Site selection and capacity determination method and system for electric hydrogen production, storage and injection integrated station

Abstract

The application discloses a kind of electric hydrogen production storage injection integrated station site selection and capacity determination method and system, obtain the parameter for electric hydrogen production storage injection integrated station site selection and capacity determination;Using Latin hypercube sampling method extracts several original operation scenarios;Using K-means algorithm carries out original operation scenario clustering, generates the typical operation scenario set S suitable for electric hydrogen production storage injection integrated station site selection and capacity determination decision;Under the site selection and capacity determination-operation check integrated decision framework, construct electric hydrogen production storage injection integrated station optimization planning model;Solving electric hydrogen production storage injection integrated station optimization planning model, obtains the site selection and capacity determination scheme of electric hydrogen production storage injection integrated station economy optimum.The application effectively improves the economy and multi-scenario adaptability of electric hydrogen production storage injection integrated station site selection and capacity determination scheme, provides practical and effective technical route for the optimization planning of electric hydrogen production storage injection integrated station in actual engineering.

Description

Technical Field

[0001] This invention belongs to the field of hydrogen energy technology, specifically relating to a method and system for site selection and capacity determination of an integrated electric hydrogen production, storage and injection station. Background Technology

[0002] An integrated power-hydrogen production, storage, and injection station is an electro-hydrogen coupling system that integrates power generation, energy storage, hydrogen production, hydrogen storage, and hydrogen injection. It can utilize renewable energy power generation to drive on-site production of green hydrogen, providing a clean, stable, and reliable supply of electricity and hydrogen energy. When the integrated power-hydrogen production, storage, and injection station is connected to the power distribution network, it can work in conjunction with renewable energy and an electro-hydrogen hybrid energy storage system to improve the flexible regulation capability of the regional power grid as a controllable power source. Simultaneously, the integrated power-hydrogen production, storage, and injection station can proactively respond to electricity price signals, fully leveraging its operational flexibility and achieving friendly interaction with the power distribution system. Therefore, the construction of integrated power-hydrogen production, storage, and injection stations is of great significance for promoting the low-carbon transformation of my country's energy and transportation systems and enhancing the flexible regulation capability of the power system.

[0003] The site selection and capacity configuration of integrated power generation, storage, and injection (HGG) stations will significantly impact the operational status of the power system and its downstream transportation systems. The site selection and capacity determination decisions for these stations not only affect the density distribution of hydrogen refueling loads for fuel cell vehicles, but also influence power flow distribution due to their flexible interaction with the distribution network, thereby affecting power flow congestion and network losses. Furthermore, related research indicates that the location of flexible resource access is closely related to the renewable energy carrying capacity of the distribution network.

[0004] Therefore, scientific and rational site selection not only increases the energy supply efficiency of integrated power generation, storage, and injection (HGG) stations, but also enhances the power distribution network's capacity to absorb and utilize new energy power generation. However, theoretical research and technological applications regarding site selection and capacity determination methods for integrated HGG stations are rarely reported. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for site selection and capacity determination of integrated electric hydrogen production, storage and injection stations, which addresses the shortcomings of the prior art. This provides technical support for scientific decision-making in the planning and design of integrated electric hydrogen production, storage and injection stations, improves the economic efficiency and multi-scenario adaptability of site selection and capacity determination decisions for integrated electric hydrogen production, storage and injection stations, and promotes the clean and low-carbon transformation of energy and power systems and transportation systems.

[0006] The present invention adopts the following technical solution:

[0007] A method for site selection and capacity determination of an integrated hydrogen production, storage, and injection station includes the following steps:

[0008] S1. Obtain parameters for site selection and capacity determination of the integrated hydrogen production, storage and injection station;

[0009] S2. Based on the parameters obtained in step S1, several original operating scenarios are extracted using the Latin hypercube sampling method; the original operating scenarios are clustered using the K-means algorithm to generate a typical operating scenario set S suitable for the site selection and capacity determination decision of the integrated electric hydrogen production, storage and injection station.

[0010] S3. Based on the typical operation scenario set S obtained in step S2, construct an optimization planning model for an integrated electric hydrogen production, storage and injection station under the integrated decision-making framework of site selection, capacity determination and operation verification.

[0011] S4. Solve the optimization planning model of the integrated electric hydrogen production, storage and injection station constructed in step S3 to obtain the site selection and capacity setting scheme with the best economic efficiency for the integrated electric hydrogen production, storage and injection station.

[0012] Specifically, in step S1, the parameters for site selection and capacity determination of the integrated hydrogen production, storage and injection station include equipment parameters, meteorological parameters, electrical parameters, load parameters and economic parameters.

[0013] Specifically, in step S2, several original operating scenarios are extracted using the Latin hypercube sampling method based on the random probability distribution functions of wind speed, clearness index, electrical load, and hydrogen load.

[0014] Specifically, in step S2, each typical operating scenario includes wind and solar resource fluctuation factors, electrical load of each node in the power distribution network, and hydrogen refueling load data of each sub-region during different time periods of the day in the target park.

[0015] Specifically, in step S3, the optimization planning model for the integrated hydrogen production, storage and injection station includes two parts: site selection and capacity determination for hydrogen and electricity facilities and medium- and long-term operation optimization. The goal is to minimize the overall annual cost while meeting investment and operational constraints.

[0016] Furthermore, the objective function for the optimization planning of the integrated hydrogen production, storage, and injection station is as follows:

[0017] minφ=φ capex +φ opex -φ rev

[0018] Where φ is the horizontal annual comprehensive cost, φ capex φ is the equivalent annual investment cost. opex Annual operating cost, φ rev Annual operating revenue.

[0019] Furthermore, the equivalent annual investment cost φ capex for:

[0020]

[0021] Annual operating cost φ opex for:

[0022]

[0023] Annual operating revenue φ rev for:

[0024]

