Thermal-electric comprehensive energy system fault influence quantity evaluation method and system

Abstract

A kind of thermal-electric comprehensive energy system failure influence quantitative evaluation method and system, comprising: constructing power grid, heat grid diagram structure and power grid, heat grid model;The power transmission efficiency and power supply proportion of power grid node are calculated, the heat transmission efficiency and heat supply proportion of heat grid node;And based on this, the power importance index of power grid node, the power importance index of power grid branch and the heat importance index of heat grid node are calculated;Each fault characteristic data of power grid and each fault characteristic data of heat grid are calculated;Thermal-electric coupling strength and failure strength are calculated;The importance weight of power grid and heat grid is calculated based on thermal-electric coupling strength and failure strength, and the importance weight is weighted and summed to each fault characteristic data of power grid and heat grid respectively, to obtain final failure risk index.This method considers the topological characteristics of different networks and the priority of each fault characteristic in the process of quantitative risk level calculation, and provides strong support for the safety analysis of thermal-electric comprehensive energy system.

Description

Technical Field

[0001] This invention belongs to the field of energy system fault assessment, specifically relating to a method and system for quantitatively assessing the impact of faults in a thermal-electric integrated energy system. Background Technology

[0002] As the global energy crisis and environmental pollution situation continue to worsen, the energy sector is facing a profound transformation. Although the construction of new energy systems is accelerating, it still generates significant carbon emissions. Therefore, it is imperative to improve the overall efficiency of energy utilization to reduce pollutant and carbon dioxide emissions and promote a low-carbon, clean energy transition. Integrated energy systems, based on the fundamental principles of "multi-energy complementarity and tiered utilization," can fully leverage the complementary advantages of electricity, gas, cooling, and heating, offering more flexible resources, higher overall energy utilization efficiency, and lower carbon emissions. Against this backdrop, given that electricity and heat consumption dominate in industrial, commercial, residential, and office load scenarios, integrated heat and power (CHP) energy systems are one of the most widely deployed and applied typical forms of integrated energy.

[0003] Integrated heat and power (CHP) energy systems involve generators, pipelines, storage tanks, and other equipment with various potential faults and risks, which can have significant impacts on personnel, system, energy, and environmental safety. Furthermore, the generation and consumption of energy are dynamic in time and space, and unforeseen events such as weather changes and equipment failures often lead to imbalances between energy supply and demand in CHP systems. In addition, due to the high coupling between the power network and the heating network, faults occurring in the heating network can propagate to the power network, increasing the risk of cascading failures. Therefore, it is necessary to quantitatively assess the severity of faults in CHP systems to provide an information foundation for fault prevention and recovery strategies.

[0004] Fault impact assessment of integrated heat and power energy systems is divided into two types: fuzzy assessment and quantitative assessment. Fuzzy assessment focuses on classifying the system state into zones based on several qualitative indicators after a fault occurs, while quantitative assessment emphasizes distinguishing the degree of impact of different faults through the calculation results of quantitative indicators. The latter provides a higher degree of refinement and stronger intuitiveness in the evaluation results. Currently, there is no universally applicable standard system for fault impact assessment indicators of integrated heat and power energy systems. The core issue of quantitative fault impact assessment is how to comprehensively consider the characteristics of the fault itself and its impact on power and heat operating conditions to establish a complete and representative fault assessment system. Simultaneously, considering the topological characteristics of different networks, selecting fault characteristic data that maximizes coverage of power and heat network operating conditions; and considering the stability priority of each indicator with different networks, assigning weights to the evaluation indicators to obtain a unique fault risk index for the system for different faults are also core issues of quantitative assessment.

[0005] CN118350540A discloses a quantitative assessment method for the support capacity of a multi-energy coupled distribution network, comprising: analyzing the operation modes of multi-energy coupled equipment under different scenarios; constructing quantitative models of the support capacity of the multi-energy coupled distribution network from the gas network side and the heating network side respectively according to the operation modes; analyzing the impact of individual differences and seasonal factors of user-level integrated energy systems on the magnitude of support capacity in the multi-energy coupled distribution network topology model; constructing a support capacity assessment index system based on the analysis results; and determining the weight of each assessment index in the support capacity assessment index system under different scenarios using the analytic hierarchy process.

[0006] CN118941092A discloses a method and device for risk assessment of integrated energy systems based on R-vine copula, including: establishing a risk assessment index system for integrated energy systems; using a nonparametric kernel density estimation method and a normal distribution to fit the marginal probability distribution of risk factors, and using the R-vine copula method to construct a joint probability distribution model of multidimensional risk factors; performing Monte Carlo sampling based on the joint probability distribution model of multidimensional risk factors, obtaining the risk factor values ​​through inverse probability transformation, and then performing reduction clustering to obtain typical operational risk scenarios of integrated energy systems; and using the AHP-Topsis method to quantitatively calculate the operational risk value of integrated energy systems.

[0007] Although some research results have been achieved in risk assessment and performance evaluation methods for integrated energy systems, and various patents have proposed methods for supporting the multi-energy coupled distribution network and risk quantification analysis based on probabilistic modeling, the following shortcomings still exist:

[0008] 1. Existing technologies mainly focus on the supporting capabilities of distribution networks and multi-energy coupled systems, or the construction of multi-dimensional public indicator systems, paying more attention to the comprehensiveness of indicators horizontally. However, they lack a targeted quantitative analysis framework for the characteristics of fault cascading propagation and dynamic imbalance of operating conditions caused by the high degree of synergy and bidirectional influence between power networks and thermal networks in heat-electric coupling scenarios. The fine-grained differentiation and measurement of fault impacts under the complex coupling mechanisms within integrated heat-electric energy systems are significantly insufficient.

