Multi-energy coupling active power distribution network toughness optimization method

Abstract

The invention relates to a toughness optimization method for a multi-energy coupling active power distribution network, and the method comprises the steps: constructing a physical model and model parameters of the multi-energy coupling active power distribution network, carrying out the stage division of an extreme event, and obtaining a plurality of divided stages; and for each division stage, according to the physical model and the model parameters, carrying out toughness optimization processing on the multi-energy coupling active power distribution network to obtain a target optimization result. For each division stage, toughness optimization processing is performed according to the physical model and the model parameters. A clear optimization target and strategy exist in each stage, and the toughness of the power distribution network is improved from the aspects of distributed resource optimization configuration, network structure optimization reconstruction, fault repair path optimization and the like. The multi-dimensional and staged optimization mode is helpful for integrally enhancing the capabilities of resisting, adapting and recovering the multi-energy coupling active power distribution network under extreme events, reducing the influence of the extreme events on power supply, and improving the stability and reliability of the power supply.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method for optimizing the resilience of a multi-energy coupled active distribution network. Background Art

[0002] As the world's environment deteriorates, the need for energy transformation becomes more urgent. Multi-energy coupled active distribution networks with multiple energy forms have gradually attracted widespread attention from scholars in the field of promoting energy transformation and advancing the formation of "dual carbon" goals because of their advantages of aggregating multiple different energy networks and achieving multi-energy complementarity.

[0003] Active distribution networks often use overhead lines as the main components of power transmission. However, they are greatly affected by heavy rains, typhoons, floods, and severe convective weather, and are prone to serious faults such as short lines and pole collapses, which may cause large-scale and long-term power outages in the regional distribution network. In recent years, the frequency of extreme weather such as severe convective weather in my country has increased significantly. At the same time, multi-energy coupled active distribution networks have a large number of distributed resources, and their multi-energy coupling characteristics make their operating state more complicated under extreme events.

[0004] Existing research mainly focuses on improving the resilience of a single distribution network, but it is difficult to fully tap the synergy between different energy networks, resulting in poor disaster resilience of multi-energy coupled active distribution networks. Summary of the invention

[0005] Based on this, it is necessary to provide a multi-energy coupled active distribution network resilience optimization method that can improve disaster resilience in response to the above technical problems.

[0006] In a first aspect, the present application provides a multi-energy coupling active distribution network resilience optimization method, the method comprising:

[0007] Construct the physical model and model parameters of the multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0008] The extreme events are divided into multiple stages, and the stages are determined based on the impact of the movement trajectory of the extreme events on the multi-energy coupled active distribution network.

[0009] For each division stage, the multi-energy coupled active distribution network is optimized for resilience according to the physical model and model parameters to obtain the target optimization results.

[0010] In one of the embodiments, the physical model includes at least one of an active power distribution network model, a gas supply network model, a heating network model and a water supply network model.

[0011] In one embodiment, the process of constructing the active power distribution network model includes:

[0012] Acquire distribution network data in an active distribution network; the distribution network data includes at least one of node voltage, branch current, node power, and conjugate complex number;

[0013] Construct node power balance equation and voltage equation based on distribution network data;

[0014] According to the node power balance equation and voltage equation, the active distribution network model is determined.

[0015] In one embodiment, the process of constructing the gas supply network model includes:

[0016] Acquire natural gas data in the gas supply network; the natural gas data includes at least one of natural gas flow, natural gas consumption and pipeline pressure drop;

[0017] Construct the gas grid energy flow equation and gas grid pressure drop equation based on natural gas data;

[0018] The gas supply network model is determined based on the gas network energy flow equation and the gas network pressure drop equation.

[0019] In one embodiment, the process of constructing the water supply network model includes:

[0020] Acquire water supply data of the water supply network; the water supply data includes at least one of water flow, water load, transmission pressure drop loss, water pressure difference and water pump head;

[0021] Construct water network energy flow equation and water network pressure drop equation according to water supply data;

[0022] The water supply network model is determined based on the water network energy flow equation and the water network pressure drop equation.

[0023] In one embodiment, the process of constructing the heating network model includes:

[0024] Obtain heating data of the heating network; the heating data includes heat loss, node temperature, external temperature and thermal power;

[0025] Construct heat exchange equations based on heating data;

[0026] The heating network model is determined based on the water network energy flow equation, water network pressure drop equation and heat exchange equation.

[0027] In one of the embodiments, for each division stage, the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the physical model and model parameters to obtain a target optimization result, including:

[0028] For each division stage, a target constraint condition is determined according to the physical model and model parameters; the target constraint condition includes at least one of an active distribution network-related constraint, a gas supply network-related constraint, a water supply network-related constraint, and a heating network-related constraint;

[0029] According to the target constraints, the multi-energy coupled active distribution network is resiliently optimized to obtain the target optimization result.

[0030] In one embodiment, the above-mentioned division stage includes a pre-disaster resistance stage, and the target optimization result includes a first optimization result; the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the target constraint condition to obtain the target optimization result, including:

[0031] For the pre-disaster defense stage, the first objective function is constructed based on the pre-acquired fault scenario set; the first objective function aims to minimize the operating cost;

[0032] Constructing a distributed resource optimization configuration model according to the first objective function and the objective constraint conditions;

[0033] Based on the snow melting algorithm, the distributed resource optimization configuration model is solved and the first optimization result is obtained.

[0034] In one embodiment, the above-mentioned division stage includes a disaster adaptation stage, and the target optimization result includes a second optimization result; the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the target constraint condition to obtain the target optimization result, including:

[0035] A second objective function is constructed according to the load loss data acquired in advance; the second objective function takes the minimum load loss as a goal;

[0036] Constructing an energy network optimization operation model according to the second objective function and objective constraints;

[0037] Based on the solver, the energy network optimization operation model is solved to obtain a second optimization result.

[0038] In one embodiment, the above-mentioned division stage includes a post-disaster recovery stage, and the target optimization result includes a third optimization result; for each division stage, the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the target constraint condition to obtain the target optimization result, including:

[0039] Determine maintenance resource constraints;

[0040] According to the pre-acquired component repair data of the active distribution network after the disaster, a third objective function is constructed; the third objective function takes the minimum repair cost as the goal;

[0041] Constructing a path optimization model according to maintenance resource constraints, target constraints and the third objective function;

[0042] Based on the ant colony algorithm, the path optimization model is solved to obtain the third optimization result.

[0043] In a second aspect, the present application also provides a multi-energy coupling active distribution network resilience optimization device. The device includes:

[0044] A parameter construction module is used to construct a physical model and model parameters of a multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0045] The stage division module is used to divide the extreme events into stages and obtain multiple stages; the stages are determined based on the impact of the movement trajectory of the extreme events on the multi-energy coupling active distribution network;

[0046] The optimization module is used to perform resilience optimization processing on the multi-energy coupling active distribution network according to the physical model and model parameters in each division stage to obtain the target optimization result.

[0047] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0048] Construct the physical model and model parameters of the multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0049] The extreme events are divided into multiple stages, and the stages are determined based on the impact of the movement trajectory of the extreme events on the multi-energy coupled active distribution network.

[0050] For each division stage, the multi-energy coupled active distribution network is optimized for resilience according to the physical model and model parameters to obtain the target optimization results.

[0051] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0052] Construct the physical model and model parameters of the multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0053] The extreme events are divided into multiple stages, and the stages are determined based on the impact of the movement trajectory of the extreme events on the multi-energy coupled active distribution network.

[0054] For each division stage, the multi-energy coupled active distribution network is optimized for resilience according to the physical model and model parameters to obtain the target optimization results.

[0055] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0056] Construct the physical model and model parameters of the multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0057] The extreme events are divided into multiple stages, and the stages are determined based on the impact of the movement trajectory of the extreme events on the multi-energy coupled active distribution network.

[0058] For each division stage, the multi-energy coupled active distribution network is optimized for resilience according to the physical model and model parameters to obtain the target optimization results.

