Ordered charging scheduling method for electric vehicle collaborative power grid optimization

Abstract

The invention provides an orderly charging scheduling method for electric vehicle collaborative power grid optimization, relates to the technical field of intelligent scheduling, and aims at performing functional decoupling on a power grid scheduling topology of a scheduling area to determine a coupling network layer, monitoring the network layer and determining a pre-scheduling task based on inter-layer and intra-layer interaction data streams. The method comprises the steps of determining assembly scheduling factors and performing multilayer granularity subdivision to determine a multilayer decision framework, supervising a training recursive decision module in combination with a network layer, introducing a target function to select a task scheduling main factor, executing a loop decision on a pre-scheduling task from bottom to top to determine a pre-scheduling strategy, and executing decoupling based on the network layer to perform charging scheduling management. The problems that the electric vehicle charging scheduling cannot effectively respond to the influence of the charging load on the power grid, the energy supply and demand are not uniform, and the electricity price is not matched with the power grid operation efficiency are solved, and the effects of dynamically responding to the power grid change, optimizing the charging efficiency and enhancing the economic operation efficiency, stability and safety of the power grid are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent dispatching, and in particular to an orderly charging dispatching method for electric vehicles in coordination with power grid optimization. Background Art

[0002] In the existing technology, the rapid growth of electric vehicles has caused new demands for grid management and optimization, especially in charging scheduling. Traditional grid scheduling strategies are mainly designed around traditional electricity demand and have not yet fully adapted to the dynamic and intermittent characteristics of electric vehicle charging demand. Current charging scheduling technologies mostly use static time windows or simple priority queuing systems to manage the charging of electric vehicles. These methods often ignore the real-time changes in grid load and the volatility of renewable energy supply. In addition, the existing system often lacks comprehensive considerations at the level of energy cost, grid load, and user convenience, which leads to the possibility of grid overload caused by large-scale charging of electric vehicles during peak hours, while the excess electricity of renewable energy is not effectively utilized during off-peak hours. At the same time, the responsiveness of the existing scheduling system is also insufficient, and it is difficult to adapt to rapidly changing market conditions and grid conditions.

[0003] In summary, the existing electric vehicle charging scheduling often has technical problems such as being unable to effectively deal with the impact of charging load fluctuations on grid stability, uneven energy supply and demand, and the mismatch between electricity price strategies and grid operation efficiency. Summary of the invention

[0004] The present application provides an orderly charging scheduling method for electric vehicles in coordination with power grid optimization, which is used to solve the technical problems that the existing electric vehicle charging scheduling cannot effectively deal with the impact of charging load fluctuations on power grid stability, uneven energy supply and demand, and the mismatch between electricity price strategy and power grid operation efficiency.

[0005] In view of the above problems, the present application provides an orderly charging scheduling method for electric vehicles in coordination with power grid optimization.

[0006] The present application provides an orderly charging scheduling method for electric vehicles in coordination with power grid optimization, the method comprising:

[0007] For the preset dispatching jurisdiction area, functional decoupling is performed on the grid dispatching topology to determine the coupled network layer, wherein the coupled network layer includes the microgrid layer, the distribution network layer and the load layer; the coupled network layer is monitored, and the pre-dispatching task is determined based on the interactive data flow between and within the layers; the overall dispatching factor is determined, and multi-layer granularity is subdivided to determine the multi-layer decision-making architecture, and the recursive decision module is supervised and trained in combination with the coupled network layer; the objective function is introduced to select the main factors for task scheduling, and the pre-dispatching task is executed from bottom to top in combination with the recursive decision module to determine the pre-dispatching strategy; the pre-dispatching strategy is decoupled based on the coupled network layer, and charging dispatching management is performed in response to the coupled network layer.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The orderly charging scheduling method for electric vehicles coordinated with power grid optimization provided by the present application performs functional decoupling on the power grid scheduling topology for a preset scheduling jurisdiction area to determine a coupling network layer, wherein the coupling network layer includes a microgrid layer, a distribution network layer and a load layer; monitors the coupling network layer, and determines the pre-scheduling task based on the interactive data flow between layers and within layers; determines the assembly scheduling factor, and performs multi-layer granularity subdivision to determine the multi-layer decision-making architecture, and supervises the training of the recursive decision module in combination with the coupling network layer; introduces an objective function to select the main factors for task scheduling, and performs a bottom-up loop decision on the pre-scheduling task in combination with the recursive decision module to determine the pre-scheduling strategy; performs decoupling based on the coupling network layer on the pre-scheduling strategy, and performs charging scheduling management in response to the coupling network layer, thereby solving the technical problems that the existing electric vehicle charging scheduling cannot effectively handle the impact of charging load fluctuations on power grid stability, uneven energy supply and demand, and the mismatch between electricity price strategy and power grid operation efficiency. Through functional decoupling and multi-layer granularity scheduling, the optimal matching of charging demand and power grid supply is achieved, the dynamic response to changes in power grid status, the optimization of electric vehicle charging efficiency, and the enhancement of the economic operation efficiency, stability and safety of the power grid are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of an orderly charging scheduling method for electric vehicles in coordination with power grid optimization is provided for this application;

