A power distribution network and IES reliability interactive evaluation system based on multi-energy flow coupling

Abstract

This application belongs to the field of energy system assessment technology and provides a reliability interaction assessment system for distribution networks and IES based on multi-energy flow coupling. The system includes: an equipment and multi-energy flow modeling module, a reliability interaction mechanism analysis module, a load reduction strategy optimization module, a reliability assessment module, and an experimental verification module. The system first establishes and solves the distributed multi-energy flow coupling model of electricity, heat, and gas. Then, it analyzes the reliability interaction mechanism under fault scenarios and formulates collaborative strategies. Subsequently, it optimizes load reduction schemes in a distributed manner for different faults. Next, it uses the Markov chain Monte Carlo method to simulate and assess the system's reliability indicators. Finally, it verifies the accuracy of the assessment results through simulation experiments. This application can accurately characterize the coupling characteristics of electricity, heat, and gas multi-energy flows, deeply analyze the reliability interaction mechanism of integrated energy systems and distribution networks, and provide theoretical support and decision-making basis for their coordinated scheduling and reliable operation through an efficient distributed assessment method.

Description

Technical Field

[0001] This application belongs to the field of energy system reliability assessment technology, and in particular relates to a reliability interactive assessment system, method, terminal equipment and storage medium for distribution networks and IES based on multi-energy flow coupling. Background Technology

[0002] Integrated energy systems (IES), through the complementarity and cascade utilization of multiple energy sources such as electricity, heat, and gas, have become a key means to improve energy efficiency. As IES become increasingly closely coupled with the distribution network, the complexity of system structure and operation has increased significantly. Traditional reliability assessment methods for single energy supply systems (such as failure mode consequence analysis or sequential Monte Carlo simulation) are no longer sufficient to accurately characterize the coupling and interaction effects of multiple energy flows.

[0003] However, existing methods have the following problems: First, they do not fully consider the coupling characteristics of multiple energy sources such as electricity, heat, and gas in transmission and conversion, resulting in insufficient modeling accuracy; second, they lack in-depth analysis of the mutual influence mechanism of faults between IES and distribution networks, leading to the lack of collaborative operation strategies; and third, the evaluation process is computationally complex and inefficient, making it difficult to achieve a balance between accuracy and speed, which limits its application in engineering practice. Summary of the Invention

[0004] This application provides a system, method, terminal equipment, and storage medium for interactive reliability assessment of distribution networks and IES based on multi-energy flow coupling, which can solve the above-mentioned problems.

[0005] In a first aspect, embodiments of this application provide a reliability interaction assessment system for distribution networks and Integrated Energy Systems (IES) based on multi-energy flow coupling, comprising: a device and multi-energy flow modeling module, used to establish mathematical models of power generation equipment in the distribution network and energy conversion equipment in the Integrated Energy System (IES), and to construct network models and power flow matrix equations for the power system, thermal system, and natural gas system, so as to solve the multi-energy flow distribution using a distributed sequential method; a reliability interaction mechanism analysis module, used to analyze the reliability change characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults based on the multi-energy flow distribution and reliability and power quality indicators, and to establish a multi-energy coordinated operation strategy; a load reduction strategy optimization module, used to solve the optimal load reduction strategy for the distribution network and IES in a distributed manner based on the target cascade analysis method for different fault scenarios, with tie-line power as the coupling variable; a reliability assessment module, used to perform sampling simulation of the operating state of the distribution network and IES based on the Markov chain Monte Carlo simulation method, and calculate the system reliability index by combining the multi-energy flow distribution and the optimal load reduction strategy; and an experimental verification module, used to perform simulation verification of the system reliability index under different power interaction modes and fault conditions.

[0006] In one possible implementation of the first aspect, the aforementioned mathematical models of power generation equipment and IES energy conversion equipment in the distribution network are established, and network models and power flow matrix equations of the power system, thermal system, and natural gas system are constructed to solve the multi-energy flow distribution using a distributed sequential method, specifically including:

[0007] A mathematical model of the power generation equipment in the distribution network is established. This model is based on the physical characteristics of the equipment's operation and quantifies the power output boundary and state transition rules through multiple constraint relationships. These constraints include:

[0008] Output constraints:

[0009]

[0010] in, To represent the state of the i-th unit at time t. Let i be the output of the i-th unit at time t. , These are the minimum and maximum output limits for the i-th unit, respectively;

[0011] Climbing / landslide constraints:

[0012]

[0013] in, , These represent the upper limits of the unit's climb / slippage.

[0014] Minimum start / stop constraints:

[0015]

[0016]

[0017] in, , These represent the minimum start-up / shutdown times of the unit, respectively. Indicates the length of the scheduling period. , These represent the continuous start / stop time of the unit at the initial moment; , These represent the time that the i-th unit needs to be in the powered-on and powered-off states after the scheduling begins; I Gi (0) indicates the state of the i-th unit at the initial moment of scheduling;

[0018] Establishing a mathematical model for the IES conversion device, specifically including:

[0019] Cogeneration unit model:

[0020]

[0021] Among them, at time t, the electrical power output, thermal power output, and intake power of the combined heat and power unit CHP are respectively , , The electrical and thermal efficiencies of CHP are respectively... , express;

[0022] Gas boiler model:

[0023]

[0024] in, , These represent the thermal output and air intake power of the gas-fired boiler GB at time t, respectively. Indicates the thermal efficiency of GB;

[0025] Electric boiler model:

[0026]

[0027] in, , Let represent the thermal output and air intake power of the electric boiler EB at time t, respectively. For the thermal efficiency of EB;

[0028] The power system network model is constructed as follows:

[0029]

[0030] in, Let i be the active power injected into the i-th node. U is the reactive power injected into the i-th node. i U j Let be the voltage amplitude at the i-th node and the j-th node. and For the conductance and susceptance of the transmission line, δ ij The voltage phase angle difference between the i-th node and the j-th node;

[0031] A thermal system network model is constructed, which includes both hydraulic and thermal models, as detailed below:

[0032] Hydraulic model:

[0033]

[0034] In this matrix, matrix A represents the connection relationship between branches and nodes in the heating network, m represents the pipe flow rate, and the heat load demand is expressed in terms of q. hIndicated, matrix B is used to represent the connection relationship between branches and loops in the heating pipe network, h f This represents the pressure drop generated when a fluid flows in a pipe, and K represents the pipe's resistance coefficient parameter.

[0035] Thermal model:

[0036]

[0037] in, For user-side heat exchange power, T is the specific heat capacity of water. s For water supply temperature, T r For return water temperature, m q The mass of hot water flowing into the heat user per unit time;

[0038] The natural gas system network model is constructed as follows:

[0039]

[0040] Where, p i p j Let q represent the pressure at node i and node j, respectively. ij k represents the flow rate from the i-th natural gas pipeline to the j-th natural gas pipeline. ij It is a parameter constant;

[0041] Construct the power flow matrix equation of the power system based on the power system network model:

[0042] Based on the power system network model, for the i-th node in the system, the deviation equation between its active power and reactive power is established as the power flow matrix equation of the power system:

[0043]

[0044] in, For nodes i The active power imbalance, For nodes j Active power imbalance;

[0045] Construct the natural gas system power flow matrix equation based on the natural gas system network model:

[0046] Based on Kirchhoff's Current Law (KCL) and Voltage Law (KVL), the nodal equilibrium equations and loop equations of the natural gas system are constructed as the natural gas system power flow matrix equations. Here, the j-th node is connected to the i-th node via a compressor or pipeline, denoted as j∈i. The natural gas system power flow matrix equations are expressed as follows:

[0047]

[0048]

[0049] Where, q i q Gi q Li Let L represent the injection flow rate, gas source flow rate, and load flow rate of the i-th node; the network pipeline set is denoted by L. This represents the flow rate of natural gas delivered by the compressor from node i to node j. s is the gas compression ratio coefficient corresponding to the compressor. j s is the compressor connection state coefficient; when the compressor draws air from the i-th node, s j Set to 1, when the compressor does not draw gas from the i-th node or there is no gas delivery path from node i to node j, s j Take 0; b li ΔP represents the correlation coefficient between pipe l and the i-th node. l This indicates the pressure drop in the pipeline, p m p n These represent the pressure values ​​at both ends of pipe 1;

[0050] Construct the power flow matrix equation of the thermal system based on the thermal system network model:

[0051] The state matrix equation of the heating network is used as the power flow matrix equation of the thermal system. ;

[0052] in, Let be the unbalance vector in the power flow equations of the thermodynamic system. This refers to the nodal thermal power imbalance. This refers to the pressure imbalance at pipeline nodes. Let this be the vector of water supply temperature deviation. H is the return water temperature deviation vector, M is the pipe-node correlation matrix, used to describe the topological connection relationship between pipes and nodes in the heating network; SP The known thermal power data of the system; matrix C s b s C r b r This is related to the topology of the heating network, the topology of the return water network, the flow rate of the heat medium, and the node temperature;

[0053] By employing a distributed sequential solution method, when solving the power flow matrix equation of any energy system, nodes coupled with other energy systems are treated as source nodes or load nodes, thus obtaining a multi-energy flow distribution.

