Urban power grid viability assessment method and device considering main and distribution cooperation

Abstract

The invention provides an urban power grid survivability assessment method and device considering main and distribution cooperation, and relates to the technical field of power grid survivability assessment. The method comprises the following steps: simulating a damaged scene of a power grid by adopting a Monte Carlo algorithm; before a target disaster occurs, constructing a first main network scheduling model and a first distribution network scheduling model, and calculating a first evaluation index; in the occurrence of a target disaster, constructing each second distribution network scheduling model and a first distribution network return scheduling model under each load node, calculating a corresponding net load demand, constructing a second main network scheduling model, and calculating a second evaluation index; after a target disaster occurs, constructing each distribution network recovery model and a second distribution network return scheduling model under each load node, calculating a corresponding distribution network total load demand, constructing a third main network scheduling model, and calculating a third evaluation index; and evaluating the survivability of the target urban power grid according to the three evaluation indexes. The method can improve the evaluation accuracy of the survivability of the urban power grid.

Description

Technical Field

[0001] The present application relates to the technical field of power grid survivability assessment, and in particular to a method and device for assessing the survivability of an urban power grid taking into account the coordination of main and distribution networks. Background Art

[0002] At present, in terms of the survival risk issues faced by urban power grids, the construction of related equipment and systems for monitoring the risks faced by urban power grids has been studied, and detection is mainly based on node collection data. Patent CN202411479181.0 establishes transmission evaluation values ​​related to voltage amplitude, power consumption, and voltage loss, and constructs a monitoring system including transmission, processing, analysis, and alarm units, and forms evaluation values ​​through monitoring node data to achieve risk monitoring. Patent CN202411178921.7 combines grid operation indicators such as load rate, voltage, and load loss, and risk inducement indicators such as temperature, precipitation, and wind speed, and uses the entropy weight method to confirm the correlation of each influencing factor and form a trust function to determine the risk monitoring value. Patent CN202411139532.3 integrates the node feature vectors of the load shedding rate, line loss sensitivity, and voltage sensitivity matrix to evaluate the node's supply guarantee capability.

[0003] In addition, on the issue of the survivability of urban power grids caused by extreme disaster risks, Patent CN202410960847.8 specifically analyzes the water accumulation index under heavy rain and flood disasters as equipment vulnerability, combines the resulting vulnerability of the grid topology, and uses the coefficient of variation method and VIKOR method to evaluate and rank 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 and subway impact coefficient under deliberate attack. Patent CN202410455872.0 considers the impact of ice disasters on the transmission system, establishes the failure probability curve of transmission lines under the combined influence of wind and ice, and evaluates the reliability of the transmission system. Patent CN202410776088.X models the urban waterlogging model under heavy rain disasters in detail, and constructs multiple random scenarios based on the Monte Carlo simulation method to optimize and solve the reconstruction of the distribution network and drainage network topology.

[0004] It can be seen that in response to the survival risks faced by urban power grids, existing technologies mainly focus on the research of transmission systems or distribution systems, and often ignore the synergy between the main grid and the distribution network, especially the power reverse support of the active distribution network to the main grid. It is also impossible to evaluate the survivability of the urban power grid as a whole under risk events, resulting in inaccurate assessment of the survivability of the urban power grid. Summary of the invention

[0005] The present application provides a method and device for evaluating the survivability of an urban power grid taking into account the coordination of main and distribution networks, so as to solve the problem of inaccurate risk assessment of urban power grids in the prior art.

[0006] In a first aspect, the present application provides a method for evaluating the viability of an urban power grid taking into account the coordination of main and distribution networks, including:

[0007] Monte Carlo algorithm is used to simulate the damage scenario of the power grid in the target city under the target disaster;

[0008] Before the target disaster occurs, a first main network dispatching model and a first distribution network dispatching model are constructed, and a first evaluation index is calculated;

[0009] When the target disaster occurs, the second distribution network dispatching model of each distribution network system under each load node in the main network and the first distribution network return dispatching model are constructed, the net load demand of each load node is calculated, the second main network dispatching model is constructed, and the second evaluation index is calculated;

[0010] After the target disaster occurs, the distribution network recovery model of each distribution network system under each load node in the main network and the second distribution network return dispatch model are constructed, the total load demand of the distribution network under each load node is calculated, the third main network dispatch model is constructed, and the third evaluation index is calculated;

[0011] The survivability of the target city power grid under the target disaster is evaluated according to the first evaluation index, the second evaluation index and the third evaluation index.

[0012] In a second aspect, the present application provides a device for evaluating the viability of an urban power grid taking into account the coordination of main and distribution networks, comprising:

[0013] An acquisition module is used to simulate the damage scenario of the power grid of the target city under the target disaster by using the Monte Carlo algorithm;

[0014] A first calculation module is used to construct a first main network scheduling model and a first distribution network scheduling model and calculate a first evaluation index before a target disaster occurs;

[0015] The second calculation module is used to construct a second distribution network dispatching model of each distribution network system under each load node in the main network and a first distribution network return dispatching model when the target disaster occurs, calculate the net load demand of each load node, construct a second main network dispatching model, and calculate the second evaluation index;

[0016] The third calculation module is used to construct a distribution network recovery model for each distribution network system under each load node in the main network and a second distribution network return dispatch model after the target disaster occurs, calculate the total load demand of the distribution network under each load node, construct a third main network dispatch model, and calculate the third evaluation index;

[0017] An evaluation module is used to evaluate the survivability of the target city power grid under the target disaster based on the first evaluation index, the second evaluation index and the third evaluation index.

