Integrated energy system weak link identification method based on information theory

Abstract

The invention discloses an integrated energy system weak link identification method based on an information theory. The method comprises the following steps: S1, constructing an integrated energy system optimization scheduling model; the method comprises the steps of S1, carrying out fault simulation, S2, simulating an N-1 fault condition, S3, carrying out vulnerability evaluation based on an entropy evaluation method, S4, carrying out comprehensive vulnerability evaluation, and S5, carrying out line sorting and vulnerable line identification. According to the method, the concept of information entropy is utilized to evaluate and identify the fragile line in the integrated energy system. The information entropy is used as a tool for measuring the uncertainty and complexity of the system and can effectively reflect the stability of the system on energy distribution and network structure, and the system power flow under normal operation and N-1 fault conditions is calculated in detail by establishing an optimal scheduling model of the comprehensive energy system, so that the optimal scheduling of the comprehensive energy system is realized. Comprise the current flow of each line, the voltage of each node and the air pressure of each air node.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-integration of energy technology and information technology, and in particular to a method for identifying weak links in an integrated energy system based on information theory. Background Art

[0002] Since the research on the vulnerability of integrated energy systems was proposed in 1994, it has gradually become a core research direction in this field. Early research mainly relied on four basic methods: complex network theory, probability theory, system analysis and intelligent algorithms. With the continuous deepening of research, the vulnerability assessment of integrated energy systems has gradually formed two main directions: system topological structure vulnerability and system operation vulnerability. In terms of system topological structure vulnerability, researchers not only introduced a variety of structural parameters, such as betweenness and electrical distance, but also combined complex network theory to conduct a more comprehensive and in-depth assessment of the system's vulnerability. These parameters reflect the structural characteristics of the system from different angles, providing strong support for the formulation of targeted improvement measures. In terms of system operation vulnerability, researchers have developed a variety of evaluation methods, such as energy function method, risk theory method, betweenness analysis method and entropy theory method. These methods have their own characteristics and can quantify the vulnerability of the system from different angles. In addition, information entropy, as a classic method for quantifying information value, has also been widely used in the field of integrated energy systems. By introducing the concept of information entropy, researchers can more deeply explore the information value in the system, thereby improving the safety and reliability of the system. For example, in risk prediction, information entropy helps researchers assess the uncertainty of various risk factors and develop corresponding response strategies; in system fault diagnosis, information entropy can be used to quantify the redundancy and effectiveness of fault information, thereby improving the accuracy and efficiency of fault diagnosis.

[0003] As the scale of integrated energy systems continues to expand, their operational complexity and structural coupling have increased significantly. If local faults are not handled properly, they can easily trigger a chain reaction, causing the entire system to collapse. Historical large-scale accident data reveal four core causes: natural disasters, internal system defects, human errors or sabotage, and cyber attacks, highlighting the urgency of vulnerability research in integrated energy systems. Taking the major power outage in California, USA in August 2020 as an example, the incident not only caused huge economic losses, but also exposed the vulnerability of the system structure, emphasizing the urgency of vulnerability research. In the integrated energy system, a comprehensive vulnerability assessment framework needs to be constructed to accurately identify the weakest links by assigning vulnerability indicators to each line or node. Especially in the context of long-term high-load operation of the system, these vulnerable links are susceptible to external interference and failure, causing chain failures.

[0004] In summary, the present invention proposes a method for identifying weak links in an integrated energy system based on information theory, which aims to reveal potential risk points in the early stage of construction and identify weak links during operation. Through optimization and monitoring, the system stability can be significantly improved, large-scale accidents can be effectively prevented, the energy supply security can be ensured, and the sustainable development of the integrated energy system can be promoted. Summary of the invention

[0005] The purpose of the present invention is to provide a method for identifying weak links in an integrated energy system based on information theory, and to use the concept of information entropy to evaluate and identify vulnerable lines in an integrated energy system. Information entropy, as a tool to measure system uncertainty and complexity, can effectively reflect the stability of the system in terms of energy distribution and network structure. First, the present invention establishes an optimal scheduling model for an integrated energy system, and calculates in detail the system flow under normal operation and N-1 fault conditions, including the current flow of each line, the voltage of each node, and the gas pressure of each gas node. Subsequently, two indicators, flow entropy and topological entropy, are introduced. The former is used to evaluate the uniformity of the system flow, and the latter is used to analyze the complexity of the network structure and the degree of connection. By combining these two indicators, this study comprehensively evaluates the overall vulnerability of the system and identifies the weakest link.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention proposes a method for identifying weak links in an integrated energy system based on information theory, comprising the following steps:

[0008] S1. Constructing an optimal dispatching model for an integrated energy system, wherein the model includes an optimal dispatching model for an integrated energy system under normal operation, wherein the optimal dispatching model for an integrated energy system includes an objective function of a system optimal dispatching model, constraints of an electric power system, and constraints of a natural gas system;

[0009] S2, Simulate N-1 fault scenario: Construct an optimal dispatch model of the power system under fault conditions, which includes an objective function and comprehensive energy system constraints;

[0010] S3. Vulnerability assessment based on entropy method: including operation vulnerability calculation based on power flow change entropy and structural vulnerability calculation based on network topology entropy;

[0011] S4, comprehensive vulnerability assessment: Based on the two indicators of power flow entropy and topological entropy obtained in step S3, comprehensive vulnerability assessment is performed;

[0012] S5. Line sorting and identification of vulnerable lines: Based on the calculated vulnerability index, all lines are sorted to identify the most vulnerable lines.

