Proceedings of the 3rd International Symposium on New Energy and Electrical Technology

The 10 kV distribution line load prediction method suffers from the problem of large absolute errors in the prediction results, and a data-driven 10 kV distribution line load prediction method is designed. The actual values of demand coefficients in the region are derived from historical data, the power characteristics of 10 kV distribution lines are obtained, the set of upstream load points at each of the two end nodes of the contact line is obtained, the load transfer threshold is set, the percentage of heavy-duty distribution substations is calculated, and the data-driven load prediction model is constructed. Experimental results: The mean absolute errors of the 10 kV distribution line load prediction method designed this time and the other two 10 kV distribution line load prediction methods are: 6.896%, 10.461% and 11.224% respectively, indicating that the designed 10 kV distribution line load prediction method works better when combined with data-driven technology.

In recent years, with the increasing improvement of more electric technology, more electric aircraft has gradually attracted everyone's attention. Transformer rectifier unit is one of the important components in the more electric system that converts the alternating current power of the aircraft into direct current power. The quality of its output voltage and input current is the key to the stable operation of the more electric system. In this paper, a 18-pulse unbalanced D-type auto transformer rectifier with adjustable output voltage is designed to meet the increasing demand for power transformation of more electric aircraft. Its working principle is analyzed, and the relationship between output DC voltage and variable boost ratio is given, which provides theoretical support for system design, and matlab/simulink simulation verification is carried out. Finally, the input power factor, input current harmonic and output voltage of 165 kVA auto transformer rectifier under typical conditions are given to verify the correctness of the theoretical analysis.

High-speed permanent magnet motor is widely used for its excellent performance. With the development of technology, the motor needs be designed to meet more features, such as low torque ripples, low noise, low loss, and so on. This paper focuses on the design of a 500W, 120000rpm high-speed permanent magnet motor, and the torque ripples effected by two pole-slot combinations are analyzed by both the theoretical solution and the finite element analysis with Fourier transformation. It is clarified that because of the different pole pairs and different magnetizations, the electromagnetic torque of 4p6s is smaller than that of 2p3s, while the ripple torque of 4p6s is larger than that of 2p3s. In addition, as the width of the slot opening increasing, the torque ripples of 4p6s are always greater than that of 2p3s.

Transmission line is an important part of power system. The geological and topographical conditions of the area through which the transmission line passes are complex and vulnerable to geological disasters. In order to avoid the damage caused by geological disasters to the transmission line and improve the safety of the transmission and distribution system, an effective means is to monitor the micro displacement of the area where the transmission line is located. Considering that the transmission line often crosses the no man’s land and the communication environment is complex, the GPS-PPP method is used to continuously observe the substation for 11 months. After model correction and calculation, the accuracy of the monitoring data can reach millimeter level. In addition, through the analysis of precipitation in this area in the same period, geological displacement and settlement mainly occur in the time period with abundant precipitation. This shows that GPS-PPP monitoring has a good application prospect in transmission line geological disaster monitoring and early warning, and can effectively ensure the safety of transmission line.

As the power source of the power grid, the stable operation of the generator is the premise of the normal operation of the whole power system. The control method of power system voltage regulator (PSVR) is to introduce the voltage variation of generator main transformer high-voltage side, where participate in automatic voltage regulator (AVR) control through PSVR to maintain voltage stability. In this paper, considering damping windings to avoid analysis bias, an improved Heffron-Phillips model of single-machine infinite-bus power system with PSVR has been established to analyze the influence of PSVR access on generator power-angle characteristics. The simulation results show that PSVR not only improves stability of system voltage, but also has great influence on the dynamic power-angle characteristics of generator.

Efficient detection and classification of devices are very important for effective maintenance in hydropower stations. However, the conventional manual approach suffers from low accuracy, efficiency, and reliability. Nowadays, object detection based on deep learning has achieved great development. In particular, the YOLO series models demonstrate significant advantages in terms of high accuracy and speed and robustness in complex image backgrounds, which have been widely used in numerous contexts for multi-object recognition tasks. Thus, in this paper, a YOLOv5-based algorithm is proposed to realize real-time multi-device recognition in hydropower stations. We labelled about 600 device photos of 27 categories collected from the site, based on which comparative experiments were carried out to evaluate the performance of YOLOv5 models of different configurations. The experimental results show that the mAP of YOLOv5m outperforms the others, the mAP of YOLOv5m can reach 95.3% and its precision can reach 94.1%. The study tests and reveals the effectiveness of YOLOv5 for device detection in hydropower stations. As such, the study on the one hand provides a useful asset management tool for the maintenance team, for another provides valuable empirical data for other studies that apply deep learning models in similar industrial situations.

Power and signal composite modulation (PSCM) has been applied for achieving communication among power converters. Strategy for transmitting signals multi-directionally among power converters is rarely detailed in literatures. In this paper, bidirectional communication between two Buck converters is achieved by PSCM via dc bus by employing the PWM-frequency-shift keying (FSK) and PWM-phase-shift keying (PSK) methods. In addition, the frequency division multiplexing access (FDMA) is employed to identify the signal accesses from different power converters. The calculation of output voltage ripples considering data transmission is analyzed and simulations are conducted to verify the feasibility of the proposed communication method. The results show that amplitude of the switching voltage ripples for signal communication can be limited below 0.5% to guarantee power quality.

In order to better achieve the life prediction and reliability analysis of motor bearings, a bearing life prediction method based on deep learning is proposed in this paper. The method firstly extracts features from the original vibration signal of the bearing and uses Weibull distribution to fit the extracted features. Then the fitted features are applied to the training phase of the SFAM (Simplified Fuzzy ARTMAP) neural network, and the extracted original features are applied to the testing phase, after SFAM neural network classification, a category representing the bearing degradation rate is given for each input vector. Finally, the classification results are made more continuous by a smoothing algorithm. The results show that this method can realize the life prediction and reliability analysis of motor bearings, and it is universal.

This paper proposes a multiple three-phase Fractional Slot Concentrated Winding (FSCW) Permanent-Magnet Synchronous Reluctance Motor (PM-SynRM) with high reliability for the aerospace field, it controls three winding modules through three sets of independent inverters. The machine torque, winding currents and temperature rise of the motor were evaluated by finite element simulation under healthy operation, open-circuit fault and short-circuit fault, and a 5kW prototype was designed and built for experiments. The experimental results verify the correctness of the simulation and the excellent fault tolerance of the motor.

This paper investigates the effect of hairpin winding topology on the electromagnetic and thermal performance in order to improve the torque, power density and efficiency of traction motors. First, traction motors’ importance in developing electric vehicle (EV) and the advantages of hairpin windings compared to round windings are introduced. The winding losses of the traction motor operating at 3000 rpm and 10000 rpm, and the efficiency during the whole speed process are then analyzed. The results show that eight layers of hairpin winding will cause more losses and lower efficiency. Moreover, the effect of hairpin winding topology on spray cooling performance is evaluated. Finally, the optimal number of hairpin winding layers and dimensions for the proposed motor are determined by weighing the effects of AC and DC losses and considering the efficiency of the traction motor under the drive cycle.

The inherent financial attribute of blockchain technology can be well applied to power transactions. However, due to the throughput constraints and network delay of blockchain, it is difficult to improve the transaction confirmation throughput while ensuring the delay. This paper proposes a blockchain power trading model with hierarchical governance and layer by layer confirmation, which provides a reference for the State Grid Corporation of China to use blockchain technology in power transaction, as well as for distributed high-throughput blockchain application scenarios.

The interturn short-circuit fault of motor stator winding has been recognized as one of the most fatal faults of permanent magnet synchronous motor (PMSM). And fast and reliable detection method is required to indicate when to take protective action. In this paper, an interturn short-circuit fault detection method for PMSM fitted with sensorless control, based on high-frequency signal injection technology, is proposed. The fault indicator applied in the article is independent of the operating condition and sensitive to short-circuit fault. Meanwhile, the overall cost of drive system is reduced due to no additional hardware.

Hexverter is a direct AC–AC modular multilevel converter based on cascaded full-bridge sub-modules, which is well suited to connect two systems operating at different frequencies. However, its operating range is limited by the capacitor voltage of sub-modules. A strategy based on the optimal neutral voltage and circulating current is proposed in this paper to limit the capacitor voltage within a specific range while minimizing the switching loss. First, the capacitor voltage's general fluctuation law is studied, and the detailed mathematical expression of the capacitor voltage is given. After that, constant neutral voltage and circulating current are introduced to limit its magnitude. In addition, the optimal values of neutral voltage and circulating current are calculated by minimizing switching loss, and the complete suppression strategy is put forward. Finally, it is verified by simulation that the proposed strategy suppresses the magnitude of the capacitor ripple voltage and reduces the switching loss.

Because Vienna rectifier has the advantages of simple structure and high work efficiency, it is widely used in communication power supply, motor drive, wind power generation and other field. In order to improve robustness and dynamic tracking performance of Vienna rectifier, this article proposes a model predictive current control (MPCC) method combined with super-twisting sliding mode control (STSM). To achieve higher robustness under parameter mismatch and load perturbation, a Luenberger disturbance observer (LDO) is designed in MPCC to estimate and compensate the disturbances. To further enhance the control performance of voltage loop, a STSM control strategy is adopted. The simulation results validate the better dynamic tracking performance and strong robustness in Vienna rectifier compared with conventional methods.

