Charging pile networking communication method and split type charging pile system

Abstract

The invention relates to the field of electric vehicle charging infrastructures, and discloses a charging pile networking communication method and a split type charging pile system.The charging pile networking communication method comprises the following steps that a game model between charging piles is established based on the power dispatching requirements of the charging piles and the load condition of a power grid, and the game model is established based on the power dispatching requirements of the charging piles and the load condition of the power grid; the Nash equilibrium principle in the game theory is utilized to realize optimal scheduling of resources between the charging piles; based on charging pile power scheduling, an optimal control method is adopted to carry out cooperative scheduling of charging pile power output; a stochastic differential equation is used to process the power of the charging pile and the load condition of the power grid so as to process the load fluctuation of the power grid and the uncertainty of charging requirements. Through combination of the game theory, the optimal control, the stochastic differential equation and the Lyapunov stability theory, optimal distribution of charging pile power scheduling and communication bandwidth is realized, and the charging efficiency, the communication stability and the adaptive capacity of the system in power grid fluctuation are improved.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle charging infrastructure, and in particular to a charging pile networking communication method and a split-type charging pile system. Background Art

[0002] With the popularity of electric vehicles, charging piles, as an important infrastructure, have become an indispensable part of daily life. However, the number of charging piles and charging demand often fluctuate with the increase in the number of electric vehicles, which brings certain challenges to the grid load and charging pile power scheduling. Traditional charging pile power scheduling methods usually do not consider the impact of grid load fluctuations on charging efficiency, resulting in overload or inefficient operation of the charging pile system during high demand.

[0003] In the prior art, charging pile power scheduling mostly adopts static or preset strategies, which can operate effectively when the grid load is stable. For example, some scheduling methods ensure that the grid load does not exceed the upper limit through a simple load balancing mechanism, which can guarantee the basic functions and charging efficiency of the charging pile in the short term. This type of method is suitable for situations where the charging demand is not high or the grid load fluctuation is small, and under low load conditions, it can reasonably allocate the power of the charging pile, reduce energy efficiency loss, and provide basic charging services.

[0004] However, existing technologies often expose obvious deficiencies when faced with grid load fluctuations and uncertain charging demand. First, existing charging pile scheduling methods do not fully consider the real-time changes in grid loads, and the scheduling strategy lacks flexibility and cannot cope with the problems of overload or low charging efficiency caused by load fluctuations. Secondly, traditional methods do not fully utilize game theory to optimize resource allocation between charging piles, resulting in the system being unable to effectively coordinate power output when multiple charging piles are running at the same time, which can easily lead to power waste or resource conflicts. In addition, existing systems often do not fully consider the optimal allocation of bandwidth, and the communication delay between charging piles is high, which affects the transmission of real-time data and the response speed of the system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a charging pile networking communication method and a split charging pile system, aiming to solve the problems of lack of flexibility in charging pile power scheduling, unreasonable bandwidth allocation and poor system stability in the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A charging pile networking communication method, comprising the following steps:

[0007] Based on the power dispatching requirements of charging piles and the load conditions of the power grid, a game model between charging piles is established, and the Nash equilibrium principle in game theory is used to achieve optimal dispatching of resources between charging piles.

[0008] Based on the power dispatch of charging piles, the optimal control method is used to coordinate the power output of charging piles;

[0009] Use stochastic differential equations for charging pile power and grid load conditions to handle grid load fluctuations and charging demand uncertainties;

[0010] Combined with game theory, the communication bandwidth selection between charging piles is optimized so that the communication bandwidth is optimally allocated among the charging piles;

[0011] Based on Lyapunov stability theory, the stability analysis of the coordinated dispatching process of charging piles and power grid is carried out.

[0012] Preferably, the optimization scheduling of resources between charging piles includes:

[0013] Each charging pile in the game model is regarded as a "player", and the strategy is adjusted according to the mutual influence between the charging pile power and the load to achieve overall load balance and maximize energy efficiency;

[0014] In the process of mutual game between charging piles, Nash equilibrium is used to ensure that each charging pile maximizes its own benefits without changing the strategies of other charging piles.

[0015] Preferably, the charging pile power scheduling includes:

[0016] The optimal control method adjusts the power output of the charging pile so that the grid load can be kept within the set target range;

[0017] By minimizing energy efficiency losses, the overall charging efficiency of the charging pile system is improved.

[0018] Preferably, the stochastic differential equation comprises:

[0019] By using stochastic differential equations, the dynamic processes of charging piles and grid loads can be accurately modeled, taking into account the randomness of grid load fluctuations and charging demand changes;

[0020] The model can adjust power scheduling according to historical data and real-time load changes to reduce system fluctuations.

