Comprehensive energy system operation reliability rapid evaluation method based on feasible region matching

Abstract

The invention relates to the technical field of energy system reliability analysis, and discloses a feasible region matching-based comprehensive energy system operation reliability rapid evaluation method, which comprises the following steps of 1, acquiring basic data; step 2, constructing a fault probability model and calculating a time-varying fault probability of the equipment at an evaluation moment; 3, randomly generating a system state based on the probability; 4, calculating feasible regions of an energy supply side and a load use side in the state; step 5, matching the two feasible regions, judging whether the system is sufficient or not according to a matching result, if yes, turning to step 7, and if not, executing step 6 to calculate the load cutting capacity; 7, calculating a convergence criterion factor; 8, judging whether the factor is smaller than a threshold value or not; if so, executing the step 9 to calculate a system reliability index at the current moment; otherwise, returning to the step 3 to regenerate the state and circularly executing the steps 3-7. According to the method, the operation reliability evaluation of the integrated energy system can be accurately and efficiently completed.

Description

Technical Field

[0001] This invention relates to the field of energy system reliability analysis technology, and specifically to a rapid assessment method for the operational reliability of a comprehensive energy system based on feasible domain matching. Background Technology

[0002] Under the dual-carbon development goals, traditional thermal power plants are facing the dual challenges of emission reduction pressure and efficiency improvement. To achieve a green and low-carbon transformation, direct participation in the construction of integrated energy systems in industrial parks has become a key direction for the upgrading and efficient utilization of thermal power plants. An integrated energy system in an industrial park is an integrated energy supply and utilization system designed to improve the reliability and flexibility of the energy system by leveraging smart grid technology, demand-side management, and decentralized coordination to achieve the coordinated operation of multiple energy forms (such as electricity, cooling, and heat) in energy production, transmission, distribution, and utilization. The energy conversion process of an integrated energy system in an industrial park is typically described using an energy hub model. The energy hub, through equipment such as electric heat pumps, combined heat and power units, and chillers, converts input electricity and natural gas into various energy forms such as electricity, heat, and cooling to meet diverse end-user load demands. However, integrated energy systems in industrial parks still face multiple uncertainties during operation, including unexpected equipment failures and fluctuations in renewable energy output. Multi-energy coupling structures provide pathways for the propagation of faults between different energy systems, posing a significant threat to the reliable operation of the system; the uncertain output of external new energy sources further exacerbates the operational risks of the system.

[0003] While some research progress has been made in the reliability assessment of integrated energy systems, existing technologies still have the following shortcomings:

[0004] (1) Regarding the assessment scale: Existing reliability assessment methods for integrated energy systems in industrial parks mainly focus on medium- and long-term steady-state reliability analysis, making it difficult to quantify the time-varying reliability level under real-time operating conditions. The flexible operation mode of energy conversion equipment, the time-varying failure probability, and the high flexibility of user-side demand response in integrated energy systems in industrial parks have a significant impact on the short-term operation of the system and have strong time coupling characteristics. (2) Existing assessment methods usually rely on nonlinear optimal load reduction optimization models to calculate and analyze each possible system state during the operation phase. However, due to the large number of system states, the analysis process is often time-consuming, making it difficult to meet the computation time requirements at the operational level and thus difficult to apply in actual engineering. (3) In addition, there is currently a lack of a time-varying failure probability model for integrated energy system equipment in industrial parks that can describe the coupling effects of service life, seismic vulnerability, and external time-varying meteorological environment, making it difficult to provide a reasonable equipment model basis for system operation reliability assessment.

[0005] Therefore, accurately assessing the operational reliability of the integrated energy system in the park under time-varying conditions and quantitatively analyzing the impact of various internal and external factors on the reliable operation of the integrated energy system in the park, so as to provide a quantitative decision-making basis for risk prevention and control and operation optimization of the integrated energy system in the park, has become one of the key issues facing power companies in promoting the transformation and upgrading of thermal power plants and building a new multi-energy complementary energy system. Summary of the Invention

[0006] The present invention aims to provide a rapid assessment method for the operational reliability of integrated energy systems based on feasible domain matching, which can accurately and efficiently complete the operational reliability assessment of integrated energy systems and provide decision support for risk prevention and optimized scheduling of integrated energy systems in industrial parks.

