Distributed regulation and control method for multi-stage electricity-hydrogen integrated charging station group

Abstract

The invention discloses a multi-stage electricity and hydrogen integrated charging station group distributed regulation and control method. The method comprises three stages: an inter-charging-station optimization stage, an inter-charging-station energy distributed transaction stage and a charging station network optimization stage. In the inter-station optimization stage of the charging stations, each charging station performs inter-station optimization regulation and control according to information such as prediction data; in the inter-station energy distributed transaction stage of the charging stations, each charging station adopts a related algorithm to carry out distributed transaction according to the regulation and control result and the distributed transaction strategy of the inter-station optimization stage; and in the station network optimization stage of the charging stations, each charging station performs station network optimization regulation and control according to the transaction result of the inter-station energy distributed transaction stage. According to the regulation and control method, the electricity-hydrogen integrated charging station group can be efficiently scheduled through related strategies and algorithms under a distributed framework.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization and control of a group of electric-hydrogen integrated filling stations, and specifically to a distributed control method for a group of multi-stage electric-hydrogen integrated filling stations. Background Art

[0002] With the current global energy structure transformation and increasingly stringent environmental protection requirements, the electric-hydrogen integrated energy system, as a new type of green energy system, is gradually becoming an important way to achieve efficient energy utilization. It effectively combines electric energy and hydrogen energy to promote the diversified use of energy and provide support for the flexible dispatch of power grids. Driven by this technological advancement, the electric-hydrogen integrated energy supply station has become a key infrastructure for the comprehensive supply of electricity and hydrogen energy.

[0003] However, with the development of productivity, the scale of the electric hydrogen system continues to increase, and the limitations of the traditional centralized control framework are becoming more and more obvious, especially in terms of scalability and computational burden. At present, a number of research results on the electric hydrogen integrated filling station group have been published, generally focusing on system modeling and centralized collaborative optimization, while there are relatively few studies on distributed collaborative optimization. However, the existing distributed collaborative optimization methods still face some challenges in application, such as the algorithm iteration time is too long, resulting in insufficient system response speed, and it is difficult to meet the requirements of high efficiency and the requirements of the distribution network. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies in the prior art and propose a distributed control method for a multi-stage integrated electric-hydrogen filling station group, which can quickly dispatch the integrated electric-hydrogen filling station group under a distributed framework and improve the overall efficiency in the distributed collaborative optimization process, thereby better meeting the efficient scheduling and operation requirements of the integrated electric-hydrogen filling station group and improving the system economy.

[0005] In order to solve the above technical problems, the technical solution of the present invention is:

[0006] A distributed control method for a multi-stage electric-hydrogen integrated charging station group comprises the following steps:

[0007] Step S100, establishing an objective function with the goal of economic optimization as the inter-station optimization model of the filling station, the inter-station optimization model of the filling station performs optimization scheduling according to the inter-station transaction price, predicted electricity load, predicted hydrogen load, and predicted photovoltaic power generation of each filling station, and the optimization result of the optimization scheduling includes the inter-station purchase and sale of electricity power and purchase and sale of hydrogen flow at each time;

[0008] Step S200: Each filling station adopts a distributed trading strategy and algorithm to conduct distributed trading of hydrogen energy and electric energy according to the optimization result of step S100 and the topological relationship between the filling stations, and determines the trading electric energy power and trading hydrogen energy flow at each moment;

[0009] Step S300, establish a filling station network optimization model with the goal of minimizing the total cost of purchasing and selling energy to the superior power grid and hydrogen network. Each filling station uses the filling station network optimization model to optimize the station network scheduling based on the transaction power and transaction hydrogen flow at each moment, as well as the station network transaction price, predicted electricity load, predicted hydrogen load, and predicted photovoltaic power generation power. The power flow is verified and optimized through the forward and backward method to generate the optimal control strategy for each filling station.

