Toughness evaluation method, device and equipment based on multi-energy coupling active power distribution network

Abstract

The invention relates to a toughness evaluation method, device and equipment based on a multi-energy coupling active power distribution network. The method comprises the steps of determining energy network types of the multi-energy coupling active power distribution network, constructing multi-stage evaluation indexes according to network characteristics corresponding to the energy network types, and constructing an energy network multi-stage evaluation index system according to the multi-stage evaluation indexes. Determining index levels of the energy network multi-level evaluation index system, determining a paired comparison matrix corresponding to each index level, and if it is determined that an index consistency characteristic value determined according to the paired comparison matrix meets an index consistency requirement, determining an index weight corresponding to each evaluation index according to the paired comparison matrix, and determining the weight of each screened target toughness index, determining a fuzzy measure according to each target toughness index and the respective index weight, and performing weighted fusion according to the fuzzy measure and the index value of each target toughness index to obtain a toughness evaluation result. The method can improve the accuracy of a power grid toughness evaluation result.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a resilience assessment method, apparatus, computer equipment, computer-readable storage medium and computer program product based on a multi-energy coupled active distribution network. Background Art

[0002] With the development of power technology and the promotion and application of power systems in different meteorological environments, pole collapse, line breakage and other faults caused by various extreme weather conditions have damaged various infrastructures of power systems (such as multi-energy coupling active distribution networks), resulting in power outages, water outages, heating and cooling interruptions of varying degrees of severity for users. In order to ensure the normal and stable operation of the power system under different extreme weather conditions, it is necessary to improve the ability of the multi-energy coupling active distribution network to cope with extreme weather. Therefore, it is necessary to evaluate the resilience of the multi-energy coupling active distribution network when it suffers a fault, so as to improve the ability of the multi-energy coupling active distribution network to cope with extreme weather in a targeted manner based on the obtained resilience evaluation results.

[0003] Traditional resilience assessment methods usually require obtaining dynamic operating data of multi-energy coupled active distribution networks during extreme weather events in order to evaluate the resilience of multi-energy coupled active distribution networks based on the dynamic operating data. However, multi-energy coupled active distribution networks are affected by the coupling of multiple energy forms in the network, and their response characteristics in response to extreme weather events are more complex than those of traditional distribution networks. It is difficult to quantitatively evaluate their resilience from all aspects and angles based on the characteristics of different energy networks, and the obtained assessment results still have the problem of low accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide a resilience assessment method, device, computer equipment, computer-readable storage medium and computer program product based on a multi-energy coupled active distribution network, which can improve the accuracy of the resilience assessment results of the multi-energy coupled active distribution network in response to the above-mentioned technical problems.

[0005] In the first aspect, the present application provides a resilience assessment method based on a multi-energy coupled active distribution network, comprising: determining the type of energy network associated with the multi-energy coupled active distribution network, and for each of the energy network types, constructing a multi-level evaluation index corresponding to the energy network type according to the network characteristics corresponding to the energy network type; constructing a multi-level evaluation index system for an energy network corresponding to the multi-energy coupled active distribution network according to the multi-level evaluation indicators corresponding to each of the energy network types, and determining the indicator levels included in the multi-level evaluation index system for an energy network; for each of the indicator levels, determining a pairwise comparison matrix corresponding to the indicator level, and determining, based on the pairwise comparison matrix, a pairwise comparison matrix corresponding to the indicator level. corresponding indicator consistency characteristic value; if it is determined that the indicator levels all meet the indicator consistency requirements according to the indicator consistency characteristic value, then according to the pairwise comparison matrix, the indicator weights corresponding to the evaluation indicators in the indicator level are determined; based on the multi-level evaluation indicator system of the energy network, the target resilience indicators are screened out, and the indicator weights of each target resilience indicator are determined, so as to determine the fuzzy measure between each target resilience indicator according to each target resilience indicator and the indicator weight of each target resilience indicator; according to the fuzzy measure between each target resilience indicator and the indicator value of each target resilience indicator, weighted fusion is performed to obtain the resilience evaluation result corresponding to the multi-energy coupled active distribution network.

[0006] In the second aspect, the present application also provides a resilience assessment device based on a multi-energy coupled active distribution network, including: a multi-level evaluation index construction module, used to determine the energy network type associated with the multi-energy coupled active distribution network, and for each of the energy network types, construct a multi-level evaluation index corresponding to the energy network type according to the network characteristics corresponding to the energy network type; an energy network multi-level evaluation index system construction module, used to construct an energy network multi-level evaluation index system corresponding to the multi-energy coupled active distribution network according to the multi-level evaluation indicators corresponding to each of the energy network types, and determine the indicator levels included in the energy network multi-level evaluation index system; an indicator consistency eigenvalue determination module, used to determine, for each of the indicator levels, the pairwise comparison matrix corresponding to the indicator level, and determine the pairwise comparison matrix corresponding to the indicator level. The indicator consistency characteristic value corresponding to the indicator level; an indicator weight determination module, which is used to determine the indicator weights corresponding to the evaluation indicators in the indicator level according to the paired comparison matrix if it is determined that the indicator levels all meet the indicator consistency requirements according to the indicator consistency characteristic value; a fuzzy measurement determination module, which is used to screen out target resilience indicators based on the multi-level evaluation indicator system of the energy network, and determine the indicator weights of each target resilience indicator, so as to determine the fuzzy measurement between each target resilience indicator according to each target resilience indicator and the indicator weights of each target resilience indicator; a resilience assessment result acquisition module, which is used to perform weighted fusion according to the fuzzy measurement between each target resilience indicator and the indicator value of each target resilience indicator, so as to obtain the resilience assessment result corresponding to the multi-energy coupled active distribution network.

[0007] In a third aspect, the present application provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above-mentioned resilience assessment method for a multi-energy coupled active distribution network when executing the computer program.

[0008] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for assessing the resilience of a multi-energy coupled active distribution network.

[0009] In a fifth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for assessing the resilience of a multi-energy coupled active distribution network are implemented.

[0010] In the above-mentioned resilience assessment method, device, computer equipment, computer-readable storage medium and computer program product based on multi-energy coupling active distribution network, by determining the energy network type associated with the multi-energy coupling active distribution network, and for each energy network type, according to the network characteristics corresponding to the energy network type, constructing a multi-level evaluation index corresponding to the energy network type, so that according to the multi-level evaluation index corresponding to each energy network type, a multi-level evaluation index system of energy network corresponding to the multi-energy coupling active distribution network can be constructed to evaluate the resilience of the multi-energy coupling active distribution network in an all-round and multi-angle manner. Among them, by determining the indicator level included in the multi-level evaluation index system of energy network, and for each indicator level, determining the pairwise comparison matrix corresponding to the indicator level, so as to determine the indicator consistency eigenvalue corresponding to the indicator level according to the pairwise comparison matrix, when it is determined that the indicator level meets the indicator consistency requirement according to the indicator consistency eigenvalue, the indicator weight corresponding to the evaluation indicator in the indicator level can be determined according to the pairwise comparison matrix. Furthermore, based on the multi-level evaluation index system of energy networks, target resilience indicators are screened out, and the indicator weights of each target resilience indicator are determined. According to each target resilience indicator and the indicator weights of each target resilience indicator, the fuzzy measurement between each target resilience indicator is determined. Therefore, weighted fusion can be performed according to the fuzzy measurement between each target resilience indicator and the indicator value of each target resilience indicator to obtain the resilience assessment result corresponding to the multi-energy coupled active distribution network. This realizes the reasonable quantification and accurate evaluation of the resilience of the multi-energy coupled active distribution network by comprehensively considering the coupling relationship between different energy networks, reasonably analyzing the fuzzy measurement and influencing mechanism between different indicators, and weighting the indicators according to the mutual influence between the indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 This is an application environment diagram of a resilience assessment method based on a multi-energy coupled active distribution network in one embodiment;

[0013] Figure 2 It is a flowchart of a resilience assessment method based on a multi-energy coupled active distribution network in one embodiment;

