A Distributed Regulation Method for a Multi-Stage Electric-Hydrogen Integrated Charging Station Group

Abstract

The present invention discloses a distributed regulation method for a multi-stage integrated electric-hydrogen charging station group. This method encompasses three stages: the inter-station optimization stage of the charging stations, the distributed energy trading stage between the charging stations, and the station-network optimization stage of the charging stations. In the inter-station optimization stage of the charging stations, each charging station conducts inter-station optimization regulation based on information such as prediction data; in the distributed energy trading stage between the charging stations, each charging station performs distributed trading using relevant algorithms according to the regulation results of the inter-station optimization stage and the distributed trading strategy; in the station-network optimization stage of the charging stations, each charging station conducts station-network optimization regulation based on the trading results of the distributed energy trading stage between the charging stations. This regulation method can efficiently schedule the integrated electric-hydrogen charging station group through relevant strategies and algorithms under a distributed framework.

Description

Technical Field

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

[0002] With the transformation of the global energy structure and the increasingly stringent environmental protection requirements, the integrated electric-hydrogen energy system, as a new type of green energy system, is gradually becoming an important way to achieve efficient energy utilization. By effectively combining electric energy and hydrogen energy, it promotes the diversified utilization of energy and provides support for the flexible scheduling of the power grid. Driven by this technological progress, the integrated electric-hydrogen filling station has become a key infrastructure for the comprehensive supply of electric power and hydrogen energy.

[0003] However, with the development of productivity, the scale of the electric-hydrogen system has been continuously increasing, and the limitations of the traditional centralized regulation framework have become more and more obvious, especially in terms of scalability and computational burden. At present, several research results on integrated electric-hydrogen filling stations have been published, generally focusing on system modeling and centralized collaborative optimization, while the research on distributed collaborative optimization is relatively less. And the existing distributed collaborative optimization methods still face some challenges in application, such as the excessive algorithm iteration time, resulting in insufficient system response speed, and it is difficult to meet the requirements of high efficiency and the requirements of the distribution network, etc. Summary of the Invention

[0004] The purpose of the present invention is to propose a multi-stage distributed regulation method for integrated electric-hydrogen filling stations in view of the deficiencies of the existing technology, which can quickly schedule the integrated electric-hydrogen filling stations under a distributed framework and improve the overall efficiency in the process of distributed collaborative optimization, so as to better meet the requirements of high-efficiency scheduling and operation of the integrated electric-hydrogen filling stations and improve the system economy.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A multi-stage distributed regulation method for integrated electric-hydrogen filling stations, comprising the following steps:

[0007] Step S100: Establish an objective function with the optimal economy as the goal as the inter-station optimization model of the filling station. The inter-station optimization model of the filling station performs optimal scheduling according to the inter-station transaction price, predicted electric load, predicted hydrogen load, and predicted photovoltaic power generation power of each filling station. The optimization result of the optimal scheduling includes the power of electricity purchase and sale and the flow of hydrogen purchase and sale between stations at each moment;

[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 each filling station, and determines the power of electricity trading and the flow of hydrogen energy trading at each moment;

[0009] Step S300: Establish a charging station network optimization model with the goal of minimizing the total cost of selling and purchasing energy to the superior power grid and the hydrogen energy market. Each charging station applies the charging station network optimization model for network optimization dispatching based on the trading electric power and hydrogen energy flow at each moment, as well as the network trading price, predicted electric load, predicted hydrogen load, and predicted photovoltaic power generation. Then, verify and optimize the power flow through the forward-backward substitution method to generate the optimal control strategy for each charging station.

[0010] Preferably, in the step S100, the objective function of the charging station inter-station optimization model: with the goal of minimizing the total cost of selling and purchasing electric energy and hydrogen energy, the expression is:

[0011] ;

[0012] Among them, , represents the price of selling and purchasing electric energy between charging stations at time , represents the price of selling and purchasing hydrogen energy between charging stations at time , represents the power of selling and purchasing electric energy of charging station at time , represents the flow rate of selling and purchasing hydrogen energy of charging station at time

[0013] Preferably, the charging station inter-station optimization model is set with constraint conditions, and the constraint conditions include electric network constraints, hydrogen network constraints, and electric-hydrogen coupling constraints.

