Power distribution network toughness improving method and device considering multi-element flexibility resources

Abstract

The invention discloses a power distribution network toughness improvement method and device considering multi-element flexibility resources, and the method comprises the steps: calculating the wind speed of each node of a power distribution network according to a typhoon wind field model, and calculating the fault probability of a power distribution line according to the wind speed of each node and a line breakage and tower fault probability model, so as to simulate the power failure process of the power distribution network under a typhoon disaster. Establishing an objective function by taking minimization of load reduction quantity and operation cost of the power distribution network as optimization objectives, establishing line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and operation constraints of the power distribution network based on the fault probability of the power distribution line, and generating a power distribution network optimization scheduling model according to the constraints of the objective function; according to the method, the load reduction amount and the operation cost of the power distribution network are effectively reduced, the power failure scale is reduced, the power failure time is shortened, and therefore the toughness level of the power distribution network is effectively improved when extreme disasters happen.

Description

Technical Field

[0001] The present invention relates to the field of improving the resilience of distribution networks, and in particular to a method and device for improving the resilience of distribution networks taking into account multiple flexibility resources. Background Art

[0002] In recent years, extreme weather events such as storms, typhoons, cold waves and floods have occurred frequently, posing unprecedented challenges to the stability and reliability of power systems. As an important part of the power system, the distribution network is directly related to the power safety and economic operation of the majority of users. Under the influence of extreme weather, a high-risk and low-probability event, some distribution network components will fail, leading to power outages. In this context, the operating environment of the distribution network has become more complex and uncertain due to the influence of extreme weather, and the traditional distribution network dispatching strategy can no longer meet current needs.

[0003] Domestic and foreign scholars have conducted a lot of research on measures to improve the resilience of distribution networks. For example, considering the timing failures caused by extreme disasters on distribution network components, a coordinated fault recovery method for AC / DC hybrid distribution networks with improved resilience is proposed; considering the regulation capacity and operating power of energy storage resource aggregates, a grid optimization operation and resilience improvement model for the entire stage of "pre-disaster-during-post-disaster" is proposed; considering emergency resources such as electric vehicles and maintenance teams, a charging station layout planning method that takes into account both distribution network resilience and charging convenience is proposed. However, these current methods have limited effects on improving the resilience of distribution networks when extreme disasters occur. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for improving the resilience of a distribution network taking into account multiple flexibility resources, which can effectively improve the resilience level of the distribution network when extreme disasters occur.

[0005] In order to solve the above technical problems, a technical solution adopted by the present invention is: A method for improving the resilience of a distribution network considering multiple flexibility resources comprises the following steps: Construct a typhoon wind field model and establish a line break and tower failure probability model; Calculating the wind speed of each node of the distribution network according to the typhoon wind field model, and calculating the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model; Establishing an objective function with minimizing load reduction and distribution network operation cost as optimization objectives, and establishing line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line; A distribution network optimization scheduling model is generated according to the objective function, the line reinforcement resource constraint, the interconnection line resource constraint, the energy storage resource constraint, the wind power resource constraint and the distribution network operation constraint, and the distribution network optimization scheduling model is solved to obtain a distribution network optimization scheduling plan.

[0006] Furthermore, the typhoon wind field model is an improved Rankine model, specifically: ; In the formula, v d represents the wind speed at a distance d from the center of the typhoon, v max Indicates the maximum wind speed radius of the typhoon R m The wind speed at the location is X, and X represents the shape parameter.

[0007] Furthermore, the establishment of the line break and tower failure probability model is specifically as follows: ; ; In the formula, represents the probability of failure of the nth distribution line of the line, represents the wind speed experienced by the nth distribution line, Indicates the wind speed that the distribution line is designed to withstand. represents the wind speed with the minimum probability of failure of the distribution line, Indicates the maximum wind speed that the distribution line can withstand. represents the failure probability of the mth tower, represents the standard normal cumulative distribution function, It represents the standard deviation of engineering parameters when the tower reaches the damage threshold. Indicates the wind speed suffered by the mth tower of the line, Indicates the tower design wind speed.

[0008] Further, the failure probability of the distribution line is calculated according to the wind speed of each node and the line breakage and tower failure probability model, specifically: ; In the formula, represents the failure probability of the distribution line connecting node i and node j, N L represents the total number of distribution lines on the distribution line connecting node i and node j, M P Represents the total number of towers on the distribution line connecting node i and node j.

[0009] Furthermore, the objective function is established with the minimization of load reduction and distribution network operation cost as the optimization goal, specifically: ; In the formula, T represents the set of failure times, N represents the set of nodes, represents the active power reduction of node i at time t, D L represents the set of lines, D N represents the line node set, c HD represents the reinforcement resource cost, Indicates whether line ij is reinforced, c TL represents the tie line resource cost, Indicates whether to add a contact line between node i and node j.

