3704 results about "Charging station" patented technology

The invention discloses a power distribution management system of an intelligent charging pile, and belongs to the technical field of electric vehicle charging facilities and intelligent power grids. The load prediction module carries out space-time alignment fusion on historical data and real-time monitoring data to generate a power distribution demand prediction value, and the edge calculation controller executes a model prediction control algorithm based on multi-source data to generate a real-time control strategy containing relay time sequence parameters and a capacitance compensation scheme. And the dynamic power distribution adjustment module executes strategy parameters through the solid-state relay array and the parallel compensation capacitor bank. Self-adaptive adjustment of the power distribution network is achieved through a real-time monitoring-prediction-optimization closed-loop control mechanism, the harmonic content of the power grid is effectively reduced, the energy distribution efficiency of the charging pile group is improved, and the method is suitable for intelligent electric energy management of public charging stations and other scenes.

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

An apparatus and method relate to analyzing metrics relating to EV charging points and facilitating selection of an EV charge point based on metrics associated with the EV charge points. Methods may include: determining locations of one or more electric vehicle charging stations; determining a total power available at the one or more electric vehicle charging stations; determining, for the one or more electric vehicle charging stations, if dynamic load balancing is used; determining occupancy of charge points at each of the one or more electric vehicle charging stations; calculating an available power at a charge point of the one or more electric vehicle charging stations based on the total power available and the occupancy of charge points for the one or more electric vehicle charging stations; and calculating an estimated time to charge a vehicle at the one or more electric vehicle charging stations based on the available power.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.

The invention relates to the technical field of vehicle charging station operation and maintenance management, and particularly discloses a new energy vehicle charging station operation and maintenance management system and method based on the Internet of Things, and the method comprises the steps: collecting high-frequency harmonic characteristic data in a charging process in real time, and generating harmonic-impedance spectrum combined monitoring data; extracting dynamic attenuation gradient data of the line characteristic impedance; when the dynamic attenuation gradient data is lower than a preset critical safety threshold value, triggering a charging mode switching instruction; the charging piles are controlled to be switched into a charging mode, the phase difference parameters of the multiple charging piles are dynamically optimized, and a phase difference dynamic adjustment instruction set is generated; and finally, self-adaptive correction of the impedance compensation amount is carried out through the MEC equipment, and by self-adaptive correction of the impedance compensation amount, the system can maintain that the overall impedance is constantly greater than a critical safety threshold value, and power loss and equipment damage caused by impedance mismatch are avoided. The real-time feedback mechanism is helpful for improving the energy efficiency of the system, reducing the operation cost and ensuring the long-term reliability of the charging infrastructure.

The invention discloses a wind-solar complementary energy storage direct current charging method and system based on liquid cooling heat dissipation, and relates to the technical field of energy storage charging. Comprising the following steps: S1, collecting wind-solar complementary energy storage data in real time, and carrying out data preprocessing; s2, converting wind-solar power generation into direct current, judging the flow direction of wind-solar power generation energy, and performing corresponding wind-solar power generation energy transmission power supply; s3, judging the limit of the output power of the charging pile, and controlling and adjusting the output of the charging pile in stages; s4, quantifying the liquid cooling flow required by heat dissipation, and performing heat source heat dissipation on the energy storage system and the charging pile; and S5, carrying out coordinated regulation and control, strategy optimization and multi-dimensional visualization on the flow direction of wind and light power generation energy, charging pile output and heat source heat dissipation. The problems that an existing charging station is difficult to deploy in remote and power transmission difficult areas, high-power charging heat dissipation and energy efficiency bottlenecks are prominent, and the efficiency of traditional air cooling and alternating current conversion is low, so that efficient, safe and green energy supplementation is difficult to achieve are solved.