[0011] Figure 2 A schematic diagram of the feedback management process of power dispatching in an orderly charging dispatching method for electric vehicles in coordination with power grid optimization is provided for this application. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Examples, such as Figure 1 As shown, the present application provides an orderly charging scheduling method for electric vehicles in coordination with power grid optimization, the method comprising:

[0014] For a preset dispatching jurisdiction area, functional decoupling is performed on a power grid dispatching topology to determine a coupling network layer, wherein the coupling network layer includes a microgrid layer, a distribution network layer and a load layer.

[0015] Specifically, for the preset dispatching jurisdiction area, the functional decoupling of the power grid dispatching topology is first performed. The core of this step is to deconstruct the complex and interdependent structure in the traditional power grid dispatching system to form an independent but coordinated multi-layer network structure. The preset dispatching jurisdiction area refers to a specific geographical or management area where power dispatching and management are required. It can be a city, a community, a commercial area or any other clearly defined area. Specifically, the coupling network layer includes the microgrid layer, the distribution network layer and the load layer. Each layer has specific functions and responsibilities to ensure the flexibility and responsiveness of power grid management. Among them, the microgrid layer is mainly responsible for the optimized management of energy generation, storage and consumption in local areas. For example, a microgrid may include solar panels, wind turbines and electric vehicle charging stations. Its goal is to achieve self-sufficiency and efficient use of energy without relying on the external power grid; the distribution network layer is responsible for the distribution and scheduling of electricity in larger areas, for example, the power supply of residential or industrial areas in a city, ensuring the safe and efficient transmission of electricity from power stations to end users; and the load layer is directly related to the end consumers of the power grid, such as households, factories or commercial facilities. The scheduling focus of this layer is to dynamically allocate electricity according to the user's real-time electricity demand, so as to optimize the user's electricity bill expenditure and improve the efficiency of electricity utilization.

[0016] Functional decoupling not only clearly defines the responsibilities of each network layer, making management and response more flexible and efficient, but also achieves optimal allocation and efficient utilization of power grid resources by coupling the independent operation of these layers, greatly improving the grid's adaptability and stability to complex load conditions, and providing a strong guarantee for achieving efficient and reliable power grid operation.

[0017] The coupling network layer is monitored, and pre-scheduling tasks are determined based on the interactive data flows between layers and within layers.

[0018] Optionally, in order to achieve the optimal coordinated dispatch of electric vehicles and power grids, the coupled network layer is monitored and the pre-dispatching tasks are determined based on the interactive data flows between and within the layers. Each layer in the coupled network layer is responsible for collecting data related to its operation and exchanging data with other layers to achieve information integrity and real-time performance. The interactive data flow mainly refers to the data exchange between the layers and between different nodes within the layers, which includes but is not limited to power consumption data, charging demand, power grid status, output of renewable energy, etc. In particular, for the charging demand of electric vehicles, due to its discrete and irregular load characteristics, the data of these load targets are usually difficult to effectively predict and analyze. Therefore, advanced data analysis techniques, such as machine learning algorithms and pattern recognition techniques, are used to analyze the data flows of the inter-layer interactions from the data dimension to identify potential load change patterns and trends. Afterwards, in the process of determining the pre-dispatching tasks, the real-time data collected from each layer is first integrated and processed to analyze the overall load situation of the power grid and the time and spatial distribution of the charging demand of electric vehicles. For example, if data analysis shows that the demand for electric vehicle charging in a certain area increases sharply during a specific period of time, the pre-dispatch system will adjust the power distribution in the area in advance, or optimize the operation plan of the charging station to ensure grid load balance and charging efficiency.

[0019] Through data-based pre-dispatching methods, we can not only respond more accurately to the uncertainty and dynamic changes in electric vehicle charging, but also effectively improve the operating efficiency and response speed of the power grid, ensure the safety and stability of the power grid, and optimize the use of power resources, thereby achieving the goal of energy conservation and emission reduction.