[0010] Furthermore, the line reinforcement resource constraints are: ; ; Where N HD Indicates the maximum number of reinforcements. represents the failure probability after line reinforcement, represents the failure probability of the distribution line connecting node i and node j, It represents the failure probability reduction factor after line reinforcement.

[0011] Furthermore, the tie line resource constraint is: ; ; Where N TL Indicates the maximum number of contact lines, u ij Indicates whether the line between node i and node j is connected.

[0012] Furthermore, the energy storage resource constraint is: ; ; ; ; ; ; ; In the formula, It indicates the power transmitted by node i to the grid at time t. Indicates battery n s Whether it is in the discharge state at time t, Indicates battery n s The discharge power at time t is: Indicates battery n sWhether it is in charging state at time t, Indicates battery n s At time t, the charging power is Indicates battery n s The maximum charging power, Indicates battery n s The maximum discharge power, Indicates battery n s The battery capacity at time t is Indicates battery n s The battery capacity at time t-1 is: Indicates the charging time of electric vehicles, Indicates the charging efficiency of the battery. Indicates the discharge efficiency of the battery. Indicates the battery capacity, T n Indicates the longest charging time for electric vehicles. Indicates battery n s The lower limit of battery capacity, Indicates battery n s The upper limit of battery capacity, Represents the number of batteries at node i.

[0013] Furthermore, the wind power resource constraint is: ; In the formula, Indicates the actual power of the fan. Indicates the rated power of the fan, v i,t represents the wind speed of node i at time t, v in Indicates the wind speed at which the fan is cut in, v out Indicates the fan cut-out wind speed, v rated Indicates the rated wind speed of the fan.

[0014] In order to solve the above technical problems, another technical solution adopted by the present invention is: A distribution network resilience improvement device considering multiple flexibility resources, comprising: Model building module, used to build typhoon wind field model and establish line break and tower failure probability model; A calculation module, used to calculate the wind speed of each node of the distribution network according to the typhoon wind field model, and calculate the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model; An objective function and constraint establishment module, used to establish an objective function with minimization of load reduction and distribution network operation cost as optimization objectives, and to establish line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line; The model solving module is used to generate a distribution network optimization scheduling model according to the objective function, the line reinforcement resource constraints, the interconnection line resource constraints, the energy storage resource constraints, the wind power resource constraints and the distribution network operation constraints, and solve the distribution network optimization scheduling model to obtain a distribution network optimization scheduling plan.

[0015] The beneficial effects of the present invention are as follows: a typhoon wind field model is constructed, and a line break and tower failure probability model is established; the wind speed of each node of the distribution network is calculated according to the typhoon wind field model to analyze the relationship between the typhoon wind field and the distribution line; the failure probability of the distribution line is calculated according to the wind speed of each node and the line break and tower failure probability model to simulate the power outage process of the distribution network under the typhoon disaster; an objective function is established with minimizing the load reduction and the distribution network operation cost as the optimization goal; and line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and the like are established based on the failure probability of the distribution line. and distribution network operation constraints, and generates a distribution network optimization dispatching model according to the constraints of the objective function, and solves it to obtain the distribution network optimization dispatching scheme. The distribution network optimization dispatching model makes full use of multiple flexible resources such as energy storage resources and wind power resources, and considers the line failure problem under the influence of typhoon factors. The model is more in line with reality, and is deployed and adjusted according to actual needs. It can adapt to different load demands and environmental changes, effectively reduce the load reduction and distribution network operation cost, and reduce the scale and time of power outages, thereby effectively improving the resilience of the distribution network when extreme disasters occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of the steps of a method for improving the resilience of a distribution network considering multiple flexibility resources according to an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a distribution network resilience enhancement device taking into account multiple flexibility resources according to an embodiment of the present invention; Figure 3 A schematic diagram of an IEEE 33-node distribution network system in a distribution network resilience enhancement method considering multiple flexibility resources in an embodiment of the present invention; Figure 4 A schematic diagram of the failure probability distribution of distribution lines during a typhoon in a method for improving the resilience of a distribution network considering multiple flexibility resources in an embodiment of the present invention; Figure 5 A schematic diagram of changes in system load reduction when 3-7 lines are reinforced in a distribution network resilience improvement method considering multiple flexibility resources in an embodiment of the present invention; Figure 6 A schematic diagram of system load reduction in different scenarios in a method for improving distribution network resilience considering multiple flexibility resources according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0018] Please refer to Figure 1 , a distribution network resilience improvement method considering multiple flexibility resources, comprising the steps of: Construct a typhoon wind field model and establish a line break and tower failure probability model; Calculating the wind speed of each node of the distribution network according to the typhoon wind field model, and calculating the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model; Establishing an objective function with minimizing load reduction and distribution network operation cost as optimization objectives, and establishing line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line; A distribution network optimization scheduling model is generated according to the objective function, the line reinforcement resource constraint, the interconnection line resource constraint, the energy storage resource constraint, the wind power resource constraint and the distribution network operation constraint, and the distribution network optimization scheduling model is solved to obtain a distribution network optimization scheduling plan.

