An Optimization Method for Energy Network Security Management Based on Large-Scale New Energy Grid Connection

Abstract

This invention belongs to the field of multi-energy system collaborative optimization scheduling, specifically, it relates to an energy network security management optimization method based on large-scale renewable energy grid connection, including the following steps: establishing an EH model and a P2G unit model for the multi-energy system; constructing a collaborative optimization scheduling model among multi-energy hub units containing large-scale renewable energy grid connection before and after a sudden event occurs in the system's power grid or natural gas network; solving the constructed model to obtain the optimal collaborative optimization scheduling scheme among multi-energy hub units containing large-scale renewable energy grid connection. The energy network security management optimization method based on large-scale renewable energy grid connection proposed in this invention can not only significantly improve the system's energy supply security, but also effectively solve the problem of renewable energy power generation absorption.

Description

Technical Field

[0001] This invention belongs to the field of collaborative optimization scheduling of multi-energy systems, and specifically relates to an energy network security management optimization method based on large-scale new energy grid connection. Background Technology

[0002] In recent years, multi-energy systems have shown increasing advantages in optimizing energy structure and improving energy efficiency, thus attracting widespread attention. Energy hubs serve as the interface platform between sources, grid, and loads in multi-energy systems. With the increasing number of energy hubs (EHs), it is necessary to optimize and manage the energy network from the perspective of overall system energy security. On the other hand, with the increasing penetration rate of new energy sources, the randomness and volatility of their output pose significant challenges to the normal operation of the power grid. Therefore, coordinated and optimized scheduling of multi-energy systems with large-scale new energy grid integration is of great significance.

[0003] P2G enables bidirectional energy flow between the power grid and the natural gas grid, possessing the potential to balance system supply and demand. It can serve as an emergency alternative energy source for multi-energy system emergencies and effectively mitigate the randomness and volatility of new energy generation, significantly improving the dispatchability of multi-energy systems. Therefore, constructing a multi-energy hub collaborative optimization scheduling model considering P2G participation can not only significantly improve system energy security but also effectively solve the problem of renewable energy consumption. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems by proposing an energy network security management optimization method based on large-scale new energy grid connection. This method aims to achieve safe and economical operation of multi-energy systems with multiple large-scale new energy grid connections, and to provide a reference for the scheduling and operation of multi-energy systems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An energy network security management optimization method based on large-scale renewable energy grid connection includes the following steps:

[0007] Step S1: Establish the EH model and P2G unit model of the multi-energy system;

[0008] Step S2: Construct a collaborative optimization scheduling model among multi-energy hub units, including large-scale new energy grid connection, before and after a sudden event occurs in the system power grid or natural gas grid;

[0009] Step S3: Solve the established model;

[0010] Step S4: Obtain the optimal collaborative scheduling scheme among multi-energy hub units containing large-scale new energy grid connection.

[0011] Furthermore, step S1 specifically includes the following steps:

[0012] S101: Establish an EH model for a multi-energy system. The EH model consists of three parts: a transformer, a CHP unit, and a gas boiler. Electricity and natural gas serve as the energy inputs of the hub. The transformer and CHP unit meet the electrical load requirements, and the CHP unit and gas boiler meet the system heat load requirements.

[0013] S102: Construct a P2G unit model. The P2G unit model takes electricity and water as inputs. The H2 generated by electrolysis combines with CO2 and enters the methanation unit to generate methane and water. The generated methane gas is directly injected into the natural gas pipeline.

[0014] Furthermore, the total amount of methane injected into each gas pipe in S102 is calculated using equations (1) and (2):

[0015]

[0016]

[0017] In the formula, the superscripts 0 and 1 represent the conditions before and after emergency fault handling, respectively, and P m,out (t) and ptg represent the total amount of methane produced and the amount of methane injected into each gas network at time t, respectively; s is the scene set; lg is the number of natural gas networks;

[0018] To ensure that the hydrogen injected into the pipeline is within the standard limit, the standard limit is set to 1% of the total gas energy in the pipeline, as shown in equations (3) and (4):

[0019]

[0020]

[0021] Among them, P Gnet (t,lg) and P H (t,lg) represent the total gas consumed and the total power of hydrogen injected into the lg-th gas network branch, respectively.

