902 results about "Electrical load" patented technology

A vehicle having a primary mover, a charging system, multiple electric loads, and a controller. The charging system includes a charge storing device and an alternator. The alternator is configured to convert mechanical energy generated by the prime mover into electrical energy to charge the charge storing device. The multiple electrical loads are electrically coupled to the charging system via a power distribution module. The controller is communicably coupled to the charging system and is configured to receive an indication that an electrical output of the charging system is unable to provide sufficient electrical energy to each electrical load in the electrical loads. The controller is also configured to provide a control signal to the power distribution module in response to the indication. The control signal is configured to cause the power distribution module to decouple at least one of the electrical loads from the charging system.

The invention discloses a non-intrusive load decomposition method and system based on an xLSTM-Transform model, and is applied to the technical field of non-intrusive load decomposition. XLSTM is constructed by combining sLSTM (scalar LSTM) and mLSTM (matrix LSTM), so that the processing capability of the model on time sequence data is enhanced; a causal convolution module is added behind an input layer, a sliding window in a fixed direction of the causal convolution module can more accurately extract a local load mode of an electric appliance while ensuring causality, and future information leakage is avoided; a self-attention mechanism in a Transform architecture is introduced to establish an xLSTM-Transform combination model, historical data is adopted to train the model, and finally, the model is used to perform electric appliance load decomposition. According to the method, a multi-scale electrical load mode can be effectively processed, and the problem of long-term dependence faced by a traditional time sequence model is solved.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention discloses a power grid dispatching method based on emergencies and a graph structure. The method comprises the following steps: establishing a baseline load prediction model by collecting historical electrical load data; recognizing emergencies influencing the power utilization demand in real time; and in combination with real-time power consumption behavior data, constructing a time-varying power consumption disturbance factor, and dynamically correcting load prediction. Geographic distribution and historical power generation data of new energy power stations such as wind power and photovoltaic power stations are acquired, a power generation graph structure model is constructed, the power generation amount is independently predicted by adopting a long-short-term memory network, and a prediction result is corrected in combination with a graph structure. And based on the corrected load and power generation prediction, considering the capacity and the energy state of the energy storage system, optimizing multi-time-sequence-scale energy scheduling, and formulating an energy storage charging and discharging and generator set operation scheme. According to the method, the dynamic influence of emergencies and the spatial relevance of new energy are fused, the load prediction precision and the new energy power generation prediction accuracy are remarkably improved, more reasonable power grid dispatching is achieved, and the safety and stability of a power grid are enhanced.

The invention discloses a household electrical load optimization scheduling method and system based on an MAPPO algorithm. The method comprises the following steps: (1) collecting multi-source heterogeneous data from a user side and an external environment and carrying out preprocessing; (2) estimating the future equipment use probability of the user by collecting historical equipment use data of the user, and outputting an equipment behavior prediction vector; (3) acquiring real-time electricity price information, performing short-term electricity price trend prediction by using the time sequence prediction model based on a historical electricity price sequence, and outputting an electricity price prediction sequence; (4) generating a scheduling strategy by the MAPPO network based on the equipment behavior prediction vector and the electricity price prediction sequence, and outputting a corresponding scheduling result; and (5) sending a scheduling result to the home gateway, controlling the equipment, collecting an execution feedback result in real time, updating internal parameters of the MAPPO network, and realizing strategy iteration and adaptive adjustment of the MAPPO network.

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

The invention relates to a data center equipment type selection method based on electrical and heating ventilation load linkage, and belongs to the technical field of data center infrastructure design. The model selection method comprises the steps of setting basic data, obtaining the number of machine rooms and the number of IT devices in a single machine room, obtaining the total power of the IT devices, setting an energy efficiency index set value, obtaining the project power consumption scale, obtaining the total cooling load, selecting models of air conditioners at the tail ends of the machine rooms and substations, selecting models of water chilling units, selecting models of heat exchangers, selecting models of cooling water pumps and chilled water pumps and selecting models of cooling towers. And obtaining the total power of the air conditioner, obtaining an energy efficiency index accounting value, and carrying out PUE verification. According to the method, through closed-loop iteration of electrical load-heating and ventilation load-PUE, model selection and quantity selection of all key equipment are completed at a time; professional splitting, repeated adjustment and modification and capacity redundancy caused by traditional serial design are avoided, the design period is remarkably shortened, and initial investment is reduced.