[0059] The above-mentioned resilience optimization method for multi-energy coupling active distribution network first constructs the physical model and model parameters of the multi-energy coupling active distribution network, divides the extreme events into stages, and obtains multiple division stages; for each division stage, according to the physical model and model parameters, the multi-energy coupling active distribution network is optimized for resilience to obtain the target optimization result. This application performs resilience optimization according to the physical model and model parameters for each division stage. There are clear optimization goals and strategies in each stage, which improves the resilience of the distribution network in many aspects, from distributed resource optimization configuration, network structure optimization reconstruction to fault repair path optimization. This multi-dimensional, phased optimization method helps to enhance the overall ability of the multi-energy coupling active distribution network to resist, adapt and recover from extreme events, reduce the impact of extreme events on power supply, and improve the stability and reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 An application environment diagram of a multi-energy coupling active distribution network resilience optimization method in one embodiment;

[0061] Figure 2 A schematic diagram of a process of optimizing the resilience of a multi-energy coupling active distribution network in one embodiment;

[0062] Figure 3 A multi-stage schematic diagram of a multi-energy coupled active power distribution network under extreme weather conditions divided in one embodiment;

[0063] Figure 4 A schematic diagram of typical fault scenarios divided in an embodiment;

[0064] Figure 5 is a schematic diagram of a process of constructing an active power distribution network model in an embodiment;

[0065] Figure 6 A schematic diagram of a construction process of a gas supply network model in one embodiment;

[0066] Figure 7 A schematic diagram of a process of constructing a water supply network model in one embodiment;

[0067] Figure 8 A schematic diagram of a construction process of a heating network model in one embodiment;

[0068] Fig. 9 One of the flow charts for obtaining a target optimization result in one embodiment;

[0069] Fig.10 The second flowchart of obtaining a target optimization result in one embodiment;

[0070] Fig.11 A schematic diagram of a typical fault scenario in an embodiment;

[0071] Fig.12 A comparison diagram of a snow melting algorithm and other algorithms in one embodiment;

[0072] Fig.13 A schematic diagram of distributed resource configuration in one embodiment;

[0073] Fig.14 A schematic diagram of load recovery rate in one embodiment;

[0074] Fig.15a One of the schematic diagrams of coupling device utilization in one embodiment;

[0075] Fig.15b This is a second schematic diagram of coupling device utilization in one embodiment;

[0076] Fig.16a This is one of the schematic diagrams of the network structure of a multi-energy coupling active distribution network in the adaptation stage in an embodiment;

[0077] Fig.16b This is a second schematic diagram of a network structure of a multi-energy coupling active power distribution network in an adaptation stage in an embodiment;

[0078] Fig.17 A schematic diagram of load recovery rate in an adaptation phase in an embodiment;

[0079] Fig.18 The third flowchart of obtaining a target optimization result in one embodiment;

[0080] Fig.19The fourth flowchart of obtaining a target optimization result in one embodiment;

[0081] Fig. 20 This is a graph showing the convergence comparison of algorithms in the post-disaster repair phase in one embodiment;

[0082] Fig.21 A schematic diagram of load recovery rate in the post-disaster emergency repair phase in one embodiment;

[0083] Fig.22a This is one of the schematic diagrams of coupling device utilization in the post-disaster repair phase in one embodiment;

[0084] Figure 22b This is a second schematic diagram of coupling device utilization in the post-disaster repair phase in an embodiment;

[0085] Fig.23 It is a structural block diagram of a multi-energy coupling active distribution network resilience optimization device in one embodiment;

[0086] Fig.24 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0087] 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.

[0088] First of all, before specifically introducing the technical solution of the embodiments of the present application, the technical background based on the embodiments of the present application is introduced.

[0089] As the world's environment deteriorates, multi-energy coupled active distribution networks with multiple energy forms have gradually received widespread attention from scholars in the field of promoting energy transformation and advancing the formation of the "dual carbon" goal because of their advantages of aggregating multiple different energy networks and achieving multi-energy complementarity. However, since active distribution networks often use overhead lines as the main components of power transmission, they are greatly affected by heavy rains, typhoons, floods and severe convective weather, and are prone to serious faults such as short lines and pole collapse, which may cause large-scale and long-term power outages in the regional distribution network. In recent years, the frequency of extreme weather such as severe convective weather in my country has increased significantly. At the same time, multi-energy coupled active distribution networks have a large number of distributed resources, and their multi-energy coupling characteristics make their operating state under extreme events more complicated. Therefore, how to make full use of the configuration of distributed resources and optimize the operation mode of different energy systems under extreme events, and improve the ability of multi-energy coupled active distribution networks to resist extreme events, adapt to extreme events, and recover from the impact of extreme events, is a key scientific problem that needs to be solved urgently.

[0090] However, existing research results often consider a specific stage after an extreme event occurs, and rarely consider the resilience improvement of multi-energy coupled active distribution networks in multiple stages. At the same time, existing research mainly focuses on a single distribution network, and some studies involve electrical coupling networks and electrical-water coupling networks. Few studies consider the resilience improvement strategy of active distribution networks that comprehensively consider electrical, water and heat multi-energy coupling. Therefore, it is difficult to give full play to the synergy between different energy networks to improve the disaster resilience of multi-energy coupled active distribution networks.

[0091] Based on this, the present application provides a multi-energy coupled active distribution network resilience optimization method, aiming to solve the above technical problems.

[0092] The multi-energy coupling active distribution network resilience optimization method provided in the embodiment of the present application can be applied to Figure 1 In 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. The server constructs a physical model and model parameters of the multi-energy coupled active distribution network, divides the extreme events into stages, and obtains multiple division stages; for each division stage, according to the physical model and model parameters, the multi-energy coupled active distribution network is optimized for resilience to obtain the target optimization result. Among them, the terminal 102 can be but is not limited to smart meters, sensors, data collectors, and metering instruments. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0093] In an exemplary embodiment, Figure 2 As shown in the figure, a multi-energy coupling active distribution network resilience optimization method is provided, which is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0094] S201, construct a physical model and model parameters of a multi-energy coupled active distribution network.

[0095] Among them, the physical model is established based on the operating requirements of active power distribution network, gas supply network, water supply network and heating network.

[0096] In the embodiments of the present application, the multi-energy coupled active distribution network is a distribution network system that integrates multiple energy forms (such as electricity, natural gas, water, thermal energy, etc.). In this system, different energy networks are coupled with each other to achieve the complementarity and coordinated operation of multiple energy sources. For example, electric energy can be converted into thermal energy for heating, and natural gas can be converted into electric energy and thermal energy through cogeneration and other methods. The active distribution network part has active control and management functions, and can monitor, control and optimize the operating status of the power grid in real time, which is different from the traditional passive distribution network.

[0097] Construction of heating network related models: In terms of the construction of the physical model of the heating network, the working fluid energy flow equation is similar to the water network energy flow equation. It is necessary to consider the balance between the total heat flow into the node and the node heat load, and also to satisfy the heat exchange relationship of the node, including the heat power input to the node, the heat loss of the line transmission, the node temperature and the external temperature. Parameters such as heat flow and temperature constitute the model parameters of the heating network.

[0098] S202, dividing the extreme event into stages to obtain a plurality of stages.

[0099] Among them, the division stages are determined based on the impact of the movement trajectory of extreme events on the multi-energy coupled active distribution network.

[0100] In the embodiment of the present application, the server first analyzes extreme events, which include rainstorms, typhoons, floods, severe convective weather, etc. The server needs to obtain relevant information about extreme events, such as meteorological data, geographic information, etc., to analyze the possible impact of these events on the multi-energy coupled active distribution network.

[0101] Then the stages are divided based on the impact, such as Figure 3 As shown in Figure 1, according to the movement trajectory of extreme events (such as typhoon path, rainstorm coverage, etc.) and the degree of impact on various parts of the multi-energy coupled active distribution network (active power distribution network, gas supply network, water supply network and heating network), the extreme event process is divided into multiple stages. For example, before the arrival of the typhoon is the pre-disaster stage; when the typhoon affects the distribution network and causes line failures and energy supply interruptions, it is the mid-disaster stage; after the typhoon, when the distribution network is damaged and needs to be repaired, it is the post-disaster stage. This division is dynamic and is based on the actual operating characteristics of the multi-energy coupled active distribution network and the impact of extreme events.

