Flexible partitioning and efficient networking method and system for power distribution system in extreme weather

Abstract

The invention relates to a flexible partitioning and efficient networking method and system for a power distribution system in extreme weather, belongs to the technical field of disaster prevention scheduling of power systems, and solves the problems of insufficient continuous power supply capacity of important loads and low new energy utilization rate of a power distribution network in the prior art when an extreme weather disaster occurs. Comprising the steps of obtaining rainfall information and a power distribution network topological structure of a power distribution network region, and further obtaining a power distribution network prediction topological structure of each time period of continuous occurrence of a disaster; district division is carried out on the power distribution network prediction topological structure in each time period when the disaster continuously occurs, and a power supply redundancy index of each district in the corresponding time period is obtained; under the constraint condition of power distribution network district networking, on the basis of districts divided by a power distribution network prediction topological structure in each time period of disaster continuous occurrence and a power supply redundancy index, a district networking scheme in each time period of disaster continuous occurrence when the load recovery function of the power distribution network is the maximum value is obtained by taking the maximization of the load recovery function of the power distribution network as a target. And power restoration is carried out on the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system disaster prevention dispatching, and particularly relates to a flexible partitioning and efficient networking method and system for a power distribution system under extreme weather. BACKGROUND

[0002] In recent years, with the intensification of global climate change, extreme weather events occur frequently, and the disaster risk of power systems is increasingly prominent. Extreme disasters such as heavy rain pose a serious challenge to the safe and reliable operation of distribution networks. The traditional fault recovery strategy has been unable to cope with the complex and changing extreme disaster situation, and it is difficult to fully utilize the potential of distributed resources. There have been a large number of studies on the volatility of new energy power generation and load and the uneven distribution of power in the substation under extreme disasters, but there is still a lack of research on how to effectively use various types of flexible load and multiple types of power supply resources strategies in the dynamic process of disaster timing development, and improve the power supply reliability and load recovery value of the island in the disaster scene of multiple faults.

[0003] Currently, the main research on the power supply strategy method for line fault recovery of distribution networks under extreme weather disaster scenarios is to supply power to the substation with a single power source. The problem of dynamically adjusting the system partition by fully mobilizing the synergistic effect of multiple power supply resources and the active regulation and control capability of flexible controllable load has not been studied, resulting in insufficient continuous power supply capacity for important loads and low utilization rate of new energy when extreme weather disasters occur. SUMMARY

[0004] In view of the above analysis, the embodiments of the present application aim to provide a flexible partitioning and efficient networking method and system for a power distribution system under extreme weather, to solve the problem of insufficient continuous power supply capacity for important loads and low utilization rate of new energy of the existing distribution network when extreme weather disasters occur.

[0005] In one aspect, the embodiments of the present application provide a flexible partitioning and efficient networking method for a power distribution system under extreme weather, comprising the following steps:

[0006] Obtain rainfall information of the distribution network region and the distribution network topology, and then obtain the predicted topology structure of the distribution network in each period of disaster duration. The predicted topology structure of the distribution network includes predicted fault lines;

[0007] Divide the substation according to the predicted topology structure of the distribution network in each period of disaster duration, and obtain the power supply redundancy index of each substation in the corresponding period;

[0008] Under the constraint condition of power distribution network area networking, each area and power supply redundancy index of the power distribution network prediction topology structure in each period of disaster duration are obtained, and a power distribution network load recovery function is maximized to obtain a power distribution network area networking scheme in each period of disaster duration when the power distribution network load recovery function is maximum, so as to restore power supply of the power distribution network.

[0009] In one aspect, the embodiment of the present application provides a flexible partitioning and efficient networking system of a power distribution system under extreme weather, which comprises:

[0010] A topology prediction module is configured to obtain rainfall information of a power distribution network area and a power distribution network topology, and then obtain a power distribution network prediction topology structure in each period of disaster duration, wherein the power distribution network prediction topology structure comprises a predicted fault line.

[0011] An area division and power supply redundancy index construction module is configured to divide areas of the power distribution network prediction topology structure in each period of disaster duration, and obtain power supply redundancy indexes of each area in the corresponding period.

[0012] A power supply recovery module is configured to, under the constraint condition of power distribution network area networking, obtain each area and power supply redundancy index of the power distribution network prediction topology structure in each period of disaster duration, maximize a power distribution network load recovery function as an objective, obtain a power distribution network area networking scheme in each period of disaster duration when the power distribution network load recovery function is maximum, and restore power supply of the power distribution network.

[0013] Compared with the prior art, the present application can achieve at least one of the following beneficial effects:

[0014] The flexible partitioning and efficient networking method and system of the power distribution system under extreme weather provided by the embodiment of the present application can obtain rainfall information of a power distribution network area and a power distribution network topology, and then obtain a power distribution network prediction topology structure in each period of disaster duration, divide areas of the power distribution network prediction topology structure in each period of disaster duration, obtain power supply redundancy indexes of each area in the corresponding period, maximize a power distribution network load recovery function as an objective, obtain a power distribution network area networking scheme in each period of disaster duration when the power distribution network load recovery function is maximum, and restore power supply of the power distribution network, so that the synergistic effect of various power supply resources and the active regulation and control capability of flexible controllable loads are fully mobilized to dynamically adjust the system partitioning, realize linkage and cooperation of the whole process of disaster partitioning, networking and recovery, significantly improve new energy utilization rate and load recovery amount, and reduce the wind and light abandonment rate.

