A method for quantifying operation risk and risk mitigation of a regional integrated energy system

Abstract

The present application relates to a kind of quantification regional integrated energy system operation risk and the method for stabilizing risk, comprising the following steps: step 1, the deterministic analysis of RIES operating state is carried out;Step 2, a large number of random operation scenarios are generated according to the original data obtained in step 1, a risk index system is constructed, and the initial state of the system is selected using non-sequential Monte Carlo simulation technology, after the state of the system is updated according to the failure element, the calculation result of the system is obtained using deterministic flow calculation, and the relationship between the variance coefficient and the given threshold is judged after being recorded;If the variance coefficient of all state variables is less than the given threshold, then end the simulation process and calculate the risk index;Step 3, in the face of multiple types of risks, build an incident risk list to locate the root fault and analyze the weak link, and find out the potential threats in the system from the perspectives of risk sources and consequences.The present application can provide decision support for operation and dispatch personnel.

Description

Technical Field

[0001] This invention belongs to the field of risk assessment technology for regional integrated energy systems, and in particular, it is a method for quantifying and mitigating the operational risks of regional integrated energy systems. Background Technology

[0002] Regional Integrated Energy Systems (RIES), a concept originating from the Energy Internet, are considered a promising technology by many countries to strengthen the connections between various energy carriers. In cities, RIES are located on the energy load side, covering several square kilometers to meet the diverse energy needs of regional users such as functional buildings or neighborhoods. Based on geographical factors, RIES integrate power distribution systems, medium- and low-pressure gas systems, and hot and cold water systems into a unified whole, demonstrating flexibility and synergy among multiple energy systems. However, the complex interactions between these subsystems can pose significant challenges in assessing the potential operational risks of RIES. Therefore, more effective methods are needed to explore the interaction mechanisms and operational risks within RIES.

[0003] Currently, most studies on risk assessment mainly use various complex optimization models to guide system scheduling after an accident. The severity of the risk mainly depends on the load shedding and operating costs. However, for RIES composed of three subsystems (electric, thermal, and gas), few studies have focused on analyzing the impact of fault propagation on system operation. Although some studies have considered the uncertainty of component failure, they have not systematically explained the interaction process and propagation law between them, nor have they been able to quantitatively give the consequences of each type of failure on RIES.

[0004] A search revealed no prior art documents that are identical or similar to this invention. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and propose a method for quantifying and mitigating the operational risks of a regional integrated energy system, which can provide decision support for operation and dispatch personnel.

[0006] The present invention solves its practical problem by adopting the following technical solution:

[0007] A method for quantifying and mitigating the operational risks of a regional integrated energy system includes the following steps:

[0008] Step 1: Input the raw data of each energy system and perform deterministic analysis on the operating status of RIES;

[0009] Step 2: Generate a large number of random operating scenarios based on the raw data obtained in Step 1, construct a risk indicator system, and select the initial state of the system using non-sequential Monte Carlo simulation technology. After updating the system state based on the faulty components, use deterministic power flow calculation to obtain the system calculation results. After recording them, determine the relationship between the variance coefficient and the given threshold. If the variance coefficients of all state variables are less than the given threshold, then end the simulation process and calculate the risk indicators; otherwise, more samples need to be drawn.

[0010] Step 3: In the face of various types of risks, construct an accident risk list to locate the root cause of the failure and analyze the weak links, and identify potential threats in the system from the perspectives of risk source and consequences.

[0011] Moreover, the specific method of step 1 is as follows:

[0012] (1) Input the grid information of each energy system, multi-element load curves, probability distribution of distributed energy and component unavailability data;

[0013] (2) Analyze the consequences of various faults on RIES, and use the deterministic performance flow calculation method to analyze the electrical and thermal flow distribution after the fault.

[0014] Furthermore, the risk indicator system in step 2 includes:

[0015] 1) Branch overload severity function

[0016] The actual transmission capacity F of the line i and maximum transmission energy F i，max The ratio of F is denoted as f1, as shown in equation (1). The overload severity function can be defined using f1, where f1 is the ratio of F to F1. i From 0 to f1F i，max When F is between, the risk of the line exceeding the limit is 0, but when F i Exceeding f1F i，max At that time, the severity of overload will increase with F i It increases linearly with the increase of .