The invention relates to the technical field of operation and maintenance management, and discloses a cloud operation and maintenance management method, system and equipment for a charging station, and the method comprises the steps: monitoring the WiFi signal strength, 5G network quality and wired network delay parameters of each floor of a stereo garage in real time, and building a network environment database; when the charging pile detects that the SOC change amplitude exceeds a set threshold value, an optimal target transmission path set is screened out; carrying out differential bit coding on the collected charging data to generate a charging data frame, and sending the charging data frame to a cloud server in a multi-path concurrent mode; the cloud server receives and gathers the charging data frames from different floors of the stereo garage, and analyzes the charging load distribution condition of the whole garage; and calculating a power distribution scheme of the charging pile of each floor based on the whole garage charging load distribution condition, and issuing a scheduling instruction to the charging pile of the corresponding floor, thereby improving the intelligent operation and maintenance management level and resource utilization efficiency of the stereo garage charging station.

A modular, multi-directional, smart electric vehicle (EV) charging station. In an embodiment, the EV charging station has a field-serviceable, replaceable modular construction, a fully multi-directional power routing architecture, buffered EV-to-grid and EV-to-EV charging, multi-EV charging capability, and the ability to integrate multiple modular charging stations into a networked local and / or wide-area charging system.

A multipurpose lounge chair device is provided. The device is comprised of a foldable and reclining frame that supports a seating body fabricated from cooling bungee weave. The device comprises a shade canopy made from UV-reflective fabric, which is pivotally mounted to the frame via adjustable hinges or articulated brackets to maximize solar exposure and shade coverage. At least one flexible monocrystalline solar panel is embedded in the canopy and configured to charge an onboard lithium-based battery. The battery powers at least one fan and at least one Bluetooth speaker, both of which may be controlled by a wired or wireless remote. A swing-out charging station table is attached to the frame by a pivot mechanism and includes multiple charging ports, such as USB or wireless charging pads, along with a non-slip surface for item placement.

The invention discloses a Nash negotiation-based energy management scheduling method for an optical storage and charging integrated energy station, and the method comprises the steps: constructing a multi-target optimization model which comprehensively considers the interest demands of an electric vehicle user, a charging station operator and a power grid operator; determining participants of Nash negotiation as a charging station operator, a power grid operator and an electric vehicle user, and determining a strategy space of each participant; on the basis of the Nash negotiation theory, all the benefit subjects are regarded as negotiation participants, utility functions of all the benefit subjects are constructed, and negotiation reference points of all the benefit subjects are determined; solving a Nash equilibrium solution of the multi-objective optimization model, and determining an optimal energy management scheduling strategy through iterative negotiation; and according to the optimal energy management scheduling strategy, real-time control and scheduling are carried out on the optical storage and charging integrated energy station. According to the invention, the flexibility, adaptability and economy of an energy scheduling scheme can be improved, and multi-party win-win and stable and efficient operation of the system are realized.

The invention relates to the technical field of charging station management, and particularly discloses an intelligent cooperative charging station management system and method for an electric automobile, and the system comprises the steps: integrating real-time communication and honeycomb partition management through a cooperative decision module, constructing an efficient and state-aware charging network, and carrying out the state-aware management of the charging network; the decision model can accurately adapt to the actual operation state of the target charging station; the cooperative scheduling module optimizes resource allocation by using a state matrix and an aggregation space, so that the utilization efficiency of charging station resources is remarkably improved; and the intelligent charging scheduling decision module combines reinforcement learning rapid iteration and historical data driven prior knowledge and demand prediction, so that the generation of an optimal scheduling strategy is accelerated, the excessive dependence of decision on real-time data is reduced, and the response capability and operation stability of the system for responding to dynamic demands and complex scenes are greatly enhanced, and thus, the system is more intelligent. And finally, efficient, intelligent and networked collaborative management of the electric vehicle charging service is realized.

The invention discloses an electric vehicle charging power aggregation distributed regulation and control method, and relates to the technical field of AC charging pile charging power regulation and control, and the method specifically comprises the following steps: carrying out the cleaning and stabilization processing of power load and new energy data, employing an ARIMA model to predict the load, and employing an LSTM-Transform model to predict the new energy generation power; the upper layer aggregates the total state of the charging station, including the electricity price, the load, the total vehicle energy, the total docking power of the lower-layer single vehicle model and the battery demand; based on an MCTS enhanced DDPG algorithm, the total charging power is optimized through cost discount rewards and energy penalty; an improved ADMM algorithm distributes total power to each pile, and battery stress and heat loss are minimized; and comparing the actual battery energy with the virtual battery energy, and updating the data and re-deciding when the energy is inconsistent. According to the invention, three industry bottlenecks of poor real-time response of electric vehicle charging, slow large-scale scheduling and difficulty in consideration of economy and battery health are overcome, and power grid dynamic response acceleration, decision-making efficiency jump and comprehensive benefit breakthrough are realized.