[0015] The technical solutions in the present application can be combined with each other to realize more preferred combination solutions. Other features and advantages of the present application will be described in the following description, and some advantages will become apparent from the description, or will be understood by those skilled in the art through implementation of the present application. The objects and other advantages of the present application can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application.

[0017] Figure 1 A flowchart of a flexible partitioning and efficient networking method of a power distribution system under extreme weather for an embodiment 1 of the present application is shown in FIG. 1.

[0018] Figure 2 A structural diagram of a flexible partitioning and efficient networking system of a power distribution system under extreme weather for an embodiment 2 of the present application is shown in FIG. 2.

[0019] Figure 3 An improved 97-node power distribution system disaster 14:00 line fault diagram for an embodiment 3 of the present application is shown in FIG. 3.

[0020] Figure 4 An improved 97-node power distribution system disaster 15:15 line fault diagram for an embodiment 3 of the present application is shown in FIG. 4.

[0021] Figure 5 A new energy power generation and load fluctuation prediction diagram for an embodiment 3 of the present application is shown in FIG. 5.

[0022] Figure 6 A 14:00 partitioning result and tie switch state diagram for a strategy 2 for an embodiment 3 of the present application is shown in FIG. 6.

[0023] Figure 7 A 15:15 partitioning result and tie switch state diagram for a strategy 2 for an embodiment 3 of the present application is shown in FIG. 7.

[0024] Figure 8 A 14:00 partitioning result and tie switch state diagram for a strategy 1 for an embodiment 3 of the present application is shown in FIG. 8.

[0025] Figure 9 A 15:15 partitioning result and tie switch state diagram for a strategy 1 for an embodiment 3 of the present application is shown in FIG. 9.

[0026] Figure 10 A 14:00 partitioning result and tie switch state diagram for a strategy 3 for an embodiment 3 of the present application is shown in FIG. 10.

[0027] Figure 11Strategy 3 provided for embodiment 3 of the present application is shown in the 15:15 partition result and contact switch state schematic diagram;

[0028] Figure 12 Strategy 3 provided for embodiment 3 of the present application is shown in the 15:15 partition result and contact switch state schematic diagram;

[0029] Figure 13 Strategy 3 provided for embodiment 3 of the present application is shown in the 15:15 partition result and contact switch state schematic diagram;

[0030] Figure 14 Strategy 3 provided for embodiment 3 of the present application is shown in the 15:15 partition result and contact switch state schematic diagram;

[0031] Figure 15 Strategy 3 provided for embodiment 3 of the present application is shown in the 15:15 partition result and contact switch state schematic diagram;

[0032] Fig. 16(a) is a schematic diagram of the load recovery amount of cell 1 and the utilization of each power generation resource when strategy 1 and 2 are provided for embodiment 3 of the present application;

[0033] Fig. 16(b) is a schematic diagram of the load recovery amount of cell 2 and the utilization of each power generation resource when strategy 2 is provided for embodiment 3 of the present application;

[0034] Fig. 16(c) is a schematic diagram of the load recovery amount of cell 3 and the utilization of each power generation resource when strategy 2 is provided for embodiment 3 of the present application;

[0035] Fig. 16(d) is a schematic diagram of the load recovery amount of cell 4 and the utilization of each power generation resource when strategy 2 and 3 are provided for embodiment 3 of the present application;

[0036] Fig. 16(e) is a schematic diagram of the load recovery amount of cell 5 and the utilization of each power generation resource when strategy 2 and 3 are provided for embodiment 3 of the present application;

[0037] Fig. 16(f) is a schematic diagram of the load recovery amount of cell 6 and the utilization of each power generation resource when strategy 1 and 2 are provided for embodiment 3 of the present application;

[0038] Fig. 16(g) is a schematic diagram of the load recovery amount of cell 6 and the utilization of each power generation resource when strategy 1 and 2 are provided for embodiment 3 of the present application;

[0039] Fig. 16(h) is a schematic diagram of the load recovery amount of cell 6 and the utilization of each power generation resource when strategy 1 and 2 are provided for embodiment 3 of the present application;

[0040] Fig. 16(i) is a schematic diagram of the load recovery amount of cell 6 and the utilization of each power generation resource when strategy 1 and 2 are provided for embodiment 3 of the present application;

[0041] Fig. 16(j) is a schematic diagram of the load recovery amount of cell 6 and the utilization of each power generation resource when strategy 1 and 2 are provided for embodiment 3 of the present application. DETAILED DESCRIPTION

[0042] Preferred embodiments of the present application will be described in detail below with reference to the drawings, in which:

[0043] Embodiment 1

[0044] In one specific embodiment of the present application, a flexible partitioning and efficient networking method for power distribution systems under extreme weather is disclosed, as shown in the accompanying drawings, comprising the following steps: Figure 1

[0045] S1, obtaining extreme weather information of the power distribution network region, power distribution network topology, and then obtaining predicted topology of the power distribution network in each period of disaster duration; wherein the predicted topology of the power distribution network includes predicted fault lines.

[0046] Specifically, the extreme weather information includes rainfall information and wind load information.

