Active power distribution network risk assessment method and system

Abstract

The invention discloses an active power distribution network risk assessment method and system, and the method comprises the steps: taking a day as a calculation time period, combining a constructed fault influence scale, and carrying out the calculation of an annual equipment maintenance work schedule model considering the minimum power failure loss and element strengthening plan offset, and obtaining an annual equipment maintenance work plan; combining the novel source side / load side response model to construct a system unit commitment optimization scheduling model with minimum load shedding and optimal economical efficiency as objective functions; and inputting the obtained annual equipment maintenance work scheduling plan result, carrying out state sampling on the system by adopting a sequential Monte Carlo simulation method, and carrying out system state estimation by applying a system unit combination optimization scheduling model to obtain system operation state data so as to realize reliability evaluation and risk analysis of the power distribution network.

Description

Background Art

[0002] As the "last mile" connecting users, the reliability of the distribution network will directly affect the user's electricity experience, and the risk assessment of the distribution network has received more and more attention. With the large-scale access to the distribution system of new energy, electric vehicle charging infrastructure, and new energy storage, the scale of the system continues to increase, and the number of equipment in the system is growing rapidly. The impact of the corresponding interaction between sources and loads and the load outage caused by equipment maintenance on the system reliability cannot be ignored. The original risk assessment theory and method are no longer applicable.

[0003] The essence of source-load bidirectional response is the interaction between the supply on the source side and the demand on the load side, which promotes the peak load regulation of the power grid and the consumption of new energy. However, in view of the actual situation of a large number of new sources and loads connected to the distribution system, no new and effective risk assessment scheme has been proposed in the existing studies. Equipment maintenance is a very important part of the daily operation and maintenance of the power system. The preparation of a scientific and reasonable maintenance plan is an important way to improve the operating efficiency and reliability of the distribution network, and it is of great significance to ensure the stable operation of the social economy. Considering the rapid growth of the number of system equipment, the increasing demand for maintenance, and the impact of maintenance shutdown on system operation is similar to that of system power outage caused by equipment failure, the load reduction caused by maintenance work cannot be ignored. The current risk assessment schemes rarely take into account the impact of component shutdown on system operation during the system maintenance process.

[0004] Therefore, in response to the above problems, it is urgent to find a risk assessment method for active distribution networks that takes into account source-load response and equipment maintenance, so as to accurately provide system reliability assessment results and risk analysis results. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an active distribution network risk assessment method and system in view of the deficiencies in the above-mentioned prior art, which is used to solve the technical problem that the active distribution network risk assessment does not take into account the impact of new energy source-load response interaction and equipment maintenance on system risk in the context of large-scale access of new energy and a large increase in maintenance demand.

[0006] The present invention adopts the following technical solutions:

[0007] A method for risk assessment of an active distribution network comprises the following steps:

[0008] S1. Generate different maintenance conditions through state enumeration method, use distribution network reconstruction optimization model to analyze system load reduction under different maintenance conditions, and construct fault impact table;

[0009] S2, taking the day as the calculation period, combining the fault impact scale obtained in step S1, and calculating the annual equipment maintenance work plan by the annual equipment maintenance work scheduling model considering the minimization of power outage losses and component reinforcement plan offset;

[0010] S3. Divide the source-load response of the distribution network into the distributed energy storage output part and the flexible load response part on the source-load side, and establish a new source-side / load-side response model that takes into account flexible resources, energy storage output and flexible load response; combine the new source-side / load-side response model to construct a system unit combination optimization scheduling model with minimum load shedding and optimal economy as objective functions; input the annual equipment maintenance work scheduling plan results obtained in step S2, use the sequential Monte Carlo simulation method to sample the system state, and use the system unit combination optimization scheduling model to estimate the system state, obtain the system operation status data, and realize the distribution network reliability assessment and risk analysis.

[0011] Preferably, step S1 specifically includes:

[0012] S101, constructing a reasonable distribution network topology by disconnecting the tie switch and the section switch;

[0013] S102, determining the constraint conditions of the distribution network reconstruction optimization model;

[0014] S103. Based on the distribution network topology obtained in step S101 and the constraints obtained in step S102, a mixed integer programming problem is established to solve the system operation mode that maximizes the load recovery amount and the load recovery value of each node in the system under the corresponding maintenance condition, and the calculation results are organized into a fault impact table.

[0015] Preferably, in step S102, the constraint conditions include:

[0016] The operating power flow constraints are:

[0017]

[0018] Where c(ij) represents the operating state of branch ij, δ(j) represents the set of downstream branches of node j, π(j) represents the set of upstream branches of node j; N represents the set of nodes in the system; P S,j , Q S,j They represent the active power and reactive power injected by the power source connected to node j respectively; P L,j , Q L,j They represent the active and reactive power requirements of the j node load respectively; H ij , G ij Respectively represent the active and reactive power flowing through the ij branch; U i represents the voltage of load node i; r ij represents the resistance of branch ij; x ij It represents the reactance of branch ij; VR represents the rated voltage of the system;

[0019] Radial topology constraints:

[0020]

[0021] Where Br represents the set of branches in the system; N Sr represents the number of controllable power supply nodes; R represents the set of virtual power supply points of power outage islands; F jk represents the power flow on line jk, F ij represents the power flow on line ij, N source represents the set of controllable power nodes, M represents a sufficiently large constant, W j represents the virtual power provided by source node j.

[0022] Preferably, step S2 specifically includes:

[0023] S201. For a given set of equipment to be repaired, the maintenance plan is equivalent to the power outage plan of the corresponding equipment, and an annual equipment maintenance work scheduling model that considers minimizing power outage losses and component reinforcement plan offsets is constructed, and the annual maintenance plan is obtained by using the model optimization solution;

[0024] S202, determining the constraint conditions of the annual maintenance plan scheduling model;

[0025] S203. Based on the annual maintenance plan obtained in step S201 and the constraints obtained in step S202, a mixed integer programming problem is established to obtain an optimized scheduling result of the annual maintenance plan.

[0026] Preferably, in step S202, the constraint conditions are specifically:

[0027] Engineering resource constraints:

[0028]

[0029] Among them, x it Indicates the operating status of the i-th device in the t-th time period. 0 indicates normal operation, and 1 indicates component strengthening work; Ω c Represents the set of all components in the system; M t It represents the maximum number of components that the engineering company can inspect and strengthen simultaneously in the tth time period;

[0030] At the same time constraints:

[0031] x it =x jt

[0032] Among them, x it and x jt are the working states of the i-th and j-th components in the t-th time period respectively;

[0033] Mutually exclusive constraints:

[0034]

[0035] Where n is the total number of devices whose power outage relationship is mutually exclusive;

[0036] Deterministic power outage constraints:

[0037] x iT =1 or x iT =0

[0038] Among them, x iT Indicates the maintenance status of the i-th equipment in the T-th time period;

[0039] Power outage continuity constraints:

[0040] x it =1,t∈[t i,1 ,t i,2 ]

[0041] Minimum power outage interval constraints:

[0042] t i ' , ′1-t i,2 ≥B i

[0043] Among them, t i,2 Indicates the end time of the power outage plan for component i, t" i,1 Indicates the start time of the next power outage plan for component i, B i Indicates the minimum interval between two power outages of component i.

