Energy station elasticity assessment method and system taking into account equipment vulnerability characteristics

Abstract

The present invention relates to an energy station elasticity assessment method and system taking into account equipment vulnerability characteristics, the method comprising the following steps: obtaining preset parameters of the energy station, taking into account equipment vulnerability characteristics under earthquake disasters, establishing an energy station fault model, and generating a set of energy station fault states under earthquake disasters; based on the energy station fault state set, using an improved hierarchical energy hub model to establish an energy station optimal load reduction model, and obtaining optimal load reduction results for different fault states of the energy station after the earthquake disaster; based on the optimal load reduction results for different fault states of the energy station, calculating the energy station performance quantification value and the energy station elasticity assessment index under earthquake disasters, and identifying the weak links of the energy station at the system and component levels based on the energy station elasticity assessment index. Compared with the prior art, the present invention has the advantages of being able to effectively assess the changes in the elastic performance of the energy station before and after the earthquake disaster, and then accurately identifying the weak links of the energy station at both the system and component levels.

Description

Technical Field

[0001] The present invention relates to the technical field of energy station performance prediction, and in particular to an energy station elasticity assessment method and system taking into account equipment vulnerability characteristics. Background Art

[0002] Damage to energy stations caused by earthquake stress can lead to interruptions in multiple types of energy supply, seriously threatening energy supply security. As the infrastructure for coupling heterogeneous energy subsystems, energy stations are composed of various energy conversion and storage devices, and are the medium for the elastic interaction of heterogeneous energy subsystems. Accurately evaluating their elastic performance under earthquake disasters is the basis for studying and improving the elasticity of heterogeneous energy subsystems in energy stations.

[0003] The difficulty of evaluating the resilience of energy stations under extreme earthquake disasters lies in the following: as a key facility for coupling heterogeneous energy subsystems, energy stations have both energy conversion and supply functions compared to heterogeneous energy networks. There is a lack of effective quantification methods for the time-varying characteristics of their performance under earthquake disasters, and there is also a lack of effective analysis methods for the complex relationship between output energy and input energy, and the mutual influence between heterogeneous energy sources. Therefore, taking into account the changes in the multi-energy conversion storage capacity and multi-energy supply capacity of energy stations before and after earthquake disasters, quantifying the resilience performance of energy stations before and after earthquake disasters, and identifying the weak links of energy station systems and components have important theoretical value and practical significance for studying and improving the resilience of energy stations to earthquake disasters.

[0004] However, the prior art does not disclose an effective energy station resilience assessment method for earthquake disasters. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide an energy station resilience assessment method and system taking into account the vulnerability characteristics of the equipment, which can effectively evaluate the changes in the resilience performance of the energy station before and after the earthquake disaster, and then accurately identify the weak links of the energy station at both the system and component levels, providing a basis for improving the performance of the energy station and stabilizing its operation.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for evaluating the resilience of an energy station taking into account the vulnerability characteristics of equipment comprises the following steps:

[0008] Obtain the preset parameters of the energy station, take into account the vulnerability characteristics of the equipment under earthquake disasters, establish the energy station fault model, and generate the energy station fault state set under earthquake disasters;

[0009] Based on the energy station fault state set, the energy station optimal load reduction model is established using the improved hierarchical energy hub model, and the optimal load reduction results of energy stations with different fault states after the earthquake disaster are obtained.

[0010] Based on the optimal load reduction results of different fault states of the energy station, the quantitative performance value of the energy station and the resilience evaluation index of the energy station under earthquake disasters are calculated, and the weak links of the energy station at the system and component levels are identified based on the resilience evaluation index of the energy station.

[0011] Furthermore, the preset parameters of the energy station include the energy station's earthquake fortification intensity, the energy station's equipment type, the energy equipment's earthquake vulnerability function, and the energy station's fault state set order.

[0012] Furthermore, the equipment vulnerability characteristic under the earthquake disaster is expressed as:

[0013]

[0014] Where: fail It represents the comprehensive loss coefficient of the number of energy equipment under earthquake disaster; represents the quantity loss coefficient corresponding to the energy equipment in the kth damaged state; v and V represent the index and set of energy equipment types respectively; n v Indicates the number of energy equipment before the earthquake disaster occurs; Δn v represents the amount of energy equipment lost after an earthquake disaster; k represents the probability of public infrastructure reaching the kth damaged state; k max Number the most severely damaged state.

[0015] Furthermore, the energy station fault model is expressed as:

[0016]

[0017] Where: is the energy station fault status set; represents the set of damaged device type combinations u; Represents the set of damaged equipment quantity in different states; is the probability set of different fault states; represents the probability of the zth fault state; represents the set of damaged equipment type combinations u under the zth fault state; represents the probability that the i-th type of equipment is in the most seriously damaged state under an earthquake disaster; o represents the order of the energy station fault set; u is the combination of equipment failure types under the fault state order o, V represents the set of energy equipment types, u is a subset of the set V; Card(u) represents the number of damaged equipment types.