[0017] f1 = F i / F i，max (1)

[0018] 2) Node Exceedance Severity Function

[0019] The limits of node state variables are divided into two types: exceeding the upper limit and exceeding the lower limit. We can define the upper and lower limit ranges of node i's state variables as [F...]. i，min F i，max ], midpoint F i，mid and width w iIt can be calculated from equation (2). Let f2 be a proportionality coefficient close to 1, and the actual safe operating range of node i is [F i，mid +f2w i F i，mid -f2w i ], when F i If a node is outside the safe operating range, the severity function for exceeding the limit will increase linearly.

[0020] F i，mid =(F i，max +F i，min ) / twenty two)

[0021] w i =(F i，max -F i，min ) / twenty three)

[0022] The above two types of severity functions are applied to power grids, heating networks and gas networks; variables including bus voltage, power supply temperature of heating nodes and pressure of gas nodes can be used to quantify the severity of node overload, as shown in equations (4)-(6); variables including power flow, mass flow and gas flow can be used to quantify the severity of branch overload, as shown in equations (7)-(9).

[0023]

[0024] in This represents the transmission capacity of the line between buses i and j. This indicates the transmission capacity limit of the line, f1 E This indicates the load ratio coefficient of the line. This indicates the total number of lines in the power grid.

[0025]

[0026] Where V i V represents the voltage magnitude of bus i. i，max V i，min V represents the maximum and minimum allowable voltage amplitude of bus i. i，mid It is V i，max V i，min Midpoint between; f2 E w represents the voltage proportionality coefficient of the busbar. i E V represents i，max With V i，min Width of space This indicates the total number of busbars in the power grid;

[0027]

[0028] Where mij The actual mass flow rate of the heating pipeline, in m ij，max f1 represents the maximum permissible mass flow rate of the heating pipe. H This represents the mass flow rate proportionality coefficient of the pipeline. This indicates the total number of pipes in the heating network;

[0029]

[0030] Where T i T represents the water supply temperature at thermal node i. i，max T i，min T represents the maximum and minimum allowable water supply temperatures for thermal node i. i，mid It is T i，max T i，min The midpoint, f2 H The proportionality constant representing the temperature of a thermal node, w i H T represents i，max With T i，min Width of space This represents the total number of nodes in the heating network;

[0031]

[0032] Where g ij g represents the actual gas flow rate of the gas pipeline. ij，max f1 represents the maximum permissible gas flow rate of the gas pipeline. G This represents the proportionality coefficient of gas flow rate in the pipeline. This indicates the total number of pipes in the gas network.

[0033]

[0034] Where P i G This indicates the gas pressure at gas node i. This indicates the maximum and minimum allowable gas pressure at gas node i. yes The midpoint, f2 G This represents the gas pressure proportionality coefficient of the gas node. express and Width between;

[0035] In addition, some accidents can cause load nodes to lose their connection with source nodes and fail to meet the energy demand of users. The degree of load loss of electricity, heat and gas users can be measured by Eqs.(24)-(26).

[0036]

[0037]

[0038]

[0039] in This indicates the load loss of electricity, heat, and gas systems. This represents the set of passive, isolated nodes in an electrical, thermal, or gas system. This represents the load value of the user at node i.

[0040] The risk index is defined as the product of the probability of an accident occurring and its degree of impact. Combining this with the severity function above, we obtain the following nine categories of risk indices:

[0041] (1) Voltage Violation Risk Index (VVRI)

[0042]

[0043] (2) Line Overload Risk Index (LORI)

[0044]

[0045] (3) Electric Load Loss Index (ELLI)

[0046]

[0047] (4) Supply temperature Violation Risk Index (STVRI)

[0048]

[0049] (5) Heating Pipeline Overload Risk Index (HPORI)

[0050]

[0051] (6) Heat Load Loss Index (HLLI)

[0052]

[0053] (7) Pressure Violation Risk Index (PVRI)

[0054]

[0055] (8) Gas Pipeline Overload Risk Index (GPORI)

[0056]

[0057] (9) Gas Load Loss Index (GLLI)

[0058]

[0059] Among them, P r (E i ) indicates an emergency E i The probability of occurrence, Sev(X, E) i ) indicates that in event E i The severity of the system variable X exceeding its limits.