[0025] Where k is the index of the candidate system / equipment; K1 = {WT, PV, ELZ, HT}, K2 = {BS}, K3 = {HD} are the sets of candidate systems / equipment, where WT, PV, BS, ELZ, HT, and HD represent wind power generation systems, photovoltaic power generation systems, battery energy storage systems, electrolyzers, hydrogen storage tanks, and hydrogen refueling machines, respectively; j is the index of the distribution network node; Λ represents the set of nodes in the target park's distribution network that are allowed to be connected to the integrated hydrogen production, storage, and injection station due to geographical restrictions, policy factors, etc.; c ehs For the basic construction costs of the integrated hydrogen production, storage and injection station, c k Let k be the unit investment cost of system / equipment. The unit power price and unit energy price of battery energy storage systems; u j X is a 0-1 variable used to measure the connection status of the integrated hydrogen production, storage, and injection station at node j; k,j The configured capacity for node j system / device k, For node j, the power capacity and energy storage capacity of the battery energy storage system; n k,j γ represents the number of hydrogen refueling units at node j; k Each investment cost item is evaluated using a capital recovery factor, N. + ={1,2,…} is the set of nodes in the target park's power distribution network, satisfying m is the index of each sub-region within the target park, and M is the set consisting of all sub-regions of the target park; Λ m Let B be the set of candidate nodes for the distribution network in each sub-region; let (i,j) be the set of distribution network branches, where (i,j) represents a branch of the distribution network with nodes i and j as vertices; π s Let λ be the probability of running scenario s; ehs The annual operation and maintenance cost of an integrated hydrogen production, storage and injection station is λ k The unit annual maintenance cost of system / device k; t is the index of the runtime segment, and T is the total number of runtime segments for each runtime scenario; To meet the hydrogen refueling needs of sub-region m during time period t in scenario s; For the operation scenario, the hydrogen refueling load that can be obtained at node j during time period t is s. σ is the interruptible hydrogen refueling load compensation cost coefficient; r is the network loss cost coefficient; ij Let (i,j) be the resistance value of branch (i,j). ξ is the square of the current in branch (i,j) during time period t in the running scenario s;E This is the cost coefficient for interruptible power load compensation; This represents the electricity demand at node j during time period t in the running scenario s; This represents the electrical load that node j can satisfy during time period t in operating scenario s; This represents the active power purchased from the upper-level power grid during time period t in the operating scenario s; This represents the electricity price purchased from the upper-level power grid during time period t, using a time-of-use pricing mechanism. This is a time conversion factor used to convert operating costs from a typical daily timescale to an annual timescale; Δ t The granularity of medium- to long-term operation time is considered for optimization planning; The price of hydrogen; α E,t For internal electricity sales price; θ exp The electricity price sold by the superior power grid; The active power output to the upper-level power grid during time period t in the operating scenario s.

[0026] Furthermore, the specific investment constraints of the optimization planning model for the integrated hydrogen production, storage, and injection station are as follows:

[0027] Annual investment cost constraints:

[0028]

[0029] Minimum limit constraint on the number of access points per region:

[0030]

[0031] Non-access node constraints for integrated hydrogen production, storage, and injection stations:

[0032]

[0033] Component procurement constraints:

[0034]

[0035]

[0036]

[0037]

[0038] Where, φ capex Annual investment cost; The upper limit of the equivalent annual value investment cost acceptable to investors; j The variable is a 0-1 variable used to measure the access status of the integrated hydrogen production, storage and injection station at node j; m is the index of each sub-region within the target park; M is the set consisting of all sub-regions of the target park. Configure upper / lower limits for the system / device's k capacity; The upper / lower limits for the capacity configuration of the battery energy storage system, k∈K2; the energy storage capacity of the battery energy storage system is determined by the energy storage coefficient κ. min and κ max constraint; Let k represent the upper / lower limit of the number of hydrogen refueling machines to be installed, where k ∈ K3.

[0039] Furthermore, the specific operational constraints of the optimization planning model for the integrated hydrogen production, storage, and injection station are as follows:

[0040] Wind power generation systems and photovoltaic power generation systems need to meet the following active / reactive power output constraints:

[0041]

[0042]

[0043]

[0044] in, The active power output of the new energy power generation system at node j during time period s in the operating scenario; The fluctuation factor of the target park's new energy source during the time period t in the operating scenario s; WT represents the configuration capacity of system / equipment k at node j; WT represents the wind power generation system; PV represents the photovoltaic power generation system; Λ represents the set of nodes in the target park's power distribution network that are allowed to connect to the integrated hydrogen production, storage, and injection station. This represents the upper and lower limits of the grid-connected power factor angle for new energy power generation systems. For the reactive power output of the new energy power generation system at node j during time period s in the operating scenario; S k,j The apparent power of the inverter in the new energy power generation system;

[0045] Battery energy storage system operating constraints:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] Where K2 = {BS} is a set of battery energy storage systems, and BS represents a battery energy storage system; M1 represents the charging / discharging power of the battery energy storage system at node j during time period s in the operating scenario; M1 is a very large positive real number. For each time period t in the operating scenario s, there are 0-1 variables representing the charging / discharging state of the battery energy storage system at node j. The energy storage status of the battery energy storage system at node j during time period s in the operating scenario; η represents the maximum depth of discharge of the battery energy storage system. k The charging / discharging efficiency of battery energy storage systems; For the power capacity and energy storage capacity of the battery energy storage system at node j; Δ t The granularity of medium- to long-term operation time is considered for optimization planning;

[0055] Electrolyzer operating constraints:

[0056] The electro-hydrogen conversion equation for an electrolyzer:

[0057]

[0058] Electrolyzer capacity constraints:

[0059]

[0060] in, χ represents the injected electrical power of the electrolytic cell at node j during time period s. ELZ η represents the electro-hydrogen conversion efficiency of the electrolyzer; ELZ To improve the working efficiency of the electrolytic cell;

[0061] Hydrogen storage tank operating constraints:

[0062] Material flow balance constraints of the hydrogen system:

[0063]

[0064] Hydrogen storage capacity constraints:

[0065]

[0066] in, X represents the hydrogen storage capacity of the hydrogen storage tank at node j during time period t in the operating scenario s; HT,j The installation capacity of the hydrogen storage tank at node j; Hydrogen loss due to hydrogen storage tanks; operational constraints of the hydrogen refueling unit:

[0067]

[0068] Among them, v k To improve the refueling capacity of the hydrogen dispenser, For the operating scenario, the hydrogen refueling load that can be satisfied at node j during time period t is n. k,j K3 represents the number of hydrogen refueling machines installed at node j; K3 = {HD} is the set of hydrogen refueling machines, where HD represents a hydrogen refueling machine.

[0069] Hydrogen refueling demand allocation constraints

[0070]

[0071]

[0072]

[0073] in, For the integrated hydrogen production, storage, and injection station operating at node j during time period s in scenario s, the proportion of hydrogen refueling demand in the sub-region should be met. To meet the hydrogen refueling needs of sub-region m during time period t in scenario s;

[0074] Power balance constraints:

[0075]

[0076]

[0077]

[0078]

[0079] in, The active / reactive power interaction between the flexible electric hydrogen production, storage and injection station and the power distribution network is represented by the active / reactive power of the station at node j during time period s in the operation scenario. For the operating scenario, the active / reactive power of node j during time period s can actually meet the electrical load. The active / reactive power required by the electrical load at node j during time period s in the operating scenario;

[0080] AC power flow constraints in distribution networks:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] in, Let Θ(j) be the active / reactive power flowing through branch (i,j) in time period t of scenario s; Θ(j) is the set of downstream child nodes of node j; The active / reactive power transmitted by the main transformer during time period t in the operating scenario s; x is the square of the voltage amplitude at node j during time period t in the running scenario s; ij Let (i,j) be the reactance of branch (i,j);

[0088] Node voltage constraints:

[0089]

[0090] in, Indicates the upper / lower limit of the voltage amplitude at node j;

[0091] Branch current constraints:

[0092]

[0093] in, This represents the upper limit of the current in branch (i,j);

[0094] Transformer gate interactive power constraints:

[0095]

[0096] Among them, S lv,j The capacity of the low-voltage transformer at the candidate node j;

[0097] The interaction power between the target industrial park's distribution network and the upstream power grid is limited by the rated capacity of the main transformer as follows:

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] in, In the operating scenario s, time period t represents a 0-1 variable indicating the power purchase / sale status from the upstream power grid; M2 is a large positive real number; S sub The capacity of the main transformer.

