Based on the problem that traditional energy storage scheduling strategy is difficult to achieve economic optimization, a scheduling model of energy storage system of photovoltaic-storage charging station considering battery life loss is propoesd. Firstly, the physical models of energy storage, photovoltaic and EV((electric vehicle) charging are established through OpenDSS(open distribution system simulation) to simulate the daily operation of the charging station. Secondly, combined with the rain-flow counting method, the operation life of the energy storage system under different operating conditions is calculated and converted into the battery loss cost, which is included in the optimization goal. In this way, the daily charging cost of photovoltaic-storage charging station can be minimized. Finally, the improved particle swarm optimization (PSO) algorithm is used to solve the optimization problem to obtain the economic optimal energy storage capacity allocation and the daily energy storage operation scheme under this configuration. The feasibility of the model is verified by example simulation and the influence of different energy storage operation schemes on revenue and component cost is analyzed.

In order to improve the utilization rate of user energy storage and reduce the centralized energy storage configuration of micro-grid, a cloud energy storage capacity configuration method of aggregated user energy storage is proposed, and the pricing of energy storage transaction is studied theoretically. Cloud energy storage operator aggregate user energy storage and configure centralized energy storage at the same time. Cloud energy storage operator and users are regarded as an alliance to conduct external power trading, and individuals are encouraged to conduct energy storage capacity trading through aggregation fees and service fees within the alliance. Considering the respective interest demands of cloud energy storage operator, energy storage provider and energy storage demander, each subject model is established. In order to take into account the overall interests of the alliance and the individual interests within the alliance, an energy storage transaction pricing model aiming at maximizing the classical social welfare and Nash social welfare is established. The example results show that the method proposed in this paper can make full use of user energy storage, reduce the cost of centralized energy storage, and realize the maximum profit of the alliance and the profit balance of each subject.

A real-time electricity management strategy is studied for the prosumers without energy storage device, which have dual role of power supplier and consumer in smart grid. Firstly, a real-time power management model with the constraint of maximum tolerance of power demand is constructed. Based on Lyapunov optimization theory, a real-time electricity management algorithm is proposed to maximize the power user’s revenue generated by selling electricity to external grid while guaranteeing the service delay of electricity demand within given tolerable period, considering time-varying price of power system, uncertainty of user generation and flexible load to control and manage the power consumption of the prosumer and the electricity trading with the grid. The proposed algorithm has low complexity and does not need to know the statistical characteristics of power demand, electricity price and renewable energy power generation. Theoretical analysis shows that the proposed algorithm can make the optimization target infinitely close to optimum, with guarantee of the delay of electricity demand in tolerance range. Finally, simulation results are given to show that the proposed algorithm outperforms the existing algorithms and several other greedy algorithms, and the impact of the different parameters on the performance of algorithm is analyzed, which provides a theoretical basis of power management of prosumer and helps the stable operation of power grid.

Aiming at the low recognition rate of non intrusive load monitoring for steel plant load action, a non-intrusive load detection decomposition algorithm of steel mill based on event perception is studied in this paper. The algorithm firstly divides the events into two types: state change events caused by load switching behavior and mode change events caused by load working process. The state change event is detected by feature difference, and the intersection number of the obtained power curve and the average power line is introduced as a new feature to detect the mode change event. Finally, load identification is realized by analyzing events. In this paper, the effectiveness of the algorithm is verified by the actual obtained electricity consumption data of the steel mill. The algorithm improves the accuracy of detection, reduces the rate of missed detection, has the capacity of load identification, and has a better effect on the restoration of production details.

Short term daily electricity forecasting is helpful to construct power market and improve power supply service level. With the help of the accurate identification of different users’ characteristics, a short-term daily electricity demand prediction modeling of industrial users based on doubly decomposition is proposed. Firstly, statistical tools are used to identify the seasonality, temperature correlation and holiday correlation of users; Then, according to the recognition results, the seasonal-trend decomposition procedure based on regression is customized; After that, the electricity demand is decomposed by doubly decomposition combined with the regression cycle method and the ensemble empirical mode decomposition; Next, based on the characteristics of the obtained components, long/short term memory networks, weighted least squares support vector machine and convolutional neural network is used for prediction; Finally, the prediction results of each component are superimposed, and the holiday prediction results are adjusted by using the probability distribution to obtain the final prediction value. Through the analysis of practical examples, the proposed method has high prediction accuracy.