The electromagnetic immunity test of vehicle electronic steering control system is a mandatory test item corresponding to the national standard GB 17675–2021 “Basic Requirements for Vehicle Steering System”. By simulating the scene of electromagnetic interference from the surrounding environment during the driving process of the vehicle in a 10-m semi-anechoic chamber, it is tested whether the electromagnetic immunity performance for the steering system of the tested vehicle meets the requirements of the national standard. The steering auxiliary equipment can solve the errors caused by the failure of the test equipment and human factors in the existing experiment process, obtain the test information of the electronic steering control system in real time, and realize the quantifiable, evaluable and traceable standard requirements of the test results.

A new active arc suppression method based on reversely switched dynistor (RSD) is proposed to solve the muzzle arc generated by high current. This method is validated by simulation and experiments. Equivalent circuit model of electromagnetic gun is established in Sentaurus TCAD. In this model, a resistor and a full-controlled switch are used to simulate the function of armature and the formation of power current. The model of RSD is established to simulate current transfer process. Simulation results show that 7 kA is transferred by RSD within 100 μs when it turns on under the main voltage of 220 V. Arc suppression equipment based on RSD is developed, achieving the extinguishing of muzzle arc by transferring 98.5% of the main current (7 kA) when RSD turns on under the main voltage of 220 V in the first shot, which indicates the effectiveness of the equivalent circuit model. The 60% of the main current is transferred by arc suppression equipment within 200 μs when main current is 35 kA in the second shot, realizing the suppression of muzzle arc.

Rolling bearing is a kind of precision mechanical component bearing that changes the sliding friction between the running shaft and the shaft seat into rolling friction, so as to reduce the friction loss. It is a very important part of mechanical equipment, and its life prediction is of great significance. In this regard, a residual life prediction method based on bi-directional Gate recurrent unit is proposed. Firstly, the time domain, frequency domain and time-frequency domain features are screened, and three evaluation indexes are defined to quantitatively evaluate the effect of feature parameters characterizing the bearing degradation process. The sensitive feature set is screened. A bi-directional Gate recurrent unit network is built, and the sensitive feature set is used as input, The normalized single point life value is changed to segment life value as a label to train the neural network, and finally the life prediction of rolling bearing is realized. Finally, it is verified on the public data set that this method can accurately predict the remaining life of rolling bearings.

Non-isolated two-stage inverter is widely used in new energy vehicles, photovoltaics, energy storage and other fields due to its advantages of small size and high conversion efficiency. In order to enhance dynamic performance and robustness of non-isolated two-stage inverter, a model predictive control (MPC) method based on linear extended state observer (LESO) is proposed in this paper. Firstly, mathematical model of the inverter and a cascade MPC strategy are presented. Secondly, the cost functions are evaluated by using deadbeat control method to achieve better dynamic performance. Furthermore, to solve the problems of external disturbance and parameter mismatches, the LESOs are designed to estimate and compensate each control loop. Finally, the simulation and experiment results validate the faster dynamic performance and strong robustness of the proposed method compared with conventional method.

The short-term load forecasting of the power system is an important basis for the safe and economical operation of the power system. With the introduction of the price competition mechanism into the power system to form the power market, higher requirements are put forward for the accuracy and response speed of short-term load forecasting. The key problem of power system load forecasting is to establish a corresponding mathematical model according to the historical data of the forecast object to describe its development law. Support vector machine theory (SVM) can better solve practical problems such as small samples, nonlinearity, high dimensionality and local minima, and can be used to establish a relatively complete load forecasting model. The research shows that the application of SVM for load forecasting of power system has the advantages of high accuracy and fast speed, which significantly improves the effect of load forecasting.

SiC MOSFETs are gaining in popularity due to their excellent performance. In the applications of high voltage and high current power converters, a multi-chip parallel method can increase current level to meet the load demand but suffers from a potentially current imbalance between different chips. In this paper, the influence of peripheral parameters on parallel flow sharing effect is studied extensively. Where an imbalance occurs between parallel SiC MOSFETs, the proposed method introduces a differential mode choke to suppress the unbalanced current. The physical concepts, working principles and design criteria of the differential mode choke suppression method are described in detail. Simulation tests are carried out to verify the effectiveness of the technique.

With the development of society, electric cars are increasingly popular. Electric taxi will still become one of the important transportation tools for residents in the future due to its stop-and-go characteristics. In this paper, considering that electric taxi travel is affected by the traffic characteristics of the road network itself and the vehicle charge, an electric taxi charging load prediction model is established which is affected by daily dynamic traffic trip changes and real-time monitoring of charging conditions. First of all, the urban area is planned as a road network, then in view of the influence of multiple factors on traffic congestion, such as the restriction of the morning and evening bus lane, the prohibition of left and the signal light at the intersection, the corresponding road resistance model and speed flow model are established. Dijkstra algorithm is used to repeatedly calculate the shortest path considering real-time traffic information to guide the driving of electric taxi, and then the charging load of electric taxi is predicted. Finally, an example is given to illustrate the effectiveness of the method.

A complete linearized model of DFIG (doubly fed induction generator) is established using PMT (plug-in modeling technique). Considering the influence of the wake effect on the wind speed of wind turbines in the downwind direction of the wind farm, the equivalent wind speed of wind turbine nodes is corrected. The eigenvalue analysis method is used to analyze the small-signal stability of the multimachine system. The influence of the increase of wind power permeability rate and wake effect on the small signal stability of the system is studied. The results show that the wake effect decreases the system stability and has a greater impact on the DFIG-dominated oscillation modes; different types of oscillation modes have different trends of damping ratios when the proportion of wind power is increased.

Battery electric vehicles are environmental-friendly transport. However, the fast charging of Lithium-Ion batteries remains problematic. Because fast charging could introduce severe or non-reversible degradation. Thus, this paper dedicates to investigating the aging mechanism caused by fast charging. In the paper, the cylindrical Lithium Iron Phosphate battery is cycled at 4C-rate of more than three thousand times. Then, the battery is dissembled and specific positions are selected on both sides of the anode electrode for Scanning Electrode Microscopy and Energy-Dispersive X-ray Spectroscopy analysis. Results from the aged battery are compared with those captured from a fresh battery having the same calendar aging condition. The post-mortem analysis demonstrates the change in material structure and element composition. It shows that the predominant aging mechanism is lithium deposition, the loss of active material caused by phase transitions, and the dissolution of the cathode current collector. These aging mechanisms will lead to capacity loss or battery resistance increment.

Distributed renewable generation has attracted extensive attentions and research efforts for resolving the severe issues of environmental deterioration and energy shortage. Communication plays an important role in improving the operation performance of distributed power systems. Power & signal composite modulation (PSCM) is newly emerged and applied to power converter systems for communication without requirement of independent signal lines. In this paper, a power & signal synchronous transmission (PSST) strategy is proposed for three-phase voltage source rectifier (VSR) by using frequency shift keying (FSK) method. The performance of FSK based signal and power modulation method in the VSR is discussed by injecting signal carriers to voltage and current regulators. The experiment results are presented for validating the feasibility of PSST in three-phase VSR systems.

Dual Active Bridge (DAB) converters are widely used in energy storage systems, fuel cell systems and microgrids for its excellent characteristics. Recently, model predictive control (MPC), which is known for its high dynamic performance and multi-objective optimization capability, has been applied to DAB converters. MPC relies on feedback variables to achieve state variable prediction, where the sensor sampling noise will degrade MPC performance and greatly limits its application. In this work, we analyze the effect of sensor noise on the MPC performance of a DAB converter, and propose an MPC method with active sensor noise suppression. The proposed method independently sets the MPC discretization cycle and the switching cycle, so that the control performance deterioration can be suppressed without reducing the switching frequency or adding filters. The simulation results verify that compared with the classical MPC method, the proposed method can effectively reduce the inductor current stress and the output voltage ripple, suppress the transient bias, and improve the efficiency of the DAB converter, when the sensors suffer from sampling noise.

This study aims to address the effect of noise interference on the quality of speech recognition in the cabin during the running of multiunit rubber-wheel rail vehicles. Thus, a speech enhancement method for multiunit rubber-wheel rail vehicle cabins is proposed based on active noise control. Active noise control is used to eliminate specific noise signals in the noisy speech signal in the multiunit rubber-wheel rail vehicle cabin, and then noise-reduced mixed signal collected by the microphone is sent to a deep neural network model for speech enhancement processing. Simulation shows that the speech enhancement method based on active noise control can suppress the noise in the cabin space of multiunit rubber-wheel rail vehicles and extract purer speech signals, improving the quality of speech recognition in the cabin’s complex noise environment.

Startup pre-charging is essential to the operation of modular multilevel converter (MMC). The startup procedure of MMC includes uncontrolled stage and controlled stage. The conventional startup pre-charging methods mainly focus on the controlled stage, while the performance of uncontrolled stage is deteriorated by the inrush currents. To achieve a soft-startup performance of MMC, this paper presents a dc-link startup pre-charging control strategy which contains three stages. The first stage is pre-charging with current-limiting resistors based on the principle of half-bridge MMC. The second stage is bypassing the current-limiting resistors sequentially. The mathematical model of bypassing resistors sequentially is established, and the optimal bypassing times are achieved to minimize the inrush current during the uncontrolled startup stage. In the last controllable charging stage, constant current charging is adopted to reduce the inrush current effectively. Finally, the performance of the proposed method is verified by simulations.

This paper proposes a new permanent magnet vernier machine (PMVM) with asymmetrical stator modulated teeth design to enhance its working field harmonics and torque capability. The air-gap field modulation mechanism in proposed PMVM is theoretically analyzed by magnetomotive force-permeance model. Besides, in order to confirm the merits of the asymmetrical modulated teeth design, a traditional PMV machine with symmetrical teeth design is selected as a benchmark to perform a fair comparison. Additionally, the basic electromagnetic performances of the two PMVMs are investigated and compared by finite element method. The simulation results show that the asymmetrical modulated teeth design exhibits an improved air-gap working harmonic amplitudes and torque capability than conventional counterpart.