[0010] Preferably, in step S100, the objective function of the inter-station optimization model of the filling station is to minimize the total cost of purchasing and selling electricity and hydrogen energy, and the expression is:

[0011] ;

[0012] in, , express The price of electricity purchased and sold between charging stations at all times; , express The price of buying and selling hydrogen between filling stations at any given moment; , express Time filling station Purchase and sale of electrical energy; , express Time filling station Purchase and sell hydrogen energy flow.

[0013] Preferably, the filling station inter-station optimization model is provided with constraints, which include electricity network constraints, hydrogen network constraints and electricity-hydrogen coupling constraints.

[0014] Preferably, the electric network constraints include power balance constraints, solar abandonment constraints, electric energy storage system constraints, and electric energy storage charge state constraints;

[0015] The power balance constraint is expressed as follows:

[0016] ;

[0017] In the formula, express Time filling station Electrical load power; express Time filling station Input power of electrolyzer equipment; , Respectively Time filling station Charging power and discharging power of electric energy storage equipment; express Time filling station Output power of photovoltaic power generation equipment;

[0018] The light abandonment constraint is expressed as follows:

[0019] ;

[0020] In the formula, express Time filling station Abandoned power of photovoltaic power generation equipment; express Time filling station Power generation capacity of photovoltaic power generation equipment;

[0021] The electric energy storage system constraint is expressed as follows:

[0022] ;

[0023] In the formula, , is a binary variable to ensure Time filling station Electrical energy storage devices exist in only one charging and discharging state; , Indicates the upper limit of charging and discharging power of the electric energy storage equipment at each charging station;

[0024] The electric energy storage charge state constraint:

[0025] ;

[0026] In the formula, express Time filling station State of charge of the electrical energy storage device; , Indicates the upper and lower limits of the state of charge of the energy storage equipment at each charging station; Indicates filling station Capacity of electrical energy storage equipment; , Indicates the charging and discharging efficiency of the electric energy storage equipment at each charging station; Optimize time cycles for each filling station.

[0027] Preferably, the hydrogen network constraints include hydrogen energy balance constraints and hydrogen energy storage constraints.

[0028] Hydrogen energy balance constraints:

[0029] ;

[0030] In the formula, express Time filling station Hydrogen load flow rate; express Time filling station The hydrogen output flow rate of the electrolyzer equipment; , express Time filling station The hydrogen charging and discharging flow of hydrogen energy storage equipment.

[0031] The hydrogen energy storage constraint is expressed as follows:

[0032] ;

[0033] In the formula, , is a binary variable to ensure Time filling station Hydrogen energy storage devices only exist in one charging and discharging state; , Indicates the upper limit of charging and discharging power of hydrogen energy storage equipment at each filling station; express Time filling station The total amount of hydrogen stored in the hydrogen energy storage device; Indicates filling station The upper limit of hydrogen storage capacity of hydrogen energy storage equipment.

[0034] Preferably, the electric-hydrogen coupling constraint is an electrolyzer equipment constraint, and the expression is as follows:

[0035] ;

[0036] In the formula, Indicates the efficiency of the electrolyzer equipment at each filling station; Indicates the upper limit of the input power of the electrolyzer equipment at each charging station.

[0037] Preferably, the step S200 specifically includes:

[0038] Step S210: taking the purchase and sale hydrogen energy flow in step S100 as the initial hydrogen energy imbalance, and obtaining the hydrogen energy flow after the transaction through iterative distributed transaction algorithm;

[0039] Step S220: Obtain the convergence result of hydrogen energy trading according to the distributed exchange algorithm in step 210, so as to determine whether to convert the hydrogen energy imbalance into equivalent purchased power and generate an initial electric energy imbalance;

[0040] Step S230: adopting a distributed trading algorithm to iterate the initial electric energy imbalance to obtain the electric energy power after the transaction.

[0041] Preferably, the distributed transaction algorithm comprises the following steps:

[0042] First, determine the adjacency matrix and node imbalance;

[0043] Modify the adjacency matrix based on the imbalance amount and generate weight coefficients;

[0044] The imbalance is updated iteratively until one of the following convergence conditions is met: global balance, all imbalances are positive or negative, and the preset upper limit of iterations is reached.