[0060] In the Monte Carlo simulation algorithm, the probability of the occurrence of a system state can be estimated by the sampling frequency. When the sample is large enough, the sampling frequency can be regarded as an unbiased estimate of the probability of occurrence, as shown in equation (22).

[0061]

[0062] Where M represents the total number of samples. Indicates drawing event E i The number of times.

[0063] Furthermore, the accident risk list in step 3 includes:

[0064] (1) Node risk indicators include: over-limit risk indicators at each bus, heating node and gas node;

[0065] (2) Branch risk indicators include: over-limit risk indicators for each power line, heating pipeline and gas pipeline;

[0066] (3) Load loss indicators include: electricity, heat and gas load loss indicators; and those in the risk table. and These represent the over-limit risk index and load loss index at node k under accident j, respectively. This indicates the overload risk index of branch k under accident j;

[0067] (4) By adding the risk indicators in the table horizontally, the overall operational risk caused by each accident can be quantified.

[0068] Advantages and beneficial effects of the present invention:

[0069] 1. This invention constructs risk indicators applicable to regional integrated energy systems to quantify the operational risks of the system. In step 2, the branch overload severity function and the node limit exceedance severity function are applied to the electricity-heat-gas regional integrated energy system, respectively. A series of risk indicators are constructed based on the probability of accidents: voltage limit exceedance risk indicators, line overload risk indicators, and power load loss indicators can be used to measure the operational risks of the power grid; heating temperature risk indicators, heat pipeline overload risk indicators, and heat load loss indicators can be used to measure the operational risks of the heating network; and gas pressure limit exceedance risk indicators, gas pipeline overload risk indicators, and gas load loss indicators can be used to measure the operational risks of the gas network. This invention transforms the threats posed by uncertainties such as equipment failure and source-load fluctuations to the operational safety of the electricity, gas, and heating subsystems in RIES into quantitative indicators, providing decision support for operation and dispatch personnel.

[0070] 2. This invention proposes specific ideas and methods for mitigating risks in regional integrated energy systems. After expanding the risk indicators into a risk list in step 3, the root cause of the system failure and weak link analysis can be performed. The problems causing system operation risks can be found from both the source and consequences of the risks, thereby assisting in the accurate formulation of risk mitigation strategies. Attached Figure Description

[0071] Figure 1 This is a flowchart of the multi-energy power flow discrete solution algorithm in RIES of the present invention;

[0072] Figure 2 This is a schematic diagram of the overload severity function of the present invention;

[0073] Figure 3 This is a schematic diagram of the severity function of exceeding the limit in this invention;

[0074] Figure 4 This is a flowchart of the RIES operational risk assessment algorithm of the present invention;

[0075] Figure 5 This is a schematic diagram of the present invention for identifying root cause faults and weak points;

[0076] Figure 6 This is a schematic diagram of the computational topology of the present invention;

[0077] Figure 7 This is a schematic diagram illustrating the operational risks of various busbars and lines in the power grid according to the present invention;

[0078] Figure 8This is a schematic diagram illustrating the operational risks of various nodes and pipelines in the heating network of this invention;

[0079] Figure 9 This is a schematic diagram illustrating the operational risks of various nodes and pipelines in the gas network of this invention. Detailed Implementation

[0080] The embodiments of the present invention will be further described in detail below:

[0081] A method for quantifying and mitigating the operational risks of a regional integrated energy system includes the following steps:

[0082] Step 1: Input the raw data of each energy system and perform deterministic analysis on the operating status of RIES;

[0083] The specific method for step 1 is as follows:

[0084] (1) Input the grid information of each energy system, multi-element load curves, probability distribution of distributed energy and component unavailability data;

[0085] (2) Analyze the consequences of various faults on RIES, and use the deterministic performance flow calculation method to analyze the electrical and thermal flow distribution after the fault.