[0022] Furthermore, step S2 specifically includes the following steps:

[0023] S201: Construct a collaborative optimization objective function among multiple energy hub units, with the goal of minimizing the total operating cost of the energy hub network before and after unexpected failures in the power system and natural gas system. The optimization scheduling model is as follows:

[0024]

[0025] In the formula, This represents the total operating cost prior to the occurrence of the unexpected failure. This represents the expected operating costs after an unexpected failure; each cost consists of two parts: the first part is the cost of the electrical or gas energy used within each hub, and the second part is the penalty cost for failing to meet the internal electrical and thermal demands of the hub. α is the safety factor; C p The equivalent economic loss incurred when wind power generation is not used is also caused by unforeseen events; the specific expressions for each part of the cost are shown in equations (6)-(8):

[0026]

[0027]

[0028]

[0029] In the formula, n is the total number of power hubs; π e and π g The price of electricity and natural gas per kilowatt-hour, and P is the penalty cost incurred for each kilowatt-hour of electricity and gas load shedding. e (t,n), P g (t,n),L e (t,n) and L h (t,n) represent the total electricity consumption, natural gas consumption, and interrupted electrical and thermal loads, respectively. c (t) represents the reduction in wind power generation;

[0030] S202: Constraints that need to be met when constructing an optimized scheduling model before and after emergency response;

[0031] (1) Energy balance

[0032] Before and after an emergency event, the balance between power supply and demand satisfies equations (9) and (10):

[0033]

[0034]

[0035] The heat load supply and demand balance satisfies equations (11) and (12):

[0036]

[0037]

[0038] Among them, I ec (n,j) and I ec(n,j) represent the grid and gas grid connection status between each hub unit, respectively, and are 0-1 variables; n is the total number of hub units, and the power exchange losses are modeled using the transmission efficiency of the lines between hubs; η t and η CHP The power conversion efficiencies of the transformer and CHP are respectively; P t (t,n) represents the input power on the primary side of the transformer; P CHP (t,n) and P gb (t,n) represent the amount of natural gas consumed by the CHP and the gas boiler, respectively; and η gb P represents the variables representing the heat production efficiency of the CHP unit, the efficiency of the gas-fired boiler, and the amount of electricity exchanged with other hubs, respectively. ex (t,n,j) and P hx (t,n,j) represent the electrical and thermal interaction power between hub units, respectively;

[0039] The power consumption inside the hub and the power consumption of the power grid and gas grid also satisfy the following relationship: Equations (13) and (14) show that the power input from the power grid to the hub is equal to the power consumed by the transformer inside the hub; Equations (15) and (16) show that the amount of natural gas input from the gas grid to the hub is equal to the total amount of natural gas consumed by the CHP unit and GB.

[0040]

[0041]

[0042]

[0043]

[0044] In the formula, P e (t,n), P g (t,n) represent the input electrical power and input natural gas volume of each hub, respectively;

[0045] Similar to other storage technologies, the hydrogen storage inside a P2G cell satisfies a certain energy balance relationship. The hydrogen storage inside a P2G cell at time t is equal to the storage at time t-1 plus the difference between the amount of hydrogen produced and released by the P2G at time t, as shown in equations (17) and (18):

[0046]

[0047]

[0048] In the formula, P st (t) represents the hydrogen storage capacity; P ch (t) and Pdis (t) represents the power of the device for storing and releasing hydrogen, respectively; η ch and η dis These represent the storage and release efficiency of hydrogen, respectively.

[0049] (2) Equipment unit operation constraints

[0050] The devices contained in a hub must meet the following constraints during operation:

[0051] The operating constraints of CHP and gas-fired boilers are shown in equations (19)-(22):

[0052]

[0053]

[0054]

[0055]

[0056] The constraints on the exchange of electrical and thermal power between hub units are shown in equations (23)-(26).

[0057]

[0058]

[0059]

[0060]

[0061] In the formula, This indicates the maximum permissible value for power exchange between hubs. This represents the maximum permissible heat exchange value.