[0021] Preferably, the game theory includes:

[0022] The information game theory optimization process enables charging piles to communicate effectively under limited bandwidth through spectrum allocation and bandwidth selection algorithms;

[0023] Each charging pile is optimized based on its communication needs and bandwidth allocation using strategies from game theory to ensure communication efficiency and reduce latency.

[0024] Preferably, the Lyapunov stability theory includes:

[0025] The Lyapunov stability theory is used to analyze dynamic instability factors that are prone to occur during charging pile power scheduling and grid load changes;

[0026] By constructing the Lyapunov function, the stability of the system in the face of external disturbances and internal fluctuations is analyzed.

[0027] Preferably, the charging pile power scheduling further includes:

[0028] Dynamically adjust the power output of each charging pile based on real-time changes in grid load;

[0029] The adjustment process introduces a grid load feedback mechanism so that the power output of the charging pile can respond to the fluctuation of the grid load in a timely manner, thereby optimizing the response capability of the system.

[0030] In this way, the charger is able to reduce power output during peak load periods.

[0031] Preferably, the stochastic differential equation model further comprises:

[0032] The dynamic process of charging pile power and grid load is modeled by introducing random disturbance terms. The core of the stochastic differential equation is to use historical data and real-time changes for prediction;

[0033] The stochastic differential equation can reflect the power adjustment of the charging pile during the peak or valley period of charging demand, making the grid load more stable in a fluctuating environment;

[0034] Through this modeling method, charging piles can more flexibly respond to the randomness of grid load and charging demand, reducing power scheduling fluctuations and energy efficiency losses.

[0035] Preferably, the game theory further includes:

[0036] By dynamically allocating spectrum resources and combining them with a game theory strategy model, bandwidth resources are optimized to minimize communication delays between charging piles.

[0037] Each charging pile determines the bandwidth allocation strategy based on its communication needs through the game theory optimization algorithm, so that under limited bandwidth conditions, real-time and efficient communication can be achieved between charging piles, reducing transmission delays and improving system throughput.

[0038] The present invention also provides a split charging pile system, comprising the following steps:

[0039] A charging unit module is used to perform an electric energy transmission operation, transmit electric energy to the electric vehicle, and realize a charging function;

[0040] A control unit module is used to control the charging process in real time and perform communication operations between charging piles;

[0041] The grid load monitoring module is used to monitor the load status of the grid and adjust the power output of each charging pile according to the grid load conditions;

[0042] The communication module is used for communication between charging piles. It uses a bandwidth allocation algorithm based on information game theory to optimize the bandwidth allocation between charging piles.

[0043] The resource scheduling module uses the Nash equilibrium principle and optimal control algorithm in game theory to optimize the scheduling of resources between charging piles according to the power scheduling requirements of charging piles and the load conditions of the power grid;

[0044] The stability analysis module performs stability analysis on the charging pile power scheduling and grid load change process based on Lyapunov stability theory;

[0045] The data processing and prediction module is used to analyze and process historical data, adjust the power output strategy of the charging pile according to the real-time load and charging demand forecast, so as to cope with the randomness of grid load fluctuations and charging demand changes.

[0046] The present invention provides a charging pile networking communication method and a split charging pile system.

[0047] Beneficial effects:

[0048] 1. The present invention uses the Nash equilibrium principle in game theory to optimize the power of charging piles, achieving reasonable resource allocation under dynamic grid load changes. Compared with the simple power scheduling method in the prior art, the present invention can dynamically adjust the power output according to the mutual influence between charging piles, avoid grid load overload and power waste, realize the global optimization of the system, and improve the charging efficiency and grid load balance.

[0049] 2. The present invention uses the optimal control method to accurately coordinate the charging pile power scheduling, ensuring that the grid load remains within the target range. Compared with the traditional static scheduling method, the present invention can dynamically adjust the charging pile power according to the real-time grid load fluctuation, thereby improving the charging efficiency, reducing energy efficiency loss, and avoiding the risk of unstable grid load.

[0050] 3. The present invention uses stochastic differential equations to model the dynamic process of grid load and charging pile power, which can effectively deal with grid load fluctuations and uncertainty in charging demand. Compared with the existing methods that do not adequately handle load fluctuations, the present invention introduces random disturbance terms to accurately describe the random changes in grid load and charging pile power, so that the charging pile system can operate stably in a complex environment, reducing the negative impact of uncertainty on the system.

[0051] 4. The present invention uses Lyapunov stability theory to analyze the coordinated dispatching process of charging piles and power grids to ensure the stability of the system when the power grid load fluctuates. Compared with the lack of effective stability analysis methods in traditional technologies, the present invention analyzes the stability of the system by constructing a Lyapunov function. When facing external disturbances and power grid load fluctuations, the strategy can be quickly adjusted to restore stability and ensure long-term and efficient operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the process of the present invention;

[0053] Figure 2 Schematic diagram of the construction of the system of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Please see attached Figure 1 The embodiment of the present invention provides a charging pile networking communication method and a split charging pile system, comprising the following steps:

[0056] S1. Based on the power dispatching requirements of charging piles and grid load conditions, a game model between charging piles is established, and the Nash equilibrium principle in game theory is used to achieve optimal dispatching of resources between charging piles.