[0086] In this embodiment, to conduct a risk assessment, the consequences of various faults on RIES must first be analyzed. This invention employs a deterministic performance flow calculation method to analyze the electrical and thermal flow distribution after a fault, as detailed in [link to documentation]. Figure 1 The thick-lined boxes represent the energy flow solutions for the heating network, power grid, and gas network; the dashed boxes represent the interaction of information between coupling links and systems; nFlag indicates the type of system coupling mode; K1 represents the number of electrical coupling iterations; K2 represents the number of electrothermal coupling iterations; K1Max represents the maximum number of electrical coupling iterations; and K2Max represents the maximum number of electrothermal coupling iterations.

[0087] First, the calculation order for the heating network and the power grid can be determined based on the operating mode of the electro-thermal coupling equipment. If all equipment operates in a "heat-driven power" mode, calculations can be performed in the order of nFlag=1. If all equipment operates in a "power-driven heat" mode, calculations can be performed in the order of nFlag=2. If both operating modes exist in the equipment, there will be unknown data regardless of which system's energy flow is calculated first. In this case, initial values ​​need to be set first, and electro-thermal iterative solutions should be performed (see the flowchart for nFlag=3). It is worth noting that if an electric compressor is present, the energy flow calculation results of the gas network will, in turn, affect the input data of the power grid. In this case, electro-gas iterative solutions are required.

[0088] Step 2: Generate a large number of random operating scenarios based on the raw data obtained in Step 1, construct a risk indicator system, and select the initial state of the system using non-sequential Monte Carlo simulation technology. After updating the system state based on the faulty components, use deterministic power flow calculation to obtain the system calculation results. After recording them, determine the relationship between the variance coefficient and the given threshold. If the variance coefficients of all state variables are less than the given threshold, then end the simulation process and calculate the risk indicators; otherwise, more samples need to be drawn.

[0089] The risk indicator system in step 2 includes:

[0090] In this embodiment, the function characterizing the system's operating state is called the severity function, which is the basis for calculating risk indicators. This invention uses two linear severity functions to describe the risks of branch overload and node exceeding limits. Severity functions include linear, quadratic, and exponential types, etc. Different types of severity functions are more sensitive to certain different regions of the system in the algorithm.

[0091] 3) Branch overload severity function

[0092] The actual transmission capacity F of the line can be... i and maximum transmission energy F i，max The ratio is denoted as f1, as shown in equation (1). The overload severity function can be defined using f1, see... Figure 2 When F i From 0 to f1F i，max When F is between, the risk of the line exceeding the limit is 0, but when F i Exceeding f1F i，max At that time, the severity of overload will increase with F i It increases linearly with the increase of .

[0093] f1 = F i / F i，max (1)

[0094] 4) Node Exceedance Severity Function

[0095] The limits of node state variables are divided into two types: exceeding the upper limit and exceeding the lower limit. We can define the upper and lower limit ranges of node i's state variables as [F...]. i，min F i，max ], midpoint F i，mid and width w i It can be calculated from equation (2). Let f2 be a scaling factor close to 1, then the actual safe operating range of node i is [F i，mid +f2w i F i，mid -f2w i ], when F iIf a node is outside the safe operating range, the severity function of exceeding the limit will increase linearly, such as... Figure 3 As shown

[0096] F i，mid =(F i，max +F i，min ) / twenty two)

[0097] w i =(F i，max -F i，min ) / twenty three)

[0098] The above two types of severity functions can be applied to power grids, heating networks and gas networks; variables including bus voltage, power supply temperature of heating nodes and pressure of gas nodes can be used to quantify the severity of node overload, as shown in equations (4)-(6); variables including power flow, mass flow and gas flow can be used to quantify the severity of branch overload, as shown in equations (7)-(9).

[0099]

[0100] in This represents the transmission capacity of the line between buses i and j. This indicates the transmission capacity limit of the line, f1 E This indicates the load ratio coefficient of the line. This indicates the total number of lines in the power grid.