[0062] Hydrogen storage in P2G should meet the following constraints:

[0063]

[0064]

[0065] In the formula, and To store the maximum and minimum values ​​of the coefficients, C st Total storage capacity;

[0066] (3) Constraints on grid connection of P2G and wind turbine integration

[0067] When P2G is connected to the system, both the power grid and the gas grid connected to it will be affected. The power distribution relationship between the nodes after P2G and wind turbines are connected to the grid is as follows:

[0068]

[0069]

[0070] Among them, P Enet (t,le) represents the input power of the power grid; le represents the number of power grid units; the binary parameter I p (le) and I w (le) is used to determine the location of P2G and wind turbines within the power grid; This represents the connection status between the power grid and the input terminals of each hub unit, and is a variable of 0 or 1.

[0071] The total amount of natural gas supplied by P2G and the gas grid is equal to the natural gas consumed by the grid, as shown in the following expression:

[0072]

[0073]

[0074] In the formula, P Gnet (t,lg) represents the input power of the natural gas network; This represents the connection status between the gas network and the input terminals of each hub unit, and is a 0-1 variable.

[0075] Furthermore, the optimized scheduling model proposed in this invention contains numerous equality and inequality constraints, making its solution somewhat complex. The GAMS language offers greater flexibility and versatility, along with high solution efficiency, making it more suitable for solving large-scale, complex mathematical problems. Therefore, in step S3, the model is solved on the GAMS platform using MIP programming and the CPLEX solver.

[0076] Compared with the prior art, the method of the present invention has the following advantages:

[0077] To address the network security management challenges of multi-energy systems with large-scale renewable energy grid integration, a collaborative optimization scheduling method among multiple hubs in such systems is proposed. With the goal of minimizing the total operating cost of the multi-energy system, a collaborative optimization scheduling model is constructed for multi-energy hub units, including those with large-scale renewable energy grid integration, before and after a sudden event occurs in the system's power grid or natural gas network. The model is solved using MIP programming and the CPLEX solver. This method significantly improves system power supply security while effectively addressing the problem of renewable energy consumption.

[0078] This invention, based on the objective of minimizing the total operating cost of multi-energy systems, establishes a collaborative optimization scheduling model for multi-energy hubs, solving the problem of network security operation of multi-energy systems under large-scale renewable energy grid integration.

[0079] (1) By making reasonable use of the supply and demand potential of the P2G system, the power supply security level of the multi-energy system has been improved, the system operation instability caused by high-penetration new energy power generation has been reduced, and the utilization rate of new energy power generation has been increased.

[0080] (2) For multi-energy systems with large-scale new energy power generation, a multi-energy hub collaborative optimization scheduling model was established to take into account system safety, economy and new energy power generation utilization rate. This model maintains system safety and stability while improving wind and solar power utilization rate, providing a scientific basis for the optimized scheduling of multi-energy systems. Attached Figure Description

[0081] Figure 1 This is a flowchart of the optimized method of the present invention;

[0082] Figure 2 This refers to the single-energy hub model (EH model) described in this invention;

[0083] Figure 3 This refers to the P2G unit model described in this invention;

[0084] Figure 4 This is the multi-energy hub network model described in the embodiments of the present invention;

[0085] Figure 5 The basic electrical load and thermal load of the multi-energy system described in this embodiment of the invention;

[0086] Figure 6 This refers to the system's heat load demand shortfall after an unexpected event occurs, as described in this embodiment of the invention.

[0087] Figure 7 This refers to the system's heat load demand shortfall after an unexpected event occurs, as described in this embodiment of the invention.

[0088] Figure 8 This refers to the total natural gas production of the P2G unit under different scenarios before and after the unexpected event described in this embodiment of the invention.

