A smart grid electric vehicle charging load optimization scheduling method and system

Abstract

The application discloses a kind of smart grid electric vehicle charging load optimization scheduling method and system, the method is used to complete the task of task party issued task, by scheduling system, the charging of electric vehicle is completed, including: real-time acquisition from task party instruction information and grid information and the electric vehicle charging related data.According to the task party instruction information, grid real-time information and electric vehicle charging related data prediction charging scheduling strategy;The electric vehicle charging related data includes the voltage of electric vehicle battery management system, current, state of charge, current power, battery temperature and battery health sensitivity index.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and more specifically, to a smart grid electric vehicle charging load optimization scheduling method and system. Background Technology

[0002] In modern energy management, especially in the context of smart grids, optimizing the charging load of electric vehicles is a crucial task. Existing smart charging management systems are typically designed to balance the growing charging demand of electric vehicles with the stable operation of the power grid and economic benefits. Their core objective is to ensure that vehicles are fully charged on time, while reducing costs for users and operators and avoiding grid overload. These systems often rely on complex algorithms that comprehensively consider multiple factors such as the real-time state of the power grid, aggregated charging requests, and dynamically changing electricity prices to formulate the optimal charging plan.

[0003] However, in real-world operating environments, especially in large commercial settings, complex operating conditions can present unexpected challenges that undermine the effectiveness of these advanced scheduling methods.

[0004] The core optimization algorithm of the original charging scheduling system is based on the assumption that the non-charging load within the center is relatively stable and predictable. When the sorting system suddenly enters a high-load operation state, it will quickly occupy most of the capacity of the central power grid, causing the capacity originally reserved for electric vehicle charging to be reduced drastically in an instant.

[0005] At the same time, the external power grid environment has also changed. When the power grid company issues a load reduction order, the system often has no choice but to take the most direct "one-size-fits-all" approach, that is, to forcibly reduce the power of all vehicles that are charging, or even suspend the charging of some vehicles, in order to meet the external requirements.

[0006] Therefore, in the complex environment of highly dynamic and unpredictable non-charging loads within logistics centers, and real-time demand response commands from the external power grid, existing smart grid electric vehicle charging load optimization scheduling methods are difficult to perceive, predict, and adaptively adjust in real time. Summary of the Invention

[0007] To address the aforementioned shortcomings, the present invention provides a smart grid electric vehicle charging load optimization scheduling method and system. This system aims to solve the problem that existing smart grid electric vehicle charging load optimization scheduling methods struggle to perceive, predict, and adaptively adjust in real-time under complex environments where non-charging loads within a logistics center are highly dynamic and unpredictable, while the external power grid provides real-time demand response commands. The goal is to dynamically balance the operational needs of internal automated equipment, the real-time constraints of the external power grid, and the charging needs and battery health of the electric vehicle fleet. This ensures optimal allocation of charging load while maintaining the overall operational efficiency of the logistics center and the safety of the power grid, avoiding problems such as insufficient charging, increased costs, and shortened battery life.

[0008] The first aspect of this invention provides a smart grid electric vehicle charging load optimization scheduling method. This method is used to complete tasks assigned by a task provider and to charge electric vehicles through a scheduling system. The method includes: real-time collection of instruction information from the task provider, information from the power grid, and electric vehicle charging-related data. A charging scheduling strategy is predicted based on the task provider's instruction information, real-time power grid information, and electric vehicle charging-related data. The electric vehicle charging-related data includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle's battery management system.

[0009] The charging scheduling results executed by the predicted charging scheduling strategy are compared with the target operating results, and the charging scheduling strategy is adjusted based on the comparison results.

[0010] According to one embodiment of the present invention, the real-time acquisition of instruction information from the task party includes real-time communication between the task party and the scheduling system, and real-time acquisition of early warning signals containing the type, duration, and instantaneous threshold power of the task instruction.

[0011] According to one embodiment of the present invention, the real-time acquisition of power grid information includes: The virtual terminal node module collects information in real time, including power grid event identifier, event type, second duration, and power grid demand response command information.

[0012] According to one embodiment of the present invention, the step of predicting a charging scheduling strategy based on the tasker's instruction information, real-time grid information, and electric vehicle charging-related data includes: The data collection time window period is determined. The information within the time window period, including the type and duration of the task instruction, the warning signal with the instantaneous threshold power demand, the grid event identifier, the event type, the second duration, the target load reduction amount and the corresponding compensation price, as well as the voltage, current, state of charge, current power, battery temperature and battery health sensitivity index of each electric vehicle battery management system, are used as the first input items. The corresponding scenario identifier and scenario parameter set are output.

[0013] Based on the scenario identifier and scenario parameter set, the load demand for early warning signals is reserved to obtain the dynamic scheduling capacity of electric vehicles.

[0014] The dynamic scheduling capacity of electric vehicles, the real-time state of charge (SOC) of each electric vehicle, the target SOC, the departure time, the battery health sensitivity index, the grid electricity price, and the grid demand response instruction information are used as the second input. Different weighting coefficients are assigned to each input, and the following formula is used as the output: min (Cx + Cy + Cz + Ck), where Cx is the total electricity cost; Cy is the penalty for failing to meet grid demand response; Cz is the equivalent cost of shortened battery life due to improper charging; and Ck is the operational loss due to electric vehicles not being fully charged on time. An iterative algorithm is used to calculate the output charging power value for each charging station under the dynamic scheduling capacity, while satisfying the following constraints: The load demand for warning signals must not be lower than the threshold load demand; the total load reduction requirements of the grid demand response command must be met; each electric vehicle must reach the required threshold power level before departure the next day; the output charging power of each charging pile must not exceed its rated power; electric vehicles exceeding the battery health sensitivity index threshold shall be charged using constant current and constant voltage charging methods.

