Flexible interconnection power distribution network reliability evaluation method and related device

Abstract

The invention discloses a reliability evaluation method for a flexible interconnected power distribution network and a related device, and belongs to the technical field of power distribution network evaluation.The method considers the influence of an energy storage type intelligent soft switch on the power distribution network, and comprises the steps that firstly, modeling is conducted on the energy storage type intelligent soft switch in the power distribution network, and a mathematical model of the switch is obtained; generating a typical wind and light scene based on the wind power and photovoltaic data of the power distribution network; establishing a power distribution network recovery planning model with the minimum average power failure frequency of the system as a target by taking the wind and light scene as input and the switch model as a constraint condition, solving the model to obtain an optimal solution, and finally calculating a reliability evaluation result of the power distribution network according to the optimal solution. According to the method provided by the invention, the wind and light uncertainty and the operation characteristics of the energy storage type intelligent soft switch can be considered at the same time, the reliability evaluation precision and calculation efficiency of the power distribution network are improved, and technical support is provided for planning and operation of the flexible interconnection power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network assessment technology, and in particular relates to a method and related apparatus for assessing the reliability of flexible interconnected power distribution networks. Background Technology

[0002] With the penetration of new energy sources, power distribution networks are developing towards higher proportions of new energy and greater flexibility. However, the continuous increase in the penetration rate of new energy sources will have a significant impact on the safe and stable operation of power distribution networks, making system reliability assessment increasingly important.

[0003] Distribution network reliability measures a system's ability to meet load demands during operation. Traditional methods, such as the minimum path method and failure mode and consequences analysis, can calculate reliability indices, but they typically involve complex nonlinear characteristics, resulting in large computational loads and difficulty in directly converting them into mixed-integer linear programming models, thus limiting their application in practical planning and operation. Subsequent improved methods have included some studies considering load recovery behavior or unknown topology scenarios, but generally have not addressed the impact of flexible interconnection devices on reliability.

[0004] Smart soft switches (SOPs), as typical flexible interconnection devices, can improve power flow regulation capabilities and distributed generation absorption levels, and have been increasingly introduced into reliability assessment research in recent years. However, existing methods often fail to consider the uncertainties of new energy sources such as photovoltaics and wind power, making it difficult to accurately reflect the reliability level under conditions of high proportion of new energy sources. With the development of flexible interconnection technology, SOPs are integrated with energy storage systems to form energy storage-type smart soft switches (E-SOPs), which have significant advantages in terms of regulation flexibility and fault support. However, current research on the reliability assessment of distribution networks containing E-SOPs is still limited, and the description of their operating characteristics and fault impacts is insufficient.

[0005] Therefore, in the context of flexible interconnection and the coexistence of a high proportion of new energy sources, it is urgent to carry out research on the reliability assessment of distribution networks with energy storage-type smart soft switches, so as to make up for the shortcomings of existing methods in terms of model accuracy, description of operating characteristics and handling of new energy uncertainties. Summary of the Invention

[0006] Based on this, the present invention aims to propose a reliability assessment method and related device for flexible interconnected distribution networks. In the reliability assessment, a planning model is established with operational constraints including E-SOP as constraints. The planning model is solved, and the reliability assessment index is calculated based on the optimized solution, thereby overcoming the problem of insufficient accuracy of existing assessment methods.

[0007] In a first aspect, the present invention provides a method for reliability assessment of flexible interconnected distribution networks, comprising:

[0008] The E-SOP model is obtained by modeling the E-SOP in the distribution network;

[0009] Wind and solar power scenes are generated based on wind power and solar power data in the power distribution network.

[0010] Using wind and solar scenarios as input and the E-SOP model as constraints, a distribution network restoration planning model is established with the objective of minimizing the average power outage frequency of the system. The optimal solution is obtained by solving the distribution network restoration planning model.

[0011] The reliability assessment results of the distribution network are calculated based on the optimized solution.

[0012] Furthermore, modeling the E-SOP in the distribution network yields the following E-SOP model:

[0013] To model the E-SOP in the distribution network, we consider different fault scenarios of the E-SOP and obtain the corresponding fault scenario and E-SOP model.

