A method for optimizing the operation of a distribution network including a hydrogen production, storage and refueling station

Abstract

The present invention relates to the technical field of power system optimization, and in particular to a method for optimizing the operation of a distribution network containing a hydrogen production, storage and refueling station, including: step one, establishing a mathematical model of a photovoltaic system and a mathematical model of a hydrogen system, and constructing an objective function and constraint condition for optimizing the operation of a distribution network, and obtaining a multi-objective optimization model containing a hydrogen production, storage and refueling station; step two, linearizing the objective function and constraint condition to obtain a multi-objective optimization model of a mixed integer linear programming; step three, solving the multi-objective optimization model of the mixed integer linear programming using an ε constraint method, and obtaining an optimal scheduling scheme for an active distribution network containing a hydrogen production, storage and refueling station; step four, scheduling a photovoltaic system, a hydrogen system and a distribution network using the optimal scheduling scheme obtained in step three. This method can effectively solve the problem of voltage deviation when a hydrogen refueling station supplies power to a power grid, and improve the quality of power supplied to the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a method for optimizing the operation of a distribution network including a hydrogen production-storage-refueling station. Background Art

[0002] In the current context of vigorously advocating the development of green energy, photovoltaic power generation as the main form of new energy power generation has become a current research hotspot. Since new energy power generation will be affected by environmental conditions, its output power will fluctuate, which will in turn affect the quality of its output power. At present, the mainstream solution is to cooperate with the energy storage system to convert the electricity generated by photovoltaics into other energy and store it, and then convert it into stable electricity for output. Among them, hydrogen energy has attracted widespread attention for its advantages such as cleanliness and efficiency. The unstable electricity is converted into hydrogen storage by electrolyzing water with the electricity generated by photovoltaics. When the electricity of this department is needed, the stored hydrogen is converted into stable electricity through fuel cells. This conversion process is not only efficient and stable, but also green and clean, which is in line with the development trend of the current power grid. Therefore, the hydrogen energy conversion mechanism has become a popular conversion mechanism for photovoltaic power generation. However, in actual use, since both photovoltaic systems and electric hydrogen are affected by environmental conditions, user behavior and other factors, there is a certain degree of uncertainty. In order to ensure the stability of the entire process and provide stable electricity to the power grid, some scholars in the industry have established an optimization scheduling model for an electric hydrogen hybrid energy storage system that includes photovoltaic systems, batteries, electrolyzers, hydrogen storage tanks, and fuel cells, and used optimization methods such as particle swarm optimization, genetic algorithm, and simulated annealing algorithm to solve it.

[0003] Through the above method, the hydrogen energy conversion mechanism can be used to stably supply power to the power grid. However, this method requires the establishment of a special hydrogen energy transfer mechanism, which is relatively costly. In order to maximize resource utilization, the applicant has proposed a technical solution for directly using distributed hydrogen refueling stations for energy conversion. This method can not only reduce the number of energy conversion mechanisms and save costs, but also improve the economic benefits of hydrogen refueling stations, which is conducive to the promotion of hydrogen refueling stations.

[0004] However, in actual use, since the hydrogen refueling station itself is not an institution dedicated to energy conversion, its main function is to provide hydrogen energy for hydrogen energy vehicles. If the hydrogen refueling station is used for energy conversion to power the power grid, then while supplying power to the power grid, the hydrogen refueling station must also provide hydrogen energy for hydrogen energy. This will lead to voltage deviation problems when the hydrogen refueling station supplies energy to the power grid, especially during the peak period of hydrogen energy vehicle hydrogenation, the voltage deviation problem of the hydrogen refueling station to the power grid will become more serious. This may affect the safe and stable operation of the power system. The implementation effect of this plan is not ideal. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for optimizing the operation of a distribution network including a hydrogen production-storage-refueling station, which can effectively solve the voltage deviation problem when the hydrogen refueling station supplies power to the power grid and improve the quality of power supplied to the power grid.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for optimizing the operation of a distribution network including a hydrogen production-storage-refueling station comprises the following steps:

[0008] Step 1: Establish mathematical models of the photovoltaic system and the hydrogen system, and construct objective functions and constraints for optimizing the operation of the distribution network, and obtain a multi-objective optimization model including hydrogen production, hydrogen storage and hydrogen refueling station; wherein the hydrogen system includes an electrolyzer, a hydrogen storage tank and a fuel cell; the objective function includes an economic objective function and a power quality objective function; the constraints include photovoltaic system constraints, hydrogen system constraints and distribution network constraints;

[0009] Step 2, linearizing the objective function and constraint conditions to obtain a multi-objective optimization model of mixed integer linear programming;

[0010] Step 3, using the ε constraint method to solve the multi-objective optimization model of the mixed integer linear programming to obtain the optimal scheduling scheme of the active distribution network containing hydrogen production-storage-refueling station;

[0011] Step 4: Use the optimal scheduling plan obtained in step 3 to schedule the photovoltaic system, hydrogen system and distribution network.

