A method and device for evaluating survivability of urban power grid considering main coordination

Abstract

The application provides a kind of urban power grid survivability evaluation method and device considering main coordination, it is related to power grid survivability evaluation technical field.The method comprises: using Monte Carlo algorithm to simulate the damaged scene of power grid;Before the occurrence of target disaster, a first main grid dispatching model and a first distribution network dispatching model are constructed, and a first evaluation index is calculated;During the occurrence of target disaster, each second distribution network dispatching model under each load node and the first distribution network return dispatching model are constructed, the corresponding net load demand is calculated, the second main grid dispatching model is constructed, and the second evaluation index is calculated;After the occurrence of target disaster, each distribution network recovery model under each load node and the second distribution network return dispatching model are constructed, the corresponding total load demand of distribution network is calculated, the third main grid dispatching model is constructed, and the third evaluation index is calculated;According to the above three evaluation indexes, the survivability of target urban power grid is evaluated.The application can improve the evaluation accuracy of urban power grid survivability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid survivability evaluation, and particularly relates to a method and device for evaluating the survivability of urban power grid considering the coordination of main grid and distribution grid. BACKGROUND

[0002] At present, in the problem of survivability risk faced by urban power grid, the related equipment and system for monitoring the risk faced by urban power grid are researched, and the detection is mainly based on node data collection. Patent CN202411479181.0 establishes the power transmission evaluation value related to voltage amplitude, power consumption and voltage loss, and constructs a monitoring system including transmission, processing, analysis and alarm unit, and realizes risk monitoring by forming evaluation value through monitoring node data. Patent CN202411178921.7 comprehensively considers the power grid operation indexes such as load rate, voltage and loss load, and the risk inducing indexes such as air temperature, precipitation and wind speed, uses the entropy weight method to confirm the correlation degree of each influence factor and forms the trust function to determine the risk monitoring value. Patent CN202411139532.3 comprehensively considers the node characteristic vector of load shedding rate, line loss sensitivity and voltage sensitivity matrix to evaluate the node power supply capacity.

[0003] In addition, in the problem of survivability of urban power grid caused by extreme disaster risk, patent CN202410960847.8 specifically analyzes the water accumulation index as the device vulnerability under the rainstorm flood disaster, combines the result vulnerability of the network frame topology, and uses the variation coefficient method and VIKOR method to evaluate and sort the node vulnerability. Patent CN202410992544.4 analyzes the system of urban power grid coupled with subway power supply network, and evaluates the vulnerability of urban power grid by analyzing the load shedding amount and subway influence coefficient under deliberate attack. Patent CN202410455872.0 considers the influence of ice disaster on power transmission system, establishes the failure probability curve of power transmission line under the combined influence of wind and ice, and evaluates the reliability of power transmission system. Patent CN202410776088.X models the urban waterlogging accumulation model under rainstorm disaster in detail, and optimizes the solution of reconfiguring the distribution network and drainage network topology based on Monte Carlo simulation method.

[0004] It can be seen that, for the survivability risk faced by urban power grid, the existing technology mainly focuses on the research of power transmission system or distribution system, often ignoring the coordination of main grid and distribution grid, especially the power reverse support of active distribution grid to main grid, and cannot evaluate the survivability of urban power grid as a whole under risk events, resulting in inaccurate evaluation of the survivability of urban power grid. SUMMARY

[0005] The present application provides a method and device for evaluating the survivability of urban power grid considering the coordination of main grid and distribution grid, to solve the problem of inaccurate risk evaluation of urban power grid in the prior art.

[0006] In a first aspect, the application provides a method for evaluating the survivability of an urban power grid in consideration of the coordination between the main grid and the distribution grid, comprising:

[0007] adopting a Monte Carlo algorithm to simulate the damage scenario of the target urban power grid under a target disaster;

[0008] before the target disaster occurs, constructing a first main grid dispatching model and a first distribution grid dispatching model, and calculating a first evaluation index;

[0009] during the target disaster, constructing a second distribution grid dispatching model of each distribution grid system under each load node in the main grid and a first distribution grid return dispatching model, calculating the net load demand of each load node, constructing a second main grid dispatching model, and calculating a second evaluation index;

[0010] after the target disaster occurs, constructing a distribution grid recovery model of each distribution grid system under each load node in the main grid and a second distribution grid return dispatching model, calculating the total load demand of the distribution grid under each load node, constructing a third main grid dispatching model, and calculating a third evaluation index;

[0011] evaluating the survivability of the target urban power grid under the target disaster according to the first evaluation index, the second evaluation index, and the third evaluation index.

[0012] In a second aspect, the application provides a device for evaluating the survivability of an urban power grid in consideration of the coordination between the main grid and the distribution grid, comprising:

[0013] an acquisition module configured to adopt a Monte Carlo algorithm to simulate the damage scenario of the target urban power grid under a target disaster;

[0014] a first calculation module configured to, before the target disaster occurs, construct a first main grid dispatching model and a first distribution grid dispatching model, and calculate a first evaluation index;

[0015] a second calculation module configured to, during the target disaster, construct a second distribution grid dispatching model of each distribution grid system under each load node in the main grid and a first distribution grid return dispatching model, calculate the net load demand of each load node, construct a second main grid dispatching model, and calculate a second evaluation index;

[0016] a third calculation module configured to, after the target disaster occurs, construct a distribution grid recovery model of each distribution grid system under each load node in the main grid and a second distribution grid return dispatching model, calculate the total load demand of the distribution grid under each load node, construct a third main grid dispatching model, and calculate a third evaluation index;

[0017] An evaluation module is configured to evaluate the survivability of the target urban power grid under the target disaster according to the first evaluation index, the second evaluation index and the third evaluation index.

[0018] The application provides a method and device for evaluating the survivability of an urban power grid considering the coordination between a main grid and a distribution grid. The Monte Carlo algorithm is used to simulate the damage scenario of the target urban power grid under the target disaster. Before the target disaster occurs, a first main grid dispatching model and a first distribution grid dispatching model are constructed to calculate a first evaluation index. During the target disaster, a second distribution grid dispatching model and a first distribution grid return dispatching model of each distribution grid system under each load node of the main grid are constructed to calculate the net load demand of each load node, and a second main grid dispatching model is constructed to calculate a second evaluation index. After the target disaster occurs, a distribution grid recovery model and a second distribution grid return dispatching model of each distribution grid system under each load node of the main grid are constructed to calculate the total load demand of the distribution grid under each load node, and a third main grid dispatching model is constructed to calculate a third evaluation index. The survivability of the target urban power grid under the target disaster is evaluated according to the first evaluation index, the second evaluation index and the third evaluation index. The application highlights the risk-averse tendency by calculating the evaluation indexes of the main grid and the distribution grid before, during and after the disaster, and improves the evaluation accuracy of the survivability of the urban power grid by coordinating the main grid and the distribution grid. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, without creative labor, can also obtain other drawings according to these drawings.