[0015] According to one embodiment of the present invention, comparing the charging scheduling result executed according to the predicted charging scheduling strategy with the target operating result, and adjusting the charging scheduling strategy according to the comparison result, includes: The operational data collected after the execution of the predictive charging scheduling strategy is compared with its target data to obtain comparison results and data, including: Whether the grid demand response command is met, the actual electricity load value, the time difference between the actual charging completion time and the target departure time for each electric vehicle, the actual electricity cost incurred, and the impact of the changing trend of the battery health sensitivity index on battery health are all considered. The weight allocation of input items in the charging scheduling strategy is adaptively adjusted based on the comparison results.

[0016] According to one embodiment of the present invention, the method further includes: real-time acquisition of data information from the charging pile, the data information including actual charging power, battery temperature and battery health sensitivity index of the charging pile, and change information of the local adjustment strategy of the charging pile.

[0017] According to one embodiment of the present invention, the step of predicting the charging scheduling strategy based on the tasker instruction information, real-time grid information and electric vehicle charging-related data further includes: simultaneously using the actual charging power from the charging pile, the battery temperature of the charging pile, the battery health sensitivity index of the charging pile and the local adjustment strategy change information of the charging pile as second input items, and assigning corresponding weight coefficients to them.

[0018] A second aspect of the present invention provides a smart grid electric vehicle charging load optimization scheduling system, the system being used to complete tasks assigned by a tasking party, comprising: The data acquisition module is used to collect real-time information from the task force and the power grid, as well as data related to electric vehicle charging. The data related to electric vehicle charging includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system.

[0019] The scheduling module is used to predict charging scheduling strategies based on the tasker's instruction information, real-time power grid information, and electric vehicle charging-related data.

[0020] The feedback module is used to compare the charging scheduling results executed according to the predicted charging scheduling strategy with the target running results, and adjust the charging scheduling strategy according to the comparison results.

[0021] According to one embodiment of the present invention, the acquisition module includes: a first acquisition unit, used to communicate with the task party in real time and acquire in real time an early warning signal containing the type of task instruction, duration, and instantaneous threshold power required.

[0022] The second acquisition unit is used to acquire information in real time, including power grid event identifier, event type, second duration, and power grid demand response command information.

[0023] The third acquisition unit is used to acquire electric vehicle charging-related data in real time. The electric vehicle charging-related data includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system.

[0024] According to one embodiment of the present invention, the scheduling module includes: The first calculation unit is used to determine the acquisition time window period, and takes the data, instructions and events containing the first acquisition unit, the second acquisition unit and the third acquisition unit as the first input item, and outputs the corresponding scenario identifier and scenario parameter set.

[0025] The second calculation unit is used to obtain the reserved load demand from the scenario identifier and scenario parameter set, obtain the dynamic scheduling capacity of electric vehicles, and allocate the dynamic scheduling capacity.

[0026] The beneficial effects provided by this invention are as follows: By collecting real-time task-side instruction information, power grid information, and electric vehicle charging-related data, and predicting charging scheduling strategies based on this information, and then adjusting the strategies according to the comparison between actual execution results and target results, this effectively solves the problem in existing technologies where it is difficult to perceive, predict, and adaptively adjust in real-time under complex environments where non-charging loads within a logistics center are highly dynamic and unpredictable, while the external power grid has real-time demand response instructions. This method can dynamically balance the operational needs of internal automated equipment, the real-time constraints of the external power grid, and the charging needs and battery health of the electric vehicle fleet. Thus, while ensuring the overall operational efficiency of the logistics center and the safety of the power grid, it achieves optimal allocation of charging load, avoiding problems such as insufficient charging, increased costs, and shortened battery life. Specifically, by comprehensively perceiving task-side early warning signals, power grid demand response instructions, and electric vehicle battery status, combined with a multi-objective optimization model and adaptive adjustment mechanism, this application can achieve refined load management, significantly improving the intelligence level and economic benefits of charging scheduling, and effectively extending battery life. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0028] Figure 1 This is a flowchart of the smart grid electric vehicle charging load optimization scheduling method disclosed in an embodiment of the present invention; Figure 2 This is a block diagram of a smart grid electric vehicle charging load optimization scheduling system disclosed in an embodiment of the present invention.

[0029] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0031] Traditional smart grid electric vehicle charging load optimization scheduling methods struggle to dynamically balance the operational needs of internal automated equipment, the real-time constraints of the external power grid, and the charging needs and battery health of the electric vehicle fleet in a complex environment where non-charging loads within the logistics center are highly dynamic and unpredictable, while the external power grid provides real-time demand response commands. This makes it difficult to achieve optimal allocation of charging load while ensuring the overall operational efficiency of the logistics center and the safety of the power grid, thus avoiding problems such as insufficient charging, increased costs, and shortened battery life.

[0032] To address this issue, this application proposes a smart grid electric vehicle charging load optimization scheduling method. This method is used to complete tasks assigned by the task provider and to charge electric vehicles through a scheduling system, such as... Figure 1 As shown, it includes: Real-time acquisition of command information from the task force and information from the power grid, as well as electric vehicle charging-related data; the electric vehicle charging-related data includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system; Based on the task force's instructions, real-time power grid information, and data related to electric vehicle charging, a charging scheduling strategy is predicted.