[0012] Preferably, in step 1, the mathematical model of the photovoltaic system is:

[0013]

[0014] Where P ST is the rated output power of photovoltaic power generation under standard test conditions; G t is the light intensity; ε is the power temperature coefficient; T j is the ambient temperature; The number of modules connected in series for photovoltaic cells; is the number of photovoltaic cell modules connected in parallel.

[0015] Preferably, in step 1, the mathematical model of the hydrogen system is:

[0016]

[0017]

[0018]

[0019] In the formula, is the hydrogen production of the electrolyzer; η EL is the efficiency of the electrolyzer; P t EL,jo P2H is the electrical power consumed by the electrolytic cell; EL is the power conversion factor of hydrogen production by electrolyzer; is the hydrogen consumption of the fuel cell; P t FC is the electrical power output of the fuel cell; η FC is the efficiency of the fuel cell; H2P FC The conversion factor for the fuel cell to convert hydrogen into electricity; is the hydrogen storage state of the hydrogen storage tank at time t; is the hydrogen storage state of the hydrogen storage tank at time t-1; ε DSP is the hydrogen dissipation rate of the hydrogen storage tank; HDE t To meet the demand for hydrogen for hydrogen-powered vehicles.

[0020] Preferably, the electric power consumed by the electrolytic cell is t EL,jo The sources of electricity include the electric power output from the upstream distribution network and the electric power output from photovoltaic power generation.

[0021] Preferably, in step 1, the economic objective function is:

[0022]

[0023] Where P t UP is the electric energy purchased by the distribution network from the upstream power grid at time t; is the unit electricity price of the upstream power grid; P t DG is the distributed generation output power at time t; is the unit operating cost of distributed generation; P t PV is the photovoltaic power output power at time t; is the unit operating cost of photovoltaic power generation; P t EL P is the electric power consumed by the electrolytic cell at time t; t FC is the electrical power output by the fuel cell at time t.

[0024] Preferably, in step 1, the power quality objective function is:

[0025]

[0026] In the formula, ωi is the voltage quality coefficient; V t,i Represents the actual voltage of the ith node in the distribution network; Represents the rated voltage of node i.

[0027] Preferably, in step 1, the photovoltaic system constraint condition includes a capacity constraint of a photovoltaic system inverter:

[0028]

[0029] The hydrogen system constraints include the operating constraints of the electrolyzer:

[0030]

[0031]

[0032] Fuel cell operating constraints:

[0033]

[0034]

[0035] Operation constraints of hydrogen storage tanks:

[0036]

[0037]

[0038] And the capacity constraints of the hydrogen system inverter:

[0039]

[0040] The distribution network constraints include AC power flow constraints:

[0041]

[0042]

[0043]

[0044]

[0045] Among them, P t PV is the photovoltaic power output power at time t; is the reactive power output by the photovoltaic inverter at time t; is the capacity of the PV inverter;

[0046] is the minimum capacity of the electrolytic cell; P tEL,jo The power of the electrolyzer in the coordinated mode using photovoltaic power and upstream grid power; P h EL,Max is the maximum capacity of the electrolytic cell; is a binary variable in the hydrogen system, and defines when δ t HS =1, the fuel cell output is 0. When the hydrogen production of the electrolyzer is 0;

[0047] is the amount of hydrogen produced by the electrolyser; is the maximum amount of hydrogen produced by the electrolyzer;

[0048] P h FC,Min The minimum output of the fuel cell; P t FC is the output of the fuel cell at time t; P h FC,Max is the maximum output of the fuel cell;

[0049] is the amount of hydrogen consumed by the fuel cell at time t; The maximum amount of hydrogen consumed by the fuel cell;

[0050] is the minimum value of the hydrogen mass in the hydrogen storage tank; is the mass of hydrogen in the hydrogen storage tank at time t; is the maximum value of the hydrogen mass in the hydrogen storage tank;