A facility for predicting on site electric power requirements for maintaining a fully electric fleet of vehicles in a mining operation to ensure sustainable electric operations, comprising a server comprising communications module configured to receive data and to transmit load management instructions; plurality of electrical charging stations; plurality of electric vehicles configured to be recharged by a selected electrical charging station; wherein the server comprises processor coupled with non-volatile memory, the non-volatile memory storing processing instructions that, when executed by the processor, causes the processor to simulate a total electrical power demand of the mine site due to the plurality of cabled mine equipment and the plurality of electric vehicles for each electric vehicle and each charging station within the facility over a prescribed period thereby to simulate and determine an energy storage capacity requirement of the charging stations of the facility through the prescribed period; thereby to forecast the electrical load management needs of the facility through the prescribed period.

The invention discloses an electrical system load prediction method based on a data center, and the method comprises the steps: obtaining electrical parameters, environmental parameters and historical operation data of each subsystem through a data collection module, carrying out the normalization and time sequence reconstruction of the collected data, and forming standardized time sequence data; a combined neural network model comprising a convolutional neural network (CNN), an improved long-short term memory (LSTM) network and an attention mechanism is utilized to extract local fluctuation, mutation, frequency and statistical characteristics, and capture of data long-term dependence and key time sequence information is realized, so that an electrical load prediction value is output, and finally real-time regulation and control of a data center electrical system are realized. The method can effectively improve the prediction precision and response speed, and provides reliable technical support for the full life cycle management of the data center.

The invention discloses a microgrid electrical load prediction method and system, and the method comprises the steps: building a power grid load prediction model based on an energy consumption data classification record, and generating a real-time load prediction result; based on the real-time load prediction result, identifying a power use mode of the user, and generating a user behavior analysis result; optimizing the power grid load prediction model based on the user behavior analysis result, and generating an energy influence evaluation result; generating a node stability evaluation result by considering node positions and connectivity through an energy influence evaluation result; and based on a node stability evaluation result, adjusting abnormal load early warning parameters by considering node stability, predicting a power grid abnormal load in combination with the optimized power grid load prediction model, and generating power grid load prediction data. The monitoring capability and response speed of the power system are improved by analyzing the user power use data in the micro-grid in real time, and the accuracy of the prediction model is enhanced by combining classified processing of the user power data and analysis of the use mode.

The invention discloses a source network load storage cooperative control system, and the system comprises the following modules: an uploading module which is used for uploading the historical power information of a local power generation end, a power distribution end, a power utilization end, and an energy storage end; a data analysis module; the calculation module analyzes and calculates the obtained data to find the optimal working time period and load capacity of each type of equipment; the virtual power plant system establishes and stores a virtual power plant model; the electrical load management platform comprises a user file management module, a response protocol management module, a load dynamic monitoring and sensing module, a multi-scale load prediction module, an adjustable resource potential evaluation module, a demand response strategy engine module, an optimization scheduling and control instruction generation module, a user incentive settlement module and an energy efficiency and carbon efficiency analysis module. A storage management platform; and the regulation and control device stores a virtual power plant optimization regulation and control model. According to the method, the cross-regional and multi-time-scale dynamic adjustment requirement can be met conveniently, comprehensive, considerable, measurable and controllable power balance maintenance is achieved, and flexible and adjustable resources are reasonably applied.