[0054] Optionally, in another possible implementation of the first aspect, the above-mentioned multi-energy flow distribution, based on reliability indicators and power quality indicators, analyzes the reliability variation characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults, and establishes a multi-energy coordinated operation strategy, specifically including:

[0055] Construct a set of multiple fault scenarios, including setting fault scenarios such as IES fault, distribution network fault, and simultaneous fault of IES and distribution network;

[0056] Under various fault scenarios, based on the power flow matrix equations of the power system, thermal system, and natural gas system, the multi-energy flow distribution results of the power system, thermal system, and natural gas system under each fault condition are obtained.

[0057] The system reliability index and power quality index are calculated based on the multi-energy flow distribution results. The reliability index includes the power supply reliability index and the comprehensive energy supply reliability index, and the power quality index includes the voltage deviation index and the frequency deviation index.

[0058] By comparing the changing characteristics of various reliability and power quality indicators under different fault scenarios, the mutual support relationship and energy complementarity between the distribution network and the IES are extracted. Based on the energy supply capacity, load demand and system operating status under different fault scenarios, a multi-energy coordinated operation strategy is formed.

[0059] Optionally, in another possible implementation of the first aspect, the optimal load reduction strategy for the distribution network and IES is solved in a distributed manner based on the target cascade analysis method, using tie-line power as the coupling variable, for different fault scenarios. Specifically, this includes:

[0060] In the scenario of IES failure, the distribution network is regarded as a backup power source, and priority is given to increasing the power output of CHP and gas boilers, while utilizing the output of photovoltaic, wind power and energy storage equipment. When the output of photovoltaic, wind power and energy storage equipment cannot meet the preset load demand, electricity is purchased from the distribution network, and the reduction ratio of electricity and heat load is adjusted according to the preset load weight. The target cascade analysis method (ATC) is adopted, with tie line power as the coupling variable, and sub-optimization models of the distribution network and IES are constructed respectively. By iteratively updating the tie line power and other coupling variables and coordinating the operating status of each subsystem, the optimal load reduction strategy under the IES failure scenario is obtained until the convergence condition is met.

[0061] In the scenario of distribution network failure, it is determined whether the downstream of the failure can form an island with the IES (Instrument Engineering System). If so, the IES increases the power of its internal equipment to supply power to the island. When the increased power of its internal equipment cannot cover the island load, its non-core loads are reduced. The Objective Cascaded Analysis (ATC) method is adopted, with tie line power as the coupling variable, to establish sub-optimization problems for the distribution network and the IES respectively. By iteratively coordinating tie line power and the operating status of each system, the optimal load reduction strategy under the distribution network failure scenario is obtained. Core loads include medical emergency power load, communication base station power supply load, basic residential power supply and heating load, and industrial key production equipment power load.

[0062] In scenarios where both the IES (Integrated Equipment) and the distribution network fail simultaneously, the importance of the loads in the distribution network and the IES is weighed, the thermal deficit is converted into equivalent electrical load, and the electrical and thermal loads in the system are reduced in a coordinated manner according to a preset ratio, with priority given to reducing non-core loads to ensure the continuous power supply of core loads. The Objective Cascaded Analysis (ATC) method is adopted, with tie-line power as the coupling variable. By iteratively solving the sub-optimization problems of the distribution network and the IES and coordinating the power exchange of each system, the optimal load reduction strategy under the scenario of simultaneous failure of the IES and the distribution network is obtained.

[0063] Optionally, in another possible implementation of the first aspect, the above-mentioned Markov chain Monte Carlo simulation method is used to sample and simulate the operating state of the distribution network and IES, and the system reliability index is calculated by combining multi-energy flow distribution and optimal load shedding strategy, specifically including:

[0064] Assuming that each device in the system has two states, normal and failure, the operating states of all devices in the distribution network and IES are sampled by Gibbs sampling through the construction of Markov chains to generate multiple system state sequences, and the first m sampled states that have not reached a steady state in the Markov chains are removed.

[0065] For each system state sequence, determine the corresponding equipment fault state, and calculate the load reduction amount corresponding to the system state sequence by combining multi-energy flow distribution and optimal load reduction strategy;

[0066] Based on the load reduction corresponding to the system state sequence, the system reliability index is calculated. The system reliability index includes the distribution network reliability index and the IES' own power supply reliability index.

[0067] Distribution network reliability indicators include:

[0068] System average power outage frequency index:

[0069] ;

[0070] Where, N iLet λ be the number of users at the i-th load point, and R be the set of all load points in the system; λ is the expected number of outages at each load point within the statistical time period. i The calculation formula is as follows:

[0071] ;

[0072] Where, λ j Let be the failure rate of the j-th component; I is the set of system components associated with load point i.

[0073] System average outage duration metric:

[0074] ;

[0075] The expected duration of outage at the load point within the statistical time period is T. i The calculation formula is as follows:

[0076] ;

[0077] in, This represents the average repair time after the i-th component fails.

[0078] Average power outage duration per user:

[0079] ;

[0080] Average power outage frequency per user:

[0081] ;

[0082] Among them, M i This represents the number of users experiencing power outages due to faults.

[0083] Average power availability index:

[0084] ;

[0085] Expected annual power shortage:

[0086] ;

[0087] Among them, P L,i Let be the average power consumption of the i-th load point;

[0088] The reliability indicators of IES' own power supply include:

[0089] Annual load reduction frequency:

[0090] ;

[0091] Where S is the set of system states with load shearing, and T is the total simulation time;

[0092] Annual load reduction probability:

[0093] ;

[0094] Among them, t i It is the duration of the i-th state with load shedding;

[0095] Annual load reduction expectations:

[0096]

[0097] Among them, C i It is the reduction in IES electrical / thermal load under the i-th load shearing condition.

[0098] Optionally, in another possible implementation of the first aspect, the aforementioned system reliability indicators also include the contribution indicators of IES to the distribution network. These contribution indicators quantify the degree to which the overall IES access improves the reliability indicators of the distribution network, including the improvement rate of the system's average outage frequency, the improvement rate of the system's average outage duration, the improvement rate of the user's average outage duration, the improvement rate of the user's average outage frequency, the improvement rate of the average power supply availability, and the expected improvement rate of the annual power shortage. The contribution indicators are represented as follows:

[0099]

[0100] Among them, SAIFI0, SAIFI1, SAIDI0, SAIDI1, CAIDI0, CAIDI1, CAIFI0, CAIFI1, ASAI0, ASAI1, EENS0, and EENS1 are respectively the system average power outage frequency index, system average power outage duration index, user average power outage duration index, user average power outage frequency index, average power supply availability index, and expected annual power shortage before and after IES access.

[0101] Optionally, in another possible implementation of the first aspect, the simulation verification of system reliability indicators under different power interaction modes and fault conditions specifically includes:

[0102] Construct a simulation test scenario that includes a distribution network and an IES, and set different power interaction modes. The power interaction modes include at least the distribution network supplying power to the IES, the IES feeding back power to the distribution network, and the bidirectional power interaction mode between the distribution network and the IES.