[0044] Preferably, step S3 is specifically:

[0045] S301, constructing a new source-side response model;

[0046] S302, construct a new load-side response model, divide the load into basic load and flexible load, where the flexible load is divided into reducible load, transferable load and shiftable load; model each load and realize the load demand response, so as to achieve dynamic balance at the load end;

[0047] S303, for each time-sequentially sampled system state, a production simulation is performed, with the goal of achieving the optimal economic performance of the unit combination used, and a system unit combination optimization scheduling model is established based on the new source-side response model obtained in step S301, the new load-side response model obtained in step S302, and the power balance constraint;

[0048] S304, construct a distribution system reliability assessment model, use the sequential Monte Carlo simulation method to perform sequential time sampling on each component, simulate the system state transfer process, and use the system unit combination optimization scheduling model in step S303 to calculate the system state data obtained by each sampling, so as to realize the production simulation of the system;

[0049] S305, based on the power system status data obtained by the production simulation in step S304, using statistical methods to calculate the values ​​of the relevant reliability indicators;

[0050] S306: Establish a risk assessment system for the power distribution system, and perform risk level analysis based on the system reliability index results obtained in step S305 and the system operation status data results of different time periods calculated in step 304.

[0051] Preferably, in step S301, the new source-side response model includes:

[0052] Wind turbine model:

[0053]

[0054] in, is the actual output of the wind turbine at time t; is the predicted output of the wind turbine at time t;

[0055] Photovoltaic unit model:

[0056]

[0057] in, is the actual output of the photovoltaic unit at time t; is the predicted output of the photovoltaic unit at time t;

[0058] Energy storage model

[0059] E ES,min ≤E ES,t ≤E ES,max

[0060]

[0061] Among them, E ES,t E is the capacity of the energy storage battery at time t; ES,max 、E ES,min P is the upper and lower limits of the energy storage battery capacity; ESc,min , P ESdc,min P is the lower limit of charging and discharging power of the energy storage battery; ESc,max , P ESdc,max P is the upper limit of charging and discharging power of the energy storage battery; ESc,t , P ESdc,tThe charging and discharging power of the energy storage battery at time t;

[0062] SOC state of the energy storage battery at time t t for:

[0063]

[0064] Among them, λ ES is the self-discharge rate of the energy storage battery; η ESc It is the charging efficiency of the energy storage battery.

[0065] Preferably, in step S302, the total load P of the system at time t t load for:

[0066] P t load =P t f +P t re +P t sh +P t di

[0067] Among them, P t f is the basic load at time t; P t re is the load that can be reduced at time t; P t sh is the translatable load at time t; P t di is the transferable load at time t.

[0068] Preferably, in step S304, the objective function is:

[0069] min[cost net,t +cost new,t +cost ES,t -cost pv,t +cost curt,t ]

[0070] Among them, cost net,t Cost is the cost of purchasing electricity from the main grid. new,t Cost is the electricity purchase cost of new energy units. ES,t is the total operating cost of the energy storage unit, cost pv,t is the distributed photovoltaic consumption incentive item, cost curt,t Penalty costs for load shedding;

[0071] The constraints include:

[0072] Power balance constraints:

[0073]

[0074] Among them, P new,i,t P represents the output of the i-th renewable energy unit at time t; ES,i,t To represent the output of the i-th energy storage unit at time t, charging is negative and discharging is positive, P ES,i,t =P ESdc,i,t -P ESc,i,t ;P t represents the total load demand at time t;

[0075] Unit output upper and lower limit constraints:

[0076]

[0077] in, are the outputs of the i-th wind turbine and photovoltaic generator at time t; P i wmax , P i pvmax are the maximum outputs of the i-th wind turbine and photovoltaic generator respectively;

[0078] Energy storage charging and discharging power and SOC state constraints:

[0079]

[0080] Among them: U ES,i,t is the energy storage state of the i-th energy storage unit at time t; SOC i,t is the SOC state of the i-th energy storage unit at time t;

[0081] Line flow constraints:

[0082] P l,min ≤P l,t ≤P l,max

[0083] Where: P l,t is the power of line l at time t; P l,max , P l,min is the bidirectional power flow constraint of line l.

[0084] In a second aspect, an embodiment of the present invention provides an active distribution network risk assessment system, including:

[0085] The working condition module generates different maintenance working conditions through the state enumeration method, uses the distribution network reconstruction optimization model to analyze the system load reduction under different maintenance working conditions, and constructs a fault impact table;

[0086] The calculation module uses the day as the calculation period, combines the fault impact table, and considers the annual equipment maintenance work scheduling model that minimizes power outage losses and component reinforcement plan offsets to calculate the annual equipment maintenance work plan;

[0087] The evaluation module divides the source-load response of the distribution network into the distributed energy storage output part and the flexible load response part on the source-load side, and establishes a new source-side / load-side response model that takes into account flexible resources, energy storage output and flexible load response; combines the new source-side / load-side response model to construct a system unit combination optimization scheduling model with minimum load shedding and optimal economy as objective functions; inputs the annual equipment maintenance work scheduling plan results, uses the sequential Monte Carlo simulation method to sample the system state, and uses the system unit combination optimization scheduling model to estimate the system state, obtain the system operation status data, and realize distribution network reliability evaluation and risk analysis.

[0088] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned active distribution network risk assessment method when executing the computer program.

[0089] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which implements the steps of the above-mentioned active distribution network risk assessment method when executed by a processor.

[0090] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned active distribution network risk assessment method when executing the computer program.

[0091] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which implements the steps of the above-mentioned active distribution network risk assessment method when executed by the electronic device.

[0092] Compared with the prior art, the present invention has at least the following beneficial effects:

[0093] A risk assessment method for an active distribution network, firstly, realizes the optimized scheduling of annual equipment maintenance work by constructing an annual equipment maintenance work scheduling model, secondly, realizes the source-load response interaction of the distribution network by constructing a new source-side response model and a new load-side response model, and finally, constructs an active distribution network risk assessment model considering the interaction between equipment maintenance and system source-load response, and realizes the risk assessment of the active distribution network by performing production simulation on the active distribution network under different sampling states; the method proposed by the present invention can better adapt to the reliability analysis application under the background of increasing demand for distribution network maintenance and large-scale access to new energy, and improves the accuracy and effectiveness of risk assessment.