[0051] is the initial value of the hydrogen mass in the hydrogen storage tank; is the initial hydrogen production in the hydrogen storage tank;

[0052] P t EL P is the electric power consumed by the electrolytic cell at time t; t FC is the electric power output of the fuel cell at time t; Q t HS is the reactive power output by the hydrogen energy storage inverter at time t; is the capacity of the hydrogen system;

[0053] is the net injected active power of the node; P t UG , represents the active power from the upstream power grid at time t; represents the reactive power from the upstream power grid at time t; is the net injected reactive power of the node; P tDG , is the active power output of the distributed generator at time t; Q t DG is the reactive power output by the distributed generator at time t; G i,j is the conductivity parameter of the line between nodes i and j; B i,j is the susceptance parameter of the line between nodes i and j; V t,i is the actual voltage of the ith node of the distribution network at time t; V t,j is the actual voltage of the jth node in the distribution network at time t; θ t,i is the phase angle of the voltage at node i at time t; θ t,j is the phase angle of the voltage at node j at time t.

[0054] Preferably, in step 2, the power quality objective function is linearized and becomes:

[0055]

[0056]

[0057]

[0058] In the formula, Min.PQI is the minimum value of the voltage deviation rate; ω i is the voltage quality coefficient; δV t,i is the voltage deviation of node i at time t; is the positive voltage deviation of node i at time t; is the negative voltage deviation of node i at time t; V t,i Represents the actual voltage of the ith node of the distribution network at time t; represents the rated voltage of node i at time t; is the positive voltage deviation of node i at time t; is the negative voltage deviation of node i at time t.

[0059] Preferably, in step 2, the AC power flow constraint condition is linearized to become:

[0060]

[0061]

[0062] PL t,(i,j) =-G i,j (V t,i +V t,j )+B i,j (θ t,i -θ t,j )

[0063] QLt,(i,j) =B i,j (V t,i +V t,j )+G i,j (θ t,i -θ t,j )

[0064] Where PL t,(i,j) ,QL t,(i,j) are the active power and reactive power between nodes i and j respectively.

[0065] Preferably, step three includes:

[0066] Step 3.1, taking the economic objective function as the main objective function and the linearized power quality objective function as the constraint condition, the multi-objective optimization model of the mixed integer linear programming is transformed into:

[0067] Objective function: minimize OC t

[0068] Constraints:

[0069] And solve the optimal target value;

[0070] Step 3.2, taking the linearized power quality objective function as the main objective function, and taking the optimal objective value obtained in step 3.1 as the constraint condition, the multi-objective optimization model of the mixed integer linear programming is transformed into:

[0071] Objective function: minimize PQI t

[0072] Constraints:

[0073] In the formula, represents the minimum cost obtained in step 3.1; λ OC It represents the parameter that achieves a compromise between the two objective functions. This parameter represents the economic goal that can be sacrificed in order to minimize the voltage deviation during the optimization process of step 3.2.

[0074] And through the parameter λ OC A compromise between the two objective functions is achieved, and the optimal scheduling scheme for the active distribution network containing hydrogen production, storage and refueling stations is obtained.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] 1. This method fully considers the characteristics of the photovoltaic system, hydrogen system and distribution network. Using this method to dispatch the photovoltaic system, hydrogen system and distribution network can not only achieve the economy of the distribution network operation, but also solve the problem of large voltage drop at the hydrogen system node caused by the large demand for hydrogen by fuel vehicles, that is, improve the power quality of the distribution network containing the hydrogen system. In summary, this method solves the voltage deviation problem when the hydrogen station supplies power to the power grid, and can improve the power quality of the power supply to the power grid.

[0077] 2. Tests show that the model built by this method can be solved within seconds, and can realize online real-time scheduling of photovoltaic systems, hydrogen systems and distribution networks. It has the advantage of online programmed calculation and is of great significance to the economic, safe and stable operation of future green urban distribution networks.