[0018] The present application provides a method and device for evaluating the survivability of an urban power grid taking into account the coordination of main and distribution networks. The Monte Carlo algorithm is used to simulate the damaged scenario of the power grid of the target city 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 the 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 in the main grid are constructed to calculate the net load demand of each load node, construct a second main grid dispatching model, and calculate the 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 in the main grid are constructed to calculate the total load demand of the distribution grid under each load node, construct a third main grid dispatching model, and calculate the third evaluation index. According to the first evaluation index, the second evaluation index and the third evaluation index, the survivability of the target city power grid under the target disaster is evaluated. The present application highlights the tendency of "risk aversion" by calculating the evaluation indexes of the main grid and the distribution grid before, during and after the disaster, and improves the accuracy of the evaluation of the survivability of the urban power grid by coordinating the main grid and the distribution grid with each other. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative labor.

[0020] Figure 1 It is a flow chart for implementing the method for evaluating the viability of an urban power grid taking into account the coordination of the main and distribution networks provided in the embodiment of the present application;

[0021] Figure 2 It is a structural schematic diagram of an urban power grid survivability assessment device taking into account the coordination of main and distribution networks provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

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

[0024] When urban power grids face survival risks, there are mainly three problems:

[0025] First, existing technologies consider the survival risks faced by urban power grids mainly focus on high-probability high and low-loss events, and insufficient consideration is given to the risks brought by low-probability high-loss events. The survival pressure brought to urban power grids by extreme disasters still needs to be studied.

[0026] Second, the existing technology is not comprehensive enough in examining the survivability of urban power grids. It mainly focuses on reliability assessment in the early stages of risk events, lacks quantitative characterization of the survivability of urban power grids in the mid- and late stages of risk events, and ignores the supporting role of post-disaster distribution system topology reconstruction and maintenance and recovery on survivability.

[0027] Third, existing technologies mainly focus on the research of transmission systems or distribution systems, ignoring the synergy between the main grid and the distribution network, especially the power reverse support of the active distribution network to the main grid, and are unable to evaluate the survivability of the urban power grid as a whole under risk events.

[0028] Based on the above three problems, this application proposes a method for evaluating the survivability of urban power grids taking into account the coordination of main and distribution networks.

[0029] The design idea of ​​this application is: based on the principle that various types of disasters affect power grid equipment, identify and calculate the disaster-causing factors of extreme disasters, and build component vulnerability curves based on the disaster-causing factors to calculate the probability of component failure; for pre-disaster survivability assessment, use the Monte Carlo algorithm to generate grid damage scenarios under various disaster intensities, run the optimal scheduling models of the transmission system and distribution system respectively, calculate the load loss power, average line blocking rate and transmission capacity as pre-disaster survivability indicators, and use the segmented multi-objective risk framework to distinguish the conditional risk expectations under low, medium and high probability intervals; for disaster survivability assessment, consider The network topology of the distribution system is reconstructed, and the dispatch optimization is performed to calculate the power support capacity for the main grid, and the real-time load loss power, line transmission congestion degree and the improvement rate compared with the pre-disaster assessment are obtained as indicators of survivability during a disaster. For the post-disaster survivability assessment, the maintenance process of the distribution system maintenance team and the power reverse support capacity are considered, and a post-disaster recovery model is established to obtain the load and line transmission capacity recovery rate within 24 hours as an indicator of post-disaster survivability. Finally, based on the pre-disaster survivability indicators, the disaster survivability indicators and the post-disaster survivability indicators, an indicator matrix is ​​formed to evaluate the entire process and generate the overall survivability of the power grid.

[0030] The beneficial effects achieved are as follows;

[0031] First, in response to the problem that existing technologies ignore the relevant assessment of the survivability of urban power grids under high-risk and low-probability events, this technology divides the disaster set into intervals according to the frequency of occurrence, and calculates the expected values ​​of the assessment indicators in the corresponding intervals, highlighting the tendency of "risk aversion".

[0032] Second, the existing technology divides the urban power grid into the main grid and the distribution network for separate evaluation, ignoring the issue of interaction between the main grid and the distribution network. This technology takes into account the ability of the distributed power sources in the distribution network to provide power support and the ability to feed power back to the main grid through the substation, coordinates the main grid and the distribution network, and conducts disaster assessment on the urban power grid as a whole.

[0033] Third, in view of the fact that existing technologies lack the ability to evaluate the survivability of urban power grids from the perspective of resilience and ignore the problem of coordinated recovery of the main distribution network after a disaster, this technology takes into account the power reverse transmission capability of the distribution network at all times while considering the maintenance team recovery and network topology reconstruction process of the distribution network. Load and transmission capacity recovery rate indicators are designed to evaluate the survivability of urban power grids after a disaster.

[0034] Figure 1 The implementation flow chart of the urban power grid survivability assessment method taking into account the coordination of the main and distribution networks provided in the embodiment of the present application is described in detail as follows:

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

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

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

[0038] Obtain the disaster factors of the target disasters at different intensities in the target city, and divide the disaster scenarios of the target city under the target disaster into high-frequency scenario intervals, medium-frequency scenario intervals, and low-frequency scenario intervals according to the disaster factors at different intensities;

[0039] For each scenario interval, the historical meteorological data, power grid historical data and corresponding disaster-causing factors under the scenario interval are obtained to construct the component vulnerability curve corresponding to the scenario interval;

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

[0041] Accordingly, the Monte Carlo algorithm is used to simulate the damage scenario of the power grid in the target city under the target disaster, including:

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

[0043] Optionally, for each target city, the disaster factors of the target disasters of the target city at different intensities are obtained, and then the disaster scenarios of the target city under the target disasters are divided into high-frequency scenario intervals, medium-frequency scenario intervals and low-frequency scenario intervals using different disaster factors. Then, for each scenario interval, the historical meteorological data, power grid historical data and corresponding disaster factors under the scenario interval are obtained, and the component vulnerability curve corresponding to the scenario interval is constructed. According to the component vulnerability curve corresponding to the scenario interval, the component failure probability of all power grid equipment in the scenario interval is calculated. Finally, using the component failure probability under each scenario interval, the Monte Carlo algorithm is used to simulate the damaged scenarios of the power grid of the target city under target disasters of different intensities.