[0018] Furthermore, the objective function of the energy station optimal load reduction model is to minimize the multi-energy load reduction of the energy station after the earthquake, and the constraints include energy equipment operation characteristic constraints, multi-energy load power balance constraints and energy station energy purchase upper limit constraints.

[0019] Furthermore, the YALMIP toolkit and Gurobi solver are called to solve the optimal load reduction model of the energy station.

[0020] Furthermore, the performance decline ratio of the energy station after the disaster is used as the quantified value of the energy station performance, and the expression is:

[0021]

[0022] Where: m and Ω m are the number and set of energy types respectively; F m,t is the percentage of energy station performance decline after the disaster; and They represent the performance of the mth energy source of the energy station in period t before and after the earthquake disaster.

[0023] Furthermore, the energy station resilience assessment index under earthquake disasters includes a robustness index, a sensitivity index and a dependence index, wherein:

[0024] The robustness index is expressed as:

[0025]

[0026] Where: R m is the robustness index of the energy station; i is the probability of occurrence of the i-th fault;

[0027] The sensitivity index is expressed as:

[0028]

[0029] Where: It represents the sensitivity of the m1th energy to the m2th energy in the energy station under earthquake disaster;

[0030] The dependency index is expressed as:

[0031]

[0032] Where: It indicates the similarity of the degree to which the m1th performance and m2th performance of the energy station are affected by earthquake disasters.

[0033] Furthermore, the probability of occurrence of the i-th fault is calculated based on the Markov method through the probability of a fault state in the energy station and the failure rate of the equipment.

[0034] The present invention also provides an energy station resilience assessment system that takes into account equipment vulnerability characteristics, comprising one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the energy station resilience assessment method that takes into account equipment vulnerability characteristics as described above.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) The present invention takes into account the changes in the multi-energy conversion storage capacity and multi-energy supply capacity of the energy station before and after the earthquake disaster, quantifies the elastic performance of the energy station under the earthquake disaster, and can accurately and reliably identify the weak links of the energy station, providing a basis for improving the performance of the energy station and stabilizing its operation.

[0037] (2) The present invention takes into account the different vulnerability characteristics of energy equipment under earthquake disasters, constructs resilience indicators of energy stations from three aspects: robustness, sensitivity and dependence, and identifies the weak links of energy stations at the system and component levels, respectively, with high reliability.

[0038] (3) The present invention utilizes an improved hierarchical energy hub model to establish an optimal load reduction model for an energy station, which can achieve decoupling of input energy and output energy, facilitate the establishment of a multi-energy power balance equation, and further construct a simpler and more reliable optimal load reduction model for an energy station. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the process of the present invention;

[0040] Figure 2 This is a typical structure of an energy station in an embodiment;

[0041] Figure 3 It is the comprehensive loss coefficient curve of energy equipment quantity under earthquake disaster;

[0042] Figure 4 To improve the hierarchical energy hub model;

[0043] Figure 5 A flowchart for calculating resilience indicators of energy stations facing earthquake disasters;

[0044] Figure 6 It is the multi-energy load curve of the energy station of the embodiment;

[0045] Figure 7 To reduce the load of energy stations after earthquake disasters;

[0046] Figure 8 is the percentage of energy station performance decline after the earthquake;

[0047] Fig. 9 It is the robustness index of the energy station;

[0048] Fig.10 It is the sensitivity index of the energy station;

[0049] Fig.11 It is an indicator of energy station dependence;

[0050] Fig.12 Comparison of energy station load reduction before and after adding disaster recovery capacity. DETAILED DESCRIPTION

[0051] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0052] Example 1

[0053] This embodiment provides an energy station resilience assessment method taking into account the equipment vulnerability characteristics, such as Figure 1 As shown, the method comprises the following steps:

[0054] S1. Considering the different seismic vulnerabilities of energy equipment under earthquake disasters, an energy station failure model is established that takes into account the equipment vulnerability characteristics under earthquake disasters.

[0055] In this embodiment, the energy station fault model can be constructed based on the earthquake model and the equipment vulnerability model.

[0056] (1) Earthquake disaster model

[0057] Peak ground acceleration r PGA It is often used to assess the potential losses that may be caused by earthquake disasters. PGA The size of is mainly determined by the magnitude and epicenter distance, as shown in formula (1).

[0058] lg(r PGA )=0.583+0.651M-1.6521lg(D+0.182e 0.707M ) (1)

[0059] Where: M is the magnitude of the earthquake; D is the distance from the calculation point to the epicenter of the earthquake.

[0060] Formula (1) is the earthquake model. The earthquake location is the epicenter. The propagation model and impact of earthquake waves are reflected by various typical parameters. Among them, the peak ground acceleration is a typical parameter commonly used to reflect earthquake intensity parameters, that is, the impact of an earthquake on non-epidemic source areas can be reflected by the peak ground acceleration.

[0061] (2) Equipment Vulnerability Model

[0062] The Hazus Technical Manual of the United States divides the degree of damage to public infrastructure under earthquake disasters into states such as slight damage, moderate damage, severe damage, and complete damage. On this basis, a corresponding earthquake vulnerability model is established, as shown in formula (2).