The present accelerator magnet power topology scheme has the disadvantage of serious pollution to the power grid. Considering the characteristics of adjustable power factor of matrix converter, the possibility of matrix converter applied to accelerator DC power supply is studied. According to the actual situation of accelerator DC power supply, a direct-coupled matrix rectifier power supply scheme is studied. The modulation strategy of the power supply with direct-coupled matrix rectifier was designed. The modulation strategy combined the current space vector modulation with the closed-loop control of the output current is simulated and verified. Experimental results show that the topology has good performance on the grid side, and there is prospective feasibility in applying direct-coupled matrix rectifier to accelerator DC power supply.

SiC MOSFETs are particularly suitable for high power density high frequency applications due to their fast switching speed, low power loss, and high thermal conductivity. However, the high switching frequency also leads to voltage spikes and high switching oscillations. This paper proposes a new multi-level SiC MOSFET drive circuit and control method, which can adjust the gate resistance of the main control chip to optimize the switching characteristics of SiC MOSFETs, effectively solving the shortcomings of traditional gate drive circuits in dealing with voltage spikes and switching oscillations.

As a key interface, Dual-Active-Bridge (DAB) converters are widely used in new energy system (NES). For the NES, which usually requires fast dynamic performance and high efficiency, model predictive control (MPC) can be a promising option. Classical MPC, depended on the model parameters, will produce steady-state deviation due to the inaccurate model. Among all parameters, the inductor has a major impact, which may even lead to control failure. To tackle this issue, we propose a novel MPC strategy combined with the recursive least square (RLS) in this paper. The inductance can be accurately identified and updated online in real time without adding new sampling devices. Finally, experimental results show the influence of the inaccurate inductance on MPC and prove that the proposed strategy: (i) can eliminate the steady-state deviation (ii) maintain the excellent dynamic performance of MPC.

Lithium (Li) metal is considered to be one of the promising anode materials because of its light mass, low potential, and high theoretical specific capacity, however, dendritic growth and low battery coulomb efficiency will shorten the life of lithium metal battery (LMB). To solve this problem, adding film-forming additives to the LMB electrolyte to control the composition of the solid electrolyte interface (SEI) is a simple and effective method of lithium anode modification. Herein, this work homogenizes lithium deposition by adding a long-chain ionic liquid (IL) surface-active additive [C16MIM]BF4 to take advantage of the electrostatic shielding effect of the long-chain, at the same time, the anion group is reacted into BN, which inhibits electron transport and electrolyte degradation, both of them inhibit the growth of lithium dendrite. Stable cycling of symmetric Li||Li cells for 600 h was achieved by adding the additive to the electrolyte, and the LiFePO4||Li cells exhibited a lower polarization (51 mV) and a reversible capacity of 165.47 mAh g−1 after 30 cycles compared to the blank sample, which was highly compatible with LiFePO4.

As forced air cooling heat sinks are widely used in the cooling process of electrical and electronic equipment, their thermal design is of critical importance in order to maintain cooling capacity while reducing the size and weight of the equipment. This paper presents a method based on the combination of computational fluid dynamics (CFD) simulation and surrogate model to optimize the aluminum extruded heat sinks for high-end energy storage converters. The design experiment takes reducing the thermal resistance and mass of the heat sink as the optimization goals, and looks for the best design points of the base thickness, fin height, thickness and spacing. The calculation and simulation results show that the thermal resistance and mass of the optimized heat sink are reduced, so are the temperatures of the heating elements. Test results verify the effectiveness of the optimization method combining CFD simulation with surrogate models. Finally, the paper also puts forward the method of adding heat pipes to further improve the heat dissipation performance of the heat sink.

Due to its special working conditions, the completion pipe string of the gas storage adopts special threaded tubing, and the working conditions of gas injection and gas extraction change every year. The pipe string elongates during gas injection and compresses during gas extraction, which has a direct impact on the performance of the special connection seal. High-pressure gas injection and corrosive gas have high requirements on the sealing performance of special threaded tubing, the sealing performance of the whole pipe string is directly related to the safe production of gas storage, and the operation of special threaded tubing is a direct factor affecting the sealing quality. This paper introduces the field application of special threaded tubing in Suqiao gas storage and measures to improve the sealing performance.

The proportion of new energy power generation in the power grid is increasing, which puts forward higher requirements for the time scale of energy release link in energy storage system. This paper presents a new type of compressed air energy storage system with ejector and combustor, which can realize energy release in short-time scale under adiabatic expansion and non-supplementary combustion conditions, and also in long time scale with jet supplementary combustion conditions. It can achieve continuous output of energy release power generation link when energy storage and energy release operate simultaneously. So, it is a new energy storage system that can meet the requirements of power generation with multiple time scales and take into account the economy, which has broad application space.

In this paper, in order to optimize the denitrification efficiency and equipment structure of selective catalytic reduction (SCR) flue gas denitrification technology, a mathematical model of the internal flow field of SCR system was established according to the principle of fluid mechanics. The ICEM software was used to complete the grid generation, to process boundary conditions and to create the 3d models. The Simple algorithm is adopted for the coupling of speed and pressure. The flow in the reactor is turbulent flow and is simulated by standard K - ε model. The main conclusions can be summarized as follows: (1) Through the construction and calculation of the flow field model of the denitration system, the optimized SCR denitration reactor model and its performance distribution characteristics were obtained. (2) In the SCR denitration reactor installed with the porosity type nozzle, the pressure distribution at the inlet changed little and was more uniform, and the nozzle of porosity type can effectively prevent ash accumulation and blockage and improve the uniformity of distribution.

With the increasing proportion of PV (photovoltaic) power generation connected to the power system, there are large quantities of outstanding problems in low-frequency oscillation that cannot be ignored. In this paper, an equivalent aggregation module of large-scale PV and a PMT (Plug-in Modeling Technology) module of a multimachine power system with PV is established first. Then, the eigenvalue analysis method is adopted to analyze small-signal stability on low-frequency oscillations of the entire power system before and after PV replaces the synchronous generator and PSS (Power System Stabilizer) participates. The root locus method is adopted to discuss the change in eigenvalues and damping ratios of a multimachine power system with a high PV percentage when the parameters of the PV converter are changing. The results indicate that the damping ratio of the system oscillation mode is reduced by replacing the synchronous generator with PV, and the greater the replaced generator is involved in the original mode, the more drastic the change is; At a low ratio, the participation of PSS can enhance the stability of the power system with PV; At a high ratio, the stability can be improved by appropriately adjusting the parameters of the outer voltage controller loop and phase locked loop.

In this paper, a 50 kW stator yokeless modular axial flux motor with strong overload capacity, wide operating speed range and high operating efficiency is designed for the high torque and high speed requirements of the M/G motor in the flywheel energy storage system. Considering the overload capacity and the operating speed domain, three operating conditions of high efficiency, high speed and overload are determined, and the magnetic circuit calculation of the motor and the selection of the pole slot are carried out to determine the motor structure. Considering the influence of the stator material on the motor performance, the motor torque performance and loss characteristics under the three materials of silicon steel sheet, SMC and AMM were compared. A three-dimensional motor electromagnetic field model is established and combined with the finite element method to improve the torque performance. The YASA motor is optimized for the purpose of suppressing losses. After optimization, the maximum efficiency of the motor exceeds 98%, and the peak power density exceeds 5 kW/kg.

Export trade is not only the driving force for the economic growth of the manufacturing industry but also the key to its low-carbon transformation. To enrich the research on the embodied carbon in the export trade of manufacturing and manufacturing, this paper combines the factor decomposition method with the decoupling elasticity index model to establish an extended decoupling elasticity model to measure the decoupling relationship and degree between the measurable export trade and the corresponding decoupling elasticity values of the driving factors affecting the decoupling of the total effect; finally, the evaluation results of energy consumption decoupling in the industry is discussed. The input-output model is used to effectively calculate the embodied carbon in export trade; the decoupling elasticity model is used to analyze the decoupling of the embodied carbon in the export trade of the manufacturing industry. The research results are conducive to adjusting the trade structure according to local conditions and promoting the development of low-carbon economy. This provides literature support for the manufacturing industry to reasonably control the scale of exports of high-carbon products, adjust the export structure and develop low-carbon trade strategies. It is of great importance to achieve the goal of “carbon neutrality” and “carbon peaking”, and to form a green and low-carbon dual-cycle pattern at home and abroad.

Cloud energy storage is a new form for energy storage service which establishes shared energy storage resource pool at grid level, and can meet resource using requirements of electric users without building. In order to improve the capacity of renewable energy accommodation and system benefit, a day ahead scheduling model is proposed in which cloud energy storage agents participate in cooperative games. Firstly, various stakeholders including system operation side and cloud energy storage side are analyzed. Secondly, an optimal model is established based on cooperative game theory to maximize the overall income of the system which is divided into two layers. The upper layer is the cooperative game between the system operation side and the cloud energy storage agent, and the lower layer is the internal operation decision game between the cloud energy storage agent. Finally, the actual calculation examples show that the proposed method can improve the income of participation in cloud energy storage system.