A coordination strategy based on blockchain technology for the grid connection of electric vehicles and distributed energy is proposed. According to the total redundant power of regional power grid and total power demand of electric vehicles with time, electric vehicles are guided to different charging stations in different periods, which benefits the stability of the power grid. The electricity price of each charging station is adjusted competitively by means of independent application, smart contract approval and notification to the whole network. Smart contracts are jointly formulated by the regional power grid, charging stations and electric vehicle owners to effectively protect the interests of all parties. The application approval process based on smart contract eliminates the interference of human consciousness. The effect of the price guidance strategy proposed in this paper on the stability of the power grid and the improvement of the total income of all charging stations in the region is verified by case analysis. The proposed coordination strategy meets the development needs of electric vehicles and distributed energy grid connection in the future, and provides a reference for the application of blockchain technology in related fields.

As a distributed database technology, blockchain technology is widely used in finance, energy, electricity and other fields. Studying the application of blockchain technology in the field of electric vehicles will promote the development of electric vehicles in data sharing, optimal scheduling and intelligent management. Firstly, the problems existing in the process of intelligent development of electric vehicles are given, and the application advantages of blockchain in the intelligent management of electric vehicles are analyzed from its technical characteristics. Secondly, the existing or potential research contents of blockchain technology in the field of electric vehicles are divided into three attributes: value, energy and social, and the existing problems, research status and potential treatments of problems after the introduction of blockchain technology are analyzed. Thirdly, the present actual application progress of blockchain technology in the field of electric vehicles are summarized. Finaly, some issues and suggestions in the application of blockchain technology in the field of electric vehicles in the future are put forward.

With the continuous improvement of the quality requirements of MMC-HVDC transmission system, the detection of the power module of its core components will be more comprehensive. The traditional power module test device can not measure the capacitance and voltage resistance of the power module separately, resulting in its failure to locate the fault, and the aging condition can not be monitored which makes the power module of MMC-HVDC potentially dangerous. In order to effectively improve the defects of the current power module test device, this paper presents a method of using the least square algorithm to realize the high-precision and fast measurement of the resistance and capacitance of the power module. This method improves the measurement accuracy step by step through the input of multi-stage least square method. The multiple accuracies are verified by the engineering power module which makes the method be able to monitor and quickly screen the health status of the power module capacitors and voltage equalizing resistors on the project site, effectively avoides the potential hazards of the power modules of the straight line project, and fills the gap of high precision and fast resistance capacitance calculation of the flexible and direct engineering testing device.

In recent years, with the growth of modular multilevel converter based high voltage direct current (MMC-HVDC) transmission systems, the power loss of the main grid increases gradually. The efficient and accurate loss calculation and analysis of MMC-HVDC systems is the key step to study the loss composition and loss reduction measures. An effective method is proposed to calculate the MMC-HVDC power loss based on finite sampling. The method considers the nonlinear characteristics of pulse waveform and device impedance, and it can calculate the power loss during a long time period quickly by changing the input data, effectively resolve the challenging issue of long time period loss calculation with high accuracy. Finally, the accuracy of the algorithm is verified by comparing with the measured power loss of the actual project.

In order to improve the deficiencies of traditional power electronic transformer (PET) which are not suitable for high-voltage high-power power system, modular multilevel converter (MMC) is applied to the input stage of PET to increase the voltage level and power level of PET. The output stage of PET is composed of a three-phase full-bridge inverter, and a permanent magnet synchronous motor (PMSM) is connected to the output stage of PET, and the passivity-based control (PBC) method based on port controlled Hamiltonian with dissipation (PCHD) model is proposed to control the output stage of PET, which can be further applied to train traction system and wind power generation system. Finally, the simulation experiment is carried out on the simulation platform, and four different working conditions are simulated, including no-load operation, constant speed operation, variable speed operation and load torque mutation, the results show that the proposed control strategy has the characteristics of strong robustness and good dynamic performance.

VSC-HVDC (voltage source converter-based high voltage direct current) transmission technology has been successfully applied to large-scale new energy grid connection, grid interconnection and island power supply. However, in the process of switching between islanding and grid-connected control modes, there are still problems such as complex switching logic, and current surge caused by the position change of the grid-connected switch being not synchronized with the control logic switch. This article proposes a mode switching method for the Nanao VSC-HVDC Project to improve the success rate of control modes switching, adding a "transition state" based on droop control during the switch from island to grid connection. A new converter control structure is proposed for the "transition state". The current droop control is used as the inner loop, and the AC voltage amplitude and frequency are the outer loop. It has the external characteristics similar to the synchronous machine, stable operation under the two conditions. Using the "transition state" to decouple the AC switch position and the switching time of the control structure can improve the switching success rate, and virtual impedance is added to change the system pole configuration. A mathematical model is established to analyze the improvement of the system response characteristics by the additional poles of the virtual impedance. The proposed droop control strategy and control mode switching method are simulated and engineering verified to verify their effectiveness and correctness.