[0013] Further: In step S1, the objective function of the system optimization scheduling model is as follows:

[0014]

[0015] Where: is the unit fuel cost of non-gas generating set g: F g is the heat consumption characteristic of the generator set g; is the planned output value of the generator set g in the period ι under the normal operation model; It is a binary variable used to indicate the working status of the generator set. A value of 1 indicates that the generator set is in operation, and a value of 0 indicates that the generator set is in shutdown. and Divided into the start / stop fuel cost of generator set g; is the production cost of natural gas well k; is the gas output arrangement of gas well k in time period t; is the operating cost of the gas storage equipment; is the gas output of gas storage equipment s in time period t; is the operating cost of P2G device h; is the power consumed by P2G device h in time period t.

[0016] Further: In step S1, the power system constraints are:

[0017] Formulas (2)-(13) are the operating constraints of the system, where formula (2) is the constraint on the active output of generator set g, formula (3) is the constraint on the reactive output of generator set g, formula (4) (5) is the up / down ramp constraint of generator set g, formula (6) (7) is the minimum start / stop time constraint of generator set g, formula (8) is the start / stop state logic constraint, formula (9) is the node active power balance constraint, formula (10) is the node reactive power balance constraint, formula (11) is the voltage balance constraint, formula (12) is the voltage amplitude constraint, and formula (13) is the branch apparent power flow constraint.

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] In the formula: the parameters with superscripts min / max represent the minimum / maximum limits of the variables respectively; is the up / down ramp rate of the generator set g; MU i / MD i is the minimum continuous start / stop time of the generator set g; R i,j , X i,j is the branch resistance and branch reactance; P ij , Q ij is the branch active power and branch reactive power; the variables representing the start and stop status of the generator set in equations (6) and (7) are 0-1 variables, which belong to nonlinear constraints and are linearized, that is, based on Benders decomposition, they are iteratively solved, and the objective function is still to minimize the operating cost of the power system.

[0031] Further: In step S1, the natural gas system constraints are as follows:

[0032] Formulas (14) to (24) are the operation constraints of the natural gas system, where formula (14) is the gas output capacity constraint of gas well k; formulas (15) and (16) are the gas storage capacity constraints of gas storage equipment s; formulas (17) and (18) are the natural gas inflow / outflow rate constraints of gas storage equipment s, respectively; formula (19) is the node flow balance constraint; formula (20) calculates the amount of natural gas consumed by gas-driven compressor equipment c in time period t; formulas (21) and (22) are the transmission flow constraints of the natural gas pipeline; formula (23) is the node pressure value constraint in the natural gas network; and formula (24) is the relationship constraint between the node pressures at both ends of compressor equipment c:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] Where: is the gas storage capacity of gas storage equipment s in time period t; is the air intake of the gas storage device s in time period t; is the amount of natural gas produced by P2G device h in period t; is the power flow of the natural gas pipeline mn in period t; is the amount of natural gas consumed by gas generator set g in period t; G w,t is the size of the natural gas load w in time period t; is the amount of natural gas consumed by the gas-driven compressor device c during period t; is the amount of natural gas compressed by compressor equipment c in time period t; is the efficiency of the compressor equipment; K mn To calculate the constant of natural gas pipeline flow, its value is affected by the characteristics of natural gas pipeline; is the square of the node gas pressure of natural gas node m in time period t; is the gas compression constant of the compressor device c; It indicates the flow direction of natural gas pipeline mn in time period t. When its value is 1, it means that the natural gas flow flows from node m to node n. When its value is -1, it means that the natural gas flow flows from node n to node m.

[0045] Further: in the step S2, the objective function of the power system optimization dispatch model under the fault condition is:

[0046]

[0047] Where: and are the demand and supply imbalances of the power system respectively; and are the demand and supply imbalance of the natural gas system respectively; Ω is the fault A collection of; is the set of power system nodes e.

[0048] Further: In step S2, the integrated energy system constraint condition is:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] In formula (33) and (34), and are used to describe the difference between supply and demand of active power, and and It is used to describe the difference between reactive power supply and demand, where the demand imbalance of each node is limited by the connected electrical load to ensure that it does not exceed the sum of its actual loads, and the supply imbalance of each node is also limited by the output of the connected generator set to ensure that it does not exceed the maximum output of the generator set;

[0065] In formula (38), (39), (40), are the demand imbalance and supply imbalance of the natural gas system respectively. The demand imbalance at each node in the natural gas system is less than the sum of the gas load connected to the node, the gas consumption of the gas-fired unit and the gas consumption of the gas-driven compressor equipment. The remaining formulas are the constraints on the active output of unit g, the reactive output of unit g, the up / down ramp constraint of unit g, the minimum start / stop time constraint of generator unit g, the start / stop state logic constraint, the node active power balance constraint, the node reactive power balance constraint, the voltage balance constraint, the voltage amplitude constraint and the branch apparent power flow constraint.

[0066] Further: In the step S3, the method for calculating the operation vulnerability based on the power flow change entropy is as follows:

[0067] S3.11. First, obtain the initial data of the system and determine the basic power flow of each line under normal operation through power flow analysis method. Mark these parameters as

[0068] S3.12. Next, simulate the disconnection of each line in the system and recalculate the power flow parameters of each line after the system stabilizes, recorded as

[0069] S3.13. Calculate the power flow parameter differences before and after the line is disconnected, so as to obtain the power flow change of each line after the line disconnection fault occurs:

[0070] S3.14. Calculate the change in the power flow of the entire system caused by the disconnection of line ij, denoted as δP k ;

[0071] S3.15, evaluate the impact of disconnection of line ij on the system, i.e. i-j,k ;

[0072] S3.16. Calculate the power flow entropy H corresponding to line ij i-j ;

[0073] S3.17. Calculate the operational vulnerability VF of line ij i-j ;

[0074] S3.8. Sort the lines according to their operational vulnerability.