[0007] The basic solution provided by this invention is: a rapid assessment method for the operational reliability of an integrated energy system based on feasible domain matching, comprising the following steps:

[0008] Step 1: Collect basic data; the basic data includes weather forecast data and equipment ledger data;

[0009] Step 2: Construct a failure probability model and evaluate the time-varying failure probability of system equipment in the integrated energy system of the park to be analyzed at the evaluation time based on the basic data;

[0010] Step 3: Based on the time-varying fault probability, randomly generate a system state;

[0011] Step 4: Calculate the corresponding feasible region on the energy supply side and the feasible region on the load usage side under the system state;

[0012] Step 5: Match the feasible domains on the energy supply side and the feasible domains on the load usage side, and determine whether the current system status is sufficient based on the matching results; if yes, proceed to step 7; if no, proceed to step 6.

[0013] Step 6: Calculate the load reduction amount corresponding to the current system state;

[0014] Step 7: Calculate the reliability assessment convergence criterion factor;

[0015] Step 8: Determine if the convergence criterion factor is less than the given threshold. If yes, proceed to step 9; otherwise, return to step 3, regenerate a system state, and re-execute steps 3-7.

[0016] Step 9: Calculate the system reliability index at the current evaluation moment.

[0017] The working principle and advantages of this invention are as follows:

[0018] This invention presents a rapid reliability assessment method for integrated energy systems based on feasible domain matching. This method can accurately and efficiently assess the operational reliability of integrated energy systems, providing decision support for risk prevention and optimized scheduling of integrated energy systems in industrial parks. The key points are:

[0019] This solution significantly improves the efficiency and practicality of reliability assessment for integrated energy systems. By dynamically integrating weather forecasts and equipment data to establish a time-varying failure probability model, it more realistically reflects the impact of environmental factors on equipment failure rates, achieving real-time linkage between environmental factors and equipment status, thus enhancing the accuracy of probability assessment. Secondly, it innovatively adopts a decoupled calculation and matching mechanism for feasible regions on the energy supply side and load usage side, replacing the traditional time-consuming optimization solution process and significantly reducing the computational complexity of a single state assessment. Combined with condition-triggered load shedding calculation (executed only when the system is underutilized) and the setting of convergence criteria, it can further reduce the number of samplings required for Monte Carlo simulation and reduce redundant calculations. Through the synergistic effect of the above features, reliability assessment can be completed quickly while ensuring assessment accuracy, providing efficient decision support for real-time risk prevention and optimized scheduling of the park system.

[0020] Furthermore, compared to existing evaluation schemes, which typically employ unified optimization models or static segmented failure rates, this scheme breaks away from traditional modeling approaches. It creatively decouples the system into supply-side and load-side feasible regions for independent calculation, and then uses geometric matching (rather than numerical optimization) to determine system adequacy. This significantly reduces redundant calculations and improves computational efficiency. Secondly, the feasible region decoupling, matching judgment, conditional calculation load reduction, and convergence mechanisms set in this scheme can form an efficient computational closed loop, meeting the needs of online computation. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the method flow of an embodiment of the rapid assessment method for the operational reliability of an integrated energy system based on feasible domain matching according to the present invention;

[0022] Figure 2 This is a schematic diagram illustrating the method modeling of an embodiment of the rapid assessment method for the operational reliability of an integrated energy system based on feasible domain matching according to the present invention.

[0023] Figure 3 This is a schematic diagram showing the comparison of load reduction and calculation time between the two methods under a single system state condition in an application example of the rapid assessment method for the operational reliability of integrated energy systems based on feasible domain matching, as described in the embodiment of the present invention.

[0024] Figure 4This diagram illustrates the comparison of load reduction and computation time between two methods under the state enumeration scenario of an application example of the rapid reliability assessment method for integrated energy systems based on feasible domain matching, as described in this invention. Detailed Implementation

[0025] The following detailed explanation illustrates the specific implementation methods:

[0026] The basic implementation examples are as follows: Figure 1 and Figure 2 The following is a rapid assessment method for the operational reliability of an integrated energy system based on feasible domain matching, comprising the following steps:

[0027] Step 1: Collect basic data; the basic data includes weather forecast data and equipment ledger data.