[0009] 2. Existing methods are often based on static or single energy flow scenarios, failing to fully consider the actual impact of coupled topology and key nodes of multi-energy networks such as electric and thermal networks on fault propagation and evolution. The selection of important state variables under different spatiotemporal operating states of multiple networks lacks systematicity, making it difficult to dynamically respond to the contribution and weakening effect of thermal-electric interaction on system stability.

[0010] 3. Traditional fuzzy assessment or purely expert-based methods often result in qualitative or coarse-grained classifications, which are insufficient to provide quantitative decision support for ranking the impact of various types of faults and prioritizing their recovery. This fails to meet the actual needs for precise risk prevention and control in the operation and maintenance of integrated thermal-electric energy systems. Summary of the Invention

[0011] To address the challenges in fault assessment of integrated thermal-electric energy systems, such as frequent parameter interactions and strong operational coupling between the thermal and electrical systems, making it difficult to quantify the severity of different fault impacts, this invention considers key state variables during the operation of the electrical and thermal subsystems and, combined with the system's subnetwork topology characteristics, proposes a fault assessment system for integrated thermal-electric energy systems to achieve quantitative assessment of the system's impact on multiple types of faults.

[0012] To achieve the above-mentioned objectives, the present invention specifically adopts the following technical solution.

[0013] This invention discloses a method for quantitatively assessing the impact of faults in a combined heat and power energy system, comprising the following steps:

[0014] S1. Construct the power grid and heating network diagram structure, as well as the power grid and heating network model;

[0015] S2. Based on the power grid and heat network diagrams, calculate the power transmission efficiency and power supply ratio of the power grid nodes, and the heat transmission efficiency and heat supply ratio of the heat network nodes. The power importance index of a power grid node is the sum of its power transmission efficiency and power supply ratio. The power importance index of a power grid branch is the average of the power importance indices of its two ends. The heat importance index of a heat network node is the sum of its heat transmission efficiency and heat supply ratio.

[0016] S3. Simulate the power grid and heating network models to obtain the power and heat data of each node after the fault. Combine the power importance index of each node and branch of the power grid and the heat importance index of each node of the heating network to calculate the fault characteristic data of each fault in the power grid and the heating network.

[0017] S4. Calculate the ratio of the total output power of the heating network's electric drive equipment to the total heat load as the thermoelectric coupling strength, and calculate the fault frequency of the power grid and heating network in the previous control cycle as the fault intensity. Calculate the importance weights of the power grid and heating network based on the thermoelectric coupling strength and fault intensity, and sum the fault characteristic data of the power grid and heating network according to the importance weights to obtain the final fault risk level.

[0018] More preferably,

[0019] The power transmission efficiency is specifically:

[0020]

[0021] In the formula, Indicates the first i Power transmission efficiency of individual power grid nodes; m This represents the number of nodes in the power grid. d ij Represents a node i , j The shortest impedance path length; Z m This indicates the branch impedance.

[0022] More preferably,

[0023] The shortest impedance path length is calculated as follows:

[0024] Based on the power grid diagram structure, the sum of the electrical impedances of the lines or transformers between nodes is used as the distance between nodes;

[0025] At the start of the calculation, a distance value is initialized for each node in the power grid model, representing the distance of that node from the source node. i Distance to the source node; i The distance to the source node is set to 0, indicating that the source node is closest to itself. The distances to all other nodes are set to infinity. The source node is then set to... i Add to the priority queue, where nodes are sorted in ascending order of distance value;

[0026] During the iteration process, retrieve the node currently furthest from the source node from the priority queue. i The node with the shortest distance k traversal k Calculate all adjacent nodes of a node that are directly connected by a line or transformer. k Node distance from source node i distance and k Point and its current adjacent nodes m The sum of the distances between adjacent nodes is used as the temporary path length; if the temporary path length is less than that of adjacent nodes... m The current record is related to the source node. i If the distance is known, then update the adjacent nodes. m With source node i The distance is the temporary path length, recording the current node. k For this adjacent node m The predecessor node, and the adjacent node. m Add to priority queue and update its distance from source node i The distance value;

[0027] Iterate until the priority queue is empty, or until the target node exists. j At that time, the target node jThe process ends after retrieving the priority queue. The shortest impedance path is reconstructed by backtracking through the predecessor node, and the total impedance of this path is the target node. j To the source node i The shortest impedance path length.

[0028] More preferably,

[0029] The heat transfer efficiency is specifically:

[0030]

[0031] In the formula, Indicates the first i Heat transfer efficiency of each heat network node; n This refers to the number of nodes in the heating network. Indicates heating network nodes i , j Shortest flow path length; l x Indicates pipeline x The length of the pipe; m x Indicates pipeline x Pipeline mass flow rate.

[0032] More preferably,

[0033] The fault characteristic data of the power grid include the maximum rate of change of node voltage, the maximum value of node voltage, the active power change coefficient of the line, the reactive power change coefficient of the line, the average rate of change of generator power, and the load loss rate.

[0034] More preferably,

[0035] The fault characteristic data of the heating network include the maximum rate of change of node temperature, the reciprocal mean of the maximum node temperature, the average relative rate of change of node pressure, the average total change of heat source power, and the heat load loss rate.