[0045] Preferably, in step S300, the filling station network optimization model is expressed as:

[0046] ;

[0047] In the formula, , express The price of electricity purchased and sold by the charging station to the upper grid at any given moment; , express The filling station always sells hydrogen energy at the price of the superior hydrogen network.

[0048] Preferably, the filling station network optimization model is provided with constraints, which include electricity balance constraints, solar power abandonment constraints and hydrogen energy balance constraints.

[0049] Preferably, the distributed trading algorithm includes an anomaly detection mechanism, and when it is detected that the global imbalance is negative and has a large negative value, the sign of the imbalance is reversed and it is iterated again.

[0050] The present invention has the following characteristics and beneficial effects:

[0051] The above technical solution includes three stages: the inter-station optimization stage, in which each charging station performs inter-station optimization and regulation based on prediction data and other information, thereby avoiding the computational pressure brought by the centralized framework; the inter-station energy distributed trading stage, in which each charging station uses relevant algorithms to perform distributed trading based on the control results and distributed trading strategies of the inter-station optimization stage, effectively improving the calculation speed; the station network optimization stage, in which each charging station performs station network optimization and regulation based on the trading results of the inter-station energy distributed trading stage, making full use of the energy complementarity between charging stations, thereby improving the economic benefits of the system. Under the distributed framework, this control method uses relevant strategies and algorithms to quickly dispatch the electric-hydrogen integrated charging station group, avoiding the computational pressure under the centralized framework, and improving the overall efficiency of the distributed collaborative optimization process, effectively meeting the efficient dispatching and operation requirements of the electric-hydrogen integrated charging station group, thereby improving the economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 A flow chart of a distributed optimization and control method for a multi-stage electric-hydrogen integrated charging station group proposed by the present invention;

[0054] Figure 2 This is a schematic diagram of the structure of the electric-hydrogen integrated filling station group;

[0055] Figure 3 The distributed algorithm flow chart for the distributed transaction phase of energy between charging stations is provided;

[0056] Figure 4 This is the result diagram of the power balance of node 6 during the inter-station optimization phase of the charging station;

[0057] Figure 5 This is the hydrogen energy balance result diagram of node 6 in the inter-station optimization stage of the filling station;

[0058] Figure 6 This is the result diagram of distributed energy transaction of node 6 during the distributed energy transaction stage between charging stations;

[0059] Figure 7 This is the hydrogen energy distributed transaction result diagram of node 6 in the distributed energy transaction stage between charging stations;

[0060] Figure 8 This is the energy balance result diagram of node 6 in the charging station network optimization stage;

[0061] Fig. 9 This is the hydrogen energy balance result diagram of node 6 in the filling station network optimization stage;

[0062] Fig.10 This is the flow verification result diagram of the charging station network optimization stage. DETAILED DESCRIPTION

[0063] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0064] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0065] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0066] The present invention provides a distributed control method for a multi-stage electric hydrogen integrated filling station group, such as Figure 2 As shown, in this embodiment, the electric-hydrogen integrated filling station includes an electric energy storage module, a hydrogen energy storage module, an electrolyzer module, and a possible photovoltaic power generation module. The electrolyzer module uses electric energy to electrolyze water to produce hydrogen, and stores hydrogen in the hydrogen energy storage or meets the hydrogen energy load demand; the electric energy storage module is used to cooperate with the electrolyzer module to absorb the output of the photovoltaic system.

[0067] It should be noted that this embodiment involves a distributed control method for a multi-stage electric hydrogen charging station group. In the inter-station optimization stage, each charging station performs inter-station optimization and control based on prediction data and other information; in the inter-station energy distributed trading stage, each charging station uses relevant algorithms to perform distributed trading based on the control results and distributed trading strategies in the inter-station optimization stage; in the station network optimization stage, each charging station performs station network optimization and control based on the trading results in the inter-station energy distributed trading stage. The flow chart of the related distributed algorithm in the inter-station energy distributed trading stage is shown in the figure. Figure 3 As shown. To illustrate the effect of the present invention, a certain electric hydrogen filling station group is taken as the implementation object of the present invention. The electric hydrogen filling station group in this embodiment includes 6 electric hydrogen filling stations. Among them, the No. 2, No. 3, and No. 6 electric hydrogen filling stations are equipped with photovoltaic power generation modules. The parameters of the electric hydrogen filling station group are shown in Table 1.