[0033] The charging scheduling results executed by the predicted charging scheduling strategy are compared with the target operating results, and the charging scheduling strategy is adjusted based on the comparison results.

[0034] The smart grid electric vehicle charging load optimization scheduling method of this application can effectively cope with the complex and ever-changing power grid and internal load environment by collecting multi-source information in real time and predicting and adjusting the charging scheduling strategy based on this information, thereby achieving refined management of charging load.

[0035] To better understand the technical solution proposed in this application, some key terms involved are explained first. "Task-side instruction information" refers to instructions issued by an external task-side party (such as a logistics center operator) related to the electric vehicle charging task. These instructions may include information such as task type, duration, and instantaneous threshold power demand, used to guide the charging dispatch system in load management. "Grid information" refers to real-time operational data and instructions from the power grid, such as grid event identifiers, event types, duration, and grid demand response instruction information. This information is crucial for maintaining stable grid operation and responding to grid demands.

[0036] "Electric vehicle charging-related data" refers to data directly related to the state of electric vehicle batteries and the charging process, including voltage, current, state of charge (SOC), current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system. This data forms the basis for assessing battery status, predicting charging behavior, and protecting battery health.

[0037] "Charging scheduling strategy" refers to a series of charging plans and control instructions formulated by the scheduling system based on the above multi-source information. It aims to optimize the charging process and balance the needs of all parties, such as meeting the charging needs of vehicles while responding to the needs of the power grid, reducing operating costs and protecting battery life.

[0038] The "Battery Health Sensitivity Index" is an indicator that measures the health status of electric vehicle batteries. The higher the value, the more sensitive the battery is to improper charging behaviors (such as overcharging, over-discharging, and high-temperature charging), and the more likely it is to shorten battery life. This index needs to be fully considered when formulating charging strategies to avoid damaging the battery.

[0039] The core of the smart grid electric vehicle charging load optimization scheduling method in this application lies in achieving refined management of electric vehicle charging load through real-time data acquisition, prediction, and feedback adjustment mechanisms.

[0040] Specifically, various methods can be employed to collect real-time data on task-side instructions, grid information, and electric vehicle charging-related data. For example, task-side instructions can be transmitted in real-time via a dedicated communication link established between the scheduling system and the task party. The task party sends warning signals to the scheduling system containing information such as task instruction type, duration, and instantaneous power demand threshold. Grid information can be acquired in real-time through a Virtual Terminal Node (VTN) module, which can receive and parse grid event identifiers, event types, second durations, and grid demand response instructions. As for electric vehicle charging-related data, such as the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system, these can be uploaded to the scheduling system in real-time via the vehicle communication unit or charging pile interface. These data collection methods ensure that the scheduling system can obtain comprehensive and real-time operational status information, providing a data foundation for subsequent strategy prediction.

[0041] In predicting charging scheduling strategies based on task-side instructions, real-time grid information, and electric vehicle charging-related data, the scheduling system comprehensively utilizes the collected multi-source data. For example, the scheduling system can employ a machine learning-based model, taking task-side instructions, real-time grid information, and electric vehicle charging-related data as input, to predict the optimal charging scheduling strategy for the current and future periods. This strategy prediction can consider various optimization objectives, such as minimizing total electricity costs, minimizing penalties for failing to meet grid demand response, minimizing the equivalent cost of shortened battery life due to improper charging, and minimizing operational losses from electric vehicles not being fully charged on time. During the prediction process, iterative algorithms can be used to calculate the output charging power of each charging pile under a series of constraints (e.g., load demand for warning signals cannot be lower than the threshold load demand, total load reduction requirements for meeting grid demand response instructions, each electric vehicle reaching the required threshold charge level before departure the next day, and the output charging power of each charging pile not exceeding its rated power).

[0042] In comparing the charging scheduling results executed according to the predicted charging scheduling strategy with the target operating results, and adjusting the charging scheduling strategy based on the comparison results, the scheduling system continuously monitors the actual operating situation after the strategy is executed. For example, the scheduling system can collect operating data after the execution of the predicted charging scheduling strategy, including whether the grid demand response command is met, the actual electricity load value, the time difference between the actual charging completion time and the target departure time of each electric vehicle, the actual electricity cost incurred, and the impact of the changing trend of the battery health sensitivity index on battery health. Then, these actual operating data are compared with the preset target operating results to obtain comparison results and data. Based on these comparison results, the scheduling system can adaptively adjust the weight allocation of input items in the charging scheduling strategy. For example, if it is found that the grid demand response command is not fully met, the weight of meeting the grid demand response can be increased; if it is found that the changing trend of the battery health sensitivity index is not ideal, the weight of battery health protection can be increased. This feedback adjustment mechanism enables the scheduling strategy to be dynamically optimized according to the actual operating situation, thereby improving the robustness and adaptability of the scheduling effect.

[0043] By introducing real-time multi-source data acquisition, strategy prediction based on multi-objective optimization, and feedback adaptive adjustment mechanisms, the challenges faced by existing technologies in complex operating environments can be effectively addressed.

[0044] Traditional methods often focus on single-dimensional optimization, such as considering only electricity prices or charging demand. However, this application constructs a comprehensive information input system by collecting real-time task-side instructions, real-time grid information, and electric vehicle charging-related data. This fusion of multi-dimensional information enables the scheduling system to more accurately understand the complexity of the current operating environment, such as the instantaneous load fluctuations of automated equipment within the logistics center and real-time demand response instructions from the external power grid.