[0020] Determine the overall scheduling factor, perform multi-layer granularity subdivision, determine the multi-layer decision architecture, combine the coupling network layer, and supervise the training of the recursive decision module.

[0021] Exemplarily, the purpose of determining the overall dispatch factors and performing multi-level granularity segmentation is to establish an effective multi-level decision-making architecture that can be combined with the coupled network layer to optimize the charging scheduling of electric vehicles and the energy distribution of the power grid through supervised training of recursive decision modules. Specifically, key dispatch factors are identified and determined, which are the core variables that affect the charging performance of the power grid and electric vehicles, such as peak power demand, renewable energy supply, power grid load capacity, and the distribution of electric vehicle charging stations. After determining these overall dispatch factors, multi-level granularity segmentation is further performed, which means that the power grid dispatch problem is decomposed into multiple levels, each of which focuses on decision-making problems of different granularity. For example, the macro level may focus on energy optimization of the entire power grid, while the micro level focuses on the daily operation scheduling of a specific area or charging station. Through this hierarchical approach, resources and response strategies at each level can be managed and optimized more finely. Subsequently, a multi-level decision-making architecture is established in combination with the characteristics of the coupled network layer. This architecture integrates data and insights from different network layers to achieve synergy between layers, thereby improving the decision-making efficiency and execution of the entire system. In this architecture, the supervised training recursive decision module uses data collected from each layer to train and optimize the decision model to ensure that the model can accurately reflect the various situations encountered in actual operation. For example, during the implementation process, if it is monitored that the grid load in a certain area continues to rise, the recursive decision module will automatically adjust the charging station scheduling plan in that area to avoid grid overload. In addition, the module will learn and adapt to changes in the electricity market, such as electricity price fluctuations, and further optimize the charging scheduling strategy to maximize economic benefits and ensure grid stability. By integrating multi-layer granular decision-making and supervised training methods, the overall performance and reliability of the power grid and electric vehicle charging system can be effectively improved, and efficient and sustainable electricity use can be achieved.

[0022] An objective function is introduced to select the main factors for task scheduling, and combined with the recursive decision module, a cyclic decision is performed from bottom to top on the pre-scheduling task to determine the pre-scheduling strategy.

[0023] Furthermore, in order to further optimize the overall performance of electric vehicle charging and power grid dispatching, an objective function is introduced to select the main factors of task dispatching. Combined with the recursive decision module, a bottom-up cyclic decision method is used to determine the pre-dispatching strategy. This process first involves defining an objective function, which is quantified based on the key performance indicators of power grid operation (such as cost minimization, energy efficiency maximization, power grid stability assurance, etc.). The main role of the objective function is to guide various possible decisions in the dispatching task to ensure that the final decision result can optimally meet the preset performance goals. After selecting the main factors of dispatching, the recursive decision module uses machine learning and data analysis techniques to perform pattern recognition and prediction on the behavior of power grids and electric vehicle charging stations based on historical data and real-time data. Then, based on this information and the objective function, the module will start from the lowest-level specific operations (such as charging dispatching of a single charging station), gradually build decision strategies, and aggregate and optimize these strategies step by step upward to form a complete set of pre-dispatching strategies. In a specific embodiment, if the charging demand in a certain area increases suddenly, and the corresponding power grid power supply capacity is limited, the recursive decision module will first evaluate the charging demand and time window of each charging station in this area, and then prioritize high-priority tasks (for example, emergency charging or high-efficiency charging) according to the objective function. Through this bottom-up cyclic decision-making process, a set of charging scheduling strategies for the entire region is finally formed, which not only ensures the load balance of the power grid, but also optimizes the charging experience of electric vehicle users.

[0024] By combining the objective function and the recursive decision module method, it is not only possible to ensure the efficient operation of the power grid and the high efficiency of electric vehicle charging, but also to quickly respond to various emergencies in the process of real-time adjustment of pre-dispatching tasks, thereby significantly improving the flexibility and responsiveness of power grid dispatch.

[0025] The pre-scheduling strategy is decoupled based on the coupling network layer, and charging scheduling management is performed in response to the coupling network layer.