[0014] Figure 3 A schematic diagram of a power supply portion of a multi-energy coupling active power distribution network in one embodiment;

[0015] Figure 4A schematic diagram of a gas supply portion of a multi-energy coupled active distribution network in one embodiment;

[0016] Figure 5 A schematic diagram of a heating portion of a multi-energy coupled active power distribution network in one embodiment;

[0017] Figure 6 A schematic diagram of a water supply portion of a multi-energy coupled active power distribution network in one embodiment;

[0018] Figure 7 A schematic diagram of a multi-level evaluation index system for an energy network corresponding to a multi-energy coupled active distribution network in one embodiment;

[0019] Figure 8 A schematic diagram of a flow chart of an evaluation algorithm for a multi-energy coupled active power distribution network in one embodiment;

[0020] Fig. 9 A schematic diagram of gas pressure at a gas supply network node under a fault in one embodiment;

[0021] Fig.10 A schematic diagram of improving the voltage of a node in a power supply network under a fault in one embodiment;

[0022] Fig.11 A schematic diagram of a resilience assessment result corresponding to a multi-energy coupled active distribution network in an embodiment;

[0023] Fig.12 A schematic diagram of a resilience assessment result corresponding to a multi-energy coupled active power distribution network in another embodiment;

[0024] Fig.13 It is a structural block diagram of a resilience assessment device based on a multi-energy coupled active distribution network in one embodiment;

[0025] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] The resilience assessment method based on multi-energy coupling active distribution network in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablet computers, and Internet of Things devices, and the server 104 can be an independent physical server or a server cluster composed of multiple physical servers. Among them, after receiving the resilience assessment request fed back by the terminal 102, the server 104 determines the multi-energy coupled active distribution network that needs to be evaluated according to the resilience assessment request, and determines the energy network type associated with the multi-energy coupled active distribution network, so as to construct a multi-level evaluation index corresponding to the energy network type for each energy network type according to the network characteristics corresponding to the energy network type, and construct a multi-level evaluation index system of energy networks corresponding to the multi-energy coupled active distribution network according to the multi-level evaluation indicators corresponding to each energy network type. Furthermore, the server 104 determines the indicator levels included in the multi-level evaluation index system of the energy network, and determines the pairwise comparison matrix corresponding to the indicator level for each indicator level, so as to determine the indicator consistency characteristic value corresponding to the indicator level according to the pairwise comparison matrix. Among them, if the server 104 determines that the indicator levels all meet the indicator consistency requirements according to the indicator consistency characteristic value, then the indicator weights corresponding to the evaluation indicators in the indicator level are determined according to the pairwise comparison matrix, and after the target resilience indicators are screened out based on the multi-level evaluation index system of the energy network, the indicator weights of each target resilience indicator are determined, so as to determine the fuzzy measure between each target resilience indicator according to each target resilience indicator and the indicator weight of each target resilience indicator, so as to obtain the resilience evaluation result corresponding to the multi-energy coupled active distribution network according to the fuzzy measure between each target resilience indicator and the indicator value of each target resilience indicator by weighted fusion. Among them, the server 104 can feed back the obtained resilience evaluation result corresponding to the multi-energy coupled active distribution network to the terminal 102 for display.

[0028] In an exemplary embodiment, Figure 2 As shown in the figure, a resilience assessment method based on multi-energy coupling active distribution network is provided. Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps S202 to S212.

[0029] in:

[0030] Step S202, determining the energy network type associated with the multi-energy coupling active distribution network, and for each energy network type, constructing a multi-level evaluation index corresponding to the energy network type according to the network characteristics corresponding to the energy network type.

[0031] Specifically, for each energy network type, the server constructs a first-level network type indicator according to the energy network type, and for each first-level network type indicator, constructs a second-level static indicator and a second-level dynamic indicator according to the energy state corresponding to the energy network type. Furthermore, for each second-level static indicator and each second-level dynamic indicator, according to the network characteristics corresponding to the energy network type, multiple third-level resilience indicators under the second-level static indicator and multiple third-level resilience indicators under the second-level dynamic indicator are respectively constructed. Among them, the energy network types associated with the multi-energy coupled active distribution network specifically include power grids, gas grids, water grids, and heat grids. The first-level network type indicators constructed according to the energy network types specifically include power grid evaluation indicators, gas grid evaluation indicators, water grid evaluation indicators, and heat grid evaluation indicators. Since the energy state corresponding to each energy network type specifically includes static and dynamic, a second-level static indicator and a second-level dynamic indicator can be constructed for each first-level network type indicator.

[0032] Furthermore, since different energy network types have the same network characteristics when the energy state is static, the three-level resilience indicators included in the second-level static indicators under each first-level network type indicator are the same, and the three-level resilience indicators included in the second-level static indicators specifically include: line redundancy, energy duplication number, source-load power ratio, and network complexity. Among them, line redundancy is used to reflect the total number of paths from the source node to the load node, the energy duplication number is used to reflect the ratio of the total number of sources connected to each load node to the total number of sources, and the source-load power ratio is used to mainly reflect the power ratio of the load node to the source node closest to it, and the network complexity is used to reflect the number of branches connected to each node.

[0033] Exemplarily, the line redundancy BR is determined by the following formula (1):

[0034]

[0035] Among them, BR represents the line redundancy of the energy network, N L Represents the set of load nodes in the entire energy network, N i represents the set of source nodes that have branches connected to load node i, n ij Represents the number of branches between source node j and load node i.

[0036] For example, the energy repetition number RR is determined using the following formula (2):

[0037]

[0038] Among them, RR represents the energy duplication in the network, N L Represents the set of load nodes in the entire energy network, N iN represents the set of source nodes that have branches connected to load node i, R Represents the total number of source nodes in the network.

[0039] Exemplarily, the source-to-load power ratio PR is determined using the following formula (3):

[0040]

[0041] Where PR represents the source-to-load power ratio in the network, N L represents the set of load nodes in the entire energy network, P i represents the load power at load node i, P i.R Represents the source power at the source node closest to the load node.

[0042] Exemplarily, the network complexity NI is determined using the following formula (4):

[0043]

[0044] Among them, NI represents the network complexity in the network, N L Represents the set of load nodes in the entire energy network, n i Represents the number of branches connected to the i-th node.

[0045] In an exemplary embodiment, since different energy network types have different network characteristics when the energy state is dynamic, the third-level resilience indicators included in the second-level dynamic indicators under each first-level network type indicator are different. Specifically:

[0046] 1) When the first-level network type indicator is the power grid evaluation indicator, the third-level resilience indicators included in the second-level dynamic indicators include: A) Electric load recovery rate, which is used to reflect the degree of load recovery of the power grid during extreme events; B) Voltage deviation rate, which is used to reflect the degree of voltage deviation during extreme events; C) Electric network loss, which is used to reflect the loss of power transmission on the line during extreme events; D) Power grid line load rate, which mainly reflects the load condition of the power grid line during extreme events.

[0047] Exemplarily, the electric load recovery rate LR is determined by the following formula (5):

[0048]

[0049] Among them, LR represents the power load recovery rate in the power grid, P i.real represents the actual load power supply at load node i, P i Represents the load power at load node i.

[0050] Exemplarily, the voltage deviation rate VD is determined by the following formula (6):

[0051]

[0052] Where VD represents the node voltage deviation in the power grid, U Ni and U i They represent the rated value and the true value of the voltage at node i respectively.

[0053] Exemplarily, the power network loss NL is determined by the following formula (7):

[0054]

[0055] Where NL represents the network loss in the power grid, I ij represents the current flowing through the branch from node i to node j, r ij Represents the branch resistance from node i to node j.

[0056] Exemplarily, the grid line load rate BR is determined by the following formula (8):

[0057]

[0058] Among them, BR represents the line load rate in the power grid, P ij.max and P ij They represent the branch transmission power limit and actual transmission power from node i to node j respectively.