[0102] S203, for each divided stage, according to the physical model and model parameters, the multi-energy coupling active distribution network is subjected to resilience optimization processing to obtain a target optimization result.

[0103] In the embodiments of the present application, for the resilience optimization in the pre-disaster stage: in the pre-disaster stage, the distributed resource configuration is optimized based on the physical model and model parameters. Taking the active distribution network as an example, the configuration nodes and capacity of the energy storage equipment are determined according to its power balance characteristics and the characteristics of the energy storage equipment, while taking into account the operation needs of the gas, water and heating networks, and reasonably arranging energy reserves and supply strategies. The optimal resource allocation plan is solved through the optimization algorithm (under the constraints of satisfying the energy balance of each network, equipment capacity, etc.), which is part of the target optimization results in the pre-disaster stage, which helps to improve the pre-disaster resilience of the multi-energy coupled active distribution network.

[0104] In the disaster stage, the network structure optimization and reconstruction and energy network optimization operation are carried out according to the operation principles of each network in the physical model (such as energy flow relationship, power balance, etc.) and model parameters. In the active distribution network, the network topology is adjusted according to the line fault situation (obtained through monitoring) and the network operation principle, such as disconnecting the faulty line and closing the backup line to reduce the power outage load. For gas, water and heating networks, the energy supply strategy is adjusted according to the energy flow relationship and the changes in parameters such as pressure and flow to ensure the stability of energy supply during the disaster. By solving the optimization problem that meets the constraints of the physical model in the disaster stage, the network structure optimization scheme and energy network operation strategy in the disaster stage, that is, the target optimization result of the disaster stage, are obtained.

[0105] In the post-disaster stage, according to the physical model and model parameters, the fault repair path is optimized. For the damaged lines and equipment of the active distribution network, the fault repair path optimization model is established by considering factors such as geographical location, repair difficulty (based on line and equipment parameter evaluation) and the impact on load recovery. The model is solved by algorithms (such as ant colony algorithms, etc.) to obtain the optimal fault repair path. Under the condition of satisfying maintenance resource constraints (such as the number of maintenance personnel and equipment, these constraints are determined based on the physical model and actual conditions), the normal operation of the distribution network is restored as soon as possible. This optimal fault repair path is the target optimization result in the post-disaster stage.

[0106] The above-mentioned resilience optimization method for multi-energy coupling active distribution network first constructs the physical model and model parameters of the multi-energy coupling active distribution network, divides the extreme events into stages, and obtains multiple division stages; for each division stage, according to the physical model and model parameters, the multi-energy coupling active distribution network is optimized for resilience to obtain the target optimization result. This application performs resilience optimization according to the physical model and model parameters for each division stage. There are clear optimization goals and strategies in each stage, which improves the resilience of the distribution network in many aspects, from distributed resource optimization configuration, network structure optimization reconstruction to fault repair path optimization. This multi-dimensional, phased optimization method helps to enhance the overall ability of the multi-energy coupling active distribution network to resist, adapt and recover from extreme events, reduce the impact of extreme events on power supply, and improve the stability and reliability of power supply.

[0107] In an exemplary embodiment, based on the above embodiments, the physical model of the embodiment of the present application includes at least one of an active power distribution network model, a gas supply network model, a heating network model and a water supply network model.

[0108] Among them, the multi-energy coupling active distribution network of the embodiment of the present application is as follows Figure 4 As shown in Figure 1, the physical model of the active distribution network model plays a key role in the physical model of the multi-energy coupled active distribution network. It mainly describes the transmission and distribution of electric energy in the distribution network and its interaction with other energy networks. This model covers the network topology of the distribution network, including the connection relationship between nodes (such as substations, distribution rooms, power load nodes, etc.) and branches (such as transmission lines). At the same time, it also describes the physical laws that electric energy follows when it flows between nodes and branches, such as the power balance relationship, that is, ensuring that at each node, the total amount of electric energy input is equal to the total amount of electric energy output plus the electric energy loss or energy storage change of the node.

[0109] The embodiments of the present application can more comprehensively and accurately describe the operating status of the multi-energy coupled active distribution network, and provide a solid foundation for optimizing resilience in response to extreme events.

[0110] In an exemplary embodiment, based on the above embodiment, see Figure 5 The process of constructing the active power distribution network model of the embodiment of the present application includes the following steps:

[0111] S301, obtaining distribution network data in an active distribution network.

[0112] The distribution network data includes at least one of node voltage, branch current, node power, and conjugate complex number.

[0113] In the embodiment of the present application, the distribution network data in the active distribution network is the basis for building an accurate model. These distribution network data include but are not limited to at least one of node voltage, branch current, node power, and conjugate complex numbers.

[0114] Data acquisition equipment and methods: Smart meters are widely deployed at various nodes and branches of active distribution networks to measure node voltage and branch current data. These smart meters can collect data in real time and transmit the data to the data processing center through the communication network. For node power data, it can be measured and acquired through equipment such as power analyzers. The acquisition of conjugate complex numbers is usually obtained through corresponding mathematical operations based on the acquired voltage, current and other data when complex power calculations are involved. This multi-source data acquisition method ensures the comprehensiveness and accuracy of distribution network data.

[0115] S302: construct node power balance equations and voltage equations according to distribution network data.

[0116] For active distribution networks, energy transmission must satisfy the node power balance equation and voltage equation as follows.

[0117] V i -V j =z ij I ij ……………………………………(1)

[0118]

[0119]

[0120] Where V i represents the voltage at node i, z ij represents the impedance of branch ij, I ij Represents the current flowing through branch ij, I ij * represents complex current I ij The complex conjugate of S. ij represents the power injected from node i to node j, S jk represents the power injected from node j into node k, S j represents the net complex power injected into node j by the outside world.

[0121] S303: Determine an active power distribution network model according to a node power balance equation and a voltage equation.

[0122] In an embodiment of the present application, after constructing the node power balance equation and the voltage equation, it is necessary to combine these equations with the network topology of the active distribution network to determine the active distribution network model. The network topology describes the connection relationship between nodes and branches in the active distribution network, such as a tree structure, a ring structure, or a more complex hybrid structure. Applying the node power balance equation and the voltage equation to this network topology means mapping the energy balance and voltage relationship to a specific network connection. For example, in a tree-structured network, the power transmission and voltage changes from the root node to the leaf node must follow the constructed equations, thereby fully describing the transmission, distribution, and conversion process of electric energy in the entire active distribution network.

[0123] The embodiment of the present application constructs a model by comprehensively acquiring distribution network data (including node voltage, branch current, node power, conjugate complex numbers, etc.), which can more accurately describe the operating status of the active distribution network and greatly improve the accuracy of the model compared to traditional model building methods.

[0124] In an exemplary embodiment, based on the above embodiment, see Figure 6 The construction process of the gas supply network model in the embodiment of the present application includes the following steps:

[0125] S401, obtaining natural gas data in the gas supply network.

[0126] The natural gas data includes at least one of natural gas flow, natural gas consumption and pipeline pressure drop.

[0127] In order to obtain natural gas flow data, the embodiment of the present application installs high-precision flow meters at key locations of the gas supply network, such as gas source access points, pipeline branch points, and pipeline connections of different diameters. These flow meters can measure the flow of natural gas in the pipeline in real time and accurately, and transmit the measured data to the data processing center through a wired or wireless communication network. For natural gas consumption data, a special gas metering device is installed at each gas consumption node. These devices are selected and installed according to the type of gas-using equipment and the scale of gas consumption to ensure that the natural gas consumption of each gas-using node can be accurately measured and the data can be transmitted to the data processing center. Pipeline pressure drop data is obtained by installing pressure sensors at different locations of the pipeline. Pressure sensors are installed at the starting end, the end, and at a certain distance (determined by factors such as pipeline length and terrain) of the pipeline to measure the pressure of natural gas in the pipeline. The pipeline pressure drop data is obtained by calculating the difference between the measured values ​​of the pressure sensors at different positions, and then the data is transmitted to the data processing center.