[0075] Further: In the step S3, the method for calculating the structural vulnerability based on network topology entropy is as follows:

[0076] S3.21. Import the initial data of the system and draw the network topology diagram according to the system electrical network structure information

[0077] S3.22. Calculate the node degree D based on the network topology graphNi ;

[0078] S3.23. Calculate the line degree D using the node degree Li ;

[0079] S3.24, calculated line importance

[0080] S3.25. According to the network topology entropy principle, we can solve H t ;

[0081] S3.26. Calculate the structural vulnerability VT of line ij i-j ;

[0082] S3.27. Rank the vulnerability of the lines.

[0083] Further: The comprehensive vulnerability assessment method of step S4 is as follows:

[0084] Vulnerability index = α × power flow entropy + β × topological entropy

[0085] Among them, α and β are weight coefficients, which are adjusted according to actual conditions. The selection of weight coefficients is determined according to the characteristics and requirements of the system.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] The present invention provides a method for optimizing the scheduling and identifying vulnerable lines of an integrated energy system, aiming to improve the reliability and stability of the system. The method first constructs an optimized scheduling model of an integrated energy system to evaluate the vulnerable lines in the system:

[0088] In the present invention, the comprehensive energy flow under normal and fault-free conditions is calculated in detail, including the energy flow on each line and the state parameters of each node, to provide basic data for subsequent vulnerability analysis. In addition, the present invention also simulates the N-1 fault situation, that is, the response of the system when any line fails, to evaluate the performance of the system under partial failure. By comparing and analyzing the data under normal operating conditions and N-1 fault conditions, potential weak links in the system can be effectively identified;

[0089] In this invention, a vulnerability assessment method based on the entropy method is introduced, and the vulnerability of the integrated energy system is comprehensively assessed by calculating the two indicators of power flow entropy and topological entropy. The power flow entropy reflects the balance of the system in energy distribution, while the topological entropy measures the stability of the system structure. This combined indicator can comprehensively analyze the overall vulnerability of the integrated energy system from different angles, providing a scientific basis for the maintenance and optimization of the system;

[0090] Through the implementation of the present invention, vulnerable lines in the integrated energy system can be effectively identified and processed with priority, thereby improving the stability and reliability of the system and providing important decision-making support for the optimized operation and risk management of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 This is a flow chart of the operation vulnerability calculation based on the power flow change entropy in the present invention;

[0092] Figure 2 It is a flow chart for calculating the structural vulnerability in the present invention. DETAILED DESCRIPTION

[0093] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0094] The present invention provides a method for identifying weak links in an integrated energy system based on information theory. By establishing an optimal dispatching model of the integrated energy system, the power flow results under normal operation and N-1 fault conditions are calculated in detail. Subsequently, two indicators, power flow entropy and topological entropy, are introduced. The former is used to evaluate the power flow uniformity of the integrated energy system, and the latter is used to analyze the complexity and connection density of the network structure. Finally, the two indicators are combined to comprehensively evaluate the overall vulnerability of the system and identify weak links. The method includes the following steps:

[0095] S1. Constructing an optimal dispatching model for an integrated energy system, wherein the model includes an optimal dispatching model for an integrated energy system under normal operation, wherein the optimal dispatching model for an integrated energy system includes an objective function of a system optimal dispatching model, constraints of an electric power system, and constraints of a natural gas system;

[0096] S2, Simulate N-1 fault scenario: Construct an optimal dispatch model of the power system under fault conditions, which includes an objective function and comprehensive energy system constraints;

[0097] S3. Vulnerability assessment based on entropy method: including operation vulnerability calculation based on power flow change entropy and structural vulnerability calculation based on network topology entropy;

[0098] S4, comprehensive vulnerability assessment: Based on the two indicators of power flow entropy and topological entropy obtained in step S3, comprehensive vulnerability assessment is performed;

[0099] S5. Line sorting and identification of vulnerable lines: Based on the calculated vulnerability index, all lines are sorted to identify the most vulnerable lines.

[0100] The specific implementation is as follows:

[0101] In step S1, a power system optimization dispatch model under fault conditions is constructed:

[0102] The model takes economy, safety and reliability as multiple objectives, and realizes the rational allocation and efficient utilization of various energy resources by optimizing the scheduling strategy. The model not only takes into account the physical characteristics of energy flow, including the transmission loss of power flow and the loss of natural gas system, but also incorporates economic cost and safety constraints. Economic constraints focus on minimizing energy costs, including fuel costs, equipment maintenance costs, etc. Safety constraints ensure that the system can meet safety standards, such as voltage stability, under various operating conditions. In addition, it can also simulate the comprehensive energy flow under normal and fault-free conditions. By simulating the system operation under normal conditions, the flow distribution and node status parameters of the system under fault-free conditions can be obtained, providing data support for subsequent vulnerability analysis.

[0103] (1) The optimal dispatching model of the integrated energy system under normal operation is as follows:

[0104] 1) Objective function of system optimization scheduling model:

[0105]

[0106] The objective function of the model is to minimize the operating cost of the integrated energy system, where: is the unit fuel cost of non-gas generating set g: F g is the heat consumption characteristic of the generator set g; is the planned output value of the generator set g in the period ι under the normal operation model; It is a binary variable used to indicate the working status of the generator set. A value of 1 indicates that the generator set is in operation, and a value of 0 indicates that the generator set is in shutdown. and Divided into the start / stop fuel cost of generator set g; is the production cost of natural gas well k; is the gas output arrangement of gas well k in time period t; is the operating cost of the gas storage equipment; is the gas output of gas storage equipment s in time period t; is the operating cost of P2G device h; is the power consumed by P2G device h in time period t;

[0107] 2) Power system constraints:

[0108] Equations (2)-(13) are the operating constraints of the system, where equation (2) is the constraint on the active output of generator set g:

[0109]

[0110] Formula (3) is the constraint of reactive output of generator set g:

[0111]