The invention discloses an automobile generator self-adaptive excitation control method based on load prediction, and relates to the technical field of automobile electrical control, and the method comprises the following steps: injecting a micro-amplitude identification pulse into a whole vehicle electrical system, capturing the dynamic response of an electrical load, building a load transition curvature map, marking a high-risk time window, and generating a buffer time slot; and in the buffer time slot, constructing a prediction model fusing time domain extrapolation calculation and frequency domain envelope regression, and outputting collaborative confidence with credibility. A load transition curvature map is constructed to identify a high-risk window, a time domain and a frequency domain are fused to predict output confidence, excitation current is guided to form an anti-shake track, and a dynamic adjustment path is generated by combining start-stop broadening of high-power equipment and voltage second-order derivative identification. And finally, a slew rate self-adaptive strategy is introduced at a chip end to reduce peak impact, power supply and chip cooperative protection is realized, and system robustness and intelligent driving electrical safety are improved.

The invention relates to the technical field of power dispatching, in particular to a photovoltaic-energy storage rolling dispatching method and system based on ultra-short-term power prediction, and the method comprises the steps: collecting the multi-source heterogeneous data of an electrical load, a cloud layer change image and photovoltaic power generation power; constructing a cloud layer shielding influence model through a neural network, and obtaining a mapping relation among shielding time, intensity and generated power; using the historical load data and the cloud layer image to predict the electrical load change and the photovoltaic power respectively; integrating various data, calculating a power difference value, and balancing the difference through an energy storage system; constructing a two-way graph model of photovoltaic and energy storage dynamic change, and adjusting dynamic connection and structures between nodes; rolling scheduling optimization is adopted, a reasonable energy storage charging and discharging strategy is formulated, and dynamic balance and efficient operation of the photovoltaic-energy storage system are achieved.

The invention provides a new energy station load stability control method and system based on mutual conversion of electricity and hydrogen energy. Obtaining a generated power prediction curve of the new energy power generation facility, and determining a power difference value between the new energy power generation load and the power grid stability demand based on the prediction curve; a surplus hydrogen production electricity consumption instruction is issued, the operation number and the hydrogen production rate of an electric hydrogen production module are dispatched, and surplus electric energy is converted into hydrogen energy to be temporarily stored; receiving a real-time charging demand signal, dynamically issuing a charging permission load instruction based on the current new energy power generation load and the hydrogen gas turbine power generation load, and scheduling the power output of each charging pile; and according to the real-time states of the new energy power generation load, the electrical hydrogen production electrical load, the charging pile electrical load and the hydrogen gas turbine power generation load, starting, stopping and output power of the hydrogen gas turbine are adjusted, and reverse compensation of the new energy fluctuation power is achieved. New energy power generation fluctuation can be effectively stabilized, abandoned wind and abandoned light are reduced, and stable power supply of the distributed charging pile is achieved.

Apparatus, systems, and methods for charging electric vehicles (EVs) at an EV charging site having a plurality of EV chargers, controlling by a management server the charging performed by a plurality of EV chargers at a charging site when connectivity quality between the management server and any of the EV chargers may be unstable, and electrical load reserve for use by an EV charging system of a site.

The invention discloses an electric power allocation method and device based on an electric vehicle electrical load and electronic equipment, and relates to the technical field of electric vehicle charging management and electric power system scheduling or other related fields, the method comprises the following steps: collecting travel behavior data of N electric vehicle users, and obtaining road network topology data, N being a specified numerical value; simulating road network driving conditions of the N electric vehicles in the target time period based on the travel behavior data and the road network topology data to obtain simulated travel information; determining charging demand information of each electric vehicle based on the simulated travel information; and summarizing the charging demand information of all the electric vehicles to obtain the spatial and temporal distribution demand of the electrical load, and generating an electric power allocation strategy based on the spatial and temporal distribution demand of the electrical load. The technical problem that the time-space allocation of electric power and electric energy is difficult to match the power demand of a user due to low accuracy of charging load analysis of the electric vehicle in the related technology is solved.