[0094] Furthermore, step S1 can solve the operating mode that maximizes system load recovery under different operating conditions by constructing a distribution network reconstruction optimization model. Through state enumeration, the fault impact scale under different maintenance conditions of the corresponding active distribution network can be obtained, and used for the calculation of annual power outage losses in the subsequent annual equipment maintenance plan.

[0095] Furthermore, step S102 uses the Distflow model and the single commodity flow model to construct the power flow constraints and radial topology constraints of the active distribution network, respectively, and realizes the power flow constraints of the active distribution network on the basis of ensuring that the distribution network meets the principle of "closed-loop design, open-loop operation".

[0096] Furthermore, step S2 equates the equipment maintenance plan to the equipment power outage plan at the operation level, and constructs an annual maintenance plan scheduling model to solve the annual equipment maintenance plan that minimizes the power outage loss and the component maintenance plan offset, thereby improving the reliability and economy of the maintenance plan and providing an annual equipment maintenance plan for subsequent active distribution network risk assessment.

[0097] Furthermore, step S202 provides constraints in the actual maintenance work process, such as mutual exclusion constraints and maintenance resource constraints, so that the scheduling of maintenance work is more in line with the needs of actual engineering management, and the practicality of the maintenance plan compiled by the present invention is improved.

[0098] Furthermore, step S3 constructs an active distribution network reliability assessment model that takes into account source-load response and equipment maintenance. By sampling the system status in a time series, the production simulation of the active distribution network is realized, and reliability assessment and system risk analysis are performed based on the system status data, which provides a basis for risk discovery and early warning of the active distribution network and ensures the safe and stable operation of the active distribution network.

[0099] Furthermore, step S301 models the system power supply side under the background of large-scale access to new energy, taking into full account the uncertainty of wind power and photovoltaic output and the smoothing effect of energy storage on new energy output.

[0100] Furthermore, step S302 models the system load side in the context of user participation in demand response, and by classifying the loads according to different load characteristics, fully considers the demand response effect of flexible loads under the incentive of electricity prices.

[0101] Furthermore, step S303 constructs a system unit combination optimization scheduling model, and obtains system operation status data under different sampling states by solving the economically optimal unit output, providing a data basis for system reliability evaluation and risk analysis.

[0102] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0103] In summary, the present invention effectively reduces the impact of the distribution network on system reliability, thereby ensuring the normal operation of the power system; realizes the efficient operation of the active distribution network, improves the economy of the distribution system operation, and enhances the accuracy and effectiveness of risk assessment.

[0104] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments.

[0105] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0106] Figure 1 Prepare a flow chart for the distribution system grid element reinforcement plan;

[0107] Figure 2 It is a flow chart of the reliability assessment method based on impact increment;

[0108] Figure 3 This is the schematic diagram of the improved IEEE 33-node distribution network;

[0109] Figure 4 is a schematic diagram of the changes in system reliability indicators under different planning strategies, where (a) is the power balance of a typical day in spring, (b) is the power balance of a typical day in summer, (c) is the power balance of a typical day in autumn, and (d) is the power balance of a typical day in winter;

[0110] Figure 5 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0111] Figure 6 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0112] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0113] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0114] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0115] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0116] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0117] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0118] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0119] The present invention provides a risk assessment method for an active distribution network. Firstly, a safe and efficient maintenance plan is generated by constructing an annual equipment maintenance work scheduling model, which effectively reduces the impact of the distribution network on the system reliability, thereby ensuring the normal operation of the power system; by constructing a new source-side response model and a new load-side response model, the source-load response interaction of the distribution network is realized, the efficient operation of the active distribution network is achieved, and the economy of the distribution system operation is improved; finally, an active distribution network risk assessment model considering the interaction between equipment maintenance and system source-load response is constructed, the reliability assessment and risk warning analysis of the active distribution network are realized, and it fully adapts to the development background of the increasing demand for distribution network maintenance and the large-scale access of new energy, thereby improving the accuracy and effectiveness of risk assessment.

[0120] See also Figure 1 The present invention provides an active distribution network risk assessment method, comprising the following steps:

[0121] S1. Generate different maintenance conditions through state enumeration method, use distribution network reconstruction optimization model to analyze system load reduction under different maintenance conditions, and construct fault impact table;

[0122] S101. Considering the complex structure of the distribution network and the presence of a large number of switches on the line, a reasonable distribution network topology can be constructed by disconnecting the tie switch and the section switch, thereby realizing load transfer and reducing the scope of power outage;

[0123] The present invention adopts the distribution network reconstruction optimization model to analyze the load reduction of the system under different maintenance conditions. The objective function of the model is:

[0124]

[0125] Among them, P L,j is the load recovery amount of the jth node, ω j represents the weight of the jth node load point, and N represents the set of all load points in the system.

[0126] S102. The constraints considered in the distribution network reconstruction optimization model are as follows:

[0127] (1) Flow constraint: According to the Distflow model, the operating flow constraint is expressed as

[0128]

[0129] Where c(ij) represents the operating state of branch ij, 0 represents the branch is out of operation, 1 represents the branch is in operation, δ(j) represents the set of downstream branches of node j, π(j) represents the set of upstream branches of node j; N represents the set of nodes in the system; P S,j , Q S,j They represent the active power and reactive power injected by the power source connected to node j respectively; P L,j , Q L,j They represent the active and reactive power requirements of the j node load respectively; H ij , G ij Respectively represent the active and reactive power flowing through the ij branch; U i represents the voltage of load node i; r ij represents the resistance of branch ij; x ij Represents the reactance of branch ij; VR represents the rated voltage of the system.

[0130] (2) Radial topology constraint: To meet the radial topology constraint of the distribution network, the single commodity flow method is adopted:

[0131]

[0132] Where Br represents the set of branches in the system; N Sr represents the set of controllable power supply nodes; R represents the set of virtual power supply points of power outage islands; F jk represents the power flow on line jk. Wherein, formula (6) indicates that the number of branches in operation in the system is equal to the number of nodes minus the number of given islands (subgraphs), and formula (7) indicates that each island (subgraph) is a connected graph.

[0133] S103, using the commercial solver Gurobi to solve the mixed integer programming (MIP) problem established in step S101 and step S102, and obtain the system operation mode that maximizes the load recovery amount and the load recovery value of each node in the system under the corresponding maintenance condition. And organize the calculation results into a fault impact table.