[0063]

[0064] Where: s is the peak ground acceleration, r PGA is the upper limit of its integral; k represents the index of the damage state of public infrastructure under earthquake disaster; λ k represents the probability of public infrastructure reaching the kth damage state; μ k and δ k They respectively represent the median value and logarithmic standard deviation of the earthquake vulnerability function corresponding to the kth damage state of public infrastructure under earthquake disasters.

[0065] The results calculated based on formula (2) can assist decision makers in judging the probability of energy equipment reaching different damage states after an earthquake disaster, but the probability of energy equipment being in each damage state cannot be obtained, and the relationship between each damage state of energy equipment and equipment performance is not quantified, so it cannot be directly applied to the field of energy station resilience assessment in response to earthquake disasters. Different damage states of energy equipment under earthquake disasters are defined as different degrees of quantitative losses. On this basis, the corresponding expected number of damaged items can be obtained based on the earthquake vulnerability function of energy equipment, that is, the vulnerability model. The specific embodiment of the vulnerability model is the quantitative loss of energy equipment under earthquake disasters, as shown in formulas (3) and (4).

[0066]

[0067] Where: fail It represents the comprehensive loss coefficient of the number of energy equipment under earthquake disaster; represents the quantity loss coefficient corresponding to the energy equipment in the kth damaged state; v and V represent the index and set of energy equipment types respectively; n v Indicates the number of energy equipment before the earthquake disaster occurs; Δn v represents the amount of energy equipment lost after an earthquake disaster; k max Number the most severely damaged state.

[0068] As an infrastructure that couples multiple heterogeneous energy sources, the types of equipment inside the energy station are different. When constructing the failure model of the energy station under earthquake disasters, it is necessary to consider the difference in the vulnerability of energy equipment, that is, the difference in vulnerability function. Figure 2 The detailed parameters of the seismic vulnerability function of the internal equipment of the energy station are shown in Table 1. Based on this, the energy station vulnerability model obtained, that is, the comprehensive loss coefficient curve of the number of equipment under earthquake disasters is shown in Figure 3 shown.

[0069] Table 1 Detailed parameters of seismic vulnerability function of internal equipment of energy station

[0070]

[0071] Based on equations (3) and (4), the energy station failure model under earthquake disaster is constructed, as shown in equations (5) to (7).

[0072]

[0073] Where: is the energy station fault status set; represents the set of damaged device type combinations u; Represents the set of damaged equipment quantity in different states; is the probability set of different fault states; represents the probability of the zth fault state; represents the set of damaged equipment type combinations u under the zth fault state; represents the probability that the i-th type of equipment is in the most seriously damaged state under an earthquake disaster; o represents the order of the energy station fault set; u is the combination of equipment failure types under the fault state order o, and u is a subset of the set V; Card(u) represents the number of damaged equipment types. When o=1, it means that one type of energy equipment is damaged.

[0074] S2. Based on the improved hierarchical energy hub model, an optimal load reduction model for energy stations facing earthquake disasters is established.

[0075] (1) Improved hierarchical energy hub model

[0076] Figure 2 In the typical structure of the energy station shown, since the energy station is not equipped with gas production equipment, the power output to the natural gas load by the original energy hub model is equivalent to the power directly transmitted to the natural gas load by the upper gas grid. The energy station does not convert or store this part of the natural gas power, so this power cannot represent the gas supply capacity of the energy station. Figure 2 Based on the energy input and output types of energy stations, the energy bus in the original energy hub model is split into a source bus connected to the upper energy network and a load bus connected to the multi-energy load, and an improved hierarchical energy hub model is constructed, such as Figure 4 shown.

[0077] Depend on Figure 4It can be seen that the input power in the improved hierarchical energy hub model must be converted and stored by energy equipment before it can be output to the multi-energy load to meet its energy demand. Compared with the original energy hub model, the decoupling of input energy and output energy is achieved. Taking the electric energy bus as an example, the energy station can convert or store the upper-level input electric energy through electric refrigerators and electric energy storage, and can also use cogeneration units and electric energy storage to meet the needs of electric loads. Therefore, the electric energy bus and the electric energy load bus are defined in the improved hierarchical energy hub model. Among them, the electric energy bus is close to the power supply side, and the size of the electric power transmitted to the electric energy bus represents the energy station's ability to convert and store input electric energy; the electric energy load bus is close to the load side, and the size of the electric power transmitted between it and the multi-energy load represents the energy station's ability to supply electricity to meet the needs of electric loads. In order to distinguish the natural gas conversion capacity and supply capacity of the energy station, the natural gas load bus is not defined in the improved hierarchical energy hub model. At the same time, since there is no upper-level heat and cold energy input in the energy station, the heat energy bus and the cold energy bus are not defined in the improved hierarchical energy hub model.