[0014] Furthermore, considering different fault scenarios of E-SOP, the E-SOP in the distribution network is modeled, and the corresponding E-SOP models for each fault scenario are as follows:

[0015] Based on the fault scenarios of E-SOP, fault power constraints corresponding to the fault scenarios are introduced into the E-SOP model.

[0016] Furthermore, the constraints based on the E-SOP model include:

[0017] The switching operation constraints and energy storage operation constraints are determined based on the power transfer characteristics of the E-SOP.

[0018] The switching operation constraints are expressed as follows:

[0019] ,

[0020] The energy storage operation constraints are expressed as follows:

[0021] ,

[0022] in, and P represents the active power transmitted at ports i and j of the AC / DC converter within the E-SOP. DC The active power transmitted by the DC / DC converter within the E-SOP; and These represent the reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and These represent the maximum reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and These represent the minimum reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and P represents the losses at ports i and j of the AC / DC converter within the E-SOP, respectively. DC,loss This indicates the losses of the DC / DC converter within the E-SOP. and Port capacity; δ c and δ dc For the charge / discharge state of E-SOP, δ c =1 indicates that it is in a charging state, δ dc =1 indicates that it is in a discharge state; and These represent the charging power and discharging power of the E-SOP, respectively; A c and A dc These represent charging efficiency and discharging efficiency, respectively. For maximum discharge power, This is the maximum charging power; Let be the charge at time t. and These represent the maximum and minimum values ​​of the charge, respectively; T is the optimization period.

[0023] Furthermore, the inputs to the distribution network restoration planning model also include:

[0024] Network parameters of the distribution network, branch fault probability distribution, and system fault recovery time.

[0025] Furthermore, the distribution network reliability assessment results calculated based on the optimization solution include:

[0026] The optimal solution includes the load recovery status and the cumulative power outage time;

[0027] The average system outage time, average system power supply reliability, and system power shortage of the distribution network are calculated based on the optimized solution.

[0028] The average outage time, average power supply reliability, and power shortage of the output system are used as the results of the distribution network reliability assessment.

[0029] Furthermore, the constraints of the distribution network restoration planning model also include network operation constraints, wind and solar power output constraints, and reliability assessment constraints.

[0030] Furthermore, based on wind power data and solar power data in the distribution network, scene generation is performed, resulting in wind and solar power scenes including:

[0031] By using wind power data and solar power data to perform stochastic modeling, we obtained wind power probability distribution models and solar power probability distribution models.

[0032] Initial wind and solar scenes are generated based on wind power probability distribution models and photovoltaic probability distribution models.

[0033] The initial landscape scene is simplified to obtain the landscape scene as input.

[0034] In a second aspect, the present invention provides a reliability assessment device for flexible interconnected distribution networks, comprising:

[0035] The switch modeling module is used to model the E-SOP in the distribution network to obtain the E-SOP model;

[0036] The scene generation module is used to generate wind and solar power scenes based on wind power data and solar power data in the power distribution network.

[0037] The planning modeling and solving module is used to establish a distribution network restoration planning model with the objective of minimizing the average power outage frequency, taking the wind and solar scene as input and the E-SOP model as constraints. The optimal solution is obtained by solving the distribution network restoration planning model.

[0038] The reliability assessment module is used to calculate the reliability assessment results of the distribution network based on the optimization solution.

[0039] Thirdly, the present invention provides an electronic device including a memory storing computer-executable instructions and a processor, wherein when the computer-executable instructions are executed by the processor, the device performs the steps of the flexible interconnected distribution network reliability assessment method provided in the first aspect.

[0040] Fourthly, the present invention provides a readable storage medium storing a computer-executable program that, when executed, implements the various steps of the flexible interconnected distribution network reliability assessment method provided in the first aspect.