[0134] S2. During the equipment maintenance process, it is necessary to shut down the relevant equipment in operation and arrange a reasonable power outage window; therefore, for the operation and dispatching of the distribution network, the equipment maintenance work scheduling problem is equivalent to the power outage plan scheduling problem; generally speaking, the goals of the equipment maintenance plan scheduling problem mainly include minimizing the offset of the power outage plan, evenly distributing the power outage plan, and minimizing the economic cost of the power outage; therefore, the present invention constructs an annual equipment maintenance work scheduling model that takes days as the calculation period, combines step S1 to obtain the fault impact table, and considers minimizing the power outage loss and the offset of the component reinforcement plan;

[0135] S201. For a given set of equipment to be repaired, the maintenance plan is equivalent to the power outage plan of the corresponding equipment, and an annual equipment maintenance work scheduling model that considers minimizing power outage losses and component reinforcement plan offsets is constructed, and the model is used to optimize and solve to obtain the annual maintenance plan;

[0136] The objective function of the model is:

[0137] min F=α·ΔP t ·Δt (6)

[0138] Among them, α represents the cost coefficient of power outage loss. Power outage in the power grid will cause load loss and power outage loss. Assuming that within the maintenance cycle T, the load reduction on the tth day is ΔP t , the power outage duration is Δt.

[0139] S202. Constructing constraints considered in the annual maintenance plan scheduling model;

[0140] The constraints include:

[0141] (1) Engineering resource constraints: Engineering resources refer to the number of engineering personnel, their technical capabilities, and equipment capabilities. Due to limited resources, the number of operations that can be performed simultaneously is limited.

[0142]

[0143] Among them, x it Indicates the operating status of the i-th device in the t-th time period. 0 indicates normal operation, and 1 indicates component strengthening work; Ω c Represents the set of all components in the system; M t It represents the maximum number of components that the engineering company can simultaneously overhaul and strengthen in the tth time period.

[0144] (2) Simultaneous constraints: In a system, problems that can be solved by a single power outage need to be solved comprehensively. Repeated power outages are not allowed to occur due to inconsideration; therefore, some components must be powered off at the same time.

[0145] x it =xjt (8)

[0146] Among them, x it and x jt are the working states of the i-th and j-th components in the t-th time period respectively.

[0147] (3) Mutually exclusive constraints: To reduce the number of load points that require power outages during equipment maintenance work, some equipment cannot be powered off at the same time, such as parallel components.

[0148]

[0149] Wherein, n is the total number of devices whose power outage relationship is mutually exclusive.

[0150] (4) Deterministic power outage constraints: The working date of some equipment maintenance work is fixed, and the power outage time cannot be changed;

[0151] For example, the power outage plan formulated by the superior dispatching department, the power outage plan that extends from the previous time period to this year, etc.

[0152] x iT =1 or x iT =0(10)

[0153] Among them, x iT Indicates the maintenance status of the i-th equipment in the T-th time period. In particular, maintenance is generally not arranged during peak power consumption periods, major events, holidays, etc., which can also be classified into this type of constraint

[0154] (5) Power outage continuity constraint: To ensure the stability of distribution network operation, once a power outage plan is implemented, the power outage should be maintained until the equipment maintenance work is completed. It is not allowed to be divided into multiple sections.

[0155] x it =1, t∈[t i,1 ,t i,2 ] (11)

[0156] (6) Minimum power outage interval constraint: In order to make the distribution of power outage plans uniform and reasonable, and to avoid repeated power outages that affect the reliability of the distribution network, the time between two power outages must meet the minimum interval time requirement.

[0157] t i ' , ′1-t i,2 ≥B i (12)

[0158] Among them, t i,2 Indicates the end time of the power outage plan for component i, t" i,1 Indicates the start time of the next power outage plan for component i, Bi Indicates the minimum interval between two power outages of component i.

[0159] S203. Use the commercial solver Gurobi to solve the mixed integer programming (MIP) problem established in steps S201 and S202 to obtain the optimization scheduling result of the annual maintenance plan.

[0160] S3. Construct a risk assessment method for active distribution networks that considers source-load response and equipment maintenance: First, divide the source-load response of the distribution network into the distributed energy storage output part on the source-load side and the flexible load response part, and establish a new source-side / load-side response model that considers flexible resources, energy storage output and flexible load response; secondly, combine the new source-side / load-side response model to construct a system unit combination optimization scheduling model with minimum load shedding and optimal economy as objective functions; input the annual equipment maintenance work scheduling plan results obtained in step S2; finally, use the sequential Monte Carlo simulation method to sample the system state, and use the system unit combination optimization scheduling model to estimate the system state, obtain the system operation status data, and use statistical methods to conduct distribution network reliability assessment and risk analysis.

[0161] S301. Construct a new source-side response model. The model aims to realize the flexible power generation regulation capability of new energy units and the response adjustment of user loads. It achieves dynamic balance at the load end through demand response technology to ensure stable operation of the power system.

[0162] (1) Wind turbine model

[0163] The wind power output uncertainty model is composed of wind power forecast output and random error. The actual wind power output is represented by the forecast wind power output plus a random fluctuation error. Assuming that the output error fluctuates by 30% above and below the forecast value and conforms to the normal distribution, its expression is as follows:

[0164]

[0165] in, is the actual output of the wind turbine at time t; is the predicted output of the wind turbine at time t.

[0166] (2) Photovoltaic unit model

[0167] The photovoltaic power generation output uncertainty model is composed of the photovoltaic power generation forecast output and random error. The actual photovoltaic power generation output is represented by the predicted photovoltaic output plus a random fluctuation error. Assuming that the output error fluctuates by 30% around the predicted value and conforms to the normal distribution, its expression is shown in formula (16):

[0168]

[0169] in, is the actual output of the photovoltaic unit at time t; is the predicted output of the photovoltaic unit at time t.

[0170] (3) Energy storage model

[0171] Energy storage is a smaller-scale but faster-responding power source that is not restricted by geographical location. It can make the output of the power generation side more balanced and smooth the output of new energy. It can also make the power consumption on the load side more reliable, thereby improving the flexibility of the distribution network system.

[0172] Energy storage batteries need to meet capacity constraints and charge and discharge constraints, which can be expressed as:

[0173] E ES,min ≤E ES,t ≤E ES,max (15)

[0174]

[0175] Among them, E ES,t is the capacity of the energy storage battery at time t (kWh); E ES,max 、E ES,min P is the upper and lower limits of the energy storage battery capacity (kWh); ESc,min , P ESdc,min P is the lower limit of charging and discharging power of energy storage battery (kW); ESc,max , P ESdc,max P is the upper limit of the charging and discharging power of the energy storage battery (kW); ESc,t , P ESdc,t is the charging and discharging power (kW) of the energy storage battery at time t. In addition, the energy storage battery also needs to meet the SOC state constraint, and the cycle power balance within a cycle can be expressed as:

[0176]

[0177] Among them, SOC t is the SOC state of the energy storage battery at time t, ranging from [0, 1]; λ ES is the self-discharge rate of the energy storage battery; η ESc It is the charging efficiency of the energy storage battery.