[0078] 3. The present invention also fully considers the coordinated operation of the photovoltaic system and the hydrogen system. The electricity for the electrolyzer can not only be purchased from the power grid, but also be supplied by the photovoltaic system, which can reduce the production cost of hydrogen, thereby reducing the economic threshold for the application of hydrogen refueling stations in the power supply system, which is conducive to the promotion and application of this technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0080] Figure 1 is a flow chart in an embodiment;

[0081] Figure 2 Schematic diagram of the hydrogen system in the embodiment. DETAILED DESCRIPTION

[0082] The following is a further detailed description through specific implementation methods:

[0083] Example

[0084] like Figure 1 , Figure 2 As shown, this embodiment discloses a method for optimizing the operation of a distribution network including a hydrogen production-storage-refueling station, comprising the following steps:

[0085] Step 1: Establish mathematical models of the photovoltaic system and the hydrogen system, and construct objective functions and constraints for optimizing the operation of the distribution network to obtain a multi-objective optimization model including hydrogen production, storage and refueling station. The hydrogen system includes an electrolyzer, a hydrogen storage tank and a fuel cell; the objective function includes an economic objective function and a power quality objective function; and the constraints include photovoltaic system constraints, hydrogen system constraints and distribution network constraints.

[0086] Specifically, the mathematical model of the photovoltaic system is:

[0087]

[0088] Where P ST is the rated output power of photovoltaic power generation under standard test conditions; G t is the light intensity; ε is the power temperature coefficient; T j is the ambient temperature; The number of modules connected in series for photovoltaic cells; is the number of photovoltaic cell modules connected in parallel.

[0089] The mathematical model of the hydrogen system is:

[0090]

[0091] In the formula, is the hydrogen production of the electrolyzer; η EL is the efficiency of the electrolyzer; P t EL,jo P2H is the electrical power consumed by the electrolytic cell; EL is the power conversion factor of hydrogen production by electrolyzer; is the hydrogen consumption of the fuel cell; P t FC is the electrical power output of the fuel cell; η FC is the efficiency of the fuel cell; H2P FC The conversion factor for the fuel cell to convert hydrogen into electricity; is the hydrogen storage state of the hydrogen storage tank at time t; is the hydrogen storage state of the hydrogen storage tank at time t-1; ε DSP is the hydrogen dissipation rate of the hydrogen storage tank; HDE t To meet the demand for hydrogen for hydrogen-powered vehicles.

[0092] Among them, the electric power consumed by the electrolytic cell is P t EL,jo The sources of electricity include the electric power output from the upstream distribution network and the electric power output from photovoltaic power generation.

[0093] The economic objective function is expressed by the total operating cost of the distribution network, specifically:

[0094]

[0095] Where P t UP , is the active power from the upstream power grid at time t; is the unit electricity price of the upstream power grid; P t DG is the distributed generation output power at time t; is the unit operating cost of distributed generation; P t PV is the photovoltaic power output power at time t; is the unit operating cost of photovoltaic power generation; P t EL P is the electric power consumed by the electrolytic cell at time t; t FC is the electrical power output by the fuel cell at time t.

[0096] The power quality objective function is measured by the voltage deviation of the distribution network node and is expressed as:

[0097]

[0098] In the formula, ω i is the voltage quality coefficient; V t,i Represents the actual voltage of the ith node in the distribution network; Represents the nominal voltage of node i. By increasing , a better voltage distribution can be obtained.

[0099] The constraints include:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] Formula (5) represents the capacity constraint of the photovoltaic system inverter; Formulas (6)-(12) represent the operating constraints of the hydrogen system, including the operating constraints of the electrolyzer (Formulas (6) and (7)), the operating constraints of the fuel cell (Formulas (8) and (9)), the operating constraints of the hydrogen storage tank (Formulas (10) and (11)) and the capacity constraint of the hydrogen system inverter (Formula (12)); Formulas (13)-(16) represent the AC power flow constraints of the distribution network.

[0113] Among them, P t PV is the photovoltaic power output power at time t; is the reactive power output by the photovoltaic inverter at time t; is the capacity of the PV inverter;

[0114] P h EL,min is the minimum capacity of the electrolytic cell; P t EL,jo Power in a synergistic mode using PV output and upstream grid power for the electrolyzer; is the maximum capacity of the electrolytic cell; is a binary variable in the hydrogen system and defines When the fuel cell output is 0, When the hydrogen production of the electrolyzer is 0;

[0115] is the amount of hydrogen produced by the electrolyser; is the maximum amount of hydrogen produced by the electrolyzer;

[0116] P h FC,Min The minimum output of the fuel cell; P t FC is the output of the fuel cell at time t; P h FC,Max is the maximum output of the fuel cell;

[0117] is the amount of hydrogen consumed by the fuel cell at time t; The maximum amount of hydrogen consumed by the fuel cell;