[0036] More preferably,

[0037] In S4, the importance weights of the power grid and heating network are calculated based on the thermoelectric coupling strength. The specific calculation method is as follows:

[0038]

[0039] In the formula, Weighting for the importance of the heating network; Assigning importance weights to the power grid; The degree of thermoelectric coupling; k Sensitivity coefficient To adjust the coefficient and .

[0040] The importance weights of the power grid and heating network are recalculated based on the fault intensity, specifically as follows:

[0041]

[0042] In the formula, This is the weighting adjustment factor. To adjust the importance weight of the heating network, The revised importance weights for the power grid;

[0043] The weight correction coefficient Based on the frequency of heating network failures in the previous control cycle Compared with the frequency of power grid faults in the previous control cycle Perform calculations; when > hour, For positive; when < hour, Negative; when = hour, It is 0.

[0044] More preferably,

[0045] The specific calculation method for the heating network fault frequency in the previous control cycle is as follows:

[0046]

[0047] In the formula, This represents the total number of all fault events that occurred in the integrated electric-thermal energy system during the previous control cycle. This represents the total number of all fault events that occurred in the heating network during the previous control cycle.

[0048] The specific calculation method for the power grid fault frequency in the previous control cycle is as follows:

[0049]

[0050] In the formula, This represents the total number of all fault events that occurred in the power grid during the previous control cycle.

[0051] Another aspect of this invention discloses a quantitative assessment system for the impact of faults in a combined heat and power energy system based on the aforementioned method. This system includes a power grid and heating network model construction module, a module for calculating the importance index of power grid nodes and branches and heating network nodes, a module for calculating fault characteristic data of the power grid and heating network, and a final fault risk level calculation module.

[0052] The power grid and heating network model building module is used to build the power grid and heating network diagram structure and the power grid and heating network model;

[0053] The module for calculating the importance index of power grid nodes and branches, and heating network nodes, is used to calculate the power transmission efficiency and power supply ratio of power grid nodes, and the heat transmission efficiency and heat supply ratio of heating network nodes based on the structure of the power grid and heating network diagrams. The power importance index of a power grid node is the sum of its power transmission efficiency and power supply ratio, and the power importance index of a power grid branch is the average of the power importance indices of its two endpoints. The heat importance index of a heating network node is the sum of its heat transmission efficiency and heat supply ratio.

[0054] The module for calculating fault characteristic data of power grid and heating network is used to simulate the power grid and heating network models, obtain the power and heat data of each node after the fault, and calculate the fault characteristic data of power grid and heating network by combining the power importance index of each node and branch of the power grid and the heat importance index of each node of the heating network.

[0055] The final fault risk level calculation module is used to calculate the ratio of the total output power of the heating network's electric drive equipment to the total heat load as the thermoelectric coupling strength, and to calculate the fault frequency of the power grid and heating network in the previous control cycle as the fault intensity. Based on the two, the importance weights of the power grid and heating network are calculated, and the fault characteristic data of the power grid and heating network are weighted and summed according to the importance weights to obtain the final fault risk level.

[0056] The beneficial effects of this invention are compared with those of the prior art:

[0057] This invention proposes a quantitative assessment method for the impact of faults in integrated thermal-electric energy systems. In these systems, faults are diverse and their impact ranges vary greatly. Therefore, a fault risk index formed by combining multi-source quantitative fault characteristic data offers advantages such as ease of comparison of different fault impact levels, high precision, and strong intuitiveness. Furthermore, considering the topological characteristics of different networks and the priority of various fault characteristic data during the calculation process ensures accurate and comprehensive assessment results for different systems.

[0058] This invention also considers adjusting the importance weight of thermoelectric coupling strength to thermal and electronic systems. By dynamically adjusting the weights, the evaluation bias caused by the solidification of a single weight can be avoided, making the results more consistent with the actual operating state of the system. On the other hand, this invention further adjusts the importance weights of thermal and electronic systems based on the frequency of failures in the previous cycle. The weight adjustment based on historical failure data enables the evaluation system to more keenly capture potential risk points, providing a clear basis for preventive measures and reducing failure losses. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the process for quantitatively assessing the fault impact of the integrated thermal-electric energy system of the present invention.

[0060] Figure 2 This is a network topology diagram according to an embodiment of the present invention;

[0061] Figure 3 The figure shows the simulation results under the fault scenario in the embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0063] like Figure 1 As shown, this invention discloses a method for quantitatively assessing the fault impact of a thermal-electric integrated energy system, comprising the following steps:

[0064] S1. Construct the power grid and heating network diagram structure, as well as the power grid and heating network model;

[0065] S2. Based on the power grid and heat network diagrams, calculate the power transmission efficiency and power supply ratio of the power grid nodes, and the heat transmission efficiency and heat supply ratio of the heat network nodes. The power importance index of a power grid node is the sum of its power transmission efficiency and power supply ratio. The power importance index of a power grid branch is the average of the power importance indices of its two ends. The heat importance index of a heat network node is the sum of its heat transmission efficiency and heat supply ratio.

[0066] Specifically, the power transmission efficiency of the power grid node is as follows:

[0067]

[0068] In the formula, Represents a power grid node i Power transmission efficiency; m This represents the number of nodes in the power grid. d ij Represents a node i , j The shortest impedance path length; Z m This indicates the branch impedance.