[0019] From the above description, it can be seen that the beneficial effects of the present invention are: constructing a typhoon wind field model, and establishing a line break and tower failure probability model, calculating the wind speed of each node of the distribution network according to the typhoon wind field model, so as to analyze the relationship between the typhoon wind field and the distribution line, and calculating the failure probability of the distribution line according to the wind speed of each node and the line break and tower failure probability model, so as to simulate the power outage process of the distribution network under the typhoon disaster, establish an objective function with minimizing the load reduction and the distribution network operation cost as the optimization goal, and establish line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints based on the failure probability of the distribution line. Source constraints and distribution network operation constraints are taken into account. A distribution network optimization scheduling model is generated according to the constraints of the objective function, and it is solved to obtain the distribution network optimization scheduling plan. The distribution network optimization scheduling model makes full use of multiple flexible resources such as energy storage resources and wind power resources, and considers the line failure problem under the influence of typhoon factors. The model is more in line with reality and can be deployed and adjusted according to actual needs. It can adapt to different load demands and environmental changes, effectively reduce the load reduction and distribution network operation cost, and reduce the scale and time of power outages, thereby effectively improving the resilience of the distribution network when extreme disasters occur.

[0020] Furthermore, the typhoon wind field model is an improved Rankine model, specifically: ; In the formula, v d represents the wind speed at a distance d from the center of the typhoon, v max Indicates the maximum wind speed radius of the typhoon R m The wind speed at the location is X, and X represents the shape parameter.

[0021] From the above description, it can be seen that by modeling the typhoon wind field based on the improved Rankine model, the typhoon wind field is modeled as a double concentric circle model. The actual typhoon wind field data is combined to simulate the real typhoon travel range, which can more accurately reflect the typhoon's wind field structure, wind speed distribution and other characteristics.

[0022] Furthermore, the establishment of the line break and tower failure probability model is specifically as follows: ; ; In the formula, represents the probability of failure of the nth distribution line of the line, represents the wind speed experienced by the nth distribution line, Indicates the wind speed that the distribution line is designed to withstand. represents the wind speed with the minimum probability of failure of the distribution line, Indicates the maximum wind speed that the distribution line can withstand. represents the failure probability of the mth tower, represents the standard normal cumulative distribution function, It represents the standard deviation of engineering parameters when the tower reaches the damage threshold. Indicates the wind speed suffered by the mth tower of the line, Indicates the tower design wind speed.

[0023] From the above description, it can be seen that in typhoon weather, strong winds can easily cause distribution line failures, mainly including line breakage, tower collapse, etc. In the established line break and tower failure probability model, the probability of line breakage is linearly related to wind speed, and the vulnerability of towers can be described as a log-normal distribution, so as to accurately calculate the failure probability of distribution lines in the future.

[0024] Further, the failure probability of the distribution line is calculated according to the wind speed of each node and the line breakage and tower failure probability model, specifically: ; In the formula, represents the failure probability of the distribution line connecting node i and node j, N L represents the total number of distribution lines on the distribution line connecting node i and node j, M P Represents the total number of towers on the distribution line connecting node i and node j.

[0025] From the above description, it can be seen that the failure probability of the distribution line is calculated according to the wind speed of each node and the line break and tower failure probability model. The failure probability of the distribution line is represented by the series connection of its components, which effectively simulates the power outage of the distribution network under typhoon disasters.

[0026] Furthermore, the objective function is established with the minimization of load reduction and distribution network operation cost as the optimization goal, specifically: ; In the formula, T represents the set of failure times, N represents the set of nodes, represents the active power reduction of node i at time t, D L represents the set of lines, D N represents the line node set, c HD represents the reinforcement resource cost, Indicates whether line ij is reinforced, c TL represents the tie line resource cost, Indicates whether to add a contact line between node i and node j.

[0027] From the above description, it can be seen that the objective function is established with the minimization of load reduction and distribution network operation cost as the optimization goal, which ensures the operation economy and stability while improving the resilience level of the distribution network.

[0028] Furthermore, the line reinforcement resource constraints are: ; ; Where N HD Indicates the maximum number of reinforcements. represents the failure probability after line reinforcement, represents the failure probability of the distribution line connecting node i and node j, It represents the failure probability reduction factor after line reinforcement.

[0029] From the above description, it can be seen that establishing the line reinforcement resource constraint takes into account the impact of the number of reinforced lines on the load reduction change and the operating cost.

[0030] Furthermore, the tie line resource constraint is: ; ; Where N TL Indicates the maximum number of contact lines, u ij Indicates whether the line between node i and node j is connected.