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

[0045] λ comp =f(d) (1)

[0046] Among them, λ comp is the component failure rate.

[0047] Taking the main threat mechanism of rainstorm disasters to urban distribution networks as an example, urban waterlogging caused by rainstorms makes important equipment at system nodes such as the ring network high-voltage cabinet of the distribution network face the risk of cable joint flooding. The disaster factor is the depth of urban waterlogging under rainstorms. The occurrence of rainstorm disasters is simulated by the Chicago rain type, as shown in the following formula:

[0048]

[0049] Among them, q(t) is the average rainstorm intensity at time t, in units of L / (s·hm 2 ), A1 is the rainfall per unit time, C is the rainfall intensity variation parameter, P is the rainstorm recurrence period, b is the historical correction parameter, n is the rainstorm attenuation index, and t is the time.

[0050] Combining the gridded geographic information of the distribution system area, taking into account the urban drainage capacity and surface infiltration, and using a two-dimensional hydrodynamic model to simulate the rainstorm waterlogging process, we can obtain the simulation data of the water level depth of the disaster-causing factors at specific times.

[0051] Specifically, the two-dimensional hydrodynamic model can be abstracted into a binary linear equation about water flow velocity to calculate precipitation, water flow between grids and drainage, and based on this, calculate the water depth of inland flooding. The calculation process is shown in the following formula:

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

[0053]

[0054] f k =f c +(f0-f c ) -βt (5)

[0055]

[0056] Q Neib =V x dΔyΔt (7)

[0057]

[0058] Where W(t) is the net increase in surface water, q(t) is the average rainstorm intensity at time t, and f k is the surface infiltration, Q drain is the drainage volume of the rainwater well, k is the unit conversion coefficient, n is the Manning coefficient, d is the disaster factor, V is the water velocity vector, g is the gravitational 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 lateral component of the water velocity vector, Δy is the longitudinal length of each grid, Δt is the time interval, Δd is the increase in water depth, θ is the building coverage, and Δx is the lateral length of each grid.

[0059] In this way, the disaster factor d can be calculated, and the relationship between the failure rate of the power distribution equipment and the disaster factor, as well as the failure probability of the components related to the power distribution equipment, can be established. That is, the component failure probability in each scenario interval can be calculated by the first formula. The first formula is:

[0060]

[0061] Among them, P comp (t) is the probability of component failure, λ comp (k) is the component failure rate, γ is the damping coefficient, ξ is the attenuation coefficient, d(t) is the disaster factor, D BwD is the height of the cable joint in the power distribution room from the ground. w is the design height for flood control of the power distribution room, t is the time, and k is the component category index number.

[0062] Then, based on the historical meteorological data of the city and the historical data of damage to power grid components, the relevant parameters of disaster-causing factors and component failure rate design are determined. Then, based on the historical frequency of disasters of different intensities, the set of disaster events with corresponding frequencies, i.e., the disaster scenario interval, is divided into multiple small sets according to the frequency, and the multiple small sets are divided into high-frequency scenario intervals, medium-frequency scenario intervals, and low-frequency scenario intervals.

[0063] In the embodiment of the present application, fuzzy functions and other means can also be used to derive and calculate the component failure rate and component failure probability.

[0064] In cities of average size, the disaster intensity faced by the transmission system and the distribution system is not much different, so they can be analyzed in the same disaster scenario. For each disaster scenario of the target disaster, the Monte Carlo algorithm is used to generate random numbers that obey uniform distribution corresponding to each component in each time period, and by comparing with the failure probability of the component, the component damage scenario is sampled. As shown in the following formula:

[0065]

[0066] Among them, η i,t is a random number ranging from 0 to 1 randomly generated for element i at time t, P i,comp (t) is the component failure probability of component i at time t.

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

[0068] In an embodiment of the present application, after simulating the damaged scenario of the power grid in the target city under the target disaster, for each damaged scenario, a first main grid dispatching model of the city's main grid topology and a first distribution network dispatching model of each distribution network under each load node under 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 network are calculated respectively as pre-disaster evaluation indicators.

[0069] Among them, the first distribution dispatching model takes minimizing the load reduction of the distribution network as the optimization goal, and its objective function is:

[0070]

[0071] Among them, f1 is the load reduction of the distribution network, α power,j,t is the load weight of each node, P shed,j,tis the active load shedding power of node j in the distribution network in time period t, j is the node, N is the total number of nodes, t is the time period, and T is the time period set.

[0072] The corresponding constraints include node power balance constraints, branch power flow constraints, various types of power load constraints, and distribution network topology reconstruction constraints during disasters.

[0073] Among them, the node power balance constraint is:

[0074]

[0075] Among them, P Buy,j,t is the active power of substation node j in time period t, P DG,j,t is the active power output of distributed power generation, P L,j,t is the actual active load connected, H l,t is the active power flowing through the line, Q Buy,j,t is the reactive power of substation node j in time period t, Q DG,j,t is the reactive power output of distributed power generation, Q L,j,t is the actual connected reactive load, G l,t is the reactive power flowing through the line, l is the line, j is the node, N is the total number of nodes, t is the time period, T is the time period set, ∏(j) is the set of lower-level lines of node j, and Ω(j) is the set of upper-level lines of node j.