At some special conditions, it exists a huge challenge about flow assurance at the choke for the high-pressure and low-temperature. Analyzing and judging the subcooling degree of hydrate risk and its existence time at choke is of great significance for hydrate management. In this study, the hydrate risk at the choke was analyzed during the low production process, and restart process, and then the subcooling degree of hydrate risk and its existence time was calculated. The results show that in the early stage of low production with high pressure of reservoir when gas production is lower than 6 × 104 Sm3/d, it always exists hydrate risk at the upstream and downstream of the choke, and its subcooling degree is 3.79 ℃ and 18.45 ℃, respectively; with gas production increasing, the subcooling degree at of choke gradually decreases; when gas production is higher than 8 × 104 Sm3/d, there is no risk of hydrate formation at upstream of the choke; While when the gas production is higher than 18 × 104 Sm3/d, there is no risk of hydrate formation at upstream and downstream. When the gas well is restarted at the rate of production Δ(50 × 104 Sm3/d)/60 min, the maximum subcooling degree at downstream of the choke is 33.25 ℃, and the hydrate risk duration is 102 min.

At present, there are many power quality problems in the production lines of domestic factories, such as the drop of grid voltage amplitude, the low power factor in the operation of motor, etc. In order to ensure the power quality of the power grid, the SVG system is designed based on the scrap crushing production line of a steel enterprise in Anhui province. The design has been combined with the actual working conditions of the site. A dynamic reactive power compensation device for high power production line is proposed, and the relevant parameters of the system are calculated. Finally, through MATLAB simulation and field application tests, the starting process of the high-power motor and the compensation effect under different load conditions is compared, the results are in agreement with the theory, and the correctness of the design is verified.

In the process of crude oil pipeline transportation, if the transportation volume is lower than the minimum allowable pipeline transportation, the pipeline will increase the operation economy due to the increase in energy consumption, and the transportation process is not conducive to the efficient operation of equipment. Intermittent transportation is the main means to solve this problem, but the process is affected by pipeline throughput and intermittent time. In order to rationally distribute the pipeline hourly throughput and determine the intermittent time to reduce the pipeline energy consumption, a crude oil pipeline intermittent transportation optimal operation model is proposed in this paper. The model innovatively considers the pipeline shutdown time constraint and pipeline flow constraint and establishes the minimum energy consumption optimization model of crude oil pipeline intermittent transportation. Taking an oil pipeline in China as an example, the optimization model is solved by the FPA algorithm. After optimization, the energy consumption of the pipeline is reduced by 44% compared with the longest shutdown time and 18% compared with the shortest shutdown time.

Under the dual-carbon target, as the intermediate hub connecting power production and consumption, the low-carbon development of power grid is of great significance to the development of power industry and even the whole society. Comprehensive evaluation of carbon emissions from power grid construction projects is an important part of realizing low-carbon power grid. Based on the existing research results, this paper studies the index system and evaluation method of low-carbon power grid construction projects. According to the analysis of carbon emission sources of power grid construction projects, the comprehensive evaluation and core evaluation index system of low-carbon power grid are designed, and the low-carbon comprehensive quantitative evaluation method is constructed to form a comprehensive evaluation model of low-carbon power grid. In the construction of the index system, through the horizontal comparison and vertical comparison of power grid construction projects, the analytic hierarchy process is selected to calculate the index weight. Finally, through the comparison of the advantages and disadvantages of the construction project options, a comprehensive analysis of the evaluation criteria of low-carbon power grid construction is made, and a comprehensive evaluation system suitable for evaluating the carbon emissions of power grid construction projects is obtained.

This paper first introduces the operation status of the oilfield metering system, and then through different natural gas pipeline layouts, a metering system that is compared with the Tarim oilfield flowmeter is added to the compressor station at the first station of the long-distance natural gas pipeline. Then, in order to avoid the problem of bias flow in the manifold of the metering system at the first station, an adiabatic model was used to simulate the flow field of the metering system to simulate the setting of the natural gas fluid, and the influence of the arrangement of the natural gas pipeline on the bias flow of the metering branch was analyzed. Although the flow distribution of the middle inlet and outlet is relatively uniform, the flow rate of the middle branch is too high, which is not conducive to measuring the flowmeter, and may bring adverse effects such as vibration and noise. The speed of entry and exit on the same side is relatively gentle, and the measurement and pipeline operations are more convenient, which helps to improve the accuracy of the comparison measurement system at Lunnan Shi Station, protect the economic interests of both supply and demand, and provide reference for solving other domestic and foreign measurement problems.

Acid gases such as H2S, CO2, SO2 carried in natural gas will form acid liquid with water during transportation, which is prone to serious corrosion problems such as perforation and leakage. Corrosion inhibitors are often used as one of the main anti-corrosion processes. With regard to the serious internal corrosion of the gas gathering pipeline in a well in Moxi, based on the actual operational conditions, this paper establishes a CFD model to study the distribution of corrosion inhibitors. The simulation results show that: ① the larger the pipeline angle and the faster the injection velocity, the better the distribution of the inhibitor particles in the pipe. Although the injection rate will affect the inhibitor concentration in the pipe, the regularity remains unchanged; ② when the injection velocity increases, the inhibitor becomes better distributed and can protect the longer range of the pipeline; ③ the corrosion inhibitor at the top of the pipe will settle and attach to the wall due to the gravity, and the fluid would rise to the middle and upper part of the pipeline due to the influence of secondary flow after passing through the elbow, causing the inhibitor concentration to increase rapidly.

In recent years, the comprehensive utilization of solar photovoltaic (PV/T) technology is the current research focus. Water is a commonly used circulating fluid in PV/T collectors. A part of the heat of the heater, the temperature of the collector fluctuates greatly. In this case, latent heat energy storage methods provide a possible solution for collectors to store and release absorbed solar energy, and phase change materials are one of the methods for latent heat energy storage. In this paper, based on the rib-tube PV/T collector, the phase change material is combined with the rib-tube PV/T collector to design a phase-change thermal storage solar photovoltaic thermal collector (PCM-PV/T), using the sensible heat and latent heat of the phase change material to thermally manage the collector, so as to improve the photoelectric efficiency and overall efficiency of the collector. The results show that the addition of phase change materials can effectively reduce the temperature of photovoltaic cells. The maximum temperature difference of photovoltaic cells between PCM-PV/T and rib-tube PV/T collectors is 6.38 ℃; Compared with the rib-tube PV/T collector, the efficiency and overall efficiency are improved to a certain extent. The cumulative photoelectric efficiencies of the PCM-PV/T collector and the rib-tube PV/T collector are 10.98% and 10.98%, respectively. 10.74%, the comprehensive efficiency is 54.76% and 47.8% respectively, the PCM-PV/T collector has greater practical application value.

Achieving the goal of “carbon peaking and carbon neutrality” is a major energy strategy in China. To accelerate the construction of a new power system with new energy as the main body, and to build a clean, low-carbon, safe and efficient energy system, we must take effective measures to vigorously develop new power energy system. As a clean low-carbon and renewable green energy, photovoltaic power generation has the characteristics of low-carbon and zero-emission. Vigorously developing the photovoltaic industry is of great significance for adjusting the energy structure, promoting energy transformation, and achieving the goal of “carbon peaking and carbon neutralization”. By the end of 2021, the global photovoltaic installed capacity has been 170 GW, and brought the cumulative installed capacity to 926 GW. Among them, China’s newly installed capacity was 54.88 GW, accounting for 32.3% of the global increase; the cumulative installed capacity of China was up to 307.88 GW, accounting for 33.2% of the global total. This paper systematically analyzes the current electricity market, solar energy resources, photovoltaic power generation, and the economics of photovoltaic power generation in various regions in China. At the same time, this paper analyzes the main problems existing in the actual construction of photovoltaic projects, such as high land, strong allocation of energy storage, industrial support, limited consumption, and transaction price reduction, and studies its impact on the economics of the project investment. Finally, it is suggested that the development of photovoltaic power generation in China should adhere the four principles of “regional, strategic, integrated, and economical”, systematically realize the high-quality, large-scale, healthy and orderly development of photovoltaic power generation, and support China to achieve the goal of carbon peak and carbon neutralization on schedule.

In this paper, a rarely reported failure mechanism of multichip IGBT module caused by the aging of the emitter bonding wires on a single IGBT chip is revealed. It is found that when the emitter bonding wires bonded on a single chip are all electrically disconnected due to lift-off or cracking, there is a chance that the chip gate can be broken down when the device is blocking busbar voltage and eventually leading to the malfunction of the entire module. Due to the internal parasitics distribution, an over-voltage might be applied across the E-G terminals resulting in a breakdown on the gate oxide layer of device. In the paper, the failure mechanism of IGBT’s gate overvoltage is studied via equivalent circuit modeling and followed by simulation and experiment validations.

Shadow shading will cause the barrel effect of PV modules, based on the dual-diode solar cell model, a mathematical model of PV module output characteristics under shadow shading is established, and the output characteristics of PV modules arranged horizontally under different shading ratios are simulated and studied. The results show: the influence of shading on the current and power of PV modules is greater than that on the voltage, and the output characteristics are negatively correlated with the shading ratio; The horizontal arrangement of PV modules can moderately reduce the sensitivity of the influence of shading on the output characteristics, compared with the vertical arrangement, the horizontal arrangement of PV modules can obtain more output power under the same shading ratio; the PV module has multiple maximum power points under the shadow, and the output power of the PV module can be properly increased by increasing the number of bypass diodes.