[0041] Compared with existing evaluation methods, the present invention has the following advantages:

[0042] This invention proposes a reliability assessment method and related apparatus for flexible interconnected distribution networks. The method involves detailed modeling of energy storage-type smart soft switches (E-SOPs) in the distribution network. Using uncertainties from wind and solar power as input, a mixed-integer linear programming model is established to minimize the average system outage frequency. This yields an optimized solution for distribution network restoration, enabling accurate calculation of distribution network reliability indicators and significantly improving the accuracy and reliability of reliability assessment. A further embodiment incorporates power constraints corresponding to different E-SOP fault scenarios into the distribution network restoration planning model, allowing the model to reflect the actual operating behavior of flexible interconnected equipment and covering various equipment failure scenarios in the reliability assessment. The proposed assessment method ensures the representativeness of the assessment results while improving computational efficiency, thus providing scientific and quantifiable technical support for the planning, operation optimization, and reliability management of flexible interconnected distribution networks. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the implementation of the flexible interconnected distribution network reliability assessment method provided in this embodiment of the invention;

[0045] Figure 2 This is an electrical schematic diagram of an E-SOP provided in an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of the connection relationship of E-SOP in the power distribution network provided in the embodiments of the present invention;

[0047] Figure 4 This is a schematic diagram of the structure of the flexible interconnected distribution network reliability assessment device provided in an embodiment of the present invention;

[0048] Figure 5 This is an electronic device architecture diagram provided for an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] In the following embodiments of the present invention, a mixed-integer linear programming (MILP) model is established for reliability assessment of flexible interconnected distribution networks containing energy storage-type smart soft switches (E-SOPs) to solve the distribution network recovery planning problem. This MILP model aims to minimize the average system outage frequency and achieves a quantitative description of the distribution network's operating state by introducing decision variables and constraints.

[0051] See Figure 1 An embodiment of the present invention provides a method for reliability assessment of a flexible interconnected distribution network, comprising the following steps:

[0052] Step S110. Model the E-SOP in the distribution network to obtain the E-SOP model.

[0053] This step involves modeling the Energy Storage Smart Soft Switch (E-SOP) deployed in the distribution network to obtain its mathematical model. The E-SOP is a novel flexible interconnection device, mainly composed of a bidirectional AC / DC converter, a DC / DC energy storage converter, and controllable switches and control units. The AC / DC converter is used for active and reactive power transmission between nodes in the distribution network, achieving flexible closed-loop operation. The DC / DC energy storage converter connects energy storage units and is used for power charging and discharging regulation and energy balance. The controllable switches and control units are used to implement power flow control, charging and discharging strategy execution, and fault protection. This structure allows the E-SOP to flexibly adjust the power flow between nodes under different fault and operating conditions, improving the flexibility and resilience of the distribution network. The modeling determines the active and reactive power transmission characteristics, charging and discharging constraints, port capacity, and loss characteristics of the E-SOP.

[0054] In a further embodiment, since there is a DC link between the converters, there is no need to consider the reactive power loss of the AC-DC-AC conversion link. The E-SOP is mainly composed of a smart soft switch (SOP) and an energy storage system (ESS). Therefore, the solution of the distribution network restoration planning model needs to satisfy both the SOP operation constraints and the ESS operation constraints.

[0055] Specifically, Figure 2 and Figure 3 An exemplary structure for E-SOP is given. Based on the modeling of E-SOP, the switching operation constraints and energy storage operation constraints are expressed as follows:

[0056] The switching operation constraints are:

[0057]

[0058] Energy storage operation constraints are:

[0059]

[0060] in, and P represents the active power transmitted at ports i and j of the AC / DC converter (VSC) within the E-SOP. DC The active power transmitted by the DC / DC converter within the E-SOP; and These represent the reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and These represent the maximum reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and These represent the minimum reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and P represents the losses at ports i and j of the AC / DC converter within the E-SOP, respectively. DC,loss This indicates the losses of the DC / DC converter within the E-SOP. and Port capacity; δ c and δ dc For the charge / discharge state of E-SOP, δ c =1 indicates that it is in a charging state, δ dc =1 indicates that it is in a discharge state; and These represent the charging power and discharging power of the E-SOP, respectively; A c and A dc These represent charging efficiency and discharging efficiency, respectively. For maximum discharge power, This is the maximum charging power; Let be the charge at time t. and These represent the maximum and minimum values ​​of the charge, respectively; T is the optimization period.