[0128] S402, constructing a gas network energy flow equation and a gas network pressure drop equation according to natural gas data.

[0129] For the gas supply network, the energy flow equation and pressure drop equation that need to be satisfied for the transmission of natural gas are as follows.

[0130]

[0131]

[0132] In the formula, mn represents the branch connected to node m, up m represents the upstream branch set of node m, G in.m represents the natural gas flow rate injected into node m by the upstream branch, S m Indicates the natural gas flow rate injected into node m by the natural gas source connected to node m; down m represents the downstream branch set of node m, G out.m represents the natural gas flow from node m into the downstream branch, L m represents the gas consumption of node m. n represents the air pressure at node n, p mn Indicates the pipeline pressure drop of pipeline mn.

[0133] S403, determining a gas supply network model according to a gas network energy flow equation and a gas network pressure drop equation.

[0134] The network topology of the gas supply network in the embodiment of the present application describes the connection mode between the gas source, pipeline, and node, such as a tree structure, a ring structure, or a mesh structure. Combining the gas network energy flow equation and the gas network pressure drop equation with the network topology of the gas supply network is a key step in determining the gas supply network model. For example, in a tree-structured gas supply network, the gas source is located at the root of the tree, and each gas-consuming node is located at the end of the branch of the tree. Under this structure, natural gas starts from the gas source and passes through each node along the pipeline in sequence. The gas network energy flow equation must be satisfied at each node, and the gas network pressure drop equation must be satisfied in each pipeline. By applying the equation to this specific network topology, the entire process of transmission, distribution, and pressure change of natural gas from the gas source to each gas-consuming node can be fully described.

[0135] In an exemplary embodiment, based on the above embodiment, see Figure 7 The construction process of the water supply network model of the embodiment of the present application includes the following steps:

[0136] S501, obtaining water supply data of the water supply network.

[0137] The water supply data includes at least one of water flow, water load, transmission pressure drop loss, water pressure difference and water pump head.

[0138] In order to obtain water flow data, the embodiment of the present application can install flow measurement devices at key locations of the water supply network, such as water source outlets, pipeline branch points, connections between pipelines of different diameters, and water inlets of large water consumption nodes. These flow measurement devices can be selected according to the size of the pipeline and the flow range. Suitable types, such as electromagnetic flowmeters, turbine flowmeters, etc., can be selected.

[0139] S502: construct a water network energy flow equation and a water network pressure drop equation according to the water supply data.

[0140] For the water supply network in the embodiment of the present application, the working energy flow equation and pressure drop equation of the water network are as follows.

[0141]

[0142] ΔH b.ij +Z ij -H p.ij -ΔH ij =0…………………………(7)

[0143] In the formula, C i represents the set of nodes connected to node i, W i represents the water flow into node i, -W i represents the water flow out of node i, W Si W represents the water source connected to node i. Li Represents the water load at node i. ΔH b.ij Represents the transmission pressure drop loss of pipeline ij. ij Indicates the water pressure difference between the two ends of the pipeline, H p.ij Indicates the branch pump head. ΔH ij Represents the pressure drop across the pipe ij.

[0144] S503, determining a water supply network model according to a water network energy flow equation and a water network pressure drop equation.

[0145] The network topology of the water supply network of the embodiment of the present application describes the connection mode between the water source, the pipeline, and the nodes, such as a tree structure, a ring structure, or a mesh structure. Combining the water network energy flow equation and the water network pressure drop equation with the network topology of the water supply network is a key step in determining the water supply network model. For example, in a water supply network with a tree structure, the water source is located at the root of the tree, and each water-using node is located at the end of the branch of the tree. Under this structure, water starts from the water source and passes through each node along the pipeline in sequence. The water network energy flow equation must be satisfied at each node, and the water network pressure drop equation must be satisfied in each pipeline. By applying the equation to this specific network topology, the whole process of transmission, distribution, and pressure change of water from the water source to each water-using node can be fully described.

[0146] In an exemplary embodiment, based on the above embodiment, see Figure 8 , the construction process of the heating network model of the embodiment of the present application includes:

[0147] S601, obtaining heating data of the heating network, wherein the heating data includes heat loss, node temperature, external temperature and thermal power.

[0148] In order to obtain heat loss data, the embodiment of the present application installs heat flow sensors and temperature sensors at different positions of the heating pipeline. By measuring the difference in heat flow at different positions of the pipeline, and combining the material, length, insulation and other parameters of the pipeline, the heat loss can be calculated. Temperature sensors are installed at various nodes of the heating network (such as heat sources, heat exchange stations, heat users, etc.) to obtain node temperature data. These temperature sensors should have a suitable measurement range and accuracy, and can accurately reflect the temperature changes of the nodes. External temperature data can be obtained by installing a meteorological station near the heating network, or by using a special outdoor temperature sensor for measurement. Thermal power data can be obtained by installing a power measurement device at the outlet of the heating source (such as a thermal power plant, a boiler room, etc.), and thermal power can also be calculated based on data such as heat flow and temperature difference at the heat exchange station and the heat user end. All these data are transmitted to the data processing center via a wired or wireless communication network.

[0149] S602: construct a heat exchange equation based on the heating data.

[0150] In the embodiment of the present application, for the heating network, the energy flow equation of the working fluid is consistent with the energy flow equation of the water network. In addition, the heat exchange equation of the node must also be satisfied as follows.

[0151]

[0152] In the formula, C i Represents the set of nodes connected to node i, E ij Represents the heat loss of line ij. i and T j represents the temperature of node i and node j, T out.i represents the external temperature of node i. in represents the thermal power of the input node.

[0153] S603, determining a heating network model according to a water network energy flow equation, a water network pressure drop equation and a heat exchange equation.

[0154] The network topology of the heating network of the embodiment of the present application describes the connection mode between the heat source, pipelines, nodes (including heat exchange stations, heat users, etc.), such as a tree structure, a ring structure or a mesh structure, etc. Combining the water network energy flow equation (because the energy flow equation of the working medium of the heating network is consistent with the water network energy flow equation), the water network pressure drop equation (relevant when it comes to hot water heating, etc.) and the heat exchange equation with the network topology of the heating network is a key step in determining the heating network model.

[0155] In an exemplary embodiment, based on the above embodiment, see Fig. 9 In the embodiment of the present application, for each division stage, according to the physical model and model parameters, a multi-energy coupling active distribution network is subjected to resilience optimization processing to obtain a target optimization result, including the following steps:

[0156] S701, for each division stage, determine the target constraint condition according to the physical model and model parameters, wherein the target constraint condition includes at least one of the constraints related to the active distribution network, the constraints related to the gas supply network, the constraints related to the water supply network, and the constraints related to the heating network.

[0157] In the embodiment of the present application, first, a physical model of a multi-energy coupled active distribution network is constructed according to the method described above, including an active distribution network model, a gas supply network model, a water supply network model, and a heating network model. During the construction process, relevant data of each network is accurately obtained, such as node voltage, branch current, node power and other data in the active distribution network, natural gas flow, consumption, pipeline pressure drop and other data in the gas supply network, water flow, water load, water pressure difference and other data in the water supply network, heat loss, node temperature, thermal power and other data in the heating network.

[0158] The collected data are analyzed and processed to determine the model parameters in each physical model, such as line impedance and energy storage equipment parameters in the active distribution network, pipeline resistance coefficient and gas supply capacity in the gas supply network, pipeline roughness and water pump performance parameters in the water supply network, heat exchange coefficient and heat source supply capacity in the heating network.

[0159] (II) Determination of the division phase

[0160] According to factors such as the type (such as natural disasters, equipment failures, etc.), intensity, duration, and expected impact on the multi-energy coupled active distribution network of extreme events, the entire process of extreme events is divided into different stages, such as pre-disaster, mid-disaster, and post-disaster stages.

[0161] Analyze the situations that each part of the multi-energy coupled active distribution network (active distribution network, gas supply network, water supply network, and heating network) may face at each stage, such as the potential risks that may be faced before the disaster, the types of faults that may occur during the disaster and the scope of impact, and the damage conditions after the disaster.