[0101]

[0102] Where V i V represents the voltage magnitude of bus i. i，max V i，min V represents the maximum and minimum allowable voltage amplitude of bus i. i，mid It is V i，max V i，min Midpoint between; f2 E w represents the voltage proportionality coefficient of the busbar. i E V represents i，max With V i，min Width of space This indicates the total number of busbars in the power grid.

[0103]

[0104] Where m ij The actual mass flow rate of the heating pipeline, in m ij，max f1 represents the maximum permissible mass flow rate of the heating pipe. H This represents the mass flow rate proportionality coefficient of the pipeline. This indicates the total number of pipes in the heating network.

[0105]

[0106] Where T i T represents the supply water temperature (or return water temperature) of thermal node i. i，max T i，min T represents the maximum and minimum allowable water supply temperatures for thermal node i. i，mid It is T i，max T i，min The midpoint, f2 H The proportionality constant representing the temperature of a thermal node, w i H T represents i，max With T i，min Width of space This represents the total number of nodes in the heating network.

[0107]

[0108] Where g ij g represents the actual gas flow rate of the gas pipeline. ij，max f1 represents the maximum permissible gas flow rate of the gas pipeline. G This represents the proportionality coefficient of gas flow rate in the pipeline. This indicates the total number of pipes in the gas network.

[0109]

[0110] Where P i G This indicates the gas pressure at gas node i. This indicates the maximum and minimum allowable gas pressure at gas node i. yes The midpoint, This represents the gas pressure proportionality coefficient of the gas node. express and Width between.

[0111] In addition, some accidents can cause load nodes to lose their connection with source nodes and fail to meet the energy demand of users. The degree of load loss on the user side for electricity, heat, and gas can be measured by Eqs.(24)-(26).

[0112]

[0113]

[0114]

[0115] in This indicates the load loss of electricity, heat, and gas systems. This represents the set of passive, isolated nodes in an electrical, thermal, or gas system. This represents the load value of the user at node i.

[0116] Risk indicators can be defined as the product of the probability of an accident occurring and its degree of impact. Combining this with the severity function above, we obtain the following nine categories of risk indicators:

[0117] (1) Voltage Violation Risk Index (VVRI)

[0118]

[0119] (2) Line Overload Risk Index (LORI)

[0120]

[0121] (3) Electric Load Loss Index (ELLI)

[0122]

[0123] (4) Supply temperature Violation Risk Index (STVRI)

[0124]

[0125] (5) Heating Pipeline Overload Risk Index (HPORI)

[0126]

[0127] (6) Heat Load Loss Index (HLLI)

[0128]

[0129] (7) Pressure Violation Risk Index (PVRI)

[0130]

[0131] (8) Gas Pipeline Overload Risk Index (GPORI)

[0132]

[0133] (9) Gas Load Loss Index (GLLI)

[0134]

[0135] Among them, P r (E i ) indicates an emergency E i The probability of occurrence, Ω ctg Let Sev(X, E) represent the set of incidents. i ) indicates that in event E i The severity of the system variable X exceeding its limits.

[0136] In the Monte Carlo simulation algorithm, the probability of the occurrence of a system state can be estimated by the sampling frequency. When the sample is large enough, the sampling frequency can be regarded as an unbiased estimate of the probability of occurrence, as shown in equation (22).

[0137]

[0138] Where M represents the total number of samples. This indicates that the accident E was drawn. i The number of times.

[0139] By combining deterministic analysis of RIES states with the proposed risk indicators, a method for assessing RIES operational risks that considers cross-system failures can be developed, such as... Figure 4 As shown.

[0140] Step 3: In the face of various types of risks, construct an accident risk list to locate the root cause of the failure and analyze the weak links, and identify potential threats in the system from the perspectives of risk source and consequences.

[0141] In this embodiment, facing various types of risks, potential threats in the system can be identified from the perspectives of root cause failures and weak points. This is of great significance for developing targeted preventative measures. Root cause failures refer to component failures that lead the system into a serious accident state. Weak points refer to nodes or branches that are significantly damaged. A system can be constructed as follows: Figure 5 The accident risk list shown is used to locate the root cause of the failure and analyze the weak points.