[0089] Figure 9 This describes the relationship between the P2G unit and the wind curtailment from the power grid as described in this embodiment of the invention. Detailed Implementation

[0090] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0091] Example 1

[0092] like Figure 1 As shown, this embodiment provides an energy network security management optimization method based on large-scale renewable energy grid connection, including the following steps:

[0093] Step S1: Establish the EH model and P2G unit model of the multi-energy system;

[0094] Step S2: Construct a collaborative optimization scheduling model among multi-energy hub units, including large-scale new energy grid connection, before and after a sudden event occurs in the system power grid or natural gas grid;

[0095] Step S3: Solve the established model;

[0096] Step S4: Obtain the optimal collaborative scheduling scheme among multi-energy hub units containing large-scale new energy grid connection.

[0097] The optimization scheduling model proposed in this invention contains numerous equality and inequality constraints, making its solution quite complex. The GAMS language offers high flexibility and versatility, along with high solution efficiency, making it more suitable for solving large-scale, complex mathematical problems. Therefore, the model is solved on the GAMS platform using MIP programming and the CPLEX solver.

[0098] Furthermore, step S1 specifically includes the following steps:

[0099] S101: Establish a hub model (EH model) for a multi-energy system. This model consists of three parts: a transformer, a CHP unit, and a gas-fired boiler. Figure 2 As shown. Electricity and natural gas serve as the energy inputs for this hub, meeting electrical load demands through transformers and CHP units, and meeting system heat load demands through CHP units and gas boilers;

[0100] S102: Construct the P2G unit model, such as Figure 3 As shown in the figure. The P2G unit model takes electricity and water as inputs. The H2 generated by electrolysis combines with CO2 and enters the methanation unit to generate methane and water. The generated methane gas is directly injected into the natural gas pipeline. As shown in the figure, the generated hydrogen gas can also be directly injected into the natural gas pipeline. The total amount of methane injected into each gas pipeline is calculated by equations (1) and (2):

[0101]

[0102]

[0103] In the formula, the superscripts 0 and 1 represent the conditions before and after emergency fault handling, respectively, and Pm,out (t) and ptg represent the total amount of methane produced and the amount of methane injected into each gas network at time t, respectively; s is the scene set; lg is the number of natural gas networks;

[0104] To ensure that the hydrogen injected into the pipeline is within the standard limit, the standard limit is set to 1% of the total gas energy in the pipeline, as shown in equations (3) and (4):

[0105]

[0106]

[0107] Among them, P Gnet (t,lg) and P H (t,lg) represent the total gas consumed and the total power of hydrogen injected into the lg-th gas network branch, respectively.

[0108] Step S2 in this embodiment specifically includes the following steps:

[0109] S201: Construct a collaborative optimization objective function among multiple energy hub units, with the goal of minimizing the total operating cost of the energy hub network before and after unexpected failures in the power system and natural gas system. The optimization scheduling model is as follows:

[0110]

[0111] In the formula, This represents the total operating cost prior to the occurrence of the unexpected failure. This represents the expected operating costs after an unexpected failure; each cost consists of two parts: the first part is the cost of the electrical or gas energy used within each hub, and the second part is the penalty cost for failing to meet the internal electrical and thermal demands of the hub. α is the safety factor; C p The equivalent economic loss incurred when wind power generation is not used is also caused by unforeseen events; the specific expressions for each part of the cost are shown in equations (6)-(8):

[0112]

[0113]

[0114]

[0115] In the formula, n is the total number of power hubs; π e and π g The price of electricity and natural gas per kilowatt-hour, and P is the penalty cost incurred for each kilowatt-hour of electricity and gas load shedding. e (t,n), Pg (t,n),L e (t,n) and L h (t,n) represent the total electricity consumption, natural gas consumption, and interrupted electrical and thermal loads, respectively. c (t) represents the reduction in wind power generation;

[0116] S202: Constraints that need to be met when constructing an optimized scheduling model before and after emergency response;

[0117] (1) Energy balance

[0118] Before and after an emergency event, the balance between power supply and demand satisfies equations (9) and (10):

[0119]

[0120]

[0121] The heat load supply and demand balance satisfies equations (11) and (12):

[0122]

[0123]