[0078] Based on the various energy buses in the improved hierarchical energy hub model, the multi-energy power balance equations are established, as shown in equations (8) to (18). Among them, equations (8) and (9) are the power balance equations of the energy station purchasing electricity and gas, respectively; equations (10) to (13) are the multi-energy load power balance equations; equations (14) and (15) are the electric power balance equations established based on the electric energy bus and the electric energy load bus, respectively; equation (16) is the gas power balance equation established based on the natural gas source bus; equations (17) and (18) are the thermal power balance equations and cold power balance equations established based on the thermal energy load bus and the cold energy load bus, respectively.

[0079]

[0080]

[0081] Where: t and T are the index and set of time periods respectively; P e,pur,t The sum of the electric power purchased by the energy station and multi-energy load from the upper power grid; P g,pur,t The sum of the natural gas power purchased by energy stations and multi-energy loads from the upper gas grid; P e,in,t and P g,in,t are the input electric power and input gas power of the energy station respectively; P e,DL,t P is the electric power transmitted from the upper power grid to the electric load; g,DL,t P is the gas power transmitted from the upper gas grid to the natural gas load; e,out,t , P h,out,t and P c,out,t are the electrical power, thermal power and cooling power output by the energy station respectively; L e,t , L g,t , L h,tand L c,t are the multi-energy load power of electricity, gas, heat and cold respectively; P e1,CHP,t and P e2,CHP,t They are the electric power transmitted to the electric energy bus and the electric energy load bus by the cogeneration unit respectively; and are the electric power transmitted from the electric energy storage to the electric energy bus and the electric energy load bus respectively; P e1,EC,t and P e2,EC,t are the electric power transmitted from the electric energy bus and the electric energy load bus to the electric refrigerator; P e1,EB,t and P e2,EB,t The electric power transmitted from the electric energy bus and the electric energy load bus to the electric boiler respectively; and are the electric power transmitted from the electric energy bus and the electric energy load bus to the electric energy storage; P g,CHP,t and P g,GB,t are the input gas power of the cogeneration unit and the gas boiler respectively; P h,CHP,t , P h,EB,t , P h,GB,t and P dis,HES,t are the output thermal power of the cogeneration unit, electric boiler, gas boiler and thermal energy storage respectively; P h,AC,t is the input heat power of the absorption refrigerator; P c,EC,t and P c,AC,t are the cooling power outputs of the electric refrigerator and the absorption refrigerator respectively.

[0082] (2) Optimal load reduction model for energy stations

[0083] Based on the energy station fault model and improved hierarchical energy hub, an optimal load reduction model for energy stations is established.

[0084] (1) Objective function

[0085] The optimal load reduction model of energy stations takes the minimum multi-energy load reduction of energy stations under earthquake disasters as the objective function, as shown in formula (19).

[0086]

[0087] Where: and They are respectively the reduction in power of electricity, gas, heat and cold multi-energy loads of the energy station after the earthquake disaster.

[0088] (2) Constraints

[0089] Taking the operation constraints of the energy station after the earthquake disaster as the constraint conditions, it is written as follows:

[0090] 1) Multi-energy load power balance

[0091] Figure 4 The power balance equations of the energy hub model are shown in equations (8) to (18). After an earthquake disaster occurs, the equipment may be damaged, and the energy station may not be able to meet the energy demand of the multi-energy load, resulting in load reduction. It is necessary to add a post-disaster multi-energy load power balance equation, as shown in equations (20) to (23).

[0092]

[0093] Where: and The electricity power and natural gas power transmitted to multiple energy loads by the upper energy network after an earthquake disaster; and They are respectively the electrical power, thermal power and cold power output by the energy station after the disaster.

[0094] 2) Energy equipment operating characteristics

[0095] The operating characteristics of different equipment in the energy station are significantly different and need to be constructed separately. The operating characteristics of gas boilers, electric boilers, absorption chillers and electric chillers are shown in equations (24) to (28). Among them, equations (24) to (27) are energy conversion equations, and equation (28) indicates that the input power of energy equipment is constrained by the number of energy equipment and the maximum input power of a single device.

[0096]

[0097] Where: η GB , η EB , η AC and η EC are the energy conversion coefficients of gas boilers, electric boilers, absorption chillers and electric chillers respectively; Indicates the upper limit of input power of a single type v energy device.

[0098] The cogeneration unit can convert the input natural gas into electricity and heat energy at the same time. Its operating characteristics are shown in equations (29) to (31).

[0099]

[0100] Where: η CHP Represents the energy conversion coefficient of the cogeneration unit; R he is the heat-to-electricity ratio of the cogeneration unit; Indicates the upper limit of natural gas input power for a single cogeneration unit.

[0101] The electric energy storage equipment has the functions of shaving peaks and filling valleys, promoting the consumption of clean energy, and providing disaster recovery power supply. The overall characteristics shown to the outside are shown in equations (32) to (36). Among them, equation (32) is the state transition equation of the electric energy storage; equations (33) and (34) are the upper and lower limit constraints of the charge and discharge power of the electric energy storage and the mutually exclusive constraint of the charge and discharge state; equation (35) is the upper and lower limit constraints of the stored energy of the electric energy storage; equation (36) indicates that the energy stored at the beginning and end of the electric energy storage scheduling cycle is equal.