[0178] S302. Construct a new load-side response model. For load-side resources, actively guide users to participate in demand response and optimize the load curve. Therefore, the load can be divided into basic load and flexible load. The flexible load can be divided into curtailable load, transferable load and shiftable load. The system load can be expressed as:

[0179] P tload =P t f +P t re +P t sh +P t di (19)

[0180] Among them, P t load is the total load at time t; P t f is the basic load at time t; P t re is the load that can be reduced at time t; P t sh is the translatable load at time t; P t di is the transferable load at time t.

[0181] (1) Shiftable load refers to the load in the power grid that is affected by the compensation price reduction, which is shifted to the appropriate time for production to achieve the effect of peak shaving and valley filling and cost reduction and efficiency improvement. The original load start time of the shifted load is t sh , the duration is T sh , using the state variable τ sh represents the transfer load, and its value is [0,1], so the translation load can be expressed as:

[0182]

[0183] Among them, τ sh is the state of transfer at a certain moment, taking the value [0,1]; P t sh is the transferable load power at time t; The upper and lower limits of the transferable load power; is the dispatch cost of the total transferable load at time t; ΔC t It is the cost caused by the incentive at time t and the time-of-use electricity price difference.

[0184] Considering the inconvenience caused by frequent start and stop during the transfer process to the unit or its use, the minimum operating time constraint is imposed on the transferable load, which can be expressed as:

[0185]

[0186] in, is the minimum continuous transfer time.

[0187] (2) Load reduction

[0188] The curtailable load refers to the power load in the power grid that is affected by the compensation electricity price. The curtailable load model can be expressed as:

[0189]

[0190] Among them, ΔP t re is the actual amount of load reduction that can be reduced at time t; is the excitation elastic coefficient of the load that can be reduced, and its value is related; is the compensation amount for load reduction at time t; is the dispatching cost of load reduction at time t; t Whether time t is a reducible interval, the range is [0,1]; N max The maximum number of times the load can be reduced.

[0191] The reducible load excitation elastic coefficient is expressed as:

[0192]

[0193] in, and Time-sharing load segment when load reduction is performed for users; is the incentive price sequence that can reduce load, The smaller the value, the less sensitive the user is to the incentive electricity price; max and λ min Minimum and maximum sensitivity index for load reduction; N peak is the peak load length; To compensate for the sequence of high and low electricity prices; The load excitation elastic coefficient can be reduced, and

[0194] S303, construct a reliability assessment model for the power distribution system, use the sequential Monte Carlo simulation method to perform sequential time sampling on each component, and then simulate the system state transition process, and finally use a statistical method to calculate the system reliability index. Monte Carlo simulation is a commonly used production simulation method and will not be elaborated here.

[0195] S304, performing production simulation for each time-sequentially sampled system state;

[0196] Among them, unit combination is an important step in optimizing the dispatching and operation arrangement of the power system. It achieves the purpose of optimal economy by reasonably arranging the start and stop status and output plan of each unit at different times on the basis of meeting the load demand and other constraints. The present invention aims to achieve the optimal economy by using the unit combination, and establishes a model considering constraints such as power balance.

[0197] (1) Objective function

[0198] Assuming that there are l energy storage units and k new energy units (k1 wind turbine units and k2 photovoltaic units) in the system, the economic optimization is taken as the objective function, while considering load shedding, main grid electricity purchase cost and distributed photovoltaic incentive items. The formula is as follows:

[0199] min[cost net,t +cost new,t +cost ES,t -cost pv,t +cost curt,t ](28)

[0200] The objective function includes five items. The first item is the cost of purchasing electricity from the main grid, the second item is the cost of purchasing electricity from new energy units, the third item is the total operating cost of energy storage units, the fourth item is the distributed photovoltaic consumption reward item, and the fifth item is the load shedding penalty cost.

[0201]

[0202] Among them, P net,i,t represents the output of the main network at time t; C net,i Indicates the operating cost under the unit output of the main grid.

[0203]

[0204] Among them, P new,i,t represents the output of the i-th renewable energy unit at time t; C new,i It represents the operating cost of the i-th renewable energy unit under unit output.

[0205]

[0206] Among them, C ESc,i , C ESdc,i Respectively represent the cost per unit output when the i-th energy storage unit is charged and discharged; P ESc,i,t , P ESdc,i,t They respectively represent the charging and discharging output of the i-th energy storage unit at time t.

[0207]

[0208] Among them, C PvS P represents the unit economic reward for absorbing photovoltaic power; v S t Represents the amount of photovoltaic power consumed at time t.

[0209]

[0210] Among them, C LSIndicates the economic loss when cutting unit load; LS t represents the load shedding at time t.

[0211] (2) Constraints:

[0212] 1) Power balance constraints:

[0213]

[0214] Among them, P new,i,t P represents the output of the i-th renewable energy unit at time t; ES,i,t To represent the output of the i-th energy storage unit at time t, charging is negative and discharging is positive, P ES,i,t =P ESdc,i,t -P ESc,i,t ;P t represents the total load demand at time t.

[0215] 2) Upper and lower limits of unit output (new energy):

[0216]

[0217] in, are the outputs of the i-th wind turbine and photovoltaic generator at time t; P i wmax , P i pvmax are the maximum outputs of the i-th wind turbine and photovoltaic generator respectively.

[0218] 3) Energy storage charging and discharging power and SOC state constraints:

[0219]

[0220] Among them: U ES,i,t is the energy storage state of the i-th energy storage unit at time t, 0 represents charging and 1 represents discharging; SOC i,t is the SOC state of the i-th energy storage unit at time t.

[0221] 4) Line flow constraints:

[0222] P l,min ≤P l,t ≤P l,max (39)

[0223] Where: P l,t is the power of line l at time t; P l,max , P l,min is the bidirectional power flow constraint of line l.

[0224] S305, based on the power system status data obtained by the production simulation, using statistical methods to calculate the values ​​of the relevant reliability indicators;

[0225] Reliability indicators are mainly used to describe the probability of load shedding, power, electricity, time, etc. of the power system during the production simulation cycle. The main ones are lack of power probability (LOLP), expected power shortage (LOLE), expected energy shortage (EENS), and system average outage power (MOP). System risk analysis can be carried out based on these reliability indicators.