[0028] The weather forecast data includes ambient temperature, relative humidity, precipitation, air pressure, wind speed, and wind direction. The equipment ledger data includes ledger information that can be used to calculate the service life of the equipment.

[0029] Step 2: Construct a failure probability model and evaluate the time-varying failure probability of system equipment in the integrated energy system of the park to be analyzed at the evaluation time based on the basic data.

[0030] Specifically, the failure probability model includes a first failure probability model that considers service time, a second failure probability model that considers seismic vulnerability, and a third failure probability model that considers the impact of meteorological environment; the time-varying failure probability obtained in step 2 is the superposition value of the time-varying failure probabilities output by each failure probability model.

[0031] In this embodiment, for the first fault probability model P age (t):

[0032] First, an exponential function is used to describe the equipment failure rate affected by the number of years of operation, and it is used as the intensity function of a non-homogeneous Poisson process, as shown below:

[0033] λ age (t)=λ age,init ·t k ;

[0034] In the formula, λ age,init The initial aging failure rate is given by t, the system equipment running time is given by t, and the growth coefficient is given by k.

[0035] Based on the established intensity function, the probability P of aging failure of the energy conversion equipment at time t due to the increase in the service life of the equipment is calculated. age (t), as shown below:

[0036]

[0037] For the second failure probability model P seismic (t):

[0038] An improved Sigmoid distribution is used to establish a mapping model between online monitoring data of ground peak ground acceleration and the probability of seismic-induced failure of power equipment. Assume the peak ground acceleration at time t is g. seismic If (t), then the probability of equipment failure due to vibration is P. seismic (t) is specifically represented as follows:

[0039]

[0040] In the formula, α seismic β seismic and γ seismic Let g be the Sigmoid distribution parameter, when g seismic (t)≤g low Time P seismic (t, t + Δt) = 0 indicates that there is almost no failure under low acceleration, when g seismic (t)≥x high When, then P seismic (t, t + Δt) = 1. When g low <g seismic (t) <g high At that time, the probability curve of earthquake-induced failure is a smooth S-curve that gradually increases.

[0041] For the third failure probability model P weather (t):

[0042] A failure probability model due to weather effects is constructed using the cumulative risk function in the Cox proportional hazards model. The failure probability of equipment at time t due to external weather conditions is specifically represented as follows:

[0043] P weather (t)=1-exp(-H basic ·t·exp(β1TE(t)+β2RH(t)+β3WS(t)+β5SP(t)+β5LG(t)))

[0044] In the formula, TE(t), RH(t), WS(t), SP(t), and LG(t) represent the ambient temperature (°C), relative humidity (%), precipitation (mm), wind speed (m / s), and lightning strike density (times / km) of the equipment at any location at time t, respectively. 2 H basic The base risk value to be fitted is β1, β2, β3, β4, and β5, which are the influence weights of each weather factor, respectively.

[0045] Furthermore, the summation value P of the time-varying fault probabilities output by each fault probability model is... total (t) is calculated using the following formula:

[0046] P total (t)=1-(1-P) age (t))(1-P seismic (t))(1-P weather (t)).

[0047] Furthermore, in this embodiment, historical fault data of various energy conversion equipment within the park, such as electric heat pumps, combined heat and power units, and chillers, are collected. Maximum likelihood estimation (MLE) is used to estimate the parameters in the fault probability functions of various equipment. Based on the basic data collected in step 1, the time-varying fault probabilities of G system devices within the park (each energy conversion device is equivalent to a node in the integrated energy system) at evaluation time t are assessed and stored in matrix P. total (t), specifically represented as follows:

[0048] P total (t)=[P total,1 (t),P total,2 (t),P total,3 ,P total,4 (t), ..., P total,G (t)].

[0049] Step 3: Based on the time-varying fault probability, randomly generate a system state.

[0050] When generating the system state, random sampling is performed based on the Monte Carlo algorithm.