[0162] S702, performing resilience optimization processing on the multi-energy coupling active distribution network according to target constraint conditions to obtain a target optimization result.

[0163] In the embodiment of the present application, a linear programming algorithm is used for the active distribution network, based on the pre-disaster target constraint conditions, to optimize the configuration of the energy storage device. For example, the optimal configuration position and capacity of the energy storage device in the active distribution network are determined by minimizing the total cost of the energy storage device as the objective function, while satisfying the node power balance constraint, voltage constraint, line capacity constraint, etc.

[0164] For the gas supply network, nonlinear programming algorithms are used to optimize the gas supply plan and pipeline operation parameters while meeting the pre-disaster target constraints (natural gas flow balance constraints, pressure constraints, gas source supply capacity constraints, and gas demand constraints, etc.). For example, the gas supply pressure, flow distribution, and pipeline operation schedule of the gas source are adjusted with the objective function of minimizing the gas supply cost.

[0165] Disaster stage: The active distribution network uses heuristic algorithms (such as greedy algorithms) to restructure the network and adjust energy operation according to the target constraints adjusted during the disaster. For example, with minimizing the scope of power outage as the objective function, in the case of partial line failure, the stability of power supply can be maintained by switching lines and adjusting the charging and discharging strategies of energy storage devices.

[0166] The gas supply network uses a dynamic programming algorithm to make emergency gas supply adjustments based on the target constraints reassessed during the disaster. For example, the flow distribution of the gas supply pipeline and the emergency gas supply strategy of the gas source are adjusted with the primary goal of meeting the gas demand of key users (such as hospitals and emergency rescue centers).

[0167] Post-disaster stage: The active distribution network uses the ant colony algorithm to optimize the fault repair sequence and resource allocation based on the post-disaster target constraints. For example, with the shortest repair time as the objective function, the repair sequence of each fault point in the active distribution network is determined, and resources such as repair personnel, equipment and materials are reasonably allocated.

[0168] In an exemplary embodiment, based on the above embodiment, see Fig.10 The division stages of the embodiment of the present application include a pre-disaster defense stage, and the target optimization result includes a first optimization result; the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the target constraint conditions to obtain the target optimization result, including:

[0169] S801, for the pre-disaster defense stage, construct a first objective function according to a pre-acquired set of fault scenarios.

[0170] Among them, the first objective function aims to minimize the operating cost.

[0171] In an embodiment of the present application, historical fault data of the multi-energy coupled active distribution network is collected, including various types of faults that occurred in the past (such as power failures, gas supply failures, water supply failures, heating failures, etc.), the time and location of the faults, the scope of the fault impact, and other information.

[0172] like Fig.11 Shown is a schematic diagram of a typical fault scenario. A detailed equipment and network structure analysis is performed on the multi-energy coupled active distribution network, taking into account factors such as the aging of different equipment, environmental factors in different regions (such as areas with higher risks of natural disasters), and the impact of network topology on fault propagation.

[0173] Combining historical fault data and distribution network analysis results, various possible fault conditions are simulated through simulation tools to build a set of fault scenarios. For example, for active distribution networks, short circuits and open circuits of different lines, power imbalance faults of different nodes, etc. are simulated; for gas supply networks, gas source interruptions and pipeline leakage faults are simulated; for water supply networks, water pump failures and pipeline rupture faults are simulated; for heating networks, heating equipment damage and heating pipeline blockage faults are simulated.

[0174] Construction of the first objective function: Analyze the operating cost composition of each energy network in the multi-energy coupling active distribution network. For the electric energy part, the fuel consumption cost of traditional power generation equipment, the purchase, installation and maintenance cost of distributed power generation equipment, the charging and discharging loss cost of energy storage equipment, the power transmission loss cost, etc. are counted in detail. For the gas supply network, the gas source mining cost, transportation cost, pipeline construction and maintenance cost, etc. are calculated. For the water supply network, the water source acquisition cost, treatment cost, water pump operation and maintenance cost, pipeline laying and maintenance cost, etc. are determined. For the heating network, the production cost of the heat source, the purchase, installation and maintenance cost of the heating equipment, the construction and maintenance cost of the heating pipeline, etc. are counted.

[0175] According to the fault scenario set, analyze the impact of different fault scenarios on various cost items. For example, in a line fault scenario, the power transmission loss cost may increase, and the backup line may need to be activated, resulting in an increase in the activation cost of the backup line. In the gas source interruption scenario, it may be necessary to obtain natural gas from other gas sources, increasing the gas source conversion cost, etc.

[0176] According to the cost structure and the impact of the fault scenario, the first objective function is constructed. The cost items of each energy network are summarized, with the goal of minimizing the operating cost.

[0177] S802: Construct a distributed resource optimization configuration model according to the first objective function and the objective constraint condition.

[0178] The embodiments of the present application determine the variables in the distributed resource optimization configuration model. For distributed power generation equipment in the active distribution network, the variables include its installation location (such as which nodes it is installed at), capacity, etc.; for energy storage equipment, the variables are installation location, energy storage capacity, charging and discharging power, etc. For distributed resources in the gas supply network (such as small gas supply sources), the variables include their distribution location, gas supply capacity, etc.; for distributed water sources in the water supply network, the variables include distribution location, water supply flow, etc.; for local heating equipment in the heating network, the variables are installation location, heating power, etc.

[0179] S803: Based on the snow melting algorithm, the distributed resource optimization configuration model is solved to obtain a first optimization result.

[0180] The embodiment of the present application first initializes the snow particles. According to the variable range in the distributed resource optimization configuration model, a group of snow particles are randomly generated, and each snow particle represents an initial distributed resource configuration scheme. For example, the initial capacity of distributed power generation equipment at some nodes, the initial installation location and capacity of energy storage equipment, etc. are randomly determined.

[0181] Calculate the fitness of the snow particles. For each snow particle, substitute the distributed resource configuration scheme it represents into the first objective function of the distributed resource optimization configuration model to calculate the corresponding operating cost, which is the fitness value of the snow particle.

[0182] Movement and fusion of snow particles. According to the fitness value of the snow particles and the rules of the snow melting algorithm, the position of the snow particles is adjusted (i.e., the distributed resource configuration scheme is adjusted) so that the snow particles move in the direction of better fitness (lower operating cost). During the movement process, when certain conditions are met (such as the fitness values ​​of two snow particles are close and their positions are close), the fusion operation of the snow particles is performed to merge and optimize the configuration schemes represented by the two snow particles to obtain a new snow particle (new configuration scheme).

[0183] Iterative solution. Repeat the movement and fusion of snow particles until the stopping condition is met (such as reaching a predetermined number of iterations or the optimal solution found is no longer improved within a certain number of iterations). At this point, the distributed resource allocation solution corresponding to the converged snow particle position is the first optimization result. Fig.12 This is a comparison chart between the snow melting algorithm of the embodiment of the present application and other algorithms.

[0184] Specifically, for the pre-disaster fault resistance stage, the steps for pre-disaster distributed resource optimization configuration of the multi-energy coupling active distribution network are as follows:

[0185] (1) A typical fault scenario selection method based on information entropy is used to select a set of typical fault scenarios.

[0186] (2) Based on the typical fault scenarios in the typical fault scenario set and their occurrence probabilities, the objective function of constructing the distributed resource optimization configuration model is as follows.

[0187]

[0188] Where n represents the number of scenarios in the fault scenario set. i represents the probability of occurrence of the i-th fault scenario. loss.i represents the load loss under the i-th fault scenario. ESS represents the unit price of energy storage equipment, Q ESS Indicates the capacity of the energy storage device.

[0189] (3) Based on the energy flow balance constraints and safe operation constraints of the multi-energy coupled active distribution network, the constraints for establishing the distributed resource optimization configuration model are as follows.