[0057] In order to effectively optimize the resource allocation of the charging pile system and resolve the contradiction between grid load fluctuation and charging pile power scheduling, the Nash equilibrium principle in game theory is used to establish a game model for resource scheduling between charging piles based on the power scheduling requirements of charging piles and grid load conditions. This method enables charging piles to not only dynamically adjust power output according to their own needs when working together, but also ensure that the grid load is in a balanced state, thereby optimizing charging efficiency and system stability.

[0058] In this embodiment, the charging pile system realizes resource optimization scheduling among charging piles through the Nash equilibrium model in game theory. Specifically, the charging pile acts as a "player", and each charging pile adjusts its strategy according to its power output and load demand. The goal of each charging pile is to select the optimal power output strategy based on the current grid load, while taking into account the power selection of other charging piles, to ensure that the grid load is balanced and the charging efficiency is improved under the framework of global optimization.

[0059] In this game model, the mutual influence and game strategy between charging piles can be expressed in the following form:

[0060] C i (p i )=f i (p i )+λ i·i ;

[0061] Among them, C i (p i ) represents the power dispatch cost of charging pile i, f i (p i ) is the power dispatch cost function of the charging pile, λ i·i is the influence coefficient of charging pile i on the grid load.

[0062] As an option, the utility function of the charging station can be defined as:

[0063] U i (p i ,p -i )=γ i ·(f i (p i )-λ i·i )-∑ j≠i κ ij·j ;

[0064] Among them, p -i Indicates the power output of all charging piles except charging pile i, is the load influence coefficient of charging pile i on charging pile j, γ i is the utility weight of charging pile i.

[0065] In the process of adjusting the strategy of each charging pile, the charging pile continuously adjusts the power output through the game theory model until it reaches the Nash equilibrium state, that is, without changing the strategies of other charging piles, each charging pile maximizes its utility by selecting the optimal power scheduling scheme. Specifically, under Nash equilibrium, the optimal power output of charging pile i is Satisfies the following equation:

[0066]

[0067] U i (p i ,p -i ): The utility function of charging pile i, which represents the utility of charging pile i at a given power p i and other charging pile power p -i The utility function usually represents the benefit or efficiency of the charging pile, p i : The power output of charging pile i, which indicates the power provided by charging pile i to the electric vehicle, p -i : The power output of other charging piles other than charging pile i, that is, the power scheduling of other charging piles in the charging pile system. Utility function U i (p i ,p -i ) Power output p for charging pile i i The partial derivative of represents the rate of change of the utility function of charging pile iii when its power output changes.

[0068] By solving this equation, the optimal power output of charging pile i This method can achieve global optimal resource scheduling and meet the requirements of grid load balancing. This method can not only ensure that each charging pile adjusts its power output under dynamically changing load conditions, but also optimize the grid load and avoid overload.

[0069] There is a strong dynamic coupling relationship between the charging pile and the grid load. The charging pile adjusts the power output according to the change of the grid load, and the grid load is affected by the power scheduling of the charging pile. In order to simulate this dynamic process, the optimal control method is used to adjust the power output of the charging pile and keep the grid load within the set target range. Specifically, when the grid load is at a high state, the charging pile will appropriately reduce the power output; when the grid load is low, the charging pile can increase the power output to improve the overall charging efficiency.

[0070] These adjustments need to consider not only the needs of each charging pile, but also the scheduling of other charging piles. In other words, the strategy adjustment of each charging pile needs to interact with the power scheduling of other charging piles to form an overall collaborative optimization effect. This collaborative scheduling is accurately expressed through the game model, allowing the charging piles to coordinate and cooperate under the global optimization framework.

[0071] In order to further optimize the stability and efficiency of the system, the present invention introduces stochastic differential equations (SDEs) to model the dynamic changes of grid load and charging pile power. Stochastic differential equations are used to describe the fluctuations of grid load and charging demand, and can accurately simulate the impact of grid load changes on charging pile scheduling.

[0072] Specifically, the dynamic process of grid load L(t) and charging pile power p(t) can be modeled by the following stochastic differential equation:

[0073] dL(t)=μ L dt+σ L dW L (t);

[0074] dp(t)=μ p dt+σ p dW p (t);

[0075] Among them, μ L and μ p are drift terms, representing the average rate of change of grid load and charging pile power, σ L and σ p is volatility, W L (t) and W p (t) is the standard Brownian motion, indicating random disturbance, L(t) indicates the state of grid load changing with time, and p(t) indicates the state of charging pile power changing with time.