[0059] In an exemplary embodiment, Figure 3 As shown, a schematic diagram of the power supply part of a multi-energy coupling active distribution network is provided. Figure 3 It can be seen that the power supply part of the multi-energy coupling active distribution network includes multiple circuit nodes, that is, each number (including 1 to 33) corresponds to a circuit node, a solid line indicates that there is a line between two circuit nodes and they are in operation, and a dotted line indicates that there is a line between two circuit nodes but it is not in operation.

[0060] 2) When the first-level network type indicator is the gas network evaluation indicator, the third-level resilience indicators included in the second-level dynamic indicators include: A) gas load recovery rate, which is used to reflect the degree of load recovery of the gas network during extreme events; B) node gas pressure deviation, which is used to reflect the node gas pressure deviation in the gas network during extreme events; C) gas network loss, which is used to reflect the loss of transmission power on the gas network line during extreme events; D) gas pipeline load rate, which is used to mainly reflect the load condition of the gas network pipeline during extreme events.

[0061] Exemplarily, the gas load recovery rate GLR is determined by the following formula (9):

[0062]

[0063] Where GLR represents the load recovery rate in the gas grid, P g.i.real represents the actual gas load power at load node i, P g.i Represents the rated gas load power at load node i.

[0064] Exemplarily, the node pressure deviation PD is determined by the following formula (10):

[0065]

[0066] Among them, PD represents the node pressure deviation in the gas network, P Ni and P i They represent the rated value and the true value of the air pressure at node i respectively.

[0067] For example, the gas network loss NL is determined by the following formula (11): g :

[0068]

[0069] Among them, NL g represents the network loss in the gas grid, Q ij represents the natural gas flow on the branch from node i to node j, r g.ij Represents the air resistance of the branch from node i to node j.

[0070] Exemplarily, the gas pipeline load rate GR is determined by the following formula (12):

[0071]

[0072] Among them, GR represents the pipeline load rate in the gas network, P g.ij.max and P g.ij They represent the branch transmission power limit and actual transmission power from node i to node j respectively.

[0073] In an exemplary embodiment, Figure 4 As shown, a schematic diagram of the gas supply part of a multi-energy coupling active distribution network is provided. Figure 4 It can be seen that the gas supply part of the multi-energy coupled active distribution network includes multiple gas grid nodes, that is, each number (including 1 to 9) corresponds to a gas grid node. The solid line indicates that there is a line between two gas grid nodes and it is in operation, and the "×" on the solid line indicates that there is a route between the two gas grid nodes but it is not in operation.

[0074] 3) When the first-level network type indicator is the heating network evaluation indicator, the third-level resilience indicators included in the second-level dynamic indicators include: A) heat load recovery rate, which is used to indicate the degree of load recovery of the heating network during extreme events; B) node temperature deviation, which is used to indicate the node temperature deviation of the heating network during extreme events; C) heat network loss, which is used to indicate the loss of resources on the heating network line during extreme events; D) heat pipeline load rate, which is used to indicate the load condition of the heating network pipeline during extreme events.

[0075] Exemplarily, the heat load recovery rate HLR is determined by the following formula (13):

[0076]

[0077] Among them, HLR represents the load recovery rate in the heating network, P h.i.real represents the actual load thermal power at load node i, P h.i Represents the rated load thermal power at load node i.

[0078] Exemplarily, the node temperature deviation TD is determined by the following formula (14):

[0079]

[0080] Where TD represents the node temperature deviation in the heating network, T Ni and T i They represent the rated value and the actual value of the temperature at node i respectively.

[0081] Exemplarily, the thermal network loss NL is determined by the following formula (15): h :

[0082]

[0083] Where NLh represents the network loss in the heating network, q ij represents the flow of hot working fluid through the heating network branch from node i to node j, r h.ij Represents the branch impedance from node i to node j.

[0084] Exemplarily, the heat pipe load rate HR is determined by the following formula (16):

[0085]

[0086] Among them, HR represents the pipe load rate in the heating network, P h.ij.max and P h.ij They respectively represent the branch thermal medium transmission power limit and actual transmission power from node i to node j.

[0087] In an exemplary embodiment, Figure 5 As shown, a schematic diagram of the heating part of a multi-energy coupling active distribution network is provided. Figure 5 It can be seen that the heating part of the multi-energy coupled active distribution network includes multiple heating network nodes, that is, each number (including 1 to 14) corresponds to a heating network node. The solid line indicates that there is a line between two heating network nodes and it is in operation, and the "×" on the solid line indicates that there is a route between two heating network nodes but it is not in operation.

[0088] 4) When the first-level network type indicator is the water network evaluation indicator, the third-level resilience indicators included in the second-level dynamic indicators include: A) node water supply rate, which is used to reflect the load recovery degree of the water network during extreme events; B) node water pressure deviation, which is used to indicate the node air pressure deviation in the water network during extreme events; C) water resource loss, which is used to indicate the water resource loss on the water network line during extreme events; D) water pipeline load rate, which is used to indicate the load condition of the water network pipeline during extreme events.

[0089] Exemplarily, the node water supply rate WLR is determined by the following formula (17):

[0090]

[0091] Wherein, WLR represents the load recovery rate in the water network. Pw.i.real represents the actual load water power at load node i. Pw.i. represents the rated water supply power at load node i.

[0092] Exemplarily, the node water pressure deviation WD is determined by the following formula (18):

[0093]

[0094] Where WD represents the node water pressure deviation in the water network, π Ni and π i represent the rated value and true value of the water pressure at node i respectively.

[0095] For example, the water resource loss NL is determined by the following formula (19): w :

[0096] NL w =∑(q i.ij -q j.ij ); formula (19)

[0097] Among them, NL w represents the water resource loss during the transmission process, q i.ij represents the amount of water from pipe ij at node i, q j.ij Represents the amount of water in pipe ij on the node j side.

[0098] Exemplarily, the water pipe load rate WR is determined by the following formula (20):

[0099]

[0100] Among them, WR represents the pipe load rate in the water network, P w.ij.max and P w.ij They represent the branch hydraulic transmission power limit and actual transmission power from node i to node j respectively.

[0101] In an exemplary embodiment, Figure 6 As shown, a schematic diagram of the water supply part of a multi-energy coupling active distribution network is provided. Figure 6 It can be seen that the water supply part of the multi-energy coupled active distribution network includes multiple water network nodes, that is, each number (including 1 to 30) corresponds to a water network node. The solid line indicates that there is a line between two water network nodes and it is in operation, and the "×" on the solid line indicates that there is a route between the two water network nodes but it is not in operation.

[0102] Step S204, constructing an energy network multi-level evaluation index system corresponding to the multi-energy coupling active distribution network according to the multi-level evaluation index corresponding to each energy network type, and determining the index levels included in the energy network multi-level evaluation index system.

[0103] Among them, the types of energy networks specifically include power grids, gas grids, heat grids, and water grids. The multi-level evaluation indicators corresponding to each type of energy network include: 1) Multi-level evaluation indicators corresponding to the power grid: A) First-level network type indicators, namely power grid evaluation indicators, B) Second-level static indicators and second-level dynamic indicators under power grid evaluation indicators, C) Third-level resilience indicators under second-level static indicators: line redundancy, energy duplication number, source-load power ratio, and network complexity; D) Third-level resilience indicators under second-level dynamic indicators: electric load recovery rate, voltage deviation rate, electric network loss, and power grid line load rate. 2) Multi-level evaluation indicators corresponding to the gas grid: A) First-level network type indicators, namely gas grid evaluation indicators, B) Second-level static indicators and second-level dynamic indicators under gas grid evaluation indicators, C) Third-level resilience indicators under second-level static indicators: line redundancy, energy duplication number, source-load power ratio, and network complexity; D) Third-level resilience indicators under second-level dynamic indicators: gas load recovery rate, node gas pressure deviation, gas network loss, and gas pipeline load rate. 3) Multi-level evaluation indicators corresponding to the heat network: A) First-level network type indicators, namely heat network evaluation indicators, B) Second-level static indicators and second-level dynamic indicators under heat network evaluation indicators, C) Third-level resilience indicators under second-level static indicators: line redundancy, energy duplication, source-load power ratio, and network complexity; D) Third-level resilience indicators under second-level dynamic indicators: heat load recovery rate, node temperature deviation, heat network loss, and heat pipe load rate. 4) Multi-level evaluation indicators corresponding to the water network: A) First-level network type indicators, namely water network evaluation indicators, B) Second-level static indicators and second-level dynamic indicators under water network evaluation indicators, C) Third-level resilience indicators under second-level static indicators: line redundancy, energy duplication, source-load power ratio, and network complexity; D) Third-level resilience indicators under second-level dynamic indicators: node water supply rate, node water pressure deviation, water resource loss, and water pipe load rate.