[0076] The branch power flow constraint is:

[0077]

[0078]

[0079]

[0080]

[0081] Where M is a sufficiently large constant, u l,t is the access status of line l in time period t. A value of 1 indicates that the line is in operation. V i,t is the voltage amplitude of node i in time period t, V j,t is the voltage amplitude of node j in time period t, r l is the line impedance, x l is the line inductive reactance, is the line capacity, is the minimum node voltage, is the maximum node voltage, ε l is the set of lines, N l is a set of line node pairs.

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

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Among them, 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 reactive load, The start and stop status of the substation power supply. is the lower limit of the active power of the substation power load, is the upper limit of the active power of the substation power load, is the lower limit of reactive power of reactive power compensation for substation power supply, is the upper limit of reactive power of reactive power compensation of substation power supply, P Buy,j is the active power of the substation power load, Q Buy,j Reactive power for reactive power compensation of substation power supply, is the start and stop status of the distributed power source, is the lower limit of the active power of the distributed generation load, is the upper limit of the active power of the distributed generation load, is the lower limit of reactive power of distributed generation reactive power compensation, is the upper limit of reactive power of distributed generation reactive power compensation, P DG,j,t is the active power of distributed generation, Q DG,j,t It is the reactive power of the distributed generation reactive power compensation.

[0089] The distribution network needs to maintain open-loop operation. After being damaged, it needs to reconstruct the topology by adjusting the interconnection switches so that the original isolated islands can be reconnected, achieve energy mutual assistance and reduce load loss.

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

[0091]

[0092] in, Whether line l is selected for access during period t, a value of 1 indicates that it is selected for access; Is line l damaged during time period t? A value of 1 indicates that the line is intact; For When it is 1, node j becomes the root node of the microgrid in period t, and f l,t is the virtual line power flow, and the third item in formula (19) stipulates that the virtual power flow of the damaged line is zero, and the fourth item in formula (19) stipulates that for nodes other than the root node, a virtual power of 1 is consumed per time period; n is the number of distribution network nodes.

[0093] The first main grid dispatching model takes minimizing the load shedding of the transmission system as its objective function, and its objective function is:

[0094]

[0095] Where f2 is the load shedding of the transmission system, P shed,j,t is the load shedding power of transmission system node j in time period t.

[0096] The corresponding constraints include node power balance constraints, power flow constraints and various types of power load constraints.

[0097] Among them, the node power balance constraint is:

[0098]

[0099] Among them, P G,i,t is the active power of each node i in the network in time period t, P L,i,t It is the actual access active load.

[0100] The power flow constraint is:

[0101]

[0102] Among them, b ij is the mutual capacitance between lines ij, θ i,t is the voltage phase angle of node i in period t, θ j,t is the voltage phase angle of node j in period t.

[0103] The load constraints for each type of power supply are:

[0104]

[0105] In a possible implementation, calculating the first evaluation index includes:

[0106] Calculate the load loss power, available transmission capacity and transmission line congestion degree of each damage scenario of the target disaster respectively;

[0107] The load loss power, available transmission capacity and transmission line congestion degree of each damaged scenario are used to calculate the average load loss power, average available transmission capacity and average transmission line congestion degree of the target disaster, and the average load loss power, average available transmission capacity and average transmission line congestion degree of the target disaster are used as the first evaluation indicator.

[0108] Optionally, in the embodiment of the present 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, available transmission capacity and transmission line congestion degree of each damaged scene of the target disaster are calculated respectively, that is, the load loss power, available transmission capacity and transmission line congestion degree of each damaged scene of the target disaster are calculated by the second formula, and the second formula is:

[0109]

[0110] Among them, LOLE d For load loss power, TCAP d is the available transmission capacity, TCong d is the degree of transmission line congestion, α power,j,t is the load weight of each node, P shed,j,t is the load shedding power of node j in period t, is the line capacity, u l,T is the access status of line l, H l,T is the active power flowing through the line, j is the node, N is the total number of nodes, t is the time period, T is the time period set, l is the line, ε l A collection of lines.

[0111] Among them, the available transmission capacity and congestion level are evaluated after the distribution network completes the topology reconstruction.

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

[0113]

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

[0115] In one possible implementation, after calculating the first evaluation indicator, the method may include:

[0116] For each disaster scenario in the target city power grid, the high-frequency scenario interval, medium-frequency scenario interval and low-frequency scenario interval are divided. LP For example, considering all kinds of disasters such as rainstorms, earthquakes, and storms, based on the first evaluation index calculated above, the expectation of the interval range evaluation index can be expressed as:

[0117]

[0118] Among them, E_LOLE is the expected load loss power in the low-frequency scenario interval, E_TCAP is the expected available capacity of transmission in the low-frequency scenario interval, E_TCong is the expected degree of transmission line congestion in the low-frequency scenario interval, P(D s ) is the D s The probability of disasters.

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

[0120] In an embodiment of the present application, a simultaneous assessment of the survivability of the main network and distribution network in the urban power grid is achieved based on the disaster characteristics, and different risk intervals are divided according to the intensity and frequency of the disaster.

[0121] In step 103, when the target disaster occurs, a second distribution network dispatching 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 dispatching model is constructed, and a second evaluation index is calculated.