[0112] Formula (4) (5) is the up / down climbing constraint of generator set g

[0113]

[0114]

[0115] Formula (6) and (7) are the minimum start / stop time constraints of generator set g:

[0116]

[0117]

[0118] Formula (8) is the start-stop state logic constraint

[0119]

[0120] Formula (9) is the node active power balance constraint

[0121]

[0122] Formula (10) is the node reactive power balance constraint

[0123]

[0124] Formula (11) is the voltage balance constraint

[0125]

[0126] Formula (12) is the voltage amplitude constraint

[0127]

[0128] Formula (13) is the apparent power flow constraint of the branch

[0129]

[0130] In the formula: the parameters with superscripts min / max represent the minimum / maximum limits of the variables respectively; is the up / down ramp rate of the generator set g; MU i / MD iis the minimum continuous start / stop time of the generator set g; R i,j , X i,j is the branch resistance and branch reactance; P ij , Q ij is the branch active power and branch reactive power; the variables representing the start and stop status of the generator set in equations (6) and (7) are 0-1 variables, which belong to nonlinear constraints and are linearized, that is, based on Benders decomposition, they are iteratively solved, and the objective function is still to minimize the operating cost of the power system.

[0131] 3) Natural gas system constraints:

[0132] Formulas (14) to (24) are the operation constraints of the natural gas system, where formula (14) is the gas output capacity constraint of gas well k; formulas (15) and (16) are the gas storage capacity constraints of gas storage device s; formulas (17) and (18) are the natural gas inflow / outflow rate constraints of gas storage device s, respectively; formula (19) is the node flow balance constraint; formula (20) calculates the amount of natural gas consumed by gas-driven compressor device c in time period t; formulas (21) and (22) are the transmission flow constraints of the natural gas pipeline; formula (23) is the node pressure value constraint in the natural gas network; and formula (24) is the relationship constraint between the node pressures at both ends of compressor device c.

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] Where: is the gas storage capacity of gas storage equipment s in time period t; is the air intake of the gas storage device s in time period t; is the amount of natural gas produced by P2G device h in period t; is the power flow of the natural gas pipeline mn in period t; is the amount of natural gas consumed by gas generator set g in period t; G w,t is the size of the natural gas load w in time period t; is the amount of natural gas consumed by the gas-driven compressor device c during period t; is the amount of natural gas compressed by compressor equipment c in time period t; is the efficiency of the compressor equipment; K mn To calculate the constant of natural gas pipeline flow, its value is affected by the characteristics of natural gas pipeline; is the square of the node gas pressure of natural gas node m in time period t; is the gas compression constant of the compressor device c; It indicates the flow direction of natural gas pipeline mn in time period t. When its value is 1, it means that the natural gas flow flows from node m to node n. When its value is -1, it means that the natural gas flow flows from node n to node m.

[0145] In step S2, N-1 fault scenario simulation:

[0146] In the N-1 fault scenario, assume that any line in the integrated energy system fails, and observe the response and recovery of the system. By simulating this fault scenario, the comprehensive energy flow data of the system in the fault state can be obtained, providing a basis for subsequent data comparison and vulnerability analysis. During the simulation process, the impact of different line failures on the system was considered. For power line failures, the power flow transfer and voltage fluctuations caused by the failure were analyzed; for natural gas pipeline failures, the impact on gas transmission losses was set. By simulating these fault scenarios, we can fully understand the behavioral characteristics of the system under fault conditions and provide data support for the formulation of effective fault response strategies. In the integrated energy system, a line suddenly fails. Forced to disconnect from the main grid, the generator set will quickly adjust its output power to maintain the stable operation of the power grid. However, when the self-regulation ability of the power grid reaches its limit and is insufficient to maintain the balance between supply and demand, it is necessary to actively reduce part of the power load or stop the operation of some generator sets to deal with it. In this case, the supply and demand balance of the system will be broken, forming a supply and demand imbalance. At this time, the optimization goal of the dispatch model becomes to minimize the supply and demand imbalance of the power system, and its calculation method is:

[0147] 1) The optimal dispatch model of the power system under fault conditions is as follows:

[0148] Objective function:

[0149]

[0150] Where: and are the demand and supply imbalances of the power system respectively; and are the demand and supply imbalance of the natural gas system respectively; Ω is the fault A collection of; is the set of power system nodes e.

[0151] 2) Constraints of the integrated energy system:

[0152] The constraints of the integrated energy system under fault conditions are basically similar to the constraints of the power system optimization dispatch under normal operation. Slack variables are introduced into the fault condition operation constraints to quantify the degree of supply and demand imbalance under fault conditions. Specifically, in the node power balance constraints, as shown in equations (33) and (34), and are used to describe the difference between supply and demand of active power, and and It is used to describe the difference between reactive power supply and demand. It is worth noting that the demand imbalance of each node is limited by the connected electrical load to ensure that it does not exceed the sum of its actual loads; similarly, the supply imbalance of each node is also limited by the output of the connected generator set to ensure that it does not exceed the maximum output of the generator set. As shown in formula (38)(39)(40), They are the demand imbalance and supply imbalance of the natural gas system respectively. The demand imbalance at each node in the natural gas system is less than the sum of the gas load connected to the node, the gas consumption of the gas-fired unit and the gas consumption of the gas-driven compressor equipment. The remaining formulas are the constraints on the active output of unit g, the reactive output of unit g, the up / down ramp constraint of unit g, the minimum start / stop time constraint of generator unit g, the start / stop state logic constraint, the node active power balance constraint, the node reactive power balance constraint, the voltage balance constraint, the voltage amplitude constraint, the branch apparent power flow constraint, etc.