[0076] Through this modeling method, the power output of the charging pile can be adjusted according to the real-time load and historical data, so that the system can maintain stable operation in the face of grid load fluctuations and changes in charging demand. Using the method of stochastic differential equations, a more accurate dynamic model can be established between the charging pile and the grid load, thereby achieving refined power scheduling.

[0077] Through the above-mentioned game model and scheduling mechanism, the present invention can effectively optimize the power scheduling of charging piles, balance the grid load, and ensure efficient and stable communication between charging piles through reasonable bandwidth allocation. Specifically, the charging piles can be dynamically adjusted under different grid load conditions to avoid grid overload and power waste. This method not only improves the charging efficiency, but also enhances the stability of the system, and can continuously optimize resource allocation in complex environments.

[0078] S2. Based on the charging pile power scheduling, the optimal control method is used to coordinate the power output of the charging pile;

[0079] The power dispatch of charging piles plays a key role in balancing the load of the power grid and improving the charging efficiency. In order to achieve efficient power dispatch in the charging pile system, this embodiment adopts the optimal control method to coordinate the power output of the charging piles, ensure the stability of the power grid load, and improve the overall energy efficiency of the system. This dispatching method avoids the overload of the power grid and reduces the energy efficiency loss of the charging piles by adjusting the power output of the charging piles in real time.

[0080] In some embodiments, the optimal control method controls the power output of the charging pile to ensure that the grid load is within the set target range. The fluctuation of the grid load poses a challenge to the operation of the charging pile system, especially when the grid load is high, the adjustment of the charging pile power is particularly important. In response to these challenges, the optimal control method dynamically adjusts the charging pile power to ensure that the grid load does not exceed the predetermined safety range.

[0081] Specifically, in the charging pile power scheduling, the optimal control method uses the correlation between the grid load and the charging pile power to adjust the power output of each charging pile so that the grid load remains within the set target range. As an option, the optimal control algorithm uses feedback control to continuously adjust the charging pile power output by obtaining grid load data and charging demand information in real time to ensure that the system can effectively respond to changes in grid load.

[0082] In this embodiment, the power scheduling of the charging pile is performed by the optimal control method under the following optimization objectives:

[0083]

[0084] Among them, p(t) 2 is the power output of the charging pile at time t, L(t) is the grid load, and L tar get is the target load of the power grid, α and β are weight coefficients used to weigh the priority of charging pile power scheduling and power grid load balancing.

[0085] Specifically, in the power dispatch of charging piles, the power output of charging piles will be adjusted by real-time monitoring of the grid load. For example, when the grid load is high, the system will reduce the power output of charging piles to avoid grid overload; when the grid load is low, the power output of charging piles will be increased to make full use of charging resources.

[0086] To achieve this dynamic adjustment, the system uses an optimal control method to adjust the power output of the charging pile by continuously feeding back real-time information about changes in grid load and charging demand. This feedback control mechanism ensures that the charging pile can be reasonably adjusted based on real-time grid load data without causing excessive pressure on the grid load.

[0087] In one possible implementation, the optimal control model dynamically adjusts the power output by combining real-time grid load data with the charging pile power data. By minimizing energy efficiency losses, the charging pile can improve charging efficiency without affecting grid stability. For example, when the grid load is low, the charging pile will increase power output to increase charging speed, while when the grid load is high, the power output will be reduced to ensure that the grid load does not exceed the predetermined value.

[0088] This dynamic regulation is achieved through the feedback mechanism of the optimal control algorithm. Between real-time load monitoring and power output, the system automatically adjusts according to the optimal control strategy to ensure the stability of the charging pile power output and the balance of the grid load.

[0089] In this embodiment, the optimal control method not only considers the single power dispatching requirement of the charging pile, but also combines the operating status of the entire charging pile system to optimize resource allocation and avoid grid overload and energy waste. Specifically, the system implements global dispatching through the optimal control strategy to ensure that each charging pile performs the optimal power output under the premise of meeting the global optimization requirements.

[0090] In one possible implementation, the optimal control method adjusts the power output of the charging pile so that the grid load can smoothly respond to changes in charging demand. The optimal control method improves the overall charging efficiency of the charging pile system by reducing energy efficiency losses. For example, the charging pile can adjust the power in time according to changes in the grid load to ensure that the grid load is balanced and the charging efficiency is optimized.

[0091] Through the optimal control method of the present invention, the power dispatch of the charging pile becomes more accurate and efficient. By optimizing the power output of the charging pile, it is not only possible to improve the charging efficiency and avoid grid overload, but also to ensure that the system can flexibly respond to grid load fluctuations, further improving the stability of the grid and the reliability of the charging pile system.