[0104] Specifically, when the server constructs a multi-level evaluation index system for the energy network corresponding to the multi-energy coupled active distribution network according to the multi-level evaluation indicators corresponding to each energy network type, it specifically includes: 1) dividing the first-level network type indicators corresponding to multiple energy network types into the same indicator level, that is, dividing the power grid evaluation indicators, gas network evaluation indicators, heat network evaluation indicators and water network evaluation indicators into the first indicator level; 2) dividing the second-level static indicators and the second-level dynamic indicators under multiple first-level network type indicators into the same indicator level, that is, dividing the second-level static indicators and the second-level dynamic indicators under the power grid evaluation indicators, gas network evaluation indicators, heat network evaluation indicators and water network evaluation indicators into the second indicator level; 3) dividing the multiple third-level resilience indicators under each second-level static indicator and the multiple third-level resilience indicators under each second-level dynamic indicator into the same indicator level. The indicator level, that is, the secondary static indicators under the power grid / gas grid / water grid / heat grid evaluation indicators include multiple third-level resilience indicators (including line redundancy, energy duplication number, source-to-load power ratio, and network complexity), the secondary dynamic indicators under the power grid evaluation indicators include the electric load recovery rate, voltage deviation rate, power network loss and power grid line load rate, the secondary dynamic indicators under the gas grid evaluation indicators include the gas load recovery rate, node gas pressure deviation, gas network loss, and gas pipeline load rate, the secondary dynamic indicators under the heat network evaluation indicators include the heat load recovery rate, node temperature deviation, heat network loss and heat pipeline load rate, and the secondary dynamic indicators under the water network evaluation indicators include the node water supply rate, node water pressure deviation, water resource loss, and water pipeline load rate, are divided into the third indicator level (which can also be called the bottom indicator level).

[0105] Furthermore, the server determines the indicator levels included in the constructed energy network multi-level evaluation indicator system and the evaluation indicators included in each indicator level. According to the constructed energy network multi-level evaluation indicator system, it can be determined that the energy network multi-level evaluation indicator system includes 3 indicator levels, and each indicator level includes evaluation indicators corresponding to 4 energy network types.

[0106] In an exemplary embodiment, Figure 7 As shown in the figure, a multi-level evaluation index system for energy networks corresponding to multi-energy coupled active distribution networks is provided. Figure 7 It can be seen that the multi-level evaluation index system of energy network corresponding to the multi-energy coupling active distribution network includes three index levels, specifically involving four energy network types: power grid, gas grid, water grid and heat grid. Among them:

[0107] 1) The first indicator level includes four first-level network type indicators, namely: power grid evaluation indicators, gas grid evaluation indicators, heat grid evaluation indicators and water grid evaluation indicators. 2) The second indicator level includes four second-level static indicators and second-level dynamic indicators under the first-level network type indicators, namely, the second-level static indicators and second-level dynamic indicators under the power grid evaluation indicators, the second-level static indicators and second-level dynamic indicators under the gas grid evaluation indicators, the second-level static indicators and second-level dynamic indicators under the heat grid evaluation indicators, and the second-level static indicators and second-level dynamic indicators under the water grid evaluation indicators. 3) The third indicator level includes 4 third-level resilience indicators corresponding to each of the 4 second-level static indicators, and 4 third-level resilience indicators corresponding to each of the 4 second-level dynamic indicators, specifically: the second-level static indicators under the power grid / gas grid / water grid / heat grid evaluation indicators include line redundancy, energy duplication number, source-to-load power ratio, and network complexity; the second-level dynamic indicators under the power grid evaluation indicators include electric load recovery rate, voltage deviation rate, electric network loss, and power grid line load rate; the second-level dynamic indicators under the gas grid evaluation indicators include gas load recovery rate, node gas pressure deviation, gas network loss, and gas pipeline load rate; the second-level dynamic indicators under the heat network evaluation indicators include heat load recovery rate, node temperature deviation, heat network loss, and heat pipeline load rate; and the second-level dynamic indicators under the water network evaluation indicators include node water supply rate, node water pressure deviation, water resource loss, and water pipeline load rate.

[0108] Step S206: for each indicator level, determine the pairwise comparison matrix corresponding to the indicator level, and determine the indicator consistency eigenvalue corresponding to the indicator level according to the pairwise comparison matrix.

[0109] Specifically, for each indicator level, including the first indicator level constructed according to multiple first-level network types such as power grid evaluation indicators, gas network evaluation indicators, heat network evaluation indicators and water network evaluation indicators, the second indicator level constructed by the server according to the second-level static indicators and second-level dynamic indicators under multiple first-level network type indicators, and the third indicator level constructed according to multiple first-level network type indicators, multiple third-level resilience indicators under each second-level static indicator, and multiple third-level resilience indicators under each second-level dynamic indicator, respectively determine the pairwise comparison matrix corresponding to each indicator level.

[0110] Exemplarily, the pairwise comparison matrix A corresponding to the first indicator level is expressed by the following formula (21):

[0111]

[0112] Among them, 1, 2, 3, and 4 correspond to the first-level network type indicators of power grid indicators, gas grid indicators, heat grid indicators, and water grid indicators, respectively, while A abIndicates the importance of different power grid indicators. a can take any value from 1, 2, 3, and 4, and b can take any value from 1, 2, 3, and 4. For example, A 11 Indicates the importance of power grid indicators relative to power grid indicators, A 21 Indicates the importance of gas grid indicators relative to power grid indicators, A 32 Indicates the importance of heat network indicators relative to gas network indicators, A 43 Indicates the importance of water network indicators relative to heat network indicators.

[0113] Exemplarily, the following formulas (22) to (25) respectively represent the paired comparison matrices A1, A2, A3 and A4 constructed by the secondary indicator static indicators and the secondary dynamic indicators under the power grid indicator, gas grid indicator, heat grid indicator and water grid indicator in the second indicator level:

[0114]

[0115] Among them, A1, A2, A3, and A4 represent the pairwise comparison matrices constructed by the static and dynamic indicators of the secondary indicators under the power grid indicator, gas grid indicator, heat grid indicator, and water grid indicator, respectively. a.b1b2 represents the importance ranking between the b1th and b2th secondary indicators under the ath first-level network type indicator. For example, A in A1 1.21 Indicates the importance of the secondary dynamic index under the power grid index compared with the secondary static index, while A 1.12 It means that the secondary static index is more important than the secondary dynamic index, and the two are in a reciprocal relationship. 2.21 It indicates the importance of the secondary dynamic index under the gas network index compared with the secondary static index, while A 2.12 It indicates the importance of the secondary static index compared to the secondary dynamic index. 3.11 It indicates the importance of the secondary static index under the heat network index compared with the secondary static index, while A 3.12 It indicates the importance of the secondary static index compared to the secondary dynamic index. 4.21 It indicates the importance of the secondary dynamic index under the water network index compared with the secondary static index. 4.22 It indicates the importance of the secondary dynamic indicators under the water network indicators compared with the secondary dynamic indicators.

[0116] Exemplarily, the pairwise comparison matrix A constructed according to the multiple third-level resilience indicators under each second-level static indicator in the third indicator level and the multiple third-level resilience indicators under each second-level dynamic indicator is expressed by the following formula (26): a b.c1c2 :

[0117]

[0118] Among them, A ab.c1c2 It indicates the importance ranking between the c1th third-level resilience indicator and the c2th third-level resilience indicator under the bth second-level indicator (secondary static indicator or second-level dynamic indicator) under the ath first-level network type indicator. Among them, since there are 4 first-level network type indicators, and each first-level network type indicator includes 2 second-level indicators, then A ab Specifically, it can include A 11 , A 12 , A 21 , A 22 , A 31 , A 32 , A 41 , and A 42 These 8 situations.