The invention discloses an electrothermal hydrogen comprehensive energy multi-target collaborative planning operation method, which comprises the following steps: obtaining a photovoltaic and electrical load typical day by using a contour coefficient method and an ordered clustering algorithm, generating a wind power and thermal load typical day by using an ISODATA algorithm improved by a local density formula, and constructing an electrolytic cell and hydrogen fuel cell equipment refined dynamic efficiency model; the method comprises the following steps: constructing a multi-target collaborative bilevel planning operation model, solving the multi-target collaborative bilevel planning operation model by adopting an NSGA-II algorithm and a Gurobi solver to obtain a target capacity Pareto frontier solution set, selecting an optimal solution by utilizing a TOPSIS method, and outputting a capacity configuration scheme. According to the method, the economical efficiency, the low-carbon property and the hydrogen production reliability of the system are considered, meanwhile, the hydrogen energy utilization rate is increased, more accurate optimal configuration of the capacity of the wind-solar-hydrogen energy equipment is achieved, and reference is provided for actual planning operation.

The invention provides a comprehensive energy self-adaptive scheduling method based on meteorological prediction, and aims to solve the problems that an existing energy system lags in response to meteorological disturbance and is insufficient in load regulation precision, and the comprehensive energy self-adaptive scheduling method comprises the following steps: fusing second-level meteorological disturbance data, electrical load power data, energy storage device charge state data and power grid boundary power flow data; constructing a disturbance response feature set, and establishing a time correlation mapping structure between a disturbance variable and a load variable; according to the method, a disturbance trend is predicted based on a mapping structure, an index set of disturbance direction, intensity and sensitive sections is formed, a dynamic regulation capability vector is generated in combination with a current load state, a scheduling optimization model meeting multiple operation constraints is constructed on the basis, a cooperative control strategy set is output, and the dynamic regulation capability vector is obtained. Finally, the control strategy is converted into a control instruction, the control instruction is accurately acted on a power generation device, an energy storage device and a load device, second-level dynamic self-adaptive scheduling of the comprehensive energy system on meteorological disturbance is achieved, and the system response capacity, stability and energy utilization efficiency are improved.

The invention relates to the technical field of generation of power consumer electricity selling schemes, provides an electricity consumer electricity consumption behavior clustering-based electricity selling scheme generation method and system, and solves the problem that it is difficult to accurately match user demands with different electricity consumption habits. Historical electrical load data, responsivity data after electricity price adjustment and preference data during daily electricity consumption peak and valley are collected, time sequence analysis is performed on the historical electrical load data to obtain future electricity consumption behavior trend characteristics of each user, vector coding is performed on the trend characteristics, the responsivity data and the preference data, and the power consumption behavior trend characteristics of each user are obtained. The method comprises the steps of obtaining user electricity consumption feature vectors, grouping the user electricity consumption feature vectors to obtain a user clustering result, and finally optimizing electricity selling scheme parameters in combination with power grid peak and valley power supply cost and user electricity consumption satisfaction to generate differentiated electricity selling schemes adaptive to all groups, and through data collection, LSTM analysis, vector coding, clustering grouping, and combination of cost and user satisfaction, the electricity selling schemes can be obtained. And optimizing the parameters by using a gradient boosting tree, and generating a differentiated electricity selling scheme.

The invention discloses a wind-solar-storage integrated direct-current micro-grid device and a control method, and relates to the technical field of rural distributed energy. The device comprises a perovskite photovoltaic module array, a vertical axis fan set, a lithium iron phosphate energy storage module, a direct-current power distribution unit, an intelligent control module and a household direct-current terminal, each energy unit and a load are directly connected through a direct current bus, AC / DC conversion loss is reduced, the intelligent control module dynamically adjusts a wind-solar-storage cooperative operation strategy based on illumination, wind speed and electrical load data, and the functions of five-hour stable off-grid power supply and peak-valley arbitrage are achieved. The method adapts to a rural farm house roof resource concentration scene, solves the problems of high loss and poor off-grid stability of a traditional alternating current micro-grid, remarkably improves the energy utilization efficiency, reduces the electricity utilization cost of farmers, and can be widely applied to energy transformation projects in rural areas such as coastal areas.