[0047] Specifically, the power distribution network system topology of the power distribution network region is obtained from the power department, including the position of each node, the power of each node, the position of the power grid line, the position and capacity of each distributed power source, for obtaining the connection relationship between each load node and each distributed power source and the line between each load node. It should be noted that the power distribution network system topology obtained from the power department is the topology under normal operation.

[0048] Specifically, the predicted topology of the power distribution network in each period of disaster duration is obtained by the following method:

[0049] S11, based on the rainfall information in the extreme weather information of the power distribution network region, a rainstorm disaster scenario model is established.

[0050] Specifically, in this embodiment, the intensity and duration of rainstorm are calculated by using the generalized extreme value distribution method, so as to establish a rainstorm disaster scenario model, which is expressed as:

[0051]

[0052] In the formula, I is the rainfall intensity; T rain is the duration of rainfall; A, B and Π are the first, second and third empirical parameters, respectively, which are obtained by data fitting and reflect the current extreme weather; F X (x) is the cumulative distribution function of rainfall x; ξ, μ and σ are shape parameter, location parameter and scale parameter, respectively, which determine the characteristics of the tail of the distribution, the center value of the data distribution and the dispersion degree of the data, and their values are obtained by fitting historical rainstorm data, reflecting the current extreme weather.

[0053] ​S12, obtain the fault probability of each line of the power distribution network topology in each time period of disaster continuous occurrence based on the rainstorm disaster scenario model, the extreme weather information and the power distribution network topology structure.

[0054] Specifically, the fault probability of each line of the power distribution network topology in each time period of disaster continuous occurrence is obtained by the following way:

[0055] S121, obtain the rainfall impact data and wind force data in each time period of disaster continuous occurrence based on the wind load in the rainstorm disaster scenario model and the extreme weather information; wherein the rainfall impact data includes the speed of raindrop impact on the line, the diameter of raindrop, the number density of raindrop and the area of the line impacted by rain, and the wind force data includes the wind pressure and the wind speed.

[0056] S122, obtain the stress intensity per unit area of each line caused by the self-weight of the conductor, the rainfall and the wind load in each time period of disaster continuous occurrence based on the rainfall impact data and the wind force data in each time period of disaster continuous occurrence and the power distribution network topology structure.

[0057] More specifically, the stress intensity per unit area of the line caused by the self-weight of the conductor is expressed as:

[0058]

[0059] In the formula, represents the stress intensity per unit area of the line e caused by the self-weight of the conductor in the power distribution network topology structure; is the self-weight per unit length of the line e; L e is the span of the line e, i.e. the distance between the towers; S e is the cross-sectional area of the conductor of the line e.

[0060] More specifically, the stress intensity per unit area of each line caused by the rainfall is expressed as:

[0061]

[0062] wherein,

[0063] f r = n r · Δp · s r (5)

[0064] Δp = m r · v r (6)

[0065] m r = 0.17π · d r 3 · ρ w (7)

[0066] In the formula, represents the force intensity per unit area of line e caused by the current period rainfall; f r is the total impact force of the current period rainfall on the unit length of conductor; Δp is the momentum change of a single raindrop when it hits the line; n r is the number density of raindrops; s r is the area of the line hit by the current period rainfall; m r is the mass of a single raindrop in the current period; v r is the speed of a single raindrop in the current period; d r is the diameter of a single raindrop in the current period; ρ w is the density of water.

[0067] More specifically, the force intensity per unit area of each line caused by wind load is represented as:

[0068]

[0069] wherein,

[0070]

[0071] In the formula, represents the force intensity per unit area of line e caused by the current period wind load; y w is the wind pressure caused by the current period wind load; d is the conductor diameter of line e; ρ0 is the air density; v is the wind speed caused by the current period wind load.

[0072] S123, based on the force intensity per unit area of each line caused by the self-weight of the conductor, rainfall and wind load in each period of disaster duration, obtain the failure probability of each line in each period of disaster duration.

[0073] Specifically, the failure probability of each line in each period of disaster duration is represented as:

[0074]

[0075] wherein,

[0076]

[0077] In the formula, represents the failure probability of line e in the current period power distribution network topology due to disaster failure; represents the actual resultant force of line e in the current period power distribution network topology; F wind represents the design tension of the line in the power distribution network topology considering sustainable design.

[0078] wherein, The probability interval of the line is (0.3679, 1), which is designed based on line damage and subsequent system cascading failure, considering the influence on power supply reliability.

[0079] It should be noted that considering the diversity of extreme disaster weather disaster factors, the line is subjected to multiple forces, and the resultant force can be approximately equal to the superposition of each single force.

[0080] S13, based on the fault probability of each line of the distribution network topology structure during each period of disaster duration, obtain the predicted topology structure of the distribution network during each period of disaster duration.

[0081] Specifically, the line with a fault probability greater than a set fault threshold is taken as a predicted fault line, and the distribution network topology structure is updated to obtain the predicted topology structure of the distribution network; wherein the fault threshold is set according to actual needs.

[0082] S2, the distribution network topology structure during each period of disaster duration is divided into areas, and the power supply redundancy index of each area during the corresponding period is obtained.