[0118] is the minimum value of the hydrogen mass in the hydrogen storage tank; is the mass of hydrogen in the hydrogen storage tank at time t; is the maximum value of the hydrogen mass in the hydrogen storage tank;

[0119] is the initial value of the hydrogen mass in the hydrogen storage tank; is the initial hydrogen production in the hydrogen storage tank;

[0120] Pt EL P is the electric power consumed by the electrolytic cell at time t; t FC is the electrical power output of the fuel cell at time t; is the reactive power output by the hydrogen energy storage inverter at time t; is the capacity of the hydrogen system;

[0121] is the net injected active power of the node; P t UG represents the active power from the upstream power grid at time t; represents the reactive power from the upstream power grid at time t; is the net injected reactive power of the node; P t DG is the active power output by the distributed generator at time t; is the reactive power output by the distributed generator at time t; G i,j is the conductivity parameter of the line between nodes i and j; B i,j is the susceptance parameter of the line between nodes i and j; V t,i is the actual voltage of the ith node of the distribution network at time t; V t,j is the actual voltage of the jth node in the distribution network at time t; θ t,i is the phase angle of the voltage at node i at time t; θ t,j is the phase angle of the voltage at node j at time t.

[0122] By providing inverters for the photovoltaic system and the hydrogen system, more reactive power can be provided to improve the power quality of the nodes containing fuel cell vehicles in the distribution network.

[0123] Step 2: Linearize the objective function and constraint conditions to obtain a multi-objective optimization model of mixed integer linear programming.

[0124] Specifically, the power quality objective function becomes:

[0125]

[0126]

[0127]

[0128] Where, Min.PQI is the minimum value of the voltage deviation rate; ω i is the voltage quality coefficient; δV t,i is the voltage deviation of node i at time t; is the positive voltage deviation of node i at time t; is the negative voltage deviation of node i at time t; V t,i Represents the actual voltage of the ith node of the distribution network at time t; represents the rated voltage of node i at time t; is the positive voltage deviation of node i at time t; is the negative voltage deviation of node i at time t.

[0129] The AC power flow constraint condition becomes as follows after linearization:

[0130]

[0131]

[0132] PL t,(i,j) =-G i,j (V t,i +V t,j )+B i,j (θ t,i -θ t,j )

[0133] QL t,(i,j) =B i,j (V t,i +V t,j )+G i,j (θ t,i -θ t,j ) (18)

[0134] Where PL t,(i,j) ,QL t,(i,j) are the active power and reactive power between nodes i and j respectively.

[0135] The multi-objective optimization model of mixed integer linear programming is composed of objective functions (3), (17) and system operation constraints (5)-(12), (18).

[0136] Step three, using the ε constraint method to solve the multi-objective optimization model of the mixed integer linear programming to obtain the optimal scheduling plan for the active distribution network containing hydrogen production-storage-refueling stations.

[0137] Specifically, step three includes:

[0138] Step 3.1, taking the economic objective function as the main objective function and the linearized power quality objective function as the constraint condition, the multi-objective optimization model of the mixed integer linear programming is transformed into:

[0139]

[0140] And solve the optimal target value;

[0141] Step 3.2, taking the linearized power quality objective function as the main objective function, and taking the optimal objective value obtained in step 3.1 as the constraint condition, the multi-objective optimization model of the mixed integer linear programming is transformed into:

[0142] Objective function: minimize PQI t

[0143] Constraints:

[0144] in, represents the minimum cost obtained in step 3.1; λ OC It represents the parameter that achieves a compromise between the two objective functions. This parameter represents the economic goal that can be sacrificed in order to minimize the voltage deviation during the optimization process of step 3.2. OC The compromise between the two objective functions is achieved, and the optimal dispatching scheme of the active distribution network containing hydrogen production, hydrogen storage and hydrogen refueling station is obtained. OC =10%, then the final optimization result can be considered as the optimal result of power quality found when the economic index deviates from the optimal value by 10%. In specific implementation, the mixed integer programming toolkit CPLEX can be used for solving.

[0145] Step 4: Use the optimal scheduling plan obtained in step 3 to schedule the photovoltaic system, hydrogen system and distribution network.