The invention relates to a power distribution network two-stage recovery method based on an electric vehicle and mobile energy storage, and belongs to the technical field of disaster prevention and reduction and emergency recovery of a power system. According to the technical scheme, the method is divided into two stages of recovery: stage 1 pre-disaster pre-configuration and stage 2 post-disaster collaborative recovery; establishing a power distribution network line vulnerability evaluation model, and generating a fault scene in extreme weather based on a wind speed-fault probability curve; constructing a charging station and maintenance station pre-configuration optimization model, and determining the optimal site selection in the traffic network by adopting integer nonlinear programming; establishing a post-disaster recovery mixed integer programming model, coordinating and dispatching the electric vehicle, the mobile energy storage system and the maintenance team, and realizing collaborative optimization of load recovery and line repair; and designing a traffic network and power distribution network coupling mechanism, quantifying a system toughness index and optimizing a resource scheduling strategy. Through multi-resource cooperative scheduling, the post-disaster recovery capability of the power distribution network is remarkably improved, and an innovative solution is provided for improving the toughness of the power distribution network in extreme weather.

The invention relates to the field of new energy charging, in particular to a main circuit topology based on an integrated over-charging device for light storage and charging and a control method. The system is composed of the following parts: an ACDC module, a charging DCDC module, a photovoltaic DCDC module, a DCAC module and a battery, wherein the battery is used for storing redundant electric quantity and discharging the electric quantity to a charging pile for use; the invention provides a main circuit topology based on direct-current coupling, a very simple network architecture and an AI intelligent algorithm are fused to greatly expand the application scene of optical storage and charging in the overcharging field, meanwhile, a dynamic intelligent algorithm is combined, the dependence of an overcharging station on a power grid is reduced, and through an economic operation strategy, the system is more reliable and reliable. Efficient collaboration and accurate distribution of energy of all links of light, storage, charging and utilization are achieved, and the operation capacity and economical efficiency of a charging station are improved.

The invention discloses a response control method for a charging station participating in a low-voltage treatment scene, and the method comprises the following steps: S1, collecting the voltage data of a charging pile, calculating a deviation, and judging a low-voltage grade; s2, when the deviation is in a light interval, outputting reactive power support voltage in proportion; s3, when the deviation exceeds a heavy threshold value, reducing active power and starting V2G feedback; s4, constructing a graph structure model, and extracting inter-node power coupling and user behavior factors; s5, inputting the model into an improved prediction controller, predicting a future voltage state, and optimizing and outputting a current period power control value; s6, power adjustment is executed, and feedback power is slowly output in the V2G stage; and S7, comparing the control deviation with the behavior factor change, updating the node control priority, and feeding back for rolling optimization of the next period. According to the invention, while the charging regulation and control precision is improved, the user experience and the voltage stability are effectively considered, and the method is suitable for various low-voltage treatment scenes.

The invention discloses a multi-objective collaborative optimization method for a photovoltaic-energy storage-charging micro-grid. The method comprises the following steps: configuring a photovoltaic-energy storage-charging facility micro-grid system; evaluating the autonomous operation capability of the micro-grid through the dynamic internal energy autonomy index; a multi-objective optimization model is established, and the new energy consumption rate, the operation cost and the system autonomy are collaboratively optimized; an improved decomposition type multi-objective evolutionary algorithm is adopted to solve a Pareto optimal solution set; dynamically adjusting a system operation strategy through the energy management system; according to the method, a probabilistic IEA index is provided, the autonomy risk of the micro-grid is quantified, and energy storage optimization configuration is guided; a self-adaptive weight MOEA / D algorithm is designed, and multi-target efficient collaborative optimization is achieved; and a light-storage-charging multi-target cooperative control strategy is developed, and the economical efficiency and reliability of the system are improved. The invention provides a systematic solution for autonomous operation of the renewable energy microgrid, and is suitable for scenes such as intelligent charging stations, industrial parks and the like.