[0083] In implementation, the distribution network topology structure during each period of disaster duration is divided into areas in the following manner:

[0084] Based on the predicted topology structure of the distribution network during each period of disaster duration, the load level and network topology coincidence degree of each node during each period of disaster duration are obtained;

[0085] If it is the initial period of disaster duration, the distribution network topology structure during the initial period, the load level and network topology coincidence degree of each node are used for area division;

[0086] Otherwise, if the fault line in the predicted topology structure of the distribution network during the current period is the same as that in the previous period and the area division during the current period is performed based on the area division during the previous period, whether the area division during the current period meets the distribution network area networking constraint condition, the area division during the current period is performed based on the area division during the previous period, otherwise, the distribution network topology structure during the current period, the load level and network topology coincidence degree of each node are used for area division.

[0087] In implementation, under the operation constraint condition of the distribution network, based on the network topology coincidence degree of each node during each period of disaster duration, the value of the load weight coefficient of each node during each period is obtained when the value quantity function of the restored load of the distribution network is maximum, and then the load level of each node during each period is obtained.

[0088] Specifically, the load levels of the nodes include a first load, a second load, and a third load; wherein the load weight coefficient of the first load node is 1, the load weight coefficient of the second load node is 0.1, and the load weight coefficient of the third load node is 0.01.

[0089] Specifically, the value function of the load restoration of the distribution network is expressed as:

[0090]

[0091] In the formula, N is a set of nodes in the distribution network; T is a set of discrete disaster time sequence periods; DCN i,t is a weighted network topology coincidence degree of node i at period t; ω i,t is a load weight coefficient of node i at period t; is an active power of the restored load of node i at period t; λ i,t is whether the load of node i is restored at period t, wherein λ i,t = 1 means that the power is restored, and λ i,t = 0 means that the power is not restored.

[0092] More specifically, the weighted network topology coincidence degree DCN i,t of node i at period t is expressed as:

[0093]

[0094] wherein,

[0095]

[0096] In the formula, denotes the degree of node j, I(i) denotes a set of neighbor nodes of node i, and ceil() denotes the upward rounding.

[0097] Specifically, the degree of the node is obtained by the following formula:

[0098]

[0099] In the formula, denotes the degree of node i, A ij denotes whether there is a line connection between node i and node j, wherein A ij = 1 means that there is a line connection, and A ij = 0 means that there is no line connection.

[0100] Specifically, the operation constraints of the distribution network include a power balance constraint, a line safe operation constraint, a distribution network system power flow constraint, and an uninterrupted power supply operation constraint.

[0101] In implementation, the division of the transformer area based on the predicted topology structure of the power distribution network, the load level of each node and the network topology coincidence degree comprises the following steps:

[0102] S21, obtaining the edge weight of each tie line in the current period based on the predicted topology structure of the power distribution network in the current period and the network topology coincidence degree of each node;

[0103] Specifically, if the two end nodes of the tie line are a power node and a load node respectively, the edge weight of the tie line is 0; if the two end nodes of the tie line are both load nodes, the edge weight of the tie line is the maximum of the load weights of the two end nodes. It can be understood that for the tie line with both end nodes being load nodes, the distance between the load and the power supply and the size of the load should be considered comprehensively.

[0104] More specifically, the load weight of a node is represented as:

[0105] η i,t = α1ω′ i,t + α2DCN i,t + α3L Bi + α4D i,t (16)

[0106] wherein,

[0107]

[0108] wherein, η i,t is the load weight of node i in the power distribution network at period t, α1, α2, α3 and α4 are the first, second, third and fourth weight coefficients respectively; D i,t is the distance between node i and the nearest distributed power supply in the power distribution network at period t; L i , L max , L min are the load value of node i, the maximum load and the minimum load in the power distribution network system respectively, all of which are active power.

[0109] S22, obtaining the initial transformer area division based on the load level of each node in the predicted topology structure of the power distribution network in the current period and the distributed power supply in the power distribution network.

[0110] Specifically, the initial transformer area division is obtained by the following method:

[0111] A1, sequentially performing the following on each first-level load node in the predicted topology structure of the power distribution network in the current period:

[0112] B1, search for the distributed power supply closest to the current primary load node, determine whether the distribution network area networking constraint is met, if not, skip the distributed power supply, return to step B1 and continue to execute until all reachable distributed power supplies are traversed; if yes, then

[0113] determine whether the distributed power supply forms an initial area with other primary load nodes, if not, the current primary load node forms an initial area with the distributed power supply; if yes, keep the primary load node with the largest load and form an initial area with the distributed power supply, and the primary load node not included in the area skips the distributed power supply and returns to step B1 to continue to execute until all reachable distributed power supplies are traversed.

[0114] wherein, if there is a single distributed power supply, the distributed power supply is taken as an independent initial area.

[0115] S23, based on the predicted topology structure of the distribution network in the current period, the initial area division, and the edge weight of each tie line, obtain the divided areas. Including:

[0116] C1, sequentially execute the following steps on each initial area:

[0117] D1, determine whether the current area has nodes, if not, skip the current area; if yes, obtain each tie line with one end node in the current area and the other end node outside the current area;

[0118] D2, according to the edge weight of each tie line obtained from large to small, sequentially execute the following steps:

[0119] determine whether the end node of the current tie line not in the current area meets the distribution network area networking constraint, if not, the end node is not included in the current area; if yes, then

[0120] determine whether the end node of the current tie line not in the current area meets the current area not being full and not forming a loop, if yes, the node is included in the current area; if not, then

[0121] determine whether the end node of the current tie line not in the current area forms a loop in the current area, if yes, the end node is not included in the current area; if not, determine the other area closest to the node, if the area is not full, the node is included in the area, if full, the node is deleted;

[0122] D3, repeat steps D1-D3 until the current area is full or there is no node that can be included, the area is completed;

[0123] D4, update other initial areas by deleting the nodes in other initial areas that coincide with the nodes in the current area, and return to step C1.