[0226] S306. Establish a risk assessment system for the power distribution system and conduct risk level analysis based on system reliability indicators and system operating status data in different time periods.

[0227] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0228] In yet another embodiment of the present invention, an active distribution network risk assessment system is provided, which can be used to implement the above-mentioned active distribution network risk assessment method. Specifically, the active distribution network risk assessment system includes a working condition module, a calculation module and an assessment module.

[0229] Among them, the working condition module generates different maintenance working conditions through the state enumeration method, uses the distribution network reconstruction optimization model to analyze the system load reduction under different maintenance working conditions, and constructs the fault impact table;

[0230] The calculation module uses the day as the calculation period, combines the fault impact table, and considers the annual equipment maintenance work scheduling model that minimizes power outage losses and component reinforcement plan offsets to calculate the annual equipment maintenance work plan;

[0231] The evaluation module divides the source-load response of the distribution network into the distributed energy storage output part and the flexible load response part on the source-load side, and establishes a new source-side / load-side response model that takes into account flexible resources, energy storage output and flexible load response; combines the new source-side / load-side response model to construct a system unit combination optimization scheduling model with minimum load shedding and optimal economy as the objective function; inputs the annual equipment maintenance work scheduling plan results, uses the sequential Monte Carlo simulation method to sample the system state, and uses the system unit combination optimization scheduling model to estimate the system state, obtains the system operation status data, and uses statistical methods to conduct distribution network reliability evaluation and risk analysis.

[0232] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, graphics processors (GPU), tensor processors (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the active distribution network risk assessment method, including:

[0233] Different maintenance conditions are generated by the state enumeration method, and the distribution network reconstruction optimization model is used to analyze the system load reduction under different maintenance conditions, and a fault impact scale is constructed; the annual equipment maintenance work plan is calculated by taking the day as the calculation period and combining the obtained fault impact scale with the annual equipment maintenance work scheduling model that considers minimizing the power outage loss and the component reinforcement plan offset; the source-load response of the distribution network is divided into the distributed energy storage output part and the flexible load response part on the source-load side, and a new source-side / load-side response model considering flexible resources, energy storage output and flexible load response is established; the system unit combination optimization scheduling model with minimum load shedding and optimal economy as the objective function is constructed in combination with the new source-side / load-side response model; the annual equipment maintenance work scheduling plan result is input, the system state sampling is performed using the sequential Monte Carlo simulation method, and the system unit combination optimization scheduling model is used to estimate the system state to obtain the system operation state data, and the distribution network reliability assessment and risk analysis are performed using statistical methods.

[0234] See also Figure 5, the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the active distribution network risk assessment method in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the active distribution network risk assessment system in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0235] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0236] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0237] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0238] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0239] See also Figure 6 The terminal device is an electronic device 600, which is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0240] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0241] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0242] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0243] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0244] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0245] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0246] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the active distribution network risk assessment method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows:

[0247] Different maintenance conditions are generated by the state enumeration method, and the distribution network reconstruction optimization model is used to analyze the system load reduction under different maintenance conditions, and a fault impact scale is constructed; the annual equipment maintenance work plan is calculated by taking the day as the calculation period and combining the obtained fault impact scale with the annual equipment maintenance work scheduling model that considers minimizing the power outage loss and the component reinforcement plan offset; the source-load response of the distribution network is divided into the distributed energy storage output part and the flexible load response part on the source-load side, and a new source-side / load-side response model considering flexible resources, energy storage output and flexible load response is established; the system unit combination optimization scheduling model with minimum load shedding and optimal economy as the objective function is constructed in combination with the new source-side / load-side response model; the annual equipment maintenance work scheduling plan result is input, the system state sampling is performed using the sequential Monte Carlo simulation method, and the system unit combination optimization scheduling model is used to estimate the system state to obtain the system operation state data, and the distribution network reliability assessment and risk analysis are performed using statistical methods.

[0248] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0249] According to the "Regulations on Emergency Response and Investigation of Power Safety Accidents" and the definition method of important users, combined with the system operation status data, the risk level of the distribution network system can be analyzed. According to the load loss caused by the potential power outage, the system risk is divided into the following security event levels:

[0250] Table 1 Distribution network risk level classification method

[0251]

[0252]

[0253] In order to test the effectiveness of the method of the present invention, three hypotheses are proposed:

[0254] Taking the improved IEEE69 node distribution network system as an example, the reliability assessment method of the active distribution network considering source-load response and equipment maintenance proposed in the present invention is verified; the system topology is as follows Figure 2 shown.

[0255] The capacities of wind power, photovoltaic power and energy storage in distributed resources are set to 0.5MW, 2MW and 0.4MW respectively. The load and new energy output in this paper are selected from the time series forecast data of a certain area of ​​my country's power grid in 2025; the time-of-use electricity price data is shown in Table 2:

[0256] Table 2 Time-of-use electricity price data

[0257]

[0258] 1) Impact of source-load response on system reliability

[0259] In order to study the impact of maintenance work and source-load response characteristics on the reliability of the distribution system, four different distribution system operation scenarios are set:

[0260] Scenario 1 and Scenario 2 are random scheduling of maintenance plan and optimized scheduling of maintenance plan under passive load response;

[0261] Scenarios 3 and 4 show the indicators under active load response.

[0262] The reliability indicators are shown in Table 3:

[0263] Table 3 Comparison of system reliability indicators

[0264]

[0265]

[0266] (1) Analysis of flexible load response

[0267] Table 3 shows the reliability indicators of the system under different scenarios. Taking the optimization scheme as an example, it is found that the system reliability indicators under the condition of considering source-load response are significantly better than those under the condition of not considering source-load response. EENS is reduced by 1.810% and MOP is also reduced by 1.812%. This shows that source-load response can effectively improve the problem of insufficient power in the system and reduce the loss of system load.

[0268] See also Figure 3 , showing the daily maximum load and daily minimum load of the system in a simulated year. It can be found that after considering the flexible load, the high load of the system is lower than when there is no flexible load; the low load of the system is higher than when there is no flexible load, that is, during the peak load period of the power grid, incentive policies are used to encourage large industrial users to transfer their electricity consumption, thereby optimizing electricity use and making the overall power consumption of the system smoother. To a certain extent, load peaks are reduced, the occurrence of power outages is reduced, the impact range of power outages is reduced, and the reliability of the distribution system is improved.

[0269] (2) Analysis of the output of both sides of the source and load

[0270] In order to analyze the impact of flexible load and energy storage on system operation, the present invention comprehensively considers factors such as electricity purchase from the main grid, distributed renewable energy power generation, energy storage output and flexible load response. Figure 4 shows the source-load response output on typical days in the four seasons of spring, summer, autumn and winter in the form of a stacked curve chart.