[0104] Another technical solution of the present invention is a site selection and capacity determination system for an integrated electro-hydrogen production, storage, and injection station, comprising:

[0105] The parameter module obtains parameters for the site selection and capacity determination of the integrated hydrogen production, storage, and injection station.

[0106] The scenario module extracts several original operating scenarios based on the parameters obtained from the parameter module using the Latin hypercube sampling method; the K-means algorithm is used to cluster the original operating scenarios to generate a typical operating scenario set S suitable for site selection and capacity determination decisions of integrated electric hydrogen production, storage and injection stations.

[0107] The module constructs an optimization planning model for an integrated hydrogen production, storage and injection station based on the typical operating scenario set S obtained from the scenario module and under the integrated decision-making framework of site selection, capacity determination and operation verification.

[0108] Select the module, solve the optimization planning model of the integrated electric hydrogen production, storage and injection station constructed by the module, and obtain the site selection and capacity setting scheme with the best economic efficiency for the integrated electric hydrogen production, storage and injection station.

[0109] Compared with the prior art, the present invention has at least the following beneficial effects:

[0110] This invention provides a site selection and capacity determination method for integrated hydrogen production, storage, and injection (EPI) stations. Within an integrated decision-making framework of "site selection and capacity determination - operational verification," it proposes an optimized planning model for EPI stations with the objective of minimizing the overall annual cost. Based on historical data of key operational parameters of the EPI station, multiple operational scenarios are simulated to ensure that the resulting site selection and capacity determination scheme can flexibly adapt to different system operation implementations. In engineering applications, technicians can formulate appropriate EPI station site selection and capacity determination schemes based on the wind and solar resource characteristics and hydrogen and electricity load requirements of the target industrial park, using the method and system provided by this invention, ensuring that the economic efficiency and operational characteristics of the EPI station meet design requirements.

[0111] Furthermore, obtaining relevant information such as equipment parameters, meteorological parameters, electrical parameters, load parameters, and economic parameters in step S1 can provide data support for the site selection and capacity determination of the integrated hydrogen production, storage, and injection station.

[0112] Furthermore, based on the random probability distribution functions of wind speed, sunshine index, electrical load, and hydrogen load, several original operating scenarios are extracted using the Latin hypercube sampling method. The advantage of this method is that, compared to simple random sampling, fewer sampling times are required to achieve the same mean estimation accuracy, resulting in higher sampling efficiency.

[0113] Furthermore, each typical operating scenario simulated in step S2 includes wind and solar resource fluctuation factors for different time periods of the day, as well as the hydrogen refueling load and self-consumption power load that need to be met. This can provide operating boundaries for the site selection and capacity determination of the integrated hydrogen production, storage and injection station, so that the site selection and capacity determination scheme can adapt to different implementations of system operation.

[0114] Furthermore, the optimization planning model for the integrated hydrogen production, storage, and injection station in step S3 includes two parts: site selection and capacity determination for hydrogen and electricity facilities, and medium- to long-term operation optimization. The goal is to minimize the overall annual cost while meeting investment and operational constraints. The purpose of this model is to verify its effectiveness through medium- to long-term operation, ensuring the optimal economics of the site selection and capacity determination scheme for the integrated hydrogen production, storage, and injection station while satisfying a series of investment constraints and operational requirements.

[0115] Furthermore, the objective function for the optimization planning of the integrated hydrogen production, storage, and injection station in step S3 is to minimize the annual comprehensive cost, which is the sum of the equivalent annual investment cost and the annual operating cost, minus the annual operating revenue. This objective function allows for a comprehensive consideration of the investment and operational economics of the site selection and capacity determination scheme.

[0116] Furthermore, in step S3, the horizontal annual comprehensive cost can be expressed as the sum of the equivalent annual investment cost and the annual operating cost, minus the annual operating revenue. The equivalent annual investment cost includes the construction and installation costs of the integrated electric-hydrogen production, storage, and injection station, as well as the purchase costs of each system / equipment. The annual operating cost includes the annual construction and maintenance costs of the integrated electric-hydrogen production, storage, and injection station, the annual maintenance costs of each system / equipment, the interruptible hydrogen refueling load compensation cost, the distribution network loss cost, the interruptible power load compensation cost, and the cost of purchasing electricity from the target park's upstream power grid. The annual operating revenue includes hydrogen refueling revenue, revenue from selling electricity to users in the park, and revenue from selling electricity to the upstream power grid. The purpose of setting the horizontal annual comprehensive cost, equivalent annual investment cost, annual operating cost, and annual operating revenue is to provide a detailed description of the investment and operational economics of the site selection and capacity determination of the integrated electric-hydrogen production, storage, and injection station.

[0117] Furthermore, the investment constraints set in step S3 can provide investment boundaries for the annualized investment cost of the integrated hydrogen production, storage and injection station and the installation capacity of each system / equipment in the station.

[0118] Furthermore, the setting of operational constraints in step S3 can provide operational boundaries for the medium- and long-term operational verification of the integrated hydrogen production, storage, and injection station.

[0119] In summary, this invention relates to the site selection and capacity determination of integrated electric hydrogen production, storage, and injection stations for engineering needs. It can effectively improve the economic efficiency and multi-scenario adaptability of the site selection and capacity determination scheme for integrated electric hydrogen production, storage, and injection stations, and provide a practical and effective technical route for the optimized planning of integrated electric hydrogen production, storage, and injection stations in actual engineering projects.

[0120] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0121] Figure 1 This is a schematic diagram of a typical structure of the integrated hydrogen production, storage and injection station of the present invention;

[0122] Figure 2 This is a schematic diagram of the method flow of the present invention;

[0123] Figure 3 This is a topology diagram of the power distribution network in the target area of ​​this invention;

[0124] Figure 4 This is a schematic diagram of the wind speed fluctuation factor under a typical operating scenario of the present invention;

[0125] Figure 5 This is a schematic diagram of the light intensity fluctuation factor under a typical operating scenario of the present invention;

[0126] Figure 6 This is a hydrogen refueling load diagram for sub-region A under a typical operating scenario of the present invention;

[0127] Figure 7 This is a load diagram of node 3 of the park's power distribution network under a typical operating scenario of the present invention. Detailed Implementation

[0128] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0129] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0130] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0131] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0132] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0133] Please see Figure 1 A typical integrated hydrogen production, storage and injection station consists of a wind power generation system, a photovoltaic power generation system, a battery energy storage system, an electrolyzer, a hydrogen storage tank and a hydrogen refueling unit.

[0134] The wind power generation system, photovoltaic power generation system, and battery energy storage system are connected through the same bus, which connects to the electrolyzer and electrical load, and generates power interaction with the power distribution network through a step-up transformer; the hydrogen output port of the electrolyzer is connected to the input port of the hydrogen storage tank; the output port of the hydrogen storage tank is connected to the input port of the hydrogen refueling machine; the hydrogen refueling machine is connected to the hydrogen refueling demand side to provide hydrogen refueling services for fuel cell vehicles.