[0061] Including the mathematical model of E-SOP in the constraints of the planning model is due to the fact that the losses of E-SOP directly affect its power transmission efficiency. During the rapid power transfer phase after a fault occurs, the active power loss of the converter inside the switch reduces the actual transferable power, thus affecting the load restoration. The greater the loss, the fewer the recoverable loads, leading to an increase in system power shortage and a longer average outage time. Therefore, an accurate loss model can truly reflect the actual power transfer capacity of E-SOP and avoid overestimating its contribution to reliability. Secondly, the active and reactive power constraints of E-SOP limit its power regulation range under fault conditions. When a branch is permanently faulty... When a fault occurs, the maximum power that the E-SOP can transfer is limited by its capacity. This directly determines which lost loads can be restored to power through the E-SOP, thus affecting the system's average outage frequency and load point outage rate. Furthermore, the state-of-charge constraints and charging / discharging power constraints of the ESS within the E-SOP determine the duration for which the E-SOP can continuously supply power during islanded operation. When the output of distributed generation (DG) is insufficient, the ESS can compensate for the load shortfall by discharging. However, its discharge capacity is limited by the current load capacity and maximum discharge power. The larger the ESS capacity, the longer the islanded operation time and the shorter the cumulative outage time, thereby improving the system's average power supply reliability.

[0062] In a more preferred embodiment, step S110 includes:

[0063] To model the E-SOP in the distribution network, we consider different fault scenarios of the E-SOP and obtain the corresponding fault scenario and E-SOP model.

[0064] Specifically, the modeling approach for considering fault scenarios involves introducing fault power constraints on top of the operational constraints of normal operation.

[0065] For example, during normal operation, the E-SOP operates normally according to the aforementioned switching operation constraints and energy storage operation constraints; when a fault occurs, power constraints are introduced on the basis of the original constraints according to the specific fault scenario, so that the reliability assessment model can accurately reflect the impact of different fault types on the E-SOP's operating capability and system reliability.

[0066] In some embodiments, the failure scenarios for the E-SOP include both insulation failures and functional failures. Specifically, when an insulation failure occurs in the E-SOP, the device needs to be completely isolated, at which point the active and reactive power transmission capabilities of the E-SOP are reduced to zero.

[0067] Based on the original constraints, the following power constraints are introduced:

[0068]

[0069] in, Indicates an insulation fault condition. =0 indicates an insulation fault has occurred. =1 indicates normal operation. , , , This represents the active and reactive power at ports i and j during normal E-SOP operation. This constraint indicates the situation when the E-SOP experiences an insulation fault (…). When the power transfer of the E-SOP is 0, all power transfers are zero, and the E-SOP completely shuts down; while the E-SOP is operating normally ( When =1), the active and reactive power of each port operates in normal operating condition.

[0070] When an E-SOP experiences a functional failure, some functions of the equipment are impaired but not completely lost, resulting in a decrease in its power transmission capacity. A functional failure state is then introduced for capacity constraints, specifically the following:

[0071]

[0072] in, The power attenuation factor under functional faults (0 < <1), This is a functional failure state. =0 indicates a functional failure. =1 indicates normal operation. and These represent the rated capacities of E-SOP ports i and j, respectively. This constraint indicates... When =1, E-SOP operates at rated capacity. When =0, the capacity of E-SOP decreases to The rated capacity is times that of the rated capacity.

[0073] In some embodiments, considering the islanding phenomenon that occurs when a distribution network fails, applying E-SOP to islanding can effectively address the impact of the randomness of distributed energy output and capacity constraints on system reliability. Since the capacity of distributed energy is not unlimited, the principle of maximum distributed energy absorption must be followed in islanding with E-SOP, meaning the total load power within the island should not exceed the total power supply capacity of the distributed energy and E-SOP. Therefore, the mathematical model of E-SOP also includes the following power constraints:

[0074]

[0075] in, This represents the sum of the power of all loads within the island. This represents the sum of the output of distributed energy resources. This represents the sum of E-SOP discharge power.

