A resilient control method for power transmission and distribution systems in ice disaster scenarios

Abstract

The present invention discloses a method for controlling the resilience of a power transmission and distribution system in an ice disaster scenario. According to the meteorological data of the environment in which the transmission line is located, a failure rate model of the transmission line in an ice disaster scenario is constructed; according to the failure rate model of the transmission line, a hybrid sampling method that adapts to the change of the line failure rate is used to generate the failure scenario of the power transmission system in the ice disaster scenario; according to the resilience curve of the power system under extreme natural disasters, a resilience evaluation index and framework of the power transmission and distribution system are established, and then an optimal load reduction model of the power transmission and distribution system considering the failure of the transmission line is established; the resilience change of the power transmission and distribution system using different resilience improvement measures is controlled, thereby improving the safety of the power transmission and distribution system in the ice disaster scenario. The present invention evaluates the impact of the line failure of the transmission network under ice disaster weather on the distribution network to which it is connected, and can be used to describe the resilience change of the power transmission and distribution system in the ice disaster scenario to a certain extent. The present invention can also serve as a basis tool for selecting reasonable resilience improvement measures for the power transmission and distribution system.

Description

Technical Field

[0001] The present invention relates to the field of power transmission and distribution networks, and in particular to a method for controlling the resilience of a power transmission and distribution system in an ice disaster scenario. Background Art

[0002] The safety and stability of the power system is crucial to people's normal production and life. However, many extreme natural disasters, such as typhoons, earthquakes, ice disasters and other events with low probability but high impact, often cause serious damage to the power system. For example, the 2008 Hunan ice disaster affected the power grid. About 3,000 towers with voltage levels above 110kV collapsed, and about 170,000 towers with voltage levels below 35kV collapsed. [1] Therefore, mitigating the significant impact of extreme natural disasters such as ice storms on the power system is an urgent task. The consequences of the 2008 ice storm in the Hunan power grid and the impact of ice storms on the Northeast power grid in recent years demonstrate that ice storms have severe impacts on both transmission and distribution networks. Although the direct impact of ice storms on distribution lines has decreased in recent years due to the increased use of overhead cables in distribution networks, the transmission network remains the parent grid, and the impact of ice storms on the transmission network will also pose a threat to the distribution network.

[0003] Therefore, proposing a reasonable resilience control method for the transmission and distribution system under ice disaster scenarios is the current research focus on reducing the impact of ice disasters on the power system. Summary of the Invention

[0004] The present invention provides a method and apparatus for controlling the resilience of a power transmission and distribution system in ice disaster scenarios. This method assesses the impact of line faults in a transmission network during ice disasters on the connected distribution network. To a certain extent, it can be used to describe the resilience changes of the power transmission and distribution system during ice disasters. The present invention can also serve as a basis for selecting appropriate measures to improve the resilience of the power transmission and distribution system. Details are described below:

[0005] A method for controlling resilience of a power transmission and distribution system in an ice disaster scenario, the method comprising:

[0006] Based on the meteorological data of the environment in which the transmission lines are located, a failure rate model of the transmission lines under ice disaster scenarios is constructed;

[0007] Based on the failure rate model of the transmission line, a hybrid sampling method that adapts to the changes in line failure rate is used to generate failure scenarios of the transmission system under ice disaster scenarios.

[0008] Based on the power system resilience curve under extreme natural disasters, an evaluation index and framework for the resilience of the transmission and distribution system are established, and then an optimal load reduction model for the transmission and distribution system considering transmission line failures is established;

[0009] The resilience changes of the transmission and distribution system adopting different resilience improvement measures are controlled, which improves the safety of the transmission and distribution system in ice disaster scenarios.

[0010] The resilience assessment indicators and framework for the transmission and distribution system are established based on the resilience curve of the power system under extreme natural disasters:

[0011] Through the formation of transmission network failure scenarios, calculation of transmission and distribution system load reduction, formation of resilience curves, calculation of resilience assessment indicators and other steps, a resilience assessment framework for transmission and distribution systems under ice disaster scenarios was established.