Disclosed are methods and systems for suppressing extreme voltage surges due to transient electromagnetic energy disturbance such as a detonation of a nuclear weapon at high altitude generating an EMP (HEMP) comprising E1, E2, and E3 component pulses. Surge protection assemblies form a surge protection device (SPD) mounted directly on AC and DC power line conductors with embedded overcurrent protection. The inline mounted SPD eliminates connecting wire leads, propagation time delay response, and voltage drops due to wire inductance, providing accurate clamping level responses. The protecting assemblies are selected and coordinated to achieve a predefined response to react at protection threshold voltage levels and with current capacity to mitigate complex components of HEMP. The tertiary protection assembly disconnects an electrical load after a prolonged overvoltage is present associated with a Geomagnetic disturbance and reconnects the electrical load when an overvoltage condition is not present.

The invention discloses an energy control method and device for a flying vehicle, electronic equipment and a readable storage medium. A solar cell layer, a heat insulation layer and a thermoelectric power generation layer are sequentially integrated on the surface of the flying vehicle from top to bottom, and the method comprises the steps of obtaining first electric energy converted by the solar cell layer in response to the fact that the flying vehicle is located in a light environment, and obtaining second electric energy converted by the heat insulation layer in response to the fact that the temperature difference between the flying vehicle and the external environment is larger than a first preset temperature difference threshold value; acquiring second electric energy converted by the thermoelectric power generation layer; performing voltage conversion on the first electric energy to obtain first target electric energy, and performing voltage conversion on the second electric energy to obtain second target electric energy; charging a battery of the flying vehicle based on the first target electric energy and the second target electric energy; the electrical energy stored in the battery is distributed to the electrical load based on an energy consumption demand of the electrical load of the flying vehicle. The technical problem that the energy source of the flying vehicle is single is solved.

A packet energy transfer (PET) receiver comprising receiver front-end circuitry including an input, an output, and at least one switch connected at the output. There is receiver output control and conditioning circuity including an input and an output to be connected to an electrical load. There is a synchronizer circuit to close the switch to allow power to flow into the receiver output control and conditioning circuitry during each of the first plurality of transfer periods of the transmitter and to open the switch to prevent power to flow into the receiver output circuitry during a first plurality of sample periods. There is a load controller that disables the synchronizer circuit and operates the switch to close it and allow power to flow into the receiver output control and conditioning circuitry during subsequent transfer periods and to open the switch to prevent power to flow during a subsequent sample periods.

The invention discloses an electrical load analysis method and system based on multi-source data fusion. The method comprises the steps of obtaining load data and a meteorological data sequence of a target area; carrying out high-fidelity interpolation on the load missing data by adopting a strategy of combining form matching and meteorological verification; adaptively splitting the complete load sequence into a plurality of internally stable load data subsequences based on a preset change rate threshold; calculating a time weighted average value of the meteorological data corresponding to each sub-sequence as target meteorological data; performing two-stage clustering according to the data quantity and the target meteorological data to form a homogeneous load sub-sequence set; a representative sub-sequence with the meteorological closest to the central value is extracted from each set and input into the load analysis model to obtain a reference result; and efficiently deriving the reference result to all other subsequences in the set according to the meteorological difference by using a preset load correction coefficient. According to the method, on the premise of ensuring the analysis precision, the processing efficiency and the result consistency of the mass load data are remarkably improved.

Aspects of the present disclosure are directed to systems, devices, methods, and computer-readable storage medium for adaptive / dynamic thermal management of an electrical power system having variable electric loads, and components thereof. Thermal management may be driven at least partially by predicted / modeled thermal performance of the component to be managed, which may be calculated or modified using direct or indirect measurements. Embodiments may include adaptive thermal management of at least one of an energy storage system and an electric energy supply. Applications of this disclosure may include adaptive thermal management method for electric vehicles and non-mobility applications, particularly having variable electrical loads, which may impact performance or life of the application.