[0122] In an embodiment of the present application, the evaluation framework during a disaster is oriented to a specific component damage scenario, and takes into account the ability of the pre-disaster evaluation framework to coordinate the main and distribution networks. Due to the damage to the main network components, the load nodes can no longer obtain the originally planned power supply. The distribution system that relies on the main network to transmit electricity through the substation may not be able to obtain enough electricity, resulting in a further increase in the load loss power in the distribution network. On the other hand, the non-active distribution network can use distributed power sources and energy storage to achieve independent operation during a disaster, and can even provide reverse power supply to the main network to alleviate the load shedding situation. Specifically, a second distribution network scheduling model for each distribution network system under each load node in the main network and a first distribution network return scheduling model are constructed to calculate the net load demand of each load node, and then a second main network scheduling model is constructed to calculate the second evaluation index. Among them, the second evaluation index includes the load loss power and the transmission congestion reduction rate.

[0123] In a possible implementation, when a target disaster occurs, a second distribution network dispatching 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 dispatching model is constructed, and a second evaluation index is calculated, including:

[0124] When the target disaster occurs, a second distribution network dispatching model of each distribution network system under each load node is constructed to calculate the net load demand of each distribution network system, and based on the net load demand of each distribution network system, the power redundancy and power gap are calculated;

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

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

[0127] Construct the second main grid dispatching model, and use the net load demand of each load node to run the second main grid dispatching model to calculate the reduction rate of load loss power and transmission congestion in the target city when the target disaster occurs.

[0128] The specific calculation ideas of the embodiment of this application are as follows:

[0129] Based on the damage scenarios in specific target disasters, the main grid disaster topology is formed. For each main grid load node, the distribution systems under it are analyzed, the distribution network topology reconstruction process during the disaster is considered, and the distribution network scheduling model is run to obtain the disaster operation network of each distribution system and the required main grid supply power. According to the active output of distributed power sources and load demand of the distribution network, the power redundancy and power gap of the main grid on-line part are calculated.

[0130] For distribution networks with power redundancy, the distribution network return dispatch model is run to obtain the power support capacity of this part of the distribution network. The distribution system with return capacity can provide power support for other distribution systems with power gaps through the busbar of the load node. Therefore, the required main grid power supply of the distribution system with power gap can be combined to calculate the net load demand of the main grid load node.

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

[0132] Specifically, the distribution network dispatching model in the embodiment of the present application is similar to that before the disaster. In the process of considering the coordination of the main distribution network, more distribution network resources should be brought into play. Therefore, when the target disaster occurs, the second distribution network dispatching model of each distribution network system under each load node is constructed to maximize the power support role undertaken by the distributed power source as the optimization goal, and its objective function is:

[0133]

[0134] Among them, f is the power support role undertaken by distributed power sources.

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

[0136] Among them, power redundancy is the difference between the active output of the distributed power source at the grid node of the distribution network system and the load demand, and power gap is the power of the distribution network load loss at the grid node. Accordingly, power redundancy and power gap are calculated by the third formula, which is:

[0137]

[0138] Among them, P Sur,t For power redundancy, P shed,t For power shortage, is the upper limit of the active power of DG load, P Load,t is the active load demand, P shed,j,t is the load shedding power of node j in 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, considering that the substation node is treated as a load node, and it is necessary to give priority to the internal load demand of the distribution network, so the substation node can be regarded as a third-level load. SuR,t As the substation load size, the first distribution network return dispatching model is run to calculate the net load demand of each load node. Correspondingly, the objective function of the first distribution network return dispatching model is:

[0140]

[0141] Among them, f4 is the objective function of the first distribution network return dispatch model, α DT,j is the weight coefficient of load shedding when returning active power, It is the load active power reduction that occurs when active power is returned.

[0142] Ignore the power supply variable P of the main grid Buy,j,t , and modify the node balance power constraint to:

[0143]

[0144] Among them, P DT,j,t is the returned active power, Q DT,j,t is the reactive power returned.

[0145] Add the constraint on the return power as follows:

[0146]

[0147] in, Q is the load active power reduction that occurs when active power is returned. DT,t is the reactive power returned, Q is the load reactive power reduction caused by returning reactive power. Sur,t For reactive power redundancy.

[0148] The power load demand of the load node, that is, the net load demand, can be calculated based on the return power and the power gap, such as:

[0149]

[0150] in, To consider the power load demand after the return power, P shed,m,t is the load active power reduction of distribution network m, P DT,m,t is the active power returned by the distribution network m, and M is the set of distribution systems corresponding to the main grid load node i.

[0151] Construct a second main network scheduling model. The second main network scheduling model in the embodiment of the present application when the target disaster occurs is similar to the pre-disaster assessment framework and will not be repeated here.

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

[0153]

[0154]

[0155] Among them, LOLE_P is the load loss power, TCong_P is the transmission congestion reduction rate, P shed,j,t is the load node loss power obtained by the second main grid dispatching model, The main grid load loss power in the pre-disaster assessment framework for disaster scenarios, The degree of main grid transmission congestion in the pre-disaster assessment framework for disaster scenarios.

[0156] The embodiments of the present application take into account the main distribution network system process during a disaster, take into account the ability of the distribution network during a disaster to reversely supply power to the main network through topology reconstruction and distributed power generation, and evaluate the survivability of the urban power grid.

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

[0158] In the embodiment of the present application, the post-disaster assessment framework is also oriented to the specific component damage scenario. The main distribution coordination idea is similar to the disaster assessment. It considers the maintenance and recovery process of the distribution system after the disaster and examines the post-disaster recovery capability of the target city power grid. Specifically, a distribution network recovery model for each distribution network system under each load node in the main network and a second distribution network return dispatching model are constructed to calculate the total load demand of the distribution network under each load node, and then a third main network dispatching model is constructed to calculate the third evaluation index. Among them, the third evaluation index includes the load recovery rate and the transmission capacity recovery rate.