[0012] The failure rate model of the transmission line in the ice disaster scenario is:

[0013] Establish an ice thickness growth model for transmission lines per unit length:

[0014]

[0015] W i (t) = 0.067 × r i (t) 0.846 (2)

[0016]

[0017] In formula (1-3), R eq,i (t) is the ice thickness increment of line i at time t, T is the number of freezing rain hours, r i (t) is the amount of freezing rain at the location of line i at time t, v i (t) is the wind speed at the location of line i at time t, ρ w is the water density, ρ I is the ice density, W i (t) is the liquid water content in the air at the position of line i at time t, q i (t) is the ice thickness per unit length of line i at time t;

[0018] The ice load, wind load model and ice-wind load model for transmission lines per unit length are as follows:

[0019] L I,i (t) = 9.8 × 10 -3 ρ I π(D i +q i (t))q i (t) (4)

[0020] L W,i (t) = CS i v i (t) 2 (Di +2q i (t)) (5)

[0021] In formula (4-5), L I,i (t), L W,i (t) are the ice load and wind load per unit length of line i at time t; D i is the diameter of line i; C is a constant coefficient, set to 6.964×10 -3 ;S i is the span factor of line i;

[0022] The ice and wind load L per unit length of transmission line i at time t WI,i (t);

[0023]

[0024] The failure rate model of the transmission system is as follows:

[0025]

[0026] P i (t) = 1-(1-P f,i (t)) Li (8)

[0027] In formula (7), P f,i (t) is the failure rate of the transmission line per unit length of line i at time t, a WI is the lower limit threshold of ice and wind load per unit length of line, b WI is the upper threshold of ice and wind load per unit length of line;

[0028] In formula (8), P i (t) is the failure rate of transmission line i at time t, L i is the length of transmission line i.

[0029] The beneficial effects of the technical solution provided by the present invention are:

[0030] 1. The present invention applies ice disaster scenarios to the power transmission and distribution system, which is more in line with actual engineering conditions;

[0031] 2. This paper establishes a transmission and distribution load reduction model under ice disaster scenarios to calculate the load reduction of the transmission and distribution system under ice disaster scenarios, taking into account the impact of transmission line failures on the distribution network;

[0032] 3. Based on the resilience curve of the power system under extreme natural disasters, the present invention proposes a resilience assessment index and a resilience assessment framework for the transmission and distribution system, which can reasonably control the resilience changes of the transmission and distribution system under ice disaster scenarios, and provide assistance and basis for selecting effective resilience improvement measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of the resilience control framework for the transmission and distribution system under an ice disaster scenario;

[0034] Figure 2 This is a schematic diagram of the stress conditions on the transmission line after ice coverage in an ice disaster scenario;

[0035] Figure 3 This is a schematic diagram of the resilience change curve of the power system under extreme natural disasters;

[0036] Figure 4 This is a schematic diagram of the calculation example of the improved transmission and distribution system;

[0037] Figure 5 Schematic diagram of active distribution network in transmission and distribution system;

[0038] Figure 6 This is a schematic diagram of the distribution of freezing rain and wind speed during ice disaster weather;

[0039] Figure 7 This is a schematic diagram of the load changes on the transmission lines during the entire ice disaster process;

[0040] Figure 8 Schematic diagram of transmission system failure scenarios under three resilience improvement scenarios;

[0041] Figure 9 This is a schematic diagram of the change in the upper limit of active power output of renewable energy generators in the active distribution network;

[0042] Figure 10 Schematic diagram of transmission system load reduction under three resilience improvement scenarios;

[0043] Figure 11 Schematic diagram of load reduction in the distribution system under three resilience improvement scenarios;

[0044] Figure 12 Schematic diagram of the changes in transmission and distribution system resilience assessment indicators under three resilience improvement scenarios. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0046] The consequences of the 2008 Hunan power grid ice disaster and the recent impacts on power grids in Northeast China demonstrate that ice disasters have a severe impact on both transmission and distribution networks. Although the direct impact of ice disasters on distribution lines has decreased in recent years with the increasing use of overhead cables in distribution networks, the transmission network remains the parent grid of the distribution network, and the impact of ice disasters on the transmission network will also pose a threat to the distribution network. Therefore, developing reasonable resilience control methods for transmission and distribution systems in ice disaster scenarios is a current research priority to mitigate the impact of ice disasters on power systems.