[0103] Under each power interaction mode, different fault conditions are set, including IES fault, distribution network fault, and simultaneous IES and distribution network fault. Then, the equipment and multi-energy flow modeling module, reliability interaction mechanism analysis module, load reduction strategy optimization module, and reliability assessment module are called in sequence to simulate and calculate the system operation status under each fault condition and obtain the corresponding system reliability indicators.

[0104] The system reliability indicators under different power interaction modes and different fault conditions were compared and analyzed to obtain the verification results.

[0105] Secondly, embodiments of this application provide a method for evaluating the reliability interaction between a distribution network and an Integrated Energy System (IES) based on multi-energy flow coupling. This includes: establishing mathematical models of the power generation equipment in the distribution network and the energy conversion equipment in the IES; constructing network models and power flow matrix equations for the power system, thermal system, and natural gas system; and using a distributed sequential method to solve for the multi-energy flow distribution; based on the multi-energy flow distribution and reliability and power quality indicators, analyzing the reliability variation characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults, and establishing a multi-energy coordinated operation strategy; for different fault scenarios, using tie-line power as a coupling variable, and using a target cascade analysis method to distribute and solve for the optimal load reduction strategy of the distribution network and IES; using a Markov chain Monte Carlo simulation method to sample and simulate the operating state of the distribution network and IES, and calculating the system reliability indicators by combining the multi-energy flow distribution and the optimal load reduction strategy; and performing simulation verification of the system reliability indicators under different power interaction modes and fault conditions.

[0106] Thirdly, embodiments of this application provide a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned method for interactive evaluation of the reliability of a distribution network and an IES based on multi-energy flow coupling.

[0107] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned method for interactive evaluation of the reliability of a distribution network and an IES based on multi-energy flow coupling.

[0108] Beneficial Effects: This application provides a reliability interaction assessment system for distribution networks and Integrated Energy Systems (IES) based on multi-energy flow coupling, comprising: a device and multi-energy flow modeling module, used to establish mathematical models of power generation equipment in the distribution network and energy conversion equipment in the Integrated Energy System (IES), and to construct network models and power flow matrix equations for the power system, thermal system, and natural gas system, so as to solve the multi-energy flow distribution using a distributed sequential method; a reliability interaction mechanism analysis module, used to analyze the reliability change characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults based on multi-energy flow distribution and reliability and power quality indicators, and to establish a multi-energy coordinated operation strategy; a load reduction strategy optimization module, used to solve the optimal load reduction strategy for the distribution network and IES in a distributed manner based on the target cascade analysis method for different fault scenarios, with tie-line power as the coupling variable; a reliability assessment module, used to perform sampling simulation of the operating state of the distribution network and IES based on the Markov chain Monte Carlo simulation method, and calculate the system reliability index by combining multi-energy flow distribution and optimal load reduction strategy; and an experimental verification module, used to perform simulation verification of the system reliability index under different power interaction modes and fault conditions. This system achieves accurate and efficient assessment of the reliability of complex energy systems by constructing a multi-energy flow coupling model of electricity, heat, and gas, analyzing the interaction mechanism between IES and distribution network faults, and adopting an evaluation framework that combines distributed optimization and MCMC simulation. This provides a reliable decision-making basis for multi-energy collaborative planning and operation. Attached Figure Description

[0109] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0110] Figure 1 This is a schematic diagram of the structure of a distribution network and IES reliability interactive evaluation system based on multi-energy flow coupling, provided in one embodiment of this application;

[0111] Figure 2 This is a basic architecture diagram of a power distribution network provided in one embodiment of this application;

[0112] Figure 3 This is a basic architecture diagram of an IES provided in an embodiment of this application;

[0113] Figure 4 This is a schematic diagram of the multi-energy flow distribution of a power distribution network-IES provided in an embodiment of this application;

[0114] Figure 5This is a schematic diagram of the process of a reliability interaction assessment method for distribution networks and IES based on multi-energy flow coupling provided in an embodiment of this application;

[0115] Figure 6 This is a schematic diagram illustrating different integrated energy service modes provided in one embodiment of this application;

[0116] Figure 7 This is a radar chart of the primary indicator evaluation results provided in an embodiment of this application;

[0117] Figure 8 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0118] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0119] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0120] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0121] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0122] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0123] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0124] The following description, with reference to the accompanying drawings, details a reliability interactive assessment system, method, terminal equipment, and storage medium for a distribution network and IES based on multi-energy flow coupling provided in this application.

[0125] Figure 1 This illustration shows a schematic diagram of a distribution network and IES reliability interactive evaluation system based on multi-energy flow coupling provided in an embodiment of this application.

[0126] like Figure 1 As shown, the distribution network and IES reliability interactive assessment system 100 based on multi-energy flow coupling includes:

[0127] The equipment and multi-energy flow modeling module 101 is used to establish mathematical models of power generation equipment in the distribution network and energy conversion equipment in the integrated energy system IES, and to construct network models and power flow matrix equations for the power system, thermal system and natural gas system, so as to solve the multi-energy flow distribution using a distributed sequential method;

[0128] In one embodiment, Figure 2 This is a basic architecture diagram of a power distribution network. Figure 3 This is a basic architecture diagram of IES. Figure 4 This is a schematic diagram of the multi-energy flow distribution of the distribution network-IES.

[0129] Furthermore, in this embodiment, the aforementioned establishment of mathematical models for power generation equipment and IES energy conversion equipment in the distribution network, and the construction of network models and power flow matrix equations for the power system, thermal system, and natural gas system, to solve for multi-energy flow distribution using a distributed sequential method, specifically includes:

[0130] A mathematical model of the power generation equipment in the distribution network is established. This model is based on the physical characteristics of the equipment's operation and quantifies the power output boundary and state transition rules through multiple constraint relationships. These constraints include:

[0131] Output constraints:

[0132]

[0133] in, To represent the state of the i-th unit at time t. Let i be the output of the i-th unit at time t. , These are the minimum and maximum output limits for the i-th unit, respectively;

[0134] Climbing / landslide constraints:

[0135]

[0136] in, , These represent the upper limits of the unit's climb / slippage.

[0137] Minimum start / stop constraints:

[0138]

[0139]

[0140] in, , These represent the minimum start-up / shutdown times of the unit, respectively. Indicates the length of the scheduling period. , These represent the continuous start / stop time of the unit at the initial moment; , These represent the time that the i-th unit needs to be in the powered-on and powered-off states after the scheduling begins; I Gi (0) indicates the state of the i-th unit at the initial moment of scheduling;

[0141] Establishing a mathematical model for the IES conversion device, specifically including:

[0142] Cogeneration unit model:

[0143]

[0144] Among them, at time t, the electrical power output, thermal power output, and intake power of the combined heat and power unit CHP are respectively , , The electrical and thermal efficiencies of CHP are respectively... , express;

[0145] Gas boiler model:

[0146]

[0147] in, , These represent the thermal output and air intake power of the gas-fired boiler GB at time t, respectively. Indicates the thermal efficiency of GB;

[0148] Electric boiler model:

[0149]

[0150] in, , Let represent the thermal output and air intake power of the electric boiler EB at time t, respectively. For the thermal efficiency of EB;

[0151] The power system network model is constructed as follows:

[0152]

[0153] in, Let i be the active power injected into the i-th node. U is the reactive power injected into the i-th node. i U j Let be the voltage amplitude at the i-th node and the j-th node. and For the conductance and susceptance of the transmission line, δ ij The voltage phase angle difference between the i-th node and the j-th node;

[0154] A thermal system network model is constructed, which includes both hydraulic and thermal models, as detailed below:

[0155] Hydraulic model:

[0156]

[0157] In this matrix, matrix A represents the connection relationship between branches and nodes in the heating network, m represents the pipe flow rate, and the heat load demand is expressed in terms of q. h Indicated, matrix B is used to represent the connection relationship between branches and loops in the heating pipe network, h f This represents the pressure drop generated when a fluid flows in a pipe, and K represents the pipe's resistance coefficient parameter.