[0045] Compared to the closest existing technology, the advantage of this application lies in the refinement and adaptability of its prediction and adjustment strategies. Existing systems, when faced with sudden and highly real-time external demand response commands or internal load fluctuations, often resort to a crude, one-size-fits-all approach, leading to problems such as insufficient charging, increased costs, or shortened battery life. This application, by using this multi-source information as input to the prediction model, can more accurately predict charging scheduling strategies and calculate the optimal output charging power value for each charging pile under multiple constraints (including load demand thresholds, grid demand response, vehicle pre-departure battery charge, charging pile rated power, and battery health sensitivity index).

[0046] The feedback adjustment mechanism introduced in this application compares the operational data after the predicted charging scheduling strategy is executed with the target operational results and adaptively adjusts the weight allocation of the input items in the strategy, further improving the robustness and adaptability of the system. This closed-loop control scheduling method enables the system to learn and optimize itself based on actual operational results, thereby maintaining optimal scheduling performance in dynamically changing and complex environments. Therefore, this application can effectively balance the operational needs of internal automated equipment, the real-time constraints of the external power grid, and the charging needs and battery health of the electric vehicle fleet. While ensuring the overall operational efficiency of the logistics center and the safety of the power grid, it achieves optimal allocation of charging load, significantly reduces operating costs, extends battery life, and ensures that vehicles complete charging tasks on time.

[0047] The aforementioned real-time acquisition of instruction information from the task party includes real-time communication between the task party and the scheduling system, and real-time acquisition of early warning signals containing the type of task instruction, duration, and instantaneous threshold power requirement.

[0048] Real-time communication between the task provider and the scheduling system refers to the task provider establishing a stable data link, such as using TCP / IP, MQTT, or other real-time communication protocols, to exchange data continuously with the scheduling system. This communication mechanism ensures that the task provider can send instructions to the scheduling system in a timely manner, and that the scheduling system can receive and process these instructions instantly. Specifically, the types of task instructions included in the real-time collected early warning signals can indicate different scheduling needs, such as peak shaving and valley filling, emergency load response, or charging restrictions for specific time periods. The duration clarifies the effective time range of the instruction, enabling the scheduling system to make corresponding load adjustments within this time window. The instantaneous power threshold sets the power limit or target to be achieved at a specific time, providing a quantitative basis for the formulation of scheduling strategies. Through these detailed parameters, the scheduling system can accurately understand and respond to the needs of the task provider.

[0049] The solution proposed in this application ensures that task instructions are rapidly and accurately transmitted to the scheduling system through real-time communication between the task provider and the scheduling system. This real-time nature allows the scheduling system to promptly acquire the latest task requirements, such as grid load adjustment requests or response instructions for specific events. By collecting early warning signals containing the type, duration, and instantaneous threshold power of the task instruction, the scheduling system can comprehensively understand the specific intentions and constraints of the task provider, thereby providing accurate and real-time input data for subsequent charging scheduling strategy prediction. Therefore, the scheduling system can dynamically adjust the charging load based on the latest task requirements to meet the task provider's instruction requirements.

[0050] Through the aforementioned technical solutions, the scheduling system can achieve refined and real-time collection of task-side instruction information. This detailed and real-time information acquisition capability enables the scheduling system to more accurately grasp the specific needs and time constraints of the task-side when predicting charging scheduling strategies, thereby formulating more targeted and effective scheduling plans. This not only improves the response speed and accuracy of scheduling strategies but also helps avoid scheduling deviations caused by information lag or incompleteness, thus enhancing the efficiency and reliability of the overall smart grid's electric vehicle charging load optimization scheduling.

[0051] The real-time acquisition of power grid information includes: real-time acquisition of information containing power grid event identifiers, event types, second duration, and power grid demand response instructions through a virtual terminal node module.

[0052] The virtual terminal node module is an interface or device used to interact with the power grid system, capable of receiving and parsing various instructions and information from the grid. This module ensures the real-time and accuracy of grid information, providing a reliable data foundation for subsequent charging scheduling. Specifically, the grid event identifier uniquely identifies specific events occurring in the grid, such as load peaks, valleys, faults, or demand response events. The event type further refines the nature of the event, such as peak shaving and valley filling instructions, emergency power curtailment notices, or other types of grid operation adjustments. The second duration indicates the effective duration of the grid event or instruction, which is crucial for formulating reasonable charging scheduling strategies and ensuring that the scheduling scheme works within the effective time. The grid demand response instruction information includes the grid's specific requirements for load adjustment, such as target load reduction, response time windows, and corresponding compensation mechanisms, aiming to guide electric vehicle charging loads to participate in grid balance regulation.

[0053] The solution proposed in this application collects detailed information about the power grid in real time through a virtual terminal node module, enabling the dispatching system to promptly obtain the grid's operating status and demand. For example, when the power grid issues a demand response command, the dispatching system can immediately identify the power grid event identifier, event type, and second duration, and obtain specific power grid demand response command information. Therefore, based on this real-time and accurate power grid data, the dispatching system can more accurately assess the power grid's load status and demand, thus fully considering the actual operating needs of the power grid when predicting charging dispatching strategies and avoiding dispatching deviations caused by information lag or inaccuracy.