[0119] Specifically, if A ab Take A 11 When A ab.11 That is, A 11.11 , which indicates the importance ranking between the first third-level resilience indicator and the first third-level resilience indicator under the first second-level indicator under the first first-level network type indicator, that is, the importance ranking between line redundancy and line redundancy under the second-level static indicator under the power grid indicator. Since both third-level resilience indicators are line redundancy, A 11.11 is 1. Similarly, for different primary network type indicators and different secondary indicators (secondary static indicators or secondary dynamic indicators) under the corresponding primary network type indicators, they can be calculated based on A ab The different values ​​of and the different three-level toughness indicators included therein determine the value of each item in the pairwise comparison matrix and obtain the complete pairwise comparison matrix A. ab.c1c2 .

[0120] In an exemplary embodiment, after determining the pairwise comparison matrix corresponding to each indicator level, the server determines the indicator consistency feature value corresponding to the indicator level for each indicator level according to the pairwise comparison matrix corresponding to the corresponding indicator level.

[0121] Specifically, the server determines the matrix order of the pairwise comparison matrix corresponding to each indicator level, and obtains the maximum eigenvalue in the pairwise comparison matrix, so that the indicator consistency eigenvalue corresponding to the indicator level can be determined based on the maximum eigenvalue, the matrix order, and the preset average consistency indicator value.

[0122] Exemplarily, the following formula (27) is used to determine the indicator consistency characteristic value CR corresponding to each indicator level:

[0123]

[0124] Among them, CR represents the indicator consistency eigenvalue corresponding to the indicator level (specifically, the indicator consistency eigenvalue corresponding to the pairwise comparison matrix A, A1, A2, A3, A4, and A11, A21, A31, A41, A12, A22, A32, A42 can be determined), λ max Indicates the maximum eigenvalue in the pairwise comparison matrix, n indicates the order of the pairwise comparison matrix, and RI indicates the preset average consistency index value. After obtaining the indicator consistency eigenvalue CR, it is necessary to further determine whether the indicator consistency eigenvalue CR is less than the preset verification threshold 0.1. That is, if it is determined that the indicator consistency eigenvalue is less than the preset verification threshold 0.1, it indicates that the indicator level meets the indicator consistency requirements.

[0125] Step S208: If it is determined that all indicator levels meet the indicator consistency requirement according to the indicator consistency characteristic value, the indicator weights corresponding to the evaluation indicators in the indicator level are determined according to the pairwise comparison matrix.

[0126] Specifically, the server obtains a preset verification threshold (such as 0.1) and compares each indicator consistency characteristic value in each indicator level with the preset verification threshold. If it is determined that each indicator consistency characteristic value in each indicator level is less than the preset verification threshold, it is determined that the indicator levels all meet the indicator consistency requirements.

[0127] Furthermore, when it is determined that each indicator level meets the indicator consistency requirements, the server normalizes and sums each pairwise comparison matrix corresponding to each indicator level according to the matrix columns to obtain an intermediate vector, and determines the number of indicators of the evaluation indicators in the indicator level, so as to determine the indicator weights corresponding to the evaluation indicators in the indicator level according to the intermediate vector and the number of indicators.

[0128] Specifically, for the pairwise comparison matrix in each indicator level, when the server normalizes the pairwise comparison matrix by matrix column, it specifically sums the elements of each column by matrix column, and then divides each element in the matrix column by the sum of its column to achieve normalization, and summing by matrix column means adding the normalized matrix columns (which can also be understood as summing by row) to obtain an intermediate vector. Among them, the server determines the indicator weights corresponding to the evaluation indicators in the indicator level by determining the number of indicators of the evaluation indicators in the indicator level, and according to the intermediate vector and the number of indicators of the evaluation indicators, wherein specifically, the weight vector can be obtained by dividing each element in the vector obtained after addition (i.e., the intermediate vector) by the number of indicators n.

[0129] Exemplarily, the indicator weight ω corresponding to the evaluation indicator in the pairwise comparison matrix corresponding to the indicator level is determined by the following formula (28): ab.ci :

[0130]

[0131] Among them, ω ab.ci represents the indicator weight corresponding to the evaluation indicator in the pairwise comparison matrix corresponding to the indicator level, n represents the number of evaluation indicators in the pairwise comparison matrix, k represents the column of the pairwise comparison matrix, j represents the row of the pairwise comparison matrix, ci represents the cith indicator, A ab.kj Represents the pairwise comparison matrix A ab The rows and columns of the matrix A ab.cij Represents the pairwise comparison matrix A ab Specifically, the indicator weights corresponding to the pairwise comparison matrices A, A1, A2, A3, A4, and A11, A21, A31, A41, A12, A22, A32, and A42 can be determined, thereby obtaining the indicator weights of the evaluation indicators in each pairwise comparison matrix.

[0132] Step S210, based on the multi-level evaluation index system of the energy network, screen out the target resilience index and determine the index weight of each target resilience index, so as to determine the fuzzy measurement between each target resilience index according to each target resilience index and the index weight of each target resilience index.

[0133] Specifically, based on the multi-level evaluation index system of the energy network, the server determines the positive and negative values ​​and standard values ​​of each evaluation index in the bottom index level, and for each evaluation index, performs standardization processing according to the positive and negative values ​​and standard values ​​of the evaluation index, obtains the index value of the evaluation index, and sorts the evaluation indexes according to the index values ​​to obtain the evaluation index sequence, so that the preset evaluation indexes can be screened out from the evaluation index sequence according to the preset index threshold value to determine the target resilience index. Among them, the bottom index level can be understood as the third index level in the multi-level evaluation index system of the energy network, which includes multiple three-level resilience indicators, and it is necessary to determine the positive and negative values ​​and standard values ​​of each three-level resilience indicator, and for each three-level resilience indicator, performs standardization processing according to the positive and negative values ​​and standard values ​​of the evaluation index, obtains the index value of the corresponding three-level resilience indicator, so that the evaluation indicators are sorted according to the index values ​​to obtain the resilience index sequence, so that the preset resilience indicators can be screened out from the resilience index sequence according to the preset index threshold value to determine the target resilience index. Among them, the preset index threshold value and the number of target resilience indicators can be set and adjusted according to actual needs, and are not limited to one or some specific values.

[0134] Furthermore, after screening out multiple target resilience indicators, the server determines the indicator level to which each target resilience indicator belongs, and for each target resilience indicator, determines the indicator weight of the indicator level to which the target resilience indicator belongs as the indicator weight of the target resilience indicator.

[0135] In an exemplary embodiment, when the server determines the fuzzy measure between each target resilience indicator, it can specifically obtain preset fuzzy parameters and determine the fuzzy measure between each target resilience indicator according to the indicator weight of each target resilience indicator and the preset fuzzy parameters.

[0136] Exemplarily, the fuzzy measure between each target resilience indicator is determined by the following formula (29):

[0137] 1+λ=Π(1+λω(x i )); formula (29)

[0138] Among them, x i represents the target resilience index, x i Yes X i The elements in X i It means that after sorting the evaluation indicators from small to large, the target resilience indicator set composed of the first i evaluation indicators is extracted, and λ represents the fuzzy measure between the target resilience indicators, that is, X i The fuzzy measure between each target resilience indicator in this target resilience indicator set, ω(x i ) refers to X i Each target resilience index x in this target resilience index set i The weight of , Π represents continuous product.

[0139] Step S212, performing weighted fusion according to the fuzzy measurements between the target resilience indicators and the index values ​​of the target resilience indicators, to obtain a resilience assessment result corresponding to the multi-energy coupled active distribution network.