[0158] Thermal model:

[0159]

[0160] in, For user-side heat exchange power, T is the specific heat capacity of water. s For water supply temperature, T r For return water temperature, m qThe mass of hot water flowing into the heat user per unit time;

[0161] The natural gas system network model is constructed as follows:

[0162]

[0163] Where, p i p j Let q represent the pressure at node i and node j, respectively. ij k represents the flow rate from the i-th natural gas pipeline to the j-th natural gas pipeline. ij It is a parameter constant;

[0164] It should be noted that before performing power flow calculations, nodes must first be clearly classified. In power system analysis, state variables include the voltage magnitude and phase angle of nodes, as well as the injected active and reactive power. Node types are classified as slack nodes, PV nodes, and PQ nodes. State variables in thermal systems include heat medium flow rate, supply / return water temperature, and heat power injected into nodes. Node types include heat source nodes and heat load nodes. State variables in natural gas systems are mainly node pressure and pipeline gas flow rate. Node types include nodes with known pressure and nodes with known flow rate.

[0165] Construct the power flow matrix equation of the power system based on the power system network model:

[0166] Based on the power system network model, for the i-th node in the system, the deviation equation between its active power and reactive power is established as the power flow matrix equation of the power system:

[0167]

[0168] in, For nodes i The active power imbalance, For nodes j Active power imbalance;

[0169] Construct the natural gas system power flow matrix equation based on the natural gas system network model:

[0170] Based on Kirchhoff's Current Law (KCL) and Voltage Law (KVL), the nodal equilibrium equations and loop equations of the natural gas system are constructed as the natural gas system power flow matrix equations. Here, the j-th node is connected to the i-th node via a compressor or pipeline, denoted as j∈i. The natural gas system power flow matrix equations are expressed as follows:

[0171]

[0172]

[0173] Where, q i q Gi q Li Let L represent the injection flow rate, gas source flow rate, and load flow rate of the i-th node; the network pipeline set is denoted by L. This represents the flow rate of natural gas delivered by the compressor from node i to node j. s is the gas compression ratio coefficient corresponding to the compressor. j s is the compressor connection state coefficient; when the compressor draws air from the i-th node, s j Set to 1, when the compressor does not draw gas from the i-th node or there is no gas delivery path from node i to node j, s j Take 0; b li ΔP represents the correlation coefficient between pipe l and the i-th node. l This indicates the pressure drop in the pipeline, p m p n These represent the pressure values ​​at both ends of pipe 1;

[0174] It should be noted that, generally, the forward-backward substitution method is used to iteratively solve for temperature and flow rate in relatively simple radial heat networks; however, for complex multi-source annular heat networks, because their network model is nonlinear, therefore:

[0175] Construct the power flow matrix equation of the thermal system based on the thermal system network model:

[0176] The thermal network state matrix equation is used as the thermal system power flow matrix equation:

[0177]

[0178] in, Let be the unbalance vector in the power flow equations of the thermodynamic system. This refers to the nodal thermal power imbalance. This refers to the pressure imbalance at pipeline nodes. Let this be the vector of water supply temperature deviation. H is the return water temperature deviation vector, M is the pipe-node correlation matrix, used to describe the topological connection relationship between pipes and nodes in the heating network; SP The known thermal power data of the system; matrix C s b s C r b r This is related to the topology of the heating network, the topology of the return water network, the flow rate of the heat medium, and the node temperature;

[0179] By employing a distributed sequential solution method, when solving the power flow matrix equation of any energy system, nodes coupled with other energy systems are treated as source nodes or load nodes, thus obtaining a multi-energy flow distribution.

[0180] For example, power generation equipment in a power distribution network includes conventional generating units and distributed power sources (including photovoltaic generators and wind turbine generators).

[0181] It should be noted that the above constraints ensure that the unit meets the minimum start-up and minimum downtime constraints in all possible consecutive time periods, while also... During the period leading up to the end of the scheduling cycle, for units that are in operation, the operation status will continue until the end of the scheduling period; for units that are out of operation, the outage status will also continue until the end of the scheduling period.

[0182] The reliability interaction mechanism analysis module 102 is used to analyze the reliability change characteristics under IES failure, distribution network failure and simultaneous failure of IES and distribution network based on multi-energy flow distribution, reliability index and power quality index, and to establish a multi-energy coordinated operation strategy.

[0183] Furthermore, in this embodiment, the above-mentioned multi-energy flow distribution, based on reliability indicators and power quality indicators, analyzes the reliability change characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults, and establishes a multi-energy coordinated operation strategy, specifically including:

[0184] Construct a set of multiple fault scenarios, including setting fault scenarios such as IES fault, distribution network fault, and simultaneous fault of IES and distribution network;

[0185] Under various fault scenarios, based on the power flow matrix equations of the power system, thermal system, and natural gas system, the multi-energy flow distribution results of the power system, thermal system, and natural gas system under each fault condition are obtained.

[0186] The system reliability index and power quality index are calculated based on the multi-energy flow distribution results. The reliability index includes the power supply reliability index and the comprehensive energy supply reliability index, and the power quality index includes the voltage deviation index and the frequency deviation index.

[0187] By comparing the changing characteristics of various reliability and power quality indicators under different fault scenarios, the mutual support relationship and energy complementarity between the distribution network and the IES are extracted. Based on the energy supply capacity, load demand and system operating status under different fault scenarios, a multi-energy coordinated operation strategy is formed.

[0188] The load reduction strategy optimization module 103 is used to solve the optimal load reduction strategy of the distribution network and IES in a distributed manner based on the target cascade analysis method, with tie line power as the coupling variable, for different fault scenarios.

[0189] Furthermore, in the embodiments of this application, the above-mentioned optimal load reduction strategy for the distribution network and IES, based on the target cascade analysis method and using tie-line power as a coupling variable, specifically includes:

[0190] In the scenario of IES failure, the distribution network is regarded as a backup power source, and priority is given to increasing the power output of CHP and gas boilers, while utilizing the output of photovoltaic, wind power and energy storage equipment. When the output of photovoltaic, wind power and energy storage equipment cannot meet the preset load demand, electricity is purchased from the distribution network, and the reduction ratio of electricity and heat load is adjusted according to the preset load weight. The target cascade analysis method (ATC) is adopted, with tie line power as the coupling variable, and sub-optimization models of the distribution network and IES are constructed respectively. By iteratively updating the tie line power and other coupling variables and coordinating the operating status of each subsystem, the optimal load reduction strategy under the IES failure scenario is obtained until the convergence condition is met.

[0191] In the scenario of distribution network failure, it is determined whether the downstream of the failure can form an island with the IES (Instrument Engineering System). If so, the IES increases the power of its internal equipment to supply power to the island. When the increased power of its internal equipment cannot cover the island load, its non-core loads are reduced. The Objective Cascaded Analysis (ATC) method is adopted, with tie line power as the coupling variable, to establish sub-optimization problems for the distribution network and the IES respectively. By iteratively coordinating tie line power and the operating status of each system, the optimal load reduction strategy under the distribution network failure scenario is obtained. Core loads include medical emergency power load, communication base station power supply load, basic residential power supply and heating load, and industrial key production equipment power load.

[0192] In scenarios where both the IES (Integrated Equipment) and the distribution network fail simultaneously, the importance of the loads in the distribution network and the IES is weighed, the thermal deficit is converted into equivalent electrical load, and the electrical and thermal loads in the system are reduced in a coordinated manner according to a preset ratio, with priority given to reducing non-core loads to ensure the continuous power supply of core loads. The Objective Cascaded Analysis (ATC) method is adopted, with tie-line power as the coupling variable. By iteratively solving the sub-optimization problems of the distribution network and the IES and coordinating the power exchange of each system, the optimal load reduction strategy under the scenario of simultaneous failure of the IES and the distribution network is obtained.

[0193] For example, the internal equipment includes: combined heat and power (CHP) units, gas boilers (GB), electric boilers (EB), photovoltaic generator sets, wind turbine generator sets, electric energy storage equipment, gas energy storage equipment, and thermal energy storage equipment.