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

[0015] The power balance constraint, the expression is as follows:

[0016] ;

[0017] In the formula, represents the electric load power of charging station at time represents the input power of the electrolyzer equipment of charging station at time , respectively represent the power of selling and purchasing electric energy of charging station Charging power and discharging power of the electrical energy storage device denote the charging station at time Output power of the photovoltaic power generation device

[0018] The curtailment constraint of light is expressed as follows:

[0019] ;

[0020] In the formula, denote the charging station at time Curtailment power of the photovoltaic power generation device denote the charging station at time Power generation power of the photovoltaic power generation device

[0021] The constraint of the electrical energy storage system is expressed as follows:

[0022] ;

[0023] In the formula, , are binary variables to ensure that the electrical energy storage device at the charging station at time has only one charging and discharging state; , represent the upper limits of the charging and discharging powers of the electrical energy storage devices at each charging station;

[0024] The state of charge constraint of the electrical energy storage:

[0025] ;

[0026] In the formula, denote the state of charge of the electrical energy storage device at the charging station at time ; , represent the upper and lower limits of the state of charge of the electrical energy storage devices at each charging station; denote the charging station Capacity of the electrical energy storage device , represent the charging and discharging efficiencies of the electrical energy storage devices at each charging station; is the optimization time period for each charging station.

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

[0028] Hydrogen energy balance constraint:

[0029] ;

[0030] In the formula, represents the hydrogen load flow rate of the refueling station at time ; represents the hydrogen output flow rate of the electrolyzer equipment at the refueling station at time ; , represents the hydrogen charging and discharging flow rate of the hydrogen energy storage equipment at the refueling station at time .

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

[0032] ;

[0033] In the formula, , are binary variables used to ensure that the hydrogen energy storage equipment at the refueling station at time has only one charging and discharging state; , represent the upper limits of the charging and discharging powers of the hydrogen energy storage equipment at each refueling station; represents the total amount of hydrogen stored in the hydrogen energy storage equipment at the refueling station at time ; represents the upper limit of the hydrogen storage capacity of the hydrogen energy storage equipment at the refueling station.

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

[0035] ;

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

[0037] Preferably, the specific steps of step S200 include:

[0038] Step S210: Use the hydrogen energy purchase and sale flow rate in step S100 as the initial hydrogen energy imbalance amount, and obtain the traded hydrogen energy flow rate through iterative distributed trading algorithms;

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

[0040] Step S230: Iterate the initial power imbalance using a distributed trading algorithm to obtain the power after the transaction.

[0041] Preferably, the distributed trading algorithm includes the following steps:

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

[0043] Modify the adjacency matrix based on the imbalance and generate the weight coefficient.

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

[0045] Preferably, in step S300, the optimization model of the filling station network has the following expression:

[0046] ;

[0047] In the formula, 、 represent the electricity purchase and sale price of the filling station from / to the superior power grid at time 、 represent the hydrogen purchase and sale price of the filling station from / to the superior hydrogen network at time

[0048] Preferably, the optimization model of the filling station network has constraint conditions, and the constraint conditions include power balance constraint, curtailment constraint, and hydrogen balance constraint.

[0049] Preferably, the distributed trading algorithm includes an anomaly detection mechanism. When it is detected that the global imbalance is negative and there are large negative values, reverse the sign of the imbalance and re-iterate.