[0047] In order to evaluate the impact of line failures in the transmission network during ice disasters on the distribution network to which it is connected, an embodiment of the present invention provides a resilience control method for the transmission and distribution system under ice disaster scenarios. To a certain extent, the method can be used to describe the changes in the resilience of the transmission and distribution system under ice disaster scenarios, and can also serve as a basis for selecting reasonable measures to improve the resilience of the transmission and distribution system.

[0048] Example 1

[0049] An embodiment of the present invention provides a method for controlling resilience of a power transmission and distribution system in an ice disaster scenario, the method comprising the following steps:

[0050] 101: Based on the meteorological data of the transmission line environment, a failure rate model of the transmission line under ice disaster scenarios is constructed;

[0051] 102: Based on the failure rate model of the transmission line, a hybrid sampling method that adapts to the changes in line failure rate is used to generate the failure scenario of the transmission system under the ice disaster scenario;

[0052] 103: Establish transmission and distribution system resilience assessment indicators and framework based on the power system resilience curve under extreme natural disasters;

[0053] 104: Based on the resilience assessment indicators and framework of the transmission and distribution system, an optimal load reduction model for the transmission and distribution system considering transmission line failures is established;

[0054] 105: Taking the transmission and distribution system under ice disaster as an example, the resilience changes of the transmission and distribution system adopting different resilience improvement measures are controlled, thereby improving the safety of the transmission and distribution system under ice disaster scenarios.

[0055] In summary, the embodiment of the present invention establishes an optimal load reduction model for the power transmission and distribution system through the power transmission and distribution system resilience assessment indicators and framework through the above steps 101 to 105, proposes a resilience control method for the power transmission and distribution system under ice disaster scenarios, and further analyzes and compares the impact of different resilience improvement measures on the resilience changes of the power transmission and distribution network.

[0056] Example 2

[0057] The solution in Example 1 is further introduced below with reference to specific calculation formulas and accompanying drawings.

[0058] 201: Based on the meteorological data of the transmission line environment, a failure rate model of the transmission line under the ice disaster scenario is constructed;

[0059] Herein, step 201 includes:

[0060] 1) Based on the ice thickness model of the unit length line and the wind speed of the line, establish the ice thickness growth model of the unit length transmission line [2] ;

[0061]

[0062] W i (t) = 0.067 × r i (t) 0.846 (2)

[0063]

[0064] In formula (1-3), R eq,i (t) is the ice thickness increment of line i at time t, T is the number of freezing rain hours, r i (t) is the amount of freezing rain at the location of line i at time t, v i (t) is the wind speed at the location of line i at time t, ρ w is the water density, ρ I is the ice density, W i (t) is the liquid water content in the air at the position of line i at time t, q i (t) is the ice thickness per unit length of line i at time t.

[0065] 2) Based on the transmission line icing model and the wind speed during icy weather, the ice load, wind load model, and ice-wind load model per unit length of transmission line can be derived as follows:

[0066] L I,i (t) = 9.8 × 10 -3 ρ I π(D i +q i (t))q i (t) (4)

[0067] L W,i (t) = CS i v i (t) 2 (D i +2q i (t)) (5)

[0068] In formula (4-5), L I,i (t), L W,i (t) are the ice load and wind load per unit length of line i at time t; D i is the diameter of line i; C is a constant coefficient, set to 6.964×10 -3 ;S i is the span factor of line i.

[0069] The ice load and wind load on the line per unit length in the ice disaster scenario are as follows: Figure 1 According to the synthesis of ice load and wind load in the direction of force application, the ice and wind load L of the transmission line i per unit length at time t is obtained. WI,i (t);

[0070]

[0071] 3) Based on the ice and wind load model of the transmission line per unit length, combined with the line load threshold and the series model of the transmission line, a failure rate model for the transmission line of any length is established, that is, the failure rate model of the transmission system, as follows:

[0072]

[0073]

[0074] In formula (7), P f,i (t) is the failure rate of the transmission line per unit length of line i at time t, a WI is the lower limit threshold of ice and wind load per unit length of line, b WI It is the upper threshold of ice wind load per unit length of line.