[0271] From the typical day source-load response diagram, it can be seen that in terms of load demand, due to the incentive of electricity price policy, the electricity consumption behavior of flexible load users has shifted. During the peak electricity consumption period in the day, the user load demand is reduced, and during the low-peak electricity consumption period at night, the user load demand increases, which plays a role in peak load reduction and valley filling, which is conducive to improving the reliability of the distribution system.

[0272] In terms of power output, the main daily output comes from purchasing electricity from the main grid. During the day, the photovoltaic output increases significantly, making the net load curve duckbill-shaped, reducing the pressure on the main grid and improving economic benefits.

[0273] In terms of energy storage output, during the early morning when electricity consumption is low or during the day when new energy is generated frequently, energy storage will purchase low-priced electricity from the main grid or absorb new energy electricity to charge the energy storage. During the evening peak period, energy storage acts as a flexible resource and discharges, which can effectively alleviate the system's power restrictions and load shedding during peak electricity consumption.

[0274] The source-load response of the system varies in different seasons: in spring and autumn, available wind power resources are relatively abundant, which can reduce some of the power purchase needs on the main grid side and meet the load demand without the use of energy storage devices; in summer, photovoltaic resources are sufficient, and the net load curve presents an obvious duckbill shape, and energy storage is needed to increase the proportion of new energy consumption. In winter, photovoltaic sources are scarce, the main grid power supply pressure is high, and the peak-to-valley difference in load demand is greater than in other seasons. Therefore, energy storage devices are needed to adjust the peak-to-valley difference in load.

[0275] Therefore, the source-load response interaction can realize the efficient operation of the power system; the combination of energy storage facilities on the source side and flexible load response on the load side improves the economic efficiency of system operation while improving the reliability of distribution system operation.

[0276] 2) Impact of equipment maintenance on system reliability

[0277] In order to verify the optimization effect of the maintenance scheduling model, the system reliability under two maintenance strategies, random maintenance scheduling and optimized maintenance scheduling, is analyzed:

[0278] By comparing Scenario 1 with Scenario 2, and Scenario 3 with Scenario 4 in Table 3, it can be found that regardless of whether the source-load response is considered, the EENS and MOP indicators of the system that performs maintenance work according to the maintenance optimization schedule are better.

[0279] For example, compared with scenario 1, the ENNS of scenario 2 is reduced by about 15.435%, and the MOP is reduced by about 15.433%; compared with scenario 3, the EENS of scenario 4 is reduced by 15.022%, and the MOP is reduced by about 15.025%.

[0280] It shows that the optimized maintenance plan can reduce the impact of maintenance outages on the reliable and stable operation of the system, verifying the effectiveness of the distribution network maintenance strategy optimization model proposed in this paper.

[0281] In summary, the present invention provides an active distribution network risk assessment method and system, which has the following characteristics:

[0282] (1) Optimizing equipment maintenance scheduling: By building an annual equipment maintenance scheduling model, the equipment maintenance work is optimized and the utilization rate and efficiency of maintenance resources are effectively improved.

[0283] (2) Promoting source-load interaction: By constructing a new source-side response model and a load-side response model, the interactivity between the source side and the load side of the distribution network is enhanced, and the operational flexibility of the distribution network is improved.

[0284] (3) Improving the accuracy and effectiveness of risk assessment: By comprehensively considering the interaction between equipment maintenance and system source-load response, an active distribution network risk assessment model is proposed, which can more accurately assess the risk level of the distribution network under complex operating conditions and ensure the reliability and safety of the system.

[0285] (4) Adapting to the needs of new energy access and maintenance: The method of the present invention can effectively cope with the challenges of distribution networks in the context of large-scale access to new energy and increasing maintenance needs.

[0286] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0287] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0288] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0289] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0290] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0291] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0292] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0293] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or 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, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0294] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0295] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0296] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for risk assessment of an active distribution network, characterized in that: The following steps are involved: S1. Generate different maintenance conditions through state enumeration method, use distribution network reconstruction optimization model to analyze system load reduction under different maintenance conditions, and construct fault impact table; S2, taking the day as the calculation period, combining the fault impact scale obtained in step S1, and calculating the annual equipment maintenance work plan by the annual equipment maintenance work scheduling model considering the minimization of power outage losses and component reinforcement plan offset; S3. Divide the source-load response of the distribution network into the distributed energy storage output part on the source-load side and the flexible load response part, and establish a new source-side / load-side response model that takes into account flexible resources, energy storage output and flexible load response; combine the new source-side / load-side response model to construct a system unit combination optimization scheduling model with minimum load shedding and optimal economy as objective functions; input the annual equipment maintenance work scheduling plan results obtained in step S2, use the sequential Monte Carlo simulation method to sample the system state, and use the system unit combination optimization scheduling model to estimate the system state, obtain the system operation status data, and use statistical methods to conduct distribution network reliability assessment and risk analysis.

2. The active distribution network risk assessment method according to claim 1, characterized in that: Step S1 is specifically as follows: S101, constructing a reasonable distribution network topology by disconnecting the tie switch and the section switch; S102, determining the constraint conditions of the distribution network reconstruction optimization model; S103. Based on the distribution network topology obtained in step S101 and the constraints obtained in step S102, a mixed integer programming problem is established to solve the system operation mode that maximizes the load recovery amount and the load recovery value of each node in the system under the corresponding maintenance condition, and the calculation results are organized into a fault impact table.

3. The active distribution network risk assessment method according to claim 2, characterized in that: In step S102, the constraints include: The operating power flow constraints are: Where c(ij) represents the operating state of branch ij, δ(j) represents the set of downstream branches of node j, π(j) represents the set of upstream branches of node j; N represents the set of nodes in the system; P S,j , Q S,j They represent the active power and reactive power injected by the power source connected to node j respectively; P L,j , Q L,j They represent the active and reactive power requirements of the j node load respectively; H ij , G ij Respectively represent the active and reactive power flowing through the ij branch; U i represents the voltage of load node i; r ij represents the resistance of branch ij; x ij It represents the reactance of branch ij; VR represents the rated voltage of the system; Radial topology constraints: Where Br represents the set of branches in the system; N Sr represents the number of controllable power supply nodes; R represents the set of virtual power supply points of power outage islands; F jk represents the power flow on line jk, F ij represents the power flow on line ij, N source represents the set of controllable power nodes, M represents a sufficiently large constant, W j represents the virtual power provided by source node j.