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

[0051] Adopting the above technical solution, it includes three stages: the inter-station optimization stage of the charging station. Each charging station conducts inter-station optimization control based on information such as prediction data, thus avoiding the computational pressure brought by the centralized framework; the distributed energy trading stage between charging stations. Each charging station conducts distributed trading using relevant algorithms according to the control results of the inter-station optimization stage of the charging station and the distributed trading strategy, effectively improving the computational speed; the station-network optimization stage of the charging station. Each charging station conducts station-network optimization control according to the trading results of the distributed energy trading stage between charging stations, 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 quickly schedules the electric-hydrogen integrated charging station group through relevant strategies and algorithms, avoiding the computational pressure under the centralized framework and improving the overall efficiency in the distributed collaborative optimization process, effectively meeting the high-efficiency scheduling and operation requirements of the electric-hydrogen integrated charging station group, and thus improving the economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0053] Figure 1 It is a flowchart of the distributed optimization control method for the multi-stage electric-hydrogen integrated charging station group proposed by the present invention;

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

[0055] Figure 3 It is a flowchart of the distributed algorithm in the distributed energy trading stage between charging stations;

[0056] Figure 4 It is a diagram of the power balance result of node 6 in the inter-station optimization stage of the charging station;

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

[0058] Figure 6 It is a diagram of the distributed power trading result of node 6 in the distributed energy trading stage between charging stations;

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

[0060] Figure 8 It is a diagram of the power balance result of node 6 in the station-network optimization stage of the charging station;

[0061] Figure 9 It is the hydrogen energy balance result diagram of node 6 in the optimization stage of the charging station network;

[0062] Figure 10 It is the power flow verification result diagram in the optimization stage of the charging station network. Specific implementation manners

[0063] It should be noted that, without conflict, the embodiments in 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 orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is 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 thus cannot be understood as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "plurality" is two or more.

[0065] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood through specific situations.

[0066] The present invention provides a multi-stage distributed regulation method for an integrated electric-hydrogen charging station group, as Figure 2 shown. In this embodiment, the integrated electric-hydrogen charging station includes an electric energy storage module, a hydrogen energy storage module, an electrolyzer module, and possibly a photovoltaic power generation module. The electrolyzer module uses electric energy to electrolyze water to produce hydrogen, and stores the 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 relates to a distributed control method for a multi-stage electric-hydrogen charging integrated station group. During the inter-station optimization stage of the charging stations, each charging station conducts inter-station optimization control based on information such as prediction data. During the inter-station energy distributed trading stage of the charging stations, each charging station performs distributed trading using relevant algorithms according to the control results of the inter-station optimization stage of the charging stations and the distributed trading strategy. During the station-network optimization stage of the charging stations, each charging station conducts station-network optimization control based on the trading results of the inter-station energy distributed trading stage of the charging stations. The flow chart of the relevant distributed algorithm during the inter-station energy distributed trading stage of the charging stations is as shown in Figure 3 the following. To illustrate the effects of the present invention, a certain electric-hydrogen charging integrated station group is taken as the implementation object of the present invention below. The electric-hydrogen charging integrated station group in this embodiment includes 6 electric-hydrogen charging integrated stations. Among them, the 2nd, 3rd, and 6th electric-hydrogen integrated charging stations are equipped with photovoltaic power generation modules. The parameters of the electric-hydrogen integrated charging 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 will be described in detail below. As shown in Figure 1 the following, the specific steps are as follows:

[0071] Step S100: Inter-station optimization stage of the charging stations. Each charging station conducts optimal scheduling based on the inter-station trading price, predicted electric load , predicted hydrogen load , predicted photovoltaic power generation power, and the inter-station optimization model of the charging stations. Finally, each charging station obtains the power of electricity purchase and sale , and hydrogen flow , at each moment.

[0072] The inter-station optimization model of the charging stations consists of an economic objective function, electrical network constraints, hydrogen network constraints, and electro-hydrogen coupling constraints. Its specific model is as follows:

[0073] Objective function:

[0074]

[0075] In the formula, , represent the electricity purchase and sale price between the charging stations at time; , represent the hydrogen purchase and sale price between the charging stations at time; , represent Momentary charging station Purchasing and selling electric energy power; 、 Indicates Momentary charging station Hydrogen purchasing and selling flow rate.

[0076] Power grid constraints:

[0077] The power grid constraints include power balance constraints, curtailment constraints, energy storage system constraints, and energy storage state of charge constraints.