[0102]

[0103] Where: Δt is the time period; t o and t max are the initial and final periods of the energy storage dispatch cycle respectively; E BES,t represents the electric energy stored in the electric energy storage during period t; η ch,BES and η dis,BES They represent the charging and discharging efficiency of electric energy storage respectively; and Respectively represent the maximum charging and discharging power of a single energy storage unit; μ BES,t It is the charge and discharge status flag of the energy storage during time period t; and E BES They represent the maximum and minimum values ​​of the energy stored in the electric energy storage respectively.

[0104] Thermal energy storage can cooperate with cogeneration units, electric boilers and other heating equipment in energy stations to improve the flexibility of post-disaster heating in energy stations, thereby improving the elasticity of energy stations. The overall external characteristics of thermal energy storage are similar to those of electrical energy storage, as shown in equations (37) to (41). Among them, equation (37) is the state transfer equation of thermal energy storage; equations (38) and (39) indicate that the charging and discharging states of thermal energy storage are mutually exclusive; equation (40) is the upper and lower limit constraints of the thermal energy storage storage energy; equation (41) indicates that the energy stored in the thermal energy storage at the beginning and end of the scheduling cycle is equal.

[0105]

[0106] Where: E HES,t They represent the thermal energy stored in the thermal energy storage during period t; μ HES,t is the charge and discharge status flag of the thermal energy storage during period t; η ch,HES and η dis,HES They represent the charging and discharging efficiency of thermal energy storage respectively; and are the maximum values ​​of thermal energy storage charging and discharging power respectively; and E HES are the maximum and minimum values ​​of the thermal energy storage energy respectively.

[0107] 3) Energy purchase limit for energy stations

[0108] The upper limit of energy purchase of energy station is constrained by external equipment, as shown in equations (42) and (43).

[0109]

[0110]

[0111] Where: and They are respectively the maximum values ​​of electricity and gas purchased by the energy station.

[0112] S3. Taking into account the changes in the multi-energy conversion storage capacity and multi-energy supply capacity of the energy station after the earthquake, calculate the energy station's elasticity evaluation index.

[0113] (1) Energy station performance quantification method

[0114] The ratio of the energy supply level before and after the disaster can be used to describe the impact of earthquake disasters on its performance. Therefore, the ratio of the energy station performance before and after the earthquake disaster is defined as F m,t represents the impact of earthquake disasters on the performance of energy stations, as shown in formula (44). m,t The smaller the value, the greater the impact of earthquake disasters on the performance of the mth energy source of the energy station. m,t A value of 1 indicates that earthquake disasters have no effect on the performance of this type of energy.

[0115]

[0116] Where: m and Ω m are the number and set of energy types respectively; and They represent the performance of the mth energy source of the energy station in period t before and after the earthquake disaster.

[0117] (2) Robustness index

[0118] The robustness of an energy station reflects its ability to effectively resist the impact of earthquake disasters and maintain high performance. The robustness index can be reflected by the percentage of energy station performance degradation under earthquake disasters. The smaller the robustness index of an energy station under earthquake disasters, the better its robustness and the stronger its ability to resist earthquake disasters. In addition, affected by earthquake disasters, multiple types of equipment may fail simultaneously in an energy station, and the frequency of failure of different equipment reflects the weak link of the energy station. Therefore, a fault frequency index is added to the robustness index to measure the number of failures of different components per unit time. The larger the fault frequency index, the higher the probability of failure of the component, that is, the worse the elasticity, as shown in equations (45) and (46).

[0119]

[0120] Where: Rm is the robustness index of the energy station; i is the frequency of failure, which can be calculated based on the Markov method through the probability of the previous state of the energy station and the failure rate of the equipment; i-1 is the probability of the previous fault state; v is the failure probability of the vth device.

[0121] (3) Sensitivity index

[0122] When a certain energy device is damaged, it will affect the performance of multiple energy sources of the energy station at the same time. In order to quantify the relationship between the multi-energy supply capacity and conversion capacity of the energy station under earthquake disasters, equations (47) and (48) are defined to represent the sensitivity of the output energy performance of the energy station to the decline in the input energy performance under earthquake disasters. Among them, The larger the value, the more sensitive the m1 energy in the energy station is to the performance changes of the m2 energy.

[0123]

[0124] Where: It represents the sensitivity of the m1th energy to the m2th energy in the energy station under earthquake disaster; e, g, h, c represent electricity, gas, heat, and cold energy respectively.

[0125] (4) Dependence index

[0126] Earthquake disasters may cause "cascading failures" in energy stations, and the performance of heterogeneous energy subsystems in energy stations may also have "cascading effects". For example, when an energy station is equipped with only an absorption chiller as the only refrigeration equipment, if the heat-generating equipment is damaged, both the heating and cooling capacities of the energy station will be affected. In order to evaluate the relationship between different performances of energy stations during disasters, the mathematical concept of "cross-correlation" is used to describe the similarity of different performances of energy stations under earthquake disasters, as shown in Equation (49).