[0026] Specifically, during the decoupling process of the coupled network layer, the pre-dispatch strategy is refined into sub-strategies that can be implemented independently at these different levels. After decoupling, each sub-strategy is designed for its corresponding network layer to ensure that the implementation of the strategy can maximize the adaptation to the operating environment and optimization goals of each layer. For example, the strategy at the microgrid layer may focus on optimizing the use of locally generated renewable energy, while the strategy at the distribution network layer may focus on adjusting large-scale power flows to prevent overload; the strategy at the load layer directly targets the power needs of end users to ensure the balance between power supply and demand. In addition, charging scheduling management in response to the coupled network layer is achieved through real-time monitoring and dynamic adjustment, which includes continuous monitoring of the grid state and evaluation of charging demand and power supply. Based on these real-time data, the dispatch system can make adjustments quickly, such as increasing the charging rate when the power supply is sufficient, or delaying non-urgent charging tasks when the grid load is too high. Through this decoupling and response mechanism based on the coupled network layer, it is possible to ensure that the charging strategy can be effectively implemented at all levels while maintaining the stability and efficient operation of the entire power grid. This not only improves the operating efficiency of the power grid, but also optimizes the charging experience of electric vehicle users, ensuring the efficient use of energy and the economy of power grid operation.

[0027] Furthermore, after obtaining the interactive data stream, it includes:

[0028] Divide the grid margin into multiple levels and configure grid distribution scenarios.

[0029] The interactive data flow is identified, and the target power distribution scenario is determined. If the primary power grid margin is met, economic benefits are used as the baseline standard.

[0030] If the secondary grid margin is met, grid stability is used as the baseline standard.

[0031] If the third-level grid margin is met, grid safety is used as the baseline standard, wherein the first-level grid margin>the second-level grid margin>the third-level grid margin.

[0032] Optionally, after obtaining the interactive data stream, the data is first analyzed to divide the multi-level grid margin and configure the corresponding grid distribution scenario. These margins are determined according to the real-time capacity and expected demand of the grid. Specifically, the grid margin is divided into three levels, namely, the first-level grid margin, the second-level grid margin and the third-level grid margin, among which the first-level grid margin is the largest, indicating that the operation efficiency and economic benefits of the grid are optimized; the second-level grid margin is the second, focusing on the stability of the grid; the third-level grid margin is the lowest, focusing on the safety standards of the grid. In the specific operation, the key information in the interactive data stream is first identified, such as power consumption mode, charging demand and grid capacity, and then the target distribution scenario is determined based on this information. If the data shows that the current grid state can meet the first-level grid margin, the system will give priority to economic benefits and take measures such as optimizing power distribution and using more economical energy supply to improve the operation efficiency of the grid and reduce operating costs. If the grid state only meets the second-level grid margin, the system will focus on ensuring the stability of the grid, preventing grid overload and maintaining stable operation of the system by adjusting the power flow and managing load balancing. When the power supply capacity of the power grid approaches or reaches its limit, that is, it can only meet the third-level grid margin, the system will initiate safety-first measures, such as reducing the power supply of non-essential loads and ensuring the power supply of critical areas and facilities to prevent power grid collapse or major failures.

[0033] Through a data-driven multi-level grid margin management strategy, the grid operation strategy can be dynamically adjusted to ensure that the economy, stability and security of the grid are effectively managed and optimized under different grid load and resource conditions. This approach improves the flexibility and responsiveness of grid operations, enabling the grid to more effectively respond to various operational challenges and market changes.

[0034] Furthermore, a bottom-up loop decision is performed on the pre-scheduling task to determine the pre-scheduling strategy, including:

[0035] In combination with the multi-layer decision-making architecture, the pre-scheduled tasks are decomposed to determine a task pyramid.

[0036] The task pyramid is traversed, and single-task granularity decision and overall optimization between granularities are performed from bottom to top to determine the pre-scheduled task, wherein the task granularity growth direction is from bottom to top.

[0037] Exemplarily, the pre-scheduling tasks are optimized through a bottom-up cyclic decision-making process to ensure that the scheduling strategies of the power grid and electric vehicle charging stations maximize efficiency and responsiveness. This process first dissociates the pre-scheduling tasks in combination with the multi-layer decision-making architecture to form a task pyramid, which is organized according to the task granularity from the bottom to the top, with the bottom-up direction of task granularity growth. The task pyramid is arranged hierarchically from small to large according to the specificity of the operation and the scope of influence. The bottom layer contains specific, daily scheduling tasks, such as the charging scheduling arrangement of a single charging station, while the top layer contains more macro-strategic decisions, such as the energy distribution optimization of the regional power grid. The decisions of each layer depend on the output results of the layer below, forming a cyclic decision-making process, so that each decision layer can provide support and data foundation for the upper layer. During execution, the system first processes the bottom-level tasks of the task pyramid, analyzes each small-scale operation unit in detail (such as the charging queue optimization within a single charging station), and then aggregates these decision results upward to provide decision support for the upper layer. As the task granularity increases, the system integrates and optimizes decisions layer by layer until the macro scheduling strategy of the top layer is determined. For example, if among multiple charging stations in a certain area, the primary task shows that the demand for a certain station increases during a specific period of time, the system will adjust the charging priority at this level, and then report the results to the higher level responsible for regional power grid management, which will comprehensively consider the power supply and demand in the region and optimize the power distribution and dispatch strategy for the entire region. Through the bottom-up circular decision-making mechanism, not only the close coordination and efficient execution of tasks at all levels are guaranteed, but also the grid dispatching is more flexible and adaptable, and can effectively respond to various emergencies and demand changes, thereby optimizing the operation of the grid.