[0031] From the above description, it can be seen that the tie line resource constraint limits the maximum number of tie lines that can be added to ensure stable operation of the system.

[0032] Furthermore, the energy storage resource constraint is: ; ; ; ; ; ; ; In the formula, It indicates the power transmitted by node i to the grid at time t. Indicates battery n s Whether it is in the discharge state at time t, Indicates battery n s The discharge power at time t is: Indicates battery n s Whether it is in charging state at time t, Indicates battery n s At time t, the charging power is Indicates battery n s The maximum charging power, Indicates battery n s The maximum discharge power, Indicates battery n s The battery capacity at time t is Indicates battery n s The battery capacity at time t-1 is: Indicates the charging time of electric vehicles, Indicates the charging efficiency of the battery. Indicates the discharge efficiency of the battery. Indicates the battery capacity, T n Indicates the longest charging time for electric vehicles. Indicates battery n s The lower limit of battery capacity, Indicates battery n s The upper limit of battery capacity, Represents the number of batteries at node i.

[0033] From the above description, it can be seen that building energy storage resource constraints ensures the reliability when utilizing energy storage energy.

[0034] Furthermore, the wind power resource constraint is: ; In the formula, Indicates the actual power of the fan. Indicates the rated power of the fan, v i,t represents the wind speed of node i at time t, v in Indicates the wind speed at which the fan is cut in, v out Indicates the fan cut-out wind speed, v rated Indicates the rated wind speed of the fan.

[0035] From the above description, it can be seen that the wind power resource constraint limits the actual power of the wind turbine and takes into account the impact of whether wind power participates in energy supply on the load reduction change and operating cost.

[0036] Please refer to Figure 2 , a distribution network resilience improvement device considering multiple flexibility resources, comprising: Model building module, used to build typhoon wind field model and establish line break and tower failure probability model; A calculation module, used to calculate the wind speed of each node of the distribution network according to the typhoon wind field model, and calculate the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model; An objective function and constraint establishment module, used to establish an objective function with minimization of load reduction and distribution network operation cost as optimization objectives, and to establish line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line; The model solving module is used to generate a distribution network optimization scheduling model according to the objective function, the line reinforcement resource constraints, the interconnection line resource constraints, the energy storage resource constraints, the wind power resource constraints and the distribution network operation constraints, and solve the distribution network optimization scheduling model to obtain a distribution network optimization scheduling plan.

[0037] The above-mentioned method and device for improving the resilience of a distribution network considering multiple flexibility resources of the present invention can be applied to distribution networks facing extreme disasters, and the following is an explanation through specific implementation methods: Please refer to Figure 1 , Figure 3-Figure 6 , Embodiment 1 of the present invention is: A method for improving the resilience of a distribution network considering multiple flexibility resources comprises the following steps: S1. Construct a typhoon wind field model and establish a line break and tower failure probability model.

[0038] Among them, the damage caused by typhoons to the power grid is mainly reflected in two aspects: strong winds and heavy rainfall. The short-term strong winds brought by typhoons when they land will seriously affect the normal operation of components such as distribution lines and towers, thereby causing power outages. The typhoon wind field is modeled based on the improved Rankine model, in which the wind speed is the smallest at the center of the typhoon. As it moves away from the center of the typhoon, the wind speed increases rapidly and rises to the maximum wind speed at the maximum wind speed radius, and then slowly decreases as the distance increases. The typhoon wind field model is an improved Rankine model, specifically: ; In the formula, v d represents the wind speed at a distance d from the center of the typhoon, v max Indicates the maximum wind speed radius of the typhoon R m The wind speed at the location is X, which is a shape parameter with a value range of greater than 0.4 and less than 0.6.

[0039] In typhoon weather, strong winds can easily lead to distribution line failures, including line breakage, tower collapse, etc. The probability of line breakage is linearly related to wind speed, and the fragility of towers can be described as a log-normal distribution. The establishment of the line breakage and tower failure probability model is specifically as follows: ; ; In the formula, represents the probability of failure of the nth distribution line of the line, represents the wind speed experienced by the nth distribution line, Indicates the wind speed that the distribution line is designed to withstand. represents the wind speed with the minimum probability of failure of the distribution line, Indicates the maximum wind speed that the distribution line can withstand. represents the failure probability of the mth tower, represents the standard normal cumulative distribution function, It represents the standard deviation of engineering parameters when the tower reaches the damage threshold. Indicates the wind speed suffered by the mth tower of the line, Indicates the tower design wind speed.

[0040] S2. Calculate the wind speed of each node of the distribution network according to the typhoon wind field model, and calculate the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model.