[0069] Specifically, the shortest impedance path length is calculated based on Dijkstra's algorithm, and the specific calculation method is as follows:

[0070] Based on the power grid diagram structure, the sum of the electrical impedances of the lines or transformers between nodes is used as the distance between nodes;

[0071] At the start of the calculation, a distance value is initialized for each node in the power grid model, representing the distance of that node from the source node. i Distance to the source node; i The distance to the source node is set to 0, indicating that the source node is closest to itself. The distances to all other nodes are set to infinity. The source node is then set to... i Add to the priority queue, where nodes are sorted in ascending order of distance value;

[0072] During the iteration process, retrieve the node currently furthest from the source node from the priority queue. i The node with the shortest distance k traversal k Calculate all adjacent nodes of a node that are directly connected by a line or transformer. k Node distance from source node i distance and k Point and its current adjacent nodes m The sum of the distances between adjacent nodes is used as the temporary path length; if the temporary path length is less than that of adjacent nodes... m The current record is related to the source node. i If the distance is known, then update the adjacent nodes. m With source node i The distance is the temporary path length, recording the current node. k For this adjacent node m The predecessor node, and the adjacent node. m Add to priority queue and update its distance from source node i The distance value;

[0073] Iterate until the priority queue is empty, or until the target node exists. j At that time, the target node j The process ends after retrieving the priority queue. The shortest impedance path is reconstructed by backtracking through the predecessor node, and the total impedance of this path is the target node. j To the source node i The shortest impedance path length.

[0074] The power transmission efficiency of the power grid nodes is normalized, specifically as follows:

[0075]

[0076] In the formula, Indicates the normalized i-th i The power transmission efficiency of each power grid node.

[0077] The power supply ratio of each power grid node is calculated as follows:

[0078]

[0079] In the formula, For the first in the power gridi The power supply ratio of each node, m This represents the number of nodes in the power grid. For the first i The power supply of each node, For the first j The power supply of each node.

[0080] Specifically, the power importance index of power grid nodes The calculation method is as follows:

[0081]

[0082] In the formula, For the first i Electricity importance index of each power grid node Indicates the normalized i-th i Power transmission efficiency of individual power grid nodes For the first in the power grid i The power supply ratio of each node.

[0083] The heat transfer efficiency is specifically:

[0084]

[0085] In the formula, Indicates the first i Heat transfer efficiency of each heat network node; n This refers to the number of nodes in the heating network. Indicates heating network nodes i , j Shortest flow path length; l x Indicates pipeline x The length of the pipe; m x Indicates pipeline x Pipeline mass flow rate.

[0086] Specifically, the shortest flow path length is calculated based on Dijkstra's algorithm, and the specific calculation method is as follows:

[0087] In a heating network, the product of the pipe length between nodes and the mass flow rate of hot water is used as the heating network impedance.

[0088] Based on the heat network diagram structure, the sum of the heat network impedances between nodes is used as the distance between nodes;

[0089] At the start of the calculation, a distance value is initialized for each node in the heating network model, representing the distance of that node from the source node. i Distance to the source node; iThe distance to the source node is set to 0, indicating that the source node is closest to itself. The distances to all other nodes are set to infinity. The source node is then set to... i Add to the priority queue, where nodes are sorted in ascending order of distance value;

[0090] During the iteration process, retrieve the node currently furthest from the source node from the priority queue. i The node with the shortest distance k traversal k Calculate all adjacent nodes of the node. k Node distance from source node i distance and k Point and its current adjacent nodes m The sum of the distances between adjacent nodes is used as the temporary path length; if the temporary path length is less than that of adjacent nodes... m The current record is related to the source node. i If the distance is known, then update the adjacent nodes. m With source node i The distance is the temporary path length, recording the current node. k For this adjacent node m The predecessor node, and the adjacent node. m Add to priority queue and update its distance from source node i The distance value;

[0091] Iterate until the priority queue is empty, or until the target node exists. j At that time, the target node j The process ends after retrieving the priority queue. The shortest heat network impedance path is reconstructed by backtracking through the predecessor node. The total heat network impedance of this path is the target node's impedance. j To the source node i The shortest traffic path length.

[0092] The heat transfer efficiency of the heating network nodes is normalized as follows:

[0093]

[0094] In the formula, Indicates the normalized i-th i Heat transfer efficiency of each heat network node n This represents the number of nodes in the heating network. Indicates the first i Heat transfer efficiency of each heating network node.

[0095] The heating ratio of each heating network node is calculated as follows:

[0096]

[0097] In the formula, The first in the heat network i The heating ratio of each node,n This refers to the number of nodes in the heating network. For the first i Heating capacity of each heat network node For the first j Heating power of each heat network node.

[0098] Specifically, the thermal importance index of heating network nodes The calculation method is as follows:

[0099]

[0100] In the formula, For the first i The thermal importance index of each heating network node Indicates the normalized i-th i Heat transfer efficiency of each heat network node The first in the heat network i The heating ratio of each node.

[0101] S3. Simulate the power grid and heating network models to obtain the power and heat data of each node after the fault. Combine the power importance index of each node and branch of the power grid and the heat importance index of each node of the heating network to calculate the fault characteristic data of each fault in the power grid and the heating network.

[0102] The fault characteristic data of the power grid include the maximum rate of change of node voltage, the maximum value of node voltage, the active power change coefficient of the line, the reactive power change coefficient of the line, the average rate of change of generator power, and the load loss rate.