[0038] Furthermore, the bottom-up single-task granularity decision and inter-granularity overall optimization includes:

[0039] Determine the N sub-level scheduling tasks of the first level.

[0040] In combination with the objective function, N task scheduling main factors are selected for the N sub-order scheduling tasks.

[0041] Taking the N task scheduling main factors as greedy targets, greedy task decisions are made in parallel to determine N sub-order strategies.

[0042] Furthermore, the N sub-order strategies are interval strategies, which identify N strategy intervals and construct an optimization space.

[0043] The N sub-order strategies are traversed to perform iterative optimization of global equilibrium to generate a layer of coupling strategies.

[0044] A mapping of the first-layer coupling strategy is established at the second level, and a hierarchical loop decision is performed to obtain the pre-scheduled task.

[0045] Furthermore, the bottom-up single-task granularity decision and the overall optimization between granularities ensure that the scheduling tasks can be optimized at each level in a hierarchical and progressive manner. First, the N sub-level scheduling tasks of the first level are determined. These tasks are the basic elements in the scheduling system. Each task targets a specific operation or goal, such as load management of a single charging station or energy allocation in a specific time period. In order to optimize these sub-level scheduling tasks, an objective function is introduced, which defines the main objectives and evaluation criteria of task scheduling, such as cost minimization, efficiency maximization, or stability priority. For these goals, N task scheduling main factors are selected. These main factors are key variables that affect the results of task execution, such as power demand, charging rate, or available resources. Subsequently, greedy task decisions are made in parallel with these N task scheduling main factors as greedy targets. Greedy decision-making means trying to find the currently available optimal solution in each decision, that is, to achieve the best state defined by the objective function as much as possible in each sub-level task. For further optimization, the N sub-order strategies are defined as interval strategies, that is, each strategy has an optimal execution interval, which is the operating range in which the sub-order task can achieve a better state after considering various constraints and conditions. Then, all N sub-order strategies are traversed, and the iterative optimization of global equilibrium is performed, and each sub-strategy is adjusted to achieve the best balance between the overall strategies, such as adjusting the execution time or resource allocation of certain strategies to ensure the stable operation and efficiency of the entire system. This method generates a layer of coupling strategies, that is, a comprehensive execution plan that comprehensively considers all sub-order strategies. Finally, a mapping of a layer of coupling strategies is established at the second level, and hierarchical cyclic decision-making is performed. This means that the coupling strategies of the first level will be used as the basis for the second-level decisions, which consider broader factors and longer-term goals, such as the overall energy management and optimization of the regional power grid. Through this hierarchical cyclic decision-making method, the pre-scheduling tasks of the entire system are finally obtained, ensuring that the coordinated optimization scheduling of the power grid and electric vehicle charging meets both immediate needs and long-term strategic goals.

[0046] Furthermore, the method further comprises:

[0047] In response to the charging load of electric vehicles, a market incentive mechanism for time-of-use electricity prices is set based on the grid’s energy consumption and power supply ramping requirements.

[0048] The market incentive mechanism is introduced to implement peak-valley management constraints for charging scheduling.