[0142] The accident risk list in step 3 includes:

[0143] (1) Node risk indicators include: over-limit risk indicators at each bus, heating node and gas node;

[0144] (2) Branch risk indicators include: over-limit risk indicators for each power line, heating pipeline and gas pipeline;

[0145] (3) Load loss indicators include: electricity, heat and gas load loss indicators; and those in the risk table. and These represent the over-limit risk index and load loss index at node k under accident j, respectively. This indicates the overload risk index of branch k under accident j;

[0146] (4) In order to enable comparison and weighting of different types of risk indicators, each column in the risk list needs to be normalized as follows:

[0147]

[0148] Where, x ij This represents the i-th element of the j-th column vector in the matrix. and These represent the maximum and minimum values ​​in the j-th column vector, respectively. It is x ij The normalized value.

[0149] (5) By adding the normalized risk indicators in the table horizontally, the overall operational risk caused by each accident can be quantified.

[0150] Based on the magnitude of the overall risk indicators, the impact of an accident on each subsystem can be viewed macroscopically, with the most severe accidents identified as root cause failures. Similarly, summing each column in the list yields the expected risk value for each node and branch, reflecting its degree of damage and aiding in identifying weak points.

[0151] The invention will be further illustrated below with specific examples:

[0152] The example section of this invention comprises a regional integrated energy system consisting of an IEEE 33-node power distribution network, a Bali 32-node regional heating network, and a 36-node single-stage medium-pressure gas pipeline network. The specific network topology is described in [link to network diagram]. Figure 6 The main description of the system is as follows:

[0153] 1) The balancing node E1 of the power grid is connected to the upstream power grid, and its unavailability is set to 0; G22 and G36, as pressure points, are always maintained at 3.6 × 10⁻⁶. 5 Pa's gas source node

[0154] 2) G35 supplies gas to the gas-fired boiler and gas turbine unit, which in turn supply power to H32 and E25, respectively; G34 supplies gas to the CHP unit, which in turn supplies power to E18 and H31, respectively.

[0155] 3) The heat pump receives electrical energy from E1 and provides thermal power to H1, which is a thermal relaxation node. Photovoltaic power generation system and wind power generation system are installed at E30 and E31, respectively.

[0156] 4) The circulating pump consumes electrical energy from E1 and E22 to maintain sufficient head pressure between the water supply network and the return network; the compressor consumes electrical energy from E1 and E33 to provide sufficient gas pressure for long-distance gas transmission.

[0157] The component unavailability data in the example section are all from the literature, with a given coefficient of variance of 2 × 10⁻⁶. -3 The proposed algorithm framework is used to conduct a risk assessment of the system.

[0158] After normalizing each type of calculated risk indicator, it can be processed according to... Figure 5 Organizing these data in a structured manner allows for the generation of an incident ranking table where the overall risk index (the sum of the nine standardized risk categories) is not less than 1, as shown in the appendix table. These incidents can be considered root cause failures. It should be noted that some incidents can have similar effects on RIES, for example:

[0159] After the gas pipelines 9 and 12 in accidents 3 and 4 are damaged, G34 is isolated. Since it is connected to a critical gas load (accounting for about 80% of the total gas load), it will cause a high GLLI. On the other hand, accidents 3 and 4 will also trigger supply shortage faults that affect the normal operation of CHP, thereby creating overload and limit risks in the power and heating networks.

[0160] The impacts of accidents 7, 8, 9, and 11 on RIES are similar. They all damage branches E26-E33 of the power grid, resulting in power load loss. In addition, since E33 is a coupling node, the accident will trigger a supply shortage fault, causing the voltage station to shut down. This will affect the normal operation of gas source G22, leading to the risk of low gas pressure in the gas network.

[0161] Risk indicators for each node and branch, such as Figures 7-9 As shown in the figure, weak points can be identified. Table 1 summarizes the root cause failures and weak points, where the root cause failure numbers correspond to the incident numbers in the appendix table. The earlier the root incident is listed, the greater its impact on the weak point.