[0068]

[0069] Table 1: Equipment parameters of the electric-hydrogen integrated charging station group

[0070] The method of the present invention is described in detail below. Figure 1 As shown, the specific steps are as follows:

[0071] Step S100: Inter-station optimization stage. Each charging station optimizes the charging station based on the inter-station transaction price and predicted power load. , predict hydrogen load , predict photovoltaic power generation And the inter-station optimization model of charging stations is used for optimal scheduling. Finally, each charging station obtains the inter-station purchase and sale power at each time. , and hydrogen flow , .

[0072] The inter-station optimization model of charging stations consists of economic objective functions, power network constraints, hydrogen network constraints, and power-hydrogen coupling constraints. The specific model is:

[0073] Objective function:

[0074]

[0075] In the formula, , express The price of electricity purchased and sold between charging stations at all times; , express The price of buying and selling hydrogen between filling stations at any given moment; , express Time filling station Purchase and sale of electrical energy; , express Time filling station Purchase and sell hydrogen energy flow.

[0076] Electric network constraints:

[0077] Electric network constraints include power balance constraints, solar curtailment constraints, electric energy storage system constraints, and electric energy storage charge state constraints.

[0078] Power balance constraints:

[0079]

[0080] In the formula, express Time filling station Electrical load power; express Time filling station Input power of electrolyzer equipment; , Respectively Time filling station Charging power and discharging power of electric energy storage equipment; express Time filling station Photovoltaic power generation equipment output power.

[0081] Abandonment Constraints:

[0082]

[0083] In the formula, express Time filling station The abandoned light power of photovoltaic power generation equipment. express Time filling station Power generation capacity of photovoltaic power generation equipment;

[0084] Electric energy storage system constraints:

[0085]

[0086] In the formula, , is a binary variable to ensure Time filling station Electrical energy storage devices exist in only one charging and discharging state; , Indicates the upper limit of charging and discharging power of the energy storage equipment at each charging station.

[0087] Energy storage state of charge constraints:

[0088]

[0089] In the formula, express Time filling station State of charge of the electrical energy storage device; , Indicates the upper and lower limits of the state of charge of the energy storage equipment at each charging station; Indicates filling station Capacity of electrical energy storage equipment; , Indicates the charging and discharging efficiency of the electric energy storage equipment at each charging station; Optimize time cycles for each filling station.

[0090] Furthermore, hydrogen network constraints include hydrogen energy balance constraints and hydrogen energy storage constraints.

[0091] Hydrogen energy balance constraints:

[0092]

[0093] In the formula, express Time filling station The hydrogen output flow rate of the electrolyzer equipment; , express Time filling station The hydrogen charging and discharging flow of hydrogen energy storage equipment.

[0094] Hydrogen storage constraints:

[0095]

[0096] In the formula, , is a binary variable to ensure Time filling station Hydrogen energy storage devices only exist in one charging and discharging state; , Indicates the upper limit of charging and discharging power of hydrogen energy storage equipment at each filling station; express Time filling station The total amount of hydrogen stored in the hydrogen energy storage device; Indicates filling station The upper limit of hydrogen storage capacity of hydrogen energy storage equipment.

[0097] Furthermore, the constraints on electricity-hydrogen coupling are electrolyzer equipment constraints.

[0098] Electrolyzer equipment constraints:

[0099]

[0100] In the formula, Indicates the efficiency of the electrolyzer equipment at each filling station; Indicates the upper limit of the input power of the electrolyzer equipment at each charging station.