[0051] Specifically, based on the time-varying failure probabilities of the G system devices within the park at assessment time t, the operating state of an integrated energy system is randomly generated, i.e., the operating state (i.e., system state) of these G system devices at time t is determined, including the following steps:

[0052] A 1-row, G-column RN matrix is ​​generated based on Monte Carlo random sampling, where each element follows a uniform distribution in the interval [0,1], as shown below:

[0053] RN=[RN1,RN2,RN3,RN4,…,RN G ];

[0054] Associate each element of matrix RN with the corresponding device's failure probability vector P. total (t) Perform bit-by-bit comparisons to determine the operating state of each system device, thus obtaining the system state n. The specific rules are as follows:

[0055]

[0056] In the formula, a value of 1 indicates that the equipment is in normal operation, and a value of 0 indicates that the equipment is in a fault shutdown state.

[0057] Step 4: Calculate the corresponding feasible region on the energy supply side and the feasible region on the load usage side under the system state.

[0058] Furthermore, in this embodiment, the construction of the fault probability model in step 2 and the calculation of the corresponding feasible domains on the energy supply side and load usage side under the system state in step 4 are both completed in advance under offline conditions.

[0059] Specifically, the feasible region on the energy supply side is calculated through the following steps:

[0060] S4.1, a coupling matrix is ​​used to represent the energy conversion link under the condition of no equipment failure, and the coupling matrix corresponding to each node in the integrated energy system of the park is calculated, so as to obtain the energy conversion matrix corresponding to the whole system.

[0061] The coupling matrix is: Z g =H g A g ;

[0062] In the formula, Z g Let A be the energy conversion matrix of node g. g This is a port-branch incidence matrix, used to represent the connection relationship between node g and all its branches, H. g Let H be the transformation characteristic matrix of node g, used to characterize the energy conversion efficiency within node g. g and A g The specific calculations are as follows:

[0063]

[0064] Furthermore, the energy conversion matrix Z corresponding to the entire system is:

[0065]

[0066] Since the input and output energy of each node must remain conserved after the conversion, based on the definition of the matrix above, the energy conversion matrix of the system can be further expressed as:

[0067] ZV = 0;

[0068] In the formula, matrix V consists of the energy flow in each branch of the integrated energy system, such as gas pipelines, power transmission lines, and heat transmission pipelines.

[0069] S4.2, based on the operating status of the equipment corresponding to each node, correct the energy conversion matrix.

[0070] Specifically, the energy conversion process corresponding to each node can be further modified as follows:

[0071]

[0072] Correspondingly, the energy conversion matrix of the entire system can be corrected as follows:

[0073]

[0074] In the formula, This represents the energy conversion matrix corresponding to the energy hub when the park's integrated energy system is running in system state n.

[0075] S4.3, construct the input correlation matrix C between each node and each branch. in and output correlation matrix C out And the system's total energy flow equation considering the operating status of the energy conversion equipment is obtained.

[0076] Specifically,

[0077]

[0078] Based on the input correlation matrix C in and output correlation matrix C out This leads to the overall energy flow equation for the integrated energy system (i.e., the energy hub) that takes into account the operating status of energy conversion equipment, as shown below:

[0079]

[0080] In the formula, Γ V Represents the feasible set of energy flows. and This represents the energy flow input and output matrix under the current system state n.

[0081] S4.4 sets the operating constraints for the energy storage devices in the system.

[0082] Specifically, the operating constraints of energy storage devices are characterized as follows:

[0083]

[0084] In the formula, This represents the output power of the energy storage device at time t. This indicates the pre-scheduled capacity status of the energy storage device at time t-1, which can be obtained by performing pre-scheduled calculations.

[0085] S4.5, combining the total energy flow equation and operating constraints, obtains the feasible energy supply region of the park's integrated energy system under system conditions.

[0086] Specifically, at time t, when the park's integrated energy system is operating in system state n, the feasible region of the energy supply side at this time is... Characterized as:

[0087]

[0088] In the formula, This represents the set of all feasible combinations of various energy powers, such as electricity, heat, and cooling, that the integrated energy system of the park can provide to the energy user side under system state n.