[0162] Table 1 summarizes the root causes and weak points in the example:

[0163] Table 1 Summary of root cause faults and weak points in the case study

[0164]

[0165]

[0166] Some operators focus on the extent of damage to a specific energy system, while others may be more concerned with the consequences of an incident on the entire RIES. Therefore, different risk mitigation plans can be developed for different operating departments. For example, if the goal is only to mitigate operational risks within the RIES's network, the focus can be on root cause failures and weaknesses that lead to higher VVRI, LORI, and ELLI. If the aim is to mitigate overall RIES operational risks, measures can be developed targeting root cause failures that simultaneously exacerbate the operational risks of multiple energy systems. Following this line of thought, precise control measures can be formulated.

[0167] Table 2. Measures and plans for risk mitigation from different perspectives.

[0168]

[0169] Since many nodes in the power and gas networks are at risk of exceeding limits, the power supply voltage can be increased from 1.0 pu to 1.02 pu, and the gas source pressure can be increased from 0.36 MPa to 0.38 MPa. Redundancy should also be implemented in the source-side outlet lines or pipelines to improve the quality and reliability of the power supply. In addition, the risk of low water supply temperature at H31 can be mitigated by improving the reliability of CHP heating.

[0170] For heavily loaded lines and pipes, such as power line 1, heating pipe 32, and gas pipes 4 and 22, line and pipe models with larger transmission capacity can be selected. Note that the shutdown of gas pipes 11 and 38 would pose a serious overload risk; therefore, redundancy needs to be implemented for them to ensure that the flow distribution of the gas network does not experience significant disturbances.

[0171] The busbars located on branch lines E26-E33 in the power grid are prone to load loss risks. A tie line can be installed between E18 and E33. When a line fault occurs on branch lines E26-E33, this tie line can be closed, effectively ensuring uninterrupted load supply. Redundancy in pipelines connecting critical loads can mitigate load loss risks in gas and heating networks.

[0172] Therefore, risk mitigation schemes can be developed for the power grid, heating network, gas network, and RIES, as shown in Table 2. The four schemes were applied to the RIES for risk assessment, and the results are shown in Table 3. The bolded data in each column of Cases 1-3 represent the risk indicators that should be prioritized for improvement in that scenario. It can be seen that these indicators show a significant reduction compared to the risk outcome without improvement measures. It is worth noting that some other risk indicators in Cases 1-3 are also lower than their corresponding indicators without improvement measures, such as PVRI in Case 1 and VVRI in Case 2 and Case 3. This indicates that focusing on improving the risk of a particular system will also lead to varying degrees of reduction in the risk of other energy systems.

[0173] After mitigating the risk of RIES from a global perspective, most risk indicators in the system are lower than those without improvement measures. It can be seen that the bolded portions in Case 4 are very similar to the bolded portions in Cases 1-3 on the same line. For example, for LORI, the value after implementing improvements to RIES is 1.146%, while the value after implementing improvements to the power grid is 1.521%. This indicates that the effect of mitigating the risk of RIES from a global perspective is no less than the effect of mitigation from a single system perspective.

[0174] Table 3 Comparison of risk assessment results after the implementation of four improvement measures.

[0175]

[0176] Appendix Table

[0177]

[0178]

[0179]