[0076] Step S120. Based on wind power data and photovoltaic data in the distribution network, generate a scene to obtain a wind and solar scene.

[0077] This step involves stochastic modeling of the power output of each wind farm and photovoltaic power station in the distribution network, resulting in wind power probability distribution models and photovoltaic probability distribution models. Beta distribution, Weibull distribution, or other statistical distributions can be used to describe the power at a single moment. At the same time, time series models are combined to reflect the dynamic changes and correlations of wind and solar power. Subsequently, based on these probability models, initial wind and solar scenarios are generated, and multiple possible future power output sequences are generated through Monte Carlo sampling or Latin hypercube sampling.

[0078] Furthermore, in order to reflect real wind power data, the scene generation uses the Copula function, and a more preferred implementation uses the Gaussian Copula function for scene generation.

[0079] In a further embodiment, since there is a large amount of real wind power data, a large number of scenarios will be generated, which will lead to an accumulation of computational load. In order to use a small number of classic scenarios to describe the uncertainty of wind and solar power, the initial scenarios can be simplified. For example, based on the Kantorovich distance, a large number of original scenarios can be compressed into a small number of representative typical scenarios as input to the distribution network restoration planning model, so as to reduce computational complexity and ensure the representativeness of the evaluation results.

[0080] Step S130. Using the wind and solar scene as input and the E-SOP model as constraints, establish a distribution network restoration planning model with the objective of minimizing the average power outage frequency of the system, and solve the distribution network restoration planning model to obtain the optimized solution.

[0081] This step combines the E-SOP mathematical model established in the previous steps with the wind and solar scenario to construct a distribution network restoration planning model with the goal of minimizing the average outage frequency of the system. This step determines the load restoration status, branch status, E-SOP power transmission, ESS charging and discharging power, and state of charge through optimization solutions, so as to minimize the impact of power outages on the overall reliability of the distribution network.

[0082] Specifically, using the MILP optimization framework, wind and solar uncertainties, E-SOP power transmission constraints, and energy storage constraints are integrated into the distribution network restoration planning. By using the minimum system average outage frequency as the objective function, the model can calculate how to arrange the load restoration sequence and schedule E-SOP power flow under different wind and solar power output conditions and equipment failure scenarios, in order to minimize user outage time and power interruption risks. The linearization characteristics of MILP ensure the feasibility and computational efficiency of the solution, while accurately constraining operational limitations such as E-SOP power capacity, port active / reactive power, energy storage charging and discharging efficiency, and upper and lower limits of charge capacity.

[0083] In this step, the inputs to MILP mainly include distribution network topology and branch parameters, branch fault scenarios, wind and solar scenarios, and E-SOP model. The decision variables of the model are defined as load recovery status, E-SOP switch status, energy storage charging and discharging power, branch power flow variables, cumulative outage time, etc. At the same time, the constraints of the model are constructed, including network operation constraints, wind and solar power output constraints, and reliability index constraints, in addition to E-SOP operation constraints.

[0084] In some embodiments, the branch scenarios input to the model are represented by the probability of occurrence of fault scenarios, and the fault probability distribution is calculated as follows:

[0085]

[0086] in, Let be the failure rate of line ij. As the baseline failure rate, Let ij be the length of the line.

[0087] The objective function of the model is expressed as follows:

[0088]

[0089] in, This indicates the average frequency of power outages in the system. This represents the power outage rate at load point i. This represents the number of users at load point i. This represents the set of load points.

[0090] The network operation constraints in the constraints include power balance constraints, voltage constraints, and line capacity constraints. Therefore, the network operation constraints are expressed as follows:

[0091]

[0092] Among them, P ij Q ij and S ij These represent the active power, reactive power, and capacity of branch ij, respectively. , These represent the active power output and reactive power output of photovoltaic systems, respectively. , These represent the active power output and reactive power output of wind power, respectively. and The active and reactive power of E-SOP are respectively. and These represent the active power and reactive power of the load, respectively; V i Line voltage, and These represent the upper and lower limits of the voltage.