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

[0160] After the target disaster occurs, a distribution network recovery model is constructed for each distribution network system under each load node to calculate power redundancy and power gap;

[0161] Construct a second distribution network return dispatch model, and run the second distribution network return dispatch model based on power redundancy and power gap;

[0162] After running the second distribution network return dispatch model, the total load demand of the distribution network under each load node is calculated;

[0163] Construct the third main grid dispatching model, and use the total load demand of the distribution network under each load node to run the third main grid dispatching model to calculate the load recovery rate and transmission capacity recovery rate of the target city after the target disaster occurs.

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

[0165] Since the embodiment of the present application considers the process of deploying a maintenance team for recovery after the target disaster occurs, it is necessary to add maintenance team recovery related constraints in the model. The specific constraints are as follows:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] Among them, y d,c is a variable that records whether location d has entered the maintenance point path sequence of maintenance team c. When it is 1, it means that it has entered; x d,s,c is the order of the maintenance points of the maintenance team. When it is 1, it means that the maintenance team c is immediately after the passing point s and the passing point d, where d is the passing point, N d Gather at the location where the maintenance team passes. o is the starting station of the maintenance team, d e The maintenance team is stationed at the maintenance point, c is the maintenance team, C ar is the assembly of the maintenance convoy, s is the passing point; For the maintenance team from the maintenance completion station point d e After departure, the starting point of the route is d o The sequence of maintenance points is 0, which means that the sequence cannot be selected; For the maintenance team from the starting station d o After departure, the route maintenance is completed and the station point is d e The sequence of maintenance point paths. When it is 0, it means that the sequence cannot be selected; The starting point d o Whether to enter the maintenance point of maintenance team c, 1 means must enter; Complete station point d for maintenance e Whether to enter the maintenance point of maintenance team c, 0 means no entry, φ d and φ s To ensure that the maintenance path has no auxiliary variables of sub-loops, n des is the total number of faulty components.

[0177] Formulas (31)-(33) stipulate that except for the starting point, there is a unique corresponding route location before and after the maintenance point on the route. Formulas (34)-(39) stipulate the path selection decision of the maintenance team at the starting point and the end point.

[0178] The route selection decision of the maintenance team is combined with the component maintenance time and road travel time. The relevant constraints are as follows:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185] in, is the time for maintenance team c to arrive at location d, is the repair time of the faulty component at maintenance point d, is the road travel time from location d to location s, is the time it takes for the maintenance team c to reach location s, τ d,t It is used to record the maintenance completion time period of the maintenance point. When it is taken as 1, it means that the faulty component at location d is restored in time period t; ε is a very small positive number.

[0186] Formula (41) indicates that the arrival time at location s is equal to the sum of the arrival time at the previous location d, the component repair time, and the travel time between the two locations. Formula (42) links the route point selection for maintaining the fleet with the arrival time. Formulas (43)-(46) are used to determine the corresponding time period for maintenance completion.

[0187] After the component repair is completed, the connectivity can be restored, such as:

[0188]

[0189] in, It is the repair transient state of component l, and 1 indicates that it has been repaired.

[0190] In the embodiment of the present application, a scheduling plan can also be obtained based on the Dijistra algorithm, and then distribution network recovery optimization can be performed.

[0191] As the maintenance process progresses and the network topology is reconstructed, the power redundancy or power gap of the distribution system will be different in each time period. There may be a situation where the power redundancy suddenly changes to a power gap. Therefore, it is necessary to calculate the power redundancy or power gap for each time period. In addition, the substation node is considered as a load or power source in the second distribution network reverse transmission scheduling model. Then the second distribution network reverse transmission scheduling model can introduce the substation node state constant O sub,t , when there is a power gap in time period t, it is set to 1, indicating the power supply state, otherwise it is set to 0. Then the node balance power constraint of the second distribution network return dispatch model is as shown in formula (9). And modify P representing the flow power between the main distribution network Buy,j,t , Q Buy,j,t , P DT,t , Q DT,t The relevant constraints are:

[0192]

[0193]

[0194]

[0195] The subsequent evaluation process in the embodiment of the present application is similar to the disaster assessment framework. After the available power of each load node is allocated to the distribution system with power gap according to the importance of each distribution system, the main network optimization scheduling model is run again to obtain the recovery status of the distribution network load and lines, which will not be repeated.

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

[0197]

[0198] Among them, 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 city power grid under the target disaster is evaluated based on the first evaluation index, the second evaluation index and the third evaluation index.

[0200] In the embodiment of the present application, various processes of the target disaster are taken into consideration, and the survivability of the target city power grid under the target disaster is evaluated based on 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 method for evaluating the survivability of an urban power grid taking into account the coordination of main and distribution networks, by using a Monte Carlo algorithm to simulate the damaged scenario of the power grid of a target city under a 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 occurrence of the target disaster, 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 are constructed to calculate the net load demand of each load node, 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 in the main grid are constructed to calculate the total load demand of the distribution grid under each load node, a third main grid dispatching model is constructed to calculate a third evaluation index; according to the first evaluation index, the second evaluation index and the third evaluation index, the survivability of the target city power grid under the target disaster is evaluated. The present application highlights the tendency of "risk aversion" by calculating the evaluation indicators of the main grid and the distribution grid before, during and after the disaster, and improves the accuracy of the evaluation of the survivability of the urban power grid by coordinating the main grid and the distribution grid with each other.