[0124] Preferably, each substation in the current period power distribution network prediction topology division can also be optimized, and the load nodes not divided into substations are adjusted and removed, so as to optimize the substation division.

[0125] It can be understood that, through the substation division process in the embodiment, the key load and power supply nodes are adjusted by the weight priority connection, so as to maximize the disaster initial load recovery value.

[0126] In implementation, the power supply redundancy index of each substation in each period of disaster duration is represented as:

[0127]

[0128] In the formula, G g represents the power supply redundancy index of the substation g in the current period, P DG,j,g represents the output of the distributed power supply of the node j in the substation g in the current period, represents the remaining capacity of the uninterrupted power supply of the node j in the substation g in the current period, P L,j,g represents the load power of the node j in the substation g in the current period, N g represents the node set in the substation g in the current period.

[0129] S3, under the power distribution network substation networking constraint condition, based on the substation and the power supply redundancy index of the power distribution network prediction topology division in each period of disaster duration, a substation networking scheme is obtained when the power distribution network load recovery quantity function is maximum in each period of disaster duration, so as to supply power to the power distribution network.

[0130] Specifically, each networking in the substation networking scheme at least includes a power sufficient substation; if multiple substations are included, the closed loop design tie line between the substations is obtained according to each substation of the specific networking, that is, the tie line of the nodes connected by the substations in the networking.

[0131] Specifically, the substation networking is to perform power mutual aid between the power sufficient substation and the power deficient substation; wherein the substation with the power supply redundancy index greater than or equal to 0 is power sufficient, and the substation with the power supply redundancy index less than 0 is power deficient.

[0132] In implementation, the power distribution network load recovery quantity function is represented as:

[0133]

[0134] In implementation, the power distribution network substation networking constraint condition includes flexible networking constraint, flexible networking operation constraint, and flexible networking load recovery adjustment constraint.

[0135] In specific implementation,

[0136] Flexible networking constraints are represented as:

[0137]

[0138] In the formula, u t,i and u t,j are the voltage amplitudes of nodes i and j at the two ends of the inter-substation closed-loop design tie line at time period t; μ is the voltage coefficient; ΔU and Δf are the maximum voltage and frequency deviation of the nodes at the two ends of the inter-substation closed-loop design tie line; f t,k and f t,g are the voltage amplitudes of substation k and substation g at time period t; Ω k is the set of closed-loop design tie lines; K represents the set of formed substations.

[0139] Flexible networking operation constraints are represented as:

[0140]

[0141]

[0142] In the formula, f min and f max are the maximum and minimum values of the power supply frequency; is the power generation frequency of power supply node i in substation k at time period t; Rate max is the maximum allowed value of the frequency change rate; f nom is the nominal frequency, RMS max is the maximum allowed root mean square deviation of the frequency deviation, and T' represents the frequency period.

[0143] It can be understood that when different substations are networked, the change of load may cause fluctuations in the power grid frequency, and flexible networking operation constraints need to be performed on the power supply frequency of each power supply.

[0144] Flexible networking load recovery adjustment constraints are represented as:

[0145]

[0146] In the formula, is the load recovery state of load node i s at time period t, wherein, is the load recovery power supply of load node i s , is the load non-recovery power supply of load node i s , I s is the set of load nodes in the distribution network that lose power, and P L,i is the total active power of load node i.​ Pi is the active power of uncontrollable load of the load node i; Pi is the active power of controllable load of the load node i; Pi is the active power maximum of controllable load of the load node i; Pi is the load recovery amount of active and reactive power of the node i at the time period t, respectively; Pi is the demand amount of active and reactive power of the node i at the time period t, respectively.

[0147] It can be understood that when the power shortage area and the power redundant area perform power mutual aid, the flexible networking load recovery adjustment constraint should be considered, the optimization networking range is adjusted according to the voltage frequency constraint and the flexible load, the new energy utilization rate and the load recovery amount are significantly improved, and the abandoned wind and light rate is reduced.

[0148] It should be noted that in the embodiment, the power distribution network area networking constraint condition is relaxed to be converted into a convex constraint, so that the whole model is converted into a mixed integer second-order cone programming (MIQCP) model, which belongs to a convex optimization model. The mixed integer programming (MILP) model can be solved by using a mature commercial solver (such as MOSEK, CPLEX, GUROBI, etc.) for optimization and solution.

[0149] Compared with the prior art, the embodiment provides a flexible partitioning and efficient networking method for a power distribution system under extreme weather, obtains rainfall information of a power distribution network area and a power distribution network topology structure, and then obtains a predicted topology structure of the power distribution network during each time period when a disaster continues to occur, divides the areas of the power distribution network during each time period when the disaster continues to occur according to the predicted topology structure, and obtains a power supply redundancy index of each area during the corresponding time period. Under the constraint condition of the power distribution network area networking, based on the areas and the power supply redundancy index of the power distribution network during each time period when the disaster continues to occur, which are divided according to the predicted topology structure of the power distribution network during each time period when the disaster continues to occur, a maximum power distribution network load recovery amount function is taken as a target, a networking scheme of the areas of the power distribution network during each time period when the disaster continues to occur is obtained when the power distribution network load recovery amount function is maximum, power supply recovery of the power distribution network is performed, the collaborative action of various power supply resources and the active regulation and control capacity of the flexible controllable load are fully mobilized to dynamically adjust the system partitioning, the whole process of disaster partitioning, networking and recovery is linked and cooperated, the new energy utilization rate and the load recovery amount are significantly improved, and the abandoned wind and light rate is reduced.