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] In step S3, the vulnerability assessment method based on the entropy method is:

[0169] The entropy method originated from the concept of entropy in thermodynamics and is now widely used in the fields of system science and information science. In integrated energy systems, entropy can be used to evaluate the unevenness and predictability of energy flow, as well as the complexity and density of network structure. The unevenness of source flow reflects the degree of balance in energy distribution in the system. When energy flows unevenly, the stability and reliability of the system may be affected. Predictability focuses on the predictability of the system state. When the system state is difficult to predict, its vulnerability will increase accordingly. The complexity and density of network structure reflect the complexity of the system structure and the connection relationship between nodes. When the network structure is complex and the connection is dense, the vulnerability of the system may also be higher.

[0170] The essence of information entropy is to quantify the degree of uncertainty in the system. When the scope of an event is relatively concentrated, its sample space is smaller, the probability of the event is higher, so the amount of information required is less, and the information entropy is also lower. In this case, the system is in a more ordered state, corresponding to the low entropy state in the traditional concept of entropy. On the contrary, when the scope of an event is relatively dispersed, that is, when the sample space is larger, the probability of the event is lower, the amount of information required is more, and the information entropy is also higher. In this case, the system is in a more disordered state, corresponding to the high entropy state in the traditional concept of entropy. In general, information entropy is an indicator to measure the amount of information and uncertainty. The level of information entropy directly corresponds to the degree of order and stability of the system. Low entropy indicates high stability and high order, and vice versa.

[0171] When extending information entropy to integrated energy systems, it is necessary to understand the concepts of orderly and disorderly operation of the system. iis fixed, that is, the circuits and components in the components. The sample space x discussed in the present invention i It covers all the lines in the network. Under the normal operation of the system, each line carries a specific amount of power flow. If the power flow distribution of the system is set to be uniform, that is, the power flow shared by each line is roughly equal, then when a line is disconnected due to overload or failure, the original power flow of the line is small, and the load of each line is far below its limit value, so this fluctuation will not lead to a chain of failures. In this scenario, the power flow change entropy of the system reaches the maximum value of lnn, which represents the most orderly and stable operating state of the system. However, if the power flow distribution of the system is extremely uneven, and the power flow carrying ratio of each line is significantly different, the order and stability of the system will be greatly reduced in this case. At this time, it is difficult for the lines within the system to find a pair with the same power flow distribution ratio, and the power flow entropy value is the smallest. Therefore, the principle of judging disorder and order according to the size of the entropy value in the integrated energy system is opposite to the traditional entropy method.

[0172] Operational vulnerability calculation based on power flow change entropy

[0173] The operational fragility of the integrated energy system is reflected in how, when the system is running, once the relevant parameters of a certain line are disturbed, it will have a negative impact on the operating status of other lines. In order to evaluate this fragility, the present invention selects the flow parameters as the main basis. In the integrated energy system, the disconnection of any line will lead to the secondary distribution of the flow. These changing flow quantities are important considerations for the dynamic balance of the system and are also key factors in triggering cascading failures. Each line has its own load-bearing flow limit. Once this limit is exceeded, the line will be interrupted due to overload, which may cause overload of other lines, forming a chain reaction of failures, and even cause the decoupling of the entire system in severe cases. Therefore, the fragile lines in the network are those that are greatly affected by the flow transfer. The flow entropy is usually used to measure the operational fragility of the line. The operational fragility is evaluated by calculating the impact of the secondary distribution of the flow on the integrated energy system after the line is disconnected.

[0174] The present invention considers that lines with poor resistance to power flow interference are fragile lines. Once these critical lines are disconnected from the system due to faults, they will cause significant power flow fluctuations, threatening the operation of other lines, thereby causing the fault to spread further. Therefore, the core of identifying fragile lines in operation is to accurately identify those lines that will pose the greatest threat to system stability and security once disconnected. Analogous to the concept of information entropy, the present invention regards the impact as the degree of chaos in the internal operating state of the system caused by the disconnection of the fragile line, and introduces the concept of entropy value based on power flow transfer to quantify the power flow distribution of the system. The power flow entropy is used to observe the chaos inside the system after a line failure, thereby identifying the fragile lines in the system.

[0175] In the integrated energy system, P(x) can be understood as the degree of impact of the redistribution of power flow on the power grid after a line disconnection occurs. Specifically, when a line ij is disconnected, the system will adjust itself to reach a new steady state. In this process, the power flow of all lines except the disconnected line ij will change. These changes reflect the redistribution of power flow after the fault. The meaning of P(x) is the impact caused by this redistribution. The degree of this impact can be quantified by calculating the difference between the new power flow data and the initial data before the fault. And summarizing the difference between all affected lines and their original power flow values, the power flow impact parameters of the entire network after the line ij is disconnected can be obtained.

[0176] In the integrated energy system, if line ij is disconnected, the power flow distribution of the system will be adjusted accordingly. At this time, the power flow change of line u is δP u , and its specific calculation method is shown in formula (41). The present invention defines the power flow change of node k as the sum of its demand imbalance, supply imbalance, and the power flow change of the line connected to it, as shown in formula (42).

[0177]

[0178]

[0179] The power flow change ratio at node k caused by the disconnection of line ij is r i-j,k The specific calculation formula is shown in formula (43). By integrating the data of all nodes in the system and calculating the power flow change entropy H of the power system after ij is disconnected i-j , and its calculation method is given by formula (44). Based on the above analysis, the operating characteristic vulnerability VF of line ij is further defined as i-j , as shown in formula (45).

[0180]

[0181]

[0182]

[0183] In order to describe the vulnerability of line ij from the perspective of operating status, the design variable VF i-j , to achieve when VF i-j The larger the value of , the greater the impact of the disconnection of line ij on the system. i-j The value is inversely proportional to the degree of impact on the power system. The sum of the node flow changes of the system It is proportional to the degree of impact on the power system, so the operating characteristic vulnerability VF considering the two judgment indicators can be obtained: i-j Calculation formula (45).