[0101] Further configuration of this embodiment, step S200: distributed energy trading stage between charging stations. Each charging station buys and sells electric energy based on its own topological relationship and power at each time. , and hydrogen flow , , using distributed trading strategies and related algorithms to calculate the trading power at each moment , and trading hydrogen flows , Specifically, the following steps are included:

[0102] Step S210: Hydrogen energy trading at filling stations. The purchase and sale flow of each filling station to other filling stations obtained in step S100 is calculated. , As the initial hydrogen energy imbalance , using the relevant distributed transaction algorithm for iteration. After the iteration is completed, the flow rate of each filling station after the transaction is obtained. , .

[0103] Step S220: Determine the unbalanced amount of electric energy at the charging station. Refer to the convergence result of the distributed trading algorithm in step S210 Determine the electrical energy imbalance between charging stations.

[0104] If the convergence result is that all hydrogen energy imbalances are negative, it means that there is a demand for hydrogen purchase in the charging station group as a whole. At this time, if the price of direct hydrogen purchase is higher than the price of purchasing electricity for hydrogen supply, the hydrogen energy imbalance will be converted into an equal amount of hydrogen supply electricity purchase power. Considering the working conditions of the electrolyzers at each filling station, the power purchased for hydrogen supply The power purchased and sold from other filling stations obtained by each filling station in step S100 , The sum will be used as the initial power imbalance ,Right now:

[0105]

[0106] In the formula for or .

[0107] If the convergence result is other, the purchase and sale power of each filling station to other filling stations obtained in step S100 is , As the initial power unbalance ,Right now:

[0108]

[0109] In the formula for or .

[0110] Step S230: Charging station power trading. Use the relevant distributed trading algorithm to calculate the initial power imbalance determined in step S220. After the iteration is completed, the power purchased and sold by each charging station after the transaction , .

[0111] The relevant distributed transaction algorithms mentioned in the above steps are as follows:

[0112] 1) Determine the overall adjacency matrix and unbalance

[0113] Using the matrix Represents the node connection relationship of the graph, where:

[0114]

[0115] in , indicating that the node is connected to itself.

[0116] Using column vectors Represents the imbalance of each node (positive means surplus, negative means loss), where:

[0117]

[0118] 2) Based on the amount of imbalance With the adjacency matrix Generate correction matrix

[0119] Regenerate the adjacency matrix based on the imbalance .

[0120]

[0121] From the adjacency matrix Determine the weight coefficient of each node .

[0122]

[0123] The corrected matrix The elements in are:

[0124]

[0125] 3) Unbalance Iterative Updates

[0126]

[0127] 4) Anomaly detection and processing

[0128] When it is detected that the total unbalanced value is negative and there is a large negative value in the unbalanced value, it is necessary to reverse the sign of the unbalanced value for iteration. That is, when:

[0129]

[0130] in is a large positive number.

[0131] at this time

[0132]

[0133] 5) Convergence conditions

[0134] There are 4 convergence conditions:

[0135] Convergence condition 1: Global equilibrium

[0136]

[0137] in is a very small positive number.

[0138] Convergence condition 2: All imbalances are positive

[0139]

[0140] Convergence condition 3: All imbalances are negative

[0141]

[0142] Convergence condition 4: reaching the upper limit of the number of iterations

[0143] .

[0144] Step S300: Charging station network optimization stage. Each charging station will optimize the network according to the transaction results, station network transaction price, and predicted load. , predict hydrogen load , predict photovoltaic power generation The optimization model of the charging station network is used for optimization scheduling. The overall power flow is verified and optimized through the forward-backward method to ensure the effectiveness of the optimization results, and finally form an optimized operation plan for each charging station.

[0145] The charging station network optimization model consists of economic objective functions, power network constraints, hydrogen network constraints, and power-hydrogen coupling constraints. The charging station network optimization model is similar to the inter-station optimization model of charging stations, with only differences in economic objective functions, power balance constraints, solar curtailment constraints, and hydrogen balance constraints, as follows:

[0146] Objective function:

[0147]

[0148] In the formula, , express The price of electricity purchased and sold by the charging station to the upper grid at any given moment; , express The filling station always sells hydrogen energy at the price of the superior hydrogen network.