[0020] Figure 1 is the implementation flowchart of the method for evaluating the survivability of an urban power grid considering the coordination between a main grid and a distribution grid provided by the embodiments of the application;

[0021] Figure 2 is the structural schematic diagram of the device for evaluating the survivability of an urban power grid considering the coordination between a main grid and a distribution grid provided by the embodiments of the application. DETAILED DESCRIPTION

[0022] In the following description, specific details are set forth such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the application. However, it will be apparent to those skilled in the art that the application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the application with unnecessary detail.

[0023] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below with reference to the drawings.

[0024] When the urban power grid faces survival risk, the following three problems mainly exist:

[0025] First, the existing technology mainly considers the survival risk of the urban power grid in high-loss events with high probability, and the risk brought by high-loss events with small probability is not considered enough, and the survival pressure of the urban power grid brought by extreme disasters still needs to be studied.

[0026] Second, the existing technology is not comprehensive enough in the dimension of the survivability of the urban power grid, mainly focusing on reliability evaluation in the early stage of the risk event, lacking quantitative characterization of the survivability of the urban power grid in the middle and late stages of the risk event, and ignoring the supporting role of post-disaster distribution system topology reconstruction and maintenance recovery on survivability.

[0027] Third, the existing technology mainly focuses on the research of the power transmission system or the distribution system, ignoring the synergistic effect of the main grid and the distribution network, especially the power reverse support of the active distribution network to the main grid, and cannot evaluate the survivability of the urban power grid as a whole under the risk event.

[0028] Based on the above three problems, the present application proposes a method for evaluating the survivability of the urban power grid considering the synergy of the main grid and the distribution network.

[0029] The design idea of the present application is: based on the principle of the influence of each type of disaster on the power grid equipment, the extreme disaster causing factor is identified and calculated, and the element vulnerability curve is constructed based on the causing factor to calculate the element failure probability; for pre-disaster survivability evaluation, the Monte Carlo algorithm is used to generate the power grid damage scene under each disaster intensity, the optimal scheduling model of the power transmission system and the distribution system is run respectively, the loss of load power, the average line congestion rate and the transmission capacity are calculated as the pre-disaster survivability index, and the segmented multi-objective risk framework can also be used to distinguish the conditional risk expectation in the low, medium and high probability intervals; for the in-disaster survivability evaluation, the network topology reconstruction of the distribution system is considered, the power support capability of the main grid is calculated by optimizing the dispatch, and the real-time loss of load power, the line transmission congestion degree and the improvement rate compared with the pre-disaster evaluation are obtained as the in-disaster survivability index; for the post-disaster survivability evaluation, the repair process of the distribution system maintenance team and the power reverse support capability are considered, the post-disaster recovery model is established, and the load and line transmission capacity recovery rate within twenty-four hours is obtained as the post-disaster survivability index; finally, based on the pre-disaster survivability index, the in-disaster survivability index and the post-disaster survivability index, the index matrix is formed to evaluate the overall survivability of the power grid in the whole process.

[0030] The beneficial effects achieved are as follows:

[0031] First, in view of the fact that the prior art ignores the related assessment of the survivability of the urban power grid under high-risk small-probability events, the technology divides the disaster set into intervals according to the occurrence frequency, and respectively calculates the expected value of the assessment index of the corresponding interval, highlighting the tendency of "risk aversion".

[0032] Second, in view of the fact that the prior art divides the urban power grid into a main grid and a distribution network for separate assessment, ignoring the interaction capability between the main grid and the distribution network, the technology considers the power support capability of the distributed power supply of the distribution network and the capability of the distribution network to send power back to the main grid, and performs mutual coordination between the main grid and the distribution network, and performs disaster assessment on the urban power grid as a whole.

[0033] Third, in view of the fact that the prior art lacks assessment of the survivability of the urban power grid from the perspective of resilience recovery, and ignores the coordinated recovery capability of the main grid and the distribution network after a disaster, the technology considers the repair team recovery and network topology reconstruction process of the distribution network, and takes into account the power back-feeding capability of the distribution network at each time. The load and power transmission capacity recovery rate index is designed to assess the survivability of the urban power grid after a disaster.

[0034] Figure 1 The implementation flowchart of the urban power grid survivability assessment method considering the coordination of the main grid and the distribution network provided by the embodiments of the present application is described in detail as follows:

[0035] In step 101, the Monte Carlo algorithm is used to simulate the damage scenario of the power grid of the target city under the target disaster.

[0036] The embodiments of the present application use the Monte Carlo algorithm to simulate the damage scenario of the power grid of the target city under the same target disaster with different intensities.

[0037] In one possible implementation, before simulating the damage scenario of the power grid of the target city under the target disaster using the Monte Carlo algorithm, the method further includes:

[0038] Obtain the disaster-causing factors of the target disaster of the target city under different intensities, and divide the disaster scenario of the target city under the target disaster into a high-frequency scenario interval, a medium-frequency scenario interval and a low-frequency scenario interval according to the disaster-causing factors under different intensities;

[0039] For each scenario interval, obtain the historical meteorological data, the historical data of the power grid and the corresponding disaster-causing factors in the scenario interval, and construct the element vulnerability curve corresponding to the scenario interval;

[0040] Based on the element vulnerability curve of each scenario interval, the element failure probability of the corresponding scenario interval is calculated;

[0041] Correspondingly, simulating the damage scenario of the power grid of the target city under the target disaster using the Monte Carlo algorithm includes:

[0042] Based on the element failure probability of each scenario interval, the Monte Carlo algorithm is used to simulate the damage scenarios of the power grid of the target city under the target disaster.

[0043] Optionally, for each target city, the disaster-causing factors of the target city under different intensities of the target disaster are obtained, and then the disaster scenarios of the target city under the target disaster are divided into a high-frequency scenario interval, a medium-frequency scenario interval and a low-frequency scenario interval by using different disaster-causing factors. Then, for each scenario interval, the historical meteorological data, the historical data of the power grid and the corresponding disaster-causing factors in the scenario interval are obtained, the element vulnerability curve corresponding to the scenario interval is constructed, and the element failure probability of all power grid devices in the scenario interval is calculated according to the element vulnerability curve corresponding to the scenario interval. Finally, the Monte Carlo algorithm is used to simulate the damage scenarios of the power grid of the target city under different intensities of the target disaster by using the element failure probability of each scenario interval.

[0044] For example, the embodiment of the present application first performs segmentation and grid-based geographic information modeling on the transmission lines of the power transmission system and the power distribution systems of the target city respectively, analyzes and obtains the corresponding disaster-causing factors according to the threat principle of the disaster to the urban power grid. Then, the corresponding element failure rate model is constructed based on the disaster-causing factor d, and the corresponding element failure probability is calculated. That is, the element failure rate λ comp is expressed as a function of the disaster-causing factor, and the expression is:

[0045] λ comp = f(d) (1)

[0046] wherein λ comp is the element failure rate.