[0184] The calculation process of line operation vulnerability based on power flow change entropy is mainly divided into the following steps:

[33] :

[0185] (1) First, obtain the initial data of the system and determine the basic power flow of each line under normal operation through power flow analysis method. These parameters are marked as

[0186] (2) Next, simulate the disconnection of each line in the system and recalculate the power flow parameters of each line after the system stabilizes, denoted as

[0187] (3) Calculate the power flow parameter differences before and after the line is disconnected, and obtain the power flow change of each line after the line disconnection fault occurs:

[0188] (4) Calculate the change in the power flow of the entire system caused by the disconnection of line ij, denoted as δP k

[0189] (5) Evaluate the impact of disconnection of line ij on the system, i.e., r i-j,k

[0190] (6) Calculate the power flow entropy H corresponding to line ij i-j

[0191] (7) Calculate the operational vulnerability VF of line ij i-j

[0192] (8) Sort the lines according to their operational vulnerability.

[0193] like Figure 1 As shown, the vulnerability of power grid operation is assessed.

[0194] Structural Vulnerability Calculation Based on Network Topology Entropy

[0195] The structural vulnerability of the integrated energy line aims to assess the criticality of a certain line in the topology of the system. For example, if the connecting lines between two small systems are disconnected, the connection between the two systems will be seriously affected. Secondly, some lines in the network have a one-to-one correspondence with the load. If these lines fail, the system connectivity will be greatly affected, hindering the generator set from supplying electricity and failing to meet the energy required by the load. In addition, the circuits at the generator set outlets in the system are also extremely important. Once these lines fail, the power transmission of the entire power system will directly collapse. These lines have extremely high criticality levels in the power grid topology and are key factors affecting the robustness of the power grid structure. Compared with other branches in the network, once these critical lines fail, there is a greater probability that the fault will be further expanded, thereby seriously affecting the stable operation of the entire power grid. Therefore, the concept of "vulnerability" is introduced to identify those lines with a higher risk of potential fault propagation, which show higher structural vulnerability. By accurately identifying these lines and implementing targeted operation monitoring and structural optimization measures, the robustness of the system topology can be significantly improved. At present, the mainstream method for evaluating this structural vulnerability is based on the foundation of complex network theory, and quantitative analysis is performed by comprehensively using key indicators such as betweenness or degree. This invention chooses to use network topological entropy to quantify the imbalance of network topological structure and reflect the structural vulnerability of the line.

[0196] According to the definition of network topology entropy in formula (40) above, the node importance parameter is analogous to the line importance. Then the importance of line ij is defined as the ratio of its line degree to the sum of all line degrees. Based on this, the mathematical expression of network topology entropy in the present invention is formula (46)

[0197]

[0198] The line degree is defined as formula (39), and the line importance is defined as formula (40)

[0199]

[0200]

[0201] In the formula is the line importance of line ij; D Ni1 , D Ni2 are the node degrees corresponding to the nodes at both ends of line ij.

[0202] According to formula (46), when the connection between each node and branch in the system reaches a completely balanced state, that is, the number of branches connected to each node is exactly the same, the topological entropy of the system will reach its maximum value; however, if the remaining nodes in the system are only connected to a specific central node, the network structure will be extremely unbalanced at this time, and the topological entropy of the system will reach the minimum value. Based on the above analysis, the structural vulnerability VT of line ij is defined as i-j , the calculation formula is shown in formula (49).

[0203] VT i-j =1 / H i-j (49)

[0204] The topological entropy is calculated according to the following steps to evaluate the operational vulnerability of the line.

[0205] (1) Import the initial data of the system and draw the network topology diagram based on the system electrical network structure information

[0206] (2) Calculate the node degree D based on the network topology graph Ni

[0207] (3) Calculate the line degree D using the node degree Li

[0208] (4) Calculate the importance of the line

[0209] (5) According to the network topology entropy principle, we can solve H t

[0210] (6) Calculate the structural vulnerability VT of line ij i-j

[0211] (7) Rank the vulnerability of the lines.

[0212] like Figure 2 As shown in the figure, this is the calculation flow chart of structural vulnerability.

[0213] The topological structure vulnerability and operational characteristic vulnerability of the line are comprehensively considered, and the two indicators are normalized to be in the same order of magnitude. The vulnerability index V of the system line ij is i-j Defined as

[0214] V i-j =ω T VT i-j +ω F VF i-j (50)

[0215] Where ω T and ω Fare the weight values ​​corresponding to the structural vulnerability and operational characteristic vulnerability respectively. i-j The local concentration of nodes reflects the topological vulnerability of the line, VF i-j The power flow change entropy reflects the fragility of the line operation characteristics. Therefore, the line fragility index V designed in the present invention is i-j It can reasonably identify the vulnerable lines in the system. Further analysis shows that V i-j The larger the value of , the higher the vulnerability of line ij, that is, the more important line ij is to the system, and the greater the impact on the system after disconnection.

[0216] The topological structure vulnerability and operational characteristic vulnerability of the line are comprehensively considered, and the two indicators are normalized to be in the same order of magnitude. The vulnerability index V of the system line ij is i-j Defined as

[0217] V i-j =ω T VT i-j +ω F VF i-j (50)

[0218] Where ω T and ω F are the weight values ​​corresponding to the structural vulnerability and operational characteristic vulnerability respectively. i-j The local concentration of nodes reflects the topological vulnerability of the line, VF i-j The power flow change entropy reflects the fragility of the line operation characteristics. Therefore, the line fragility index V designed in the present invention is i-j It can reasonably identify the vulnerable lines in the system. Further analysis shows that V i-j The larger the value of , the higher the vulnerability of line ij, that is, the more important line ij is to the system, and the greater the impact on the system after disconnection.