[0054] Through the aforementioned technical solutions, the dispatching system can obtain detailed operational information and demand response instructions from the power grid in real time and accurately, significantly improving the precision of its perception of the power grid's state. This allows the formulation of charging dispatching strategies to better align with the actual operating conditions of the power grid, enhances the ability of electric vehicle charging loads to participate in power grid demand response, and contributes to the stable operation and load balancing of the power grid. Simultaneously, the standardized interface of the virtual terminal node module also improves the efficiency and reliability of data interaction between the system and the power grid.

[0055] In some embodiments described above, this application proposes a scheme for predicting charging scheduling strategies based on task-side instruction information, real-time grid information, and electric vehicle charging-related data. However, in practical applications, if the complex interaction of multi-source information and multi-objective optimization requirements are not fully considered, the predicted scheduling strategy may suffer from insufficient optimization or inability to balance the interests of multiple parties when dealing with dynamically changing grid load, task-side demands, and the characteristics of electric vehicles themselves. Therefore, this application further proposes a specific implementation method for predicting charging scheduling strategies based on the aforementioned task-side instruction information, real-time grid information, and electric vehicle charging-related data. This aims to improve the accuracy and overall efficiency of the scheduling strategy through refined data processing, scenario recognition, and multi-objective optimization models.

[0056] Specifically, the above-mentioned charging scheduling strategy, based on the task force instruction information, real-time power grid information, and electric vehicle charging-related data, includes: The data collection time window period is determined. The information within the time window period, including the type of task instruction, duration, and instantaneous threshold power demand of the warning signal, the grid event identifier, event type, second duration, target load reduction and corresponding compensation price, as well as the voltage, current, state of charge, current power, battery temperature and battery health sensitivity index of each electric vehicle battery management system, are used as the first input item. The corresponding scenario identifier and scenario parameter set are output. Based on the scenario identifier and scenario parameter set, the load demand reserved for the early warning signal is obtained to determine the dynamic scheduling capacity of electric vehicles. The dynamic scheduling capacity of electric vehicles, the real-time state of charge of each electric vehicle, the target state of charge, the departure time, the battery health sensitivity index, the grid electricity price, and the grid demand response command information are used as the second input item. Different weight coefficients are assigned to each of the input items, and the following formula is used as the output: min (Cx + Cy + Cz + Ck) where Cx is the total electricity cost; Cy is the penalty for failing to meet grid demand response; Cz is the equivalent cost of shortened battery life due to improper charging; and Ck is the operational loss of electric vehicles not being fully charged on time. The output charging power value for each charging pile under the dynamic scheduling capacity is calculated using an iterative algorithm, while satisfying the following constraints: The load demand for warning signals must not be lower than the threshold load demand; the total load reduction requirements of the grid demand response command must be met; each electric vehicle must reach the required threshold power level before departure the next day; the output charging power of each charging pile must not exceed its rated power; electric vehicles exceeding the battery health sensitivity index threshold shall be charged using constant current and constant voltage charging methods.

[0057] The acquisition time window period can be understood as the time granularity used for data acquisition and strategy prediction. For example, it can be set to 15 minutes, 30 minutes or 1 hour. Its purpose is to ensure that the scheduling strategy can respond to the dynamic changes of the power grid and the task force in a timely manner.

[0058] Specifically, the construction of the first input item aims to comprehensively capture key information affecting charging scheduling. This includes warning signals such as the type and duration of the task instruction, and the instantaneous threshold power demand, reflecting the immediate control needs of the task party for the load; information such as the grid event identifier, event type, second duration, target load reduction, and corresponding compensation price, characterizing the grid's demand response events and their economic incentives; and the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of each electric vehicle battery management system, providing the charging status and battery health status of the electric vehicle itself. This information is comprehensively processed to output scenario identifiers and scenario parameter sets, with the aim of classifying and quantifying the operating environment for the current and future period, such as identifying scenarios like "peak grid load period," "demand response event activation period," or "task party emergency control period."

[0059] Furthermore, by reserving load demand for early warning signals based on the scenario identifiers and scenario parameter sets, the aim is to ensure that, under specific scenarios, the emergency load regulation requirements of the task party or the power grid can be prioritized. Thus, the dynamic dispatch capacity of electric vehicles can be obtained. This capacity refers to the flexible space that electric vehicles can use to participate in load dispatch, under the premise of meeting basic operating requirements and battery health, for example, for peak shaving and valley filling or responding to demand-side management.

[0060] In practical applications, the second input term is constructed to provide the optimization model with comprehensive decision variables and target parameters. Specifically, the dynamic scheduling capacity of electric vehicles defines the schedulable range; the real-time state of charge (SOC), target SOC, and departure time of each electric vehicle are key parameters ensuring that user charging needs are met; the battery health sensitivity index is used to assess the impact of charging on battery life; and grid electricity prices and grid demand response instructions are directly related to the economic cost of charging and the contribution to the grid. By assigning different weight coefficients to these input terms, the priority of the optimization objective can be flexibly adjusted under different scenarios. For example, the weight of meeting demand response can be increased during grid emergencies, or the weight of electricity cost can be reduced when users are sensitive to charging costs.

[0061] The formula min (Cx + Cy + Cz + Ck) is a multi-objective optimization function. Its purpose is to find a charging scheduling strategy that minimizes the total cost while satisfying all constraints. Here, Cx is the total electricity cost, representing the electricity expenses incurred during charging; Cy is the penalty for failing to meet grid demand response, used to quantify the economic loss from failing to respond to grid regulation; Cz is the equivalent cost of shortened battery life due to improper charging, aiming to incorporate battery health degradation into economic considerations; and Ck is the operational loss of electric vehicles not being fully charged on time, reflecting users' expectations for charging completion time.

