The relationship between energy utilization and environmental protection and energy security are key issues that can not be ignored in the process of economic development. Energy Internet provides a good platform and technical support to solve these problems, which helps to reduce carbon dioxide emissions, promote peak carbon dioxide emissions, and improve national energy security. Based on the background of environmental problems and energy security, this paper systematically introduces the purpose of building Energy Internet, the characteristics of energy Internet and the specific implementation methods. It puts forward three levels solution schemes of energy structure transformation, interval energy flow model construction and regional energy efficient utilization. Finally, the role of marketization in the process of Energy Internet construction is discussed, and the potential research is prospected.

Integrated energy system (IES) has the characteristics of multi energy coupling and complementarity, which is conducive to improve energy efficiency. Simultaneously, electric vehicle (EV) is conducive to environmental energy conservation and emission reduction to promote sustainable energy development. Firstly, this paper expounds the basic architecture and planning connotation of IES, and describes the research status of IES planning in domestic and overseas. Then, the operation models of various energy supply storage equipment (ESSE) of IES are described. Based on this, the capacity configuration models of various ESSEs are established, and the power flow models of each energy supply system are summarized. In the aspect of EV charging and swapping facilities planning, firstly, various EV load forecasting methods are discussed, thus the current situation of EV charging and swapping facilities planning is classified and discussed. Then, the research status of collaborative planning of EV charging and swapping facilities and grid, the EV charging and swapping facilities planning considering carbon emission are summarized, respectively. Under the above research background, this paper summarizes the research status of EV charging and swapping facilities planning in IES environment in domestic and overseas. Finally, the bottlenecks faced by the planning and development of EV charging and swapping facilities under IES environment are summarized, and the future research direction is prospected.

±800 kV Kunliulong multi-terminal HVDC project consists of three converter stations, namely Kunbei, Liuzhou and Longmen. Kunbei station is the sending end LCC converter station; Liuzhou and Longmen station both are MCC-VSC converter stations usually operating as receiving ends. Kunliulong HVDC project adopts 8 000 MW large capacity transmission design, and would significantly impact the stability of the sending end and the receiving end systems when the project is put into operation. At the same time, the flexible inter-pole and inter-station power transfer during three terminal HVDC faults may further complicate the stability control design. This work collates the action sequences of multi-terminal HVDC line fault recovery, station online de-paralleling and stability control process, analyzes the system stability variation due to the power unbalance during DC fault and recovery, and proposes a control measure for different AC and DC faults of each converter stations. At last, based on the annual operation planning data of China Southern Power Grid, different types of faults are simulated to verify the stability control strategy of Kunliulong multi-terminal HVDC system, which provides the technical basis and design reference for the stability control strategy of multi-terminal HVDC stability control system construction.

In a flexible interconnected substation based on back-to-back modular multilevel converters(B2B-MMC), when the main transformer fails and exits operation, the converter needs to switch from the power transmission mode to the island control mode. The impulse voltage and impulse current generated by this process will seriously threaten the safety of equipment operation and the reliability of system power supply. For this reason, this paper proposes an improved island control mode. Based on the analysis of the operating relationship between the instantaneous power of the MMC bridge arm and the DC bus voltage, the fixed droop coefficient of the active power-DC voltage square distribution characteristic is combined with the adaptive inertial droop coefficient using virtual inertia. Among them, the adaptive inertia technology is to couple the active power of the DC side and the frequency of the AC system in the B2B-MMC system, and quickly adjust the droop coefficient to suppress the voltage shock during the switching process, reduce the peak current, and improve the stability of the system. Based on B2B-MMC, a flexible interconnected substation model is built in PSCAD/EMTDC transient simulation software, and the response characteristics of the proposed method during converter switching are analyzed. The results verify the effectiveness and practicability of the proposed method.

In recent years, DC microgrid cluster has attracted attention for its higher permeability, stronger stability, better energy efficiency and so on. In this paper, a fully distributed optimal strategy is proposed to achieve the consistency of incremental cost, which is about generation cost and carbon emission. Based on the distributed communication, the consensus algorithm calculates the objective function at the microgrid level and the cluster level respectively. The results change the droop control of each distributed generators (DG). Considering the influence of line resistance on the accuracy of power proportional sharing, a novel droop controller is designed. The simulation model is built on the MATLAB /Simulink to verify the feasibility of the proposed control strategy.

Demand response plays an important role in helping to maintain the stability of the power system, improve the consumption capacity of renewable energy, reduce the peak valley difference of the power system, and delay the construction investment of the power grid. The real-time demand response based on real-time price pushes the demand response to a new height from real-time and optimal. Based on the analysis of the existing problems of quasi real time price, this paper puts forward the basic characteristics of real-time price, and puts forward a new form of demand response:real-time demand response. In the specific algorithm, based on the load adjustment capacity model of single equipment potential aggregation method, a complete mathematical model of real-time demand response is established, and its solution and benefit evaluation process are clarified. The effectiveness of the real-time demand response model is verified by case analysis, and the model is proved to be suitable for fast real-time calculation for real-time price.