[0149] Power balance constraints:

[0150]

[0151] Abandonment Constraints:

[0152]

[0153] Hydrogen energy balance constraints:

[0154]

[0155] In order to demonstrate the effectiveness of the method, a specific example is given below. The present invention selects a typical day for simulation experiment. The embodiment of the present invention simulates the multi-stage electric hydrogen filling integrated station group distributed control method on the Python platform. Figure 4 , Figure 5 The results of the electricity and hydrogen energy balance of the No. 6 electric-hydrogen integrated charging station during the inter-charging station optimization phase are shown; Figure 6 , Figure 7 The results of electricity and hydrogen energy trading at the No. 6 electric-hydrogen integrated charging station during the inter-charging station distributed energy trading phase were displayed; Figure 8 , Fig. 9 The results of the electricity and hydrogen energy balance of the No. 6 electric-hydrogen integrated charging station in the station network optimization stage are shown; Fig.10 The voltage results of the integrated electric and hydrogen filling station group at different times are displayed.

[0156] In the inter-station optimization stage of filling stations, the No. 6 electric-hydrogen integrated filling station cooperates with the electric energy storage module, hydrogen energy storage module, photovoltaic power generation module and electrolyzer module to effectively utilize solar energy to optimize the inter-station through the inter-station optimization model of the filling station, and determines the inter-station purchase and sale of electric energy power and hydrogen flow. In the inter-station distributed energy trading stage of the filling station, the No. 6 filling station conducts distributed inter-station energy trading according to the distributed trading strategy, the purchase and sale of electric energy power, the purchase and sale of hydrogen flow and related algorithms determined in the inter-station optimization stage of the filling station, and determines the transaction power and transaction hydrogen flow with other filling stations. In the station network optimization stage of the filling station, the No. 6 filling station considers the forecast data and the transaction data determined between the stations in the inter-station distributed energy trading stage of the charging station, and optimizes the station network through the station network optimization model of the charging station to determine the final optimization plan. It completes the rapid coordination and optimization of the filling station under the distributed framework, effectively meeting the efficient scheduling and operation requirements of the electric-hydrogen integrated filling station group.

[0157] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments including components are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

Claims

1. A distributed control method for a multi-stage electric hydrogen integrated filling station group, characterized in that: The following steps are involved: Step S100, establishing an objective function with the goal of economic optimization as the inter-station optimization model of the filling station, the inter-station optimization model of the filling station performs optimization scheduling according to the inter-station transaction price, predicted electricity load, predicted hydrogen load, and predicted photovoltaic power generation of each filling station, and the optimization result of the optimization scheduling includes the inter-station purchase and sale of electricity power and purchase and sale of hydrogen flow at each time; Step S200: Each filling station adopts a distributed trading strategy and algorithm to conduct distributed trading of hydrogen energy and electric energy according to the optimization result of step S100 and the topological relationship between the filling stations, and determines the trading electric energy power and trading hydrogen energy flow at each moment; Step S300, establish a filling station network optimization model with the goal of minimizing the total cost of purchasing and selling energy to the superior power grid and hydrogen network. Each filling station uses the filling station network optimization model to optimize the station network scheduling based on the transaction power and transaction hydrogen flow at each moment, as well as the station network transaction price, predicted electricity load, predicted hydrogen load, and predicted photovoltaic power generation power. The power flow is verified and optimized through the forward and backward method to generate the optimal control strategy for each filling station.

2. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 1, characterized in that: In step S100, the objective function of the inter-station optimization model of the filling station is to minimize the total cost of purchasing and selling electricity and hydrogen energy, and the expression is: ； in, , express The price of electricity purchased and sold between charging stations at all times; , express The price of buying and selling hydrogen between filling stations at any given moment; , express Time filling station Purchase and sale of electrical energy; , express Time filling station Purchase and sell hydrogen energy flow.

3. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 2, characterized in that: The inter-station optimization model for filling stations is provided with constraints, which include electricity network constraints, hydrogen network constraints and electricity-hydrogen coupling constraints.

4. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 3, characterized in that: The electric network constraints include power balance constraints, solar curtailment constraints, electric energy storage system constraints, and electric energy storage charge state constraints; The power balance constraint is expressed as follows: ； In the formula, express Time filling station Electrical load power; express Time filling station Input power of electrolyzer equipment; , Respectively Time filling station Charging power and discharging power of electric energy storage equipment; express Time filling station Output power of photovoltaic power generation equipment; The light abandonment constraint is expressed as follows: ； In the formula, express Time filling station Abandoned power of photovoltaic power generation equipment; express Time filling station Power generation capacity of photovoltaic power generation equipment; The electric energy storage system constraint is expressed as follows: ； In the formula, , is a binary variable to ensure Time filling station Electrical energy storage devices exist in only one charging and discharging state; , Indicates the upper limit of charging and discharging power of the electric energy storage equipment at each charging station; The electric energy storage charge state constraint: ； In the formula, express Time filling station State of charge of the electrical energy storage device; , Indicates the upper and lower limits of the state of charge of the energy storage equipment at each charging station; Indicates filling station Capacity of electrical energy storage equipment; , Indicates the charging and discharging efficiency of the electric energy storage equipment at each charging station; Optimize time cycles for each filling station.

5. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 4, characterized in that: The hydrogen network constraints include hydrogen energy balance constraints and hydrogen energy storage constraints. Hydrogen energy balance constraints: ； In the formula, express Time filling station Hydrogen load flow rate; express Time filling station The hydrogen output flow rate of the electrolyzer equipment; , express Time filling station The hydrogen charging and discharging flow of hydrogen energy storage equipment; The hydrogen energy storage constraint is expressed as follows: ； In the formula, , is a binary variable to ensure Time filling station Hydrogen energy storage devices only exist in one charging and discharging state; , Indicates the upper limit of charging and discharging power of hydrogen energy storage equipment at each filling station; express Time filling station The total amount of hydrogen stored in the hydrogen energy storage device; Indicates filling station The upper limit of hydrogen storage capacity of hydrogen energy storage equipment.

6. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 3, characterized in that: The electric-hydrogen coupling constraint is the electrolyzer equipment constraint, and the expression is as follows: ； In the formula, Indicates the efficiency of the electrolyzer equipment at each filling station; Indicates the upper limit of the input power of the electrolyzer equipment at each charging station.

7. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 1, characterized in that: The step S200 specifically includes: Step S210: taking the purchase and sale hydrogen energy flow in step S100 as the initial hydrogen energy imbalance, and obtaining the hydrogen energy flow after the transaction through iterative distributed transaction algorithm; Step S220: Obtain the convergence result of hydrogen energy trading according to the distributed exchange algorithm in step 210, so as to determine whether to convert the hydrogen energy imbalance into equivalent purchased power and generate an initial electric energy imbalance; Step S230: adopting a distributed trading algorithm to iterate the initial electric energy imbalance to obtain the electric energy power after the transaction.

8. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 7, characterized in that: The distributed transaction algorithm includes the following steps: First, determine the adjacency matrix and node imbalance; Modify the adjacency matrix based on the imbalance amount and generate weight coefficients; The imbalance is updated iteratively until one of the following convergence conditions is met: global balance, all imbalances are positive or negative, and the preset upper limit of iterations is reached.

9. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 5, characterized in that: In step S300, the filling station network optimization model is expressed as: ； In the formula, , express The price of electricity purchased and sold by the charging station to the upper grid at any given moment; , express The filling station always sells hydrogen energy at the price of the superior hydrogen network.

10. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 9, characterized in that: The charging station network optimization model is provided with constraints, which include electricity balance constraints, solar power abandonment constraints and hydrogen energy balance constraints.

11. A distributed control method for a multi-stage electric-hydrogen integrated filling station group according to claim 1, characterized in that: The distributed trading algorithm includes an anomaly detection mechanism. When it is detected that the global imbalance is negative and has a large negative value, the sign of the imbalance is reversed and it is iterated again.

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