[0093] The power output constraints for wind and solar power are expressed as follows:

[0094]

[0095] in, and The upper and lower limits of photovoltaic power output; and These are the upper and lower limits of wind power output.

[0096] Reliability assessment constraints mainly include load recovery constraints, power outage time constraints, and power supply path constraints.

[0097] definition Let N be a binary variable, where a value of 0 indicates a power outage and a value of 1 indicates that load point i is supplied with power at time t; N is the set of nodes, L is the set of lines, and T is the set of lines. i For time sets; and These are the rated active power and reactive power of the load, respectively; Δt i Let δ be the cumulative power outage time at load point i. t For time step; is a binary variable. When its value is 0, it means that branch ij is open at time t. When its value is 1, it means that branch ij is closed at time t. M is a positive number.

[0098] The reliability assessment constraints can then be expressed as follows:

[0099]

[0100] In a further embodiment, after a fault occurs, the distribution network typically needs to go through four recovery phases in sequence, including:

[0101] (1) Fault location and isolation phase: When a permanent branch fault occurs, the system needs to locate and isolate the fault; when an E-SOP insulation fault occurs, only the faulty equipment needs to be isolated; and when a functional fault occurs, only fault isolation is required. The recovery time for this phase is defined as follows: ;

[0102] (2) E-SOP rapid power transfer phase: The E-SOP will use different methods to handle different fault types. When a permanent branch fault occurs, the E-SOP will quickly transfer power; when the E-SOP itself fails, the tie switch will be activated. The recovery time of this phase is defined as ;

[0103] (3) Interchange switch transfer phase: When a permanent branch fault occurs and E-SOP cannot transfer all power, the system will activate the interchange switch for transfer. The recovery time for this phase is defined as follows: ;

[0104] (4) Islanding Phase: If power supply fails to be restored in the above phases, the system enters the islanding operation phase based on the DG and ESS capacities. The recovery time for this phase is defined as... .

[0105] The recovery times of the four recovery stages are used as known parameters as inputs to the MILP model described above. This directly affects the calculation of the cumulative outage time. When a load point restores power through the k-th stage after a fault, its outage time is the recovery time corresponding to that stage. If a load point requires multiple stages to fully recover, its outage time is the recovery time corresponding to the final recovery stage. The differences in recovery time caused by different fault types and recovery strategies are ultimately reflected in reliability indicators such as the system average outage time and the system average power supply reliability through the cumulative outage time, thereby quantifying the impact of different fault scenarios on system reliability.

[0106] The following timing constraints are introduced into the model's constraints:

[0107]

[0108] This constraint ensures that the recovery process proceeds in the following order: fault location and isolation, E-SOP rapid power transfer, tie switch power transfer, and islanding.

[0109] Step S140. Calculate the distribution network reliability assessment results based on the optimized solution.

[0110] This step calculates the reliability index of the distribution network based on the optimized solution obtained in step S130.

[0111] Specifically, it can calculate indicators such as the average system outage time, the average system power supply reliability rate, and the system power shortage, thereby quantitatively reflecting the operational reliability of the distribution network under the uncertainty of wind and solar power and E-SOP failure.

[0112] According to the system indicators for reliability assessment of distribution networks specified in "DL / T 1563—2016 Guidelines for Reliability Assessment of Medium Voltage Distribution Networks", the system average outage frequency, system average outage time (SAIDI), system average power supply reliability rate (ASAI), and system power shortage (ENS) are used as the reliability assessment results output. In this embodiment of the invention, the system average outage frequency is used as the objective function of the model, so it can be obtained directly by solving the model. The other three indicators can be calculated from the optimized solution of the model.

[0113] Specifically, the calculations for System Average Outage Time (SAIDI), System Average Power Supply Reliability (ASAI), and System Power Shortage (ENS) are expressed as follows:

[0114]

[0115] in, This represents the number of users at load point i. This indicates the power outage time at load point i.