The invention belongs to the technical field of new energy charging, and particularly relates to a light storage and charging integrated new energy charging station which comprises a photovoltaic power generation module, an energy storage module, a charging module and an intelligent control unit. The photovoltaic power generation module predicts power generation power through an LSTM neural network in combination with meteorological data, and dynamically adjusts a prediction time window based on a weather type; the energy storage module is directly connected with the photovoltaic direct current side to improve energy efficiency; the charging module obtains the SOC / SOH state of the vehicle and the user demand through the data acquisition unit, and generates a priority coefficient by using a scoring model. The system dynamically distributes photovoltaic power, energy storage power and power grid power through a demand grading module, and when it is detected that the photovoltaic output deviation exceeds a threshold value or the energy storage SOC is insufficient, a power redistribution and compensation mechanism is automatically triggered. According to the invention, efficient utilization of renewable energy sources, intelligent scheduling of an energy storage system and differentiated supply of charging services are realized, and the problems of low photovoltaic consumption rate and response lag of a traditional charging station are solved.

This disclosure provides an improved universal interchangeable battery for electric vehicles forming part of a battery swapping ecosystem that includes electric vehicles, swappable batteries, a charging / swap station, a battery adaptor for reliable charging and communication, and a mobile application for managing battery swapping within a fleet and a network of charging stations, enabling efficient and user-friendly battery exchanges.

The invention belongs to the technical field of electric vehicle charging, and discloses a highway charging station intelligent guiding method based on multi-agent reinforcement learning, and the method comprises the steps: S1, collecting the data of a highway network, the geographic position and capacity information of a charging station, and the dynamic information of a vehicle from real order data; s2, constructing a scheduling system model, wherein the scheduling system model comprises an environment modeling and structure sensing module, a multi-agent decision module and a joint scheduling module; and S3, inputting the information acquired in the step S1 into a scheduling system model to generate a specific charging distribution scheme. The method has the following advantages that the charging congestion problem in the peak period of holidays and festivals is effectively relieved, and the average waiting time of vehicles is remarkably shortened; the overall resource utilization rate of the site is improved, and load balancing in a global range is realized; the adaptability of the scheduling strategy to the dynamic change of traffic flow is enhanced, and the charging completion rate and the system throughput are improved; and the method has high engineering feasibility and popularization price.

The invention relates to an intelligent power grid cooperative scheduling method for automobile access, and relates to the field of electric automobile charging scheduling and intelligent power grid optimization. The method comprises the following steps: acquiring multi-source real-time data of an electric vehicle, a power grid and a charging station, constructing a collaborative scheduling graph structure, and performing multi-target optimization to generate an initial charging guide strategy and a charging station resource allocation scheme; then, reinforcement learning iteratively optimizes the guide strategy to dynamically adapt to environmental changes; further optimizing charging time and power parameters through a dynamic adaptive optimization algorithm, and realizing charging station congestion early warning and selection suggestions in combination with a probability prediction algorithm; a charging station power distribution strategy is optimized based on early warning information, fine optimization is carried out by adopting a genetic algorithm, and a scheduling scheme is evaluated and adjusted in real time through a feedback control algorithm. According to the invention, dynamic, refined and global optimization of electric vehicle charging scheduling is realized, the stability of a power grid, the operation efficiency of a charging station and the charging experience of a user are improved, and new energy consumption is promoted.

The invention discloses a bimodal adaptive immersion phase change charging station thermal management system, and relates to the technical field of charging station thermal management, and the system comprises a circulation management module which carries out the circulation mapping and optimization operation of a phase change temperature result, and generates a circulation scheme; the immersion cooling module is used for intelligently regulating and controlling the nanometer phase change slurry to flow in an immersion cooling tank in a self-adaptive manner by utilizing a circulation scheme, and dynamically switching between a single-phase liquid cooling mode and a phase change boiling mode to generate heat absorption data; the heat exchange module is used for inputting the heat absorption data into a plate heat exchanger for heat exchange to generate cooling liquid temperature data, and carrying out heat dissipation and waste heat recovery operation of a dry cooler to generate backflow heat data; feature extraction and multi-dimensional nonlinear coupling analysis are carried out on the initial working condition data of the charging station, and iterative optimization is carried out in combination with a pre-training model, so that high-precision prediction of dynamic and multi-modal temperature changes is realized, and a reliable basis is provided for circulation management and cooling mode selection.