[0146] This method fully considers the characteristics of the photovoltaic system, hydrogen system and distribution network. Using this method to dispatch the photovoltaic system, hydrogen system and distribution network can not only achieve the economy of the distribution network operation, but also solve the problem of large voltage drop at the nodes of the hydrogen system due to the large demand for hydrogen by fuel vehicles, that is, improve the quality of power in the operation of the distribution network containing the hydrogen system. This method solves the problem of voltage deviation when the hydrogen station supplies power to the power grid, and can improve the quality of power supplied to the power grid. In addition, the present invention fully considers the coordinated operation of the photovoltaic system and the hydrogen system. The electricity for the electrolyzer can not only be purchased from the power grid, but also be supplied by the photovoltaic system, which can reduce the production cost of hydrogen, thereby reducing the economic threshold for the application of hydrogen stations in the power supply system, which is conducive to the promotion and application of this technology.

[0147] Tests show that the model built by this method can be solved within seconds, and can realize online real-time scheduling of photovoltaic systems, hydrogen systems and distribution networks. It has the advantage of online programmed calculation and is of great significance to the realization of economic, safe and stable operation of future green urban distribution networks.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the operation of a distribution network including a hydrogen production, storage and refueling station, It is characterized in that The following steps are involved: Step 1: Establish mathematical models of the photovoltaic system and the hydrogen system, and construct objective functions and constraints for optimizing the operation of the distribution network, and obtain a multi-objective optimization model including hydrogen production, hydrogen storage and hydrogen refueling station; wherein the hydrogen system includes an electrolyzer, a hydrogen storage tank and a fuel cell; the objective function includes an economic objective function and a power quality objective function; the constraints include photovoltaic system constraints, hydrogen system constraints and distribution network constraints; Step 2: linearize the objective function and constraint conditions to obtain a multi-objective optimization model of mixed integer linear programming; Step 3, using the ε constraint method to solve the multi-objective optimization model of the mixed integer linear programming to obtain the optimal scheduling scheme of the active distribution network containing hydrogen production-storage-refueling station; Step 4, use the optimal scheduling plan obtained in step 3 to schedule the photovoltaic system, hydrogen system and distribution network; Among them, step three includes: Step 3.1, taking the economic objective function as the main objective function and the linearized power quality objective function as the constraint condition, the multi-objective optimization model of the mixed integer linear programming is transformed into: Objective function: minimize OC t Constraints: And solve the optimal target value; Step 3.2, taking the linearized power quality objective function as the main objective function, and taking the optimal objective value obtained in step 3.1 as the constraint condition, the multi-objective optimization model of the mixed integer linear programming is transformed into: Objective function: minimize PQI t Constraints: In the formula, represents the minimum cost obtained in step 3.1; λ OC It represents the parameter that achieves a compromise between the two objective functions. This parameter represents the economic goal that can be sacrificed in order to minimize the voltage deviation during the optimization process of step 3.

2. And through the parameter λ OC A compromise between the two objective functions is achieved, and the optimal scheduling scheme for the active distribution network containing hydrogen production, storage and refueling stations is obtained.

2. The method for optimizing the operation of a distribution network containing a hydrogen production-storage-refueling station as claimed in claim 1, Features: In step 1, the mathematical model of the photovoltaic system is: Where P ST is the rated output power of photovoltaic power generation under standard test conditions; G t is the light intensity; ε is the power temperature coefficient; T j is the ambient temperature; The number of modules connected in series for photovoltaic cells; is the number of photovoltaic cell modules connected in parallel.

3. The method for optimizing the operation of a distribution network containing a hydrogen production, storage and refueling station as claimed in claim 2, Features: In step 1, the mathematical model of the hydrogen system is: In the formula, is the hydrogen production of the electrolyzer; η EL , is the efficiency of the electrolyzer; P2H is the electrical power consumed by the electrolytic cell; EL , is the power conversion factor of hydrogen production by electrolyzer; is the hydrogen consumption of the fuel cell; is the electrical power output of the fuel cell; η FC is the efficiency of the fuel cell; H2P FC The conversion factor for the fuel cell to convert hydrogen into electricity; is the hydrogen storage state of the hydrogen storage tank at time t; is the hydrogen storage state of the hydrogen storage tank at time t-1; ε DSP is the hydrogen dissipation rate of the hydrogen storage tank; HDE t To meet the demand for hydrogen for hydrogen-powered vehicles.

4. The method for optimizing the operation of a distribution network containing a hydrogen production-storage-refueling station as claimed in claim 3, Features: The electrical power consumed by the electrolytic cell The sources of electricity include the electric power output from the upstream distribution network and the electric power output from photovoltaic power generation.