[0062] The output charging power of each charging station under the dynamic scheduling capacity can be calculated using iterative algorithms, such as linear programming, mixed-integer programming, or heuristic algorithms. This calculation process must also meet a series of constraints, including: the load demand in response to the warning signal must not be lower than the threshold load demand to ensure the effectiveness of emergency control; the total load reduction requirements of the grid demand response command must be met to maintain grid stability; each electric vehicle must reach the required threshold charge level before departure the next day to ensure user travel needs; the output charging power of each charging station must not exceed its rated power to comply with equipment physical limitations; and electric vehicles exceeding the battery health sensitivity index threshold must be charged using constant current and constant voltage methods to extend battery life.

[0063] This application's solution effectively addresses the limitations of traditional charging scheduling strategies in handling complex dynamic environments by introducing refined data acquisition, scenario recognition, and multi-objective optimization models. First, by determining the data acquisition time window period and integrating multi-source information (task-side early warning signals, grid event information, and electric vehicle battery management system data) as the primary input, the system's operating status can be comprehensively and in real-time perceived. Consequently, by outputting scenario identifiers and scenario parameter sets, accurate identification and quantification of current and future scenarios are achieved, laying the foundation for subsequent refined scheduling.

[0064] Secondly, based on the identified scenarios, load demands are reserved for early warning signals, and the dynamic scheduling capacity of electric vehicles is calculated. This allows the dispatching system to flexibly respond to emergency control needs while fully tapping the load adjustment potential of electric vehicles. This dynamic capacity allocation ensures that basic charging needs are met while providing valuable resilient resources for the power grid.

[0065] Furthermore, by using the dynamic scheduling capacity of electric vehicles, real-time state of charge, target state of charge, vehicle deployment time, battery health sensitivity index, grid electricity price, and grid demand response command information as the second input, and assigning different weight coefficients to them, a multi-objective optimization model was constructed that comprehensively considers economic efficiency, grid stability, battery health, and user satisfaction. This model achieves a balance of interests among multiple parties by minimizing the total electricity cost Cx, the penalty Cy for failing to meet grid demand response, the equivalent cost Cz for shortened battery life due to improper charging, and the operational loss Ck due to electric vehicles not being fully charged on time.

[0066] Finally, an iterative algorithm was used to calculate the output charging power of each charging pile under a series of strict constraints (such as early warning signal load demand, grid demand response, next day's vehicle power consumption, charging pile rated power, and battery health charging method), ensuring the feasibility and robustness of the scheduling strategy. These constraints not only guarantee the safe and stable operation of the power grid and the basic needs of users, but also effectively extend the lifespan of electric vehicle batteries by considering the battery health sensitivity index.

[0067] This application further proposes that the method also includes: real-time collection of data information from the charging pile, the data information including actual charging power, battery temperature and battery health sensitivity index of the charging pile, and change information of the local adjustment strategy of the charging pile.

[0068] "Real-time data acquisition from charging piles" refers to the establishment of a real-time communication link between the scheduling system and each charging pile to continuously acquire various data generated by the charging piles during operation. Specifically, "actual charging power" refers to the actual electrical power output of the charging pile to the electric vehicle at a given moment, reflecting the charging pile's true operating status and potentially affected by factors such as grid fluctuations, charging pile wear and tear, or electric vehicle battery characteristics. "Charging pile battery temperature" refers to the temperature inside the charging pile or near its connection point with the electric vehicle battery. This helps assess the battery's thermal management during charging, complementing the battery temperature information provided by the electric vehicle battery management system and offering more comprehensive thermal status information. "Battery health sensitivity index" is an assessment indicator of battery health status trends based on the charging pile's interaction data with the battery, indicating the battery's health risks under current charging conditions. "Information on changes to local adjustment strategies at the charging pile" refers to any temporary or local adjustments made to the charging strategy at the local level based on its own sensor data, local grid conditions, or user operations, such as fine-tuning of charging current or voltage, or switching of charging modes.

[0069] Predicting charging scheduling strategies primarily relies on task-side instructions, real-time grid information, and data from the electric vehicle battery management system. However, during actual charging, the operating status of the charging pile itself and the local characteristics of the connected electric vehicles, such as the actual charging power, battery temperature, and local adjustment strategies, can directly and dynamically impact charging performance and battery health. If this real-time, localized charging pile data is not adequately incorporated into the predictive model for the scheduling strategy, discrepancies may arise between the predicted results and actual operating conditions, thereby affecting the accuracy of scheduling and the effectiveness of battery protection.

[0070] This application further proposes the above-mentioned charging scheduling strategy based on the tasker's instruction information, real-time power grid information, and data related to electric vehicle charging, and also includes: Meanwhile, the actual charging power from the charging pile, the battery temperature of the charging pile, the battery health sensitivity index of the charging pile, and the local adjustment strategy change information of the charging pile are used as the second input items, and corresponding weight coefficients are assigned to them.

[0071] Through the above technical solutions, this application enables more refined and optimized scheduling of electric vehicle charging loads in smart grids. Specifically, by identifying scenarios and calculating dynamic scheduling capacity, the scheduling strategy can more accurately predict and respond to real-time grid demands and emergency instructions from task parties, significantly improving the stability and reliability of grid operation. Simultaneously, by introducing a multi-objective optimization function, comprehensively considering electricity costs, grid demand response penalties, battery life depreciation costs, and operational losses from untimely charging, the generated charging scheduling strategy is not only economical and efficient but also considers the charging experience of electric vehicle users and battery health, effectively extending battery lifespan. Furthermore, strict constraints ensure the feasibility and safety of the scheduling strategy in actual operation, avoiding potential risks caused by improper scheduling, thereby achieving deep integration and collaborative optimization of smart grids and electric vehicle charging loads.