[0150] Embodiment 2

[0151] In one specific embodiment of the application, a flexible partitioning and efficient networking system for a power distribution system under extreme weather is disclosed, as shown in FIG. Figure 2 The system comprises:

[0152] a topological prediction module, configured to acquire rainfall information of a power distribution network region and a power distribution network topology, and then obtain predicted topologies of the power distribution network in each period during which a disaster lasts;

[0153] a substation area division and power supply redundancy index construction module, configured to divide the predicted topologies of the power distribution network in each period during which the disaster lasts into substation areas, and obtain power supply redundancy indexes of each substation area in the corresponding period;

[0154] a power supply recovery module, configured to, under a constraint condition of substation area networking of the power distribution network, based on each substation area divided according to the predicted topologies of the power distribution network in each period during which the disaster lasts and the power supply redundancy indexes, obtain, as an objective, a substation area networking scheme in each period during which the disaster lasts, in which a power distribution network load recovery amount function is maximum, so as to recover power supply of the power distribution network.

[0155] The specific implementation process of the embodiment of the present application can be referred to the above method embodiment, and the embodiment will not be described here.

[0156] Since the principle of the embodiment is the same as that of the above method embodiment, the system also has the corresponding technical effects of the above method embodiment.

[0157] Embodiment 3

[0158] In order to verify the correctness of embodiments 1 and 2 of the present application, a specific example is provided in this embodiment, which uses a certain city improved 97-node power distribution network system for example verification, and a heavy rain disaster occurs in the period of 14:00-16:00; the fault conditions are as shown in Figure 3 and Figure 4 At 14:00, lines (1, 2), (1, 36), (11, 12), (60, 61), and (78, 82) fail due to the heavy rain disaster; at 15:15, lines (36, 56), (40, 45), (64, 65), and (25, 26) fail, and the disaster lasts for 2 hours. According to the influence range of the rainstorm disaster and the overall recovery process of the power distribution system, it is predicted that the power supply of the region will be restored by the superior power grid at about 18:30 after 2.5h, and the total duration of the fault is 4.5 hours. According to the meteorological prediction data, the wind turbine, photovoltaic new energy generation and load fluctuation prediction are as shown in Figure 5 Under the influence of the disaster, the fault recovery analysis time interval is taken as 15 minutes, that is, Δt=15min, and the analysis time is from 12:00 to 18:30, a total of 26 periods, of which 1-8 periods are normal operation periods, 9-16 periods are disaster lasting periods, and 17-26 periods are fault recovery periods. The initial positions of two emergency power supply vehicles MPS1 and MPS2 are both located at node 1, and they are in standby state. The main parameters of the system are shown in Tables 1, 2 and 3.

[0159] Table 1 Node load level

[0160]

[0161] Table 2 Uninterruptible power supply, emergency power supply vehicle parameters

[0162]

[0163] Table 3 Fan, photovoltaic power generation parameters

[0164]

[0165] To verify the effectiveness and superiority of the system partitioning and networking of embodiments 1 and 2 in disasters, three strategy schemes are used for comparison in the emergency control stage and the load recovery stage after the fault.

[0166] Strategy 1: The system partitioning and networking method proposed in embodiments 1 and 2 is used for fault recovery in disasters. The main principle is to balance the redundancy of substation area power supply, and the flexible load is adjusted to realize flexible networking of substation area, and the dispatching of emergency power supply vehicles is considered for auxiliary power supply. Among them, when the constraint of closed tie line cannot be met, the emergency power supply vehicle is used for power supply.

[0167] Strategy 2: Flexible networking strategy is not used, only single power supply substation area is formed by substation division, and the dispatching of emergency power supply vehicles is relied on to carry out fault recovery in disasters.

[0168] Strategy 3: Flexible networking strategy is used, but the principle of balancing the redundancy of substation area power supply is not considered, only whether the networking constraint is met is considered to carry out fault recovery in disasters.

[0169] Figure 6 、 Figure 7 、 Figure 8 、 Figure 9 、 Figure 10 and Figure 11 are the networking results of strategies 1, 2 and 3 at 14:00 and 15:15, respectively.

[0170] As can be seen from 12, the total amount of load recovery of strategy 1 is always higher than that of strategies 2 and 3 in the entire recovery period after the disaster-induced fault; by comparing Figure 13 and Figure 14 , it can be seen that the first and second load recovery rates of strategy 1 are higher than those of strategies 2 and 3; by Figure 15 , it can be seen that in the off-grid operation state, the required total power of each period of load in the distribution system is higher than the total power that can be provided by the source, so it is difficult to achieve complete recovery of the lost power load in the system.