[0127]

[0128] Where: It indicates the similarity of the degree of impact of earthquake disaster on the m1th performance and the m2th performance of the energy station. The larger the value is, the more similar the impact of earthquake disasters on the m1th performance and the m2th performance of the energy station is, that is, the higher the dependence between the m1th performance and the m2th performance is.

[0129] S4. Generate a set of energy station fault states under earthquake disasters, solve the optimal load reduction model of the energy station, and calculate the elasticity index of the energy station. In a specific implementation, the YALMP toolkit and the Gurobi solver are called to solve the optimal load reduction model to obtain the optimal load reduction results of the energy station under different fault states and the elasticity index of the energy station.

[0130] Based on the energy station performance quantification method and different elasticity index calculation methods, the calculation process of energy station elasticity index for earthquake disasters is further proposed, such as Figure 5 The detailed calculation steps are as follows:

[0131] Step 1: Input preset parameters such as earthquake disaster, earthquake vulnerability function of various equipment, multi-energy load of energy station, fault order, etc.

[0132] Step 2: Establish energy equipment vulnerability model and energy station failure model;

[0133] Step 3: Initialize the energy station fault state set order o=1;

[0134] Step 4: Create an o-order fault state set according to equations (5) to (7)

[0135] Step 5: Select Medium fault state z;

[0136] Step 6: Calculate the optimal load reduction results of energy stations under earthquake disasters using the energy station optimal load reduction model based on the improved hierarchical energy hub;

[0137] Step 7: Judgement Are all fault states in the analysis? If yes, go to step 8; otherwise, let z=z+1 and go to step 5;

[0138] Step 8: Determine whether o is equal to the preset fault order N. If so, go to step 9; otherwise, set o=o+1 and go to step 4;

[0139] Step 9: Calculate the quantified value of energy station performance according to formula (44);

[0140] Step 10: Calculate the robustness index of the energy station according to equations (45) and (46);

[0141] Step 11: Calculate the energy station sensitivity index according to equations (47) and (48);

[0142] Step 12: Calculate the energy station dependence index according to formula (49).

[0143] In the above method, the energy station can convert the input energy of the upper power grid and gas grid into electricity, gas, heat and cold to meet the energy demand of multi-energy loads, that is, the system can be divided into electricity, gas, heat and cold subsystems at the system level. Due to the selection and sizing scheme of energy station equipment, the elasticity of different subsystems under earthquake disasters is different. The above method is to identify weak subsystems for energy stations with fixed equipment selection and sizing schemes; at the component level, the energy station is composed of different energy equipment, and the vulnerability characteristics of different equipment under earthquake disasters are significantly different. The above method can evaluate the impact of different equipment on the elasticity of energy station subsystems and the whole system, that is, the identification of weak components. Through the above energy station elasticity indicators constructed from the three aspects of robustness, sensitivity and dependence, the weak links of energy stations at the system and component levels can be reliably identified.

[0144] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0145] To verify the effectiveness of the above method, this example uses an energy station consisting of 4 gas boilers, 2 electric boilers, 1 absorption chiller, 2 electric chillers, 2 thermal energy storage units, 1 electric energy storage unit and 5 cogeneration units to analyze and verify the proposed method. The detailed parameters of the equipment are shown in Table 2, and the multi-energy load curve of the energy station is shown in Figure 6 shown.

[0146] Table 2 Detailed parameters of internal equipment of energy station

[0147]

[0148] Taking the first-order fault state as an example (only one type of equipment is damaged at the same time), the resilience index of the energy station under earthquake disasters is analyzed. The frequency and number of each fault type are shown in Table 3.

[0149] Table 3 Fault numbers and frequencies of various types of energy equipment

[0150]

[0151] Figure 7 The figure shows the reduction in heat load and cooling load of the energy station after the earthquake disaster. Figure 7 From (a), we can see that when the earthquake disaster damages the gas boiler, electric refrigerator, and cogeneration unit, the energy station will experience different degrees of heat load reduction. Figure 7 (a) shows that compared with the failure of the gas boiler, the heat load reduction of the energy station increases significantly when the cogeneration unit fails. This is because the vulnerability characteristics of the cogeneration unit and the gas boiler are different under earthquake disasters. There are differences in the number of damaged gas boilers and cogeneration units under the same earthquake intensity, resulting in different heat load reductions under different equipment failures. In addition, when the electric refrigerator fails, the energy station also has a heat load reduction. This is because the electric refrigerator is the main cooling equipment of the energy station. After the earthquake disaster, the cooling load of the energy station is significantly reduced (such as Figure 7 As shown in (b), the heat energy can only be converted into cold energy through absorption chillers to alleviate the cooling pressure, resulting in heat load reduction during the peak cooling period. This reflects the mutual influence between the multi-energy supply capabilities of energy stations under earthquake disasters, that is, the mutual influence between the elasticity of heterogeneous energy subsystems.