[0049] Specifically, in order to effectively manage the charging load of electric vehicles and optimize the energy consumption and power supply ramping requirements of the power grid, a market incentive mechanism based on time-of-use electricity prices is introduced. This mechanism aims to regulate the charging behavior of electric vehicles through economic incentives to match the operational needs and capabilities of the power grid. The power grid energy consumption refers to the ability of the power grid to accept and utilize external energy, especially renewable energy, such as wind and solar energy input. This ability is particularly important in power grid systems with a high degree of renewable energy participation, because the output of these energy sources is usually highly volatile and uncertain. The power supply ramping requirement describes the ability of the power grid to quickly increase energy supply when demand increases. This usually involves how the power grid can quickly increase power generation to cope with a sharp increase in demand in a short period of time, especially during peak consumption periods. The ramping ability is crucial to maintaining the stability of the power grid and responding to changes in market demand. The time-of-use electricity price system refers to setting different electricity prices in different time periods according to the energy supply and demand status of the power grid. For example, at night or other low-demand periods, the electricity price is set lower to encourage users to charge during these periods, thereby increasing the load of the power grid during low-load periods; on the contrary, during peak periods, the electricity price rises to curb excessive power consumption and help the power grid relieve the pressure of high load. The price incentive is set based on the charging load of electric vehicles and combined with the optimization of the energy consumption and power supply ramping requirements of the power grid. This price mechanism not only encourages electric vehicle users to adjust the charging time according to the changes in electricity prices to achieve cost savings, but also helps the power grid to manage the load more smoothly and avoid the pressure caused by peak demand on the power grid. After the introduction of the market incentive mechanism, the control strategy is further refined by implementing the peak-valley management constraints of charging scheduling. The peak-valley management of charging scheduling mainly involves the intelligent scheduling of charging requests of electric vehicle charging stations to ensure that charging needs are met first when the grid capacity allows, and that non-urgent charging needs are appropriately delayed when resources are tight. The peak-valley management constraints of charging scheduling are a key strategy in power system management, which aims to adjust and control the load demand of the power grid, especially during high demand (peak) and low demand (valley) periods. The core goal of this management strategy is to optimize the operating efficiency of the power grid, reduce energy waste, and ensure the stability and economy of power supply. By monitoring the state of the power grid and changes in electricity prices in real time, charging stations can dynamically adjust the operation of charging equipment and optimize the energy utilization rate during the charging process. For example, a charging station may increase the number of charging devices in operation during low electricity price periods to attract more users to charge, while it may reduce the number of devices in operation or postpone non-emergency charging during high electricity price periods to reduce costs and reduce grid load. In addition, this peak-valley management can also predict grid load and electricity price trends through prediction algorithms, so as to prepare for charging scheduling in advance and ensure the economy and efficiency of charging station operations. The market incentive mechanism of time-of-use electricity prices combined with peak-valley management of charging scheduling can not only effectively regulate the charging demand of electric vehicles, but also optimize the energy distribution and load management of the power grid, and enhance the stability and economic benefits of the power grid.

[0050] Furthermore, the charging scheduling management in response to the coupling network layer includes:

[0051] The microgrid layer, distribution network layer and load layer have relative independence and coordination.

[0052] The pre-scheduling strategy is decoupled, the sub-order strategy is determined and transmitted to the mapping network layer, and the synchronization timestamp constraint is introduced to perform charging scheduling management of electric vehicles.

[0053] Optionally, a responsive strategy based on coupled network layers is adopted for the charging scheduling management of electric vehicles. This strategy elaborates on the functions of the microgrid layer, distribution network layer and load layer in the power grid and how they work together to achieve efficient charging scheduling management. Each layer has relative independence to handle specific tasks and needs, while maintaining synergy with other layers to ensure the coordination and optimization of the entire power grid system. The microgrid layer is mainly responsible for power management and distribution in the local area, including the integration of renewable energy and the balancing of small-scale loads; the distribution network layer is responsible for power transmission and distribution in a wider area, ensuring that power is safely and efficiently transmitted from the power generation source to the consumption node; the load layer is directly related to the end users of the power grid, handling specific power demand and consumption. In order to achieve effective charging scheduling management, the pre-scheduling strategy is decoupled. This process involves splitting the overall scheduling strategy into smaller, manageable sub-strategies, each of which is specialized for the specific needs and operating conditions of different network layers. Such decoupling not only simplifies the problem, but also makes the strategy more flexible and adaptable. Furthermore, these sub-level strategies are transmitted and implemented through the mapping network layer. The mapping network layer acts as a bridge between data and control to ensure the correct implementation of strategies at each layer and the flow of information. It is responsible for collecting data from each coupled network layer and integrating this data into a unified platform. By mapping and adjusting strategies from different layers, the mapping network layer ensures that these strategies work together in the entire power grid system. In addition, the mapping network layer is also responsible for controlling the flow of information to ensure that information flows smoothly from one layer to another, which includes not only real-time operation data, but also strategy updates, performance reports, and warning information, so that power grid operators can respond to various situations immediately. The mapping network layer is not just a static data processing center. It also has the function of executing cyclic decisions, which can update and re-evaluate strategies based on real-time data, making power grid management more flexible and responsive. At the same time, in order to synchronize operations at each level and ensure data consistency, a synchronization timestamp constraint is introduced. This timestamp ensures that all operations and data updates are synchronized throughout the network, thereby avoiding data conflicts and operation delays and enhancing the system's real-time response capabilities.