[0219] In step S4, comprehensive vulnerability assessment:

[0220] Based on the two indicators of power flow entropy and topological entropy, a comprehensive evaluation method is adopted to perform weighted summation of the two indicators to obtain the vulnerability index of each line. The specific calculation method is as follows:

[0221] Vulnerability index = α × power flow entropy + β × topological entropy

[0222] Among them, α and β are weight coefficients, which can be adjusted according to actual conditions. The selection of weight coefficients can be determined according to the characteristics and needs of the system. For example, when the system pays more attention to the unevenness of energy flow, the weight coefficient of power flow entropy can be appropriately increased; when the system pays more attention to the complexity of the network structure, the weight coefficient of topological entropy can be appropriately increased.

[0223] By calculating the vulnerability index, all lines can be sorted to identify the most vulnerable lines. When these vulnerable lines fail, they may have a greater impact on the entire system. Therefore, these vulnerable lines need to be monitored and maintained to improve the overall stability and reliability of the system.

[0224] In step S5, line sorting and vulnerable line identification:

[0225] All lines are sorted according to the calculated vulnerability index. In the sorting results, the lines with higher vulnerability index are the vulnerable lines in the integrated energy system. These vulnerable lines have different positions and characteristics in the system. Some vulnerable lines may be located on the critical path of the system and undertake important energy transmission tasks; some may connect multiple important nodes or devices, which has a significant impact on the overall stability of the system.

[0226] Since these fragile lines may have a significant impact on the entire system when they fail, they need to be monitored and maintained. By strengthening monitoring methods and maintenance measures, potential safety hazards can be discovered and dealt with in a timely manner to prevent the occurrence and spread of failures. This helps to improve the overall stability and reliability of the system and ensure the safe operation of the integrated energy system.

[0227] According to steps S1 to S5, we know that:

[0228] The present invention constructs a detailed integrated energy system optimization and dispatching model, which can accurately calculate the energy flow distribution in the absence of any faults, including power flow and natural gas flow, and is detailed to the energy flow of each line and the energy status of each node, such as voltage, gas pressure, etc. Then, in order to comprehensively evaluate the system's resilience in the face of sudden failures, N-1 failures are simulated, that is, the possible failures of any line or equipment in the integrated energy system are simulated one by one, and the response status of the system is recorded.

[0229] After accumulating the data foundation, the entropy method was introduced to evaluate the vulnerability of the system. The entropy method has become a powerful tool for measuring the complexity of the integrated energy system by virtue of its ability to quantify the uncertainty and chaos of the system. Specifically, by calculating the two core indicators of energy flow entropy and topological entropy: the former focuses on the balance and predictability of energy flow, and deeply analyzes the transmission efficiency and stability of different energy sources in the system; the latter focuses on the complexity of the network structure and the analysis of the connection strength to evaluate the robustness and redundancy of the system structure.

[0230] Based on the above calculation results, all lines and equipment were further ranked according to their vulnerability, the most vulnerable links in the system were accurately found, and the identification results were verified.

[0231] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0232] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for identifying weak links in an integrated energy system based on information theory, characterized in that: The following steps are involved: S1. Constructing an optimal dispatching model for an integrated energy system, wherein the model includes an optimal dispatching model for an integrated energy system under normal operation, wherein the optimal dispatching model for an integrated energy system includes an objective function of a system optimal dispatching model, constraints of an electric power system, and constraints of a natural gas system; S2, Simulate N-1 fault scenario: Construct an optimal dispatch model of the power system under fault conditions, which includes an objective function and comprehensive energy system constraints; S3. Vulnerability assessment based on entropy method: including operation vulnerability calculation based on power flow change entropy and structural vulnerability calculation based on network topology entropy; S4, comprehensive vulnerability assessment: Based on the two indicators of power flow entropy and topological entropy obtained in step S3, comprehensive vulnerability assessment is performed; S5. Line sorting and identification of vulnerable lines: Based on the calculated vulnerability index, all lines are sorted to identify the most vulnerable lines.

2. The method for identifying weak links in an integrated energy system based on information theory according to claim 1 is characterized in that: In step S1, the objective function of the system optimization scheduling model is as follows: Where: is the unit fuel cost of non-gas generating set g: F g is the heat consumption characteristic of the generator set g; is the planned output value of the generator set g in the period ι under the normal operation model; It is a binary variable used to indicate the working status of the generator set. A value of 1 indicates that the generator set is in operation, and a value of 0 indicates that the generator set is in shutdown. and Divided into the start / stop fuel cost of generator set g; is the production cost of natural gas well k; is the gas output arrangement of gas well k in time period t; is the operating cost of the gas storage equipment; is the gas output of gas storage equipment s in time period t; is the operating cost of P2G device h; is the power consumed by P2G device h in time period t.

3. The method for identifying weak links in an integrated energy system based on information theory according to claim 1 is characterized in that: In step S1, the power system constraints are: Formulas (2)-(13) are the operating constraints of the system, where formula (2) is the constraint on the active output of generator set g, formula (3) is the constraint on the reactive output of generator set g, formula (4) (5) is the up / down ramp constraint of generator set g, formula (6) (7) is the minimum start / stop time constraint of generator set g, formula (8) is the start / stop state logic constraint, formula (9) is the node active power balance constraint, formula (10) is the node reactive power balance constraint, formula (11) is the voltage balance constraint, formula (12) is the voltage amplitude constraint, and formula (13) is the branch apparent power flow constraint. In the formula: the parameters with superscripts min / max represent the minimum / maximum limits of the variables respectively; is the up / down ramp rate of the generator set g; MU i / MD i is the minimum continuous start / stop time of the generator set g; R i,j , X i,j is the branch resistance and branch reactance; P ij , Q ij is the branch active power and branch reactive power; the variables representing the start and stop status of the generator set in equations (6) and (7) are 0-1 variables, which belong to nonlinear constraints and are linearized, that is, based on Benders decomposition, they are iteratively solved, and the objective function is still to minimize the operating cost of the power system.