The invention relates to the technical field of charging station regulation and control, and discloses a charging station regulation and control method and system considering ordered charging and demand response. The method comprises the steps of processing uncertainty of vehicle arrival time and user departure time through a demand prediction model, constructing a scheduling optimization model to maximize charging station income, and comprehensively considering charging income, V2G discharge income, electricity purchase cost and user waiting penalty. After a power grid demand response signal is received, two-stage decoupling optimization is carried out, the first stage is to optimize a charging pile shutdown strategy, and the second stage is to optimize a vehicle scheduling scheme. The time uncertainty is adaptively adjusted through an affine decision rule, a mixed integer linear programming solver is utilized to obtain an optimal regulation and control instruction of the charging station, and robust scheduling execution is realized. According to the method, the problem that the charging station cannot realize multi-target collaborative optimization scheduling under double challenges of demand prediction uncertainty and power grid demand response is solved, and the robustness and the economic benefit of a charging station scheduling scheme are improved.

The invention discloses an electric heavy truck cooperative balance control system and method based on a large-scale charging group, and the method comprises the steps: constructing a power grid load prediction model, and obtaining a power grid load prediction value; establishing a multi-objective optimization model, wherein the objective function is to minimize the deviation between the planned charging power and the actual demand power of each electric heavy truck; setting a constraint function, and constraining the planned charging power and charging time of each electric heavy truck and the power grid load of each time period; collecting multi-source data and extracting multi-source features; constructing a demand prediction model based on the multi-source features to obtain a charging demand prediction result, and performing charging priority dynamic evaluation on the electric heavy truck to obtain a charging sequence of the electric heavy truck; and based on the charging sequence and the planned charging power, power distribution is carried out on the to-be-charged electric heavy truck, and an optimal charging station and an optimal charging path are recommended. According to the actual conditions of the vehicle and the power grid, the charging power can be dynamically adjusted, the charging time is shortened, the charging efficiency is improved, and the power resource of the charging pile is fully utilized.

The invention relates to the technical field of distributed computing, and discloses a heavy truck charging load scheduling-oriented distributed energy intelligent matching method, which comprises the following steps of: establishing a charging service computing power mapping table at a scheduling node; maintaining a shadow counter in a local memory, extracting a pre-estimated computing power consumption value according to an event type and accumulating the pre-estimated computing power consumption value to the shadow counter, and executing linear numerical deduction on the shadow counter according to a reference logic subtraction rate so as to simulate a scheduling data throughput evolution process; adjusting a linear deduction rate parameter according to the deviation between the state feedback data and the numerical value of the shadow counter; and distributing the charging matching task to a charging station edge computing node of which the shadow counter value does not exceed a preset logic saturation threshold value. According to the invention, through an open-loop estimation and closed-loop calibration mechanism of a local logic state, an instantaneous congestion risk caused by physical feedback lag is eliminated; and logic state consistency and self-adaptive distribution of the whole network computing power resources are realized.

The invention discloses an electric vehicle charging dynamic pricing method and system based on user consumption psychology, and the method comprises the steps: S1, calculating the response time period and the shortest charging time of a user according to the charging behavior data of an electric vehicle user, and screening the user with the demand response capability; s2, constructing a three-dimensional psychological feature vector based on the user with the demand response capability, performing weighted calculation on a user type decision parameter, and dividing user types; s3, constructing a differentiated comprehensive satisfaction model for different types of users; s4, constructing an electric vehicle charging double-layer pricing model based on a stackelberg game; and making a charging station price strategy, thereby realizing flexible regulation and control of the charging load of the electric vehicle. The method has the advantages of reducing the deviation between the scheduling instruction and the actual response, remarkably improving the regulation and control efficiency of the price signal on the load and the like.