[0078] Power balance constraints:

[0079]

[0080] In the formula, Indicates Momentary charging station Electric load power; Indicates Momentary charging station Input power of electrolyzer equipment; 、 Respectively indicate Momentary charging station Charging power and discharging power of energy storage equipment; Indicates Momentary charging station Output power of photovoltaic power generation equipment.

[0081] Curtailment constraints:

[0082]

[0083] In the formula, Indicates Momentary charging station Curtailment power of photovoltaic power generation equipment. Indicates Momentary charging station Power generation power of photovoltaic power generation equipment;

[0084] Energy storage system constraints:

[0085]

[0086] In the formula, 、 Are binary variables used to ensure that Momentary charging station The energy storage equipment has only one charge and discharge state; 、 Indicate the upper limits of the charge and discharge powers of the energy storage equipment at each charging station.

[0087] Constraints on the state of charge of electrical energy storage:

[0088]

[0089] In the formula, represents the state of charge of the electrical energy storage device at the charging station at time ; , represent the upper and lower limits of the state of charge of the electrical energy storage devices at each charging station; represents the charging station electrical energy storage device capacity; , represent the charge-discharge efficiencies of the electrical energy storage devices at each charging station; is the optimization time period for each charging station.

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

[0091] Hydrogen energy balance constraints:

[0092]

[0093] In the formula, represents the hydrogen output flow rate of the electrolyzer device at the charging station at time ; , represent the hydrogen charge-discharge flow rate of the hydrogen energy storage device at the charging station at time ;

[0094] Hydrogen energy storage constraints:

[0095]

[0096] In the formula, , are binary variables used to ensure that at time the hydrogen energy storage device at the charging station has only one charge-discharge state; , represent the upper limits of the charge-discharge power of the hydrogen energy storage devices at each charging station; represents the total amount of hydrogen stored in the hydrogen energy storage device at the charging station at time ; represents the charging station upper limit of the hydrogen storage capacity of the hydrogen energy storage device.

[0097] Furthermore, the electro-hydrogen coupling constraint is the electrolyzer device constraint.

[0098] Electrolyzer device constraints:

[0099]

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

[0101] In a further setting of this embodiment, step S200: Inter-station energy distributed trading stage for refueling stations. Based on their own topological relationships, the power of electricity purchased and sold at each moment, , and the hydrogen flow rate , , each refueling station adopts a distributed trading strategy and its related algorithms to calculate the power of electricity traded at each moment , and the hydrogen energy flow rate traded , . Specifically, it includes the following steps:

[0102] Step S210: Hydrogen energy trading for refueling stations. The hydrogen purchase and sale flow rates , to other refueling stations obtained by each refueling station in step S100 are used as the initial hydrogen energy imbalance , and relevant distributed trading algorithms are used for iteration. After the iteration is completed, the hydrogen purchase and sale flow rates , after trading for each refueling station are obtained.

[0103] Step S220: Determination of the electricity imbalance for refueling stations. Referring to the convergence result of the distributed trading algorithm in step S210 , the electricity imbalance between refueling stations is determined.

[0104] If the convergence result shows that all hydrogen energy imbalances are negative, it means that the refueling station group as a whole has a hydrogen purchase demand. At this time, if the price of directly purchasing hydrogen is higher than the price of purchasing electricity for hydrogen supply, the hydrogen energy imbalance is converted into an equivalent power of purchasing electricity for hydrogen supply . Considering the working conditions of the electrolyzers at each refueling station, the power of purchasing electricity for hydrogen supply and the power of purchasing and selling electricity , to other refueling stations obtained by each refueling station in step S100 will be used as the initial electricity imbalance , that is:

[0105]

[0106] In the formula is or .

[0107] If the convergence result is otherwise, the power purchase and sale to other charging stations obtained by each charging station in step S100 、 is used as the initial power imbalance , that is:

[0108]

[0109] In the formula is or .

[0110] Step S230: Charging station power trading. Use the relevant distributed trading algorithm to iterate on the initial power imbalance determined in step S220. After the iteration, the power purchase and sale of each charging station after the transaction 、 .