[0089] Furthermore, a spatial projection algorithm based on vertex search is employed to... Solve the problem and output the solution. The corresponding polyhedron.

[0090] It should be noted that when assessing the operational reliability of the park's integrated energy system at time t, the aforementioned feasible region can be modeled offline in advance. Before the system actually operates at time t, [the model can be...]. Replace with the actual capacity state of the energy storage device at time t-1. This process can significantly improve the computational efficiency of online reliability assessment for the integrated energy system in the park.

[0091] The feasible region on the load-using side is calculated through the following steps:

[0092] S4.6 divides the energy use-side load of the integrated energy system into three parts: fixed load, transferable load, and alternative load, and performs load modeling.

[0093] Specifically,

[0094] In the formula, P load (t) represents the electrical load, heat load, and cooling load values ​​at time t, P s load (t) represents the fixed load at time t. This represents the transferable load at time t. This represents the alternative load at time t.

[0095] Since stationary loads such as electricity, heat, and cooling do not participate in demand response, while transferable loads can be determined once the dispatch plan is finalized, the changes in demand for these three types of energy loads at time t are mainly caused by substitute loads, which can be modeled as follows:

[0096]

[0097] Where: ΔP r x,load (t)=-η x,y ΔP r y,load (t)x,y∈{electric load, heat load, cooling load};

[0098]

[0099] In the formula, ΔP r EH,Load (t) and ΔP r EC,Load (t) represents the energy conversion value between electrical load and thermal load, and cooling load, respectively; ΔP r HE,load (t) and ΔP r HC,load (t) represents the energy conversion value between heat load and electrical load, and cooling load, respectively; ΔP r CE,load (t) and ΔP r CH,load (t) represents the energy conversion value between cooling load and electrical load, and heating load, respectively; ΔP r E,load (t), ΔP r H,load (t) and ΔP r C,load (t) represents the change in replaceable load corresponding to the three types of loads. η x,y X represents energy conversion efficiency, W represents the calorific value per unit of energy, and X represents the utilization rate per unit of energy.

[0100] S4.7, based on load modeling, constructs the energy use-side feasible domain of the park's integrated energy system under system conditions.

[0101] Specifically, based on S4.6, under system state n, the feasible region of energy use on the energy use side of the park's integrated energy system at time t. It can be described as:

[0102]

[0103] In the formula, P load (t) represents the power demand vector on the load side at time t. Let t be the power value of the energy-using side participating in the alternative demand response. Let t be the power value of the energy user side participating in the alternative demand response during system pre-scheduling at time t. The maximum value of the alternative load. This represents the power demand vector determined during system pre-scheduling. In the formula, This represents all possible combinations of electricity, heat, and cooling load demands after adjustment by the user side through demand response (such as load transfer or substitution) under system state n.

[0104] Furthermore, a spatial projection algorithm based on vertex search is employed to... Solve and output. The corresponding polyhedron.

[0105] It should be noted that when assessing the operational reliability of the park's integrated energy system at time t, the aforementioned feasible region can be modeled offline in advance. Before the system actually operates at time t, [the model can be...]. Simply substitute the values ​​into the feasible region shown in Step 4.3. This type of processing can significantly improve the computational efficiency of online reliability assessment of the park's integrated energy system.

[0106] Step 5: Match the feasible domains of the energy supply side and the feasible domains of the load usage side, and determine whether the current system status is sufficient based on the matching results; if yes, proceed to step 7; if no, proceed to step 6.

[0107] When matching the feasible regions on the energy supply side and the feasible regions on the load usage side, it is determined whether the two intersect in spatial location. If they intersect, the current system state is considered sufficient, and the system load shedding calculation can be omitted, with the load reduction amount LS... n The value is 0; if there is no intersection, the current system state is considered insufficient.

[0108] In operational reliability assessment, the system states with a high probability of occurrence are mostly those with low-order faults, and the situation of insufficient adequacy is extremely rare. Therefore, omitting the load shedding calculation for system states with sufficient adequacy can significantly reduce the computational complexity of system state analysis and improve the computational efficiency of system state analysis and reliability assessment.

[0109] Step 6: Calculate the load reduction amount corresponding to the current system state.