The wide bandgap (WBG) power semiconductors featuring the ever-increasing switching speed pose new measurement challenges to the high-fidelity measurement recently. To obtain high-fidelity measurement of voltage signals in the applications of the temperature sensitive electrical parameter (TSEP) and the degradation sensitive electrical parameter (DSEP) techniques in advanced condition monitoring of power devices, the influences of voltage probes used to perform these tasks are studied in this paper. Firstly, key properties of different voltage probes and their impacts on the signal measurement fidelity are investigated. Secondly, the impact of voltage probes connected into the power loop on the gate loop, are investigated by the presented susceptibility model. In the last, the accuracy of some reported TSEP candidates influenced by aforementioned probe effects is investigated and discussed.

This paper analyzes the case of cylinder cutting transformation of 350 MW supercritical unit. The results show that the heating capacity of the unit can be increased by about 90 MW under the same main steam flow rate after the transformation. The peak regulation capacity of the unit can be increased by 70 MW and the lower limit of peak regulation can reach 62.2 MW while keeping the same exhaust flow rate. Combined with the boundary conditions of the project, the investment of the project is 22.0693 million Yuan, and the financial internal rate of return (after-tax) is 25.46%, which has good economic benefits.

In order to reduce the steam superheat degree of the third-stage extraction steam, improve the thermal efficiency of the regenerative system, and improve the economy of the unit operation, a technical scheme of setting up an external steam cooler for a 630 MW coal-fired subcritical generating unit was proposed, and the technical scheme was carried out. The results show that the addition of a heating and draining external steam cooler can effectively utilize the superheat of the three-extraction steam, improve the efficiency of the heat recovery system, and effectively improve the reliability and economy of the unit operation. The heat consumption is reduced by 12 kJ/kWh, 11 kJ/kWh and 10 kJ/kWh respectively under the condition of 50% THA and 50% THA, which can achieve the comprehensive benefit of energy saving and emission reduction.

As the renewable energy sources have been envisaged as a major source of energy generation for future, ultra-high-voltage DC (UHVDC) transmission system is playing an increasingly important role in transmitting huge amount of renewable power to the load center. The uncertainty of wind energy together with the risk of UHVDC faults posts great challenges in the secure operation of interconnected inter-regional power systems. In view of these challenges, the security assessment of UHVDC system considering uncertain wind output is proposed in this paper. First, we perform short-term wind output prediction based on Gaussian Process Regression (GPR). Second, we use Monte Carlo simulation to obtain wind power loss expectations, and then obtain different N − 1 contingency scenarios. Third, we proposed an optimal operational strategy in the presence of the above contingences, by coordinating AGC and AVC of each region of the inter-connected power system to eliminate over-limits of frequency, voltage, current and transmission capacity. Case studies with modified IEEE 14-bus system and a synthetic Chinese power grid show that the coordinated optimal operational strategy is able to ensure the frequency security, voltage stability, and the secure operation of the interconnected power system.

There are many design parameters affecting the performance of the stiffening ring, and it is difficult to obtain the optimal structure. By adopting parametric 3D modeling, this research optimized the design of seven key parameters: pad plate height, rib plate height, top ring plate thickness, rib plate thickness, bottom ring plate thickness, top ring plate width, and bottom ring plate width. Taking maximum stress, maximum displacement and minimum safety factor as three main constraints, 450 combinations were successfully optimized in 4034 combinations. The optimal quality of the stiffening ring was obtained while meeting strength and stiffness. The results indicated that the multi-parameter multi-constraint optimization design method is easy to obtain better-optimized performance, which has important guiding significance for engineering design and certain reference value for theoretical research.

With the pressure of climate change and carbon emission, renewable energy, and distributed energy sources are promoted in the form of microgrids worldwide. However, the inherent randomness and volatility of renewable energy sources cause the dynamic behaviors of microgrids to be complicated. The boundary conditions under various operating conditions are difficult to evaluate, bringing huge challenges to microgrids' planning, operation, and maintenance. The objective of this paper is to present a multi-scenario digital twin analysis platform for microgrids for the analysis requirements of complex microgrid scenarios. The system structure, simulation algorithm, and platform workflow are proposed in this paper. In addition, a model of the microgrid was built and the validity of the model was verified through unit testing. Through multi-scenario steady-state analysis and frequency-sweeping analysis, the effectiveness of the microgrid central controller (MGCC) and the batch parallel simulation function is verified.

The complex energy conversion relationships and the volatility of renewable energy sources and loads in an integrated energy system containing multiple energy sources pose challenges to the flexible operation of the system. In order to reduce the impact of renewable energy and load uncertainty on the system, an optimal allocation method of dispatchable resources for integrated energy systems with flexibility is proposed. This paper first proposes a quantitative evaluation index of system flexibility from the perspective of supply and demand balance. Then, with the annual operating cost of the system and the flexibility index of upward and downward adjustment as the optimization objectives, an iterative optimization model of the main problem and sub-problem is established. Finally, the multi-attribute decision-making method based on TOPSIS (technique for order performance by similarity to ideal solution) is used to select the optimal allocation of the system. The results show that the proposed optimization model can meet the demand and supply balance of the system and improve the economy and flexibility of the system.

The internal energy coupling and the external environment influence make the operation scene of regional integrated energy system more and more complex and the uncertainty problem more and more prominent. In addition to the usual uncertainty of energy side and load side, the uncertainty of coupling equipment conversion efficiency is considered, and the uncertainty interval of output power is given. Based on this, puts forward the planning and evaluation model of regional integrated energy system flexibility in ascension, first to economy optimal flexibility as a target for promotion planning, safety assessment, the resulting satisfy both good economical efficiency and safety of planning as a result, and is verified by an example and analyze the effectiveness of the model.

As a heavy user of electric power, it is obviously important for the park to ensure friendly interaction with the power grid during the operation stage through the optimal capacity configuration of the integrated energy system. In this study, Taking an office park in Tianjin as an example, multi-objective particle swarm optimization algorithm (MOPSO) is used to determine the capacity configuration optimization of an integrated energy system by considering life cycle initial cost (LCIC), life cycle carbon emissions (LCCE), and utilization ratio of renewable energy (REPp) in the first stage. And then taking reduce the impact on the power grid and the operating cost of the system as the objectives, the scheduling characteristics of the capacity configuration obtained by the multi-objective optimization in the operation stage are measured respectively. Through two-stage optimization, it can be concluded that the optimized capacity configuration can not only realize friendly interaction with the power grid, but also realize carbon reduction, economical beneficial and sustainable.

Combining vulnerability with risk theory, this paper introduces a method of vulnerability analysis based on risk assessment in power system. Based on the risk theory, the impact of component outage probability and component outage on the system is considered comprehensively. Four categories of risk indexes, including risk of circuit overload, risk of over-voltage, low-voltage risk, and loss of load risk are used to establish a risk assessment index system to analyze the risk of power system components. The entropy weight method is applied to calculate corresponding risk indexes, which reveals the severity of the accident and the weaknesses of the grid structure. Then the ALARP principle is used to quantify and classify the consolidated risk, which can determine the risk level and importance degree in the system of each system component, offering a certain reference for operation personnel and setting personnel. Numerical example shows investigation of study and validity of the solution method.

Reasonable equipment configuration optimization in the early stage of the construction of the park integrated energy system is the key to its good operation. The practicability and enforceability of the optimized scheme can ensure the good execution of the plan. Considering the discrete and nonlinear distribution of the performance and price of engineering equipment products, an optimal allocation method of park integrated energy system considering engineering practicality is proposed. First, based on the characteristics of energy conversion/storage equipment, equipment models of the system are built. Then, considering the properties of engineering equipment, the objective function and constraint model are reshaped based on the discrete equipment variables, and a large-scale mixed integer linear programming solution model is established. The net present value is set as the optimization objective of the model. Finally, based on an example, the optimal configuration results are compared and analyzed. The operation results and investment sensitivity of the configuration results are Analyzed. These analyses verify the rationality and engineering practicability of the research results.

Facing the problems of large power loss and low control efficiency of HVAC in Colleges and universities, an energy-saving control method of HVAC in Colleges and universities considering the satisfaction of thermal comfort is proposed. According to the thermal comfort satisfaction index, the functional structure principle of HVAC is analyzed, and the energy-saving control model of HVAC in Colleges and universities is established. Under this model, the energy-saving control method process of HVAC in Colleges and universities is designed. According to this process, the degree of satisfaction of thermal comfort is fully considered from the three aspects of temperature, humidity and air conditioning to achieve the energy-saving control of HVAC in Colleges and universities. The experimental results show that the method has good performance in temperature, relative humidity and air conditioning control.

With the rapid and sustainable development of economy and society, the problem of energy supply has been gradually highlighted. The use of traditional fossil energy has the disadvantages of unsustainable development and high pollution, and the development and utilization of clean energy is urgent. Hydrogen energy is considered as the most developed clean energy in the global energy transition. Under the background of low-carbon emission reduction, green hydrogen energy production technology has attracted extensive attention. The purpose of this paper focuses on the green hydrogen energy production technologies, mainly including hydrogen production by water electrolysis, microbial hydrogen production and photocatalytic water splitting for hydrogen production technologies. The principle, key materials and development status of each green hydrogen technology are discussed respectively and the outlook and suggestions of green hydrogen energy are given accordingly as well.

For bidirectional inductive wireless power transfer systems, zero voltage switching (ZVS) of switching devices and system efficiency are always the keys to research. In this paper, the efficiency of the bidirectional dual-LCC compensation topology is firstly modelled, and the optimal bilateral modulation strategy is quantitatively derived. Then, a half-bridge modulation method under light load conditions is proposed. Compared with the full-bridge modulation strategy, the operating efficiency of the system is further improved. Finally, a 3.3 kW two-way wireless power transfer system simulation model is built in MATLAB. Compared with the full-bridge triple-phase-shift modulation strategy, it is proved that the half-bridge modulation scheme proposed in this paper has higher energy transfer efficiency under light load conditions.