[0103] Specifically, the calculation method is as follows:

[0104] Maximum rate of change of node voltage D u :

[0105]

[0106] In the formula, For the first i Electricity importance index of each power grid node m The number of nodes in the power grid; max(| U i t+1 - U i t | indicates a node i The maximum interval voltage between adjacent sampling points from the start of the fault to the end of data monitoring; U N Indicates the network's rated voltage; This indicates the minimum time interval for power grid data monitoring and feedback.

[0107] Maximum node voltage D um :

[0108]

[0109] In the formula, For the first i Electricity importance index of each power grid node m This represents the number of nodes in the power grid. Represents a node i The maximum voltage from the start of the fault to the end of data monitoring.

[0110] Line active power variation coefficient D p :

[0111]

[0112] In the formula, k This represents the total number of branches in the power grid. Indicates the first power grid i The power importance index of each branch line; Indicates the first i The active power of each branch during normal operation For the first time after the fault i The active power of each branch changes continuously over time. Indicates the first i The upper limit of active power of each branch; This represents the total fault observation time for the coupled system.

[0113] Line reactive power variation coefficient D q :

[0114]

[0115] In the formula, k This represents the total number of branches in the power grid. This represents the power importance index of the i-th branch of the power grid; Indicates the first i The reactive power value of each branch circuit during normal operation Indicates the first time after the fault i The reactive power of each branch changes continuously over time. Indicates the first i The upper limit of reactive power of each branch.

[0116] Average rate of change of generator power D GS :

[0117]

[0118] In the formula, g This represents the total number of generators in the power grid. Indicates the first i The active power output of the generator before the fault. Indicates the first i The change in active power output over time after a generator failure.

[0119] Electric load loss rate D lE :

[0120]

[0121] In the formula, This represents the sum of the electrical power output by all load nodes before the fault. This represents the sum of the output power of all load nodes at the end of the fault observation period.

[0122] The fault characteristic data of the heating network include the maximum rate of change of node temperature, the reciprocal mean of the maximum node temperature, the average relative rate of change of node pressure, the average total change of heat source power, and the heat load loss rate.

[0123] Specifically, the calculation method is as follows:

[0124] Maximum rate of change of node temperature D T :

[0125]

[0126] In the formula, For the first i The thermal importance index of each heating network node n The number of nodes in the heating network; max(| T i t+1 - T i t | indicates a node i The maximum temperature interval between adjacent sampling points from the onset of the fault to the system returning to stability or becoming completely out of control; T a Indicates ambient temperature; τ 1 indicates the time step of the heating network simulation.

[0127] Inverse mean of maximum node temperature D Tm :

[0128]

[0129] In the formula, For nodes i The maximum temperature from the start of the fault to the end of data monitoring.

[0130] Average relative change rate of node pressure D P :

[0131]

[0132] In the formula, ω Pi This represents the pressure weight factor, which is the same as the heat transfer efficiency of the heating network node. For the first i The node pressure value at the end of the simulation of each heating network node. The first time before the failure i Pressure values ​​at individual heating network nodes; n This indicates the total number of nodes in the heating network.

[0133] Mean of total change in heat source power D G :

[0134]

[0135] In the formula, gh Indicates the total number of heat sources in the heating network; φ i Indicates the first i The output thermal power before the heat source fails; φ i ' indicates the first i The change in output thermal power over time after a heat source failure. This represents the total fault observation time for the coupled system.

[0136] Heat load loss rate D lT :

[0137]

[0138] In the formula, This represents the sum of the thermal power output by all load nodes before the fault. This represents the sum of the output thermal power of all load nodes at the end of the simulation.

[0139] S4. Calculate the ratio of the total output power of the electric drive equipment of the heating network to the total heat load as the thermoelectric coupling strength, and calculate the fault frequency of the power grid and heating network in the previous control cycle as the fault intensity; calculate the importance weight of the power grid and heating network based on the thermoelectric coupling strength and fault intensity, and sum the fault characteristic data of the power grid and heating network according to the importance weight to obtain the final fault risk index.

[0140] Specifically, it includes the following steps:

[0141] S401. Calculate the weights of each fault characteristic data of the power grid in the power grid subsystem and the weights of each fault characteristic data of the heating network in the heating network subsystem. The calculation method is as follows:

[0142] Based on the relative importance of the indicators, judgment matrices are constructed for each fault characteristic data of the heating and power grids (the judgment matrix for the heating network is 5x5, and the judgment matrix for the power grid is 6x6). The elements in the judgment matrix... x ij Indicates the first i The first indicator is relative to the first j The importance of each indicator is evaluated using a scale from 1 to 9, where 1 indicates that both criteria are equally important, and 9 indicates that one criterion is far more important than the other. i = j hour, x ij =1, the judgment matrix is ​​the same for different faults in the same integrated thermal-electric energy system;

[0143] The analytic hierarchy process (AHP) is used in conjunction with a judgment matrix to calculate the weights of each fault characteristic data in each subsystem.

[0144] S402. Based on the weights of each fault characteristic data of the power grid in the power grid subsystem and the weights of each fault characteristic data of the heating network in the heating network subsystem, weighted fusion is performed to obtain weighted power grid fault characteristic data and weighted heating network fault characteristic data.