[0092] Through the application of optimal control methods, the charging pile system can flexibly adjust the power output according to the changes in the grid load. This automatic adjustment greatly reduces the need for manual intervention and ensures charging efficiency and system stability. In addition, the system can also adapt to fluctuations in charging demand, maximize the use of charging pile resources, and improve the operational efficiency of the entire charging network.

[0093] S3, using stochastic differential equations for charging pile power and grid load conditions to handle grid load fluctuations and uncertainty in charging demand;

[0094] In order to cope with the fluctuation of grid load and the unpredictability of charging demand, the charging pile system models the dynamic changes of grid load and charging pile power through stochastic differential equations (SDEs). This method can effectively handle the randomness of grid load fluctuation and charging demand, ensuring the stable operation and efficient energy utilization of the system during the charging process. Through this modeling method, the present invention realizes the dynamic scheduling of charging pile power and further improves the stability of grid load, so that the charging pile system can adapt to different load conditions and charging demand fluctuations.

[0095] In this embodiment, the power scheduling problem of the charging pile system adopts a stochastic differential equation model, so that random factors can be taken into account when dealing with changes in charging pile power and grid load. In traditional scheduling methods, grid load changes and charging demand are often regarded as deterministic factors, while in practical applications, their changes are usually unpredictable and uncertain. In order to overcome this uncertainty, we introduce a stochastic differential equation model to describe the dynamic process of grid load and charging pile power scheduling as a random process.

[0096] Specifically, when the grid load changes and the charging demand fluctuates, the power scheduling of the charging piles is based on the real-time load conditions of the grid and the demand of the charging piles, and the power output is calculated and adjusted to maintain the stability of the system.

[0097] In general, the power of the charging pile and the grid load state can be expressed by the following stochastic differential equation:

[0098] dL(t)=μ L dt+σ L dW L (t);

[0099] dp(t)=μ p dt+σ p dW p (t);

[0100] Among them, μ L and μ p are drift terms, representing the average rate of change of grid load and charging pile power, σ L and σ p is volatility, W L (t) and W p (t) is the standard Brownian motion, indicating random disturbance, L(t) indicates the state of grid load changing with time, and p(t) indicates the state of charging pile power changing with time.

[0101] As an option, the fluctuation of grid load and charging pile power is not only affected by charging demand, but also related to environmental factors, charging pile operation status, etc. Through the modeling of the above stochastic differential equations, we can effectively predict the fluctuation of grid load and the change of charging demand in actual operation, and adjust the power output of the charging pile in time.

[0102] Specifically, the present invention dynamically models the changes in grid load and charging pile power by introducing stochastic differential equations, and adjusts the power output of the charging pile according to the real-time monitored grid load and charging demand fluctuations. Using this method, the charging pile can make power scheduling decisions in real time according to the changes in grid load, maintain the balance of grid load, and avoid system overload.

[0103] In some embodiments, the charging pile system can accurately predict the fluctuation trend of grid load and charging demand by solving stochastic differential equations. The charging pile adjusts the power output based on this prediction information to minimize the fluctuation of power scheduling and optimize the stability of grid load. For example, when the grid load suddenly increases, the charging pile system can respond quickly, reduce the power output of the charging pile, and reduce the burden on the grid; when the grid load decreases, the power output of the charging pile can be appropriately increased to improve charging efficiency.

[0104] In one possible implementation, the charging pile adjusts the power output strategy based on historical data and real-time load changes. Specifically, when the charging pile predicts that the grid load will increase, the charging pile system will reduce the power output in advance, and vice versa. Through this dynamic adjustment, the charging pile can smooth the power output and avoid the grid load fluctuation from having a greater impact on the system, thereby improving the stability and efficiency of the overall system.

[0105] In this embodiment, the coordinated optimization of charging pile power scheduling and grid load not only improves the stability of the grid, but also improves the charging efficiency of the charging pile system. Through reasonable power scheduling, the charging pile system can maximize the charging speed when the grid load is low, and reduce power output when the grid load is high to avoid grid overload.

[0106] In addition, the advantage of using stochastic differential equations to model the grid load and charging pile power dispatch process is that it can dynamically adjust the power output based on historical data and real-time changes, which enables the system to effectively cope with sudden changes in grid load and fluctuations in charging demand. This method improves the adaptive ability of the charging pile system and ensures the stable operation of the charging pile under different grid load conditions.

[0107] By using this dynamic optimization method, the charging pile system can not only improve charging efficiency, but also make flexible adjustments during peak and valley periods of grid load to avoid overload problems, reduce energy efficiency losses, and improve the overall operation efficiency of the grid. This implementation method can make the coordination between charging piles and the grid more efficient, thus providing a more feasible and stable solution for the construction of charging pile networks.