[0116] In a further embodiment, multiple assessments can be conducted on different typical wind and solar scenarios and E-SOP fault conditions, and the average value and fluctuation range of each indicator can be statistically analyzed to achieve a comprehensive assessment of the reliability of the distribution network and provide a scientific basis for planning, scheduling and recovery strategies.

[0117] The above-disclosed embodiments describe in detail a method for reliability assessment of flexible interconnected distribution networks. The disclosed method can be implemented using various types of equipment. Therefore, the present invention also discloses an apparatus corresponding to the above method. Specific embodiments are given below for detailed description.

[0118] like Figure 4 As shown, one embodiment of the present invention provides a reliability assessment device for a flexible interconnected distribution network, comprising:

[0119] The switch modeling module 402 is used to model the E-SOP in the distribution network to obtain the E-SOP model;

[0120] The scene generation module 404 is used to generate a wind and solar scene based on wind power data and solar power data in the power distribution network.

[0121] The planning modeling and solving module 406 is used to establish a distribution network restoration planning model with the objective of minimizing the average power outage frequency of the system, taking the wind and solar scene as input and the E-SOP model as constraints, and to solve the distribution network restoration planning model to obtain the optimized solution.

[0122] The reliability assessment module 408 is used to calculate the reliability assessment results of the distribution network based on the optimization solution.

[0123] The device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0124] The methods and related apparatuses mentioned in the above embodiments are described with reference to the method flowcharts and / or structural diagrams provided in the embodiments of this application. Specifically, each block of the method flowchart and / or structural diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 A process or multiple processes and / or structures illustrate the steps of the functions specified in one or more boxes.

[0125] The following embodiments illustrate the application of this method to a computer device. It is understood that the computer device can be any device with computing and processing capabilities, including but not limited to servers or personal laptops. In one embodiment, the computer device can be an application server, which can be a server used to run the application under test.

[0126] See Figure 5This document illustrates a hardware block diagram of an electronic device intended to represent various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0127] like Figure 5 As shown, the electronic device includes: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;

[0128] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0129] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0130] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0131] The memory stores a program, which the processor can call. The program is used to implement the various processing steps of the aforementioned flexible interconnected distribution network reliability assessment scheme.

[0132] This invention also provides a readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements various processing flows of the flexible interconnected distribution network reliability assessment scheme provided by any possible implementation of the above embodiments and / or in combination with the embodiments.

[0133] The invention has been described in particular detail above with respect to possible scenarios, and those skilled in the art will recognize that the invention can be practiced through other embodiments. Specific naming of components, capitalization of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or important, and the mechanisms or features of implementing the invention may have different names, forms, or procedures. The system can be implemented through a combination of hardware and software (as described), entirely through hardware elements, or entirely through software elements. The specific division of functions among the various system components described herein is merely exemplary and not mandatory; rather, the functions performed by a single system component can be performed by multiple components, or the functions performed by multiple components can be performed by a single component.

[0134] Those skilled in the art should understand that the various steps of the disclosed methods can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using device-executable program code, which can then be stored in a storage device for execution by the computing device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, the embodiments disclosed in this invention are not limited to any specific hardware and software combination.

[0135] The programs (also referred to as programs, software, software applications, or code) executable by these computing devices include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0136] Certain aspects of this invention include the process steps and instructions described herein in algorithmic form. It should be noted that the process steps and instructions of this invention can be implemented in software, firmware, and / or hardware, and when implemented in software, they can be downloaded, stored on various operating systems and operated from said platforms.

[0137] Those skilled in the art will understand that the structures shown in the figures are merely block diagrams of some structures related to the present application and do not constitute a limitation on the terminal device to which the present application is applied. Specific terminal devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0138] In the description of this specification, the use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "possible design," etc., refers to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0139] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 the present invention.