[0145] The weighted power grid fault characteristic data are as follows:

[0146]

[0147] In the formula, For the first i Power grid fault characteristic data corresponding to the type of fault. , , , , as well as These are the maximum rate of change of node voltage, the maximum value of node voltage, the coefficient of change of active power of the line, the coefficient of change of reactive power of the line, the average rate of change of generator power, and the load loss rate. , , , , as well as These are the weighting coefficients of the maximum rate of change of node voltage, the maximum value of node voltage, the coefficient of change of active power of the line, the coefficient of change of reactive power of the line, the average rate of change of generator power, and the load loss rate in the power grid subsystem.

[0148] The weighted heating network fault characteristic data are as follows:

[0149]

[0150] In the formula, For the first i Heat network fault characteristic data corresponding to the type of fault. , , , as well as These are the maximum rate of change of node temperature, the reciprocal mean of the maximum node temperature, the average relative rate of change of node pressure, the average total change of heat source power, and the heat load loss rate, respectively. , , , as well as These are the weighting coefficients of the maximum rate of change of node temperature, the reciprocal mean of the maximum node temperature, the average relative rate of change of node pressure, the average total change of heat source power, and the heat load loss rate in the heating network subsystem, respectively.

[0151] S403. The ratio of the total output power of electrically driven equipment in the heating network to the total heat load is taken as the thermoelectric coupling strength. The frequency of each fault occurring in a single control cycle during the simulation is taken as the fault intensity. The importance weights of the power grid and the heating network are calculated based on the thermoelectric coupling strength and the fault intensity.

[0152] Specifically, the importance weights of the power grid and the heating network are calculated based on the thermoelectric coupling strength. The calculation method is as follows:

[0153]

[0154] In the formula, Weighting for the importance of the heating network; Assigning importance weights to the power grid; For the degree of thermoelectric coupling, when v When the value is 0, meaning there is no coupling, the importance weights of the heating network and the power grid are naturally equal; k Sensitivity coefficient k The larger the tanh, the greater the tanh. kv )right v The more sensitive the change, the more suitable it is for systems where the coupling strength has a significant impact on the weights; when k The smaller the value, the smoother the weight changes, making it suitable for scenarios with mild coupling effects. To adjust the coefficient and To avoid extreme weighting.

[0155] The importance weights of the power grid and heating network are recalculated based on the fault intensity, specifically as follows:

[0156]

[0157] In the formula, This is the weighting adjustment factor. To adjust the importance weight of the heating network, The revised importance weights for the power grid;

[0158] The weight correction coefficient Based on the frequency of heating network failures in the previous control cycle Compared with the frequency of power grid faults in the previous control cycle Perform calculations; when > hour, For positive; when < hour, Negative; when = hour, =0;

[0159] Specifically, in this embodiment, Preferred options are:

[0160]

[0161] In the formula, The attenuation coefficient and This reflects the effect of thermoelectric coupling strength on The degree of attenuation; To correct the amplitude coefficient and Restricted The maximum value; v For thermoelectric coupling strength, when v =0.5, meaning when the coupling is balanced, the system is closer to an independent dual system, and the local impact of the fault is more prominent, that is, the importance weight of the thermal and electronic systems is more significantly affected by the historical fault frequency; when v As the value gradually approaches 1, the local effects of the system are masked by the overall characteristics, which means that the impact of reducing the frequency of historical failures is reduced.

[0162] The specific calculation method for the heating network fault frequency in the previous control cycle is as follows:

[0163]

[0164] In the formula, This represents the total number of all fault events that occurred in the integrated electric-thermal energy system during the previous control cycle. This represents the total number of all fault events that occurred in the heating network during the previous control cycle.

[0165] The specific calculation method for the power grid fault frequency in the previous control cycle is as follows:

[0166]

[0167] In the formula, This represents the total number of all fault events that occurred in the power grid during the previous control cycle.

[0168] S404. The weighted power grid fault characteristic data and heating network fault characteristic data are then weighted and fused according to the importance weights of the power grid and heating network to calculate the final fault risk level.

[0169] Specifically, the final failure risk index is:

[0170]

[0171] In the formula, For the first i The fault risk level of the type of fault. , The first i Power grid fault characteristic data corresponding to the first type of fault and the second type of fault i Heat network fault characteristic data corresponding to the type of fault. To adjust the importance weight of the heating network, This is the revised importance weight of the power grid.

[0172] This invention also claims protection for a quantitative assessment system for the fault impact of a thermal-electric integrated energy system based on the aforementioned method, comprising a power grid and heating network model construction module, a power grid node and branch and heating network node importance index calculation module, a power grid and heating network fault characteristic data calculation module, and a final fault risk level calculation module, characterized in that:

[0173] The power grid and heating network model building module is used to build the power grid and heating network diagram structure and the power grid and heating network model;

[0174] The module for calculating the importance index of power grid nodes and branches, and heating network nodes, is used to calculate the power transmission efficiency and power supply ratio of power grid nodes, and the heat transmission efficiency and heat supply ratio of heating network nodes based on the structure of the power grid and heating network diagrams. The power importance index of a power grid node is the sum of its power transmission efficiency and power supply ratio, and the power importance index of a power grid branch is the average of the power importance indices of its two endpoints. The heat importance index of a heating network node is the sum of its heat transmission efficiency and heat supply ratio.

[0175] The module for calculating fault characteristic data of power grid and heating network is used to simulate the power grid and heating network models, obtain the power and heat data of each node after the fault, and calculate the fault characteristic data of power grid and heating network by combining the power importance index of each node and branch of the power grid and the heat importance index of each node of the heating network.