4. The method for identifying weak links in an integrated energy system based on information theory according to claim 1 is characterized in that: In step S1, the natural gas system constraints are as follows: Formulas (14) to (24) are the operation constraints of the natural gas system, where formula (14) is the gas output capacity constraint of gas well k; formulas (15) and (16) are the gas storage capacity constraints of gas storage equipment s; formulas (17) and (18) are the natural gas inflow / outflow rate constraints of gas storage equipment s, respectively; formula (19) is the node flow balance constraint; formula (20) calculates the amount of natural gas consumed by gas-driven compressor equipment c in time period t; formulas (21) and (22) are the transmission flow constraints of the natural gas pipeline; formula (23) is the node pressure value constraint in the natural gas network; and formula (24) is the relationship constraint between the node pressures at both ends of compressor equipment c: Where: is the gas storage capacity of gas storage equipment s in time period t; is the air intake of the gas storage device s in time period t; is the amount of natural gas produced by P2G device h in period t; is the power flow of the natural gas pipeline mn in period t; is the amount of natural gas consumed by gas generator set g in period t; G w,t is the size of the natural gas load w in time period t; is the amount of natural gas consumed by the gas-driven compressor device c during period t; is the amount of natural gas compressed by compressor equipment c in time period t; is the efficiency of the compressor equipment; K mn To calculate the constant of natural gas pipeline flow, its value is affected by the characteristics of natural gas pipeline; is the square of the node gas pressure of natural gas node m in time period t; is the gas compression constant of the compressor device c; It indicates the flow direction of natural gas pipeline mn in time period t. When its value is 1, it means that the natural gas flow flows from node m to node n. When its value is -1, it means that the natural gas flow flows from node n to node m.

5. The method for identifying weak links in an integrated energy system based on information theory according to claim 1 is characterized in that: In step S2, the objective function of the power system optimization dispatch model under the fault condition is: Where: and are the demand and supply imbalances of the power system respectively; and are the demand and supply imbalance of the natural gas system respectively; Ω is the fault A collection of; is the set of power system nodes e.

6. The method for identifying weak links in an integrated energy system based on information theory according to claim 1 is characterized in that: In step S2, the integrated energy system constraints are: In formula (33) and (34), and are used to describe the difference between supply and demand of active power, and and It is used to describe the difference between reactive power supply and demand, where the demand imbalance of each node is limited by the connected electrical load to ensure that it does not exceed the sum of its actual loads, and the supply imbalance of each node is also limited by the output of the connected generator set to ensure that it does not exceed the maximum output of the generator set; In formula (38), (39), (40), are the demand imbalance and supply imbalance of the natural gas system respectively. The demand imbalance at each node in the natural gas system is less than the sum of the gas load connected to the node, the gas consumption of the gas-fired unit and the gas consumption of the gas-driven compressor equipment. The remaining formulas are the constraints on the active output of unit g, the reactive output of unit g, the up / down ramp constraint of unit g, the minimum start / stop time constraint of generator unit g, the start / stop state logic constraint, the node active power balance constraint, the node reactive power balance constraint, the voltage balance constraint, the voltage amplitude constraint and the branch apparent power flow constraint.

7. The method for identifying weak links in an integrated energy system based on information theory according to claim 1 is characterized in that: In the step S3, The method for calculating the operation vulnerability based on the power flow change entropy is as follows: S3.

11. First, obtain the initial data of the system and determine the basic power flow of each line under normal operation through power flow analysis method. Mark these parameters as S3.

12. Next, simulate the disconnection of each line in the system and recalculate the power flow parameters of each line after the system stabilizes, recorded as S3.

13. Calculate the power flow parameter differences before and after the line is disconnected, so as to obtain the power flow change of each line after the line disconnection fault occurs: S3.

14. Calculate the change in the power flow of the entire system caused by the disconnection of line ij, denoted as δP k ; S3.15, evaluate the impact of disconnection of line ij on the system, i.e. i-j,k ; S3.

16. Calculate the power flow entropy H corresponding to line ij i-j ; S3.

17. Calculate the operational vulnerability VF of line ij i-j ; S3.

8. Sort the lines according to their operational vulnerability; The method for calculating the structural vulnerability based on network topology entropy is as follows: S3.

21. Import the initial data of the system and draw the network topology diagram according to the system electrical network structure information S3.

22. Calculate the node degree D based on the network topology Ni ; S3.

23. Calculate the line degree D using the node degree Li ; S3.24, calculated line importance S3.

25. According to the network topology entropy principle, we can solve H t ; S3.

26. Calculate the structural vulnerability VT of line ij i-j ; S3.

27. Rank the vulnerability of the lines.

8. The method for identifying weak links in an integrated energy system based on information theory according to claim 7 is characterized in that: Considering the topological structure vulnerability and operational characteristic vulnerability of the line, and normalizing the two indices to make them in the same order of magnitude, the vulnerability index V of the system line ij is i-j Defined as V i-j =ω T VT i-j +ω F VF i-j (50) Where ω T and ω F are the weights corresponding to the structural vulnerability and operational characteristic vulnerability, VT i-j The local concentration of nodes reflects the topological vulnerability of the line, VF i-j The entropy of power flow changes reflects the fragility of the line's operating characteristics.

9. The method for identifying weak links in an integrated energy system based on information theory according to claim 1 is characterized in that: The comprehensive vulnerability assessment method of step S4 is as follows: Vulnerability index = α × power flow entropy + β × topological entropy Among them, α and β are weight coefficients, which are adjusted according to actual conditions. The selection of weight coefficients is determined according to the characteristics and requirements of the system.

Images