[0124] Among them, I ec (n,j) and I ec (n,j) represent the grid and gas grid connection status between each hub unit, respectively, and are 0-1 variables; n is the total number of hub units, and the power exchange losses are modeled using the transmission efficiency of the lines between hubs; η t and η CHP The power conversion efficiencies of the transformer and CHP are respectively; P t (t,n) represents the input power on the primary side of the transformer; P CHP (t,n) and P gb (t,n) represent the amount of natural gas consumed by the CHP and the gas boiler, respectively; and η gb P represents the variables representing the heat production efficiency of the CHP unit, the efficiency of the gas-fired boiler, and the amount of electricity exchanged with other hubs, respectively. ex (t,n,j) and P hx (t,n,j) represent the electrical and thermal interaction power between hub units, respectively;

[0125] The power consumption inside the hub and the power consumption of the power grid and gas grid also satisfy the following relationship: Equations (13) and (14) show that the power input from the power grid to the hub is equal to the power consumed by the transformer inside the hub; Equations (15) and (16) show that the amount of natural gas input from the gas grid to the hub is equal to the total amount of natural gas consumed by the CHP unit and GB.

[0126]

[0127]

[0128]

[0129]

[0130] In the formula, P e (t,n), P g (t,n) represent the input electrical power and input natural gas volume of each hub, respectively;

[0131] Similar to other storage technologies, the hydrogen storage inside a P2G cell satisfies a certain energy balance relationship. The hydrogen storage inside a P2G cell at time t is equal to the storage at time t-1 plus the difference between the amount of hydrogen produced and released by the P2G at time t, as shown in equations (17) and (18):

[0132]

[0133]

[0134] In the formula, P st (t) represents the hydrogen storage capacity; P ch (t) and P dis (t) represents the power of the device for storing and releasing hydrogen, respectively; η ch and η dis These represent the storage and release efficiency of hydrogen, respectively.

[0135] (2) Equipment unit operation constraints

[0136] The devices contained in a hub must meet the following constraints during operation:

[0137] The operating constraints of CHP and gas-fired boilers are shown in equations (19)-(22):

[0138]

[0139]

[0140]

[0141]

[0142] The constraints on the exchange of electrical and thermal power between hub units are shown in equations (23)-(26).

[0143]

[0144]

[0145]

[0146]

[0147] In the formula, This indicates the maximum permissible value for power exchange between hubs. This represents the maximum permissible heat exchange value.

[0148] Hydrogen storage in P2G should meet the following constraints:

[0149]

[0150]

[0151] In the formula, and To store the maximum and minimum values ​​of the coefficients, C st Total storage capacity;

[0152] (3) Constraints on grid connection of P2G and wind turbine integration

[0153] When P2G is connected to the system, both the power grid and the gas grid connected to it will be affected. The power distribution relationship between the nodes after P2G and wind turbines are connected to the grid is as follows:

[0154]

[0155]

[0156] Among them, P Enet (t,le) represents the input power of the power grid; le represents the number of power grid units; the binary parameter I p (le) and I w (le) is used to determine the location of P2G and wind turbines within the power grid; This represents the connection status between the power grid and the input terminals of each hub unit, and is a variable of 0 or 1.

[0157] The total amount of natural gas supplied by P2G and the gas grid is equal to the natural gas consumed by the grid, as shown in the following expression:

[0158]

[0159]

[0160] In the formula, P Gnet (t,lg) represents the input power of the natural gas network; This represents the connection status between the gas network and the input terminals of each hub unit, and is a 0-1 variable.

[0161] Example 2

[0162] like Figure 4 As shown, a network consisting of 14 energy hub units is used as the research object, where energy exchange is possible between the hub units. The first, second, and sixth energy hubs are the energy centers for industrial areas, while the remaining units are the energy centers for residential areas. The entire network includes two power grids and two gas grids. Hubs 1 to 6 are powered by the first power grid and the first gas grid, while hubs 7 to 14 are powered by the second power grid and the second gas grid. P2G units supply gas to both gas grids. Electrical energy exchange is possible between hub units 1-6 and between hubs 7-14. Hubs 1, 5, and 6 can only exchange thermal energy with each other, while the remaining hubs can exchange thermal energy with each other. The optimal scheduling step size is one hour.