[0190] Σy ij =N-1…………………………(10)

[0191] α ij +α ji =y ij ……………………………………(11)

[0192]

[0193]

[0194] P load.imin ≤P load.i ≤P load.imax ………………………………(14)

[0195] Q load.imin ≤Q load.i ≤Q load.imax ………………………………(15)

[0196] P gas.imin ≤P gas.i ≤P gas.imax ………………………………(16)

[0197] P w.imin ≤P w.i ≤P w.imax ……………………………………(17)

[0198] P t.imin ≤P t.i ≤P t.imax …………………………………………(18)

[0199] Where P load.j , P load.jmax , P load.jmin They represent the active power of the load connected to node j and its upper and lower limits respectively; Q load.j , Q load.jmax , Q load.jmin They represent the reactive power and upper and lower limits of the distributed generator connected to node j. ij Indicates the working status of line ij, y ij =1 indicates that the line is closed. N indicates the number of distribution network nodes. ij =1 indicates that node i is the parent node of node j. gas.j , P gas.jmax , P gas.jmin They represent the power of the gas load connected to the natural gas grid node j and its upper and lower limits. w.j , P w.jmax , P w.jmin They represent the power of the load connected to the water supply network node j and its upper and lower limits. t.j , P t.jmax , P t.jmin They respectively represent the power of the load connected to the heating network node j and its upper and lower limits.

[0200] (4) The snow melting algorithm is used to solve the distributed resource optimization configuration model, such as Fig.13 The figure shows a schematic diagram of distributed resource configuration.

[0201] In the above-mentioned multi-stage resilience improvement method of multi-energy coupling active distribution network under extreme weather conditions based on resource allocation and network reconstruction, the steps of solving the distributed resource optimization configuration model using the snow melting algorithm are as follows:

[0202] (5) Initialize the algorithm, determine the dimension d of the problem to be solved and the number n of molecules to be solved, and initialize a solution space with n rows and d columns.

[0203] (6) For each molecule solution, find the objective function value of the current molecule. Sort the molecules according to the objective function value from large to small, select the first molecule as population a, and the remaining molecules as population b

[0204] (7) For the molecules of population a, the position update formula is as follows:

[0205]

[0206] Where Random(t) is a random variable, randomly selected from G(t), Z2(t), Z3(t) and Z4(t). G(t) is the optimal position of the molecule at time t, Z2(t) is the suboptimal position of the molecule at time t, Z3(t) is the third position of the molecule at time t, and Z4(t) is the average position of the molecule at time t. BMi(t) represents the random variable of the Brownian motion of the molecule at time t. θ1 represents the learning factor of the molecule from the optimal position of the molecule.

[0207] (8) For the molecules of population b, the position update formula is as follows:

[0208]

[0209] Where DDF is the snow ablation coefficient, T is the current temperature, and T1 is the outdoor temperature, which is generally set to 0. θ2 represents the random learning factor that the molecule learns from the optimal position of the molecule.

[0210] (9) Based on the updated position, recalculate the fitness and sort from large to small to verify whether the number of iterations reaches the preset value. If so, output the final configuration plan.

[0211] Fig.14 is a schematic diagram of load recovery rate, Fig.15a and Fig.15b is a schematic diagram of coupling device utilization, Fig.16a and Fig.16b Schematic diagram of the network reconstruction structure of multi-energy coupling active distribution network in the adaptation stage. Fig.17 It is a schematic diagram of load recovery rate in the adaptation stage. It can be seen from the above figure that the embodiment of the present application fully considers various fault conditions that the multi-energy coupling active distribution network may face in the optimization process of the pre-disaster resistance stage. This makes the constructed first objective function and distributed resource optimization configuration model more in line with the actual situation, and can more effectively improve the resistance of the multi-energy coupling active distribution network in the face of different fault risks.

[0212] In an exemplary embodiment, based on the above embodiment, see Fig.18 , the division stages of the embodiment of the present application include a disaster adaptation stage, and the target optimization result includes a second optimization result; according to the target constraint condition, the multi-energy coupling active distribution network is subjected to resilience optimization processing to obtain the target optimization result, including:

[0213] S901, constructing a second objective function according to the pre-acquired load loss data, wherein the second objective function aims to minimize the load loss.

[0214] Among them, the second objective function is a mathematical function constructed to achieve a specific optimization goal in the disaster adaptation stage. In the embodiment of the present application, the second objective function aims to minimize load loss. By quantifying and comprehensively considering the load losses of different energy networks (such as active distribution networks, gas supply networks, water supply networks, and heating networks), a function that can reflect the overall load loss situation is constructed. This function will serve as the goal of optimizing the operation of the energy network, aiming to minimize the load loss rate in the disaster stage by adjusting the operation strategy of the energy network.

[0215] The load loss data acquired in advance is the basis for constructing the second objective function. These data are collected in a variety of ways. For example, in the active distribution network, the voltage, current and other data of the power consumption nodes are monitored in real time through smart meters and other equipment, and the power load loss is calculated in combination with the normal power load demand of the user; in the gas supply network, the natural gas flow data is obtained through sensors installed in pipelines and gas consumption nodes, and the natural gas load loss is determined by comparing the gas demand under normal gas supply conditions; in the water supply network, the water flow is measured according to the flowmeters installed in the water pipes and water consumption nodes, and the water load loss is determined in combination with the normal water demand; in the heating network, the heating situation is measured by temperature sensors, heat flow sensors and other equipment, and the heat load loss is obtained by comparing with the normal heating demand.

[0216] S902: construct an energy network optimization operation model according to the second objective function and the objective constraint conditions.

[0217] Among them, the energy network optimization operation model is a mathematical model constructed based on the characteristics of the multi-energy coupled active distribution network in the disaster adaptation stage. The model comprehensively considers the physical characteristics (such as energy flow equations, power balance equations, etc.) of each energy network (active distribution network, gas supply network, water supply network, heating network), target constraints (such as equipment capacity constraints, energy supply demand constraints, etc.) and the second objective function (minimum load loss). By quantifying and mathematically expressing these factors, the constructed model can solve the optimal operation strategy of the energy network in the disaster adaptation stage, that is, the second optimization result.

[0218] S903: Based on the solver, the energy network optimization operation model is solved to obtain a second optimization result.

[0219] Among them, the solver is a tool or algorithm for solving mathematical models (such as the energy network optimization operation model in the embodiment of the present application). It can adopt different algorithm principles, such as linear programming algorithms, nonlinear programming algorithms, etc., by inputting relevant parameters of the model (such as objective functions, constraints, etc.), and after a series of calculations and iterations, find the optimal solution that meets the model requirements (that is, the second optimization result in the embodiment of the present application).

[0220] Specifically, for the fault adaptation stage during the disaster, the steps for optimizing the network structure of the multi-energy coupling active distribution network and optimizing the operation of the energy network to reduce the power outage load are as follows:

[0221] (1) During a disaster, more load energy supply should be guaranteed as much as possible. Based on this, the objective functions of the multi-energy coupling active distribution network structure optimization reconstruction and energy network optimization operation model are established as follows.

[0222] minf=P loss (t)……………………(21)

[0223] Where P loss (t) represents the load loss of the active distribution network at time t.

[0224] (2) In order to ensure the solution speed and solution accuracy, the embodiment of the present application uses the Cplex solver to solve the optimization model.

[0225] (3) In order to transform the nonlinear constraints into constraints that conform to the second-order cone programming, the above formula is transformed as follows.

[0226]

[0227] In the formula, l ij =|I ij | 2 , v j =|V j | 2 .

[0228] In an exemplary embodiment, based on the above embodiment, see Fig.19 , the division stage of the embodiment of the present application includes a post-disaster recovery stage, and the target optimization result includes a third optimization result; for each division stage, the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the target constraint conditions to obtain the target optimization result, including:

[0229] S1001, determining maintenance resource constraints.

[0230] Among them, maintenance resource constraints refer to the conditions that are limited by human, material, financial and other resources when restoring the multi-energy coupled active distribution network after a disaster. For example, the number of maintenance personnel, the number and types of maintenance equipment (such as repair vehicles and tools), and the reserve of maintenance materials (such as wires, pipes, accessories, etc.) are all limited. These limiting factors constitute maintenance resource constraints. It determines the number and scale of maintenance tasks that can be carried out simultaneously during the post-disaster recovery process.