[0041] Assume that there are M lines connecting nodes i and j. P towers and N L distribution lines, the failure probabilities of each tower and distribution line are independent of each other, then the failure probability of the line is The series representation of its components, the failure probability of the distribution line is calculated according to the wind speed of each node and the line breakage and tower failure probability model, specifically: ; In the formula, represents the failure probability of the distribution line connecting node i and node j, N L represents the total number of distribution lines on the distribution line connecting node i and node j, M P Represents the total number of towers on the distribution line connecting node i and node j.

[0042] S3. Establish an objective function with minimizing load reduction and distribution network operation cost as optimization goals, and establish line reinforcement resource constraints, interconnection line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line.

[0043] Among them, by minimizing the load reduction amount, the impact of typhoon disasters on the safe operation of the distribution network can be reduced. The objective function is established with minimizing the load reduction amount and the distribution network operation cost as the optimization goal, specifically: ; In the formula, T represents the set of failure times, N represents the set of nodes, represents the active power reduction of node i at time t, D L represents the set of lines, D N represents the line node set, c HD represents the reinforcement resource cost, Indicates whether line ij is reinforced, which is a 0-1 variable. When 1 means line ij is reinforced, 0 means the line is not reinforced, c TL represents the tie line resource cost, Indicates whether to add a contact line between node i and node j, which is a 0-1 variable. When 1 means adding a contact line between node i and node j, A value of 0 indicates that there is already a distribution line between node i and node j, and no tie line is added.

[0044] The line reinforcement resource constraints are: ; ; Where N HD Indicates the maximum number of reinforcements. represents the failure probability after line reinforcement, It represents the failure probability reduction factor after line reinforcement, and its value range is .when =1, the line is reinforced and the line failure probability is reduced to 1 / 2 of the original failure probability. times, and when =0, the line is not reinforced and the line failure probability remains unchanged.

[0045] The tie line resource constraints are: ; ; Where N TL Indicates the maximum number of contact lines, u ij Indicates whether the line between node i and node j is connected, which is a 0-1 variable. ij 1 indicates that the line between node i and node j is connected. ij A value of 0 indicates that the line between node i and node j is not connected.

[0046] The energy storage resource constraints are: ; ; ; ; ; ; ; In the formula, It indicates the power transmitted by node i to the grid at time t. Indicates battery n s Whether it is in the discharge state at time t is a 0-1 variable. 1 means battery n s At time t, it is in the discharge state. 0 means battery n s At time t, it is not in the discharge state. Indicates battery n s The discharge power at time t is: Indicates battery n s Whether it is in charging state at time t is a 0-1 variable. 1 means battery n s At time t, it is in the charging state. 0 means battery n s At time t, it is not in charging state. Indicates battery n s At time t, the charging power is Indicates battery n sThe maximum charging power, Indicates battery n s The maximum discharge power, Indicates battery n s The battery capacity at time t is Indicates battery n s The battery capacity at time t-1 is: Indicates the charging time of electric vehicles, Indicates the charging efficiency of the battery. Indicates the discharge efficiency of the battery. Indicates the battery capacity, T n Indicates the longest charging time for electric vehicles. Indicates battery n s The lower limit of battery capacity, Indicates battery n s The upper limit of battery capacity, Represents the number of batteries at node i.

[0047] The wind power resource constraints are: ; In the formula, Indicates the actual power of the fan. Indicates the rated power of the fan, v i,t represents the wind speed of node i at time t, v in Indicates the wind speed at which the fan is cut in, v out Indicates the fan cut-out wind speed, v rated Indicates the rated wind speed of the fan.

[0048] The distribution network operation constraints include power balance constraints, power upper and lower limit constraints, line flow constraints and node voltage constraints.

[0049] The power balance constraint indicates that the inflow power of a node i in the distribution network must be equal to the outflow power, which is: ; ; ; ; In the formula, represents the active power transmitted by the line at time t, represents the active power emitted by node i at time t, represents the active power of the load connected to node i at time t, represents the reactive power transmitted by the line at time t, represents the reactive power emitted by node i at time t, represents the reactive power of the load connected to node i at time t, represents the reactive power reduction of node i at time t, represents the total active power of the load connected to node i, represents the total reactive power of the load connected to node i. This constraint ensures the balance of active power and reactive power in the distribution network.

[0050] The power upper and lower limits are: ; ; In the formula, Indicates the upper limit of the active output of the generator. Indicates the upper limit of the reactive power output of the generator.

[0051] The line power flow constraint limits the maximum active and reactive power flowing through the line, which is: ; ; In the formula, Indicates whether the line is faulty, a 0-1 variable. 1 means the line is not faulty. 0 indicates a line fault. Indicates the maximum active power allowed to flow through the line. Indicates the maximum reactive power allowed to flow through the line. =0, the active and reactive powers flowing through the line are both 0.