Extreme weather events are occurring with increasing frequency, the research on the resilience of power systems under extreme natural disasters has received more and more attention. This paper proposes a high resilience decision-making method based on deep reinforcement learning, and the operation state and line fault state of distribution network under extreme disasters are regarded as the observation state set. In the current environment observation state, self-learning agent seeks feasible decision-making strategies for action, and defines the return function of self-learning agent for action evaluation. Based on the observed state data, the deep reinforcement learning (DRL) training is carried out based on the dueling deep Q network (DDQN). The agent selects the action by trial and error learning, and the trial and error experience is stored in the evaluation function Q matrix to realize the nonlinear mapping from the state to the real-time fault recovery strategy of active distribution network. Finally, the improved IEEE 33 node system is taken as a typical case, based on Monte Carlo method, the random fault scene is simulated, and the random optimization decision of the fault recovery generated by the proposed method is analyzed. The case study show that through the coordinated and optimized control of distributed generation, tie switches and interruptible loads in the active distribution network, power supply capacity in extreme disasters can be effectively improved.

Voltage stability is one of the important parts for the secure and stable operation in power systems. However, some credible contingencies may cause the load margin significantly reduced to a lower level and even cause voltage instability. To increase the voltage stability, the bus operation mode is applied to optimize the grid network topology to enhance the load margin of power system and ensure the voltage stability for base case and contingency cases. The contingency-constrained voltage stability preventive control model is proposed, and a stage-based bus operation mode optimization methodology is developed in this paper. To reduce the computation complexity, the sensitivity of line parameters is employed to pre-screen the candidate set in which the bus operation schemes can enhance the load margin, and the effective ones are ranked by the look-ahead margin method. Finally, the continuation power flow method is used to identify the ‘best’ bus operation schemes. Numerical studies on the IEEE 118-bus power system and 1648-bus power system demonstrate the effectiveness of the proposed methodology.

As a soft magnetic material with high permeability and low high-frequency loss, amorphous alloys have been widely used in electromagnetic devices such as high-frequency transformers. How to accurately and quickly simulate its inherent hysteresis characteristics is important for the optimal design of equipment. Aiming at the problems of time-consuming numerical solution of classical Preisach hysteresis model, complicated identification of distribution function, and low accuracy in simulating the hysteresis characteristics of amorphous alloys, this paper proposes an analytical Preisach model that can accurately and quickly simulate the hysteresis characteristics of amorphous alloys. First, for the distribution function of the irreversible magnetization component of the amorphous alloy, based on the experimental data of the amorphous alloy hysteresis characteristic curve, a special analytical function is used to fit, and then on the basis of obtaining the closed form Everett function based on the integration method, the non-crystalline alloy is constructed. Irreversible magnetization component of crystal alloy. In addition, a parameter-containing hyperbolic tangent function is introduced to characterize the reversible magnetization component of the amorphous alloy, and then the analytical Preisach model is obtained by linear superposition. By comparing the simulation results of the hysteresis characteristics of the amorphous alloy and the calculation results of the loss with the experimental results by the model, it is found that the relative error is less than 10%, and the numerical solution of the model is simple and easy to be realized by simulation, thus verifying the accuracy and practicality of the model.

It is discovered in an analysis of a metal oxide arrester (MOA) fault that the partial discharge characteristic signal is not detected when testing the arrester with defective valve plate. To deeply analyze this phenomenon, the model of MOA is established by using COMSOL multiphysics^® simulation software. The electric field distribution law of MOA valve sheet surface is studied when there are defects at different positions and different types of defects. The surface electric field distributions of MOA valve sheet column with and without defects is compared, The sensitivity of partial discharge test to MOA valve defects is studied, and the simulation test is built to verify the results. The results of verification test are consistent with the simulation results. It is found that the partial discharge test is more sensitive to the defects of the MOA at both ends, and less sensitive to the defects of the MOA in the middle. The research results provide technical reference for the partial discharge field test of MOA.

At present, most of the research on electric vehicle load forecasting does not focus on the actual area. In this paper, data driven and model driven are combined to predict the load of electric vehicles. On this basis, an automatic demand response strategy is proposed. Firstly, through the data collection of Didi, the regeneration information such as functional area division, electric vehicle travel time and space transfer matrix are obtained. Considering multi-day charging, the load forecasting model is constructed. Secondly, in order to stabilize the fluctuation of net load, considering the benefits of both sides of supply and demand, the automatic demand response strategy is formulated. Combined with the benefits of price type and incentive type demand response mechanism, the uncertainty of user participation is described by introducing logistic function. Finally, taking a certain area in Haikou as an example, the simulation results verify that the prediction model can effectively predict the load of the actual area, and the proposed strategy can achieve a win-win situation between supply and demand, consume absorb new energy locally, and stabilize the load fluctuation.