[0111] The relevant distributed trading algorithm mentioned in the above steps is specifically as follows:

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

[0113] Use the matrix to represent the node connection relationship of the graph, where:

[0114]

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

[0116] Use the column vector to represent the imbalance of each node (positive indicates surplus, negative indicates loss), where:

[0117]

[0118] 2) Generate a correction matrix based on the imbalance and the adjacency matrix

[0119] Regenerate the adjacency matrix according to the imbalance.

[0120]

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

[0122]

[0123] The corrected matrix The elements in

[0124]

[0125] 3) Unbalance Iterative update

[0126]

[0127] 4) Anomaly detection and handling

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

[0129]

[0130] Where 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 balance

[0136]

[0137] Where is a very small positive number.

[0138] Convergence condition 2: All unbalances are positive

[0139]

[0140] Convergence condition 3: All unbalances are negative

[0141]

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

[0143] .

[0144] Step S300: Optimization stage of the filling station network. Each filling station will, according to the transaction results, network transaction price, predicted load , predicted hydrogen load , predicted photovoltaic power generation And an optimized scheduling is carried out through the optimized model of the charging and refueling station network. The overall power flow is verified and optimized by the forward-backward substitution method to ensure the effectiveness of the optimization results, and finally the optimized operation plan for each charging and refueling station is formed.

[0145] Among them, the optimized model of the charging and refueling station network consists of an economic objective function, electrical network constraints, hydrogen network constraints, and electrical-hydrogen coupling constraints. The optimized model of the charging and refueling station network is similar to the optimized model between charging and refueling stations, only different in the economic objective function, power balance constraint, curtailment constraint, and hydrogen energy balance constraint. The specific details are as follows:

[0146] Objective function:

[0147]

[0148] In the formula, 、 represent The electricity purchase and sale price of the charging and refueling station from / to the superior power grid at time 、 represent The hydrogen energy purchase and sale price of the charging and refueling station from / to the superior hydrogen network at time

[0149] Power balance constraint:

[0150]

[0151] Curtailment constraint:

[0152]

[0153] Hydrogen energy balance constraint:

[0154]

[0155] To demonstrate the effectiveness of the present method, a specific example is given below. In the implementation of the present invention, a typical day is selected for simulation experiments. In the embodiment of the present invention, the multi-stage electrical-hydrogen charging and refueling integrated station group distributed control method involved is simulated on the Python platform. Figure 4 、 Figure 5 Show the power balance result and hydrogen energy balance result of the No. 6 electrical-hydrogen integrated charging and refueling station in the optimization stage between charging and refueling stations; Figure 6 、 Figure 7 Show the electricity trading result and hydrogen energy trading result of the No. 6 electrical-hydrogen integrated charging and refueling station in the energy distributed trading stage between charging and refueling stations; Figure 8 、 Figure 9 Show the power balance result and hydrogen energy balance result of the No. 6 electrical-hydrogen integrated charging and refueling station in the optimization stage of the charging and refueling station network; Figure 10 Show the voltage results of each moment of the electrical-hydrogen integrated charging and refueling station group.

[0156] During the optimization stage between charging stations, the No. 6 integrated power-to-hydrogen charging station, in cooperation with the electric energy storage module, hydrogen energy storage module, photovoltaic power generation module, and electrolyzer module, effectively utilizes solar energy to conduct inter-station optimization through the inter-station optimization model of the charging station, and determines the power of electricity purchase and sale and hydrogen flow rate between stations. During the distributed energy trading stage between charging stations, the No. 6 charging station conducts distributed inter-station energy trading according to the distributed trading strategy, the power of electricity purchase and sale, the flow rate of hydrogen purchase and sale determined in the optimization stage between charging stations, and related algorithms, and determines the power of traded electricity and the flow rate of traded hydrogen with other charging stations. During the optimization stage of the charging station network, the No. 6 charging station considers the predicted data and the transaction data determined between stations in the distributed energy trading stage between charging stations, and conducts network optimization through the charging station network optimization model to determine the final optimization plan. It completes the rapid coordinated optimization of the charging station under the distributed framework, effectively meeting the high-efficiency scheduling and operation requirements of the integrated power-to-hydrogen charging station group.