A hybrid energy scheduling method, an electronic device, and a computer program product. The method comprises: acquiring a new energy generation power hyperparameter and an electrical load power hyperparameter; respectively dividing a new energy generation power prediction layer and an electrical load power prediction layer into L-dimensional clustering subspaces, and respectively performing iterative clustering on the basis of the new energy generation power hyperparameter and the electrical load power hyperparameter to acquire a new energy generation power sequence set and an electrical load power sequence set which both have a sequence length of L; acquiring a joint probability scenario set on the basis of the new energy generation power sequence set and the electrical load power sequence set; and optimally solving a preset electricity consumption indicator on the basis of the joint probability scenario set, so as to acquire a scheduling strategy for hybrid energy scheduling.

The invention relates to the technical field of virtual power plant operation control, in particular to a virtual power plant accurate response method considering source-load double-side uncertainty, and the method comprises the steps: obtaining a source-load double-side accurate response result based on the discrete degree of the power mismatch degree of all response intervals and the difference of the power mismatch degree between any response interval and the adjacent next response interval; determining a supply and demand balance degree; determining a load schedulable index based on the similarity between the electrical load and the predicted value of the electrical load at all moments in each response interval and the dispersion degree of the predicted value of the electrical load at all moments, and determining the response intensity of the virtual power plant in each response interval in combination with the supply and demand balance degree; and judging whether the scheduling plan of the virtual power plant needs to be adjusted or not based on the response intensity. According to the invention, the uncertainty between the power generation side and the load side is considered cooperatively, so that the reliability of the virtual power plant to deal with the load fluctuation is improved.

A vacuum cleaner including a cleaner head defining a suction chamber and having an agitator arranged to be rotated by an agitator motor, a dirt separator, a vacuum motor arranged to draw air into the suction chamber and then into the dirt separator, and a controller. The controller is configured to monitor the electrical load of the agitator motor, compare the magnitude of the electrical load to a threshold, and selectively adjust the electrical power delivered to the vacuum motor. The controller is configured either to increase the electrical power delivered to the vacuum motor to a predetermined upper power level if the electrical load is greater than the threshold, or to decrease the electrical power delivered to the vacuum motor to a predetermined lower power level if the electrical load is smaller than the threshold.

This invention belongs to the field of biomass subcritical fertilizer production, specifically relating to a mobile photovoltaic-powered subcritical fertilizer production system and its product application aimed at reducing nitrogen and phosphorus emissions, increasing efficiency, and resource-based disposal of high-moisture waste. It includes a solar preheating system, a photovoltaic power supply system, an auxiliary heating system, a hydrothermal reaction system, and a post-treatment system. The photovoltaic power supply system and auxiliary heating system are connected to the electrical load of the hydrothermal reaction system; the hydrothermal reaction system is connected to both the solar preheating system and the post-treatment system. This invention utilizes electrodialysis technology to desalinate the hydrothermal charcoal before returning it to the field. Through electrodialysis, the desalination rate of the charcoal reaches 45%-90%, and the conductivity of the desalinated charcoal is 1.40-6.75 mS / cm. The desalinated charcoal significantly increases crop yield; chlorophyll content increases by 25.5-67.5%, fresh weight increases by 18.5%-175%, and lettuce root length increases by 37.0%-98.2%. The high degree of desalination avoids the negative effects of salt stress on lettuce roots.

The invention provides an electric vehicle ordered charging method and system based on an improved whale optimization algorithm, and belongs to the technical field of electric vehicle ordered charging, and the method comprises the steps: constructing a fuzzy clustering model based on threshold value optimization according to the electrical load prediction data of a micro energy grid user side, an improved particle swarm algorithm is adopted to carry out optimal division of the peak and valley periods of the micro-energy network on the fuzzy clustering model based on threshold optimization; according to historical electric vehicle charging load data, a charging load prediction method based on variable-variable variational mode decomposition and a long-short-term memory neural network is adopted; according to the electrical load prediction data, the photovoltaic output prediction data, the time-of-use electricity price and the electric vehicle charging load prediction data of the micro energy grid user side, the double-layer multi-target optimization model is solved by adopting an improved whale optimization algorithm based on a hybrid reverse learning strategy, and an optimal ordered charging strategy of the electric vehicle is obtained. According to the method, the electric vehicle can be charged more orderly, and the influence on a power grid is smaller.