[0171] As shown in FIG. 16(a), it is the power generation situation and the power generation resource utilization situation of each load recovery in the substation 1 under the strategies 1 and 2. As can be seen from the comparison between FIG. 16(d) and FIG. 16(g), under the strategies 2 and 3, the wind power abandonment phenomenon of the wind turbine WT1 in the substation 4 is more serious, and after the networking strategy of the strategy 1 is adopted, the utilization rate of new energy power generation is significantly improved.

[0172] As can be seen from the comparison between FIG. 16(c), FIG. 16(g) and FIG. 16(i), in the substation 3 under the strategy 2, the load is suddenly reduced after the fault caused by the disaster at the time period t = 9, and it is difficult to recover in the subsequent stage, which is due to the fact that in the substation 3, only PV2 supports power supply, and during the disaster, PV3 is out of service due to insufficient light. In the networking 1 under the strategy 1, the substation 3 and the substation 4 are networked by closing the tie switch 99, and the WT2 and the PV2 cooperate to supply power, so after the PV2 is out of service, the WT2 continues to guarantee the power supply of the important load in the region, and therefore the load recovery amount is obviously higher than that of the substation 3. The strategy 3 is similar to the strategy 2, although the substation 1 and the substation 3 are networked by closing the line 97, and the PV1 and the PV2 cooperate to supply power, but after the disaster occurs, the PV1 and the PV2 are out of service, and cannot support the power supply of the load in the region.

[0173] As can be seen from the comparison between FIG. 16(e) and FIG. 16(h), the load recovery amount of the substation 5 under the strategies 2 and 3 and the networking 2 under the strategy 1, the substation 5 and the substation 3 are similar to the above-mentioned situation, and both of them cannot support the power supply of the load in the region due to the fact that the PV is out of service after the disaster. In the networking 2 under the strategy 1, the substation 2 and the substation 5 are networked by closing the line 100, and the WT1 and the PV3 cooperate to supply power, so after the PV3 is out of service, the WT1 can still guarantee the power supply of the important load in the region.

[0174] As can be seen from the comparison between FIG. 16(b), FIG. 16(f) and FIG. 16(j), the load recovery amount of the substation 2 and the substation 6 under the strategy 2 and the networking 4 under the strategy 3, the change of the load recovery amount is not large, which is due to the fact that the power generation of the wind turbine WT1 and the wind turbine WT3 in the substation 2, the substation 6 and the networking 4 is redundant for the important load in the region, and therefore the improvement of the networking is not obvious.

[0175] In combination with Figure 12 , Figure 13 , Figure 14 , Figure 15 and FIG. 16, it is shown that the strategy 1 based on the system partition and the flexible networking control strategy not only improves the load recovery efficiency, but also optimizes the utilization of new energy power generation, and has strong load power supply guarantee capability. In comparison, the traditional single power supply mode and the strategy which does not take the balance of the substation power supply redundancy as the networking principle have deficiencies in the process of guaranteeing the load power supply.

[0176] In summary, the system partitioning and flexible networking method proposed in embodiments 1 and 2 can achieve the requirements of important load supply guarantee, resource efficient utilization and load rapid recovery, and can improve the power supply guarantee capability and operation reliability of the distribution network in disasters.

[0177] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiments can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. The computer readable storage medium is a disk, an optical disk, a read-only memory or a random access memory, etc.

[0178] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any changes or replacements within the technical range disclosed by the present application can be easily thought of by those skilled in the art, and should be covered within the protection scope of the present application.

Claims

1. A flexible partitioning and efficient networking method for power distribution systems under extreme weather conditions, characterized in that, The method comprises the following steps: obtaining rainfall information of a power distribution network region, a power distribution network topology, and then obtaining a predicted power distribution network topology of each time period during disaster duration; wherein the predicted power distribution network topology comprises predicted fault lines; dividing the predicted power distribution network topology of each time period during disaster duration into transformer areas, and obtaining a power supply redundancy index of each transformer area in the corresponding time period; under the constraint condition of transformer area networking of the power distribution network, based on the predicted power distribution network topology of each time period during disaster duration and the power supply redundancy index of each transformer area, taking a function of maximizing power distribution network load recovery as the target, obtaining a transformer area networking scheme in which the function of the power distribution network load recovery is maximum in each time period during disaster duration, and performing power supply recovery on the power distribution network.

2. The flexible partitioning and efficient networking method of power distribution system under extreme weather according to claim 1, characterized in that, The predicted power distribution network topology of each time period during disaster duration is divided into transformer areas in the following manner: based on the predicted power distribution network topology of each time period during disaster duration, obtaining a load level of each node and a network topology coincidence degree in each time period during disaster duration; if it is an initial time period during disaster duration, performing transformer area division based on the predicted power distribution network topology of the initial time period, the load level of each node, and the network topology coincidence degree; otherwise, if the fault lines in the predicted power distribution network topology of the current time period are the same as those in the previous time period and the transformer area division in the previous time period meets the constraint condition of transformer area networking of the power distribution network, performing transformer area division in the current time period based on the transformer area division in the previous time period, otherwise, performing transformer area division based on the predicted power distribution network topology of the current time period, the load level of each node, and the network topology coincidence degree.

3. The flexible partitioning and efficient networking method of power distribution system under extreme weather according to claim 2, characterized in that, under the constraint condition of power distribution network operation, based on the network topology coincidence degree of each node in each time period during disaster duration, taking a function of maximizing the value of power distribution network load recovery as the target, obtaining the value of the load weight coefficient of each node in each time period when the value function of the recovered load of the power distribution network is maximum, and then obtaining the load level of each node in each time period.