[0202] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

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

[0204] Figure 2 The structure diagram of the urban power grid survivability assessment device taking into account the coordination of the main and distribution networks provided in the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown, which is described in detail as follows:

[0205] like Figure 2 As shown, the urban power grid survivability assessment device 2 taking into account the coordination of main and distribution networks includes:

[0206] An acquisition module 21 is used to simulate a damage scenario of a power grid in a target city under a target disaster by using a Monte Carlo algorithm;

[0207] A first calculation module 22 is used to construct a first main network scheduling model and a first distribution network scheduling model and calculate a first evaluation index before a target disaster occurs;

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

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

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

[0211] The present application provides a device for evaluating the survivability of an urban power grid taking into account the coordination of main and distribution networks. The device simulates the 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 grid dispatching model are constructed to calculate a first evaluation index. During the occurrence of 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 in the main grid are constructed to calculate the net load demand of each load node, construct a second main grid dispatching model, and 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 in the main grid are constructed to 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. According to the first evaluation index, the second evaluation index, and the third evaluation index, the survivability of the target city power grid under the target disaster is evaluated. The present application highlights the tendency of "risk aversion" by calculating the evaluation indexes of the main grid and the distribution grid before, during, and after the disaster, and improves the accuracy of the evaluation of the survivability of the urban power grid by coordinating the main grid and the distribution grid with each other.

[0212] In a possible implementation, the device may further include a probability calculation module, which may be used to:

[0213] Obtain the disaster factors of the target disasters at different intensities in the target city, and divide the disaster scenarios of the target city under the target disaster into high-frequency scenario intervals, medium-frequency scenario intervals, and low-frequency scenario intervals according to the disaster factors at different intensities;

[0214] For each scenario interval, the historical meteorological data, power grid historical data and corresponding disaster-causing factors under the scenario interval are obtained to construct the component vulnerability curve corresponding to the scenario interval;

[0215] Based on the component vulnerability curve of each scenario interval, the component failure probability of the corresponding scenario interval is calculated;

[0216] Accordingly, the acquisition module can be used to:

[0217] Based on the component failure probability in each scenario interval, the Monte Carlo algorithm is used to simulate the damage scenario of the power grid in 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 used to:

[0219] The component failure probability in each scenario interval is calculated by the first formula, which is:

[0220]

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

[0222] In a possible implementation, the first calculation module may be used to:

[0223] Calculate the load loss power, available transmission capacity and transmission line congestion degree of each damage scenario of the target disaster respectively;

[0224] The load loss power, available transmission capacity and transmission line congestion degree of each damaged scenario are used to calculate the average load loss power, average available transmission capacity and average transmission line congestion degree of the target disaster, and the average load loss power, average available transmission capacity and average transmission line congestion degree of the target disaster are used as the first evaluation indicator.

[0225] In a possible implementation, the first calculation module may also be used for:

[0226] The load loss power, available transmission capacity and transmission line congestion degree of each damaged component in each scenario interval are calculated by the second formula, which is:

[0227]

[0228] Among them, LOLE d For load loss power, TCAP d is the available transmission capacity, TCong d is the degree of transmission line congestion, α power,j,t is the load weight of each node, P shed,j,t is the load shedding power of node j in period t, is the line capacity, u l,T is the access status of line l, H l,T is the active power flowing through the line, j is the node, N is the total number of nodes, t is the time period, T is the time period set, l is the line, ε l A collection of lines.

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

[0230]

[0231] Among them, f1 is the load reduction of the distribution network, α power,j,t is the load weight of each node, P shed,j,t is the active load shedding power of node j in the distribution network in time period t, j is the node, N is the total number of nodes, t is the time period, and T is the time period set.

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

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

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

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

[0236] Construct the second main grid dispatching model, and use the net load demand of each load node to run the second main grid dispatching model to calculate the reduction rate of load loss power and transmission congestion in the target city when the target disaster occurs.

[0237] In a possible implementation, the second calculation module may be used to:

[0238] The power redundancy and power shortage are calculated by the third formula, which is:

[0239]

[0240] Among them, P Sur,t For power redundancy, P shed,t For power shortage, is the upper limit of the active power of DG load, P Load,t is the active load demand, P shed,j,t is the load shedding power of node j in period t.

[0241] In a possible implementation, the third evaluation indicator includes a load recovery rate and a transmission capacity recovery rate, and the third calculation module may be used to:

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

[0243] Construct a second distribution network return dispatch model, and run the second distribution network return dispatch model based on power redundancy and power gap;

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

[0245] Construct the third main grid dispatching model, and use the total load demand of the distribution network under each load node to run the third main grid dispatching model to calculate the load recovery rate and 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 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.

[0247] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are 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.

[0248] If the 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. When the computer program is executed by the processor, it can implement the steps of the above-mentioned embodiments of the urban power grid survivability assessment method taking into account the coordination of the main and distribution. 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 that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium.

[0249] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for evaluating the viability of an urban power grid taking into account the coordination of main and distribution networks, characterized in that: include: Monte Carlo algorithm is used to simulate the damage scenario of the power grid in the target city under the target disaster; Before the target disaster occurs, a first main grid dispatching model and a first distribution grid dispatching model are constructed, and a first evaluation index is calculated; 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 in the main grid are constructed, the 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 grid recovery model and a second distribution grid return dispatching model of each distribution grid system under each load node in the main grid are constructed, the 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; based on the first evaluation index, the second evaluation index and the third evaluation index, the survivability of the target city power grid under the target disaster is evaluated.

2. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 1 is characterized in that: Before using the Monte Carlo algorithm to simulate the damage scenario of the power grid of the target city under the target disaster, the method further includes: Obtaining disaster factors of the target disaster at different intensities in the target city, 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 factors at different intensities; For each scenario interval, the historical meteorological data, power grid historical data and corresponding disaster-causing factors under the scenario interval are obtained to construct the component vulnerability curve corresponding to the scenario interval; Based on the component vulnerability curve of each scenario interval, the component failure probability of the corresponding scenario interval is calculated; Accordingly, the Monte Carlo algorithm is used to simulate the damage scenario of the power grid of the target city under the target disaster, including: Based on the component failure probability in each scenario interval, the Monte Carlo algorithm is used to simulate the damage scenario of the power grid of the target city under the target disaster.

3. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 2 is characterized in that: When the target disaster is a rainstorm disaster, the component vulnerability curve based on each scenario interval is used to calculate the component failure probability of the corresponding scenario interval, including: The component failure probability in each scenario interval is calculated by a first formula, and the first formula is: Among them, P comp (t) is the failure probability of the component, λ comp (k) is the component failure rate, γ is the damping coefficient, ξ is the attenuation coefficient, d(t) is the disaster factor, D Bw D is the height of the cable joint in the power distribution room from the ground. w is the design height for flood control of the power distribution room, t is the time, and k is the component category index number.

4. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 1 is characterized in that: The calculating of the first evaluation index comprises: Calculate the load loss power, available transmission capacity and transmission line congestion degree of each damaged scenario of the target disaster respectively; The load loss power, available transmission capacity and transmission line congestion degree of each damaged scenario are used to calculate the average load loss power, average available transmission capacity and transmission line congestion degree of the target disaster, and the average load loss power, average available transmission capacity and transmission line congestion degree of the target disaster are used as the first evaluation index.

5. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 4 is characterized in that: The respectively calculating the load loss power, available transmission capacity and transmission line congestion degree of each damaged scenario of the target disaster includes: The load loss power, available transmission capacity and transmission line congestion degree of each damaged scenario of the target disaster are calculated by the second formula, and the second formula is: Among them, LOLE d For the load loss power, TCAP d is the available transmission capacity, TCong d is the degree of congestion of the transmission line, α power,j,t is the load weight of each node, P shed,j,t is the load shedding power of node j in period t, is the line capacity, u l,T is the access status of line l, H l,T is the active power flowing through the line, j is the node, N is the total number of nodes, t is the time period, T is the time period set, l is the line, ε l A collection of lines.

6. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 1 is characterized in that: The objective function of the first distribution network scheduling model is: Among them, f1 is the load reduction of the distribution network, 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 in time period t, where j is the node, N is the total number of nodes, t is the time period, and T is the time period set.

7. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 1 is characterized in that: The second evaluation index includes the reduction rate of load loss power and transmission congestion degree. When the target disaster occurs, the second distribution network dispatching model of each distribution network system under each load node in the main network and the first distribution network return dispatching model are constructed, the net load demand of each load node is calculated, the second main network dispatching model is constructed, and the second evaluation index is calculated, including: When the target disaster occurs, 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 power gap are calculated according to the net load demand of each distribution network system; Constructing the first distribution network return scheduling model, and running the first distribution network return scheduling model based on the power redundancy and the power gap; After running the first distribution network return dispatching model, calculating the net load demand of each load node; The second main grid dispatching model is constructed, and the net load demand of each load node is used to run the second main grid dispatching model to calculate the load loss power and the transmission congestion reduction rate of the target city when the target disaster occurs.

8. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 7 is characterized in that: The power redundancy and power gap are calculated according to the load demand of each distribution network system, including: The power redundancy and the power gap are calculated by a third formula, and the third formula is: Among them, P Sur,t For the power redundancy, P shed,t For the electricity gap, is the upper limit of the active power of DG load, P Load,t is the active load demand, P shed,j,t is the load shedding power of node j in period t.

9. The urban power grid survivability assessment method taking into account the coordination of main and distribution networks according to claim 1 is characterized in that: The third evaluation index includes the load recovery rate and the transmission capacity recovery rate. After the target disaster occurs, the distribution network recovery model of each distribution network system under each load node in the main network and the second distribution network return dispatch model are constructed, the total load demand of the distribution network under each load node is calculated, the third main network dispatch model is constructed, and the third evaluation index is calculated, including: After the target disaster occurs, a distribution network recovery model of each distribution network system under each load node is constructed to calculate power redundancy and power gap; Constructing the second distribution network return dispatching model, and running the second distribution network return dispatching model based on the power redundancy and the power gap; After running the second distribution network return dispatching model, calculating the total load demand of the distribution network under each load node; The third main grid dispatching model is constructed, and the total load demand of the distribution network under each load node is used to run the third main grid dispatching model to calculate the load recovery rate and the transmission capacity recovery rate of the target city after the target disaster occurs.

10. A device for evaluating the viability of an urban power grid taking into account the coordination of main and distribution networks, characterized in that: include: An acquisition module is used to simulate the damage scenario of the power grid of the target city under the target disaster by using the Monte Carlo algorithm; A first calculation module is used to construct a first main network scheduling model and a first distribution network scheduling model and calculate a first evaluation index before a target disaster occurs; The second calculation module is used to construct a second distribution network dispatching model of each distribution network system under each load node in the main network and a first distribution network return dispatching model when the target disaster occurs, calculate the net load demand of each load node, construct a second main network dispatching model, and calculate the second evaluation index; The third calculation module is used to construct a distribution network recovery model for each distribution network system under each load node in the main network and a second distribution network return dispatch model after the target disaster occurs, calculate the total load demand of the distribution network under each load node, construct a third main network dispatch model, and calculate the third evaluation index; An evaluation module is used to evaluate the survivability of the target city power grid under the target disaster based on the first evaluation index, the second evaluation index and the third evaluation index.

Images