[0052] The node voltage constraint limits the voltage drop between two nodes of the line under normal operation and fault conditions, which is: ; ; ; In the formula, represents the voltage of node i at time t, represents the voltage of node j at time t, represents a constant, and the voltage variable is optimized using the big M method. ij Represents the resistance of the line, x ij Indicates the reactance of the line, U min Indicates the minimum voltage value of node i, U max Represents the maximum voltage value of node i. =1, the voltage at both ends of the line strictly meets the voltage drop constraint; when the line fails, that is, =0, there is no constraint on the voltage across the fault line.

[0053] S4. Generate a distribution network optimization scheduling model according to the objective function, the line reinforcement resource constraint, the interconnection line resource constraint, the energy storage resource constraint, the wind power resource constraint and the distribution network operation constraint, and solve the distribution network optimization scheduling model to obtain a distribution network optimization scheduling plan.

[0054] In order to verify the effectiveness of the distribution network resilience improvement method considering multiple flexibility resources described above, Figure 3 The IEEE 33-node distribution network system shown in Figure 1 is used as the object for simulation analysis. The distribution network includes 3 distributed wind power sources, 2 energy storage nodes and 5 tie lines. The deployment locations of these resources are shown in Table 1. To simulate the impact of a typhoon, it is assumed that a typhoon lands at a location of (-10km, 5km) and moves toward the northeast at a speed of 20km / h. During the typhoon, the distribution line failure probability distribution is as follows: Figure 4 shown.

[0055] Table 1 Flexibility resource allocation location

[0056] Based on the above model, this embodiment simulates the changes in system load reduction when 3-7 lines are reinforced, such as Figure 5 As shown in Table 2, with the increase in the number of reinforced lines, the system becomes more and more robust. In addition, four scenarios are set up to study the impact of energy storage resources and wind power resources on system resilience.

[0057] Table 2 Scenario settings

[0058] Among them, the resilience area index is used to quantify the resilience of the distribution network under various scenarios in the calculation. The resilience of the distribution network is evaluated by defining the integral of the load reduction amount in the fault situation over time. The calculation formula is as follows: ; In the formula, R r It represents the resilience area index, which is mathematically defined as the integral of the load reduction amount over time under a fault condition. It describes the speed and severity of the system load reduction caused by the fault. The smaller the value, the closer the fault condition is to the normal condition, and the smaller the consequences of the power outage are. t1 and t2 represent the start time and end time of the typhoon disaster impact.

[0059] As shown in Table 3 and Figure 6As shown in the figure, in the scenario without considering energy storage and wind power flexibility resources, the system load reduction is the most serious, resulting in a load reduction of 1400 kW. After considering the participation of flexible resources in power supply, the load reduction is alleviated. At t=10, when the typhoon impact ends, the load reduction in scenarios 2 and 3 is 9.38% and 14.06% lower than that in scenario 1, respectively. In particular, in scenario 4, which considers the participation of energy storage and wind power at the same time, the load reduction can be reduced by 29.69%, achieving the best result.

[0060] Table 3 Resilience area index for each scenario

[0061] In summary, the typhoon wind field model in the above method of the present invention is an improved Rankine model, which combines actual typhoon wind field data, simulates the actual typhoon travel range, and considers the line failure problem under the influence of typhoon factors. The constructed optimization model is more in line with reality, and provides a practical method for urban pre-disaster prevention and disaster repair, and provides technical theoretical support for power grid operators to pre-configure resources and defense measures. In addition, a variety of flexible resources are considered, including line reinforcement, switching of interconnection lines, energy storage resources and wind power energy scheduling configuration, and full use of energy storage energy and wind power energy and other resources. Deployment and adjustment are carried out according to actual needs, which can adapt to different load demands and environmental changes. The above method of the present invention can effectively reduce the amount of load reduction when extreme disasters occur, reduce the time and scale of power outages, and improve the resilience level of the distribution network.

[0062] Please refer to Figure 2 , Embodiment 2 of the present invention is: A distribution network resilience improvement device considering multiple flexibility resources, comprising: Model building module, used to build typhoon wind field model and establish line break and tower failure probability model; A calculation module, used to calculate the wind speed of each node of the distribution network according to the typhoon wind field model, and calculate the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model; An objective function and constraint establishment module, used to establish an objective function with minimization of load reduction and distribution network operation cost as optimization objectives, and to establish line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line; The model solving module is used to generate a distribution network optimization scheduling model according to the objective function, the line reinforcement resource constraints, the interconnection line resource constraints, the energy storage resource constraints, the wind power resource constraints and the distribution network operation constraints, and solve the distribution network optimization scheduling model to obtain a distribution network optimization scheduling plan.

[0063] In an optional implementation, the typhoon wind field model in the model building module is an improved Rankine model, specifically: ; In the formula, v d represents the wind speed at a distance d from the center of the typhoon, v max Indicates the maximum wind speed radius of the typhoon R m The wind speed at the location is X, which represents the shape parameter.