[0180] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A method for quantifying and mitigating the operational risks of a regional integrated energy system, characterized in that: Includes the following steps: Step 1: Input the raw data of each energy system to conduct a deterministic analysis of the regional integrated energy system's operating status; Step 2: Generate a large number of random running scenarios based on the raw data obtained in Step 1, construct a risk indicator system, select the initial state of the system using non-sequential Monte Carlo simulation technology, update the system state based on the faulty components, obtain the system calculation results using deterministic power flow calculation, record them, and then determine the relationship between the variance coefficient and the given threshold. If the variance coefficients of all state variables are less than a given threshold, then the simulation process ends and the risk index is calculated; otherwise, more samples need to be drawn. Step 3: In the face of various types of risks, construct an accident risk list to locate the root cause of failures and analyze the weak links, and identify potential threats in the system from the perspectives of risk sources and consequences. The specific method for step 1 is as follows: (1) Input the grid information of each energy system, multi-element load curves, probability distribution of distributed energy and component unavailability data; (2) Analyze the consequences of various faults on the regional integrated energy system, and use the deterministic performance flow calculation method to analyze the electrical and thermal flow distribution after the fault; The risk indicator system in step 2 includes: 1) Branch overload severity function The actual transmission capacity of the line and maximum transmitted energy The ratio is denoted as See equation (1); using Define an overload severity function, when From 0 When the line is in between, the risk of exceeding the limit is 0, but when Exceed At that time, the severity of overload will increase. It increases linearly with the increase of ; (1) 2) Node Exceedance Severity Function The limits of node state variables are divided into two types: exceeding the upper limit and exceeding the lower limit. Let the upper and lower limits of the state variables of node i be... ,midpoint and width It is calculated from equation (2) and formula (3); let With a scaling factor close to 1, the actual safe operating range of node i is ,when If a node is outside the safe operating range, the severity function for exceeding the limit will increase linearly. (2) (3) The above two types of severity functions are applied to power grids, heating networks and gas networks; variables including bus voltage, water supply temperature of heating nodes and pressure of gas nodes are used to quantify the severity of node overload, as shown in equations (5), (7), and (9); variables including power flow, mass flow and gas flow are used to quantify the severity of branch overload, as shown in equations (4), (6), and (8). (4) in, This indicates the total number of lines in the power grid; (5) in This represents the voltage amplitude at bus i. Indicates the maximum and minimum allowable voltage amplitudes of bus i. yes Midpoint between; This represents the voltage proportionality coefficient of the busbar. express and Width of space This indicates the total number of busbars in the power grid; (6) in, This represents the mass flow rate proportionality coefficient of the pipeline. This indicates the total number of pipes in the heating network; (7) in This represents the water supply temperature at thermal node i. This represents the maximum and minimum allowable water supply temperatures for thermal node i. yes The midpoint, The proportionality coefficient representing the temperature of a thermal node. express and Width of space This represents the total number of nodes in the heating network; (8) in, This represents the proportionality coefficient of gas flow rate in the pipeline. This indicates the total number of pipes in the gas network; (9) in This indicates the gas pressure at gas node i. This indicates the maximum and minimum allowable gas pressure at gas node i. yes The midpoint, This represents the gas pressure proportionality coefficient of the gas node. express and Width between; In addition, some accidents may cause load nodes to lose contact with source nodes and fail to meet the energy demand of users. The degree of load loss of electricity, heat and gas users is measured by formula (10) – (12). (10) (11) (12) in This indicates the load loss of electricity, heat, and gas systems. This represents the set of passive, isolated nodes in an electrical, thermal, or gas system. This represents the load value of the user at node i; The risk index is defined as the product of the probability of an accident occurring and its degree of impact. Combining this with the severity function above, we obtain the following nine categories of risk indices: (1) Voltage over-limit risk indicators (13) (2) Line overload risk indicators (14) (3) Power load loss index (15) (4) Exceeding heating temperature limits (16) (5) Overload risk indicators for heating pipelines (17) (6) Thermal load loss index (18) (7) Pressure Exceedance Risk Indicators (19) (8) Gas pipeline overload risk indicators (20) (9) Gas load loss index (21) in, Indicates an emergency The probability of occurrence Indicates in the event Lower system variables The severity of the violation; In the Monte Carlo simulation algorithm, the probability of the system state is estimated by the sampling frequency. When the sample is large enough, the sampling frequency is regarded as an unbiased estimate of the probability of occurrence, as shown in equation (22). (22) Where M represents the total number of samples. Indicates the event of drawing. The number of times.

2. The method for quantifying and mitigating the operational risks of a regional integrated energy system according to claim 1, characterized in that: The accident risk list in step 3 includes: (1) Node risk indicators include: over-limit risk indicators at each busbar, heating node and gas node; (2) Branch risk indicators include: over-limit risk indicators of each power line, heating pipeline and gas pipeline; (3) Load loss indicators include: electricity, heat and gas load loss indicators; and indicators in the risk table. ,and These represent the over-limit risk index and load loss index at node k under accident j, respectively. This indicates the overload risk index of branch k under accident j; (4) Add the risk indicators in the table horizontally to quantify the overall operational risk caused by each accident.

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