[0075] In formula (8), P i (t) is the failure rate of transmission line i at time t, L i is the length of transmission line i.

[0076] 202: Establish the failure scenario of the power transmission system under the ice disaster scenario based on the failure rate model of the power transmission system;

[0077] Herein, step 202 includes:

[0078] Considering that the failure rates of all transmission lines are independent of each other, a mixed sampling method that adapts to the changes in line failure rates is used to obtain the operation duration and repair duration of each transmission line under the ice disaster scenario. [3] , and then generate fault scenarios. The fault scenario generation process of the transmission system is as follows:

[0079] (1) According to the meteorological conditions of line i, calculate the ice thickness q per unit length of line i at time t. i (t);

[0080] (2) Calculate the ice wind load per unit length of line i based on the ice thickness and wind speed of line i at time t: WI,i (t);

[0081] (3) According to the ice wind load L WI,i (t) Calculate the failure rate P of line i i (t);

[0082] (4) Extract a random number U from the uniformly distributed set [0,1] i (t);

[0083] (5) If the random number U i (t)>Failure rate P i (t), then line i becomes faulty, and t-1 is the operation duration of line i; otherwise, let t = t + 1 and return to step (1);

[0084] (6) Extract the random number V again from the uniform distribution set [0,1] i ;

[0085] (7) According to the random number V i Calculate the repair duration t of line i repairi ;

[0086] (8) Let i = i + 1, and return to step (1) until all transmission lines are traversed.

[0087] Repair duration t repairi The calculation method is as follows:

[0088] t repairi =-t MTTR LqCy i (9)

[0089] In formula (9), t MTTR is the average repair time, V i It is a uniformly distributed random number in [0,1]. The maximum repair duration is 2 times the average repair time, and the minimum repair duration is 0.5 times the average repair time.

[0090] 203: Propose a resilience control method for the transmission and distribution system based on the resilience curve of the power system under extreme natural disasters;

[0091] Herein, step 203 includes:

[0092] 1) Establish two resilience assessment indicators based on the power system resilience curve under extreme natural disasters;

[0093] Among them, the resilience curve of the power system is as follows: Figure 2 As shown, in Figure 2 In the equation, at time t0, due to extreme natural disasters, the power system begins to fail; at time t1, the power system load begins to stabilize; at time t2, the power system load begins to recover; at time t3, the power system load returns to the state before the failure; the change process of the power system resilience starts from t0 and ends at t3. L It represents the total load power of the power system before the fault occurs, P X It indicates the lowest total load power of the power system during the entire process of the disaster.

[0094] The following two resilience evaluation indicators are then proposed to represent the resilience of the transmission and distribution system: [4] .

[0095] (1) Smallest load live percent (SLLP): This resilience evaluation indicator can describe the robustness characteristics of the transmission and distribution system under ice disaster scenarios and reflect the severity of damage to the transmission and distribution system.

[0096]

[0097] In formula (10), P L It represents the total load power of the power system before the fault occurs, P X It indicates the lowest total load power of the power system during the entire process of the disaster.

[0098] (2) Loss percent of the load electricity of disaster (LPLE): This resilience assessment indicator can directly describe the reduction in load electricity of the power system during the entire ice disaster process. It can intuitively show the impact of different resilience improvement measures on the resilience of the overall transmission and distribution system, and also has certain guiding significance for decision-making on resilience improvement.

[0099]

[0100] In formula (11), S ABCD for Figure 2 The area of the shaded part, S AEFD It refers to the total load electricity of the power system during the entire disaster process when no faults occur and the power system operates normally.

[0101] 2) Based on the proposed transmission and distribution system resilience assessment indicators, a resilience assessment framework for the transmission and distribution system under ice disaster scenarios is established.