[0047] Taking the main threat mechanism of the rainstorm disaster to the urban distribution network as an example, the urban waterlogging caused by the rainstorm makes the important equipment such as the ring network high-voltage cabinet of the distribution network system node face the risk of cable joint immersion, and the disaster-causing factor is the waterlogging water level depth of the city under the rainstorm. The occurrence of the rainstorm disaster is simulated by using the Chicago rain type, as shown in the following formula:

[0048]

[0049] wherein q(t) is the average rainstorm intensity at time t, the unit is L / (s·hm 2 ), A1 is the rainfall per unit time, C is the rain force variation parameter, P is the rainstorm return period, b is the historical correction parameter, and n is the rainstorm attenuation index.

[0050] Combined with the grid-based geographic information of the distribution system area, considering the urban drainage capacity and surface infiltration, the two-dimensional hydrodynamic model is used to simulate the rainstorm waterlogging process, and the simulation data of the disaster-causing factor waterlogging water level depth at each time can be obtained.

[0051] Specifically, the two-dimensional water dynamic model can be abstracted as a binary first-order equation about water flow velocity, and the precipitation, water flow between grids and drainage are calculated, and the water depth of waterlogging is calculated according to the calculation process shown in the following formula:

[0052] W(t)=q(t)-f k -Q drain (3)

[0053]

[0054] f k =f c +(f0-f c )e -βt (5)

[0055]

[0056] Q Neib =V x dΔyΔt (7)

[0057]

[0058] Wherein, W(t) is the net increase of water on the ground, q(t) is the average intensity of rainstorm at t moment, f k is the infiltration amount of the ground, Q drain is the drainage amount of the rainwater well, k is the unit conversion coefficient, n is the Manning coefficient, d is the disaster-causing factor, V is the water flow velocity vector, g is the gravity acceleration, is the water depth difference, f c is the minimum infiltration rate, f0 is the initial maximum infiltration rate, β is the attenuation coefficient, t is the time, C is the discharge coefficient, a is the cross-sectional area of the drainage well, Q Neib is the water flow between adjacent grids, V x is the transverse component of the water flow velocity vector, Δy is the longitudinal length of each grid, Δt is the time interval, Δd is the increase of water depth, θ is the building coverage, Δx is the transverse length of each grid.

[0059] Thus, the disaster-causing factor d can be calculated, and the relationship between the failure rate of power distribution equipment and the disaster-causing factor is established, and the element failure probability related to the power distribution equipment is calculated, that is, the element failure probability under each scene interval is calculated by the first formula, and the first formula is:

[0060]

[0061] Wherein, P comp (t) is the element failure probability, λ comp (k) is the element failure rate, γ is the damping coefficient, ξ is the attenuation coefficient, d(t) is the disaster-causing factor, D BwD is the height of cable joint to ground in power distribution room w t is time, and k is the index number of element category.

[0062] Then, according to the historical meteorological data of the city and the historical data of the damage of the power grid elements, the related parameters of the disaster-causing factor and the element failure rate design are determined. According to the historical frequency of different intensity disasters, the disaster event set corresponding to the frequency, that is, the disaster scene interval, is divided into a plurality of small sets according to the frequency, and the plurality of small sets are divided into a high-frequency scene interval, a medium-frequency scene interval and a low-frequency scene interval.

[0063] In the embodiments of the present application, the element failure rate and the element failure probability can also be derived and calculated by using fuzzy functions and other means.

[0064] In a general scale city, the disaster intensity faced by the power transmission system and the power distribution system is not much different, and the same disaster scene can be analyzed. For the disaster scene of each target disaster, the Monte Carlo algorithm is used to generate random numbers corresponding to each element in each time period, which are uniformly distributed. By comparing with the element failure probability, the element damage scene is sampled out. As shown in the following formula:

[0065]

[0066] η (t) is the element failure probability of element i at time t. i,t η (t) is the element failure probability of element i at time t. i,comp η (t) is the element failure probability of element i at time t.

[0067] In step 102, before the target disaster occurs, a first main grid scheduling model and a first distribution grid scheduling model are constructed, and a first evaluation index is calculated.

[0068] In the embodiments of the present application, after simulating the damaged scene of the power grid of the target city under the target disaster, for each damaged scene, a first main grid scheduling model of the city main grid topology and a first distribution grid scheduling model of each distribution grid under each load node of the main grid are constructed respectively, and a first evaluation index based on the main grid and a first evaluation index based on each distribution grid are calculated respectively as the pre-disaster evaluation index.

[0069] The first distribution scheduling model takes the minimum load shedding amount of the distribution grid as the optimization objective, and the objective function is:

[0070]

[0071] η (t) is the element failure probability of element i at time t. power,j,t η (t) is the element failure probability of element i at time t. shed,j,tLet N be the active load reduction power of node j in the distribution network during time period t, where j is the node, N is the total number of nodes, t is the time period, and T is the set of time periods.

[0072] The corresponding constraints include node power balance constraints, branch power flow constraints, various types of power load constraints, and disaster-related distribution network topology reconfiguration constraints.

[0073] The node power balance constraint is as follows:

[0074]

[0075] Among them, P Buy,j,t Let P be the active power of substation node j during time period t. DG,j,t For the active power output of distributed power sources, P L,j,t For the actual active load connected, H l,t Q represents the active power flowing through the line. Buy,j,t Let Q be the reactive power of substation connection node j during time period t. DG,j,t For the reactive power output of distributed power sources, Q L,j,t For the actual reactive load connection, G l,t Let l be the reactive power flowing through the line, l be the line, j be the node, N be the total number of nodes, t be the time period, T be the time period set, ∏(j) be the set of subordinate lines of node j, and Ω(j) be the set of superior lines of node j.

[0076] Branch flow constraints are:

[0077]

[0078]

[0079]

[0080]

[0081] Where M is a sufficiently large constant, u l,t This represents the access status of line l during time period t, with a value of 1 indicating that the line is in operation. i,t Let V be the voltage amplitude of node i during time period t. j,t Let r be the voltage amplitude of node j in time period t. l x is the line impedance. l For line inductance, For line capacity, This represents the minimum node voltage. ε represents the maximum node voltage. l For the set of routes, N l This is a set of line node pairs.

[0082] The constraints of each type of power supply load are:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] wherein P Load,j,t is the active load demand, Q Load,j,t is the reactive load demand, Q shed,j,t is the load shedding power of the reactive load, is the start-stop state of the substation power supply, is the lower limit of the active power of the substation power supply load, is the upper limit of the active power of the substation power supply load, is the lower limit of the reactive power of the substation power supply reactive compensation, is the upper limit of the reactive power of the substation power supply reactive compensation, P Buy,j is the active power of the substation power supply load, Q Buy,j is the reactive power of the substation power supply reactive compensation, is the start-stop state of the distributed power supply, is the lower limit of the active power of the distributed power supply load, is the upper limit of the active power of the distributed power supply load, is the lower limit of the reactive power of the distributed power supply reactive compensation, is the upper limit of the reactive power of the distributed power supply reactive compensation, P DG,j,t is the active power of the distributed power supply, Q DG,j,t is the reactive power of the distributed power supply reactive compensation.

[0089] The distribution network needs to be kept open-loop operation, and after being damaged, the topology needs to be reconstructed by adjusting the tie switch, so that the original islands can be reconnected to realize energy mutual assistance and reduce the loss of load.