[0194] For example, non-core loads include non-essential electricity consumption for ordinary residential use (such as decorative lighting and entertainment equipment), non-critical electricity consumption for commercial equipment (such as shop window display and non-core office auxiliary equipment), and non-productive heat loads for industrial use (such as workshop cleaning heat and office area heating backup loads), (or include non-essential electricity consumption for ordinary residential use, commercial auxiliary electricity consumption, and industrial auxiliary heat loads).

[0195] For example, core loads include power supply to hospital intensive care units, power supply to emergency communication base stations, basic residential heating load, power supply to core industrial production equipment and necessary production heat load, and power supply to urban public transportation hubs (or include emergency medical power supply, core communication power supply, basic residential heating load, and core industrial production power supply and heat load).

[0196] For example, load importance can be represented by a load priority coefficient ω, which ranges from 0 to 1. The priority coefficient ω for core loads is ≥ 0.8 (e.g., power supply to hospital intensive care units ω = 0.95, power supply to core industrial production equipment ω = 0.9), while the priority coefficient ω for non-core loads is ≤ 0.5 (e.g., decorative lighting ω = 0.3, power supply for shop window displays ω = 0.2). The higher the priority coefficient, the stronger the load importance, and the later the load will be reduced in the event of a failure.

[0197] The reliability assessment module 104 is used to perform sampling simulation of the operating status of the distribution network and IES based on the Markov chain Monte Carlo simulation method, and calculate the system reliability index by combining multi-energy flow distribution and optimal load reduction strategy.

[0198] Furthermore, in this embodiment of the application, the above-mentioned Markov chain Monte Carlo simulation method is used to sample and simulate the operating state of the distribution network and IES, and the system reliability index is calculated by combining multi-energy flow distribution and optimal load shedding strategy, specifically including:

[0199] Assuming that each device in the system has two states, normal and failure, the operating states of all devices in the distribution network and IES are sampled by Gibbs sampling through the construction of Markov chains to generate multiple system state sequences, and the first m sampled states that have not reached a steady state in the Markov chains are removed.

[0200] For each system state sequence, determine the corresponding equipment fault state, and calculate the load reduction amount corresponding to the system state sequence by combining multi-energy flow distribution and optimal load reduction strategy;

[0201] Based on the load reduction corresponding to the system state sequence, the system reliability index is calculated. The system reliability index includes the distribution network reliability index and the IES' own power supply reliability index.

[0202] Distribution network reliability indicators include:

[0203] System average power outage frequency index:

[0204] ;

[0205] Where, N i Let R be the number of users at the i-th load point, and R be the set of all load points in the system.

[0206] It should be noted that the higher the average failure rate of a load point, the more times a failure occurs at that load point within a year, and the worse the power supply reliability level.

[0207] The expected value of the number of outages at the load point within the statistical time period is represented by λ. i The calculation formula is as follows:

[0208] ;

[0209] Where, λ j Let be the failure rate of the j-th component; I is the set of system components associated with load point i.

[0210] System average outage duration metric:

[0211] ;

[0212] The expected duration of outage at the load point within the statistical time period is T. i The longer the average outage time at a load point, the longer the duration of power outages at that load point within a year, and the worse the power supply reliability. i The calculation formula is:

[0213] ;

[0214] in, This represents the average repair time after the i-th component fails.

[0215] Average power outage duration per user:

[0216] ;

[0217] Average power outage frequency per user:

[0218] ;

[0219] Among them, M i This represents the number of users experiencing power outages due to faults.

[0220] Average power availability index:

[0221] ;

[0222] Expected annual power shortage:

[0223] ;

[0224] Among them, P L,i Let be the average power consumption of the i-th load point;

[0225] The reliability indicators of IES' own power supply include:

[0226] Annual load reduction frequency:

[0227] ;

[0228] Where S is the set of system states with load shearing, and T is the total simulation time;

[0229] Annual load reduction probability:

[0230] ;

[0231] Among them, t i It is the duration of the i-th state with load shedding;

[0232] Annual load reduction expectations:

[0233]

[0234] Among them, C i It is the reduction in IES electrical / thermal load under the i-th load shearing condition.

[0235] As another possible implementation, the aforementioned system reliability indicators also include the contribution indicators of IES to the distribution network. These contribution indicators quantify the degree to which the overall IES integration improves the reliability indicators of the distribution network. These include the improvement rate of the system's average outage frequency, the improvement rate of the system's average outage duration, the improvement rate of the user's average outage duration, the improvement rate of the user's average outage frequency, the improvement rate of the average power supply availability, and the expected improvement rate of the annual power shortage. The contribution indicators are represented as follows:

[0236]

[0237] Among them, SAIFI0, SAIFI1, SAIDI0, SAIDI1, CAIDI0, CAIDI1, CAIFI0, CAIFI1, ASAI0, ASAI1, EENS0, and EENS1 are respectively the system average power outage frequency index, system average power outage duration index, user average power outage duration index, user average power outage frequency index, average power supply availability index, and expected annual power shortage before and after IES access.

[0238] The experimental verification module 105 is used to simulate and verify the system reliability indicators under different power interaction modes and fault conditions.

[0239] Furthermore, in the embodiments of this application, the above-mentioned simulation verification of system reliability indicators under different power interaction modes and fault conditions specifically includes:

[0240] Construct a simulation test scenario that includes a distribution network and an IES, and set different power interaction modes. The power interaction modes include at least the distribution network supplying power to the IES, the IES feeding back power to the distribution network, and the bidirectional power interaction mode between the distribution network and the IES.

[0241] Under each power interaction mode, different fault conditions are set, including IES fault, distribution network fault, and simultaneous IES and distribution network fault. Then, the equipment and multi-energy flow modeling module, reliability interaction mechanism analysis module, load reduction strategy optimization module, and reliability assessment module are called in sequence to simulate and calculate the system operation status under each fault condition and obtain the corresponding system reliability indicators.

[0242] The system reliability indicators under different power interaction modes and different fault conditions were compared and analyzed to obtain the verification results.

[0243] This application provides a reliability interaction assessment system for distribution networks and Integrated Energy Systems (IES) based on multi-energy flow coupling, comprising: a device and multi-energy flow modeling module, used to establish mathematical models of power generation equipment in the distribution network and energy conversion equipment in the Integrated Energy System (IES), and to construct network models and power flow matrix equations for the power system, thermal system, and natural gas system, so as to solve the multi-energy flow distribution using a distributed sequential method; a reliability interaction mechanism analysis module, used to analyze the reliability change characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults based on multi-energy flow distribution and reliability and power quality indicators, and to establish a multi-energy coordinated operation strategy; a load reduction strategy optimization module, used to solve the optimal load reduction strategy for the distribution network and IES in a distributed manner based on the target cascade analysis method for different fault scenarios, with tie-line power as the coupling variable; a reliability assessment module, used to perform sampling simulation of the operating state of the distribution network and IES based on the Markov chain Monte Carlo simulation method, and calculate the system reliability index by combining the multi-energy flow distribution and the optimal load reduction strategy; and an experimental verification module, used to perform simulation verification of the system reliability index under different power interaction modes and fault conditions. This system achieves accurate and efficient assessment of the reliability of complex energy systems by constructing a multi-energy flow coupling model of electricity, heat, and gas, analyzing the interaction mechanism between IES and distribution network faults, and adopting an evaluation framework that combines distributed optimization and MCMC simulation. This provides a reliable decision-making basis for multi-energy collaborative planning and operation.

[0244] In practical use, the distribution network and IES reliability interactive evaluation system based on multi-energy flow coupling provided in this application embodiment can be configured in any terminal device to perform the following distribution network and IES reliability interactive evaluation method based on multi-energy flow coupling.

[0245] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0246] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the following method embodiments, and will not be repeated here.

[0247] Figure 5 The diagram shows a flowchart of a method for interactive evaluation of the reliability of a distribution network and an IES based on multi-energy flow coupling, provided in an embodiment of this application.

[0248] like Figure 5 As shown, this method for evaluating the reliability interaction between distribution networks and IES based on multi-energy flow coupling includes:

[0249] S501. Establish mathematical models of power generation equipment in distribution networks and energy conversion equipment in integrated energy systems (IES), and construct network models and power flow matrix equations for power systems, thermal systems, and natural gas systems to solve the multi-energy flow distribution using a distributed sequential method.