[0163] Four different emergency scenarios for electricity and natural gas were considered. The first and second scenarios represent unexpected events in the first and second power grids, respectively. Similarly, the third and fourth scenarios represent unexpected events in the first and second natural gas networks, respectively.

[0164] Figure 5 This is to determine the basic power and heat requirements of the aforementioned 14 energy hubs.

[0165] To analyze the impact of P2G units on the system, the system was optimized and scheduled with and without P2G units participating. The results are shown in Table 1.

[0166] Table 1 Comparison of Total System Operating Costs

[0167]

[0168]

[0169] As shown in the table, using P2G units can reduce total operating costs by 9.85% within 24 hours. This is because P2G helps reduce load losses and also helps reduce wind energy losses.

[0170] Figure 6 and Figure 7The data shows the system's thermal and electrical load deficits after an unforeseen event, with and without P2G units. It can be seen that P2G units play a crucial role in meeting the system's energy supply needs during gas network emergencies, effectively reducing penalty costs. Compared to the system without P2G units, the thermal load deficit is reduced by 24.6% when P2G units are used. Furthermore, using P2G units reduces the electrical load deficit by 1.43%, thereby improving power supply security.

[0171] Figure 8 The data shows the total amount of methane gas produced by P2G before and after the unexpected event. Comparative data reveals that in the third and fourth scenarios, P2G produces methane during peak periods of heat shortage to maintain a balance between heat load and demand. Compared to the third and fourth scenarios, the second scenario produces less methane gas. This is because in this scenario, the secondary power grid supplying electricity to the P2G fails, and only wind turbines provide power, thus producing less methane.

[0172] During the simulation, electrical and thermal interactions occur between the various hub units, as shown in Tables 2 and 3.

[0173] Table 2 Average power exchange (kWh) between hub units

[0174]

[0175]

[0176] Table 3. Average heat exchange (kWh) between hub units

[0177]

[0178] Figure 9 This chart illustrates the relationship between P2G (Power-to-Government) turbines and grid-curtailed wind power. Total wind power generation is represented by blue bars. The reduction in power generation without P2G turbines is represented by green bars, while the degree of wind power reduction with P2G turbines is represented by red bars. It can be observed that the presence of P2G turbines significantly impacts wind curtailment. This is because during the peak operating phase of P2G turbines, wind power generation is required to provide electricity, thus significantly improving the utilization rate of renewable energy generation.

Claims

1. A method for security management optimization of an energy network based on large-scale new energy grid connection, characterized in that: The method comprises the following steps: Step S1: establishing an EH model of a multi-energy system and a P2G unit model; Step S2: constructing a collaborative optimization scheduling model between multi-energy concentrators with large-scale new energy grid-connected before and after a sudden event occurs in a system power grid or a natural gas grid; The step S2 specifically comprises the following steps: S201: constructing a collaborative optimization objective function between multi-energy concentrators, taking the minimum total operation cost of the energy concentrator network before and after the unexpected failure of the power system and the natural gas system as the goal, and constructing an optimization scheduling model as follows: (5) wherein, represents the total operating cost before the unexpected failure occurs; represents the expected operating cost after the unexpected failure occurs; is a safety factor; is the equivalent economic loss generated when wind power is not used; the specific expressions of each part of the cost are shown in equations (6)-(8): (6) (7) (8) wherein, is the total number of energy hubs; and are the prices of electricity and natural gas per kWh, and are the penalty fees for shedding electric and gas loads per kWh, , , and represent the total electric and gas loads and the interrupted electric and gas loads, respectively, is the wind power curtailment; S202: constructing constraint conditions that need to be met by the optimization scheduling model before and after the emergency treatment; Step S3: solving the constructed model to obtain an optimal scheme of collaborative optimization scheduling between multi-energy concentrators with large-scale new energy grid-connected. 2.The energy network security management optimization method based on large-scale new energy grid connection according to claim 1, characterized in that: The step S1 specifically comprises the following steps: S101: establishing an EH model of a multi-energy system, wherein the EH model is composed of a transformer, a CHP unit and a gas-fired boiler, and electricity and natural gas are energy inputs of the concentrator, the electricity demand is met through the transformer and the CHP unit, and the heat load demand of the system is met through the CHP unit and the gas-fired boiler; S102: constructing a P2G unit model, wherein electricity and water are inputs, H2 generated by electrolysis combines with CO2 to enter a methanation unit to generate methane and water, and the generated methane gas is directly injected into a natural gas pipeline. 3.The energy network security management optimization method based on large-scale new energy grid connection according to claim 2, characterized in that: The total amount of methane injected into each gas pipeline in the S102 is calculated by formulas (1) and (2): (1) (2) In the formula, the superscripts 0 and 1 represent the conditions before and after emergency fault processing, respectively, and are the total amount of methane generated at time t and the amount of methane injected into each gas network, respectively; is a set of scenarios; is the number of natural gas networks; In order to ensure that the hydrogen injected into the pipeline is within the standard limit, the standard limit is set to 1% of the total gas energy of the pipeline, as shown in formulas (3) and (4): (3) (4) wherein, and are the total gas and the total power of injected hydrogen, respectively, consumed by the th gas grid branch.