[0090] The constraints of the disaster distribution network topology reconstruction are:

[0091]

[0092] wherein, is whether the line l is selected to access operation at the time period t, and the value of 1 indicates that it is selected to access; is whether the line l is damaged at the time period t, and the value of 1 indicates that the line is intact; For being in For being 1, node j becomes the microgrid root node at time period t, f l,t is the virtual line flow, and the third rule in formula (19) stipulates that the virtual flow of the damaged line is zero, and the fourth rule in formula (19) stipulates that for nodes other than the root node, 1 of virtual power is consumed per time period; n is the number of nodes of the power distribution network.

[0093] The first main grid scheduling model takes the minimum load reduction amount of the power transmission system as the objective function, and the objective function is:

[0094]

[0095] Wherein, f2 is the load reduction amount of the power transmission system, P shed,j,t is the load reduction power of the power transmission system node j at time period t.

[0096] The corresponding constraint conditions include node power balance constraint, power flow constraint and each type of power load constraint.

[0097] The node power balance constraint is:

[0098]

[0099] Wherein, P G,i,t is the active power of each node i of the network at time period t, P L,i,t is the actual active load connected.

[0100] The power flow constraint is:

[0101]

[0102] Wherein, b ij is the mutual capacity resistance between lines ij, θ i,t is the voltage phase angle of node i at time period t, θ j,t is the voltage phase angle of node j at time period t.

[0103] The each type of power load constraint is:

[0104]

[0105] In a possible implementation manner, the first evaluation index is calculated, comprising:

[0106] The lost load power, the power transmission available capacity and the power transmission line congestion degree of each damaged scene of the target disaster are calculated respectively;

[0107] The average value of the load loss power, the average value of the transmission available capacity and the average value of the transmission line congestion degree of the target disaster are calculated by using the load loss power, the transmission available capacity and the transmission line congestion degree of each damaged scene, and the average value of the load loss power, the average value of the transmission available capacity and the average value of the transmission line congestion degree of the target disaster are taken as the first evaluation index.

[0108] Optionally, in the embodiment of the application, the calculation process of the first evaluation index of the main network and each distribution network is consistent, that is, the load loss power, the transmission available capacity and the transmission line congestion degree of each damaged scene of the target disaster are calculated respectively, that is, the load loss power, the transmission available capacity and the transmission line congestion degree of each damaged scene of the target disaster are calculated by using the second formula, and the second formula is as follows:

[0109]

[0110] wherein, LOLE d is the load loss power, TCAP d is the transmission available capacity, TCong d is the transmission line congestion degree, α power,j,t is the load weight of each node, P shed,j,t is the load shedding power of node j at time period t, is the line capacity, u l,T is the access state of line l, H l,T is the active power flowing through the line, j is a node, N is the total number of nodes, t is a time period, T is a set of time periods, l is a line, and ε l is a set of lines.

[0111] Wherein, the transmission available capacity and the congestion degree are evaluated after the topology reconstruction of the distribution network is completed.

[0112] Then, for the load loss power, the transmission available capacity and the transmission line congestion degree of each damaged scene, the average value of the load loss power, the average value of the transmission available capacity and the average value of the transmission line congestion degree of the target disaster are calculated, and the average value of the load loss power, the average value of the transmission available capacity and the average value of the transmission line congestion degree of the target disaster are taken as the first evaluation index. The calculation formula is as follows:

[0113]

[0114] wherein, LOLE_A DS is the average value of the load loss power, TCAP_A DS is the average value of the transmission available capacity, TCong_A DS is the average value of the transmission line congestion degree, and N DS is the number of damaged scenes.

[0115] In one possible implementation, after the first evaluation index is calculated, the method can comprise:

[0116] For each disaster scenario division of high-frequency scenario interval, medium-frequency scenario interval and low-frequency scenario interval of the target city power grid under each disaster, the low-frequency scenario interval D LP For example, considering all kinds of disasters such as rainstorm, earthquake, gale, etc., based on the first evaluation index calculated above, the expectation of the interval range evaluation index can be expressed as:

[0117]

[0118] Wherein, E_LOLE is the loss of load power expectation of the low-frequency scenario interval, E_TCAP is the transmission available capacity expectation of the low-frequency scenario interval, E_TCong is the transmission line congestion degree expectation of the low-frequency scenario interval, P(D s ) is the probability of the D s th disaster.

[0119] The expectation calculation of the first evaluation index of the high-frequency scenario interval and the medium-frequency scenario interval is similar to that of the low-frequency scenario interval, and the embodiments of the present application will not be described.

[0120] In the embodiments of the present application, the survivability of the main grid and the distribution network part in the city power grid is synchronously evaluated according to the disaster characteristics, and different risk intervals are divided according to the intensity and frequency of the disaster.

[0121] In step 103, in the target disaster, the second distribution network scheduling model and the first distribution network return scheduling model of each distribution network system under each load node in the main grid are constructed, the net load demand of each load node is calculated, the second main grid scheduling model is constructed, and the second evaluation index is calculated.

[0122] In the embodiments of the present application, the disaster occurrence evaluation framework faces the specific element damage scenario, and the pre-disaster evaluation framework considers the ability of the main grid and the distribution network. Due to the damage of the main grid element, the load node can no longer obtain the planned power supply, and the distribution system relying on the main grid to transmit power through the transformer substation may be unable to obtain sufficient power, resulting in further increase of the loss of load power in the distribution network. On the other hand, the active distribution network can independently run in the disaster by using distributed power and energy storage, and even can provide power supply to the main grid in reverse, to alleviate the load shedding. Specifically, the second distribution network scheduling model and the first distribution network return scheduling model of each distribution network system under each load node in the main grid are constructed, the net load demand of each load node is calculated, and then the second main grid scheduling model is constructed, and the second evaluation index is calculated. The second evaluation index includes the loss of load power and the transmission congestion degree reduction rate.

[0123] In one possible implementation, in the occurrence of the target disaster, a second distribution network scheduling model of each distribution network system under each load node in the main network and a first distribution network return dispatching model are constructed, the net load demand of each load node is calculated, a second main network scheduling model is constructed, and a second evaluation index is calculated, including:

[0124] In the occurrence of the target disaster, a second distribution network scheduling model of each distribution network system under each load node is constructed, the net load demand of each distribution network system is calculated, and the power redundancy and the power gap are calculated according to the net load demand of each distribution network system;

[0125] The first distribution network return dispatching model is constructed, and the first distribution network return dispatching model is run based on the power redundancy and the power gap;

[0126] After running the first distribution network return dispatching model, the net load demand of each load node is calculated;

[0127] The second main network scheduling model is constructed, and the second main network scheduling model is run by using the net load demand of each load node to calculate the loss of load power and the reduction rate of power transmission congestion degree of the target city in the occurrence of the target disaster.

[0128] The specific calculation idea of the embodiments of the present application is as follows:

[0129] Based on the specific damaged scene in the occurrence of the target disaster, a main network disaster topology is formed, each distribution system under each load node in the main network is analyzed, the distribution network topology reconstruction process in the disaster is considered, the distribution network scheduling model is run, the disaster running network of each distribution system and the required main network supply power are obtained, the power redundancy and the power gap of the main network in-network part are calculated according to the active power output of the distributed power supply of the distribution network and the load demand.