[0231] In the embodiment of the present application, the number of maintenance personnel available for post-disaster recovery is counted, including power maintenance personnel, gas equipment maintenance personnel, water supply equipment maintenance personnel, heating equipment maintenance personnel, etc. Different types of maintenance personnel have different professional skills and can only participate in the maintenance of corresponding energy network equipment.

[0232] Check the number and types of maintenance equipment, such as the number of repair vehicles, the number of sets of power maintenance tools, the number of sets of gas pipeline maintenance equipment, etc. Each maintenance task requires specific maintenance equipment, and the number of equipment is limited.

[0233] S1002: construct a third objective function based on pre-acquired component repair data of the post-disaster active distribution network.

[0234] Among them, the third objective function is a mathematical function constructed to achieve a specific optimization goal in the post-disaster recovery stage. In the embodiment of the present application, the third objective function takes the minimum maintenance cost as the goal, quantifies the maintenance costs of different components, and comprehensively considers factors such as maintenance resource constraints to construct a function that can reflect the overall maintenance cost situation. This function will serve as the goal of optimizing post-disaster recovery path and other decisions, aiming to minimize the post-disaster maintenance cost by reasonably arranging maintenance tasks.

[0235] In the embodiment of the present application, the pre-acquired component repair data of the post-disaster active distribution network includes information such as the degree of damage, repair difficulty, repair time, and required repair resources of each component. For components with a higher degree of damage, more repair time and resources may be required, resulting in higher repair costs. The difficulty of repair is also related to the repair cost. For example, complex repair work may require higher-level maintenance personnel, whose labor costs are higher.

[0236] The third objective function is constructed by summing the maintenance cost of each component with the goal of minimizing the maintenance cost, aiming to minimize the overall maintenance cost by optimizing the maintenance path and resource allocation.

[0237] S1003: construct a path optimization model according to the maintenance resource constraints, target constraints and the third objective function.

[0238] Among them, the path optimization model is a mathematical model built based on the characteristics of multi-energy coupled active distribution networks in the post-disaster recovery stage. The model comprehensively considers the maintenance resource constraints, the target constraints of each energy network (active distribution network, gas supply network, water supply network, heating network) (such as the operation requirements after equipment repair, etc.) and the third objective function (minimum maintenance cost). By quantifying and mathematically expressing these factors, the constructed model can solve the optimal maintenance path and resource allocation plan in the post-disaster recovery stage, that is, the third optimization result.

[0239] In the embodiment of the present application, in the ant colony algorithm, the maintenance task is regarded as a node on the path of the ants looking for food. Each ant starts from the starting point (indicating the start of post-disaster recovery work) and selects the next maintenance task based on the pheromone concentration and heuristic information (such as the urgency of the maintenance task, the estimated maintenance cost, etc.). The pheromone concentration reflects the quality of the maintenance path, and the pheromone concentration is uniformly distributed at the beginning. As the ants move, they leave pheromones on the path they pass, and the pheromones will evaporate over time.

[0240] S1004: Solve the path optimization model based on the ant colony algorithm to obtain a third optimization result.

[0241] Specifically, for the post-disaster fault recovery stage, the steps for optimizing the post-disaster fault repair path of the multi-energy coupling active distribution network to reduce the load-weighted power outage time are as follows:

[0242] (1) During the post-disaster repair process, the goal of component repair is to restore the normal operation of the distribution network as soon as possible. Therefore, the objective function for optimizing the post-disaster fault repair path of the multi-energy coupled active distribution network aimed at reducing the load-weighted power outage time is designed as follows.

[0243]

[0244] Where N represents the number of damaged components in the active distribution network after the disaster. j It represents the time spent on repairing the jth fault, including the time on the path and the time spent on repairing. j It represents the load power that has not been restored after the jth fault is repaired.

[0245] (1) In the above embodiment, maintenance resource constraints are added as follows.

[0246]

[0247] In the formula, g k is the maintenance material required for the kth faulty equipment, g max The maximum amount of supplies a maintenance team can carry.

[0248] (2) The ant colony algorithm is used to solve the optimization of post-disaster fault repair path of multi-energy coupled active distribution network aimed at reducing load-weighted power outage time.

[0249] In the above-mentioned multi-stage resilience improvement method of multi-energy coupled active distribution network under extreme weather conditions based on resource allocation and network reconstruction, the steps of using ant colony algorithm to solve the optimization of post-disaster fault repair path of multi-energy coupled active distribution network for reducing load-weighted power outage time are as follows:

[0250] (3) Initialize the algorithm, determine the topological structure of the network to be solved and the number of ants nant, and initialize the pheromone concentration on each branch of the network.

[0251] (4) The ants start to move, and use the roulette method to decide the direction of transfer according to the pheromone concentration on different paths. The probability that the ant nant transfers from the fault point i to j at time t is:

[0252]

[0253] In the formula, is the pheromone concentration distributed between fault point i and fault point j, is the heuristic factor, which indicates the expected degree of ant transfer from fault point i to fault point j; α and β are the relative importance strength of pheromone and heuristic factor respectively. fault indicates the set of unrepaired fault points.

[0254] (5) After the ant movement is completed, the pheromone concentration on the path is updated as follows:

[0255] τ ij (t+n)=(1-ρ)τ ij (t)+Δτ ij (t)…………………………(26)

[0256]

[0257]

[0258] In the formula, ρ (0<ρ<1) represents the evaporation coefficient of pheromone, and 1-ρ represents the persistence of pheromone; represents the amount of pheromone on the path (i, j) after ant k completes one iteration, Q is a constant, and L k is the total length of the path traveled by the kth ant in this cycle.

[0259] (6) Check whether the number of iterations reaches the preset value. If it does, calculate the ant with the shortest time among all ants and select its path as the final solution.

[0260] Fig. 20 This is a comparison chart of algorithm convergence in the post-disaster repair phase. Fig.21 is a schematic diagram of load recovery rate, Fig.22a and 22b It is a schematic diagram of the utilization of coupled equipment. It can be seen from the figure that the ant colony algorithm can be used to solve the optimization of post-disaster fault repair path of multi-energy coupled active distribution network aimed at reducing load-weighted power outage time.

[0261] In one embodiment, Fig.23As shown, a multi-energy coupling active distribution network resilience optimization device 1100 is provided, comprising:

[0262] Parameter construction module 1101, used to construct a physical model and model parameters of a multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0263] The stage division module 1102 is used to perform stage division processing on the extreme event to obtain multiple division stages; the division stages are determined based on the impact of the movement trajectory of the extreme event on the multi-energy coupling active distribution network;

[0264] The optimization module 1103 is used to perform resilience optimization processing on the multi-energy coupling active distribution network according to the physical model and model parameters for each division stage to obtain a target optimization result.

[0265] In one of the embodiments, the physical model includes at least one of an active power distribution network model, a gas supply network model, a heating network model and a water supply network model.

[0266] In one embodiment, the above device further comprises:

[0267] A power grid data acquisition module, used to acquire power distribution network data in an active power distribution network; the power distribution network data includes at least one of node voltage, branch current, node power, and conjugate complex number;

[0268] A power grid equation determination module, used to construct a node power balance equation and a voltage equation based on distribution network data;

[0269] The power grid model determination module is used to determine the active distribution network model according to the node power balance equation and voltage equation.

[0270] In one embodiment, the above device further comprises:

[0271] A natural gas data acquisition module, used to acquire natural gas data in the gas supply network; the natural gas data includes at least one of natural gas flow, natural gas consumption and pipeline pressure drop;

[0272] A gas network equation determination module is used to construct a gas network energy flow equation and a gas network pressure drop equation based on natural gas data;

[0273] The gas network model determination module is used to determine the gas supply network model according to the gas network energy flow equation and the gas network pressure drop equation.

[0274] In one embodiment, the above device further comprises:

[0275] A water supply data acquisition module, used to acquire water supply data of the water supply network; the water supply data includes at least one of water flow, water load, transmission pressure drop loss, water pressure difference and water pump head;

[0276] A water network equation determination module is used to construct a water network energy flow equation and a water network pressure drop equation based on water supply data;

[0277] The water supply network determination module is used to determine the water supply network model according to the water network energy flow equation and the water network pressure drop equation.