[0135] Please see Figure 2 This invention discloses a site selection and capacity determination method for an integrated electric hydrogen production, storage, and injection station. By optimizing the access location and capacity of the integrated electric hydrogen production, storage, and injection station to meet the hydrogen and electricity load requirements of various operating scenarios, it achieves optimal economic efficiency. The method includes the following steps:

[0136] S1. Obtain relevant information required for the site selection and capacity determination of the integrated hydrogen production, storage, and injection station, specifically including:

[0137] (1) Equipment parameters, such as the purchase cost, annual operation and maintenance cost, service life, and operating parameters of wind power generation system, photovoltaic power generation system, battery energy storage system, electrolyzer, hydrogen storage tank, and hydrogen refueling machine;

[0138] (2) Meteorological parameters, such as historical fluctuation factors of wind and solar resources in the target park;

[0139] (3) Electrical parameters, such as the network topology and line parameters of the target park's power distribution network;

[0140] (4) Load parameters, such as the historical power load of each node of the target park's power distribution network and the historical hydrogen refueling load of each sub-area within the target park;

[0141] (5) Economic parameters, such as annualized interest rate, time-of-use electricity price, new energy grid connection price, basic construction cost and annual operating cost of integrated electric hydrogen production, storage and injection station.

[0142] S2. Based on the historical wind and solar resource fluctuation factors, power load of each node in the distribution network, and historical hydrogen refueling load data of each sub-region in the target park from step S1, several original operating scenarios are extracted using the Latin hypercube sampling method according to the random probability distribution functions of wind speed, sunshine index, power load, and hydrogen load. Then, the K-means algorithm is used to cluster the original operating scenarios, generating a set of typical operating scenarios S suitable for site selection and capacity determination decisions of integrated power generation, storage, and injection stations. Each typical operating scenario (hereinafter referred to as "operating scenario") includes wind and solar resource fluctuation factors, power load of each node in the distribution network, and hydrogen refueling load data of each sub-region in the target park for each time period of the day. |S| represents the number of typical scenarios in the set, and the subscript s represents the index of the typical scenario.

[0143] S3. Under the integrated decision-making framework of "site selection and capacity determination - operation verification", construct an optimization planning model for the integrated hydrogen production, storage and injection station. The optimization planning model for the integrated hydrogen production, storage and injection station includes two parts: hydrogen and electricity facility site selection and capacity determination decision and medium- and long-term operation optimization. The goal is to minimize the comprehensive cost within the current year while meeting investment and operational constraints. The specific description is as follows:

[0144] 1) Objective function

[0145] The goal of the optimized planning for an integrated hydrogen production, storage, and injection station is to minimize the overall cost for the investor and operator within a given year by selecting the optimal site selection and capacity allocation. The objective function (i.e., the overall cost function) for the optimized planning of the integrated hydrogen production, storage, and injection station is expressed as follows:

[0146] minφ=φ capex +φ opex -φ rev (1)

[0147]

[0148]

[0149]

[0150] K1={WT,PV,ELZ,HT} (5)

[0151] K2={BS} (6)

[0152] K3={HD} (7)

[0153] (1) The annual comprehensive cost φ (Equation (1)) can be expressed as the equivalent annual investment cost φ capex Annual operating cost φ opex The sum of these, minus last year's operating revenue φ revThe overall cost may be positive or negative: a negative value indicates that the investment in the integrated electric hydrogen production, storage and injection station within the target park can be fully recovered within a horizontal year, thus generating a profit; a positive value indicates that the investment in the integrated electric hydrogen production, storage and injection station is difficult to recover.

[0154] (2) Equivalent annual investment cost φ capex Equation (2) includes the construction and installation costs of the integrated hydrogen production, storage, and injection station, as well as the purchase costs of each system / equipment. Here, k is the index of the candidate system / equipment, K1, K2, and K3 are the sets of candidate systems / equipment, WT, PV, BS, ELZ, HT, and HD represent wind power generation systems, photovoltaic power generation systems, battery energy storage systems, electrolyzers, hydrogen storage tanks, and hydrogen refueling machines, respectively; j is the index of the distribution network node; Λ represents the set of nodes (hereinafter referred to as "candidate nodes") that the target park's distribution network is allowed to connect to the integrated hydrogen production, storage, and injection station due to geographical restrictions, policy factors, etc.; c ehs For the basic construction costs of the integrated hydrogen production, storage and injection station, c k (k∈K1∪K3) represents the unit investment cost of system / equipment k. The unit power price and unit energy price of battery energy storage systems; u j The variable is 0-1, used to measure the connection status of the integrated hydrogen production, storage, and injection station. j =1 indicates that node j has an integrated hydrogen production, storage, and injection station connected; X k,j (k∈K1) represents the configuration capacity of system / device k at node j. For node j, the power capacity and energy storage capacity of the battery energy storage system; n k,j (k∈K3) represents the number of hydrogen refueling units at node j. Each investment cost item is calculated using the capital recovery factor γ. k Converted to equivalent annual value, the calculation formula is as follows:

[0155]

[0156] Among them, L k denoted as k, the operating life of the integrated hydrogen production, storage, and injection station / system / equipment; r is the annualized interest rate.

[0157] (3) Operating costs (Equation (3)) include the annual operation and maintenance costs of the integrated hydrogen production, storage and injection station, the annual operation and maintenance costs of each system / equipment, the interruptible hydrogen refueling load compensation costs, the distribution network loss costs, the interruptible power load compensation costs, and the cost of purchasing electricity from the target park's upstream power grid. Wherein, N + ={1,2,…} is the set of nodes in the target park's power distribution network, satisfying The distribution network is connected to the upper-level power grid through the main transformer at node 0; m is the index of each sub-region within the target park (the sub-region division follows the power grid company's distribution sub-region division and does not overlap), and M is the set consisting of all sub-regions of the target park; Λm (m∈M) is the set of candidate nodes for the distribution network in each sub-region, satisfying... B is the set of distribution network branches, where (i,j) represents a branch of the distribution network with nodes i and j as vertices; π s The probability of scenario s can be selected based on the experience of relevant technical personnel, or it can be directly taken as a constant value of 1S; λ ehs The annual operation and maintenance cost of an integrated hydrogen production, storage and injection station is λ k The unit annual maintenance cost for system / device k (k∈K1∪K2∪K3); t is the index of the runtime segment, and T is the total number of runtime segments for each runtime scenario; To meet the hydrogen refueling needs of sub-region m during time period t in scenario s; For the operation scenario, the hydrogen refueling load that can be obtained at node j during time period t is s. σ is the interruptible hydrogen refueling load compensation cost coefficient; r is the network loss cost coefficient; ij Let (i,j) be the resistance value of branch (i,j). ξ is the square of the current in branch (i,j) during time period t in the running scenario s; E This is the cost coefficient for interruptible power load compensation; This represents the electricity demand at node j during time period t in the running scenario s; This represents the electrical load that node j can satisfy during time period t in operating scenario s; This represents the active power purchased from the upper-level power grid during time period t in the operating scenario s; This represents the electricity price purchased from the upper-level power grid during time period t, using a time-of-use pricing mechanism. This is a time conversion factor used to convert operating costs from a typical daily timescale to an annual timescale; Δ t The granularity of medium- and long-term operation time is considered for optimization planning.