[0250] S502. Based on multi-energy flow distribution and reliability and power quality indicators, analyze the reliability change characteristics under IES fault, distribution network fault, and simultaneous IES and distribution network fault conditions, and establish a multi-energy coordinated operation strategy.

[0251] S503. For different fault scenarios, the optimal load reduction strategy of the distribution network and IES is solved in a distributed manner based on the target cascade analysis method, with tie line power as the coupling variable.

[0252] S504. Based on the Markov chain Monte Carlo simulation method, the operating status of the distribution network and IES is sampled and simulated, and the system reliability index is calculated by combining multi-energy flow distribution and optimal load reduction strategy.

[0253] S505. Simulation verification of system reliability indicators is performed under different power interaction modes and fault conditions.

[0254] This application provides a reliability interaction assessment method for distribution networks and Integrated Energy Systems (IES) based on multi-energy flow coupling. First, a mathematical model of the power generation equipment in the distribution network and the energy conversion equipment in the IES is established. Network models and power flow matrix equations for the power system, thermal system, and natural gas system are constructed. A distributed sequential method is used to solve for the multi-energy flow distribution. Then, based on the multi-energy flow distribution and reliability and power quality indicators, the reliability variation characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults are analyzed to establish a multi-energy coordinated operation strategy. Next, for different fault scenarios, the optimal load reduction strategy for the distribution network and IES is solved in a distributed manner using tie-line power as the coupling variable and a target cascade analysis method. Then, based on the Markov chain Monte Carlo simulation method, the operating states of the distribution network and IES are sampled and simulated. The system reliability index is calculated by combining the multi-energy flow distribution and the optimal load reduction strategy. Finally, the system reliability index is verified by simulation under different power interaction modes and fault conditions. Therefore, this application achieves accurate and efficient assessment of the reliability of complex energy systems by constructing an electric-thermal-gas multi-energy flow coupling model, analyzing the interaction mechanism between IES and distribution network faults, and adopting an evaluation framework that combines distributed optimization and MCMC simulation, thus providing a reliable decision-making basis for multi-energy collaborative planning and operation.

[0255] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0256] The solution provided in this application will be illustrated below with another embodiment.

[0257] I. Setting Basic Parameters for the Example

[0258] 1. System Topology and Service Mode

[0259] This embodiment simulates a distribution network-IES integrated system under four different integrated energy service modes. The physical structure topology of each mode is as follows: Figure 6 As shown in (a)-(d), the core differences lie in the IES access location, equipment capacity configuration, and power distribution network architecture:

[0260] Mode 1: Includes two integrated energy systems, IES1 and IES2, connected to areas with relatively dispersed loads in the distribution network;

[0261] Mode 2: IES1 and IES2 are connected near the power source side of the distribution network, making the distribution network itself more reliable;

[0262] Mode 3: IES1 and IES2 are located close to the load center, and their equipment capacity is configured slightly lower than that of Mode 1;

[0263] Mode 4: The access locations of IES1 and IES2 are the same as in Mode 3, and the distribution network architecture is optimized and adjusted.

[0264] 2. IES Equipment Parameters

[0265] (1) Failure rate and repair rate

[0266] The failure rate (γ) and repair rate (μ) of the core equipment of IES are shown in Table 1. Among them, the combined heat and power unit (CHP) has the highest failure rate (1.5 times / year), while the thermal energy storage equipment has the lowest failure rate (0.2 times / year). The repair rate ranges from 5 to 20 times / year, which is consistent with the actual equipment operation pattern.

[0267] Table 1

[0268]

[0269] (2) Equipment capacity configuration

[0270] The equipment capacities of IES1 and IES2 are shown in Table 2. CHP, as the core energy conversion equipment, has capacities of 2300kW (IES1) and 1800kW (IES2), respectively. The capacity of distributed power sources such as photovoltaic and wind power is configured according to the load demand of the access area. The capacity of energy storage equipment (electric, gas, and thermal energy storage) is 1000kW-level to ensure multi-energy complementary regulation capability.

[0271] Table 2

[0272]

[0273] II. Implementation of the Assessment Process

[0274] 1. Modeling Phase

[0275] Based on the "Equipment and Multi-Energy Flow Modeling Module" of this application, the following modeling work was completed:

[0276] Power generation equipment in the power distribution network: The model is established according to the output constraints, ramp / slippage constraints and minimum start / stop constraints. The upper limit of the output of conventional units is set at 5000kW.

[0277] IES Energy Conversion Equipment: Establish models for CHP, gas boiler (GB), and electric boiler (EB). The electrical efficiency of CHP is ηe=0.4 and the thermal efficiency is ηh=0.5. The thermal efficiency of GB is ηGB=0.9 and the thermal efficiency of EB is ηEB=0.95.

[0278] Multi-energy flow model: Network models of the power system (Newton-Raphson method for power flow solution), the thermal system (forward-backward substitution method), and the natural gas system (based on KCL / KVL laws) are constructed respectively, and the multi-energy flow distribution is obtained by using a distributed sequential solution method.

[0279] 2. Reliability Interaction Mechanism Analysis

[0280] The interaction between IES and the distribution network under four modes is analyzed using the "Reliability Interaction Mechanism Analysis Module":

[0281] In Mode 2, the IES is connected near the power source, the distribution network architecture is reliable, the IES has high power supply stability when used as a load, and the improvement on the distribution network reliability indicators (SAIFI, SAIDI) is more significant.

[0282] In Mode 1, IES devices have larger capacity and stronger multi-energy complementarity, providing stronger power support during distribution network faults, and achieving the best system-level and device-level contribution indicators.

[0283] In modes 3 and 4, the IES connection location is close to the load center, and its own power supply reliability is high, but its support for the distribution network is weaker than that in modes 1 and 2.

[0284] 3. Load reduction strategy adaptation

[0285] Based on the topology characteristics of each mode, the hierarchical distributed optimization strategy in the "Load Reduction Strategy Optimization Module" is adapted as follows:

[0286] When an IES experiences a CHP fault, energy is released through gas storage to drive GB supplementary heating. At the same time, electricity is purchased from the distribution network (not exceeding the tie line limit of 2000kW), and the reduction ratio is adjusted according to the electrical load weight m=0.6 and the heat load weight n=0.4.

[0287] When a distribution network line fails, the IES in modes 1 and 2 can quickly form islands, covering more load nodes, and the load reduction is 15%-20% lower than that in modes 3 and 4.

[0288] 4. Reliability assessment calculation

[0289] Using the MCMC simulation method in the "Reliability Assessment Module," with a sampling count of 2000 and a pre-sampling count of 500, and a convergence criterion of variance coefficient ≤ 0.05, the initial values ​​of the reliability indices for the four modes were calculated (as shown in Table 3). The results of the core indices are as follows:

[0290] Distribution network system-level indicators (C1): Mode 2 is the best (SAIFI=0.67821 times / year, SAIDI=2.21209 hours / year).

[0291] IES reliability index (C4): Mode 4 is the best (FLC=4.26123 times / year, PLC=2.38952%).

[0292] IES contribution index (C5, C6): Mode 1 is the best (αSAIFI=-0.19361%, average device-level contribution is above 0.12).

[0293] Table 3

[0294]

[0295] 5. Experimental Verification and Result Analysis

[0296] Based on the "experimental verification module", radar charts ( Figure 7 (As shown) A comprehensive evaluation of the six primary indicators yielded the following conclusions:

[0297] Evaluation based on a single indicator has limitations: when considering only the traditional indicators of the distribution network (C1-C3), Mode 2 is the best; however, when combined with the contribution of IES (C5-C6), Mode 1 has better overall performance, which verifies the comprehensiveness of the indicator system in this application.

[0298] The system evaluation results are consistent with reality: the larger the capacity of IES equipment and the more reasonable the access location (mode 1), the more significant the improvement in the reliability of the distribution network; the reliability of the distribution network architecture (mode 2) is the foundation, but it needs to be combined with the multi-energy complementary capabilities of IES to achieve global optimization;

[0299] This application system can effectively distinguish the advantages and disadvantages of different integrated energy service models, providing accurate decision-making basis for the construction site selection, capacity configuration and maintenance of IES.