[0108] S4. Combined with game theory, optimize the communication bandwidth selection between charging piles so that the communication bandwidth is optimally allocated among the charging piles;

[0109] In order to achieve efficient communication and stable operation between charging piles, game theory is used to optimize the allocation of communication bandwidth between charging piles. The communication bandwidth between charging piles directly affects the speed and reliability of data transmission. Especially when multiple charging piles are running at the same time, the reasonable allocation of bandwidth resources is particularly important. In order to ensure that the information exchange between the charging piles in the system is not restricted, the present invention optimizes the allocation of bandwidth resources between charging piles through a game theory model, thereby improving the overall communication efficiency, reducing communication delays, and ensuring the real-time performance of the system.

[0110] In this embodiment, the bandwidth selection and allocation between charging piles are optimized through the information game model in game theory. In the charging pile system, the communication demand of each charging pile is affected by multiple factors such as its power scheduling demand, the coordination demand with other charging piles, and the system load status. Each charging pile can be regarded as a "player" in the game, and its goal is to optimize its communication effect by selecting an appropriate bandwidth strategy based on the current bandwidth resource allocation of the system.

[0111] Specifically, the bandwidth selection problem of charging piles can be expressed as the following game model:

[0112]

[0113] in:

[0114] U i (x i ,x -i ) represents the utility function of charging pile i, x i is the bandwidth selection of charging pile i, S i is the signal power of charging pile i, N i is the communication noise power, λ i is the bandwidth consumption cost coefficient of charging pile i, γ i is the utility weight of charging pile i, x -i Indicates the bandwidth selection of other charging piles except charging pile i.

[0115] This utility function comprehensively considers the throughput and bandwidth consumption cost of the charging pile communication. The communication throughput is determined by the ratio of signal power to noise power, and the bandwidth consumption cost is determined by the bandwidth selection x i Multiply by the bandwidth consumption factor λ i In the game process, the charging pile selects the most appropriate bandwidth allocation strategy by maximizing its own utility function.

[0116] In this game model, each charging pile optimizes its own utility by adjusting its own bandwidth selection, while taking into account the bandwidth selection of other charging piles. During the game, the ultimate goal of the charging pile is to find a bandwidth allocation scheme that maximizes the total utility of the system. According to the Nash equilibrium principle of game theory, when each charging pile selects the optimal bandwidth strategy, the system reaches Nash equilibrium. In this equilibrium state, no charging pile can unilaterally improve its own utility by adjusting the bandwidth.

[0117] In some embodiments, the bandwidth selection of charging piles is limited by system load and bandwidth resources. When selecting bandwidth, each charging pile will continuously adjust according to the current bandwidth allocation and system load status through a game process until each charging pile meets its communication needs and maximizes the communication efficiency of the overall system.

[0118] As an option, the game theory model in the present invention will dynamically adjust the bandwidth selection between charging piles according to the changes in communication requirements and the fluctuations in system bandwidth resources. Specifically, when the grid load is high or the number of charging piles increases, the bandwidth resources may be limited, so the bandwidth allocation strategy of each charging pile needs to be flexibly adjusted. At this time, the charging pile will select the appropriate bandwidth through the game process according to the signal quality, communication requirements and bandwidth resources to ensure that each charging pile in the system can communicate with other charging piles efficiently and minimize communication delays.

[0119] Generally speaking, as the number of charging piles increases, the competition for communication bandwidth resources will become more intense. The game theory optimization method adjusts the allocation of bandwidth resources so that each charging pile can efficiently transmit data under limited bandwidth conditions and maximize its communication utility. For example, when the communication demand of a charging pile is high, the game model will allocate more bandwidth resources to the charging pile based on the bandwidth selection of other charging piles.

[0120] In one possible implementation, the system uses spectrum allocation algorithms and bandwidth selection strategies to ensure that communication between charging piles is not interfered with under limited bandwidth and minimizes delays. This approach can improve the system throughput and make communication between charging piles more efficient, thereby improving the overall performance of the system.

[0121] S5. Based on Lyapunov stability theory, the stability analysis of the coordinated dispatching process of charging piles and power grid is carried out.

[0122] In order to ensure the stable operation of the charging pile system in the dynamic grid load changes, the Lyapunov stability theory is used to analyze the stability of the coordinated dispatching process of the charging pile and the grid. There is a complex interdependence between the charging pile and the grid. The change of the grid load will directly affect the power dispatching of the charging pile, and the power dispatching of the charging pile will affect the grid load. In this complex coordinated dispatching process, the stability of the system is crucial. The Lyapunov stability theory provides an effective tool for analyzing the stability of the system, which can help determine the response behavior of the charging pile system when facing grid load fluctuations, thereby ensuring that the charging pile and the grid can be maintained in a balanced state together.

[0123] In this embodiment, the coordinated scheduling process of the charging pile and the power grid can be regarded as a dynamic system, and the relationship between the grid load and the power output of the charging pile is modeled by stochastic differential equations (SDEs). In order to analyze the stability of the system, the Lyapunov stability theory is introduced. Specifically, by constructing a Lyapunov function, the stability problem of the system is transformed into judging the changes of the function in time evolution. If the Lyapunov function tends to the minimum value at time t→∞, it means that the system is stable.