[0064] In an optional implementation, the establishment of the line break and tower failure probability model in the model building module is specifically: ; ; In the formula, represents the probability of failure of the nth distribution line of the line, represents the wind speed experienced by the nth distribution line, Indicates the wind speed that the distribution line is designed to withstand. represents the wind speed with the minimum probability of failure of the distribution line, Indicates the maximum wind speed that the distribution line can withstand. represents the failure probability of the mth tower, represents the standard normal cumulative distribution function, It represents the standard deviation of engineering parameters when the tower reaches the damage threshold. Indicates the wind speed suffered by the mth tower of the line, Indicates the tower design wind speed.

[0065] In an optional implementation, the calculation module calculates the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model, specifically: ; In the formula, represents the failure probability of the distribution line connecting node i and node j, N L represents the total number of distribution lines on the distribution line connecting node i and node j, M P Represents the total number of towers on the distribution line connecting node i and node j.

[0066] In an optional implementation, the objective function and constraint establishment module establishes an objective function with minimization of load reduction and distribution network operation cost as the optimization goal, specifically: ; In the formula, T represents the set of failure times, N represents the set of nodes, represents the active power reduction of node i at time t, D L represents the set of lines, D N represents the line node set, c HD represents the reinforcement resource cost, Indicates whether line ij is reinforced, c TL represents the tie line resource cost, Indicates whether to add a contact line between node i and node j.

[0067] The line reinforcement resource constraint in the objective function and constraint establishment module is: ; ; Where N HD Indicates the maximum number of reinforcements. represents the failure probability after line reinforcement, represents the failure probability of the distribution line connecting node i and node j, It represents the failure probability reduction factor after line reinforcement.

[0068] The tie line resource constraints in the objective function and constraint building module are: ; ; Where N TL Indicates the maximum number of contact lines, u ij Indicates whether the line between node i and node j is connected.

[0069] The energy storage resource constraint in the objective function and constraint establishment module is: ; ; ; ; ; ; ; In the formula, It indicates the power transmitted by node i to the grid at time t. Indicates battery n s Whether it is in the discharge state at time t, Indicates battery n s The discharge power at time t is: Indicates battery n s Whether it is in charging state at time t, Indicates battery n s At time t, the charging power is Indicates battery n s The maximum charging power, Indicates battery n s The maximum discharge power, Indicates battery n s The battery capacity at time t is Indicates battery n s The battery capacity at time t-1 is: Indicates the charging time of electric vehicles, Indicates the charging efficiency of the battery. Indicates the discharge efficiency of the battery. Indicates the battery capacity, T n Indicates the longest charging time for electric vehicles. Indicates battery n s The lower limit of battery capacity, Indicates battery n s The upper limit of battery capacity, Represents the number of batteries at node i.

[0070] The wind power resource constraint in the objective function and constraint establishment module is: ; In the formula, Indicates the actual power of the fan. Indicates the rated power of the fan, v i,t represents the wind speed of node i at time t, v in Indicates the wind speed at which the fan is cut in, v out Indicates the fan cut-out wind speed, v rated Indicates the rated wind speed of the fan.

[0071] In summary, the present invention provides a method and device for improving the resilience of a distribution network taking into account multiple flexibility resources, constructs a typhoon wind field model, and establishes a line break and tower failure probability model. The wind speed of each node in the distribution network is calculated according to the typhoon wind field model to analyze the relationship between the typhoon wind field and the distribution line, and the failure probability of the distribution line is calculated according to the wind speed of each node and the line break and tower failure probability model to simulate the power outage process of the distribution network under typhoon disasters, establish an objective function with minimizing the load reduction and the distribution network operation cost as the optimization goal, and establish line reinforcement resource constraints, tie line resource constraints, storage and other constraints based on the failure probability of the distribution line. Energy resource constraints, wind power resource constraints and distribution network operation constraints are taken into account. The distribution network optimization dispatching model is generated according to each constraint of the objective function, and is solved to obtain the distribution network optimization dispatching scheme. The distribution network optimization dispatching model makes full use of multiple flexible resources such as energy storage resources and wind power resources, and considers the line failure problem under the influence of typhoon factors. The model is more realistic and can be deployed and adjusted according to actual needs. It can adapt to different load demands and environmental changes, effectively reduce the load reduction and distribution network operation costs, and reduce the scale and time of power outages, thereby effectively improving the resilience of the distribution network when extreme disasters occur. In addition, the typhoon wind field is modeled based on the improved Rankine model, and the typhoon wind field is modeled as a double concentric circle model. The actual typhoon wind field data is combined to simulate the real typhoon travel range, which can more accurately reflect the typhoon's wind field structure, wind speed distribution and other characteristics.