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

Claims

1. A distributed control method for a multi-stage electric-hydrogen integrated charging station group, characterized in that, It includes the following steps: Step S100: Establish an objective function with the optimal economy as the goal as the optimization model for the charging stations among stations. The optimization model for the charging stations among stations is optimized and scheduled according to the inter-station trading prices, predicted electricity loads, predicted hydrogen loads, and predicted photovoltaic power generation of each charging station. The optimization results of the optimization scheduling include the power of electricity purchase and sale and the flow of hydrogen purchase and sale among stations at each moment; Step S200: Each charging station adopts a distributed trading strategy and algorithm to conduct distributed trading of hydrogen energy and electricity according to the optimization results of Step S100 and the topological relationship among the charging stations, and determines the power of electricity trading and the flow of hydrogen energy trading at each moment. The specific steps of Step S200 include: Step S210: Use the hydrogen energy purchase and sale flow in Step S100 as the initial hydrogen energy imbalance amount, and iteratively obtain the hydrogen energy flow after trading through the distributed trading algorithm; Step S220: According to the convergence result of the hydrogen energy trading obtained by the distributed trading algorithm in Step 210, determine whether to convert the hydrogen energy imbalance amount into an equivalent electricity purchase power to generate the initial electricity imbalance amount; Step S230: Use the distributed trading algorithm to iterate the initial electricity imbalance amount to obtain the electricity power after trading; The distributed trading algorithm includes the following steps: First, determine the overall adjacency matrix M(k) and the imbalance amount u(k) Use a matrix to represent the node connection relationship of the graph, where: where M ii (k)=1 indicates that the node is connected to itself, and the column vector represents the imbalance of each node, where: u(k) = [u1(k), u2(k) ··· u n (k)] T ; Generate the correction matrix T(k) based on the imbalance amount u(k) and the adjacency matrix M(k); Regenerate the adjacency matrix M′(k) based on the imbalance amount: Determine the weight coefficient c of each node from the adjacency matrix M′(k) i (k): The elements in the corrected matrix T(k) are: Iteratively update the imbalance amount u(k) u(k + 1) = u(k)·T(k); Iteratively update the imbalance amount until one of the following convergence conditions is met: global balance, all imbalance amounts are positive or negative, or the preset upper limit of the number of iterations is reached; Step S300: Establish an optimization model for the charging station network with the goal of minimizing the total cost of purchasing and selling energy from the superior power grid and the hydrogen grid. Each charging station applies the optimization model for the charging station network for network optimization scheduling according to the power of electricity trading and the flow of hydrogen energy trading at each moment, as well as the network trading price, predicted electricity load, predicted hydrogen load, and predicted photovoltaic power generation, and verifies and optimizes the power flow through the forward-backward substitution method to generate the optimal control strategy for each charging station.

2. The distributed control method for a multi-stage electric-hydrogen integrated charging station group according to claim 1, wherein In Step S100, the objective function of the optimization model for the charging stations among stations: with the goal of minimizing the total cost of purchasing and selling electricity and hydrogen energy, the expression is: Among them, priIEB(t) and priIES(t) represent the electricity purchase and sale prices between charging and refueling stations at time t; priIHB(t) and priIHS(t) represent the hydrogen purchase and sale prices between charging and refueling stations at time t; PIB n (t) and PIS n (t) represent the electricity purchase and sale power of charging and refueling station n at time t; VIB n (t) and VIS n (t) represent the hydrogen purchase and sale flow rate of charging and refueling station n at time t.

3. A distributed control method for a multi-stage electric-hydrogen integrated charging station group according to claim 2, characterized in that, The optimization model for the charging stations among stations is set with constraint conditions, and the constraint conditions include electrical network constraints, hydrogen network constraints, and electrical-hydrogen coupling constraints.