[0130] For the distribution network with power redundancy, the distribution network return dispatching model is run to obtain the power support capacity of the distribution network. The distribution system with return capacity can provide power support for other distribution systems with power gap through the bus of the load node, so the net load demand of the main network load node can be calculated by combining the required main network supply power of the distribution system with power gap.

[0131] After obtaining the net load demand of each load node, the main network scheduling model is run based on the main network disaster topology to calculate the loss of load power, the degree of power transmission congestion and the corresponding improvement ratio of the corresponding pre-disaster evaluation framework of the main network. Then, the supplyable power of each load node is distributed to the distribution systems with current gap according to the importance of each distribution system.

[0132] Specifically, the network dispatching model in the embodiment of the present application is similar to before the disaster, and in the process of considering the coordination of the main network and the distribution network, more distribution network resources should be played, therefore, in the target disaster, the second distribution network dispatching model of each distribution network system under each load node is constructed, and the maximum power support of the distributed power is taken as the optimization target, and the objective function is:

[0133]

[0134] Wherein, f is the power support of the distributed power.

[0135] Based on the second distribution network dispatching model of each distribution system, the load demand of each distribution network system is calculated, and the power redundancy and the power gap are calculated according to the load demand of each distribution network system.

[0136] Wherein, the power redundancy is the difference between the active power output of the in-network node distributed power and the load demand, and the power gap is the off-load power of the in-network node. Correspondingly, the power redundancy and the power gap are calculated by the third formula, and the third formula is:

[0137]

[0138] Wherein, P Sur,t is the power redundancy, P shed,t is the power gap, is the upper limit of the active power of the DG load, P Load,t is the active load demand, P shed,j,t is the load shedding power of node j at time period t.

[0139] The first distribution network return dispatching model in the embodiment of the present application is based on the distribution network dispatching model, and the substation node is treated as a load node, and the internal load demand of the distribution network needs to be preferentially guaranteed, therefore, the substation node can be regarded as a three-level load. And the calculated power redundancy P SuR,t is taken as the size of the substation load, the first distribution network return dispatching model is run, and the net load demand of each load node is calculated. Correspondingly, the objective function of the first distribution network return dispatching model is:

[0140]

[0141] Wherein, f4 is the objective function of the first distribution network return dispatching model, α DT,j is the weight coefficient of the active power return when the load shedding occurs, is the active power load shedding amount when the active power returns.

[0142] The main network supply power variable P Buy,j,t is ignored, and the node balance power constraint is modified as:

[0143]

[0144] wherein P DT,j,t is the active power of the return, Q DT,j,t is the reactive power of the return.

[0145] The constraint of increasing the return power is:

[0146]

[0147] wherein, is the active power load shedding of the load when the active power of the return occurs, Q DT,t is the reactive power of the return, is the reactive power load shedding of the load when the reactive power of the return occurs, Q Sur,t is the redundancy of the reactive power.

[0148] The power load demand of the load node, i.e. the net load demand, can be calculated according to the return power and the power gap, i.e.:

[0149]

[0150] wherein, is the power load demand considering the return power, P shed,m,t is the active power load shedding of the load of the distribution network m, P DT,m,t is the active power of the return of the distribution network m, and M is the set of distribution systems corresponding to the load node i of the main network.

[0151] A second main network dispatching model is constructed. The second main network dispatching model in the target disaster in the embodiment of the present application is similar to the pre-disaster assessment framework, and will not be repeated.

[0152] Then, the net load demand of each load node is used to run the second main network dispatching model to calculate the loss of load power and the reduction rate of power transmission congestion of the target city in the target disaster. The specific calculation formula is:

[0153]

[0154]

[0155] wherein, LOLE_P is the loss of load power, TCong_P is the reduction rate of power transmission congestion, P shed,j,t is the loss of load power of the load node obtained by the second main network dispatching model, is the main network loss of load power of the disaster scenario in the pre-disaster assessment framework, is the main network power transmission congestion degree of the disaster scenario in the pre-disaster assessment framework.

[0156] The embodiment of the application considers the process of the main grid and distribution grid system in the disaster, considers the ability of the distribution grid in the disaster to send power back to the main grid through topology reconstruction and distributed power generation, and evaluates the survivability of the urban power grid.

[0157] In step 104, after the target disaster occurs, a distribution grid recovery model of each distribution grid system under each load node in the main grid and a second distribution grid return dispatching model are constructed, total load demand of the distribution grid under each load node is calculated, a third main grid dispatching model is constructed, and a third evaluation index is calculated.

[0158] In the embodiment of the application, the post-disaster evaluation framework is also directed to a specific determined element damage scenario, the main grid and distribution grid collaborative idea is similar to the disaster evaluation, the repair and recovery process of the post-disaster distribution grid system is considered, and the post-disaster recovery ability of the target urban power grid is investigated. Specifically, a distribution grid recovery model of each distribution grid system under each load node in the main grid and a second distribution grid return dispatching model are constructed, total load demand of the distribution grid under each load node is calculated, and then a third main grid dispatching model is constructed, and a third evaluation index is calculated. The third evaluation index includes a load recovery rate and a transmission capacity recovery rate.

[0159] In a possible implementation, after the target disaster occurs, a distribution grid recovery model of each distribution grid system under each load node in the main grid and a second distribution grid return dispatching model are constructed, total load demand of the distribution grid under each load node is calculated, a third main grid dispatching model is constructed, and a third evaluation index is calculated, including:

[0160] After the target disaster occurs, a distribution grid recovery model of each distribution grid system under each load node is constructed, and power redundancy and power gap are calculated;

[0161] A second distribution grid return dispatching model is constructed, and the second distribution grid return dispatching model is run based on the power redundancy and the power gap;

[0162] After the second distribution grid return dispatching model is run, total load demand of the distribution grid under each load node is calculated;

[0163] A third main grid dispatching model is constructed, and the third main grid dispatching model is run by using the total load demand of the distribution grid under each load node, and a load recovery rate and a transmission capacity recovery rate of the target urban power grid after the target disaster occur are calculated

[0164] Optionally, the power distribution network recovery model and the second power distribution network return scheduling model are both established based on the power distribution network scheduling model in the disaster evaluation framework, and the optimization period is set to twenty-four hours. That is, after the occurrence of the target disaster, the power distribution network recovery model of each power distribution network system under each load node is constructed, the power redundancy and the power gap are calculated, then the second power distribution network return scheduling model is constructed, and the power redundancy and the power gap are used to run the second power distribution network return scheduling model, and the total load demand of the power distribution network under each load node is calculated. Then a third main network scheduling model is constructed, the total load demand of the power distribution network under each load node is used to run the third main network scheduling model, and the load recovery rate and the transmission capacity recovery rate of the target city after the occurrence of the target disaster are calculated. The third main network scheduling model is established based on the disaster evaluation framework.