[0140] Specifically, the server sorts each target resilience indicator according to the value of each target resilience indicator to obtain a target resilience indicator sequence, and determines the fuzzy measure corresponding to the target resilience indicator sequence based on the fuzzy measure between each target resilience indicator in the target resilience indicator sequence and the indicator weight of each target resilience indicator.

[0141] Exemplarily, the fuzzy measure μ(A) corresponding to the target resilience index sequence is determined by the following formula (30): ( i ) ):

[0142] μ(A (i) )=ω(x (i) )+ω(x(j) )+λω(x (i) )ω(x (j) ); formula (30)

[0143] Among them, μ(A (i) ) represents the fuzzy measure corresponding to the target resilience index sequence, A (i) ={x (i) ,x (i+1) ,…,x (n)}, representing multiple target resilience indicators x (i) ~x (n) The target resilience index sequence is composed of λ, which represents the fuzzy measure between the target resilience indicators, ω(x (i) ) refers to the target resilience index x in the target resilience index sequence (i) The indicator weight, ω(x (j) ) refers to the target resilience index x in the target resilience index sequence (j) The weight of the indicator.

[0144] Furthermore, the server performs weighted fusion according to the fuzzy measure corresponding to the target resilience indicator sequence and the index value of each target resilience indicator in the target resilience indicator sequence to obtain a resilience assessment result corresponding to the multi-energy coupled active distribution network.

[0145] Exemplarily, the resilience evaluation result C corresponding to the multi-energy coupled active distribution network is obtained by the following formula (31): μ (f(x (1) ),f(x (2) ),…,f(x (n) )):

[0146]

[0147] Among them, C μ (f(x (1) ),f(x (2) ),…,f(x (n) )) represents the resilience assessment result of the multi-energy coupling active distribution network, f(x (i) ) represents the index value of each target resilience index after reordering, μ(A (i) ) represents the target resilience index sequence A (i) The fuzzy measure A (i) ={x (i) ,x (i+1) ,…,x (n)}, representing multiple target resilience indicators x (i) ~x (n) The target resilience indicator sequence is formed.

[0148] The above-mentioned resilience assessment method based on multi-energy coupled active distribution network constructs a multi-level evaluation index system of energy network corresponding to the multi-energy coupled active distribution network, so as to evaluate the resilience of the multi-energy coupled active distribution network from all-round and multi-angle perspectives. It realizes the reasonable quantification and accurate evaluation of the resilience of the multi-energy coupled active distribution network by comprehensively considering the coupling relationship between different energy networks, reasonably analyzing the fuzzy measurement and influence mechanism between different indicators, and weighting the indicators according to the mutual influence between the indicators.

[0149] In an exemplary embodiment, Figure 8 As shown, a processing flow of an evaluation algorithm for a multi-energy coupled active distribution network is provided. Figure 8 It can be seen that the evaluation algorithm specifically includes the following processing steps:

[0150] 1) Establishment of a multi-level evaluation index system for energy networks of multi-energy coupled active distribution networks: A) First-level index: grid index, gas grid index, water grid index and heat grid index; B) Second-level index: Second-level static index and second-level dynamic index corresponding to grid index, gas grid index, water grid index and heat grid index; C) Third-level index: multiple third-level resilience indexes under each second-level static index and each second-level dynamic index. 2) Determination of the weights of evaluation indicators and fuzzy measures between indicators: A) First-level index weight, B) Second-level index weight, C) Third-level index weight, D) λ fuzzy measure. 3) Determination of target resilience index and data processing: A) Dynamic index calculation, B) Static index calculation, C) Target resilience index screening, D) Data normalization. 4) Determination of resilience evaluation results corresponding to multi-energy coupled active distribution networks: A) Index sorting, B) Fuzzy measure sorting, C) Integral operation to determine resilience evaluation results.

[0151] For example, Fig. 9 As shown, a schematic diagram of the gas pressure of a gas supply network node under a fault condition is provided. Fig. 9 It can be seen that when a fault occurs, the node gas pressure of the gas supply network node rises and falls suddenly, and the node gas pressure is unstable, so the fault needs to be solved in time. Fig.10 As shown, a schematic diagram of improving the node voltage of the power supply network under fault conditions is provided. By performing fault processing and improvement when a fault occurs, the node voltage of the power supply network node is obtained, and the node voltage shows a trend of first decreasing, then increasing, and then decreasing again during processing.

[0152] In an exemplary embodiment, a specific example of a method for assessing the resilience of a multi-energy coupled active distribution network is provided, which specifically includes the following steps:

[0153] Step 1: Construct a multi-level evaluation index system for energy networks corresponding to the multi-energy coupling active distribution network, and calculate the index value of each evaluation index. The index values ​​of each evaluation index in the multi-level evaluation index system for energy networks are shown in Table 1 (Table of multi-level evaluation index system for energy networks):

[0154] Table 1 Multi-level evaluation index system of energy network

[0155]

[0156] Step 2: Use the analytic hierarchy process and λ-fuzzy measure to calculate the weight of each evaluation index and the fuzzy measure between the indicators.

[0157] Step 2.1: Establish the pairwise comparison matrix of the first-level indicators through the following formula (32):

[0158]

[0159] Step 2.2, through the following formula (33), reflects the pairwise comparison matrix of the secondary indicators constructed by the hierarchical analysis method:

[0160]

[0161] Step 2.3, through the following formulas (34) to (38), the pairwise comparison matrix of the three-level indicators constructed by the hierarchical analysis method is reflected:

[0162]

[0163] Step 2.4: Use the following formulas (39) to (42) to perform a hierarchical single-rank consistency test on the pairwise comparison matrix of each level of indicators:

[0164]

[0165] CR 1 =CR 2 =CR 3 =CR 4 =0<0.1 formula (40);

[0166] CR 11 =CR 21 =CR 31 =CR 41 =0.0296<0.1; Formula (41)

[0167]

[0168] Among them, CR AIt refers to the index consistency eigenvalue of the pairwise comparison matrix A, 4.0062 represents the maximum eigenvalue of the pairwise comparison matrix A, 4 represents the order of the pairwise comparison matrix A, and 0.89 refers to the preset average consistency index value. 1 , CR 2 , CR 3 , and CR 4 refers to the indicator consistency eigenvalue of the pairwise comparison matrix A1, A2, A3, and A4, while CR 11 , CR 21 , CR 31 , CR 41 Refers to the index consistency eigenvalues ​​corresponding to the pairwise comparison matrices A11, A21, A31, and A41, CR 12 , CR 22 , CR 32 , CR 44 Refers to the index consistency eigenvalues ​​corresponding to the pairwise comparison matrices A12, A22, A32, and A42

[0169] Step 2.5, perform a consistency test of the hierarchical total ranking on the pairwise comparison matrix A of the overall indicators.

[0170] Step 2.6, calculate the indicator weights of each level of indicators through the following formulas (43) to (46).

[0171] ω=[0.5177,0.2444,0.1222,0.1157];Formula (43)

[0172] ω 1 =ω 2 =ω 3 =ω 4 =[0.5,0.5]; formula (44)

[0173] ω 11 =ω 21 =ω 31 =ω 41 =[0.5595, 0.2397, 0.1247, 0.0761]; Formula (45)

[0174]

[0175] Among them, ω represents the indicator weight of each evaluation indicator in the comparison matrix A, ω 1 ,ω 2 ,ω 3 , and ω 4 Refers to the indicator weights of the evaluation indicators in the pairwise comparison matrices A1, A2, A3, and A4, ω 11 ,ω 21 ,ω31 , and ω 41 Refers to the indicator weights of the evaluation indicators in the pairwise comparison matrices A11, A21, A31, and A41, ω 12 ,ω 22 ,ω 32 , and ω 42 Refers to the indicator weights of the evaluation indicators of the pairwise comparison matrices A12, A22, A32, and A42.

[0176] Step 2.7, for each level of sub-indicators belonging to the same indicator, determine the λ-fuzzy measure between each indicator.

[0177] Step 3: According to the established multi-level evaluation index system of multi-energy coupled active distribution network, data processing is performed on the resilience indicators of different energy networks.