[0110] When calculating the load reduction amount corresponding to the current system state, the load reduction amount is determined by calculating the minimum Euclidean distance between point pairs with the minimum distance between the feasible regions on the energy supply side and the feasible regions on the load use side in three-dimensional space.

[0111] Specifically,

[0112]

[0113] in, and respectively feasible regions and Surface points Represented as point pairs The formula for calculating the Euclidean distance between them. u1, u2, and u3 represent the importance of each type of load; the higher the value, the less likely that type of load is to be reduced.

[0114] Step 7: Calculate the reliability assessment convergence criterion factor.

[0115] The reliability assessment convergence criterion factor is calculated based on the following formula:

[0116]

[0117] Where χ is the reliability assessment convergence criterion factor, and N sum The number of system states that have been generated and evaluated; LS aver and LS f N sum The average load shedding amount for each system state and the load shedding amount corresponding to the f-th system state.

[0118] Step 8: Determine if the convergence criterion factor is less than the given threshold. If yes, proceed to step 9; otherwise, return to step 3, regenerate a system state, and re-execute steps 3-7.

[0119] Step 9: Calculate the system reliability index at the current evaluation moment.

[0120] The system reliability metrics include the expected load underload metric:

[0121]

[0122] Wherein, LCM(t) is the expected load shortfall index of the integrated energy system at time t; N sum The number of system states that have been generated and evaluated; LS n This represents the system load shedding amount corresponding to system state n.

[0123] This embodiment provides a rapid assessment method for the operational reliability of integrated energy systems based on feasible domain matching. This method can accurately and efficiently assess the operational reliability of integrated energy systems, providing decision support for risk prevention and optimized scheduling of integrated energy systems in industrial parks.

[0124] Furthermore, to illustrate the effectiveness of this method, an application example is introduced in this embodiment to verify its effectiveness.

[0125] This method is applied to evaluate the individual system status and operational reliability of a comprehensive energy system in a certain industrial park at a specific time. The main parameter settings are as follows:

[0126] The capacity parameters and conversion efficiency of each energy conversion device in the park's integrated energy system are shown in Table 1 below;

[0127] The conversion efficiency between the three types of loads—electricity, heat, and cooling—is shown in Table 2.

[0128] Table 1 Parameters of various energy conversion devices within the park's integrated energy system

[0129]

[0130] Table 2 Conversion efficiency between electrical, heating, and cooling loads.

[0131]

[0132] This scheme eliminates the need for load shedding calculations under conditions of sufficient energy supply when analyzing each system state. Load shedding calculations are only required when the feasible regions of energy consumption and supply do not intersect (i.e., the system is not sufficient), using a model based on minimum Euclidean distance. To verify the accuracy of this model, a comparative analysis is conducted using specific system states:

[0133] Case 1 is analyzed using the feasible region matching model proposed in this method; Case 2 is analyzed using the traditional optimal load shedding calculation model. A comparison of the load shedding amount and calculation time of the two methods is provided below. Figure 3 As shown.

[0134] Depend on Figure 3 It can be seen that, under the premise of ensuring the consistency of the load shedding calculation results, the system state analysis time using the feasible region matching and minimum Euclidean distance model (Case 1) is shortened by about 63% compared with the traditional optimal load shedding model (Case 2), and the calculation efficiency is improved by about 2.7 times, which verifies the accuracy of this method for system state analysis.

[0135] Based on this, the operational reliability of the park's integrated energy system at a specific moment is assessed through state enumeration.

[0136] In the approximately 13,000 system state samples generated, two methods were used for comparative evaluation: a method based on the traditional optimal load reduction model and an evaluation method based on feasible region matching proposed in this invention. The evaluation results are as follows: Figure 4 As shown, the comparison data of the two methods in terms of system load reduction index and calculation time is presented.

[0137] Depend on Figure 4As can be seen, while ensuring the accuracy of operational reliability assessment, the system operational reliability assessment efficiency based on this method is improved by 82.54% compared with the traditional assessment method based on the optimal load reduction model. This significant performance improvement mainly stems from three key innovations of the method of this invention: 1) it eliminates the redundant load shedding calculation under the system's ample state; 2) it reduces the number of system states that actually need to be analyzed from 13,000 to less than 6,000; and 3) it replaces the complex optimal load optimization process with the shortest Euclidean distance calculation. These comparative data strongly demonstrate that this method is both efficient and accurate in operational reliability assessment.