[0072] This application's solution, through refined collection and comparison of multi-dimensional operational data after the execution of the predictive charging scheduling strategy, can comprehensively evaluate the actual performance of the current scheduling strategy in terms of grid response, operational efficiency, user experience, economic cost, and battery health. As a result, the system can accurately identify deviations and deficiencies in strategy execution. It is precisely this detailed feedback information that makes it possible to subsequently adaptively adjust the weight allocation of input items in the charging scheduling strategy. By dynamically adjusting the weights of various optimization objectives, the system can flexibly reconfigure resources based on actual operating conditions and priority changes, thereby generating an optimized scheduling strategy that better meets the needs of the current environment and effectively compensating for the potential limitations of the initial predictive strategy.

[0073] The aforementioned technical solutions significantly improve the accuracy and robustness of smart grid electric vehicle charging load optimization scheduling. Specifically, this solution ensures that grid demand response commands are met more effectively, thereby reducing penalties for non-compliance. Simultaneously, monitoring and feedback of actual electricity load and electricity costs helps further optimize charging costs and maximize economic benefits. Furthermore, focusing on the time difference between the actual charging completion time and the target departure time for each electric vehicle improves user satisfaction and reduces operational losses due to untimely charging. More importantly, analyzing the changing trends of the battery health sensitivity index effectively prevents shortened battery life caused by improper charging, thus extending the lifespan of electric vehicle batteries. Overall, this solution enables continuous learning and optimization of charging scheduling strategies to adapt to dynamically changing operating environments, achieving smarter, more efficient, and sustainable electric vehicle charging management.

[0074] A second aspect of the present invention provides a smart grid electric vehicle charging load optimization scheduling system 20, such as... Figure 2As shown, the system is used to complete the tasks assigned by the tasker, including: The data acquisition module is used to collect real-time information from the task force and the power grid, as well as data related to electric vehicle charging. The data related to electric vehicle charging includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system.

[0075] The scheduling module is used to predict charging scheduling strategies based on the tasker's instruction information, real-time power grid information, and electric vehicle charging-related data.

[0076] The feedback module is used to compare the charging scheduling results executed according to the predicted charging scheduling strategy with the target running results, and adjust the charging scheduling strategy according to the comparison results.

[0077] The acquisition module includes: a first acquisition unit, used to communicate with the task party in real time and acquire early warning signals containing the type of task instruction, duration, and instantaneous threshold power required.

[0078] The second acquisition unit is used to acquire information in real time, including power grid event identifier, event type, second duration, and power grid demand response command information.

[0079] The third acquisition unit is used to acquire electric vehicle charging-related data in real time. The electric vehicle charging-related data includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system.

[0080] The scheduling module includes: a first calculation unit, used to determine the acquisition time window period, take the data, instructions and events containing the first acquisition unit, the second acquisition unit and the third acquisition unit as the first input item, and output the corresponding scenario identifier and scenario parameter set.

[0081] The second calculation unit is used to obtain the reserved load demand from the scenario identifier and scenario parameter set, obtain the dynamic scheduling capacity of electric vehicles, and allocate the dynamic scheduling capacity.

[0082] Suppose that within a certain data collection window, the first data collection unit receives a "peak shaving and valley filling" warning signal from a task provider, with an instantaneous power demand threshold of 500kW and a duration of 2 hours. Simultaneously, the second data collection unit collects a demand response command issued by the power grid, requiring a load reduction of 300kW within the same time period. The third data collection unit provides real-time charging data for multiple electric vehicles, including their current state of charge, target state of charge, deployment time, and battery health sensitivity index.

[0083] At this point, the first computing unit processes this information as the first input item, identifies the current scenario of "peak shaving and valley filling combined with demand response", and outputs the corresponding scenario identifier (e.g., "Scenario A") and scenario parameter set (e.g., including peak shaving demand of 500kW, demand response reduction of 300kW, and detailed state parameters of each electric vehicle).

[0084] Subsequently, the second calculation unit receives "Scenario A" and its parameter set. Based on this information, the second calculation unit first calculates that the total reserved load requirement to meet peak shaving and demand response is 800kW. Next, the second calculation unit comprehensively considers factors such as the battery health sensitivity index, departure time, and target state of charge of each electric vehicle to calculate the dynamic capacity available for load dispatch for each electric vehicle without affecting its normal use. For example, the dynamic dispatch capacity may be larger for an electric vehicle that is about to be fully charged and has a late departure time; while the dynamic dispatch capacity will be strictly limited for an electric vehicle with a low battery level that is about to be dispatched. Finally, the second calculation unit will optimize the allocation of the output charging power value of each charging pile based on these dynamic dispatch capacities to ensure that all electric vehicles can be fully charged on time while meeting the 800kW reserved load requirement and taking into account battery health.

[0085] Obviously, the above specific implementation examples are merely illustrative of the application of this method and not intended to limit the implementation. Those skilled in the art can make other variations and modifications based on the above description to study other related issues. Therefore, the scope of protection of this invention should be limited to the scope of the claims.