[0102] Based on the two transmission and distribution system resilience assessment indicators introduced above, and through the formation of transmission network failure scenarios, calculation of transmission and distribution system load reduction, formation of resilience curves, calculation of resilience assessment indicators and other steps, a resilience assessment framework for transmission and distribution systems under ice disaster scenarios was established, such as Figure 3 shown.

[0103] 204:Based on the resilience assessment framework, an optimal load reduction model for the transmission and distribution system considering transmission line failures is established;

[0104] Herein, step 204 includes:

[0105] 1) Objective function of the optimal load reduction model for the transmission and distribution system;

[0106] For the transmission and distribution system under extreme natural disasters, it is necessary to establish an optimal load reduction model to reduce load reduction after encountering a line failure. The goal of this optimization model is to maximize the weighted load sum of the transmission and distribution system. Its objective function is: [5] :

[0107]

[0108] In formula (12), ω ti 、ω dj are the weights of transmission network load i and distribution network load j respectively. t and γ d are the load weights between the transmission network and the distribution network respectively. Xti and P Xdj are the reduced load active power of transmission network load i and distribution network load j respectively. lt and n ld are the number of load nodes in the transmission network and the distribution network, respectively.

[0109] 2) Transmission system constraints;

[0110] The transmission system is modeled using a DC power flow model, with the following constraints: [5] .

[0111] C gt P gt +C lt (P Xt -P Lt )=C t P t (13)

[0112] m tij =(1-ztij )M,i,j∈N t (14)

[0113] θ ti -θ tj ≥-m tij +P tij x tij ,i,j∈N t (15)

[0114] θ ti -θ tj ≤m tij +P tij x tij ,i,j∈N t (16)

[0115]

[0116] P gtmin ≤P gt ≤P gtmax (18)

[0117] P tmin ≤P t ≤P tmax (19)

[0118] 0≤P Xt ≤P Xtmax (20)

[0119]

[0120] Formula (13) is the node active power balance constraint. In formula (13), C gt 、C lt and C t are the transmission network node-source node correlation matrix, the transmission network node-load node correlation matrix, and the transmission network node-line correlation matrix respectively; gt 、P Xt 、P Lt and P t They are the output active power vector of the power node of the transmission network, the active power vector of the load node, the active power vector of the load node, and the active power flow of the line.

[0121] Formula (14) is the line state constraint. In formula (14), m tij is the intermediate vector of the transmission line state; z tij is a 0-1 binary vector representing the operating status of the transmission line; M is a very large number; N t is the number of transmission lines.

[0122] Formula (15-16) is the line constraint. In formula (15-16), θ ti and θ tj are the voltage phase angle vectors of the first and last nodes of the transmission line respectively; P tij and x tij are the active power flow vector and line reactance vector of transmission line ij respectively.

[0123] Formula (17) is the transmission and distribution coupling constraint. In formula (17), C td is the correlation matrix between the load nodes of the transmission network and the power nodes of the distribution network; gd and P gdmax They are the output active power vector and output active power upper limit vector of the main power supply of the distribution network respectively.

[0124] Formula (18) is the upper and lower limit constraints of the active power output of the power node. In formula (18), P gt 、P gtmin and P gtmax They are the output active power vector, output active power lower limit vector and output active power upper limit vector of the power node of the transmission network respectively.

[0125] Formula (19) is the upper and lower limit constraints of the active power flow of the transmission line. In formula (19), P t 、P tmin and P tmax They are the active power flow vector, the lower limit vector and the upper limit vector of the active power flow of the transmission line respectively.

[0126] Formula (20) is the upper and lower limit constraints of load node active power reduction. In formula (20), P Xtmax Reduce the active power upper limit vector for the load nodes of the transmission network.

[0127] Formula (21) is the upper and lower limit constraints of the node voltage phase angle. In formula (21), θ t is the voltage phase angle vector of the transmission network node.

[0128] 3) Constraints of the power distribution system.

[0129] The distribution system is calculated using the AC power flow model based on distflow [6] .