[0152] Starting from 1:00, at one-hour intervals, the average performance degradation ratio of the energy station in a typical day, that is, in 24 moments, is calculated from the perspectives of both the subsystem and the whole system, such as Figure 8 As shown. Figure 8 It can be seen that when any energy equipment is damaged under earthquake disasters, the performance of the power subsystem of the energy station will decline more than other subsystems. This is because in this energy station, electric energy has the most abundant form of energy conversion, which can be directly coupled with the gas, heat, and cold subsystems through equipment such as cogeneration units, electric boilers, and electric refrigerators. The performance of the power subsystem of the energy station is easily affected by changes in the performance of other subsystems. For example, when the absorption chiller fails, the demand for thermal energy conversion in the energy station decreases, the cogeneration unit reduces both the electrical power and thermal power output, and the electric boiler reduces the electrical power input and thermal power output. The power supply capacity and power conversion capacity of the energy station are reduced at the same time, resulting in a decline in the performance of the power subsystem of the energy station.

[0153] according to Figure 8 Energy station performance degradation ratio and Fig. 9 The robustness index of the energy station shown in the figure analyzes the weak equipment of the energy station from the component level. Figure 8 It can be seen that after the earthquake disaster, the performance of the entire system in the fault state of Energy Station 4 decreased most seriously. Fig. 9It can be seen that in the fault state No. 4, that is, when the electric refrigerator is damaged, the robustness indicators of various subsystems and the entire system of the energy station are the highest. The reasons for the above phenomenon are: the electric refrigerator is damaged, the number of electric energy conversion equipment in the energy station is reduced, the electric and cold subsystems are decoupled to a certain extent, the electric energy conversion paths are reduced, and the electric energy conversion capacity of the energy station is reduced. For the natural gas subsystem, since the electric and gas subsystems are deeply coupled based on the cogeneration unit, the power consumption paths are reduced, resulting in a reduction in the energy station's demand for electric power from the cogeneration unit and a decrease in the natural gas conversion capacity. For the thermal subsystem, after a large number of electric refrigerators are damaged, due to the limited number of absorption refrigerators, it is difficult to increase the cooling capacity of the energy station by increasing the thermal power input of the absorption refrigerator, so it is impossible to generate more thermal energy conversion demand, and the performance of the thermal subsystem changes are limited. At the same time, the electric, thermal, and gas subsystems are deeply coupled based on the cogeneration unit, and the reduction in electric power demand leads to a simultaneous reduction in the natural gas power input and thermal power output of the cogeneration unit. Therefore, the electric refrigerator can be regarded as the weak equipment of the energy station, and the overall resilience of the energy station can be improved by appropriately increasing the capacity of the electric refrigerator or reinforcing it first.

[0154] Analyze the weak links of the energy station from the system level. Fig. 9 It can be seen that under different fault conditions, the robustness index of the power subsystem is the highest compared with other subsystems, indicating that the performance of the power subsystem is more affected by earthquake disasters. The reasons for the above phenomenon are: first, there are many types of equipment directly coupled with the power subsystem. Once the above equipment fails, it will directly affect the performance of the power subsystem of the energy station; second, compared with other types of equipment, the electrical equipment in the energy station, that is, electric refrigerators, cogeneration units, and electric energy storage, have a higher probability of failure under earthquake disasters and a greater failure frequency, resulting in a higher robustness index of the power subsystem, indicating that the weak subsystem of the energy station is the power subsystem.

[0155] The input of the energy station is electricity and natural gas, and the output is electricity, heat, and cold energy. The sensitivity of the energy station output energy to the input energy under different fault conditions can be analyzed according to the sensitivity index of the energy station after the earthquake disaster, such as Fig.10 As shown. Fig.10 It can be seen that the cooling performance of the energy station will be affected by the power subsystem and the natural gas subsystem only when the fault state 4, that is, the electric refrigerator fails. The reason for the above phenomenon is that the electric refrigerator is the main cooling equipment of the energy station. When other equipment fails, the electric refrigerator can be disconnected from other energy equipment according to the energy supply tension of the energy station, and directly driven by the upper input power, ensuring the stability of the cooling performance of the energy station after the disaster.

[0156] Fig.11 The figure shows the dependence index of the energy station under different fault conditions. Fig.11It can be seen that when different equipment is damaged, the dependence index of the energy station is significantly different. This is because the input and output energy types of the faulty equipment directly affect the similarity of the performance of different subsystems of the energy station under earthquake disasters. Fig.11 In the figure, the cooling performance of the energy station is affected by the performance of other subsystems only when the electric refrigerator is damaged. This indicates that after the earthquake, in order to reduce the energy supply pressure of the energy station, the electric refrigerator directly draws power from the upper energy network and is disconnected from other subsystems. Fig.11 It can be seen that when the energy station is in fault state 6, that is, when the thermal energy storage is damaged, the higher the dependence index of the thermal subsystem on the natural gas subsystem and the power subsystem, the higher the dependence index of the thermal subsystem on the natural gas subsystem and the power subsystem. This shows that thermal energy storage can increase the flexibility of the post-disaster energy station and reduce the dependence of the thermal subsystem of the post-disaster energy station on the performance of the power subsystem and the natural gas subsystem.