[0054] Through the above steps, the charging needs of electric vehicles can be efficiently managed, the use of grid resources can be optimized, and energy consumption can be reduced and the stability of system operation can be improved. The implementation of the entire process not only improves the operational efficiency of the grid, but also improves the charging experience and satisfaction of users.

[0055] Furthermore, if Figure 2 As shown, after the charging scheduling management is performed in response to the coupling network layer, it includes:

[0056] Carry out charging dispatch tracking for electric vehicles and obtain dispatch data link.

[0057] The scheduling data chain is traversed to perform a time phase indicator curve conversion, wherein the time phase indicator curve represents a multivariate indicator trend under a time axis.

[0058] Based on the time phase index curve, the strategy response deviation analysis of power dispatching and the trend change analysis of power grid distribution scenarios are carried out to perform feedback management of power dispatching.

[0059] Specifically, the charging schedule of electric vehicles is tracked in real time, and then the scheduling data chain is obtained, which contains all relevant charging activities and parameters, such as charging time, duration, power consumption and operating status of charging stations. First, the obtained scheduling data chain is used to generate time phase indicator curves, which are graphical representations that associate the time axis with multivariate indicators of power grid operation (such as power demand, supply changes, charging frequency, etc.), so that the power grid operator can intuitively observe the performance trend of the power grid over time. Through the detailed analysis of these time phase indicator curves, the patterns and potential problems in the operation of the power grid can be effectively identified, providing a basis for further decision-making. Then, based on the time phase indicator curve, the strategic response deviation analysis of power dispatch and the trend change analysis of power grid distribution scenarios are performed, including in-depth analysis of real-time and historical operation data of the power grid, evaluating the effect of the current power dispatch strategy, and detecting deviations from the expected strategy. Deviation analysis helps identify those strategies that have not achieved the expected effect and analyzes the possible causes of deviations, such as market dynamics, changes in consumer behavior or the influence of external factors. Finally, the conclusions obtained from deviation analysis and trend analysis are used to conduct feedback management of power dispatch, including adjusting or reformulating charging dispatch strategies to better adapt to the actual operation of the power grid and market conditions. Feedback management not only ensures the flexibility and adaptability of power grid operation, but also promotes the continuous improvement of power grid efficiency and the optimal allocation of energy resources. Through the above analysis and management steps, the efficiency and sustainability of power grid operation can be ensured, while optimizing the charging dispatch of electric vehicles, ensuring the stable operation of the power system and maximizing energy efficiency.

[0060] Through the technical solutions of the above embodiments, the orderly charging scheduling method for electric vehicles coordinated with power grid optimization provided by the present application has the following technical effects:

[0061] 1. This application significantly improves the flexibility of grid management by functionally decoupling the grid dispatching topology and establishing a coupled network layer including a microgrid layer, a distribution network layer, and a load layer. The above structure allows the grid to respond independently to load changes at different levels, thereby effectively optimizing the overall power resource allocation and enhancing the grid's responsiveness and management efficiency to different power demands.

[0062] 2. By establishing a multi-layer granular decision-making architecture and combining it with a recursive decision-making module, this application can perform precise scheduling at multiple decision-making levels, thereby ensuring that the scheduling decisions at each level are based on optimized information processing. This method improves the precision and accuracy of power grid charging scheduling, making power grid operations more economical and efficient.

[0063] 3. This application adopts a bottom-up cyclic decision-making method to dissociate and optimize pre-scheduling tasks through the task pyramid. This structured decision-making process ensures the comprehensiveness and systematicness of the decision, improves the implementation efficiency of the scheduling plan, and enhances the economy and stability of power grid operation through global optimization.

[0064] 4. By implementing interval strategies and iterative optimization of global equilibrium, the dispatching task is ensured to achieve the optimal strategy within different operating intervals, which not only enhances the adaptability and stability of the power grid under different operating conditions, but also improves the economic benefits of power grid operation through fine adjustment of strategies.

[0065] 5. Introduce market incentive mechanisms such as time-of-use electricity prices and effectively manage the peak and valley periods of charging loads, thereby encouraging users to charge during periods when electricity prices are lower. This strategy not only optimizes the load management of the power grid and reduces the pressure on the power grid during peak periods, but also improves the energy utilization efficiency and economy of the entire power grid.