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] P dgmin (Q dgmin )≤P dg (Q dg )≤P dgmax (Q dgmax ) (27)

[0136] P dmin (Q dmin )≤P d (Q d )≤P dmax (Q dmax ) (28)

[0137] 0≤P dX (Q dX )≤P dXmax (Q dXmax ) (29)

[0138] U dmin ≤U d ≤U dmax (30)

[0139] Formula (22) is the active power flow balance constraint of the node injection. In formula (22), C dg 、C dl and C d are the distribution network node-power node correlation matrix, node-load node correlation matrix and node-line correlation matrix respectively; P dg 、P dl 、P dX 、P d 、r d and I d They are the output active power vector of the power supply node of the distribution network, the active power vector injected by the load node, the active power vector reduced by the load node, the line active power flow vector, the line resistance vector and the line current vector.

[0140] Formula (23) is the node injection reactive power flow balance constraint. In formula (23), Q dg , Q dl , Q dX , Q d and x d They are the reactive power vector output by the power node of the distribution network, the reactive power vector injected by the load node, the reactive power vector reduced by the load node, the reactive power flow vector of the line and the reactance vector of the line.

[0141] Formula (24) is the line voltage loss constraint. In formula (24), ΔU dis the voltage loss of the distribution network line.

[0142] Formula (25) is the power flow balance constraint of active power and reactive power of the line. In formula (25), S d is the line apparent power vector of the distribution network.

[0143] Formula (26) is the active power and reactive power flow balance constraint of the line after second-order cone relaxation. In formula (26), U d is the node voltage vector.

[0144] Formula (27) is the upper and lower limit constraints of the generator output active power and reactive power. In formula (27), P dgmin 、P dgmax , Q dgmin and Q dgmax They are the output active power lower limit vector, active power upper limit vector, reactive power lower limit vector and reactive power upper limit vector of the power supply node of the distribution network respectively.

[0145] Formula (28) is the upper and lower limit constraints of the active power and reactive power flow of the line. In formula (28), P dmin 、P dmax , Q dmin and Q dmax They are the lower limit of active power flow, the upper limit of active power flow, the lower limit of reactive power flow and the upper limit of reactive power flow of distribution network lines.

[0146] Formula (29) is the upper and lower limit constraints of load reduction active power and reactive power. In formula (29), P dXmax and Q dXmax They are the active power vector and reactive power vector for the load nodes in the distribution network.

[0147] Formula (30) is the upper and lower limit constraints of the node voltage. In formula (30), U dmin and U dmax are the lower limit vector and upper limit vector of the voltage at the distribution network node respectively.

[0148] 205: Take certain resilience-enhancing measures. For resilience-enhancing measures that reduce the line failure rate, such as line reinforcement, it is necessary to regenerate the transmission system failure scenario after the line failure rate is reduced, and calculate the load reduction and resilience evaluation index. For resilience-enhancing measures that do not change the line failure rate, it is only necessary to use the optimal load reduction model of the transmission and distribution system after the resilience-enhancing measures are installed to solve the new transmission and distribution system load reduction based on the original failure scenario and calculate the resilience evaluation index.

[0149] 206: Compare the resilience assessment indicators after taking resilience enhancement measures and without taking resilience enhancement measures. If the resilience assessment indicators after taking resilience enhancement measures can meet the actual project needs, then take these resilience enhancement measures when the ice disaster arrives. If they do not meet the actual project needs, then take new resilience enhancement measures until the actual work needs are met.

[0150] In summary, the embodiment of the present invention establishes a resilience control method for the power transmission and distribution system under ice disaster scenarios through the above steps 201 to 206. This resilience control method can reasonably evaluate the resilience changes of the power transmission and distribution system under ice disaster scenarios and provide assistance and basis for selecting effective resilience improvement measures.

[0151] Example 3

[0152] The following is a specific example, Figure 4-12 The feasibility of the schemes in Examples 1 and 2 was verified, as described below:

[0153] This embodiment is based on the improved IEEE RTS-79 power transmission system [7] and IEEE33 node power distribution system [8] The transmission and distribution system of T24D4 coupled is used as an example to verify the effectiveness of this method. The transmission and distribution system schematic diagram and active distribution network schematic diagram are shown in Figure 2. Figure 4 、 Figure 5 shown. Figure 4 The middle triangle shaded area is the high-weight load node of the transmission network, and the elliptical shaded area is the low-weight load node of the transmission network.