With the goal of “carbon neutrality and carbon peaking”, the carbon emission rights trading market and green power trading market have ushered in rapid development. At present, China’s green power market and carbon market are still operating independently. The synergy to promote the “carbon peak, carbon neutral” goal has not yet been formed, and it is urgent to break the “data island” dilemma in the two fields. This paper further builds a new model of carbon-electricity market synergy and interaction by carrying out research on carbon-electricity market synergy mechanism and cross-chain technology. First, a collaborative clearing model of electricity-carbon market is designed, which is based on smart contract plug-ins and relay chains in blockchain technology. The cross-chain method builds a cross-chain model of carbon-electricity market synergy clearing. This paper studies and forms replicable and generalizable systematic results, which can effectively support the rational allocation of carbon-electricity market resources. It has important reference value.

The power battery is the only source of power for battery electric vehicles, and the safety of the battery pack box structure provides an important guarantee for the safe driving of battery electric vehicles. The battery pack box structure shall be of good shock resistance, impact resistance, and durability. This paper uses the finite element model analysis method of the whole vehicle to verify the mechanical properties of the foamed aluminum material through experiments, and optimizes the design of the weak links in the structure of the power battery pack box, which effectively reduces the maximum deformation of the battery pack box and the maximum stress value, while reducing the weight of the battery pack box, thus meeting the requirements of lightweight design.

With the construction of a new power system and the implementation of China’s clean energy strategic goals, the installed capacity of domestic new energy has grown rapidly. This will lead to the increasingly prominent problem of new energy consumption in regional power grids. Therefore, it is urgent to carry out a comprehensive assessment of the new energy consumption capacity in combination with the actual characteristics of the regional power grid, and to establish a simulation computing architecture. To this end, this paper firstly affects the key factors of new energy power generation and consumption capacity, and establishes optimization objectives and constraints. On this basis, this paper proposes a simulation computing architecture design scheme, and finally carries out a comprehensive analysis of the factors affecting the cost of new energy consumption in combination with a certain regional power grid. The numerical example verifies the effectiveness of the scheme, and can provide a basis for the evaluation and optimization of the new energy consumption capacity under the new situation.

The traditional proportional integral (PI) controllers used for vector control of permanent magnet synchronous motors (PMSM) are characterized by poor adaptability, unsatisfying the requirement of optimal control. To solve the issue, this paper proposes a fuzzy adaptive PI regulator to regulate the machine speed. By designing the components of the fuzzy adaptive PI regulator, which include fuzzification, fuzzy control rules, membership functions and defuzzification, the structure of the controller is established. Besides, in terms of the problem that it is difficult to tune the parameters of the fuzzy adaptive PI regulator, a parameter tuning method based on classic control theory is proposed. The proposed parameter design method is able to maintain the system to keep stable during the whole control process and improve the dynamics of the system. It is easy to implement in practice. Finally, simulation results prove that the proposed fuzzy adaptive control and the parameter tuning methods are effective.

As the proportion of renewable energy sources and emerging loads in the system gradually increases, electricity networks become more complex. In order to cope with the multiple demands for electrical energy, experts in various countries have developed the concept of an energy internet. This paper investigates its key devices and proposes a virtual motor control strategy for multi-port energy routers, which can effectively improve system inertia and enhance system stability. A simulation model of the system was built in PLECS software and compared with the traditional droop control strategy to verify the effectiveness of the control strategy proposed in this paper, which can smooth out the fluctuation of the DC bus voltage.

As the number of substations in the power system continues to increase, the management of power equipment becomes increasingly complex. Using image recognition technology to perform text recognition on power equipment nameplates can effectively improve the management level of power system equipment. Text detection is the first step in text recognition, and traditional text detection techniques are complex and time-consuming. This paper proposes a nameplate text detection model based on the deep learning model EAST. Experiments show that this method can greatly accelerate the efficiency of power equipment nameplate recognition.

The price of NEV credit is determined by a combination of factors including the value of credits, supply and demand relationship (supply-demand ratio and concentration), and prior price expectations. Among them, the supply-demand ratio of credits is the most sensitive price signal for enterprises, but the industry currently lacks an effective method for solving the supply-demand ratio of credits. This paper introduces a simulation method with multiple steps-based offset logic, which based on the offset principle of CAFC and NEV Credit Policy, divides the enterprises’ credit compliance strategy into five stages with 11 steps for offset, and builds a simulation software of “Supply-Demand Conditions of CAFC and NEV Credit” based on this simulation method to enable the scientific, accurate and efficient solution of the supply-demand ratio of credits in the industry. The above simulation method and software have been applied in the NEV credit price prediction and CAFC and NEV Credit Policy research.

China has officially launched its national carbon market in July 2021. As the previous biggest and most liquid emission trading scheme, the study on EU ETS will provide valuable insights for China to develop its carbon derivatives market. This paper investigates the relationship between the EUA spot and futures markets within the third commitment period of the EU ETS by combing the VEC and BEKK models. Based on daily data, we study the transmission of information in these two markets. We reveal the leading position of EUA futures market in the price discovery process and this result is further confirmed by our subsequent volatility analysis. However, this link is weakened or even disrupted in the second sub-period. We argue that the completion of backloading of emission allowances and the upcoming more stringent regulations account for this change. In addition, our BEKK models identify the existence for a close link between the volatility dynamics of two markets, whereas in particular a bilateral volatility spillover is observed. Accordingly, we give the policy advice that regulators should value the role the futures market plays in leading the price discovery process and keep a good balance between the development of derivative markets and the spot market to establish a more integrated ETS.

With the rapid economic development, the world is looking for renewable energy to replace traditional energy. Natural gas is a clean renewable energy source, and building a hybrid power generation system with natural gas pressure power generation and energy storage devices will effectively improve the utilization rate of renewable energy. This thesis proposes to use the deep reinforcement learning (DRL) algorithm to optimize the scheduling of the differential pressure power generation energy management system. A data-driven approach is proposed to train the energy management system offline, and test the trained model online. Through the experimental verification and the comparison of different algorithms, the effectiveness of the energy management system based on the Double deep Q-Network (DDQN) algorithm for differential pressure power generation is verified.

Compared with AC microgrids, DC microgrids have attracted much attention due to the advantages of integrating distributed renewable energy generation systems, energy storage units, electric vehicles and other DC loads efficiently and reliably, without considering the issues of the frequency synchronization, the reactive power compensation, the power quality and so on. However, the current research on DC microgrid mainly focuses on the optimization and control of a single DC microgrid while ignoring the energy exchange and mutual support among multiple DC microgrids. In this paper, based on the consistency theory, we propose a distributed cooperative control method and apply this method to multiple DC microgrids which are connected by a multiple-active-bridge (MAB) converter. Each terminal port of the MAB converter is regarded as the internal node of the corresponding sub-microgrid. Thus, the accuracy of the average bus voltage is improved and the bus voltage of each sub-microgrid is stable and error-free. The output power of each distributed generation unit in a sub-microgrid is balanced. Meanwhile, the MAB converter can regulate the power flow according to the average voltage error and the output power of different microgrids, thereby achieving the energy exchange and mutual support between multiple DC microgrids. The output power of each distributed generation unit in all DC microgrids can also be balanced. Finally, the performance of the distributed cooperative control method is verified by simulation results.

High resistance connection (HRC) is a typical permanent magnet motor fault, which is caused by material fatigue and overheating in the motor winding. If it is not handled in time, the fault will cause more serious faults and even fire, so its HRC fault diagnosis is of great significance. This paper presents an HRC fault diagnosis method based on monitoring the magnetic field signal, which uses sensor to collect the magnetic field signal, processes the test data characteristics through neural network, and then identifies the HRC fault category of permanent magnet motor. The simulation results show that it is very effective to detect the high resistance contact fault of PMSM by using the magnetic field signal and the accuracy reaches 98%.

As the penetration of renewable energy and power electronic devices increases in power grids, electromagnetic transient (EMT) simulation is essential for analysing the system characteristics in the future. However, constructing and analysing a large-scale power system in EMT simulation software cost too much time and computation resources. This paper first develops a tool to automate the modelling process, then proposes a method to optimize the network partition scheme based on the long transmission line decoupling model (LTLD), and finally balances the computation load for real-time EMT simulation applications. The proposed method is implemented on the CloudPSS platform and verified with a large-scale provincial grid. The test results show that the simulation results of the scheme are reliable, and the real-time simulation of the grid can be achieved.

In traditional grid-connected photovoltaic inverters, the SPWM signal generation process is complex and inflexible, and the phase-locked loop is easily affected by grid fluctuations and voltage waveform distortion. Based on that, a phase-locked loop control strategy for the grid-connected photovoltaic inverter is designed on the customized IP core technology of FPGA. The strategy realizes real-time tracking and adjustment of the phase difference between the photovoltaic inverter system and the grid. The proposed Graded Phase Control uses multiple delay shift register groups to adjust the phase of the SPWM signal by grades so that the inverter can output sinusoidal waves with high precision and good flexibility through the SPWM technology. The SPWM signal generation process is independently completed by the logic resources of the FPGA. During the process, a large number of CPU resources is released, thereby improving the overall performance and efficiency of the system. The simulation results and the sampling results of the embedded logic analyzer show that IP core can improve the quality of the SPWM signal waveform as well as increase the speed and stability of the system phase adjustment. The simulation experiment verifies the feasibility of IP core, which meets the requirements of fast speed and high reliability of phase-locked loops of the grid-connected photovoltaic inverter.