4. The energy network security management optimization method based on large-scale new energy grid connection according to claim 1, characterized in that, The constraint conditions of the step S202 include: (1) Energy balance Before and after the emergency event, the electricity load supply and demand balance satisfies formulas (9)-(10): (9) (10) The heat load supply and demand balance satisfies formulas (11) and (12): (11) (12) where, and denote the grid and gas network connection status between each hub unit, respectively, and are 0-1 variables; is the total number of hub units; and are the electrical energy conversion efficiencies of the transformer and the CHP, respectively; is the input electrical power at the primary side of the transformer; and denote the amount of natural gas consumed by the CHP and the gas boiler, respectively; and denote the CHP unit heat production efficiency and the gas boiler efficiency and the variables of the electrical power exchange with other hubs, respectively; and denote the electrical and thermal interaction power between hub units, respectively. The power consumption of the concentrator and the power consumption of the power grid and the gas grid also satisfy the following relationship, (13) (14) (15) (16) wherein , Pi and P2are the input electric power and the input natural gas quantity of each hub, respectively. The hydrogen storage in the P2G unit satisfies a certain energy balance relationship, and the hydrogen storage in the P2G unit at time t is equal to the storage at time t-1 plus the difference between the hydrogen generated and released by the P2G at time t, as shown in formulas (17) and (18): (17) (18) wherein, represents the hydrogen storage amount; and respectively represent the power of the device to store and release hydrogen gas; and respectively represent the storage and release efficiency of hydrogen gas; (2) Device unit operation constraint The devices contained in the concentrator need to meet the following constraint conditions during operation: The operation constraints of the CHP and the gas-fired boiler are shown in formulas (19)-(22): (19) (20) (21) (22) The electricity and heat power exchange constraints between the concentrator units are shown in formulas (23)-(26): (23) (24) (25) (26) represents the maximum allowed value of the inter-hub power exchange, represents the maximum allowed value of the thermal energy exchange; The hydrogen storage in the P2G should satisfy the following constraints: (27) (28) wherein and is the maximum and minimum value of the stored coefficients, is the total storage amount; (3) P2G and wind turbine grid-connected constraint When the P2G is connected to the system, the power grid and the gas grid connected to the P2G will be affected, and the electric energy distribution relationship of the nodes after the P2G and the wind turbine are connected to the power grid is as follows: (29) (30) wherein, is the input power to the grid; represents the number of grids; binary parameter and is used to determine the location of P2G and wind turbines in the grid; is the connection status of the grid to the input end of each hub unit, which is a 0, 1 variable; The total amount of natural gas supplied by the P2G and the gas grid is equal to the natural gas consumed by the power grid, and the specific expression is as follows: (31) (32) wherein is the input power to the natural gas network; is the connection status of the gas network to the input of each hub unit, being a 0-1 variable.

5. The energy network security management optimization method based on large-scale new energy grid connection according to claim 1, characterized in that, The step S3 is solved on the GAMS platform, and the model is solved by using MIP programming and the CPLEX solver.

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