[0154] To validate the proposed resilience assessment metrics and framework, three resilience improvement scenarios were used. Case 1 represents a transmission network with no line reinforcement strategy. Case 2 employs a distributed line reinforcement strategy, which reinforces all transmission lines. Case 3 employs a centralized line reinforcement strategy, which reinforces the longest lines in the transmission network. Both the centralized and distributed line reinforcement strategies share the same line reinforcement resources.

[0155] Ice disaster weather data such as Figure 6 As shown in Figure 2, the ice wind load under the ice disaster weather line is obtained based on the relevant meteorological model of the transmission line, as shown in Figure 2. Figure 7 As shown in the figure, loads 1-3 represent wind load, ice load and ice-wind load respectively. Figure 6It can be seen from the figure that the wind speed on the line is small, and the impact of wind load on the overall load is small. In the early stage of the ice and snow disaster, the ice thickness of the transmission line is small, and the ice load is small, resulting in a low probability of line failure. Therefore, only the 10 days after the ice disaster are considered. At this time, the ice disaster scenario when the power grid is prone to failure is considered. The 10 days after the ice disaster is used as the simulation time. During the simulation time, a time section is formed every 6 hours, and a total of 40 fault scenarios (line state sequences) are generated, including all transmission line failures and repair processes. According to the mixed time sampling method, the transmission network failure scenarios under the three example scenarios within the simulation time can be obtained, such as Figure 8 As shown. Figure 8 (a) corresponds to the transmission network failure scenario under Case 1, Figure 8 (b) corresponds to the transmission network failure scenario under Case 2, Figure 8 (c) corresponds to the transmission network failure scenario under Case 3. At the same time, the changes in the active power output upper limit of renewable energy generators (wind turbines and photovoltaic generators) in the active distribution network under various transmission network failure scenarios when the transmission network is affected by ice disasters are shown in Figure 2. Figure 9 shown.

[0156] pass Figure 8 The transmission network failure scenarios under the three example scenarios shown in the figure are combined with the optimal load reduction model of the transmission and distribution system to calculate the load changes of the transmission network under the three example scenarios and the load changes of the total distribution network (the loads of the four distribution networks are weighted and summed to form a total distribution network). Figure 10 and Figure 11 shown.

[0157] from Figure 10 and Figure 11 It can be seen that the load reduction of the transmission network and the load reduction of the total distribution network after considering the transmission line reinforcement strategy are significantly improved compared with the load reduction of the transmission and distribution network without adopting the line reinforcement strategy.

[0158] Then, according to the resilience curve of the transmission and distribution network (i.e. Figure 10 and Figure 11 The resilience evaluation index of the transmission and distribution system under the ice disaster scenario is calculated as shown in Figure 12 As shown in Figure 2, the changes in the resilience assessment indicators of the transmission and distribution system under the ice disaster show that the SLLP and LPLE of the transmission network and the main distribution network in Cases 2 and 3, where line reinforcement measures are implemented, are significantly improved compared to Case 1, where no reinforcement strategy is implemented. This demonstrates that the line reinforcement strategy, as a resilience-enhancing measure, can improve the resilience of the transmission and distribution system and also verifies the effectiveness of the proposed resilience assessment indicators.

[0159] In addition, by Figure 10 and Figure 12 It can be seen that the centralized line reinforcement strategy is more effective in improving the resilience of the transmission network than the distributed line reinforcement strategy. This is because the failure scenarios of the transmission network are mainly composed of long line failures in the transmission network, and the centralized line reinforcement strategy is precisely for reinforcing long lines of the transmission network. The centralized line reinforcement strategy applies more line reinforcement resources on long lines than the distributed line reinforcement strategy. Figure 11 and Figure 12 It can be seen that the distributed line reinforcement strategy is more effective than the centralized line reinforcement strategy in improving the resilience of the overall distribution network. This is because the distribution of the four distribution networks within the transmission network and the distribution of the transmission network's load weights are not identical. As a result, the centralized line reinforcement strategy cannot effectively improve the resilience of distribution networks located in low-load weight areas of the transmission network. However, the distributed line reinforcement strategy can, by leveraging its distributed reinforcement characteristics, improve the resilience of some distribution networks located in low-load weight areas of the transmission network. Of course, given the randomness of the sampling of fault scenarios in the mixed time sampling method that adapts to changes in line failure rates, the effectiveness of resilience-enhancing measures on the resilience of the transmission and distribution network is not always static.

