposed method, and shows the influence of the iteration times, sample number, the way of sample combination on the prediction errors of the proposed algorithm.

At present, the penetration rate, charging frequency and charging capacity of electric buses are relatively high, so the charging load has a non-negligible impact on the operation and dispatch of the power grid. So, the charging load forecasting research has important theoretical and practical significance, but the intermittent and random charging behavior increase the spatial forecasting difficulty. Therefore, the charging load forecasting method of electric buses is proposed on the basis of spectral clustering and long short-term memory (LSTM) neural network. First of all, the charging load curve is clustered according to spectral clustering considering the distance and the shape. And then, considering the key factors that affect the charging load, such as historical load, temperature and day type, the model parameter of LSTM neural network is trained using each cluster charging load, and the charging load of each cluster is predicted. Then, the total charging load of the forecasting day is to sum the forecasting results of different clusters. Finally, on the basis of the historical real data in a certain city, the proposed method is verified. The result shows the mean absolute percentage error (MAPE) of charging load prediction result of the proposed method is below 11%, and the accuracy of load forecasting is improved.

With the increase in the penetration rate of new energy based distributed power generation, its characteristics such as strong intermittent and high volatility will have a greater impact on grid voltage fluctuations. How to calculate the steady-state voltage of the power grid with complex distributed power access more quickly is of great significance. In this paper, by sampling a great deal of real output data of photovoltaic and wind power, a comprehensive probability model is improved and generated on the basis of the traditional probability model, and the probability distribution deviation caused by the temporal and spatial characteristics is corrected through the Markov transition probability matrix. Then, taking the actual output data of adjacent photovoltaic and wind power stations in a certain area of southern China as a sample, the steady-state voltage at each node of the distribution network under different scenarios is calculated in the distribution network topology. Finally, the results of the calculation example show that the power grid model generated by the improved method simulation in this paper has high authenticity and applicability, and the calculated voltages have high accuracy rate. Moreover, compared with the traditional power system flow calculation, the calculation time is greatly reduced, so that it has better follow-up in the control effect, and is suitable for the calculation of the steady-state voltage of the complex power system with new energy.