[0300] To implement the above embodiments, this application also proposes a terminal device.

[0301] Figure 8 This is a schematic diagram of the structure of a terminal device according to an embodiment of this application.

[0302] like Figure 8 As shown, the terminal device 200 includes:

[0303] The system includes a memory 210 and at least one processor 220, and a bus 230 connecting different components (including the memory 210 and the processor 220). The memory 210 stores a computer program, which, when executed by the processor 220, implements a method for interactive evaluation of the reliability of a distribution network and an IES based on multi-energy flow coupling, according to an embodiment of this application.

[0304] Bus 230 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0305] Terminal device 200 typically includes various electronically readable media. These media can be any available media that can be accessed by terminal device 200, including volatile and non-volatile media, removable and non-removable media.

[0306] Memory 210 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 240 and / or cache memory 250. Terminal device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 260 may be used to read and write non-removable, non-volatile magnetic media (… Figure 8 Not shown; usually referred to as a "hard drive"). Although Figure 8 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 230 via one or more data media interfaces. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0307] A program / utility 280 having a set (at least one) of program modules 270 may be stored in, for example, memory 210. Such program modules 270 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 270 typically perform the functions and / or methods described in the embodiments of this application.

[0308] Terminal device 200 can also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), and with one or more devices that enable a user to interact with terminal device 200, and / or with any device that enables terminal device 200 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 292. Furthermore, terminal device 200 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 293. As shown, network adapter 293 communicates with other modules of terminal device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0309] The processor 220 performs various functional applications and data processing by running programs stored in the memory 210.

[0310] It should be noted that the implementation process and technical principles of the terminal device in this embodiment are explained in the foregoing description of a method for interactive evaluation of the reliability of a distribution network and IES based on multi-energy flow coupling in this application embodiment, and will not be repeated here.

[0311] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0312] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0313] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0314] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0315] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0316] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0317] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0318] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A reliability interactive assessment system for distribution networks and IES based on multi-energy flow coupling, characterized in that, include: The equipment and multi-energy flow modeling module is used to establish mathematical models of power generation equipment in the distribution network and energy conversion equipment in the integrated energy system (IES), and to construct network models and power flow matrix equations for the power system, thermal system and natural gas system, so as to solve the multi-energy flow distribution using a distributed sequential method; The reliability interaction mechanism analysis module is used to analyze the reliability change characteristics under the conditions of IES failure, distribution network failure, and simultaneous failure of IES and distribution network based on the multi-energy flow distribution and reliability indicators and power quality indicators, and to establish a multi-energy coordinated operation strategy. The load reduction strategy optimization module is used to solve the optimal load reduction strategy of the distribution network and IES in a distributed manner based on the target cascade analysis method for different fault scenarios, with tie line power as the coupling variable. The reliability assessment module is used to perform sampling simulations of the operating status of the distribution network and IES based on the Markov chain Monte Carlo simulation method, and calculate the system reliability index by combining multi-energy flow distribution and optimal load reduction strategy. The experimental verification module is used to simulate and verify the system reliability indicators under different power interaction modes and fault conditions.

2. The system as described in claim 1, characterized in that, The process involves establishing mathematical models for power generation equipment and IES energy conversion equipment in the distribution network, and constructing network models and power flow matrix equations for the power system, thermal system, and natural gas system. A distributed sequential method is then used to solve for the multi-energy flow distribution. Specifically, this includes: A mathematical model of the power generation equipment in the distribution network is established. This model is based on the physical characteristics of the equipment's operation and quantifies power output boundaries and state transition rules through multiple constraint relationships. These multiple constraint relationships include: Output constraints: ； in, To represent the state of the i-th unit at time t. Let i be the output of the i-th unit at time t. , These are the minimum and maximum output limits for the i-th unit, respectively; Climbing / landslide constraints: ； in, , These represent the upper limits of the unit's climb / slippage. Minimum start / stop constraints: ； ； in, , These represent the minimum start-up / shutdown times of the unit, respectively. Indicates the length of the scheduling period. , These represent the continuous start / stop time of the unit at the initial moment; , These represent the time that the i-th unit needs to be in the start-up and stop-down states after the scheduling begins; I Gi (0) indicates the state of the i-th unit at the initial moment of scheduling; Establishing a mathematical model for the IES conversion device, specifically including: Cogeneration unit model: ； Among them, at time t, the electrical power output, thermal power output, and intake power of the combined heat and power unit CHP are respectively , , The electrical and thermal efficiencies of CHP are respectively... , express; Gas boiler model: ； in, , These represent the thermal output and inlet power of the gas-fired boiler GB at time t, respectively. Indicates the thermal efficiency of GB; Electric boiler model: ； in, , Let represent the thermal output and air intake power of the electric boiler EB at time t, respectively. For the thermal efficiency of EB; The power system network model is constructed as follows: ； in, Let i be the active power injected into the i-th node. U is the reactive power injected into the i-th node. i U j Let be the voltage magnitudes at the i-th node and the j-th node. and For the conductance and susceptance of the transmission line, δ ij The voltage phase angle difference between the i-th node and the j-th node; A thermal system network model is constructed, which includes a hydraulic model and a thermal model, as detailed below: Hydraulic model: ； In this matrix, matrix A represents the connection relationship between branches and nodes in the heating network, m represents the pipe flow rate, and the heat load demand is expressed in terms of q. h Indicated, matrix B is used to represent the connection relationship between branches and loops in the heating pipe network, h f This represents the pressure drop generated when a fluid flows in a pipe, and K represents the pipe's resistance coefficient parameter. Thermal model: ； in, For user-side heat exchange power, T is the specific heat capacity of water. s For water supply temperature, T r For return water temperature, m q The quality of hot water flowing into heat users per unit time; The natural gas system network model is constructed as follows: ； Where, p i p j Let q represent the pressure at node i and node j, respectively. ij k represents the flow rate from the i-th natural gas pipeline to the j-th natural gas pipeline. ij It is a parameter constant; Construct the power flow matrix equation of the power system based on the power system network model: Based on the power system network model, for the i-th node in the system, the deviation equation between its active power and reactive power is established as the power flow matrix equation of the power system: ； in, For nodes i The active power imbalance, For nodes j Active power imbalance; Construct the natural gas system power flow matrix equation based on the natural gas system network model: Based on Kirchhoff's Current Law (KCL) and Voltage Law (KVL), the nodal equilibrium equations and loop equations of the natural gas system are constructed as the natural gas system power flow matrix equations. Here, the j-th node is connected to the i-th node via a compressor or pipeline, denoted as j∈i. The natural gas system power flow matrix equations are expressed as follows: ； ； Where, q i q Gi q Li Let L represent the injection flow rate, gas source flow rate, and load flow rate of the i-th node; the network pipeline set is denoted by L. This represents the flow rate of natural gas delivered by the compressor from node i to node j. s is the gas compression ratio coefficient corresponding to the compressor. j s is the compressor connection state coefficient; when the compressor draws air from the i-th node, s j Set to 1, when the compressor does not draw gas from the i-th node or there is no gas delivery path from node i to node j, s j Take 0; b li ΔP represents the correlation coefficient between pipe l and the i-th node. l This indicates the pressure drop in the pipeline, p m p n These represent the pressure values ​​at both ends of pipe 1; Construct the power flow matrix equation of the thermal system based on the thermal system network model: The state matrix equation of the heating network is used as the power flow matrix equation of the thermal system. ; in, Let be the unbalance vector in the power flow equations of the thermodynamic system. This refers to the nodal thermal power imbalance. This refers to the pressure imbalance at pipeline nodes. Let this be the water supply temperature deviation vector. H is the return water temperature deviation vector, M is the pipe-node correlation matrix, used to describe the topological connection relationship between pipes and nodes in the heating network; SP The known thermal power data of the system; matrix C s b s C r b r This is related to the topology of the heating network, the topology of the return water network, the flow rate of the heat medium, and the node temperature; By employing a distributed sequential solution method, when solving the power flow matrix equation of any energy system, nodes coupled with other energy systems are treated as source nodes or load nodes to obtain the multi-energy flow distribution.