[0165] Since the embodiment of the present application considers the process of starting to deploy maintenance teams for recovery after the occurrence of the target disaster, it is necessary to add maintenance team recovery related constraints in the model, and the specific constraints are as follows:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] wherein y d,c is a record variable of whether the site d enters the maintenance point of the maintenance team c, and when it is 1, it indicates that it has entered; x d,s,c is the maintenance point of the maintenance team, and when it is 1, it indicates that the maintenance team c is on the way to the site d after the site s, d is the site on the way, N d is the set of sites on the way of the maintenance team, d o is the starting point of the maintenance team, d e is the maintenance completion point of the maintenance team, c is the maintenance team, C ar is the set of maintenance teams, s is the site on the way; = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. e = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. o = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. o = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. e = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. o = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. e = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. d = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. s = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise. des = 1 if the maintenance team c has to pass through the repair point d on the way from the start point to the end point, and = 0 otherwise.

[0177] Equations (31)-(33) specify that for each repair point d, there is a unique repair point d' such that the maintenance team c has to pass through d on the way from d' to the end point. Equations (34)-(39) specify the repair point selection decisions of the maintenance team at the start point and the end point.

[0178] The repair point selection decisions of the maintenance team are combined with the repair times of the faulty elements and the road travel times to obtain the following constraints:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185] wherein = the time at which the maintenance team c reaches the location d, = the repair time of the faulty element at the repair point d, = the road travel time from the location d to the location s, = the time at which the maintenance team c reaches the location s, d,t = 1 if the repair of the faulty element at the location d is completed at the time period t, and = 0 otherwise. ε is a small positive number.

[0186] Equation (41) shows that the arrival time at site s is equal to the arrival time at the previous site d, the repair time of the element, and the travel time between the two sites. Equation (42) links the way point selection of the vehicle fleet to the arrival time. Equations (43)-(46) are used to determine the respective time periods in which the repair of the elements is completed.

[0187] The completion of the repair of the elements can restore the connectivity, such as:

[0188]

[0189] wherein, is the repair transient of the element l, and 1 indicates that the repair has been completed.

[0190] In the embodiments of the present application, the dispatching scheme can also be obtained based on the Dijkstra algorithm, and then the distribution network restoration optimization is performed.

[0191] With the progress of the repair and the reconstruction of the network topology, the power redundancy or the power gap of the distribution system will be different at each time period, and the power redundancy may suddenly become a power gap. Therefore, the power redundancy or the power gap needs to be calculated at each time period. In the second distribution network dispatching model, the substation nodes are considered as loads or power sources. The state constant O of the substation nodes is introduced into the second distribution network dispatching model. sub,t When there is a power gap at the time period t, it is set to 1, indicating the power supply state, and otherwise it is set to 0. The node balance power constraint of the second distribution network dispatching model is shown in equation (9). The related constraints of P Buy,j,t , Q Buy,j,t , P DT,t , Q DT,t are:

[0192]

[0193]

[0194]

[0195] In the embodiments of the present application, the subsequent evaluation process is similar to the in-disaster evaluation framework. The available power of each load node is distributed to the distribution systems with power gaps according to the importance of each distribution system. The restoration of the distribution network load and line can be obtained by running the main network optimization dispatching model again, and the details are not repeated.

[0196] The load recovery rate and the power transmission capacity recovery rate within 24 hours are calculated as follows:

[0197]

[0198] Wherein, Load_re is the load recovery rate, and TCAP_re is the transmission capacity recovery rate.

[0199] In step 105, the survivability of the target urban power grid under the target disaster is evaluated according to the first evaluation index, the second evaluation index, and the third evaluation index.

[0200] In the embodiments of the present application, each process of the target disaster is considered, and the survivability of the target urban power grid under the target disaster is evaluated according to the first evaluation index before the disaster, the second evaluation index during the disaster, and the third evaluation index after the disaster.

[0201] The present application provides a kind of urban power grid survivability evaluation method considering main coordination, by using Monte Carlo algorithm simulation target city under target disaster the damaged scene of grid;Before the occurrence of target disaster, first main grid dispatching model and first distribution network dispatching model are constructed, and first evaluation index is calculated;During the occurrence of target disaster, the second distribution network dispatching model of each distribution network system under each load node in main grid and first distribution network return dispatching model are constructed, and the net load demand of each load node is calculated, second main grid dispatching model is constructed, and second evaluation index is calculated;After the occurrence of target disaster, distribution network recovery model of each distribution network system under each load node in main grid and second distribution network return dispatching model are constructed, and the total load demand of distribution network under each load node is calculated, third main grid dispatching model is constructed, and third evaluation index is calculated;The survivability of the target urban power grid under the target disaster is evaluated according to the first evaluation index, the second evaluation index and the third evaluation index.The present application calculates the evaluation index of main grid and distribution network before disaster, during disaster and after disaster, highlights the tendency of "risk aversion", and improves the evaluation accuracy of the survivability of urban power grid by the mutual coordination of main grid and distribution network.

[0202] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0203] The following is the device embodiment of the present application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0204] Figure 2 The structure schematic diagram of the urban power grid survivability evaluation device considering main coordination provided by the embodiments of the present application is shown, only the parts related to the embodiments of the present application are shown for convenience of description, and the details are as follows:

[0205] As Figure 2 indicated, the urban power grid survivability evaluation device considering main coordination 2 comprises:

[0206] The acquisition module 21 is configured to simulate a damaged scenario of the power grid of the target city under the target disaster by using a Monte Carlo algorithm.

[0207] The first calculation module 22 is configured to, before the target disaster occurs, construct a first main grid dispatching model and a first distribution network dispatching model, and calculate a first evaluation index.

[0208] The second calculation module 23 is configured to, during the target disaster, construct a second distribution network dispatching model of each distribution network system under each load node in the main grid and a first distribution network return dispatching model, calculate a net load demand of each load node, construct a second main grid dispatching model, and calculate a second evaluation index.

[0209] The third calculation module 24 is configured to, after the target disaster occurs, construct a distribution network recovery model of each distribution network system under each load node in the main grid and a second distribution network return dispatching model, calculate a total load demand of the distribution network under each load node, construct a third main grid dispatching model, and calculate a third evaluation index.

[0210] The evaluation module 25 is configured to evaluate the survival ability of the power grid of the target city under the target disaster according to the first evaluation index, the second evaluation index and the third evaluation index.

[0211] The application provides a device for evaluating the survival ability of a city power grid considering the coordination of a main grid and a distribution network. The device simulates a damaged scenario of the power grid of a target city under a target disaster by using a Monte Carlo algorithm. Before the target disaster occurs, a first main grid dispatching model and a first distribution network dispatching model are constructed, and a first evaluation index is calculated. During the target disaster, a second distribution network dispatching model of each distribution network system under each load node in the main grid and a first distribution network return dispatching model are constructed, a net load demand of each load node is calculated, a second main grid dispatching model is constructed, and a second evaluation index is calculated. After the target disaster occurs, a distribution network recovery model of each distribution network system under each load node in the main grid and a second distribution network return dispatching model are constructed, a total load demand of the distribution network under each load node is calculated, a third main grid dispatching model is constructed, and a third evaluation index is calculated. The survival ability of the power grid of the target city under the target disaster is evaluated according to the first evaluation index, the second evaluation index and the third evaluation index. The device calculates the evaluation indexes of the main grid and the distribution network before, during and after a disaster, highlights the tendency of "risk aversion", and improves the evaluation accuracy of the survival ability of the city power grid by coordinating the main grid and the distribution network.