Under the background of carbon peaking and carbon neutrality goals and new power system with new energy as the main body, new energy such as wind power and photovoltaic will develop continuously and rapidly. The problem of line power flow blocking caused by the high permeability new energy connecting to the power grid has become an important factor that hinders the absorption of new energy. In this paper, a distributed power flow controller (DPFC) is proposed to facilitate the optimal allocation of high permeability new energy sources. The objective function of the strategy is to minimize the system line congestion in a single planning cycle, and the optimal allocation of the DPFC for the access system is proposed. Finally, an IEEE 24-node example is used to verify the effectiveness of the optimal allocation strategy of DPFC. The results show that the DPFC configuration strategy and operation control strategy can reduce the system operation cost and improve the new energy absorption level compared with the unified power flow controller (UPFC) without power flow control device.

Aiming at the problem that there are many measured harmonic data curves with similar CP95 values and significant differences in fluctuation characteristics in the existing high-voltage scenarios, and the current harmonic CP95 index can not characterize the difference in harmonic level, this paper proposes a comprehensive evaluation method of high-voltage harmonic quality of power grid considering the discrete fluctuation characteristics of data. Firstly, the fluctuation characteristics of measured harmonic data is analyzed, a high-voltage harmonic comprehensive evaluation index system is screened， designed and built， covering the trend characteristics of data set, discrete fluctuation characteristics of data and other data fluctuation characteristics; Then, considering that the existing evaluation system rarely involves data fluctuation characteristics, and there is a conflict between the established trend characteristic index of data set and the discrete fluctuation characteristic index of data, expert experiences are considered and critical method is used to weight; In order to solve the problem of disharmony between indicators and weights, a comprehensive evaluation model of high voltage harmonics based on game theory is established; Finally, the rationality of this method is verified by an example. The trend characteristic index of the data set established in this paper can preliminarily divide the harmonic quality level, and the discrete fluctuation characteristic index of the data can characterize the local fluctuation characteristics of the data; The established high voltage harmonic comprehensive evaluation model can reasonably and harmoniously distribute the index weight; The proposed index and method can effectively characterize the harmonic quality difference of high voltage monitoring points.

In order to improve the identification accuracy of sub-synchronous oscillation parameters in power system, aiming at the problems of strong noise interference in the sampled signals of sub-synchronous oscillation and difficulty in determining the system modal order, a sub-synchronous oscillation identification method combining fast independent component analysis (FastICA) technology and improved stochastic subspace of accurate modal order determination is proposed. Firstly, the FastICA technology is used to separate the noise from the original signal, and then the processed signal is input into the stochastic subspace algorithm as a new signal. Aiming at the problem that it is difficult to determine the order of the system modal in the stochastic subspace algorithm, the accurate modal order determination method is improved to avoid the phenomenon of false modal and modal omission, and finally the main modal is determined quickly, and the modal parameters of the signal is accurately identified. Through further analysis and verification of the ideal calculation example, and the wind farm standard model and the measured data of power grid, the results show that the method can effectively reduce noise interference, shorten the identification time, accurately identify the main modal of the signal, and improve the identification accuracy.

The increasing diversity of access elements and complexity of network structure bring new difficulties to the fault location in distribution network. With the help of regional integration of electrical information, a fault location method based on fault-network state estimation for distribution networks is proposed. Firstly, the fault network description model is established, and the fault event is described as the change of network topology and expressed by Kirchhoff equations. Then, to uniformly represents the fault conditions of different components through fault network, a standardized fault network description to represent different possible fault occurrence at a fault component is pointed out. Furthermore, the distribution network state estimation is carried out with the help of different fault networks representing different component faults, and the matching degree of the state estimation results of different fault networks is calculated The distribution network fault location function is finally obtained through the matching degree comparison of the state estimation results. Finally, the effectiveness of the proposed method is verified by case simulation under different fault types and transition resistors, and the fault location performance is further tested in the case of abnormal data. The results show that the proposed method also has a good ability to resist bad data.

With numbers of renewable energy generations penetrating low-voltage networks, the operation problems of low-voltage distribution networks, such as low voltage ride-through, three-phase unbalance and distribution transformer overload, are further aggravated. Because of the characteristics of unclear topology and frequent changes in operation modes of low voltage distribution, an edge-end power-voltage operation control method based on the Internet of Things under distribution network is proposed. Firstly, these photovoltaics are incorporated into the edge-end system, and the edge-end distributed communication architecture of low voltage network is established through topology identification. Based on that, a multi-mode distributed control model is established to further improve the maximum renewable energy consumption. Then, a power control method under transformer operation constraints is proposed to realize power exchange control of the power control center in the distribution network. Finally, this paper provides the principle of controlled point selection, optimizes the weight of the communication network and analyses the system stability. The feasibility and superiority of the proposed method are verified by simulation analysis.