[0124] Specifically, the dynamic process of grid load and charging pile power scheduling can be expressed as follows:

[0125] dL(t)=μ L dt+σ L dW L (t);

[0126] dp(t)=μ p dt+σ p dW p (t);

[0127] Among them, μ L and μ p are drift terms, representing the average rate of change of grid load and charging pile power, σ L and σ p is volatility, W L (t) and W p (t) is the standard Brownian motion, indicating random disturbance, L(t) indicates the state of grid load changing with time, and p(t) indicates the state of charging pile power changing with time.

[0128] These equation models provide the evolution law of the system on the time axis. On this basis, this embodiment further defines the Lyapunov function of the system to analyze the stability of the system.

[0129] In this embodiment, the Lyapunov function is constructed as the weighted sum of the charging pile power and the grid load. Specifically, the Lyapunov function V(p(t), L(t)) of the system can be defined as:

[0130]

[0131] in:

[0132] p(t) is the power output of the charging pile, p * (t) is the optimal power output of the charging pile, L(t) represents the grid load, and L target is the target load of the power grid.

[0133] Specifically, the Lyapunov function measures the stability of the system by measuring the deviation between the charging pile power and the grid load and its optimal value. If the Lyapunov function tends to zero, it means that the system tends to be stable.

[0134] In general, the derivative of the Lyapunov function It can be used to analyze the stability of the system. By taking the derivative of the Lyapunov function, the stability criterion of the system can be obtained. If the system meets the following conditions:

[0135]

[0136] This indicates that the system is in a stable state.

[0137] As an option, the stability analysis of the system can be performed by calculating the derivative of the Lyapunov function. In the coordinated scheduling of the grid load and the charging pile power, when the charging pile power and the grid load change, the change trend of the Lyapunov function can reflect whether the system tends to be stable. For example, when the load is high, the power output of the charging pile will be reduced to avoid grid overload. At this time, the value of the Lyapunov function will gradually decrease, and the system will tend to be stable.

[0138] In this way, the Lyapunov stability theory ensures that the system can respond promptly when the grid load changes, avoiding instability in the system. For example, if the grid load suddenly increases, the charging pile will reduce the burden on the grid by reducing power output, and as the system gradually returns to balance, the Lyapunov function will tend to zero, and the system will eventually return to a stable state.

[0139] This embodiment realizes the stability analysis of the coordinated scheduling process between the charging pile system and the power grid load by applying the Lyapunov stability theory. By constructing the Lyapunov function and taking its derivative, the system can effectively cope with the fluctuation of the power grid load and the change of charging demand, and ensure the stability of the charging pile power scheduling process.

[0140] In actual operation, when the changes in grid load and charging pile power are disturbed, Lyapunov stability theory helps determine whether the system can automatically adjust and return to a stable state. Through this method, the charging pile system can dynamically adapt to changes in grid load and remain stable in an environment with large load fluctuations, thereby improving the coordinated operation efficiency of the charging pile and the grid.

[0141] Through the Lyapunov stability theory, the coordinated dispatching process of charging piles and power grids is effectively guaranteed to be stable. This method ensures that the system can recover quickly in the face of external disturbances and internal fluctuations, and can maintain charging efficiency and grid load balance during system operation, thereby achieving long-term stable operation of the system.

[0142] The split-type charging pile system described below and the charging pile networking communication method described above can refer to each other.

[0143] Please see attached Figure 2 , a split charging pile system, comprising the following steps:

[0144] A charging unit module is used to perform an electric energy transmission operation, transmit electric energy to the electric vehicle, and realize a charging function;

[0145] A control unit module is used to control the charging process in real time and perform communication operations between charging piles;

[0146] The grid load monitoring module is used to monitor the load status of the grid and adjust the power output of each charging pile according to the grid load conditions;

[0147] The communication module is used for communication between charging piles. It uses a bandwidth allocation algorithm based on information game theory to optimize the bandwidth allocation between charging piles.

[0148] The resource scheduling module uses the Nash equilibrium principle and optimal control algorithm in game theory to optimize the scheduling of resources between charging piles according to the power scheduling requirements of charging piles and the load conditions of the power grid;

[0149] The stability analysis module performs stability analysis on the charging pile power scheduling and grid load change process based on Lyapunov stability theory;

[0150] The data processing and prediction module is used to analyze and process historical data, adjust the power output strategy of the charging pile according to the real-time load and charging demand forecast, so as to cope with the randomness of grid load fluctuations and charging demand changes.