Accurately evaluating the minimum miscibility pressure (MMP) of CO2-crude oil systems is a critical problem in the design of CO2 miscible flooding of reservoirs. Based on optimization algorithm and artificial intelligence model, a novel model integrating grey wolf optimizer (GWO) algorithm and radial basis function (RBF) neural network is built through measured MMP data fitting, which is named GWO-RBF. On the basis of Spearman rank correlation analysis, the proposed model selects reservoir temperature, oil C5+ molecular weight and mole fraction of intermediate oil components (C2-C4, CO2, H2S) as the independent variables. The GWO-RBF model parameters (base function width σ, hidden layer node number M) are optimized using 40 sets of published CO2-crude oil system MMP data. Prediction is performed on extra 10 sets of non-training MMP data. The results show that the average absolute relative deviation (AARD) of the GWO-RBF model is 2.73% on training sets, while the AARD of model prediction is only 3.11%. On training and prediction sets, the maximum absolute relative deviations (ARDs) are 8.30% and 9.99%, respectively, while the minimum ARDs are 0.19% and 0.00%.

China's crude oil is mostly condensable and highly viscous crude oil, which is usually transported by heating, and the pipeline system consumes a lot of energy. In order to further reduce the energy consumption of crude oil pipelines, a general optimization model for long-distance crude oil pipelines is established in this paper. The model comprehensively considers the pipeline constraints, pump constraints, and heater constraints, and uses the PPSO algorithm to optimize them. It can realize the optimal calculation of the temperature and pressure of the pipeline system and gives the corresponding pump and heater operation scheme.

With the continuous progress of industrialization, enterprises have put forward stricter requirements for electricity safety and energy consumption management, and accurate and efficient distribution room monitoring system is the key means to achieve this requirement. The currently widely used online distribution monitoring system will cause massive data calculation and transmission to bring great pressure on the master station data processing and communication channel. Based on the science and technology plan project, this paper designs a power distribution monitoring system based on edge computing. The system uses edge computing technology to optimize the dynamic and static performance of data processing of existing distribution monitoring systems from the aspects of system architecture and edge load distribution algorithm. Through the engineering demonstration application of the system, the field data shows that the system has practical engineering application value.

Once the transformer fails, it will bring blackouts to the power network. The existing protection methods are difficult to detect and locate faults. This paper analyzes the electrical characteristics of inter-turn short circuit of dual-winding transformer, discusses the possibility of on-line monitoring of inter-turn short circuit by innovation graph, and proposes an inter-turn protection method based on current ratio innovation. When the innovation value of current ratio exceeds the threshold value, the fault can be judged and the protection action can be protected. This method can avoid the transient process of transformer, and is not affected by inter-phase fault and external fault, which improves the reliability of the protection algorithm. The feasibility of the proposed protection algorithm is verified by simulation experiments.

The large scale integration of new energy leads to great changes in the characteristics of power flow and fault characteristics of traditional radial distribution network, which has a great impact on the correct action of relay protection devices. The influence of different types of short circuit faults on the protection device is analyzed when the distributed power is connected to the starting bus and the intermediate bus. A current protection scheme using the innovation graph to calculate the correct estimate ratio of lines is proposed. In this scheme, the innovation graph parameters are calculated by the current innovation before the fault and after the fault, and the fault is judged only by comparing the correct estimate ratio of each line in the region. Compared with the conventional method of current protection, the scheme has lower requirements for communication system and improves the complex coordination between contiguous lines. A simplified distribution network model is established in PSASP software to verify the effectiveness of the proposed protection scheme.

In order to solve the problem of energy shortage, many countries have called on people to establish the concept of low-carbon and environmentally friendly travel. Among them, electric vehicles, as an important means of transport for energy saving and emission reduction, are widely concerned. However, as far as the construction of charging stations is concerned, there are still problems such as high investment costs and unreasonable layout. This paper takes the optimal layout of charging stations as an object, takes into account the construction costs of charging stations, operation costs, government subsidies, user travel costs, queuing time and other factors, combines multi-source data, constructs a charging station layout model based on multi-objective genetic algorithm and provides specific layout schemes.

In recent years, in order to solve the problem of limited available space in the power system, increasing the power transmission density of the system has become a key development direction. A multi-port DC/DC isolated converter is proposed, which has been widely used in energy storage and bidirectional transmission. This article introduces a topological structure of a three-port DC/DC converter, which combines the characteristics of a common dual-port converter, so that it has a higher overall power density. At the same time, in order to solve the problem of inherent coupling between the power flows of multiple ports, this paper proposes an improved variable step size dichotomy decoupling control strategy, which is more concise and effective than the traditional decoupling control strategy. Finally, the converter model was built in MATLAB/Simulink, and the correctness of the decoupling method was simulated and verified.

To address the subjective experience in medium- and low-voltage (MV/LV) switch fault diagnosis and the deviation between diagnosis results and the actual occurrence, this paper provides an improved dynamic adaptive fuzzy Petri net-based method for diagnosing MV/LV switch faults. The effectiveness of this model is then verified by combining typical MV/LV switch fault cases. The research results show that our proposed method can effectively deal with the uncertainty factors in the fault probability and has an excellent performance in fault tolerance and high operational efficiency.

Since 2020, the battery swapping mode has returned to the public view again, and its business prospects have been recognized by all parties. Catalyzed by various factors, the battery swapping industry is expected to develop rapidly. In order to analyze the calculation of the profit balance point of pure electric vehicle swapping stations under different utilization conditions, this paper constructs a net profit margin calculation model based on different scenarios of passenger car and commercial vehicle swapping stations under different service frequencies. Focus on four first-level indicators and 12 s-level indicators, including charging cost, service electricity price difference, operating cost and service frequency, and allocate infrastructure construction costs to ten-year depreciation costs. By investigating the characteristics of the scene, the obtained data is substituted into the model for empirical research. In general, the break-even point can be reached when the utilization rates of passenger vehicle and commercial vehicle swap stations reach about 20% and 10%, respectively.

Using a single heuristic algorithm to optimize the trajectory of a UAV (unmanned aerial vehicle) patrol inspection transmission tower body and accessories will result in trajectories overlapping and easily falling into the local optimal solution. As a result, this paper presents a hybrid GA (genetic algorithm)-SA (simulated annealing algorithm) optimization algorithm for UAV 3D trajectory. To optimize the trajectory of the UAV traversing the safe hovering point at high altitude, GA, SA, and hybrid GA-SA algorithms are used on the 500 kV UHV AC double-loop drum tower of UAV patrol inspection. The results show that the hybrid GA-SA algorithm has the shortest optimal trajectory distance and the shortest iterative convergence times, proving the effectiveness of the proposed method.

Machine vision technologies play a pivotal role in autonomous driving. It is very important for a vehicle to perceive the external environment and to make independent judgment according to environmental perception. At the same time, in the face of the increasingly complex road traffic, the road traffic signs are increasing and complex today. It is particularly important for the driving vehicles to accurately identify the traffic signs on the road. This paper has proposed a scheme of automatic trolley based on identification guidance. The scheme uses a crawler model car with a power unit carrying a vision module for simulation. The vision module OpenMV shoots is to compare and identify the traffic signs in front of the road. A NCC algorithm is to improve the automatic identification on the icon so as to identify the information contained in the traffic logo. Through the serial port with communication, the master controller manage the power unit of the vehicle and define different power output, so as to realize car driving and realize the purpose of automatic driving.

Machine vision technologies have been widely used by automation nowadays. In order to improve the efficiency of food delivery in restaurants and strengthen the prevention and control of the virus epidemic, this paper has designed an automatic food delivery robot with the STM32F103ZET6 system as the micro-processor, which can realize the function of automatic food delivery in the restaurant. The robot uses OpenMV as the visual sensor module. OpenMV has two main roles in this design, one is to identify the color and the other takes the role of patrolling. The patrol algorithm is the Telson linear regression algorithm while table number instruction being sent to the micro-processor via Bluetooth. Afterwards, a optimal route is defined according to the position based on the table number received. Then, the robot follows the route to the target location. After delivering the food to the destination, the car will return automatically so as to completes the delivery task.

In the actual use of the substation, due to the long-term operation of the isolation switch, there will be closed or opened not in place, resulting in the arc between the left and right arm of the switch, will cause leakage events or damage to the switch equipment, serious fire, therefore, the opening and closing state of the switch needs to be accurately judged. At present, the real-time status judgment of open disconnector mainly depends on manual observation, which requires a lot of human resources and is not safe. In order to solve the problem of real-time automatic detection of switching on and off in substation, a real-time tracking method for automatic detection of switching on and off in substation is proposed. Firstly, the optical flow method and the midline model of the blade arm are used to accurately locate the blade arm, and the tracking feature points of the blade arm are accurately determined. Meanwhile, the opening and closing angles of the left and right blade arms in the real-time opening and closing process are estimated to achieve accurate positioning of the blade arm. After the circuit breaker is at the position of the disconnector, the center line is used to distinguish the arm wound edges of the circuit breaker, and a symmetrical formula for the matching points of the arm wound edge line circuit breaker is proposed, which is helpful to accurately determine the arm wound edge line circuit breaker. Then, according to the edge line around the circuit breaker arm and the angle between the arms, the relationship between the circuit breaker arm and the angle before and after the real-time video frame is calculated, and the opening and closing status of the disconnector is judged in real time. A deep learning-based identification method for ice breaker brake is proposed. The algorithm takes the adjusted residual network as the basic structure, takes the network model trained on ImageNet as the pre-training model, and adopts the model-based transfer learning method to learn and optimize the model parameters. The experimental results show that the isolation switch detection method has high precision, good stability, and is not affected by the change of external light, which has important practical application value. The accuracy of state recognition method based on deep learning is up to 95%.