[0157] In order to further verify the weak components of the energy station, Figure 7 There is a load reduction fault state in the energy station. A corresponding device is added as disaster recovery capacity. The multi-energy load reduction results of the energy station after adding disaster recovery capacity are as follows: Fig.12 As shown. Fig.12 It can be seen that adding disaster recovery capacity to corresponding equipment under different fault conditions can improve the multi-energy supply capacity of the energy station after the disaster and reduce the multi-energy load reduction. Among them, under the same disaster recovery capacity, the daily average load reduction of fault states 7, 4, and 1 was reduced by 2.75MWh, 3.55MWh, and 0.18MWh, respectively, indicating that adding disaster recovery capacity of electric refrigerators can reduce load reduction to a greater extent, indicating that electric refrigerators are the weak components of energy stations.

[0158] Example 2

[0159] This embodiment provides an energy station resilience assessment system that takes into account equipment vulnerability characteristics, including one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs include instructions for executing an energy station resilience assessment method that takes into account equipment vulnerability characteristics as described in Example 1.

[0160] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0161] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for evaluating the resilience of an energy station taking into account the vulnerability characteristics of equipment, characterized in that: The following steps are involved: Obtain the preset parameters of the energy station, take into account the vulnerability characteristics of the equipment under earthquake disasters, establish the energy station fault model, and generate the energy station fault state set under earthquake disasters; Based on the energy station fault state set, the energy station optimal load reduction model is established using the improved hierarchical energy hub model, and the optimal load reduction results of energy stations with different fault states after the earthquake disaster are obtained. Based on the optimal load reduction results of the energy station under different fault states, the energy station performance quantitative value and the energy station resilience evaluation index under earthquake disaster are calculated, and the weak links of the energy station at the system and component levels are identified based on the energy station resilience evaluation index; The preset parameters of the energy station include the earthquake strength of the energy station, the type of equipment in the energy station, the earthquake vulnerability function of the energy equipment and the order of the fault state set of the energy station; The equipment vulnerability characteristics under the earthquake disaster are expressed as: Where: fail represents the comprehensive loss coefficient of energy equipment quantity under earthquake disaster; θ k represents the quantity loss coefficient corresponding to the energy equipment in the kth damaged state; v and V represent the index and set of energy equipment types respectively; n v Indicates the number of energy equipment before the earthquake disaster occurs; Δn v represents the amount of energy equipment lost after an earthquake disaster; k represents the probability of public infrastructure reaching the kth damaged state; k max Number the most severely damaged state; The energy station fault model is expressed as: Where: is the energy station fault status set; represents the set of damaged device type combinations u; Represents the set of damaged equipment quantity in different states; is the probability set of different fault states; represents the probability of the zth fault state; represents the set of damaged equipment type combinations u under the zth fault state; represents the probability that the i-th type of equipment is in the most seriously damaged state under an earthquake disaster; o represents the order of the energy station fault set; u is the combination of equipment failure types under the fault state order o, V represents the set of energy equipment types, u is a subset of the set V; Card(u) represents the number of damaged equipment types.

2. The energy station resilience assessment method taking into account equipment vulnerability characteristics according to claim 1 is characterized in that: The objective function of the energy station optimal load reduction model is to minimize the multi-energy load reduction of the energy station after the earthquake, and the constraints include energy equipment operation characteristic constraints, multi-energy load power balance constraints and energy station energy purchase upper limit constraints.

3. The energy station resilience assessment method taking into account equipment vulnerability characteristics according to claim 1 is characterized in that: The YALMIP toolkit and Gurobi solver are called to solve the optimal load reduction model of the energy station.

4. The energy station resilience assessment method taking into account equipment vulnerability characteristics according to claim 1 is characterized in that: The performance decline ratio of the energy station after the disaster is used as the quantitative value of the energy station performance, and the expression is: Where: m and Ω m are the number and set of energy types respectively; F m,t is the percentage of energy station performance decline after the disaster; and They represent the performance of the mth energy source of the energy station in period t before and after the earthquake disaster.

5. The energy station resilience assessment method taking into account equipment vulnerability characteristics according to claim 1 is characterized in that: The energy station resilience assessment index under earthquake disasters includes robustness index, sensitivity index and dependence index, among which: The robustness index is expressed as: Where: R m is the robustness index of the energy station; i is the probability of occurrence of the i-th fault; The sensitivity index is expressed as: Where: It represents the sensitivity of the m1th energy to the m2th energy in the energy station under earthquake disaster; The dependency index is expressed as: Where: It indicates the similarity of the degree to which the m1th performance and m2th performance of the energy station are affected by earthquake disasters.

6. The energy station resilience assessment method taking into account equipment vulnerability characteristics according to claim 5 is characterized in that: The probability of occurrence of the i-th fault is calculated based on the Markov method through the probability of a fault state in the energy station and the failure rate of the equipment.

7. An energy station resilience assessment system taking into account equipment vulnerability characteristics, characterized in that: It comprises one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing an energy station resilience assessment method taking into account equipment vulnerability characteristics as described in any one of claims 1-6.

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