[0086] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0087] The electronic devices and other embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0088] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0090] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0091] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A smart grid electric vehicle charging load optimization scheduling method, characterized in that, The method is used to complete the task assigned by the task provider, and to complete the charging of electric vehicles through a scheduling system, including: Real-time collection of command information from the task force, information from the power grid, and data related to electric vehicle charging; The charging scheduling strategy is predicted based on the task force's instruction information, real-time power grid information, and electric vehicle charging-related data; the electric vehicle charging-related data includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system. The charging scheduling results executed by the predicted charging scheduling strategy are compared with the target operating results, and the charging scheduling strategy is adjusted based on the comparison results.

2. The method according to claim 1, characterized in that, The real-time acquisition of instruction information from the task party includes real-time communication between the task party and the scheduling system, and real-time acquisition of early warning signals containing the type of task instruction, duration, and instantaneous threshold power requirement.

3. The method according to claim 1, characterized in that, The real-time acquisition of power grid information includes: The virtual terminal node module collects information in real time, including power grid event identifier, event type, second duration, and power grid demand response command information.

4. The method according to any one of claims 1 to 3, characterized in that, The step of predicting charging scheduling strategies based on the tasker's instruction information, real-time power grid information, and electric vehicle charging-related data includes: The data collection time window period is determined. The information within the time window period, including the type of task instruction, duration, and instantaneous threshold power demand of the warning signal, the grid event identifier, event type, second duration, target load reduction and corresponding compensation price, as well as the voltage, current, state of charge, current power, battery temperature and battery health sensitivity index of each electric vehicle battery management system, are used as the first input item. The corresponding scenario identifier and scenario parameter set are output. Based on the scenario identifier and scenario parameter set, the load demand for the early warning signal is reserved to obtain the dynamic scheduling capacity of electric vehicles; The dynamic scheduling capacity of electric vehicles, the real-time state of charge of each electric vehicle, the target state of charge, the departure time, the battery health sensitivity index, the grid electricity price, and the grid demand response command information are used as the second input items. Different weight coefficients are assigned to each of these input items, and the following formula is used as the output: min (Cx + Cy + Cz + Ck) where Cx is the total electricity cost; Cy is the penalty for failing to meet grid demand response; Cz is the equivalent cost of shortened battery life due to improper charging; and Ck is the operational loss of electric vehicles not being fully charged on time. The output charging power value for each charging pile under the dynamic scheduling capacity is calculated using an iterative algorithm, while satisfying the following constraints: The load demand for warning signals must not be lower than the threshold load demand; the total load reduction requirements of the grid demand response command must be met; each electric vehicle must reach the required threshold power level before departure the next day; the output charging power of each charging pile must not exceed its rated power; electric vehicles exceeding the battery health sensitivity index threshold shall be charged using constant current and constant voltage charging methods.

5. The method according to claims 1 and 4, characterized in that, The step of comparing the charging scheduling result executed according to the predicted charging scheduling strategy with the target operating result, and adjusting the charging scheduling strategy according to the comparison result, includes: The operational data collected after the execution of the predictive charging scheduling strategy is compared with its target data to obtain comparison results and data, including: Whether the grid demand response command is met, the actual power load value, the time difference between the actual charging completion time and the target departure time of each electric vehicle, the actual electricity cost incurred, and the impact of the changing trend of the battery health sensitivity index on battery health. The weight allocation of input items in the charging scheduling strategy is adaptively adjusted based on the comparison results.

6. The method according to claim 1, characterized in that, The method further includes: Data information from charging piles is collected in real time, including actual charging power, battery temperature and battery health sensitivity index of the charging pile, and change information of local adjustment strategy of the charging pile.

7. The method according to claims 4 and 6, characterized in that, The method of predicting charging scheduling strategies based on the tasker's instruction information, real-time power grid information, and electric vehicle charging-related data also includes: Meanwhile, the actual charging power from the charging pile, the battery temperature of the charging pile, the battery health sensitivity index of the charging pile, and the local adjustment strategy change information of the charging pile are used as the second input items, and corresponding weight coefficients are assigned to them.

8. A smart grid electric vehicle charging load optimization scheduling system, characterized in that, The system is used to complete tasks assigned by the task provider, including: The data acquisition module is used to collect real-time information from the task force and the power grid, as well as data related to electric vehicle charging. The data related to electric vehicle charging includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system. The scheduling module is used to predict charging scheduling strategies based on the tasker's instruction information, real-time power grid information, and electric vehicle charging-related data. The feedback module is used to compare the charging scheduling results executed according to the predicted charging scheduling strategy with the target running results, and adjust the charging scheduling strategy according to the comparison results.

9. The system according to claim 8, characterized in that, The acquisition module includes: The first acquisition unit is used to communicate with the task party in real time and acquire early warning signals containing the type of task instruction, duration, and instantaneous threshold power required. The second acquisition unit is used to acquire information in real time, including power grid event identifier, event type, second duration, and power grid demand response command information. The third acquisition unit is used to acquire electric vehicle charging-related data in real time. The electric vehicle charging-related data includes the voltage, current, state of charge, current charge level, battery temperature, and battery health sensitivity index of the electric vehicle battery management system.

10. The system according to claims 1 and 8, characterized in that, The scheduling module includes: The first calculation unit is used to determine the acquisition time window period, and takes the data, instructions and events containing the first acquisition unit, the second acquisition unit and the third acquisition unit as the first input item, and outputs the corresponding scenario identifier and scenario parameter set. The second calculation unit is used to obtain the reserved load demand from the scenario identifier and scenario parameter set, obtain the dynamic scheduling capacity of electric vehicles, and allocate the dynamic scheduling capacity.

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