[0158] (4) Annual operating revenue (Equation (4)) includes revenue from hydrogen refueling, revenue from selling electricity to users in the park, and revenue from selling electricity to the upper-level power grid. Among them, The price of hydrogen; α E,t For internal electricity sales price; θ exp The electricity price sold by the superior power grid; The active power output to the upper-level power grid during time period t in the operating scenario s.

[0159] 2) Investment constraints

[0160] The investment constraints for the optimized planning of the integrated hydrogen production, storage, and injection station are as follows:

[0161] (1) Annual investment cost constraint:

[0162] The sum of the construction and installation costs and the system / equipment purchase costs of the integrated hydrogen production, storage and injection station should be controlled within the investment budget.

[0163]

[0164] in, This represents the upper limit of the equivalent annual value investment cost that investors can accept.

[0165] (2) Minimum limit constraint on the number of access points per region:

[0166] To meet the hydrogen refueling needs of each sub-region, each sub-region should construct at least one integrated electric hydrogen production, storage, and refueling station.

[0167]

[0168] (3) Non-access node constraints for integrated hydrogen production, storage and injection stations:

[0169] Considering investor preferences and geographical constraints, the construction of integrated hydrogen production, storage, and injection stations is not permitted at certain nodes of the target industrial park's power distribution network. These nodes are referred to as "non-access nodes." Let Ψ be the set of non-access nodes. n / a Then there is Ψ n / a ∩Λ=Φ, and Ψ n / a ∪Λ=N + And satisfy the following constraints:

[0170]

[0171] (4) Component Procurement Constraints: Due to policy, economic, safety, and geographical factors, the capacity configuration of the integrated hydrogen production, storage, and injection station must meet the following system / equipment procurement capacity constraints:

[0172]

[0173]

[0174]

[0175]

[0176] in, Configure upper / lower limits for the system / device's k capacity; The upper / lower limits for the capacity configuration of the battery energy storage system; the storage capacity of the battery energy storage system is determined by the storage coefficient κ. min and κ max constraint; This represents the upper / lower limit for the number of hydrogen refueling units that can be installed.

[0177] 3) Operational constraints

[0178] The long-term operational optimization of integrated hydrogen production, storage, and injection stations must meet a series of constraints:

[0179] (1) Operational constraints of new energy power generation systems

[0180] The system output of wind power generation systems and photovoltaic power generation systems is mainly determined by the installed capacity and the availability of natural resources.

[0181] Wind power generation systems and photovoltaic power generation systems need to meet the following active / reactive power output constraints:

[0182]

[0183]

[0184]

[0185] Equation (16) is the output constraint of the new energy power generation system. The fluctuation factor of new energy in the target park during the time period t of the operation scenario s reflects the abundance of wind and solar resources; Let be the active power output of the new energy power generation system at node j during time period s in the operating scenario. Equation (17) is the grid connection constraint of the new energy power generation system. For the reactive power output of the new energy power generation system at node j during time period s in the operating scenario; These represent the upper and lower limits of the grid-connected power factor angle of the new energy power generation system, respectively. Equation (18) is the reactive power output constraint of the new energy power generation system, S k,j This refers to the apparent power of the inverter in a new energy power generation system.

[0186] It should be noted that in equation (18), the original... Replace with This achieves decoupling of active and reactive power output from new energy generator sets, but this conversion requires the inverter to have a certain capacity margin in its design, such as S. k,j =1.1X k,j .

[0187] (2) Operational constraints of battery energy storage system

[0188] Battery energy storage systems are mainly used to mitigate power output fluctuations in new energy power generation systems and maintain the safe and stable operation of integrated hydrogen production, storage, and injection stations. Their operational constraints are as follows:

[0189]

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197] Equations (19) to (23) represent the charge and discharge constraints of the battery energy storage system, where Let M1 be the charging / discharging power of the battery energy storage system at node j during time period s in the operating scenario; M1 is a very large positive real number, empirically taken as 1×10 6 ; Let t represent the 0-1 variables of the charging / discharging state of the battery energy storage system at node j, respectively, in the operating scenario s and time period t. Equation (24) represents the state of charge constraint of the battery energy storage system, where The energy storage status of the battery energy storage system at node j during time period s in the operating scenario; η represents the maximum depth of discharge of the battery energy storage system. Equation (25) is the energy conservation equation for the battery energy storage system, used to reflect the coupling relationship between the state of charge and the charge / discharge power in adjacent time periods, where η k The charging / discharging efficiency of the battery energy storage system is given by equation (26). Equation (26) is the initial and final energy storage state constraint of the battery energy storage system, which is used to ensure that the initial and final energy storage states are the same in each operating scenario.

[0198] (3) Electrolytic cell operation constraints

[0199] An electrolyzer produces hydrogen by electrolyzing water. The electro-hydrogen conversion equation for the electrolyzer is:

[0200]

[0201] in, The injected electrical power of the electrolytic cell at node j during time period s; χ represents the hydrogen production of the electrolyzer at node j during time period s in the operating scenario; ELZ η represents the electro-hydrogen conversion efficiency of the electrolyzer; ELZ The working efficiency of the electrolytic cell.

[0202] Furthermore, the operation of the electrolytic cell is constrained by the equipment capacity:

[0203]

[0204] (4) Operating constraints of hydrogen storage tanks

[0205] Hydrogen storage tanks are hydrogen energy storage devices that satisfy the mass flow balance equation of a hydrogen system:

[0206]

[0207] in, The amount of hydrogen stored in the hydrogen storage tank at node j during time period s in the operating scenario; The hydrogen loss from the hydrogen storage tank can be calculated using the hydrogen loss coefficient. Modeling as Linear functions:

[0208]

[0209] In addition, the operation of hydrogen storage tanks must meet hydrogen storage capacity constraints:

[0210]

[0211] (5) Operating constraints of hydrogen refueling machines

[0212] Due to the limited refueling capacity of hydrogen dispensers, the hydrogen load that can be met at the same time is constrained by the following formula:

[0213]

[0214] Among them, v k This refers to the refueling capacity of the hydrogen dispenser.

[0215] (6) Constraints on hydrogen refueling demand allocation

[0216] The hydrogen refueling load is distributed according to the sub-regions of the target park and needs to be allocated to the specific nodes where the integrated hydrogen production, storage and refueling station is located:

[0217]

[0218]

[0219]

[0220] in, For the integrated hydrogen production, storage, and injection station operating at node j during time period s, the proportion of hydrogen refueling demand in the sub-region should be met.