[0178] Step 3.1, determine the positive and negative of each third-level indicator, as shown in Table 2 (Indicator Positive and Negative Table), and the standard value of each third-level indicator, as shown in Table 3 (Indicator Standard Value Table):

[0179] Table 2 Index Positive and Negative Table

[0180] Indicator 1 Indicator 2 Indicator 3 Indicator 4 Static indicators Positive Positive Positive Positive Dynamic indicators Positive Negative Negative Negative

[0181] Table 3 Index standard value table

[0182] Indicator 1 Indicator 2 Indicator 3 Indicator 4 Static indicators Number of nodes*number of branches Number of nodes*number of branches 1 Number of nodes*number of branches Dynamic indicators 1 1 Power reference value 1

[0183] Step 3.2, according to the positivity and standard values ​​of different third-level indicators, the toughness index is standardized using the Z-score standardization method to obtain the index value of the standardized toughness index, as shown in Table 4 (index value table of standardized toughness index):

[0184] Table 4 Index value table of standardized toughness index

[0185]

[0186] Step 4: Calculate the comprehensive evaluation result of the resilience of the multi-energy coupling active distribution network based on the processed resilience index and the index value of the standardized resilience index, such as Fig.11 As shown in the figure, a resilience assessment result corresponding to a multi-energy coupled active distribution network is provided. Fig.11 It can be seen that the comprehensive evaluation result of the resilience of the multi-energy coupling active distribution network calculated by Choquet integral is C1 = 0.417. The evaluation result obtained by using the traditional weighted average evaluation method for Table 4 is C2 = 0.358.

[0187] Step 5, modify the index values ​​in Table 4, obtain Table 5 (the index value table of modified resilience index), and calculate the comprehensive evaluation results of the resilience of the multi-energy coupling active distribution network, such as Fig.12 As shown in the figure, a resilience assessment result corresponding to a multi-energy coupled active distribution network is provided. Fig.12 It can be seen that the comprehensive evaluation result of the resilience of the multi-energy coupling active distribution network calculated by Choquet integral is C3=0.4754. The evaluation result obtained by using the traditional weighted average evaluation method for Table 5 is C4=0.3886.

[0188] It is understandable that when the indicators change with the same numerical value, the change in the evaluation results obtained by the resilience assessment method based on the multi-energy coupled active distribution network reaches 14%, while the traditional method is only 8.55%. Therefore, the resilience assessment method based on the multi-energy coupled active distribution network is more sensitive to the changes in indicators and the evaluation results are more sensitive.

[0189] Table 5 Index values ​​for verifying the sensitivity of the evaluation method

[0190]

[0191] The above-mentioned resilience assessment method based on multi-energy coupled active distribution network can evaluate the resilience of multi-energy coupled active distribution network from all aspects and angles by constructing a multi-level evaluation index system of energy network corresponding to the multi-energy coupled active distribution network. It realizes the reasonable quantification and accurate evaluation of the resilience of multi-energy coupled active distribution network by comprehensively considering the coupling relationship between different energy networks, reasonably analyzing the fuzzy measurement and influence mechanism between different indicators, and weighting the indicators according to the mutual influence between the indicators.

[0192] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0193] Based on the same inventive concept, the embodiment of the present application also provides a resilience assessment device based on a multi-energy coupling active distribution network for implementing the above-mentioned resilience assessment method based on a multi-energy coupling active distribution network. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the resilience assessment device based on a multi-energy coupling active distribution network provided below can be referred to the limitations of the resilience assessment method based on a multi-energy coupling active distribution network above, and will not be repeated here.

[0194] In an exemplary embodiment, Fig.13 As shown, a resilience assessment device based on a multi-energy coupled active distribution network is provided, comprising: a multi-level evaluation index construction module 1302, an energy network multi-level evaluation index system construction module 1304, an index consistency characteristic value determination module 1306, an index weight determination module 1308, a fuzzy measurement determination module 1310 and a resilience assessment result acquisition module 1312, wherein:

[0195] The multi-level evaluation index construction module 1302 is used to determine the energy network type associated with the multi-energy coupling active distribution network, and for each energy network type, construct a multi-level evaluation index corresponding to the energy network type according to the network characteristics corresponding to the energy network type; the energy network multi-level evaluation index system construction module 1304 is used to construct an energy network multi-level evaluation index system corresponding to the multi-energy coupling active distribution network according to the multi-level evaluation indicators corresponding to each energy network type, and determine the indicator level included in the energy network multi-level evaluation index system; the indicator consistency eigenvalue determination module 1306 is used to determine the pairwise comparison matrix corresponding to the indicator level for each indicator level, and determine the indicator consistency eigenvalue corresponding to the indicator level according to the pairwise comparison matrix. ; An indicator weight determination module 1308 is used to determine the indicator weights corresponding to the evaluation indicators in the indicator hierarchy according to the pairwise comparison matrix if it is determined that the indicator hierarchy meets the indicator consistency requirements according to the indicator consistency characteristic values; a fuzzy measurement determination module 1310 is used to screen out target resilience indicators based on the multi-level evaluation indicator system of the energy network, and determine the indicator weights of each target resilience indicator, so as to determine the fuzzy measurements between each target resilience indicator according to each target resilience indicator and the indicator weights of each target resilience indicator; a resilience assessment result acquisition module 1312 is used to perform weighted fusion according to the fuzzy measurements between each target resilience indicator and the indicator value of each target resilience indicator, so as to obtain a resilience assessment result corresponding to the multi-energy coupled active distribution network.

[0196] The above-mentioned resilience assessment device based on the multi-energy coupled active distribution network can evaluate the resilience of the multi-energy coupled active distribution network from all aspects and angles by constructing a multi-level evaluation index system of energy networks corresponding to the multi-energy coupled active distribution network. It achieves reasonable quantification and accurate evaluation of the resilience of the multi-energy coupled active distribution network by comprehensively considering the coupling relationship between different energy networks, reasonably analyzing the fuzzy measurement and influence mechanism between different indicators, and weighting the indicators according to the mutual influence between the indicators.

[0197] In an exemplary embodiment, the indicator weight determination module is also used to: compare the indicator consistency characteristic value with a preset verification threshold, and if it is determined that the indicator consistency characteristic value is less than the preset verification threshold, determine that the indicator level meets the indicator consistency requirement; normalize and sum the pairwise comparison matrix corresponding to the indicator level by matrix column to obtain an intermediate vector; determine the number of evaluation indicators in the indicator level, and determine the indicator weights corresponding to the evaluation indicators in the indicator level based on the intermediate vector and the number of indicators.

[0198] In an exemplary embodiment, the fuzzy measurement determination module is also used to: determine the positivity and standard value of each evaluation indicator in the bottom indicator level based on the multi-level evaluation indicator system of the energy network; for each evaluation indicator, perform standardization processing according to the positivity and standard value of the evaluation indicator to obtain the indicator value of the evaluation indicator; sort the evaluation indicators according to the indicator value to obtain an evaluation indicator sequence, and screen out preset evaluation indicators from the evaluation indicator sequence according to the preset indicator threshold to determine them as target resilience indicators.

[0199] In an exemplary embodiment, the fuzzy measurement determination module is also used to: determine the indicator level to which each target resilience indicator belongs; for each target resilience indicator, determine the indicator weight of the indicator level to which the target resilience indicator belongs as the indicator weight of the target resilience indicator; and determine the fuzzy measurement between each target resilience indicator based on the indicator weight of each target resilience indicator and preset fuzzy parameters.

[0200] In an exemplary embodiment, the resilience assessment result acquisition module is also used to: sort each target resilience indicator according to the value of the index value of each target resilience indicator to obtain a target resilience indicator sequence; determine the fuzzy measurement corresponding to the target resilience indicator sequence according to the fuzzy measurement between each target resilience indicator in the target resilience indicator sequence and the index weight of each target resilience indicator; perform weighted fusion according to the fuzzy measurement corresponding to the target resilience indicator sequence and the index value of each target resilience indicator in the target resilience indicator sequence to obtain a resilience assessment result corresponding to the multi-energy coupled active distribution network.