[0151] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A charging pile networking communication method, characterized in that: The following steps are involved: Based on the power dispatching requirements of charging piles and the load conditions of the power grid, a game model between charging piles is established, and the Nash equilibrium principle in game theory is used to achieve optimal dispatching of resources between charging piles. Based on the power dispatch of charging piles, the optimal control method is used to coordinate the power output of charging piles; Use stochastic differential equations for charging pile power and grid load conditions to handle grid load fluctuations and charging demand uncertainties; Combined with game theory, the communication bandwidth selection between charging piles is optimized so that the communication bandwidth is optimally allocated among the charging piles; Based on Lyapunov stability theory, the stability analysis of the coordinated dispatching process of charging piles and power grid is carried out.

2. A charging pile networking communication method according to claim 1, characterized in that: The optimization scheduling of resources between charging piles includes: Each charging pile in the game model is regarded as a "player", and the strategy is adjusted according to the mutual influence between the charging pile power and the load to achieve overall load balance and maximize energy efficiency; In the process of mutual game between charging piles, Nash equilibrium is used to ensure that each charging pile maximizes its own benefits without changing the strategies of other charging piles.

3. A charging pile networking communication method according to claim 1, characterized in that: The charging pile power scheduling includes: The optimal control method adjusts the power output of the charging pile so that the grid load can be kept within the set target range; By minimizing energy efficiency losses, the overall charging efficiency of the charging pile system is improved.

4. A charging pile networking communication method according to claim 1, characterized in that: The stochastic differential equation includes: By using stochastic differential equations, the dynamic processes of charging piles and grid loads can be accurately modeled, taking into account the randomness of grid load fluctuations and charging demand changes; The model can adjust power scheduling according to historical data and real-time load changes to reduce system fluctuations.

5. A charging pile networking communication method according to claim 1, characterized in that: The game theory includes: The information game theory optimization process enables charging piles to communicate effectively under limited bandwidth through spectrum allocation and bandwidth selection algorithms; Each charging pile is optimized based on its communication needs and bandwidth allocation using strategies from game theory to ensure communication efficiency and reduce latency.

6. A charging pile networking communication method according to claim 1, characterized in that: The Lyapunov stability theory includes: The Lyapunov stability theory is used to analyze dynamic instability factors that are prone to occur during charging pile power scheduling and grid load changes; By constructing the Lyapunov function, the stability of the system in the face of external disturbances and internal fluctuations is analyzed.

7. A charging pile networking communication method according to claim 3, characterized in that: The charging pile power scheduling further includes: Dynamically adjust the power output of each charging pile based on real-time changes in grid load; The adjustment process introduces a grid load feedback mechanism so that the power output of the charging pile can respond to the fluctuation of the grid load in a timely manner, thereby optimizing the response capability of the system. In this way, the charger is able to reduce power output during peak load periods.

8. A charging pile networking communication method according to claim 4, characterized in that: The stochastic differential equation model further comprises: The dynamic process of charging pile power and grid load is modeled by introducing random disturbance terms. The core of the stochastic differential equation is to use historical data and real-time changes for prediction; The stochastic differential equation can reflect the power adjustment of the charging pile during the peak or valley period of charging demand, making the grid load more stable in a fluctuating environment; Through this modeling method, charging piles can more flexibly respond to the randomness of grid load and charging demand, reducing power scheduling fluctuations and energy efficiency losses.

9. A charging pile networking communication method according to claim 5, characterized in that: The game theory further includes: By dynamically allocating spectrum resources and combining them with a game theory strategy model, bandwidth resources are optimized to minimize communication delays between charging piles. Each charging pile determines the bandwidth allocation strategy based on its communication needs through the game theory optimization algorithm, so that under limited bandwidth conditions, real-time and efficient communication can be achieved between charging piles, reducing transmission delays and improving system throughput.

10. A split-type charging pile system, applied to a charging pile networking communication method according to any one of claims 1 to 9, characterized in that: The following steps are involved: A charging unit module is used to perform an electric energy transmission operation, transmit electric energy to the electric vehicle, and realize a charging function; A control unit module is used to control the charging process in real time and perform communication operations between charging piles; The grid load monitoring module is used to monitor the load status of the grid and adjust the power output of each charging pile according to the grid load conditions; The communication module is used for communication between charging piles. It uses a bandwidth allocation algorithm based on information game theory to optimize the bandwidth allocation between charging piles. The resource scheduling module uses the Nash equilibrium principle and optimal control algorithm in game theory to optimize the scheduling of resources between charging piles according to the power scheduling requirements of charging piles and the load conditions of the power grid; The stability analysis module performs stability analysis on the charging pile power scheduling and grid load change process based on Lyapunov stability theory; The data processing and prediction module is used to analyze and process historical data, adjust the power output strategy of the charging pile according to the real-time load and charging demand forecast, so as to cope with the randomness of grid load fluctuations and charging demand changes.

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