[0221] (7) Power balance constraint

[0222] The power supply system of the flexible integrated electric hydrogen production, storage, and injection station must meet the power balance equation:

[0223]

[0224]

[0225]

[0226]

[0227] in, The active / reactive power interaction between the flexible electric hydrogen production, storage and injection station and the power distribution network is represented by the active / reactive power of the station at node j during time period s in the operation scenario. For the operating scenario, the active / reactive power of node j during time period s can actually meet the electrical load. The active / reactive power required by the electrical load at node j during time period s in the operating scenario.

[0228] (8) Distribution network power flow constraints

[0229] The AC power flow constraints of the distribution network are as follows:

[0230]

[0231]

[0232]

[0233]

[0234]

[0235]

[0236] in, Let Θ(j) be the active / reactive power flowing through branch (i,j) in time period t of scenario s; Θ(j) is the set of downstream child nodes of node j; The active / reactive power transmitted by the main transformer during time period t in the operating scenario s; x is the square of the voltage amplitude at node j during time period t in the running scenario s; ij Let be the reactance of branch (i,j).

[0237] Note that constraint (45) contains a quadratic term for power and bilinear terms for the squares of voltage and current, making the problem non-convex, greatly increasing the difficulty of solving it, and making it difficult to guarantee the global optimality of the solution. To address the above difficulties, (45) is relaxed to the following equation:

[0238]

[0239] Furthermore, it can be expressed in the standard form of a second-order cone:

[0240]

[0241] Constraint (45) can be replaced by constraint (47). Since the objective function is linear, the planning problem is a second-order cone programming (SOCP) problem, and equations (46)-(47) are called second-order cone relaxations of power flow constraints in the distribution network. It is easy to see that the second-order cone relaxation problem of optimal AC power flow in the distribution network is a convex optimization problem. When the distribution network satisfies sufficient but not necessary conditions such as load ceiling constraint relaxation, irreversible power flow direction, and uniform network, the second-order cone relaxation problem of optimal AC power flow has accuracy, that is, the global optimal solution of the relaxation problem can accurately reproduce the actual solution of optimal power flow.

[0242] (9) Node voltage constraints

[0243] To ensure voltage quality, it is necessary to limit the fluctuation range of node voltage amplitude:

[0244]

[0245] in, This indicates the upper / lower limit of the voltage amplitude at node j.

[0246] (10) Branch current constraint

[0247] To avoid safety hazards caused by line overload, it is necessary to constrain the upper limit of branch current:

[0248]

[0249] in, This represents the upper limit of the current in branch (i,j).

[0250] (11) Transformer junction interactive power constraint

[0251] Due to the upper limit of transformer transmission power, the injection power of alternative nodes is constrained:

[0252]

[0253] Among them, S lv,j Let be the capacity of the low-voltage transformer at candidate node j. Note that constraint (50) is a nonlinear constraint, which can be approximately linearized within the allowable accuracy range as follows:

[0254]

[0255]

[0256]

[0257]

[0258] The target industrial park's power distribution network is electrically connected to the upstream power grid via a main transformer, and its interaction power with the upstream power grid is limited by the rated capacity of the main transformer.

[0259]

[0260]

[0261]

[0262]

[0263]

[0264] in, In the operating scenario s, time period t represents a 0-1 variable indicating the status of purchasing / selling electricity to the upper-level power grid; M2 is a large positive real number, empirically taken as 1×10. 6 S sub The capacity of the main transformer. Similarly, constraint (59) can be linearized to the following form:

[0265]

[0266]

[0267]

[0268]

[0269] S4. Solve the optimization planning model of the integrated electric hydrogen production, storage and injection station in step S3 to obtain the site selection and capacity setting scheme with the best economic efficiency for the integrated electric hydrogen production, storage and injection station.

[0270] In another embodiment of the present invention, a site selection and capacity determination system for an integrated electric hydrogen production, storage and injection station is provided. This system can be used to implement the above-mentioned site selection and capacity determination method for an integrated electric hydrogen production, storage and injection station. Specifically, the site selection and capacity determination system for an integrated electric hydrogen production, storage and injection station includes a parameter module, a scenario module, a construction module and a selection module.

[0271] Among them, the parameter module obtains the parameters for site selection and capacity determination of the integrated hydrogen production, storage and injection station;

[0272] The parameter module obtains parameters for the site selection and capacity determination of the integrated hydrogen production, storage, and injection station.

[0273] The scenario module extracts several original operating scenarios based on the parameters obtained from the parameter module using the Latin hypercube sampling method; the K-means algorithm is used to cluster the original operating scenarios to generate a typical operating scenario set S suitable for site selection and capacity determination decisions of integrated electric hydrogen production, storage and injection stations.

[0274] The module constructs an optimization planning model for an integrated hydrogen production, storage and injection station based on the typical operating scenario set S obtained from the scenario module and under the integrated decision-making framework of site selection, capacity determination and operation verification.

[0275] Select the module, solve the optimization planning model of the integrated electric hydrogen production, storage and injection station constructed by the module, and obtain the site selection and capacity setting scheme with the best economic efficiency for the integrated electric hydrogen production, storage and injection station.

[0276] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0277] Example

[0278] A hydrogen production, storage, and injection integrated station is planned to be constructed in a target industrial park to meet the park's power supply and fuel cell vehicle hydrogen refueling needs. The park's power distribution network contains 33 electrical nodes and 32 distribution branches, with the network topology as follows: Figure 3 As shown.

[0279] Table 1 shows the node information contained in sub-regions A, B, and C. The relevant information required for site selection and capacity determination of the integrated electric hydrogen production, storage, and injection station is obtained, along with the construction and installation cost c of the integrated electric hydrogen production, storage, and injection station. ehs 1.37×10 6 $, annual building operation and maintenance cost λ ehs It is 9.37×10 4 $, rated lifespan 20 years. In addition, Tables 2, 3, and 4 respectively show the main economic parameters of the new energy power generation system, battery energy storage system, and hydrogen production, storage, and injection equipment. Table 5 shows the time-of-use electricity price data for the target park area (assuming an electricity price α sold to users within the park). E,t (This is 0.9 of the electricity price), and the remaining planning parameters are shown in Table 6.

[0280] Table 1. Node Distribution in Subregions

[0281]

[0282] Table 2 Main Economic Parameters of New Energy Power Generation Systems

[0283]

[0284] Table 3 Key Economic Parameters of Battery Energy Storage Systems

[0285]

[0286] Table 4. Key Economic Parameters of Hydrogen Production, Storage, and Injection Equipment

[0287]

[0288] Table 5 Time-of-use electricity price data

[0289]

[0290] Table 6. Other required parameters for this embodiment.

[0291]

[0292] Based on historical data from the target park, a set of operational scenarios containing 12 scenarios was simulated and generated. Due to space limitations, only a portion of this is presented here. Figures 4-7 The data show wind speed fluctuation factor, light intensity fluctuation factor, hydrogen refueling load of sub-region A, and electrical load data of node 3 under a typical scenario.

[0293] By solving model (1)-(63), the site selection and capacity optimization results of this embodiment are shown in Table 7.