4. A distributed control method for a multi-stage electric-hydrogen integrated charging station group according to claim 3, characterized in that The electrical network constraints include power balance constraints, curtailment constraints, electrical energy storage system constraints, and electrical energy storage state of charge constraints; For the power balance constraint, the expression is as follows: PIL n (t) + PIS n (t) + PIEC n (t) + PICH n (t) = PIPV n (t) + PIB n (t) + PIDCH n (t); wherein, PIL n (t) represents the electrical load power of charging station n at time t; PIEC n (t) represents the input power of the electrolyzer equipment at the charging station n at time t; PICH n (t), PIDCH n (t) respectively represent the charging power and discharging power of the electrical energy storage equipment at the charging station n at time t; PIPV n (t) represents the output power of the photovoltaic power generation equipment at the charging station n at time t; For the curtailment constraint, the expression is as follows: Where, PIAB n (t) represents the curtailment power of the photovoltaic power generation equipment at the charging station n at time t; PFPV(t) represents the power generation power of the photovoltaic power generation equipment at the charging station n at time t; For the electrical energy storage system constraint, the expression is as follows: where αIDCH n (t) and αICH n (t) are binary variables used to ensure that there is only one charge / discharge state for the electrical energy storage device at charging station n at time t; PICH up and PIDCH up represent the upper limits of the charge / discharge power of the electrical energy storage devices at each charging station; For the electrical energy storage state of charge constraint: Where, SoCI n (t) represents the state of charge of the electrical energy storage device at charging station n at time t; SoCI up , SoCI low represent the upper and lower limits of the state of charge of the electrical energy storage devices at each charging station; CE n represents the capacity of the electrical energy storage device at charging station n; η ch and η dch represent the charge and discharge efficiency of the electrical energy storage devices at each charging station; T is the optimized time period for each charging station.

5. A distributed control method for a multi-stage electric-hydrogen integrated charging 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 constraint: VIL n (t) + VICH n (t) + VIS n (t) = VIB n (t) + VIDCH n (t) + VIEC n (t); Where, VIL n (t) represents the hydrogen load flow rate of filling station n at time t; VIEC n (t) represents the hydrogen output flow rate of the electrolyzer equipment of filling station n at time t; VICH n (t), VIDCH n (t) represents the hydrogen charge and discharge flow rate of the hydrogen energy storage equipment of filling station n at time t; For the hydrogen energy storage constraint, the expression is as follows: where βIDCH n (t) and βICH n (t) are binary variables used to ensure that there is only one charging and discharging state for the hydrogen energy storage device at charging station n at time t; VICH up and VIDCH up represent the upper limits of the charging and discharging power of the hydrogen energy storage devices at each charging station; EI n (t) represents the total amount of hydrogen stored in the hydrogen energy storage device at charging station n at time t; CHY n represents the upper limit of the hydrogen storage capacity of the hydrogen energy storage device at charging station n.

6. A multi-stage electric-hydrogen integrated charging station group distributed control method according to claim 3, characterized in that The electrical-hydrogen coupling constraint is the electrolyzer equipment constraint, and the expression is as follows: where η EC represents the electrolyzer equipment efficiency of each charging station; PIEC up represents 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 charging station group according to claim 5, characterized in that, In the step S300, the optimization model of the charging station network has the following expression: In the formula, priOEB(t) and priOES(t) represent the electricity purchase and sale prices of the charging station from / to the superior power grid at time t; priOHB(t) and priOHS(t) represent the hydrogen purchase and sale prices of the charging station from / to the superior hydrogen grid at time t.

8. A distributed control method for a multi-stage electric-hydrogen integrated charging station group according to claim 7, characterized in that, The optimization model of the charging station network is set with constraint conditions, including power balance constraint, curtailment constraint and hydrogen energy balance constraint.

9. A distributed control method for a multi-stage electric-hydrogen integrated charging 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 there are large negative values in the imbalance, the sign of the imbalance is reversed and the iteration is restarted.

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