[0176] The final fault risk level calculation module is used to calculate the ratio of the total output power of the heating network's electric drive equipment to the total heat load as the thermoelectric coupling strength, and to calculate the fault frequency of the power grid and heating network in the previous control cycle as the fault intensity. Based on the two, the importance weights of the power grid and heating network are calculated, and the fault characteristic data of the power grid and heating network are weighted and summed according to the importance weights to obtain the final fault risk level.

[0177] Examples, as shown in the appendix Figure 2 As shown, in this embodiment, there are two energy networks: a heating network and a power grid, as well as energy coupling devices such as electric boilers. The parameters of each energy network and device do not substantially affect the method proposed in this invention, and therefore will not be elaborated upon here. The total simulation time in this embodiment is 7200 seconds, and the spacetime step of the heating network simulation is... The time step for the power grid simulation is The simulation of the failure scenario where the No. 2 cogeneration unit goes offline starting from the 1800s is presented here. Due to the large amount of data, only some basic data changes before and after the failure are shown in the attached figures. Figure 3 As shown. The specific process for calculating the fault risk value corresponding to this fault is as follows:

[0178] Calculate the maximum rate of change of node voltage D u :

[0179]

[0180] Calculate the maximum node voltage D um :

[0181]

[0182] Calculate the active power variation coefficient of the line D p :

[0183]

[0184] Calculate the reactive power variation coefficient of the line D q :

[0185]

[0186] Calculate the average rate of change of generator powerD GS :

[0187]

[0188] Calculate the electrical load loss rate D lE :

[0189]

[0190] Calculate the maximum rate of change of node temperature D T :

[0191]

[0192] Calculate the inverse mean of the maximum temperature at each node. D Tm :

[0193]

[0194] Since this fault does not affect the node pressure, the average relative change rate of the node pressure is... D P =0.

[0195] Calculate the mean of the total change in heat source power. D G :

[0196]

[0197] Calculate heat load loss rate D lT :

[0198]

[0199] A fault impact assessment system was established for this system. Based on expert scoring, the preset judgment matrices for each subsystem were set as follows:

[0200]

[0201]

[0202] in, A E This represents the power subsystem judgment matrix, with the power subsystem indicators arranged from left to right as follows: D u , D um , D p , D q , DGS , D lE ; A T This represents the judgment matrix for a thermal subsystem, with the thermal subsystem indices listed from left to right as follows: D T , D Tm , D P , D G , D lT .

[0203] According to the analytic hierarchy process (AHP), for the above-mentioned electrical judgment matrix, dividing each element by the sum of all elements in its corresponding column yields its normalized judgment matrix. B E :

[0204]

[0205] The normalized judgment matrix B E Calculate the arithmetic mean of the elements in each row to obtain the weight vector of the corresponding index. The same applies to the heat judgment matrix.

[0206] In summary, the weights of each fault characteristic data in each subsystem are calculated as follows:

[0207]

[0208] Assume that for this integrated thermal-electric energy system, the standard values ​​of each key state variable are as follows:

[0209]

[0210] The calculation result for each indicator is updated as a ratio to the standard value:

[0211]

[0212] The weighted power grid fault characteristic data corresponding to this fault are calculated as follows:

[0213]

[0214] The weighted characteristic data of the heating network fault corresponding to this fault are calculated as follows:

[0215]

[0216] The importance weights of the power grid and heating network are calculated based on the thermoelectric coupling strength, which is: v =1;

[0217] Based on the frequency of heating network failures in the previous control cycle Compared with the frequency of power grid faults in the previous control cycle The importance weights for the power grid and heating network are calculated as follows:

[0218]

[0219] Final calculation fault i Risk level A i for:

[0220]

[0221] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above examples; the examples and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system, characterized in that, Includes the following steps: S1. Construct the power grid and heating network diagram structure, as well as the power grid and heating network model; S2. Based on the power grid and heat network diagrams, calculate the power transmission efficiency and power supply ratio of the power grid nodes, and the heat transmission efficiency and heat supply ratio of the heat network nodes. The power importance index of a power grid node is the sum of its power transmission efficiency and power supply ratio. The power importance index of a power grid branch is the average of the power importance indices of its two ends. The heat importance index of a heat network node is the sum of its heat transmission efficiency and heat supply ratio. S3. Simulate the power grid and heating network models to obtain the power and heat data of each node after the fault. Combine the power importance index of each node and branch of the power grid and the heat importance index of each node of the heating network to calculate the fault characteristic data of each fault in the power grid and the heating network. S4. Calculate the weight of each fault characteristic data of the power grid in the power grid subsystem, and sum them up by weight to obtain the weighted power grid fault characteristic data; calculate the weight of each fault characteristic data of the heating network in the heating network subsystem, and sum them up by weight to obtain the weighted heating network fault characteristic data. The ratio of the total output power of the electric drive equipment in the heating network to the total heat load is used as the thermoelectric coupling strength, and the fault frequency of the power grid and heating network in the previous control cycle is used as the fault intensity. The importance weights of the power grid and the heating network are calculated based on the thermoelectric coupling strength and the fault strength, respectively. The weighted power grid fault characteristic data and heating network fault characteristic data are then further weighted and fused according to the importance weights of the power grid and heating network to calculate the final fault risk index.

2. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 1, characterized in that: The power transmission efficiency is specifically as follows: In the formula, Indicates the first i Power transmission efficiency of each power grid node; m This represents the number of nodes in the power grid. d ij Represents a node i , j The shortest impedance path length; Z m This indicates the branch impedance.

3. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 2, characterized in that: The shortest impedance path length is calculated as follows: Based on the power grid diagram structure, the sum of the electrical impedances of the lines or transformers between nodes is used as the distance between nodes; At the start of the calculation, a distance value is initialized for each node in the power grid model, representing the distance of that node from the source node. i Distance to the source node; i The distance to the source node is set to 0, indicating that the source node is closest to itself. The distances to all other nodes are set to infinity. The source node is then set to... i Add to the priority queue, where nodes are sorted in ascending order of distance value; During the iteration process, retrieve the node currently furthest from the source node from the priority queue. i The node with the shortest distance k traversal k Calculate all adjacent nodes of a node that are directly connected by a line or transformer. k Node distance from source node i distance and k Point and its current adjacent nodes m The sum of the distances between them is used as the temporary path length; If the temporary path length is less than that of the adjacent nodes m The current record is related to the source node. i If the distance is known, then update the adjacent nodes. m With source node i The distance is the temporary path length, recording the current node. k For this adjacent node m The predecessor node, and the adjacent node. m Add to priority queue and update its distance from source node i The distance value; Iterate until the priority queue is empty, or until the target node exists. j To the target node j The process ends after retrieving the priority queue. The shortest impedance path is reconstructed by backtracking through the predecessor node, and the total impedance of this path is the target node. j To the source node i The shortest impedance path length.

4. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 1, characterized in that: The heat transfer efficiency is specifically: In the formula, Indicates the first i Heat transfer efficiency of each heat network node; n This refers to the number of nodes in the heating network. Indicates heating network nodes i , j Shortest flow path length; l x Indicates pipeline x The length of the pipe; m x Indicates pipeline x Pipeline mass flow rate.

5. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 2, characterized in that: The fault characteristic data of the power grid include the maximum rate of change of node voltage, the maximum value of node voltage, the active power change coefficient of the line, the reactive power change coefficient of the line, the average rate of change of generator power, and the load loss rate.

6. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 4, characterized in that: The fault characteristic data of the heating network include the maximum rate of change of node temperature, the reciprocal mean of the maximum node temperature, the average relative rate of change of node pressure, the average total change of heat source power, and the heat load loss rate.

7. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 1, characterized in that: In S4, the importance weights of the power grid and heating network are calculated based on the thermoelectric coupling strength. The specific calculation method is as follows: In the formula, Weighting for the importance of the heating network; Assigning importance weights to the power grid; Thermoelectric coupling strength; k Sensitivity coefficient To adjust the coefficient and .

8. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 7, characterized in that: The importance weights of the power grid and heating network are recalculated based on the fault intensity, specifically as follows: In the formula, This is the weighting adjustment factor. To adjust the importance weight of the heating network, The revised importance weights for the power grid; The weight correction coefficient The calculation is based on the fault intensity, which includes the frequency of heating network faults in the previous control cycle. and the frequency of power grid faults in the previous control cycle ;when > hour, For positive; when < hour, Negative; when = hour, It is 0.

9. The method for quantitatively assessing the impact of faults in a thermal-electric integrated energy system according to claim 8, characterized in that: The frequency of heating network failures in the previous control cycle The specific calculation method is as follows: In the formula, This represents the total number of all fault events that occurred in the integrated electric-thermal energy system during the previous control cycle. This represents the total number of all fault events that occurred in the heating network during the previous control cycle. The power grid fault frequency in the previous control cycle The specific calculation method is as follows: In the formula, This represents the total number of all fault events that occurred in the power grid during the previous control cycle.

10. A quantitative assessment system for the fault impact of a thermal-electric integrated energy system based on the method of any one of claims 1-9, comprising a power grid and heating network model construction module, a power grid node and branch and heating network node importance index calculation module, a power grid and heating network fault characteristic data calculation module, and a final fault risk index calculation module, characterized in that: The power grid and heating network model building module is used to build the power grid and heating network diagram structure and the power grid and heating network model; The module for calculating the importance index of power grid nodes and branches, and heating network nodes, is used to calculate the power transmission efficiency and power supply ratio of power grid nodes, and the heat transmission efficiency and heat supply ratio of heating network nodes based on the structure of the power grid and heating network diagrams. The power importance index of a power grid node is the sum of its power transmission efficiency and power supply ratio, and the power importance index of a power grid branch is the average of the power importance indices of its two endpoints. The heat importance index of a heating network node is the sum of its heat transmission efficiency and heat supply ratio. The module for calculating fault characteristic data of power grid and heating network is used to simulate the power grid and heating network models, obtain the power and heat data of each node after the fault, and calculate the fault characteristic data of power grid and heating network by combining the power importance index of each node and branch of the power grid and the heat importance index of each node of the heating network. The final fault risk index calculation module is used to calculate the weight of each fault characteristic data of the power grid in the power grid subsystem, and to sum them up by weight to obtain the weighted power grid fault characteristic data; it also calculates the weight of each fault characteristic data of the heating network in the heating network subsystem, and to sum them up by weight to obtain the weighted heating network fault characteristic data. The ratio of the total output power of the electric drive equipment in the heating network to the total heat load is used as the thermoelectric coupling strength, and the fault frequency of the power grid and heating network in the previous control cycle is used as the fault intensity. The importance weights of the power grid and the heating network are calculated based on the thermoelectric coupling strength and the fault strength, respectively. The weighted power grid fault characteristic data and heating network fault characteristic data are then further weighted and fused according to the importance weights of the power grid and heating network to calculate the final fault risk index.

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