[0072] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0077] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for improving the resilience of a distribution network considering multiple flexibility resources, characterized in that: Includes steps: Construct a typhoon wind field model and establish a line break and tower failure probability model; Calculating the wind speed of each node of the distribution network according to the typhoon wind field model, and calculating the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model; Establishing an objective function with minimizing load reduction and distribution network operation cost as optimization objectives, and establishing line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line; A distribution network optimization scheduling model is generated according to the objective function, the line reinforcement resource constraint, the interconnection line resource constraint, the energy storage resource constraint, the wind power resource constraint and the distribution network operation constraint, and the distribution network optimization scheduling model is solved to obtain a distribution network optimization scheduling plan.

2. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 1, characterized in that: The typhoon wind field model is an improved Rankine model, specifically: ； In the formula, v d represents the wind speed at a distance d from the center of the typhoon, v max Indicates the maximum wind speed radius of the typhoon R m The wind speed at the location is X, which represents the shape parameter.

3. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 1, characterized in that: The establishment of the line break and tower failure probability model is specifically as follows: ； ； In the formula, represents the probability of failure of the nth distribution line of the line, represents the wind speed experienced by the nth distribution line, Indicates the wind speed that the distribution line is designed to withstand. represents the wind speed with the minimum probability of failure of the distribution line, Indicates the maximum wind speed that the distribution line can withstand. represents the failure probability of the mth tower, represents the standard normal cumulative distribution function, It represents the standard deviation of engineering parameters when the tower reaches the damage threshold. Indicates the wind speed suffered by the mth tower of the line, Indicates the tower design wind speed.

4. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 3, characterized in that: The failure probability of the distribution line is calculated according to the wind speed of each node and the line breakage and tower failure probability model, specifically: ； In the formula, represents the failure probability of the distribution line connecting node i and node j, N L represents the total number of distribution lines on the distribution line connecting node i and node j, M P Represents the total number of towers on the distribution line connecting node i and node j.

5. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 1, characterized in that: The objective function is established with the minimization of load reduction and distribution network operation cost as the optimization goal, specifically: ； In the formula, T represents the set of failure times, N represents the set of nodes, represents the active power reduction of node i at time t, D L represents the set of lines, D N represents the line node set, c HD represents the reinforcement resource cost, Indicates whether line ij is reinforced, c TL represents the tie line resource cost, Indicates whether to add a contact line between node i and node j.

6. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 5, characterized in that: The line reinforcement resource constraints are: ； ； Where N HD Indicates the maximum number of reinforcements. represents the failure probability after line reinforcement, represents the failure probability of the distribution line connecting node i and node j, It represents the failure probability reduction factor after line reinforcement.

7. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 5, characterized in that: The tie line resource constraints are: ； ； Where N TL Indicates the maximum number of contact lines, u ij Indicates whether the line between node i and node j is connected.

8. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 5, characterized in that: The energy storage resource constraints are: ； ； ； ； ； ； ； In the formula, It indicates the power transmitted by node i to the grid at time t. Indicates battery n s Whether it is in the discharge state at time t, Indicates battery n s The discharge power at time t is: Indicates battery n s Whether it is in charging state at time t, Indicates battery n s At time t, the charging power is Indicates battery n s The maximum charging power, Indicates battery n s The maximum discharge power, Indicates battery n s The battery capacity at time t is Indicates battery n s The battery capacity at time t-1 is: Indicates the charging time of electric vehicles, Indicates the charging efficiency of the battery. Indicates the discharge efficiency of the battery. Indicates the battery capacity, T n Indicates the longest charging time for electric vehicles. Indicates battery n s The lower limit of battery capacity, Indicates battery n s The upper limit of battery capacity, Represents the number of batteries at node i.

9. A method for improving the resilience of a distribution network considering multiple flexibility resources according to claim 5, characterized in that: The wind power resource constraints are: ； In the formula, Indicates the actual power of the fan. Indicates the rated power of the fan, v i,t represents the wind speed of node i at time t, v in Indicates the wind speed at which the fan is cut in, v out Indicates the fan cut-out wind speed, v rated Indicates the rated wind speed of the fan.

10. A distribution network resilience improvement device considering multiple flexibility resources, characterized in that: include: Model building module, used to build typhoon wind field model and establish line break and tower failure probability model; A calculation module, used to calculate the wind speed of each node of the distribution network according to the typhoon wind field model, and calculate the failure probability of the distribution line according to the wind speed of each node and the line breakage and tower failure probability model; An objective function and constraint establishment module, used to establish an objective function with minimization of load reduction and distribution network operation cost as optimization objectives, and to establish line reinforcement resource constraints, tie line resource constraints, energy storage resource constraints, wind power resource constraints and distribution network operation constraints based on the failure probability of the distribution line; The model solving module is used to generate a distribution network optimization scheduling model according to the objective function, the line reinforcement resource constraints, the interconnection line resource constraints, the energy storage resource constraints, the wind power resource constraints and the distribution network operation constraints, and solve the distribution network optimization scheduling model to obtain a distribution network optimization scheduling plan.

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