3. The system as described in claim 2, characterized in that, Based on the multi-energy flow distribution and reliability and power quality indicators, the reliability variation characteristics under IES faults, distribution network faults, and simultaneous IES and distribution network faults are analyzed to establish a multi-energy coordinated operation strategy, specifically including: Construct a set of multiple fault scenarios, including setting fault scenarios such as IES fault, distribution network fault, and simultaneous fault of IES and distribution network; Under various fault scenarios, based on the power flow matrix equations of the power system, thermal system, and natural gas system, the multi-energy flow distribution results of the power system, thermal system, and natural gas system under each fault condition are obtained. Based on the multi-energy flow distribution results, the system reliability index and power quality index are calculated, wherein the reliability index includes the power supply reliability index and the comprehensive energy supply reliability index, and the power quality index includes the voltage deviation index and the frequency deviation index. By comparing the variation characteristics of various reliability and power quality indicators under different fault scenarios, the mutual support relationship and energy complementarity characteristics between the distribution network and the IES are extracted. Based on the energy supply capacity, load demand and system operating status under different fault scenarios, a multi-energy coordinated operation strategy is formed.

4. The system as described in claim 3, characterized in that, The method for distributively solving the optimal load reduction strategy for the distribution network and IES, using tie-line power as a coupling variable and based on the target cascade analysis method, for different fault scenarios, specifically includes: In the scenario of IES failure, the distribution network is regarded as a backup power source, and priority is given to increasing the power output of CHP and gas boilers, while utilizing the output of photovoltaic, wind power and energy storage equipment. When the output of photovoltaic, wind power and energy storage equipment cannot meet the preset load demand, electricity is purchased from the distribution network, and the reduction ratio of electricity and heat load is adjusted according to the preset load weight. The target cascade analysis method (ATC) is adopted, with tie line power as the coupling variable, and sub-optimization models of the distribution network and IES are constructed respectively. By iteratively updating the tie line power and other coupling variables and coordinating the operating status of each subsystem, the optimal load reduction strategy under the IES failure scenario is obtained until the convergence condition is met. In the scenario of a distribution network fault, it is determined whether the downstream of the fault can form an island with the IES (Instrument Engineering System). If so, the IES increases the power of its internal equipment to supply power to the island. When the increased power of its internal equipment cannot cover the island load, its non-core loads are reduced. The Objective Cascaded Analysis (ATC) method is adopted, with tie-line power as the coupling variable, to establish sub-optimization problems for the distribution network and the IES respectively. By iteratively coordinating the tie-line power and the operating status of each system, the optimal load reduction strategy under the distribution network fault scenario is obtained. The core loads include medical emergency power load, communication base station power load, basic residential power supply and heating load, and industrial key production equipment power load. In scenarios where both the IES (Integrated Equipment) and the distribution network fail simultaneously, the importance of the loads in the distribution network and the IES is weighed, the thermal deficit is converted into equivalent electrical load, and the electrical and thermal loads in the system are reduced in a coordinated manner according to a preset ratio, with priority given to reducing non-core loads to ensure the continuous power supply of core loads. The Objective Cascaded Analysis (ATC) method is adopted, with tie-line power as the coupling variable. By iteratively solving the sub-optimization problems of the distribution network and the IES and coordinating the power exchange of each system, the optimal load reduction strategy under the scenario of simultaneous failure of the IES and the distribution network is obtained.

5. The system as described in claim 4, characterized in that, The Markov chain Monte Carlo simulation method is used to sample and simulate the operating state of the distribution network and the IES (Environmental Engineering System). Combined with multi-energy flow distribution and optimal load shedding strategies, the system reliability index is calculated, specifically including: Assuming that each device in the system has two states, normal and failure, the operating states of all devices in the distribution network and IES are sampled by Gibbs sampling through the construction of Markov chains to generate multiple system state sequences, and the first m sampled states that have not reached a steady state in the Markov chains are removed. For each system state sequence, determine the corresponding equipment fault state, and calculate the load reduction amount corresponding to the system state sequence by combining the multi-energy flow distribution and the optimal load reduction strategy; Based on the load reduction amount corresponding to the system state sequence, the system reliability index is calculated. The system reliability index includes the distribution network reliability index and the IES' own power supply reliability index. The reliability indicators of the power distribution network include: System average power outage frequency index: ； Where, N i Let λ be the number of users at the i-th load point, and R be the set of all load points in the system; λ is the expected number of outages at each load point within the statistical time period. i The calculation formula is as follows: ； Where, λ j Let be the failure rate of the j-th component; I is the set of system components associated with load point i. System average outage duration metric: ； The expected duration of outage at the load point within the statistical time period is T. i The calculation formula is as follows: ； in, This represents the average repair time after the i-th component fails. Average power outage duration per user: ； Average power outage frequency per user: ； Among them, M i This represents the number of users experiencing power outages due to faults. Average power availability index: ； Expected annual power shortage: ； Among them, P L,i Let be the average power consumption of the i-th load point; The IES's own power supply reliability indicators include: Annual load reduction frequency: ； Where S is the set of system states with load shearing, and T is the total simulation time; Annual load reduction probability: ； Among them, t i It is the duration of the i-th state with load shedding; Annual load reduction expectations: ； Among them, C i It is the reduction in IES electrical / thermal load under the i-th load shearing condition.

6. The system as described in claim 5, characterized in that, The system reliability indicators also include the contribution indicators of IES to the distribution network. These contribution indicators are used to quantify the degree to which the overall access of IES improves the reliability indicators of the distribution network. These include the improvement rate of the system average outage frequency indicator, the improvement rate of the system average outage duration indicator, the improvement rate of the user average outage duration indicator, the improvement rate of the user average outage frequency indicator, the improvement rate of the average power supply availability indicator, and the expected improvement rate of the annual power shortage. The contribution indicators are represented as follows: ； Among them, SAIFI0, SAIFI1, SAIDI0, SAIDI1, CAIDI0, CAIDI1, CAIFI0, CAIFI1, ASAI0, ASAI1, EENS0, and EENS1 are respectively the system average power outage frequency index, system average power outage duration index, user average power outage duration index, user average power outage frequency index, average power supply availability index, and expected annual power shortage before and after IES access.

7. The system as described in claim 6, characterized in that, The simulation verification of system reliability indicators under different power interaction modes and fault conditions specifically includes: Construct a simulation test scenario that includes a distribution network and an IES, and set different power interaction modes. The power interaction modes include at least the distribution network supplying power to the IES, the IES feeding back power to the distribution network, and the bidirectional power interaction mode between the distribution network and the IES. Under each power interaction mode, different fault conditions are set, including IES fault, distribution network fault, and simultaneous IES and distribution network fault. Then, the equipment and multi-energy flow modeling module, reliability interaction mechanism analysis module, load reduction strategy optimization module, and reliability assessment module are called in sequence to simulate and calculate the system operation status under each fault condition and obtain the corresponding system reliability indicators. The system reliability indicators under different power interaction modes and different fault conditions were compared and analyzed to obtain the verification results.

8. A reliability interaction assessment method for distribution networks and IES based on multi-energy flow coupling, characterized in that, include: Mathematical models of power generation equipment in distribution networks and energy conversion equipment in integrated energy systems (IES) are established, and network models and power flow matrix equations of power systems, thermal systems, and natural gas systems are constructed to solve the multi-energy flow distribution using a distributed sequential method. Based on the multi-energy flow distribution and based on reliability and power quality indicators, the reliability change characteristics under IES fault, distribution network fault, and simultaneous IES and distribution network fault conditions are analyzed, and a multi-energy coordinated operation strategy is established. For different fault scenarios, the optimal load reduction strategy of the distribution network and IES is solved in a distributed manner based on the target cascade analysis method, with tie line power as the coupling variable. Based on the Markov chain Monte Carlo simulation method, the operating status of the distribution network and IES is sampled and simulated, and the system reliability index is calculated by combining multi-energy flow distribution and optimal load reduction strategy. The system reliability indicators were verified by simulation under different power interaction modes and fault conditions.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in claim 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in claim 8.

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