For the multi-information fusion feature and multi-system modeling complexity of dynamic driving behavior and random charging behavior for electric vehicles(EVs), a charging navigation strategy of EVs based on multi-information interaction and deep reinforcement learning (DRL) is proposed in this paper. In this strategy, the EV actual operation data collected by ‘optimization energy storage cloud platform of electric vehicle clusters’ is firstly modeled and mined. Through data preprocessing and data visual display, the EV driving and charging information as well as urban charging station information are obtained. Then, the EV charging scheduling process conforming to Markov decision process (MDP) is analyzed, and the DRL is introduced to establish a charging navigation model. The real-time information of ‘vehicles-stations-networks’ is regarded as the state space of deep Q network(DQN), and the allocation of charging station is taken as the execution action of agents. Based on the evaluation of travel cost and time decision-making objectives in different periods of charging process, the reward functions of enroute and arrival a station is determined. The optimal action value function corresponding to the highest reward is implemented to recommend the optimal charging station and plan for the driving path for the owner. Finally, a multi-scene simulation example is designed to verify the feasibility and effectiveness of the strategy proposed in this paper.

With the high access proportion of renewable energy and power electronic equipment, the control decision optimization and dispatching methods of microgrids are faced with great problems and challenges. China Southern Power Grid is transforming into an "energy value chain integrator". It is possible for the company to operate and maintain tens of thousands of microgrids, and the traditional model-driven, plan-based control, and manual dispatch mode will be difficult to meet the demand of optimal scheduling for microgrids. Faced with the demand of artificial intelligence in the field of microgrid automatic operation, an auxiliary decision-making method of optimal dispatching for miucrogrids based on deep learning is proposed in this paper. First, the typical mathematical programming model of dayahead optimization scheduling for microgrids is introduced and the difficulties and limitations of the model-driven modeling and solution methods are analyzed in this paper. Then, a deep learning model of dayahead optimization scheduling for microgrid based on deep bidirectional long-short memory neural network is established and the principle of revision and processing of the output of the model is given. Finally, the effectiveness of the model and algorithm in this paper is verified by an example analysis.

In the isolated microgrid, the inertia level is low, and the problem of the frequency stability is obvious. Based on this background, the adaptive inertia and frequency recovery control method for voltage-controlled virtual synchronous generators (VSGs) in isolated microgrid is proposed to improve the frequency stability of the isolated microgrid. In the aspect of adaptive inertia control, the dynamic process of frequency is described by angular frequency deviation and its change rate, and the relationships between them and the virtual inertia is constructed respectively. The sensitivity of the virtual inertia to the changes of angular frequency deviation and its change rate is amplified by the hyperbolic sine function to improve the sensitivity of control. The sigmoid function is introduced to limit the adjustment range of the virtual inertia and suppress the oscillation of VSG output power in the control process. For the frequency recovery control, from the perspective of active power balance between generation and demand, the frequency recovery problem can be transformed into the rebalancing problem of the active power of the whole system. The frequency deviation and its change rate of each VSG are utilized to estimate the rebalancing amount of the active power of the system, and the rebalancing amount of the active power of the system borne by each VSG is allocated to realize power sharing according to the capacity of each VSG. The effectiveness of the proposed method is verified by simulation tests in different scenarios.

With the continuous improvement of the power market, the operation mechanism of power distribution market including multiple virtual power plants becomes increasingly complex. This paper constructs a bi-level optimization model for market clearing strategy and dynamic pricing design for distribution power market with multiple stakeholders. The upper level minimizes the cost of distribution system operators considering the power flow and operational security constraints. The lower level maximizes the revenue of the virtual power plant for economic scheduling. On this basis, the linearization techniques and the complementarity principles are adopted to transform the lower level problem into equilibrium constraints. Then, the original nonlinear bi-level optimization model is transformed into a single-level mixed integer linear programming model using strong dual theory. Case study verifies the effectiveness of the proposed model.

The conventional methods for wildfire risk assessment of transmission lines only consider few hazard indicators and largely depend on subjective experiences. This paper proposes a wildfire risk assessment model for transmission lines based on matter-element extension. First, a total of 15 risk sub-factors in four classes of anthropic, topographical, meteorological and transmission lines factors are assembled to construct a wildfire disaster risk assessment index system for transmission lines. Then, the analytic hierarchy process combined with the entropy theory is to evaluate combination weights of indexes, in which subjective and objective weights are combined. Next, a matter-element extension evaluation model is established to measure the correlation of risk indicators. Finally, case studies of transmission lines in a southern province of China are analyzed. And a comprehensive risk assessment for wildfire disasters is carried out. It provides a basis for managers to carry out differentiated wildfire prevention and control work.