4. The flexible partitioning and efficient networking method of power distribution system under extreme weather according to claim 2, characterized in that, The transformer area division based on the predicted power distribution network topology, the load level of each node, and the network topology coincidence degree comprises the following steps: obtaining the edge weight of each tie line in the current time period based on the predicted power distribution network topology of the current time period and the network topology coincidence degree of each node; obtaining an initial transformer area division based on the load level of each node in the predicted power distribution network topology of the current time period and the distributed power supply in the power distribution network; obtaining each divided transformer area based on the predicted power distribution network topology of the current time period, the initial transformer area division, and the edge weight of each tie line.

5. The flexible partitioning and efficient networking method of power distribution system under extreme weather according to claim 4, characterized in that, The initial transformer area division is performed in the following manner: A1, sequentially performing the following steps on each primary load node in the predicted power distribution network topology of the current time period: B1, searching for the distributed power supply closest to the current primary load node, judging whether the constraint condition of transformer area networking of the power distribution network is met, if not, skipping the distributed power supply, returning to step B1 for continuous execution until all reachable distributed power supplies are traversed; if yes, Determine whether the distributed power supply forms an initial transformer area with other primary load nodes, if not, the current primary load node forms an initial transformer area with the distributed power supply; if yes, keep the primary load node with the largest load amount and the distributed power supply to form an initial transformer area, and ignore the primary load node not included in the transformer area and the distributed power supply, and return to step B1 to continue execution until all reachable distributed power supplies are traversed. If there is a single distributed power supply, the distributed power supply is taken as an independent initial transformer area.

6. The flexible partitioning and efficient networking method of power distribution system under extreme weather according to claim 4, characterized in that, The divided transformer areas are obtained based on the predicted topology structure of the power distribution network in the current period, initial transformer area division, and edge weight of each tie line, and include: C1, sequentially performing the following steps on each initial transformer area: D1, determining whether the current transformer area has nodes, if not, the current transformer area is ignored, if yes, obtaining each tie line with one end node in the current transformer area and the other end node outside the current transformer area; D2, sequentially performing the following steps according to the edge weight of each tie line from large to small: Determine whether the end node of the current tie line not in the current transformer area meets the power distribution network transformer area networking constraint, if not, the end node is not included in the current transformer area; if yes, the end node is included in the current transformer area; Determine whether the end node of the current tie line not in the current transformer area meets the current transformer area not being full load and not forming a loop, if yes, the node is included in the current transformer area; if not, determine the other transformer area closest to the node, if the transformer area is not full load, the node is included in the transformer area, if full load, the node is deleted; Determine whether the end node of the current tie line not in the current transformer area forms a loop in the current transformer area, if yes, the end node is not included in the current transformer area; if not, determine the other transformer area closest to the node, if the transformer area is not full load, the node is included in the transformer area, if full load, the node is deleted; D3, repeat steps D1-D3 until the current transformer area is full load or there is no node that can be included, and the division of the transformer area is completed; D4, update other initial transformer areas by deleting the nodes in other initial transformer areas that coincide with the nodes in the current transformer area, and return to step C1.

7. The flexible zoning and efficient networking method of power distribution system under extreme weather according to claim 4, characterized in that, The power supply redundancy index of each transformer area in each period during disaster duration is represented as: In the formula, G g represents the power supply redundancy index of the current period substation g, P DG,j,g represents the output of the distributed power supply of node j in the current period substation g, represents the remaining capacity of the uninterrupted power supply of node j in the current period substation g, P L,j,g represents the load power of node j in the current period substation g, N g represents the node set in the current period substation g. 8.The flexible zoning and efficient networking method of power distribution system under extreme weather according to claim 4, characterized in that, The power distribution network load restoration amount function is expressed as: In the formula, N is a set of nodes in the power distribution network. T is a set of discrete disaster time intervals; ω i,t is the load weight coefficient of node i at time interval t; is the active power of the load recovery of node i at time interval t; λ i,t To determine whether the load at node i has been restored to power during time period t, where λ i,t =1 indicates power restoration, λ i,t =0 indicates that power has not been restored. 9.The flexible zoning and efficient networking method of power distribution system under extreme weather according to claim 4, characterized in that, The power distribution network transformer area networking constraint includes flexible networking constraint, flexible networking operation constraint, and flexible networking load recovery adjustment constraint.

10. A flexible partitioning and efficient networking system for power distribution systems under extreme weather conditions, characterized in that, It includes: A topology prediction module is configured to obtain rainfall information of a power distribution network region and a power distribution network topology, and then obtain predicted topology structures of the power distribution network in each period during disaster duration, wherein the predicted topology structures of the power distribution network include predicted fault lines. A transformer area division and power supply redundancy index construction module is configured to divide transformer areas according to the predicted topology structures of the power distribution network in each period during disaster duration, and obtain power supply redundancy indexes of each transformer area in the corresponding period. A power supply recovery module is configured to, under the power distribution network transformer area networking constraint, based on the transformer areas divided according to the predicted topology structures of the power distribution network in each period during disaster duration and the power supply redundancy indexes, obtain a transformer area networking scheme in each period during disaster duration when the power distribution network load recovery amount function is maximum, and recover power supply of the power distribution network.

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