[0212] In a possible implementation manner, the device can further include a probability calculation module, which can be configured to:

[0213] obtain the disaster-causing factors of the target disaster of the target city under different intensities, and divide disaster scenarios of the target city under the target disaster into a high-frequency scenario interval, a medium-frequency scenario interval and a low-frequency scenario interval according to the disaster-causing factors under different intensities;

[0214] For each scenario interval, obtain historical meteorological data, historical power grid data and corresponding disaster-causing factors in the scenario interval, and construct an element vulnerability curve corresponding to the scenario interval.

[0215] Based on the element vulnerability curve of each scenario interval, calculate the element failure probability of the corresponding scenario interval.

[0216] Correspondingly, the obtaining module can be configured to:

[0217] Based on the element failure probability in each scenario interval, use the Monte Carlo algorithm to simulate the damaged scenario of the power grid of the target city under the target disaster.

[0218] In a possible implementation, when the target disaster is a rainstorm disaster, the probability calculation module can also be configured to:

[0219] Calculate the element failure probability in each scenario interval by the first formula, and the first formula is:

[0220]

[0221] Where p comp (t) is the element failure probability, λ comp (k) is the element failure rate, γ is the damping coefficient, ξ is the attenuation coefficient, d(t) is the disaster-causing factor, D Bw is the height of the cable joint to the ground in the distribution room, D w is the design height of the distribution room for flood control, t is the time, and k is the element category index number.

[0222] In a possible implementation, the first calculation module can be configured to:

[0223] Calculate the lost load power, power transmission available capacity and power transmission line congestion degree of each damaged scenario of the target disaster, respectively.

[0224] Calculate the lost load power average value, power transmission available capacity average value and power transmission line congestion degree average value of the target disaster by using the lost load power, power transmission available capacity and power transmission line congestion degree of each damaged scenario, and take the lost load power average value, power transmission available capacity average value and power transmission line congestion degree average value of the target disaster as the first evaluation index.

[0225] In a possible implementation, the first calculation module can also be configured to:

[0226] The load shedding power, the transmission available capacity and the transmission line congestion degree of each damaged element in each scenario interval are calculated by a second formula, the second formula being:

[0227]

[0228] wherein, LOLE d is the load shedding power, TCAP d is the transmission available capacity, TCong d is the transmission line congestion degree, α power,j,t is the load weight of each node, P shed,j,t is the load shedding power of node j at time period t, is the line capacity, u l,T is the access state of line l, H l,T is the active power flowing through the line, j is a node, N is the total number of nodes, t is a time period, T is a set of time periods, l is a line, ε l is a set of lines.

[0229] In a possible implementation, the objective function of the first distribution network dispatching model is:

[0230]

[0231] wherein, f1 is the distribution network load shedding amount, α power,j,t is the load weight of each node, P shed,j,t is the active load shedding power of node j of the distribution network at time period t, j is a node, N is the total number of nodes, t is a time period, and T is a set of time periods.

[0232] In a possible implementation, the second evaluation index includes the load shedding power and the transmission congestion degree reduction rate, and the second calculation module can be used to:

[0233] In the target disaster, a second distribution network dispatching model of each distribution network system under each load node is constructed, the net load demand of each distribution network system is calculated, and the power redundancy and the power gap are calculated according to the net load demand of each distribution network system;

[0234] A first distribution network return dispatching model is constructed, and the first distribution network return dispatching model is run based on the power redundancy and the power gap;

[0235] After the first distribution network return dispatching model is run, the net load demand of each load node is calculated;

[0236] A second main network dispatching model is constructed, and the second main network dispatching model is run by using the net load demand of each load node, so as to calculate the load shedding power and the transmission congestion degree reduction rate of the target city in the target disaster.

[0237] In a possible implementation, the second calculation module can be configured to:

[0238] The power redundancy and the power gap are calculated by a third formula, the third formula being:

[0239]

[0240] wherein P Sur,t is the power redundancy, P shed,t is the power gap, is an upper limit of the active power of the DG load, P Load,t is the active load demand, P shed,j,t is the load shedding power of the node j at the time period t.

[0241] In a possible implementation, the third evaluation index includes a load recovery rate and a transmission capacity recovery rate, and the third calculation module can be configured to:

[0242] After the target disaster occurs, a distribution network recovery model of each distribution network system under each load node is constructed, and the power redundancy and the power gap are calculated;

[0243] A second distribution network return dispatching model is constructed, and the second distribution network return dispatching model is run based on the power redundancy and the power gap;

[0244] After the second distribution network return dispatching model is run, a total load demand of the distribution network under each load node is calculated;

[0245] A third main network dispatching model is constructed, and the third main network dispatching model is run by using the total load demand of the distribution network under each load node, so as to calculate a load recovery rate and a transmission capacity recovery rate of the target city after the target disaster occurs.

[0246] In the above embodiments, the description of each embodiment has its own focus, and the part not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0247] Those skilled in the art can realize that the templates, units and algorithm steps of each example described in combination with the embodiments disclosed in the present text can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0248] The modules / units, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various embodiments of the city power grid survivability evaluation method can be realized. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0249] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for survivability assessment of urban power grid considering main coordination, characterized in that, The utility model relates to a kind of method for evaluating the survivability of target city power grid under target disaster, comprising: Simulate the damage scenario of target city power grid under target disaster by Monte Carlo algorithm; Before target disaster occurs, build first main grid dispatching model and first distribution network dispatching model, calculate first evaluation index;During target disaster, build second distribution network dispatching model of each distribution network system under each load node in main grid and first distribution network return dispatching model, calculate net load demand of each load node, build second main grid dispatching model, calculate second evaluation index;After target disaster occurs, build distribution network recovery model of each distribution network system under each load node in main grid and second distribution network return dispatching model, calculate total load demand of distribution network under each load node, build third main grid dispatching model, calculate third evaluation index;According to the first evaluation index, the second evaluation index and the third evaluation index, form index matrix to evaluate the survivability of target city power grid under target disaster; Wherein, the first evaluation index includes: Calculate the loss of load power, transmission available capacity and transmission line congestion degree of each damage scenario of target disaster respectively; Calculate the average value of loss of load power, transmission available capacity and transmission line congestion degree of target disaster by using the loss of load power, transmission available capacity and transmission line congestion degree of each damage scenario, and take the average value of loss of load power, transmission available capacity and transmission line congestion degree of target disaster as the first evaluation index; Wherein, the second evaluation index includes loss of load power and transmission congestion degree reduction rate, and the second distribution network dispatching model of each distribution network system under each load node in main grid and the first distribution network return dispatching model are built during target disaster, the net load demand of each load node is calculated, the second main grid dispatching model is built, and the second evaluation index is calculated, including: During target disaster, build second distribution network dispatching model of each distribution network system under each load node, calculate net load demand of each distribution network system, and calculate power redundancy and power gap according to net load demand of each distribution network system; Build the first distribution network return dispatching model, and run the first distribution network return dispatching model based on the power redundancy and the power gap; After running the first distribution network return dispatching model, calculate the net load demand of each load node; Build the second main grid dispatching model, and run the second main grid dispatching model by using the net load demand of each load node, calculate the loss of load power and the transmission congestion degree reduction rate of target city during target disaster; Wherein, the third evaluation index includes load recovery rate and transmission capacity recovery rate, and the distribution network recovery model of each distribution network system under each load node in main grid and the second distribution network return dispatching model are built after target disaster, total load demand of distribution network under each load node is calculated, third main grid dispatching model is built, and third evaluation index is calculated, including: constructing a power grid recovery model of each power distribution network system under each load node after the target disaster occurs, and calculating power redundancy and power gap; constructing the second power grid return dispatching model, and running the second power grid return dispatching model based on the power redundancy and the power gap; calculating total power grid load demand under each load node after running the second power grid return dispatching model; constructing the third main grid dispatching model, and running the third main grid dispatching model by using total power grid load demand under each load node, to calculate the load recovery rate and the transmission capacity recovery rate of the target city after the target disaster occurs.