[0160] In summary, the advantages of the resilience assessment method for transmission and distribution systems under ice disaster scenarios are as follows: applying ice disaster scenarios to transmission and distribution systems is more consistent with actual engineering conditions and takes into account the impact of transmission line failures on the distribution network. In addition, it can reasonably assess the changes in the resilience of transmission and distribution systems under ice disaster scenarios and provide assistance and basis for selecting effective resilience improvement measures.

[0161] References

[0162] [1]Lu J, Zeng M, Zeng

[0163] [2] Zhao N, Yu

[0164] [3] Wang Jianxue, Zhang Yao, Wu Si, et al. Analysis of the impact of large-scale ice disasters on transmission system reliability [J]. Proceedings of the CSEE, 2011, 31(28): 49-56.

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[0170] Unless otherwise specified, the embodiments of the present invention do not limit the models of the components. Any component that can perform the above functions may be used.

[0171] Those skilled in the art will understand that the accompanying drawings are only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0172] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the resilience of a power transmission and distribution system in an ice disaster scenario, characterized in that: The method comprises: Based on the meteorological data of the environment in which the transmission lines are located, a failure rate model of the transmission lines under ice disaster scenarios is constructed; Based on the failure rate model of the transmission line, a hybrid sampling method that adapts to the changes in line failure rate is used to generate failure scenarios of the transmission system under ice disaster scenarios. Based on the power system resilience curve under extreme natural disasters, an evaluation index and framework for the resilience of the transmission and distribution system are established, and then an optimal load reduction model for the transmission and distribution system considering transmission line failures is established; Controlling the resilience changes of the transmission and distribution system with different resilience enhancement measures improves the safety of the transmission and distribution system in ice disaster scenarios; The failure rate model of the transmission line in the ice disaster scenario is: Establishing a model for ice thickness growth per unit length of transmission lines W i (t)=0.067×r i (t) 0.846 (2) In formula (1-3), R eq,i (t) is the ice thickness increment of line i at time t, T is the number of freezing rain hours, r i (t) is the amount of freezing rain at the location of line i at time t, v i (t) is the wind speed at the location of line i at time t, ρ w is the water density, ρ I is the ice density, W i (t) is the liquid water content in the air at the position of line i at time t, q i (t) is the ice thickness per unit length of line i at time t; The ice load, wind load model and ice-wind load model for transmission lines per unit length are as follows: L I,i (t)=9.8×10 -3 ρ I π(D i +q i (t))q i (t) (4) L W,i (t)=CS i v i (t) 2 (D i +2q i (t)) (5) In formula (4-5), L I,i (t), L W,i (t) are the ice load and wind load per unit length of line i at time t; D i is the diameter of line i; C is a constant coefficient, set to 6.964×10 -3 ;S i is the span factor of line i; The ice and wind load L per unit length of transmission line i at time t WI,i (t); The failure rate model of the transmission system is as follows: In formula (7), P f,i (t) is the failure rate of the transmission line per unit length of line i at time t, a WI is the lower limit threshold of ice and wind load per unit length of line, b WI is the upper threshold of ice and wind load per unit length of line; In formula (8), P i (t) is the failure rate of transmission line i at time t, L i is the length of transmission line i.

2. The method for controlling the resilience of a power transmission and distribution system in an ice disaster scenario according to claim 1, characterized in that: The resilience assessment indicators and framework for the transmission and distribution system are established based on the resilience curve of the power system under extreme natural disasters: Through the formation of transmission network failure scenarios, calculation of transmission and distribution system load reduction, formation of resilience curves, calculation of resilience assessment indicators and other steps, a resilience assessment framework for transmission and distribution systems under ice disaster scenarios was established.

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