5. The method for optimizing the operation of a distribution network containing a hydrogen production-storage-refueling station as claimed in claim 4, Features: In step 1, the economic objective function is: In the formula, is the electric energy purchased by the distribution network from the upstream power grid at time t; is the unit electricity price of the upstream power grid; is the distributed generation output power at time t; is the unit operating cost of distributed generation; is the photovoltaic power output power at time t; is the unit operating cost of photovoltaic power generation; is the electric power consumed by the electrolytic cell at time t; is the electrical power output by the fuel cell at time t.

6. The method for optimizing the operation of a distribution network containing a hydrogen production-storage-refueling station as claimed in claim 5, Features: In step 1, the power quality objective function is: In the formula, ω i is the voltage quality coefficient; V t,i Represents the actual voltage of the ith node in the distribution network; Represents the rated voltage of node i.

7. The method for optimizing the operation of a distribution network containing a hydrogen production-storage-refueling station as claimed in claim 6, Features: In step 1, the photovoltaic system constraint condition includes the capacity constraint of the photovoltaic system inverter: The hydrogen system constraints include the operating constraints of the electrolyzer: Fuel cell operating constraints: Operation constraints of hydrogen storage tanks: And the capacity constraints of the hydrogen system inverter: The distribution network constraints include AC power flow constraints: in, is the photovoltaic power output power at time t; is the reactive power output by the photovoltaic inverter at time t; is the capacity of the PV inverter; is the minimum capacity of the electrolytic cell; Power in a synergistic mode using PV output and upstream grid power for the electrolyzer; is the maximum capacity of the electrolytic cell; is a binary variable in the hydrogen system and defines When the fuel cell output is 0, When the hydrogen production of the electrolyzer is 0; is the amount of hydrogen produced by the electrolyzer; is the maximum amount of hydrogen produced by the electrolyzer; The minimum output of the fuel cell; is the output of the fuel cell at time t; is the maximum output of the fuel cell; is the amount of hydrogen consumed by the fuel cell at time t; The maximum amount of hydrogen consumed by the fuel cell; is the minimum value of the hydrogen mass in the hydrogen storage tank; is the mass of hydrogen in the hydrogen storage tank at time t; is the maximum value of the hydrogen mass in the hydrogen storage tank; is the initial value of the hydrogen mass in the hydrogen storage tank; is the initial hydrogen production in the hydrogen storage tank; is the electric power consumed by the electrolytic cell at time t; is the electric power output of the fuel cell at time t; is the reactive power output by the hydrogen energy storage inverter at time t; is the capacity of the hydrogen system; is the net injected active power of the node; represents the active power from the upstream power grid at time t; represents the reactive power from the upstream power grid at time t; The net injected reactive power of the node; is the active power output by the distributed generator at time t; is the reactive power output by the distributed generator at time t; G i,j is the conductivity parameter of the line between nodes i and j; B i,j is the susceptance parameter of the line between nodes i and j; V t,i is the actual voltage of the ith node of the distribution network at time t; V t,j is the actual voltage of the jth node in the distribution network at time t; θ t,i is the phase angle of the voltage at node i at time t; θ t,j is the phase angle of the voltage at node j at time t.

8. The method for optimizing the operation of a distribution network containing a hydrogen production-storage-refueling station as claimed in claim 7, Features: In step 2, the power quality objective function is linearized and becomes: In the formula, Min.PQI is the minimum value of the voltage deviation rate; ω i is the voltage quality coefficient; δV t,i is the voltage deviation of node i at time t; is the positive voltage deviation of node i at time t; is the negative voltage deviation of node i at time t; V t,i Represents the actual voltage of the ith node of the distribution network at time t; represents the rated voltage of node i at time t; is the positive voltage deviation of node i at time t; is the negative voltage deviation of node i at time t.

9. The method for optimizing the operation of a distribution network containing a hydrogen production-storage-refueling station as claimed in claim 8, Features: In step 2, the AC power flow constraint condition is linearized and becomes: PL t,(i,j) =-G i,j (V t,i +V t,j )+B i,j (θ t,i -θ t,j ) QL t,(i,j) =B i,j (V t,i +V t,j )+G i,j (θ t,i -θ t,j ) Where PL t,(i,j) ,QL t,(i,j) are the active power and reactive power between nodes i and j respectively.

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