Claims

1. A reliability assessment method for flexible interconnected distribution networks, characterized in that, include: The E-SOP model is obtained by modeling the E-SOP in the distribution network; Wind and solar power scenes are generated based on wind power and solar power data in the power distribution network. Using the aforementioned wind and solar scene as input and the E-SOP model as constraints, a distribution network restoration planning model is established with the objective of minimizing the average power outage frequency of the system. The optimal solution is obtained by solving the distribution network restoration planning model. The reliability assessment results of the distribution network are calculated based on the optimized solution.

2. The method according to claim 1, characterized in that, The process of modeling the E-SOP in the distribution network to obtain the E-SOP model includes: Considering different fault scenarios of E-SOP, the E-SOP in the distribution network is modeled to obtain the E-SOP model corresponding to each fault scenario.

3. The method according to claim 2, characterized in that, The modeling of E-SOP in the distribution network considering different fault scenarios of E-SOP includes: Based on the fault scenarios of E-SOP, fault power constraints corresponding to the fault scenarios are introduced into the E-SOP model.

4. The method according to claim 1, characterized in that, The constraints, based on the E-SOP model, include: The switching operation constraints and energy storage operation constraints are determined based on the power transfer characteristics of the E-SOP. The switch operation constraints are expressed as follows: ， The energy storage operation constraints are expressed as follows: ， in, and P represents the active power transmitted at ports i and j of the AC / DC converter within the E-SOP. DC The active power transmitted by the DC / DC converter within the E-SOP; and These represent the reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and These represent the maximum reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and These represent the minimum reactive power transmitted at ports i and j of the AC / DC converter within the E-SOP, respectively. and P represents the losses at ports i and j of the AC / DC converter within the E-SOP, respectively. DC,loss This indicates the losses of the DC / DC converter within the E-SOP. and Port capacity; δ c and δ dc For the charge / discharge state of E-SOP, δ c =1 indicates that it is in a charging state, δ dc =1 indicates that it is in a discharge state; and These represent the charging power and discharging power of the E-SOP, respectively; A c and A dc These represent charging efficiency and discharging efficiency, respectively. For maximum discharge power, This is the maximum charging power; Let be the charge at time t. and These represent the maximum and minimum values ​​of the charge, respectively; T is the optimization period.

5. The method according to claim 1, characterized in that, The calculation of the distribution network reliability assessment result based on the optimized solution includes: The optimized solution includes the load recovery status and the cumulative power outage time; The average system outage time, average system power supply reliability, and system power shortage of the distribution network are calculated based on the optimized solution. The system's average outage time, average power supply reliability, and power shortage are output as the results of the distribution network reliability assessment.

6. The method according to claim 1, characterized in that, The constraints also include network operation constraints, wind and solar power output constraints, and reliability assessment constraints.

7. The method according to claim 1, characterized in that, The scene generation based on wind power data and solar power data in the distribution network yields wind and solar power scenes, including: By using wind power data and solar power data to perform stochastic modeling, we obtained wind power probability distribution models and solar power probability distribution models. An initial wind and solar scene is generated based on the aforementioned wind power probability distribution model and photovoltaic probability distribution model. The initial landscape scene is simplified to obtain the landscape scene as input.

8. A reliability assessment device for flexible interconnected distribution networks, characterized in that, include: The switch modeling module is used to model the E-SOP in the distribution network to obtain the E-SOP model; The scene generation module is used to generate wind and solar power scenes based on wind power data and solar power data in the power distribution network. The planning modeling and solving module is used to take the wind and solar scene as input and the E-SOP model as constraints to establish a distribution network restoration planning model with the objective of minimizing the average power outage frequency of the system, and to solve the distribution network restoration planning model to obtain an optimized solution; The reliability assessment module is used to calculate the reliability assessment results of the distribution network based on the optimized solution.

9. An electronic device, characterized in that, It includes a memory storing computer-executable instructions and a processor, which, when executed by the processor, causes the device to perform the flexible interconnected distribution network reliability assessment method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, It stores a computer-executable program that, when executed, enables the reliability assessment method for flexible interconnected distribution networks as described in any one of claims 1 to 7.

Images