[0201] In an exemplary embodiment, the multi-level evaluation index construction module is also used to: for each energy network type, construct a first-level network type indicator according to the energy network type; for each first-level network type indicator, construct a second-level static indicator and a second-level dynamic indicator according to the energy state corresponding to the energy network type; for each second-level static indicator and each second-level dynamic indicator, construct multiple third-level resilience indicators under the second-level static indicator and multiple third-level resilience indicators under the second-level dynamic indicator according to the network characteristics corresponding to the energy network type.

[0202] Each module in the above-mentioned resilience assessment device based on multi-energy coupling active distribution network can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0203] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.14 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as energy network type, network characteristics, multi-level evaluation indicators, energy network multi-level evaluation indicator system, indicator hierarchy, pairwise comparison matrix, indicator consistency eigenvalue, indicator consistency requirement, indicator weight, target resilience indicator, fuzzy measurement between target resilience indicators, indicator value of target resilience indicator, and resilience evaluation results. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by a processor, a resilience assessment method based on a multi-energy coupled active distribution network is implemented.

[0204] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0205] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0206] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0207] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0208] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0209] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0210] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application. The above-mentioned embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present application. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several deformations and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the attached claims.

Claims

1. A resilience assessment method based on multi-energy coupling active distribution network, characterized in that: The method comprises: Determine the energy network type associated with the multi-energy coupling active distribution network, and for each of the energy network types, construct a multi-level evaluation index corresponding to the energy network type according to the network characteristics corresponding to the energy network type; According to the multi-level evaluation indicators corresponding to each of the energy network types, a multi-level evaluation indicator system for an energy network corresponding to the multi-energy coupling active distribution network is constructed, and the indicator levels included in the multi-level evaluation indicator system for an energy network are determined; For each of the indicator levels, determining a pairwise comparison matrix corresponding to the indicator level, and determining an indicator consistency eigenvalue corresponding to the indicator level according to the pairwise comparison matrix; If it is determined that the indicator levels all meet the indicator consistency requirements according to the indicator consistency characteristic values, then the indicator weights corresponding to the evaluation indicators in the indicator level are determined according to the pairwise comparison matrix; Based on the multi-level evaluation index system of the energy network, the target resilience index is screened out, and the index weight of each target resilience index is determined, so as to determine the fuzzy measurement between each target resilience index according to each target resilience index and the index weight of each target resilience index; According to the fuzzy measurements between the target resilience indicators and the index values ​​of the target resilience indicators, weighted fusion is performed to obtain a resilience assessment result corresponding to the multi-energy coupled active distribution network.

2. The method according to claim 1, characterized in that Determining the indicator consistency characteristic value corresponding to the indicator level according to the pairwise comparison matrix includes: For each pairwise comparison matrix corresponding to the indicator level, determining the matrix order of the pairwise comparison matrix, and obtaining the maximum eigenvalue in the pairwise comparison matrix; An indicator consistency eigenvalue corresponding to the indicator level is determined according to the maximum eigenvalue, the matrix order, and a preset average consistency index value.

3. The method according to claim 2, characterized in that If it is determined that the indicator levels all meet the indicator consistency requirements according to the indicator consistency characteristic values, then according to the pairwise comparison matrix, the indicator weights corresponding to the evaluation indicators in the indicator level are determined, including: Compare the indicator consistency characteristic value with a preset verification threshold, and if it is determined that the indicator consistency characteristic value is less than the preset verification threshold, determine that the indicator level meets the indicator consistency requirement; The paired comparison matrices corresponding to the indicator level are normalized and summed according to the matrix columns to obtain an intermediate vector; The number of indicators of the evaluation indicators in the indicator hierarchy is determined, and the indicator weights corresponding to the evaluation indicators in the indicator hierarchy are determined according to the intermediate vector and the number of indicators.

4. The method according to any one of claims 1 to 3, characterized in that: The target resilience indicators are screened out based on the multi-level evaluation index system of the energy network, including: Based on the multi-level evaluation index system of the energy network, determining the positivity and standard value of each evaluation index in the bottom index level; For each of the evaluation indicators, a standardization process is performed according to the positivity and negativity of the evaluation indicator and the standard value to obtain the indicator value of the evaluation indicator; The evaluation indicators are sorted according to the indicator values ​​to obtain an evaluation indicator sequence, and preset evaluation indicators are screened out from the evaluation indicator sequence according to a preset indicator threshold value to determine as the target toughness indicator.

5. The method according to claim 4, characterized in that The determining of the indicator weight of each target resilience indicator, so as to determine the fuzzy measure between each target resilience indicator according to each target resilience indicator and the indicator weight of each target resilience indicator, comprises: Determining the indicator level to which each of the target resilience indicators belongs; For each of the target resilience indicators, determining the indicator weight of the indicator level to which the target resilience indicator belongs as the indicator weight of the target resilience indicator; According to the indicator weight of each target resilience indicator and the preset fuzzy parameters, the fuzzy measurement between each target resilience indicator is determined.

6. The method according to any one of claims 1 to 3, characterized in that: The weighted fusion is performed according to the fuzzy measurement between each of the target resilience indicators and the index value of each of the target resilience indicators to obtain a resilience evaluation result corresponding to the multi-energy coupling active distribution network, including: According to the value of each target toughness indicator, the target toughness indicators are sorted to obtain a target toughness indicator sequence; Determining a fuzzy measure corresponding to the target resilience indicator sequence according to the fuzzy measures between the target resilience indicators in the target resilience indicator sequence and the indicator weight of each target resilience indicator; According to the fuzzy measure corresponding to the target resilience indicator sequence and the index value of each target resilience indicator in the target resilience indicator sequence, weighted fusion is performed to obtain a resilience assessment result corresponding to the multi-energy coupled active distribution network.

7. The method according to any one of claims 1 to 3, characterized in that: For each of the energy network types, according to the network characteristics corresponding to the energy network type, a multi-level evaluation index corresponding to the energy network type is constructed, including: For each of the energy network types, construct a first-level network type indicator according to the energy network type; For each of the first-level network type indicators, construct a second-level static indicator and a second-level dynamic indicator according to the energy state corresponding to the energy network type; For each of the secondary static indicators and each of the secondary dynamic indicators, according to the network characteristics corresponding to the energy network type, multiple tertiary resilience indicators under the secondary static indicators and multiple tertiary resilience indicators under the secondary dynamic indicators are respectively constructed.

8. A resilience assessment device based on multi-energy coupling active distribution network, characterized in that: The device comprises: A multi-level evaluation index construction module is used to determine the energy network type associated with the multi-energy coupling active distribution network, and for each energy network type, construct a multi-level evaluation index corresponding to the energy network type according to the network characteristics corresponding to the energy network type; An energy network multi-level evaluation index system construction module is used to construct an energy network multi-level evaluation index system corresponding to the multi-energy coupling active distribution network according to the multi-level evaluation indexes corresponding to each of the energy network types, and determine the index levels included in the energy network multi-level evaluation index system; An indicator consistency characteristic value determination module, used to determine, for each indicator level, a pairwise comparison matrix corresponding to the indicator level, and determine, based on the pairwise comparison matrix, an indicator consistency characteristic value corresponding to the indicator level; An indicator weight determination module, for determining, if it is determined according to the indicator consistency characteristic value that the indicator levels all meet the indicator consistency requirement, the indicator weights corresponding to the evaluation indicators in the indicator level according to the pairwise comparison matrix; A fuzzy measurement determination module is used to screen out target resilience indicators based on the multi-level evaluation indicator system of the energy network, and determine the indicator weight of each target resilience indicator, so as to determine the fuzzy measurement between each target resilience indicator according to each target resilience indicator and the indicator weight of each target resilience indicator; The resilience assessment result acquisition module is used to perform weighted fusion according to the fuzzy measurements between the target resilience indicators and the index values ​​of the target resilience indicators to obtain the resilience assessment result corresponding to the multi-energy coupled active distribution network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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