The invention discloses an energy management system and method for a charging station, and relates to the technical field of management analysis, and the method comprises the steps: carrying out the charging demand space-time distribution prediction based on the operation data of the charging station, and generating a charging load distribution prediction map; topological structure analysis is carried out on charging station power grid connection nodes to obtain power grid topological parameters, and charging and discharging strategy optimization is carried out in combination with real-time electricity price fluctuation data and energy storage system state data to generate an optimal charging and discharging strategy; planning a charging and discharging path of the energy storage system based on the optimal charging and discharging strategy to obtain a charging and discharging path, and adjusting the charging and discharging path according to a power grid load balancing index to generate a power grid safety interaction strategy; and inputting the power grid safety interaction strategy into the charging station energy management digital twin model for strategy verification to obtain an optimized energy management strategy, and issuing the optimized energy management strategy to a charging station control terminal for execution. The method has the effect of improving the energy management efficiency of the charging station.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent scheduling method and device for unmanned taxis, and the method comprises the steps: constructing a spatial topological structure for connecting a target unmanned taxi, passengers and a charging station based on the received vehicle data, passenger data and environment data; capturing a spatial dependency relationship among the target unmanned taxi node, the passenger node and the charging station node to generate a spatial coding feature and a time coding feature, and combining the spatial coding feature and the time coding feature with corresponding weights to generate a spatial-temporal feature of the target unmanned taxi; and generating a passenger matching result and / or a path plan and / or a charging decision of the target unmanned taxi based on the spatial-temporal characteristics. Therefore, the problems that a traditional unmanned taxi scheduling method does not fully consider complex factors in an actual driving scene, scheduling decisions are still mostly single-target guiding, dynamic adaptability is lacked, and the increasing travel requirements of passengers cannot be met are solved.

The invention relates to the technical field of unmanned aerial vehicle charging and the technical field of energy storage, and discloses an unmanned aerial vehicle intelligent wireless quick charging platform based on echelon utilization batteries, which comprises a battery health assessment module, a battery management module and a battery management module, performing dynamic health assessment on each echelon battery in a charging and discharging state; the charging resource scheduling module obtains charging request information sent when the unmanned aerial vehicle prepares to return to the charging station, and dynamically allocates charging resources; the wireless charging control module is used for identifying the position information when the unmanned aerial vehicle lands, adjusting the wireless charging power and adaptively optimizing the working frequency; the energy optimization configuration module is used for performing energy optimization configuration; the safety protection module is used for identifying potential safety hazards and carrying out safety protection and fault self-healing; the optimization maintenance module is used for evaluating the performance of the charging platform and carrying out closed-loop optimization and decision updating; according to the invention, precise matching between the performance of the echelon battery and the charging demand of the unmanned aerial vehicle is realized.

The invention discloses a charging pile power supply switching method and equipment based on a micro-grid, and relates to the technical field of electric vehicle charging, and the method comprises the steps: determining a target user according to a preset price threshold value and a region requirement based on the vehicle information of each vehicle in a target charging station before power supply switching; according to the energy storage state parameter and the power supply quality parameter of the current power supply of the target charging station, judging whether the charging demand of the target user is met; if yes, generating and executing a power supply switching strategy based on a power supply in the energy storage system and the first power supply switching rule; and if the power supply does not meet the preset high-quality power supply switching threshold, determining a standby power supply and a charging power supply, and after the charging power supply supplements energy storage to the standby power supply to a safety threshold, executing switching operation according to a preset switching sequence and an energy storage allocation result. By implementing the application, accurate switching and dynamic adaptation of the power supply can be realized, the problems of blind switching of the power supply and mismatching of the power supply capability and the high demand of the user in the prior art are solved while the resource utilization efficiency is improved, and finally the stability and reliability of switching of the power supply of the charging pile in the micro-grid environment are realized.

The present disclosure relates to techniques for scheduling electric vehicle (EV) charging sessions using an EV charger scheduling service. A method includes presenting a user interface on a client computing device, which prompts an end-user to select a time slot for using an EV charger. Upon receiving the selected time slot, the scheduling service reserves the EV charger for the end-user. The service then receives a signal indicating that an EV is connected to the charger during the reserved time. In response, the user interface is updated with a control element that, when activated by the end-user, instructs the scheduling service to command the EV charger to initiate power delivery. The method enhances the efficiency of EV charging station utilization and provides end-users with a convenient and controlled charging experience.