2. The urban power grid survivability evaluation method considering main coordination according to claim 1, characterized in that, Before the target city's damaged grid scenario under the target disaster is simulated by using the Monte Carlo algorithm, the method further comprises: obtaining disaster-causing factors of the target disaster in different intensities, and dividing disaster scenarios of the target city under the target disaster into a high-frequency scenario interval, a medium-frequency scenario interval and a low-frequency scenario interval according to the disaster-causing factors in different intensities; for each scenario interval, obtaining historical meteorological data, historical grid data and corresponding disaster-causing factors in the scenario interval, and constructing an element vulnerability curve corresponding to the scenario interval; based on the element vulnerability curve of each scenario interval, calculating the element failure probability of the corresponding scenario interval; correspondingly, the target city's damaged grid scenario under the target disaster is simulated by using the Monte Carlo algorithm, comprising: based on the element failure probability of each scenario interval, the target city's damaged grid scenario under the target disaster is simulated by using the Monte Carlo algorithm.

3. The method for survivability evaluation of urban power grid considering main coordination according to claim 2, characterized in that, When the target disaster is a rainstorm disaster, the element failure probability of the corresponding scenario interval is calculated based on the element vulnerability curve of each scenario interval, comprising: the element failure probability of each scenario interval is calculated by using a first formula, the first formula is: wherein, is the element failure probability, is the element failure rate, is the damping coefficient, is the decay coefficient, is the disaster-causing factor, is the height of the cable joint to the ground in the power distribution room, is the design height of the power distribution room for flood control, is the time, is the element category index number.

4. The urban power grid survivability evaluation method considering main coordination according to claim 1, characterized in that, the lost load power, the transmission available capacity and the transmission line congestion degree of each damaged scenario of the target disaster are calculated respectively, comprising: the lost load power, the transmission available capacity and the transmission line congestion degree of each damaged scenario of the target disaster are calculated by using a second formula, the second formula is: in, For the aforementioned power outage, The available power transmission capacity, The degree of congestion of the transmission line. Assign load weights to each node. For nodes During the period The load reduction power, For line capacity, For the line Access status, The active power flowing through the line. For nodes, This represents the total number of nodes. For time period, For time period sets, For the line, This is a set of routes.

5. The method for survivability evaluation of urban power grid considering main coordination according to claim 1, characterized in that, the objective function of the first power grid dispatching model is: wherein, is the load shedding amount for the distribution network, is the load weight of each node, is the node of the distribution network is the time period, is the active load shedding power of the time period, is the node, is the total number of nodes, is the time period, is the set of time periods.

6. The urban power grid survivability evaluation method considering main coordination according to claim 1, characterized in that, the power redundancy and the power gap are calculated according to the load demand of each power distribution network system, comprising: the power redundancy and the power gap are calculated by using a third formula, the third formula is: wherein, is the power redundancy, is the power gap, is an upper limit of the active power of the load, is the active load demand, is the node a load shed power at a time period .

7. A device for evaluating survivability of a city power grid considering main coordination, characterized in that, comprising: a obtaining module is configured to simulate the target city's damaged grid scenario under the target disaster by using the Monte Carlo algorithm; a first calculating module is configured to, before the target disaster occurs, construct a first main grid dispatching model and a first power grid dispatching model, and calculate a first evaluation index; a second calculating module is configured to, during the target disaster, construct a second power grid dispatching model of each power distribution network system under each load node in the main grid and a first power grid return dispatching model, calculate the net load demand of each load node, construct a second main grid dispatching model, and calculate a second evaluation index; a third calculation module, configured to, after the target disaster occurs, construct a distribution network restoration model and a second distribution network return dispatching model of each distribution network system under each load node in the main network, calculate total load demand of the distribution network under each load node, construct a third main network dispatching model, and calculate a third evaluation index; an evaluation module, configured to form an index matrix according to the first evaluation index, the second evaluation index and the third evaluation index, and evaluate the survivability of the target urban power grid under the target disaster according to the index matrix; the first calculation module is configured to: calculate the lost load power, the power transmission available capacity and the power transmission line congestion degree of each damaged scenario of the target disaster respectively; calculate the lost load power average value, the power transmission available capacity average value and the power transmission line congestion degree average value of the target disaster by using the lost load power, the power transmission available capacity and the power transmission line congestion degree of each damaged scenario, and take the lost load power average value, the power transmission available capacity average value and the power transmission line congestion degree average value of the target disaster as the first evaluation index; the second evaluation index includes the lost load power and the power transmission congestion degree reduction rate, and the second calculation module is configured to: construct a second distribution network dispatching model of each distribution network system under each load node, calculate the net load demand of each distribution network system, and calculate the power redundancy and the power gap according to the net load demand of each distribution network system when the target disaster occurs; construct the first distribution network return dispatching model, and run the first distribution network return dispatching model based on the power redundancy and the power gap; calculate the net load demand of each load node after running the first distribution network return dispatching model; construct the second main network dispatching model, and run the second main network dispatching model by using the net load demand of each load node to calculate the lost load power and the power transmission congestion degree reduction rate of the target city when the target disaster occurs; the third evaluation index includes the load recovery rate and the power transmission capacity recovery rate, and the third calculation module is configured to: construct a distribution network restoration model of each distribution network system under each load node after the target disaster occurs, and calculate the power redundancy and the power gap; construct the second distribution network return dispatching model, and run the second distribution network return dispatching model based on the power redundancy and the power gap; calculate the total load demand of the distribution network under each load node after running the second distribution network return dispatching model; construct the third main network dispatching model, and run the third main network dispatching model by using the total load demand of the distribution network under each load node to calculate the load recovery rate and the power transmission capacity recovery rate of the target city after the target disaster occurs.

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