[0294] Table 7 Results of Site Selection and Capacity Optimization

[0295]

[0296] As shown in Table 7, under the parameters of this embodiment, the target park needs to plan 3 integrated electric hydrogen production, storage and injection stations to meet the users' electricity and hydrogen supply requirements, with a total investment cost of:

[0297]

[0298] Based on the optimization results, those skilled in the art can formulate site selection schemes for integrated electric hydrogen production, storage and injection stations, as well as capacity configuration schemes for various equipment within the station, thereby achieving optimized planning of integrated electric hydrogen production, storage and injection stations.

[0299] In summary, the present invention provides a method and system for site selection and capacity determination of an integrated electric hydrogen production, storage and injection station, which effectively improves the economy and adaptability of the site selection and capacity determination scheme for integrated electric hydrogen production, storage and injection stations, and provides a practical and effective technical route for the optimized planning of integrated electric hydrogen production, storage and injection stations in actual engineering projects.

[0300] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0301] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0302] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0303] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0304] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for site selection and capacity determination of an integrated hydrogen production, storage, and injection station, characterized in that, The method comprises the following steps: S1, obtaining parameters for site selection and capacity determination of the electric hydrogen production, storage and injection integrated station, wherein the parameters for site selection and capacity determination of the electric hydrogen production, storage and injection integrated station include equipment parameters, meteorological parameters, electrical parameters, load parameters and economic parameters; S2, based on the parameters obtained in step S1, a Latin hypercube sampling method is used to extract a plurality of original operation scenarios; a K-means algorithm is used for original operation scenario clustering to generate a typical operation scenario set suitable for site selection and capacity decision of the hydrogen production, storage and injection integrated station Each typical operation scenario contains wind, light resource fluctuation factors, power distribution network node electric load and each sub-area hydrogen refueling load data of the target park at each time period in a day. S3、based on the typical operating scenario set obtained in step S2 Under the integrated decision-making framework of site selection and capacity determination-operation verification, an optimization planning model of the integrated station of electricity-hydrogen production-storage-injection is constructed, which includes two parts of hydrogen and electricity facility site selection and capacity determination and medium and long-term operation optimization. The objective is to minimize the comprehensive cost in the level year under the premise of meeting investment constraints and operation constraints. The objective function of the optimization planning of the integrated station of electricity-hydrogen production-storage-injection is as follows: wherein, is the levelized annual cost, is the equivalent annual cost of investment, is the annual operating cost, is the annual operating revenue; Equalized annualized cost of investment Is: Annual operating cost is: Annual operating revenue Is: wherein, k is an index of alternative systems / devices; , , is a set of alternative systems / devices, respectively represent a wind power generation system, a photovoltaic power generation system, a battery energy storage system, an electrolyzer, a hydrogen storage tank, and a hydrogen refueling station; j is an index of distribution network nodes; represents a set of nodes in the target park distribution network that are allowed to access the hydrogen production, storage, and injection integrated station due to geographical restrictions and policy factors; is the capital cost of the hydrogen production, storage, and injection integrated station, is the unit investment cost of the system / device k , is the unit power price and the unit energy price of the battery energy storage system; is a 0-1 variable that measures the access of the hydrogen production, storage, and injection integrated station at node j ; is the configuration capacity of the system / device j at node k , is the power capacity and the storage capacity of the battery energy storage system at node j ; is the number of hydrogen refueling stations at node j ; is the capital recovery coefficient of each investment cost item; is a set of nodes of the target park distribution network, satisfying ; is an index of each sub-region in the target park, is a set of all sub-regions in the target park; is a set of alternative nodes of the distribution network in each sub-region; is a set of distribution network branches, represents a branch of the distribution network with node , as the vertex; π s is the probability of the operation scenario s ; S4, solving the electric hydrogen production, storage and injection integrated station optimization planning model constructed in step S3 to obtain an optimal site selection and capacity determination scheme of the electric hydrogen production, storage and injection integrated station in terms of economy. ehs is the annual operation and maintenance cost of the hydrogen production, storage, and injection integrated station building, In step S2, a Latin hypercube sampling method is used to extract a plurality of original operation scenarios according to the random probability distribution functions of the wind speed, the clearness index, the electrical load and the hydrogen load. k is the unit annual operation and maintenance cost of the system / device ; t is an index of the operation period, T is the total number of operation periods for each operation scenario; is the sub-region s in the operation period t , m hydrogen refueling demand of the node; for the operation scenario s time period t node j hydrogen refueling load that can be satisfied; for the interruptible hydrogen refueling load compensation cost coefficient; for the network loss cost coefficient; for the branch resistance value; for the operation scenario s time period t branch square of the current; for the interruptible electrical load compensation cost coefficient; for the operation scenario s time period t node electrical demand; for the operation scenario s time period t node electrical load that can be satisfied; for the operation scenario s time period t active power purchased from the upper-level power grid; for the time period t electricity price for purchasing from the upper-level power grid, using a time-of-use price mechanism; for the time conversion factor, used for converting the operation cost from the typical day time scale to the annual time scale; for the medium and long-term operation time granularity considered in the optimization planning; for the hydrogen sales price; for the internal electricity sales price; for the upper-level power grid electricity sales price; for the operation scenario s time period t active power output to the upper-level power grid; The investment constraint of the electric hydrogen production, storage and injection integrated station optimization planning model is specifically as follows:

2. The method of claim 1, wherein the method further comprises: The annual investment cost constraint is as follows:

3. The method of claim 1, wherein, The lower limit constraint of the regional access quantity is as follows: The non-admission node constraint of the electric hydrogen production, storage and injection integrated station is as follows: The element purchase constraint is as follows: The operation constraint of the electric hydrogen production, storage and injection integrated station optimization planning model is specifically as follows: The active / reactive power output constraints of the wind power generation system and the photovoltaic power generation system are as follows: wherein, is the annual investment cost, is the upper limit of the annual equivalent investment cost acceptable to the investor; is a 0-1 variable measuring whether the node j is connected to the hydrogen production and storage station; is the index of each sub-area in the target park; is a set consisting of all sub-areas in the target park; is the system / device k capacity configuration upper / lower limit, ; is the battery energy storage system capacity configuration upper / lower limit, ; the battery energy storage system storage capacity is constrained by the storage coefficient and ; is the hydrogen refueling station installation quantity upper / lower limit, .