Permanent magnet synchronous motor (PMSM) has the advantages of high power density, low loss, low vibration and noise, and is widely used in energy, transportation, aerospace and other fields. Phase failure in permanent magnet synchronous motor drive system is one of the most common faults. If this kind of fault is not detected in time, it is likely to lead to secondary faults of the motor system, and even greater economic losses. In order to improve the reliability of the permanent magnet synchronous motor drive system, the open-circuit fault diagnosis of the drive system is studied. Model predictive control (MPC) has become a popular control method widely used in the field of motor drive and control because of its simple structure and excellent dynamic performance. This paper presents an open circuit fault method of permanent magnet synchronous motor drive system based on model predictive current control, and its effectiveness is verified by simulation.

After the distributed generation supply is connected to the traditional distribution network, the traditional distribution network changes from "passive" to "active", and the power flow changes from one-way to two-way. When the active distribution network fails, the short-circuit current is small, inconspicuous fault characteristics and rich harmonics make it difficult to locate faults. In this paper, a method for locating fault sections of active distribution network based on random forest-SVM algorithm is proposed. The method first performs fast Fourier transform on the voltage information collected at each measurement point to form the fundamental frequency voltage of each measurement point. And the fault feature set of the 2–7th harmonic voltage, the feature importance evaluation and feature selection of each fault feature are carried out by the random forest algorithm, and finally the fault section is located by the SVM algorithm. By connecting distributed generation and nonlinear loads in the IEEE33 node distribution network, the simulation results show that the method has rapidity, accuracy and fault tolerance for fault section location.

In the photovoltaic (PV) power systems, a detection of arc fault is necessary for maintaining the safe operation of the system. While most of the previous arc fault detection methods generally detect the arc faults according to some single time-scale fault features, they may be disturbed by the change of the environment, leading to a low accuracy. In this work, a novel multiple time-scale arc fault detection method is proposed. Unlike the previous single time-scale detection methods, our method uses a multiscale wavelet transform to extract the multiple time-scale fault features, and then uses a one-class long short-term memory autoencoders (LSTM-AEs) to implement the detection in an end-to-end manner. Theoretical analysis shows that if the proposed multi time-scale fault features are used, the ratio of the false negative and the false positive will be significantly lower than that of the single time-scale methods. Experiments on a real-world arc fault benchmark with ground truth labels show that our method outperforms the baseline approaches in terms of Accuracy and AUC.

With the increasing penetration of distributed generation (DG), active distribution network (ADN) is the development trend of future smart grid. Due to the features of uncertainties of DG, the difficulty of scheduling increases. For this concern, an optimization scheduling model of intra-layer autonomy and inter-layer coordination of ADN is proposed in this work. Aiming at intra-layer autonomy, an optimization model is established with the goals of the smallest electricity purchase cost for the ADN, the most profitable purchase and sale of electricity of microgrids and the most profitable and comfortable users. Aiming at inter-layer coordination, a "distribution network-microgrid" electricity price formation method that maximizes renewable energy sharing between microgrids and a "microgrid-user" electricity price formation method that maximizes users' willingness to generate electricity are proposed. In this paper, the modified IEEE 33-bus distribution system is built as case study, which verified the great scheduling capability and performance of the proposed model.

Big data is an important product of the information age. Integrating big data into smart grid applications and correctly grasping the key technologies of big data can effectively promote the sustainable development of power industry and the construction of strong smart grid. As far as modern smart grid is concerned, this is both an opportunity and a challenge. 3D point cloud data processing is the core content of reverse engineering technology. As an important data processing step in the preprocessing stage of 3D point cloud, point cloud registration plays an extremely important role in obtaining the complete 3D coordinates of the measured target surface. However, at present, the registration speed, accuracy and reliability of various registration algorithms still need to be improved. Cloud computing technology integrates several cheap ordinary PCs into a cloud computing cluster, which realizes the safe storage and efficient processing of massive data. Therefore, consider combining cloud computing with data mining algorithm to solve the problem of massive data conversion in smart grid. In this paper, cloud computing technology is introduced into the smart grid condition monitoring field. By introducing distributed file system, improving traditional density clustering algorithm and parallel design, the storage and clustering division of big data in condition monitoring are effectively solved, which provides a feasible method for the application of cloud computing in condition monitoring field.

In the context of "dual carbon" strategy, PV power generation will play a very important role, but the distribution network is increasingly affected by the uncertainty of PV power output. This paper proposes a dual-objective distribution network reactive power optimization method based on the adaptive equalization optimizer (AEO) algorithm, which uses adaptive inertia weighting to improve the convergence speed and accuracy of the algorithm based on the basic equalization optimizer algorithm. The AEO algorithm is established to solve the IEEE 33-node distribution network containing PV access with minimum network loss and minimum voltage deviation as the objective function, and finally, the effectiveness and superiority of the proposed algorithm are verified by simulation analysis.

With the development of social economy and the continuous improvement of people’s living standards, higher requirements are put forward for the operation of distribution network. After a fault occurs in the distribution network, it is necessary to quickly locate the fault location and remove the fault. With the construction and development of distribution network, the penetration rate of distributed generation is getting higher and higher. The output of distributed generation represented by photovoltaic and wind power has brought greater challenges to the reliability of distribution network. Based on this, a power supply restoration strategy of distribution network considering distributed generation is proposed. Based on the power supply restoration model, the flow of power supply restoration scheme is introduced. The feasibility of the proposed method is verified by using the improved genetic algorithm and ieee33 node model.

Compared with typhoon, squall line wind belongs to short-lived small-scale strong wind with narrow influence range, but the wind speed is very large and destructive. According to the demand of transmission line for squall line wind disaster early warning, a new squall line wind disaster early warning method for transmission line is proposed. Firstly, this method combines short-term prediction, mesoscale prediction and real-time observation data to obtain the radar echo in the next 0–2 h. Then, based on the convolution neural network model of lenet structure, the squall line is identified and the formation of squall line wind is judged. The early warning model has the characteristics of active identification and is suitable for the whole transmission line. Finally, through the simulation of the historical data of squall line wind disaster on a transmission line, it is proved that the early warning model has practical value, so as to guide the disaster prevention and mitigation work of the power grid and improve the flexibility of the power grid to deal with extreme weather events.

Power network load forecasting is of great significance to the robust operation of the power grid. Aiming at the problems of large amount of data and low model development efficiency in the process of load forecasting, an improved deep Boltzmann machine power grid load forecasting algorithm was proposed. The algorithm adopted the MapReduce parallel computing method to optimize the iterative process of the deep Boltzmann machine, which reduces the number of data iterations in the training process. And the validity of the algorithm is verified by the power grid load forecasting experiment. The results show that the load prediction results obtained by this algorithm are more accurate, the development efficiency and operation efficiency of the prediction model are higher, and the large-scale power network can be operated faster and more stably, which is of great significance to the operation and dispatch of the power system.

The energy revolution and the digital revolution go hand in hand, and the traditional distribution network is gradually evolving to be dominated by new energy. Therefore, a new type of distribution network digital model operation state prediction system is proposed. Firstly, a five-dimensional digital model of the distribution network is established based on the digital model method, and the realization mechanism and functional scheme of the frequency prediction of the model distribution network are discussed, and the evaluation indicators of the frequency prediction results under different prediction models are used. Then, the super-real-time computing power of the model distribution network is used to quickly simulate the future random scenarios of the distribution network, and the frequency prediction model of the distribution network is trained according to the historical data. Indicators to judge the reliability of predictions. The validity and rationality of the proposed analysis method are verified according to the actual power grid data analysis.

This paper describes a finite element-based method to calculate the core losses for the inductor in power converters operating at high frequency. The nodal flux density is calculated via finite element analysis at the given load condition. Then, the power loss of each element is calculated using the obtained nodal flux densities. Finally, the total loss is the sum of the power losses of all the elements. This approach can predict the flux density more accurately, owing to a detailed model with all the geometric parameters and nonlinear material data to account for the flux leakage effect and localized magnetic saturation effect. Experimental tests have been performed to validate the calculation method.

In accordance with the United Kingdom's goal to reach net zero by 2050, electric vehicles (EVs) play a crucial role in transportation. However, if the electricity used to charge EVs is derived from fossil fuels, this does not necessarily imply a reduction of overall emissions nationally or globally. To achieve optimal EV charging, a deeper comprehension of the unpredictability of on-site renewable energy sources (ORES) energy output is required. In this paper, the predicted renewable energy generated is used as the actual value for the reinforcement learning algorithm simulation environment. Such a model can represent the relationship between the power generation and the wind speed as well as solar irradiation, which are characterized by significant uncertainties. The uncertainty analysis shows that the wind speed at Newcastle upon Tyne can be modelled as a Weibull distribution with parameters A = 19.98 and B = 1.91. As for energy demand, this paper integrates information from an Oslo (Norway) car parking garage-based set of EV charging stations with EVs' demand statistics. The charging habits of EV users range from 800 min to 1,000 min of parking time, and from 5 kWh to 20 kWh in terms of charging energy. The maximum connection frequency for EV charging is 20 min. In addition, this paper develops methods for stochastic EV charging and parking space occupancy employing actual data. Based on the aforesaid renewable energy generation and the EV charging status, it is possible to develop a decision algorithm to optimal renewable energy efficiency.