[0278] In one embodiment, the above device further comprises:

[0279] A heating data acquisition module is used to acquire heating data of the heating network; the heating data includes heat loss, node temperature, external temperature and thermal power;

[0280] A heat exchange equation determination module is used to construct a heat exchange equation based on heating data;

[0281] The heating model determination module is used to determine the heating network model based on the water network energy flow equation, the water network pressure drop equation and the heat exchange equation.

[0282] In one embodiment, the optimization module includes:

[0283] A condition determination unit, for determining target constraint conditions for each division stage according to the physical model and model parameters; the target constraint conditions include at least one of active distribution network related constraints, gas supply network related constraints, water supply network related constraints and heating network related constraints;

[0284] The optimization unit is used to perform resilience optimization processing on the multi-energy coupling active distribution network according to target constraints to obtain target optimization results.

[0285] In one embodiment, the above-mentioned division stage includes a pre-disaster defense stage, the target optimization result includes a first optimization result; and the optimization module includes:

[0286] A first function determination unit is used to construct a first objective function according to a pre-acquired set of fault scenarios in the pre-disaster resistance stage; the first objective function takes minimizing the operating cost as a goal;

[0287] A configuration model determination unit, used to construct a distributed resource optimization configuration model according to the first objective function and the objective constraint condition;

[0288] The solution unit is used to solve the distributed resource optimization configuration model based on the snow melting algorithm to obtain a first optimization result.

[0289] In one embodiment, the division stage includes a disaster adaptation stage, the target optimization result includes a second optimization result; and the optimization module includes:

[0290] A second function determination unit is used to construct a second objective function according to the pre-acquired load loss data; the second objective function takes the minimum load loss as a goal;

[0291] An operation model determination unit, used for constructing an energy network optimization operation model according to a second objective function and objective constraints;

[0292] The solving unit is used to solve the energy network optimization operation model based on the solver to obtain a second optimization result.

[0293] In one embodiment, the above-mentioned division stage includes a post-disaster recovery stage, and the target optimization result includes a third optimization result; the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the target constraint condition to obtain the target optimization result, including:

[0294] A resource constraint determination unit, used for determining maintenance resource constraints;

[0295] A third function determination unit is used to construct a third objective function based on pre-acquired component repair data of the post-disaster active distribution network; the third objective function takes minimizing the repair cost as a goal;

[0296] An optimization model determination unit, used for constructing a path optimization model according to maintenance resource constraints, target constraints and a third objective function;

[0297] The solving unit is used to solve the path optimization model based on the ant colony algorithm to obtain a third optimization result.

[0298] Each module in the above multi-energy coupling active distribution network resilience optimization device can be implemented in whole or in part by software, hardware and their combination. The above 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 the above modules.

[0299] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.24As 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 computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. 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 the processor, a method for optimizing the resilience of a multi-energy coupled active distribution network is implemented.

[0300] Those skilled in the art will understand that Fig.24 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.

[0301] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0302] Construct the physical model and model parameters of the multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0303] The extreme events are divided into multiple stages, and the stages are determined based on the impact of the movement trajectory of the extreme events on the multi-energy coupled active distribution network.

[0304] For each division stage, the multi-energy coupled active distribution network is optimized for resilience according to the physical model and model parameters to obtain the target optimization results.

[0305] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0306] Construct the physical model and model parameters of the multi-energy coupled active distribution network; the physical model is established based on the operation requirements of the active power distribution network, gas supply network, water supply network and heating network;

[0307] The extreme events are divided into multiple stages, and the stages are determined based on the impact of the movement trajectory of the extreme events on the multi-energy coupled active distribution network.

[0308] For each division stage, the multi-energy coupled active distribution network is optimized for resilience according to the physical model and model parameters to obtain the target optimization results.

[0309] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are 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 this specification.

[0310] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-energy coupling active distribution network resilience optimization method, characterized in that: The method comprises: Constructing a physical model and model parameters of a multi-energy coupled active distribution network; the physical model is established based on the operation requirements of an active power distribution network, a gas supply network, a water supply network, and a heating network; Performing stage division processing on the extreme event to obtain a plurality of division stages; the division stages are determined based on the influence of the movement trajectory of the extreme event on the multi-energy coupling active distribution network; For each of the divided stages, the multi-energy coupled active distribution network is subjected to resilience optimization processing according to the physical model and the model parameters to obtain a target optimization result.

2. The method according to claim 1, characterized in that The physical model includes at least one of an active power distribution network model, a gas supply network model, a heating network model and a water supply network model.

3. The method according to claim 2, characterized in that The construction process of the active power distribution network model includes: Acquire distribution network data in the active distribution network; the distribution network data includes at least one of node voltage, branch current, node power, and conjugate complex number; Constructing a node power balance equation and a voltage equation according to the distribution network data; The active power distribution network model is determined according to the node power balance equation and the voltage equation.

4. The method according to claim 2, characterized in that: The construction process of the gas supply network model includes: Acquire natural gas data in the gas supply network; the natural gas data includes at least one of natural gas flow, natural gas consumption and pipeline pressure drop; Constructing a gas network energy flow equation and a gas network pressure drop equation according to the natural gas data; The gas supply network model is determined according to the gas network energy flow equation and the gas network pressure drop equation.

5. The method according to claim 2, characterized in that: The construction process of the water supply network model includes: Acquire water supply data of the water supply network; the water supply data includes at least one of water flow, water load, transmission pressure drop loss, water pressure difference and water pump head; Constructing a water network energy flow equation and a water network pressure drop equation according to the water supply data; The water supply network model is determined according to the water network energy flow equation and the water network pressure drop equation.

6. The method according to claim 5, characterized in that The construction process of the heating network model includes: Acquiring heating data of the heating network; the heating data includes heat loss, node temperature, external temperature and thermal power; Constructing a heat exchange equation according to the heating data; The heating network model is determined according to the water network energy flow equation, the water network pressure drop equation and the heat exchange equation.

7. The method according to claim 2, characterized in that For each of the divided stages, the multi-energy coupling active distribution network is subjected to toughness optimization processing according to the physical model and the model parameters to obtain a target optimization result, including: For each of the division stages, a target constraint condition is determined according to the physical model and the model parameters; the target constraint condition includes at least one of an active distribution network-related constraint, a gas supply network-related constraint, a water supply network-related constraint, and a heating network-related constraint; The multi-energy coupling active distribution network is subjected to resilient optimization processing according to the target constraint conditions to obtain a target optimization result.

8. The method according to claim 7, characterized in that The division stage includes a pre-disaster resistance stage, and the target optimization result includes a first optimization result; the multi-energy coupling active distribution network is subjected to resilience optimization processing according to the target constraint condition to obtain the target optimization result, including: For the pre-disaster defense stage, a first objective function is constructed according to a pre-acquired set of fault scenarios; the first objective function aims to minimize the operating cost; Constructing a distributed resource optimization configuration model according to the first objective function and the objective constraint condition; Based on the snow melting algorithm, the distributed resource optimization configuration model is solved to obtain the first optimization result.

9. The method according to claim 7, characterized in that: The division stage includes a disaster adaptation stage, and the target optimization result includes a second optimization result; According to the target constraint conditions, the multi-energy coupling active distribution network is subjected to resilience optimization processing to obtain the target optimization result, including: Constructing a second objective function based on the pre-acquired load loss data; the second objective function aims to minimize the load loss; Constructing an energy network optimization operation model according to the second objective function and the objective constraint condition; Based on the solver, the energy network optimization operation model is solved to obtain the second optimization result.

10. The method according to claim 7, characterized in that The division stage includes a post-disaster recovery stage, and the target optimization result includes a third optimization result; According to the target constraint conditions, the multi-energy coupling active distribution network is subjected to resilience optimization processing to obtain the target optimization result, including: Determine maintenance resource constraints; Constructing a third objective function based on pre-acquired component repair data of the post-disaster active distribution network; the third objective function takes minimizing the maintenance cost as a goal; Constructing a path optimization model according to the maintenance resource constraint, the target constraint condition and the third objective function; Based on the ant colony algorithm, the path optimization model is solved to obtain the third optimization result.

Images