4. The active distribution network risk assessment method according to claim 1, characterized in that: Step S2 is specifically as follows: S201. For a given set of equipment to be repaired, the maintenance plan is equivalent to the power outage plan of the corresponding equipment, and an annual equipment maintenance work scheduling model that considers minimizing power outage losses and component reinforcement plan offsets is constructed, and the annual maintenance plan is obtained by using the model optimization solution; S202, determining the constraint conditions of the annual maintenance plan scheduling model; S203. Based on the annual maintenance plan obtained in step S201 and the constraints obtained in step S202, a mixed integer programming problem is established to obtain an optimized scheduling result of the annual maintenance plan.

5. The active distribution network risk assessment method according to claim 1, characterized in that: In step S202, the constraint conditions are specifically: Engineering resource constraints: Among them, x it Indicates the operating status of the i-th device in the t-th time period. 0 indicates normal operation, and 1 indicates component strengthening work; Ω c Represents the set of all components in the system; M t It represents the maximum number of components that the engineering company can inspect and strengthen simultaneously in the tth time period; At the same time constraints: x it =x jt Among them, x it and x jt are the working states of the i-th and j-th components in the t-th time period respectively; Mutually exclusive constraints: Where n is the total number of devices whose power outage relationship is mutually exclusive; Deterministic power outage constraints: x iT =1 or x iT =0 Among them, x iT Indicates the maintenance status of the i-th equipment in the T-th time period; Power outage continuity constraints: x it =1,t∈[t i,1 ,t i,2 ] Minimum power outage interval constraints: t i ′ , ′1-t i,2 ≥B i Among them, t i,2 Indicates the end time of the power outage plan for component i, t" i,1 Indicates the start time of the next power outage plan for component i, B i Indicates the minimum interval between two power outages of component i.

6. The active distribution network risk assessment method according to claim 1, characterized in that: Step S3 is specifically as follows: S301, constructing a new source-side response model; S302, construct a new load-side response model, divide the load into basic load and flexible load, where the flexible load is divided into reducible load, transferable load and shiftable load; model each load and realize the load demand response, so as to achieve dynamic balance at the load end; S303, for each time-sequentially sampled system state, a production simulation is performed, with the goal of achieving the optimal economic performance of the unit combination used, and a system unit combination optimization scheduling model is established based on the new source-side response model obtained in step S301, the new load-side response model obtained in step S302, and the power balance constraint; S304, construct a distribution system reliability assessment model, use the sequential Monte Carlo simulation method to perform sequential time sampling on each component, simulate the system state transfer process, and use the system unit combination optimization scheduling model in step S303 to calculate the system state data obtained by each sampling, so as to realize the production simulation of the system; S305, based on the power system status data obtained by the production simulation in step S304, using statistical methods to calculate the values ​​of the relevant reliability indicators; S306: Establish a risk assessment system for the power distribution system, and perform risk level analysis based on the system reliability index results obtained in step S305 and the system operation status data results of different time periods calculated in step 304.

7. The active distribution network risk assessment method according to claim 6, characterized in that: In step S301, the new source-side response model includes: Wind turbine model: in, is the actual output of the wind turbine at time t; is the predicted output of the wind turbine at time t; Photovoltaic unit model: in, is the actual output of the photovoltaic unit at time t; is the predicted output of the photovoltaic unit at time t; Energy storage model AND ES,min ≤E ES,t ≤E ES,max Among them, E ES,t is the capacity of the energy storage battery at time t; E ES,max 、E ES,min P is the upper and lower limits of the energy storage battery capacity; ESc,min , P ESdc,min P is the lower limit of charging and discharging power of the energy storage battery; ESc,max , P ESdc,max P is the upper limit of charging and discharging power of the energy storage battery; ESc,t , P ESdc,t The charging and discharging power of the energy storage battery at time t; SOC state of the energy storage battery at time t t for: Among them, λ ES is the self-discharge rate of the energy storage battery; η ESc It is the charging efficiency of the energy storage battery.

8. The active distribution network risk assessment method according to claim 6, characterized in that: In step S302, the total load P of the system at time t t load for: P t load =P t f +P t re +P t sh +P t di Among them, P t f is the basic load at time t; P t re is the load that can be reduced at time t; P t sh is the translatable load at time t; P t di is the transferable load at time t.

9. The active distribution network risk assessment method according to claim 6, characterized in that: In step S304, the objective function is: min[cost net,t +cost new,t +cost ES,t -cost pv,t +cost curt,t ] Among them, cost net,t Cost is the cost of purchasing electricity from the main grid. new,t Cost is the electricity purchase cost of new energy units. ES,t is the total operating cost of the energy storage unit, cost pv,t is the distributed photovoltaic consumption incentive item, cost curt,t Penalty costs for load shedding; The constraints include: Power balance constraints: Among them, P new,i,t P represents the output of the i-th renewable energy unit at time t; ES,i,t To represent the output of the i-th energy storage unit at time t, charging is negative and discharging is positive, P ES,i,t =P ESdc,i,t -P ESc,i,t ;P t represents the total load demand at time t; Unit output upper and lower limit constraints: in, are the outputs of the i-th wind turbine and photovoltaic generator at time t; P i wmax , P i pvmax are the maximum outputs of the i-th wind turbine and photovoltaic generator respectively; Energy storage charging and discharging power and SOC state constraints: Among them: U ES,i,t is the energy storage state of the i-th energy storage unit at time t; SOC i,t is the SOC state of the i-th energy storage unit at time t; Line flow constraints: P l,min ≤P l,t ≤P l,max Where: P l,t is the power of line l at time t; P l,max , P l,min is the bidirectional power flow constraint of line l.

10. An active distribution network risk assessment system, characterized in that: include: The working condition module generates different maintenance working conditions through the state enumeration method, uses the distribution network reconstruction optimization model to analyze the system load reduction under different maintenance working conditions, and constructs a fault impact table; The calculation module uses the day as the calculation period, combines the fault impact table, and considers the annual equipment maintenance work scheduling model that minimizes power outage losses and component reinforcement plan offsets to calculate the annual equipment maintenance work plan; The evaluation module divides the source-load response of the distribution network into the distributed energy storage output part and the flexible load response part on the source-load side, and establishes a new source-side / load-side response model that takes into account flexible resources, energy storage output and flexible load response; combines the new source-side / load-side response model to construct a system unit combination optimization scheduling model with minimum load shedding and optimal economy as the objective function; inputs the annual equipment maintenance work scheduling plan results, uses the sequential Monte Carlo simulation method to sample the system state, and uses the system unit combination optimization scheduling model to estimate the system state, obtains the system operation status data, and uses statistical methods to conduct distribution network reliability evaluation and risk analysis.

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