4. The method of claim 1, wherein the method further comprises: The battery energy storage system operation constraint is as follows: The electrolyzer operation constraint is as follows: wherein, for the operating scenario s time period t node j active power of the new energy power generation system; for the operating scenario s time period t fluctuation factor of the target park new energy; for the node j system / device k configured capacity, for the wind power generation system, for the photovoltaic power generation system; for the node set of the target park power distribution network allowing access to the hydrogen electricity storage and injection integrated station; / for the grid-connected power factor angle of the new energy power generation system; for the operating scenario s time period t node j reactive power of the new energy power generation system; for the apparent power of the inverter in the new energy power generation system; The electrolyzer electric-hydrogen conversion equation is as follows: wherein, is a collection of battery energy storage systems, denotes a battery energy storage system; is an operating scenario s a time period t a node j a charge / discharge power of a battery energy storage system; is a very large positive real number; is an operating scenario s a time period t denotes a node j a 0-1 variable for the charge / discharge state of a battery energy storage system; is an operating scenario s a time period t a node j a state of charge of a battery energy storage system; is a maximum depth of discharge of a battery energy storage system; is a charge / discharge efficiency of a battery energy storage system; is a node j a power capacity and a state of charge of a battery energy storage system; is a medium-long term operating time granularity considered for the optimization planning; The electrolyzer capacity constraint is as follows: The hydrogen storage tank operation constraint is as follows: The material flow balance equation of the hydrogen system is as follows: wherein, is the scenario s is the period t is the node j is the injected electric power of the electrolytic cell; is the electric hydrogen conversion efficiency of the electrolytic cell; is the operating efficiency of the electrolytic cell; The hydrogen storage capacity constraint is as follows: The hydrogen refueling demand distribution constraint is as follows The power balance constraint is as follows: wherein, for a running scenario s time period t node j hydrogen storage amount of the hydrogen storage tank; for a node j installed capacity of the hydrogen storage tank; for hydrogen loss of the hydrogen storage tank; hydrogen dispenser operation constraint: wherein, a refueling capacity of the hydrogen dispenser; a running scenario s a time period t a node j a hydrogen refueling load that can be satisfied; a node j a number of hydrogen dispensers installed; a set of hydrogen dispensers, HD denotes a hydrogen dispenser; The alternating current power flow constraint of the distribution network is as follows: wherein, for a running scenario s time period t node j The electric hydrogen production, storage and refueling integrated station should meet the hydrogen refueling demand proportion of the sub-area where it is located. for a running scenario s time period t sub-area m hydrogen refueling demand; The node voltage constraint is as follows: wherein, representing a running scenario s a time period t a node j active / reactive power of the flexible electric hydrogen production, storage and injection integrated station and the power distribution network; for a running scenario s a time period t a node j active / reactive power that can actually satisfy the electrical load; for a running scenario s a time period t a node j active / reactive power of the electrical load demand; The branch current constraint is as follows: in, For the running scenario s Time period t branch road Active / reactive power flowing through; For nodes j The set of downstream child nodes; For the running scenario s Time period t The active / reactive power transmitted by the main transformer; For the running scenario s Time period t node j The square of the voltage amplitude; branch road The reactance; The transformer gateway interactive power constraint is as follows: wherein representing a node j upper / lower limit of voltage amplitude; The interactive power between the target park distribution network and the upper-level power grid is limited by the rated capacity of the main transformer as follows: wherein is an upper limit of the current of the branch ​ The method comprises the following steps: wherein, as an alternative node j capacity of the low voltage transformer; The parameter module obtains the parameters for site selection and capacity determination of the electric hydrogen production, storage and injection integrated station, wherein the parameters for site selection and capacity determination of the electric hydrogen production, storage and injection integrated station include equipment parameters, meteorological parameters, electrical parameters, load parameters and economic parameters. wherein, respectively, running scenarios s period t is a 0~1 variable indicating the state of buying / selling power to the upper grid; is a very large positive real number; is the capacity of the main transformer.

5. An electric hydrogen production, storage and injection integrated station site selection and sizing system, characterized by, The scenario module extracts a plurality of original operation scenarios by using a Latin hypercube sampling method based on the parameters obtained by the parameter module. The selection module solves the electric hydrogen production, storage and injection integrated station optimization planning model constructed by the construction module to obtain an optimal site selection and capacity determination scheme of the electric hydrogen production, storage and injection integrated station in terms of economy. ​ The K-means algorithm is used for original operation scene clustering to generate a typical operation scene set suitable for site selection and capacity determination of an integrated hydrogen production, storage and injection station Each typical operation scene contains wind and light resource fluctuation factors of each time period in a day, electric load of each node of a distribution network and hydrogen refueling load data of each sub-region of a target park. A construction module acquires a typical operation scenario set based on the scenario module Under the site selection and capacity determination-operation verification integrated decision framework, an optimization planning model of the integrated station of electricity, hydrogen production, storage and injection is constructed, which includes the decision of hydrogen and electricity facility site selection and capacity determination and the optimization of medium and long-term operation. The target is to minimize the comprehensive cost in the level year under the premise of meeting the investment constraints and operation constraints. The objective function of the optimization planning of the integrated station of electricity, hydrogen production, storage and injection is as follows: wherein, is the levelized annual cost, is the levelized investment cost, is the annual operating cost, is the annual operating benefit; Equalized annualized cost of investment is: Annual operating cost is: Annual operating revenue is: wherein, k is an index of alternative systems / devices; , , is a set of alternative systems / devices, respectively represent a wind power generation system, a photovoltaic power generation system, a battery energy storage system, an electrolyzer, a hydrogen storage tank, and a hydrogen refueling station; j is an index of nodes of the power distribution network; represents a set of nodes of the target park power distribution network that are allowed to access the hydrogen production, storage, and injection integrated station due to geographical restrictions, policy factors, and the like; is the capital construction cost of the hydrogen production, storage, and injection integrated station, is the unit investment cost of the system / device k , is the unit power price and the unit energy price of the battery energy storage system; is a 0-1 variable that measures the access of the hydrogen production, storage, and injection integrated station at node j ; is the configuration capacity of the system / device j at node k ; is the power capacity and the storage capacity of the battery energy storage system at node j ; is the number of hydrogen refueling stations at node j ; is the capital recovery coefficient of each investment cost item; is a set of nodes of the target park power distribution network, satisfying ; is an index of each sub-region in the target park, is a set of all sub-regions in the target park; is a set of alternative nodes of the power distribution network included in each sub-region; is a set of branches of the power distribution network, represents a branch of the power distribution network with nodes , as vertices; π s is the probability of the operation scenario s ; ​ ehs is the annual operation and maintenance cost of the hydrogen production, storage, and injection integrated station building, ​ k is the unit annual operation and maintenance cost of the system / device ; t is an index of the operation period, T is the total number of operation periods of each operation scenario; is the sub-region s in the period t of the operation scenario m hydrogen refueling demand of the node; for an operation scenario s time period t node j hydrogen refueling load that can be satisfied; for an interruptible hydrogen refueling load compensation cost coefficient; for a network loss cost coefficient; for a branch resistance value; for an operation scenario s time period t branch square of current; for an interruptible electrical load compensation cost coefficient; for an operation scenario s time period t node electrical demand; for an operation scenario s time period t node electrical load that can be satisfied; for an operation scenario s time period t active power purchased from a superior power grid; for a time period t electricity price for purchasing from a superior power grid, using a time-of-use electricity price mechanism; for a time conversion factor, used for converting operation cost from a typical day time scale to a year time scale; for a medium and long term operation time granularity considered in optimization planning; for a hydrogen sales price; for an internal electricity sales price; for a superior power grid electricity sales price; for an operation scenario s time period t active power output to a superior power grid; ​

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