[0138] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics of the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A rapid reliability assessment method for integrated energy systems based on feasible domain matching, characterized in that, Includes the following steps: Step 1: Collect basic data; the basic data includes weather forecast data and equipment ledger data; Step 2: Construct a failure probability model and evaluate the time-varying failure probability of system equipment in the integrated energy system of the park to be analyzed at the evaluation time based on the basic data; Step 3: Based on the time-varying fault probability, randomly generate a system state; Step 4: Calculate the corresponding feasible region on the energy supply side and the feasible region on the load usage side under the system state; Step 5: Match the feasible domains on the energy supply side and the feasible domains on the load usage side, and determine whether the current system status is sufficient based on the matching results; if yes, proceed to step 7; if no, proceed to step 6. Step 6: Calculate the load reduction amount corresponding to the current system state; Step 7: Calculate the reliability assessment convergence criterion factor; Step 8: Determine if the convergence criterion factor is less than the given threshold. If yes, proceed to step 9; otherwise, return to step 3, regenerate a system state, and re-execute steps 3-7. Step 9: Calculate the system reliability index at the current evaluation moment.

2. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, In step 2, the failure probability model includes a first failure probability model that considers service time, a second failure probability model that considers seismic vulnerability, and a third failure probability model that considers the impact of meteorological environment; the time-varying failure probability obtained in step 2 is the superposition value of the time-varying failure probabilities output by each failure probability model.

3. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, In step 3, random sampling is performed based on the Monte Carlo algorithm when generating the system state.

4. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, In step 4, the feasible region on the energy supply side is calculated through the following steps: S4.1, a coupling matrix is ​​used to represent the energy conversion link under the condition of no equipment failure, and the coupling matrix corresponding to each node in the integrated energy system of the park is calculated, so as to obtain the energy conversion matrix corresponding to the whole system; S4.2, Based on the operating status of the equipment corresponding to each node, the energy conversion matrix is ​​corrected; S4.3, construct the input correlation matrix and output correlation matrix between each node and each branch, and obtain the system total energy flow equation considering the operating status of the energy conversion equipment; S4.4 sets the operating constraints for the energy storage devices in the system; S4.5, combining the total energy flow equation and operating constraints, obtains the feasible energy supply region of the park's integrated energy system under system conditions.

5. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, In step 4, the feasible region for load use is calculated through the following steps: S4.6 divides the energy use-side load of the integrated energy system into three parts: fixed load, transferable load, and alternative load, and performs load modeling. S4.7, based on load modeling, constructs the energy use-side feasible domain of the park's integrated energy system under system conditions.

6. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, In step 5, when matching the feasible region on the energy supply side and the feasible region on the load usage side, it is determined whether the two intersect in spatial location. If they intersect, the current system state is considered sufficient. If there is no intersection, the current system state is considered insufficient.

7. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, In step 6, when calculating the load reduction amount corresponding to the current system state, the load reduction amount is determined by calculating the minimum Euclidean distance between point pairs with the minimum distance between the feasible regions on the energy supply side and the feasible regions on the load use side in three-dimensional space.

8. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, In step 7, the reliability assessment convergence criterion factor is calculated based on the following formula: Where χ is the reliability assessment convergence criterion factor, and N sum The number of system states that have been generated and evaluated; LS aver and LS f N sum The average load shedding amount for each system state and the load shedding amount corresponding to the f-th system state.

9. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, The system reliability metrics include the expected load underload metric: Wherein, LCM(t) is the expected load shortfall index of the integrated energy system at time t; N sum The number of system states that have been generated and evaluated; LS n This represents the system load shedding amount corresponding to system state n.

10. The rapid reliability assessment method for integrated energy system operation based on feasible domain matching according to claim 1, characterized in that, The construction of the failure probability model in step 2 and the calculation of the corresponding feasible domains on the energy supply side and load usage side under the system state in step 4 are both completed in advance under offline conditions.

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