[0066] 6. By implementing the power dispatch strategy response deviation analysis and power grid distribution scenario trend analysis based on the time-phase index curve, a real-time feedback and strategy adjustment mechanism is provided for power grid management, which not only ensures the continuity and safety of power grid operation, but also reduces operating costs and enhances the overall performance of the power grid by continuously optimizing the dispatch strategy.

[0067] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. An orderly charging scheduling method for electric vehicles in coordination with power grid optimization, characterized in that: The method comprises: For a preset dispatching jurisdiction area, functional decoupling is performed for a power grid dispatching topology to determine a coupling network layer, wherein the coupling network layer includes a microgrid layer, a distribution network layer, and a load layer; Monitoring the coupled network layer, and determining pre-scheduling tasks based on the inter-layer and intra-layer interactive data flows; Determine the overall scheduling factor, perform multi-layer granularity subdivision, determine the multi-layer decision architecture, combine the coupling network layer, and supervise the training of the recursive decision module; Introducing an objective function to select the main factors for task scheduling, combining the recursive decision module, performing a bottom-up cyclic decision on the pre-scheduling task, and determining the pre-scheduling strategy; The pre-scheduling strategy is decoupled based on the coupling network layer, and charging scheduling management is performed in response to the coupling network layer.

2. The orderly charging scheduling method for electric vehicles in coordination with power grid optimization as claimed in claim 1, characterized in that: After obtaining the interactive data flow, including: Divide the grid margin into multiple levels and configure the grid distribution scenario; Identify the interactive data flow, determine the target power distribution scenario, and if the primary power grid margin is met, use economic benefits as the baseline standard; If the secondary grid margin is met, grid stability is used as the baseline standard; If the third-level grid margin is met, grid safety is used as the baseline standard, wherein the first-level grid margin>the second-level grid margin>the third-level grid margin.

3. The orderly charging scheduling method for electric vehicles in coordination with power grid optimization as claimed in claim 1, characterized in that: A bottom-up loop decision is performed on the pre-scheduling task to determine the pre-scheduling strategy, including: In combination with the multi-layer decision-making framework, the pre-scheduled tasks are separated to determine a task pyramid; The task pyramid is traversed, and single-task granularity decision and overall optimization between granularities are performed from bottom to top to determine the pre-scheduled task, wherein the task granularity growth direction is from bottom to top.

4. The orderly charging scheduling method for electric vehicles in coordination with power grid optimization as claimed in claim 3, characterized in that: The bottom-up single-task granularity decision and inter-granularity overall optimization include: Determine the N sub-level scheduling tasks of the first level; In combination with the objective function, for the N sub-order scheduling tasks, select N task scheduling main factors; Taking the N task scheduling main factors as greedy targets, performing task greedy decision-making in parallel, and determining N sub-order strategies; Traversing the N sub-order strategies to perform iterative optimization of global equilibrium, and generating a layer of coupling strategies; A mapping of the first-layer coupling strategy is established at the second level, and a hierarchical loop decision is performed to obtain the pre-scheduled task.

5. The orderly charging scheduling method for electric vehicles in coordination with power grid optimization as claimed in claim 4, characterized in that: The N sub-order strategies are interval strategies, which identify N strategy intervals and construct an optimization space.

6. The orderly charging scheduling method for electric vehicles in coordination with power grid optimization as claimed in claim 1, characterized in that: The method further comprises: In view of the charging load of electric vehicles, a market incentive mechanism for time-of-use electricity prices is set based on the grid energy consumption and power supply ramping requirements; The market incentive mechanism is introduced to implement peak-valley management constraints for charging scheduling.

7. The orderly charging scheduling method for electric vehicles in coordination with power grid optimization as claimed in claim 1, characterized in that: The charging scheduling management is performed in response to the coupling network layer, including: The microgrid layer, distribution network layer and load layer have relative independence and coordination; The pre-scheduling strategy is decoupled, the sub-order strategy is determined and transmitted to the mapping network layer, and the synchronization timestamp constraint is introduced to perform charging scheduling management of electric vehicles.

8. The orderly charging scheduling method for electric vehicles in coordination with power grid optimization as claimed in claim 1, characterized in that: After charging scheduling management is performed in response to the coupling network layer, it includes: Track the charging schedule of electric vehicles and obtain the scheduling data link; Traversing the scheduling data chain, and performing a time phase index curve conversion, wherein the time phase index curve represents a multivariate index trend under a time axis; Based on the time phase index curve, the strategy response deviation analysis of power dispatching and the trend change analysis of power grid distribution scenarios are carried out to perform feedback management of power dispatching.

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