Emergency power supply dispatching method based on line fault pre-judgment under typhoon

Abstract

The invention discloses an emergency power supply dispatching method based on line fault pre-judgment under typhoon, relates to the technical field of power system emergency repair dispatching, and solves the problem that in the prior art, the whole emergency power supply vehicle dispatching process does not fully consider the influence of a traffic network on driving time and different levels of important users, and the dispatching efficiency is poor. And the existing scheduling model is relatively rigid and not flexible enough. The method mainly comprises two parts: the first part is used for calculating the fault probability of a power distribution network line and making pre-disaster prediction for the formulation of a subsequent emergency power supply scheduling scheme; in the second part, a multi-target planning model is established by taking the minimum total power failure loss as a main function and the surplus capacity as a secondary function and considering the running time of the emergency power supply vehicle under actual traffic and the condition that new energy power generation exists on part of nodes; prediction of line faults under typhoon and post-disaster emergency power supply vehicle dispatching are combined, an emergency power supply dispatching scheme can be made in advance, and losses caused by power failure are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system emergency repair and dispatching, and in particular to an emergency power supply dispatching method based on line fault prediction during typhoons. Background Art

[0002] In recent years, global climate change has become increasingly severe, with meteorological disasters such as typhoons, heavy rains, and ice storms becoming frequent. Most of my country's economically developed regions are located along the eastern coastal areas, which are often affected by typhoons, resulting in significant economic and social losses. Therefore, it is crucial to develop models to study the probability of these losses and to facilitate the timely and appropriate deployment of emergency rescue and relief supplies when disasters occur.

[0003] Based on current technical analysis, the evaluation of faulty lines has the disadvantages of complex data processing and huge computational effort; in terms of power resource scheduling, the models are usually too complex, with long solution times and slow calculation speeds. In addition, the impact of the traffic network on travel time is not fully considered during the entire emergency power vehicle scheduling process, and the existing scheduling models are relatively rigid and inflexible. From the perspective of the overall power emergency plan, there are few plans that combine pre-disaster prediction of distribution network line failures during typhoons with post-fault power resource scheduling. Most domestic and foreign research has discussed line failure prediction, or only discussed post-disaster emergency power scheduling. However, during typhoon disasters, sudden power outages can cause huge life and economic losses to important users such as hospitals and factories. The early prediction of line failures is conducive to preparing emergency power scheduling plans in advance, reducing power outage time, and thus reducing the losses caused by power outages to users.

[0004] In view of this, an emergency power supply scheduling method based on line fault prediction during typhoons is needed. Summary of the Invention

[0005] In view of the problem that the entire emergency power supply vehicle dispatching process in the existing technology does not fully consider the impact of the traffic network on travel time and the impact of different levels of important users, resulting in the existing dispatching model being relatively rigid and inflexible, the present invention provides an emergency power supply dispatching method based on line fault prediction during typhoons. It can accurately calculate the lines that will be disconnected after the disaster through the line fault probability model; based on the topology of the post-disaster distribution network, the importance of the power-lost users is divided, and an emergency power supply optimization dispatching model is proposed by combining renewable energy power generation and actual traffic travel time. The specific technical solution is as follows:

[0006] A method for dispatching emergency power supply based on line fault prediction during typhoons includes the following steps:

[0007] Obtain the typhoon's path, direction, maximum wind speed radius, and maximum wind speed, calculate the effective wind speed of the typhoon on the distribution network lines, and calculate the failure probability of the lines;

[0008] The objective function is constructed based on the power outage loss of the power-losing user unit and the shortest travel time as follows:

[0009]

[0010] Where c j represents the power outage loss value of the jth important user, d j represents the power shortage of the jth important user, t' represents the time required from the power outage to the time when the city power supply station's mobile emergency power supply receives the dispatching instruction and starts, t ij P represents the time it takes for the emergency power supply to reach the jth important user from the i-th power supply station. k represents the capacity of the kth type of mobile emergency power supply, z ij is a 0-1 variable. If the i-th power supply station provides at least one emergency power supply to the j-th power-off user, the value is 1, otherwise it is 0. t' is the emergency response time, x ijk It represents the number of k-th emergency power supplies provided by the i-th power supply station to the j-th important user;

[0011] Solve the objective function and obtain the optimal emergency power supply scheduling strategy.

[0012] Preferably, the method further comprises the following steps:

[0013] Add a secondary objective function as follows:

[0014]

[0015] Preferably, when processing multi-objective functions, the efficiency coefficient method is used to convert them into single-objective functions. First, the optimal and worst solutions of each objective function are solved, then normalization calculations are performed to obtain the efficiency coefficient of each objective, and finally each efficiency coefficient is weighted to convert multiple objectives into one objective.

[0016] Preferably, the method further comprises the following steps:

[0017] Set constraints, which at least include power demand constraints for power outage users, as shown below:

[0018]

[0019] Where, f sgn (x) is the sign function.

[0020] Preferably, the method further comprises the following steps:

[0021] Set constraints, which include at least the number of mobile emergency power supplies for each power supply station, as shown below:

[0022]

[0023] Where y ik is the number of the kth type of emergency power supply owned by the i-th power supply station.

[0024] Preferably, by introducing a 0-1 variable b ij , remove the maximum function, as follows:

[0025] If the spare capacity of the jth power outage user is provided by the i-th power supply, then b ij =1, otherwise b ij =0, satisfying the following formula:

[0026]

[0027] Based on variable b ij , the value of the objective function is simplified as follows:

[0028]

[0029] Preferably, variable b ij The value determination process is as follows:

[0030] Introduce the comparison variable w pij The results of the pairwise comparison are described as follows:

[0031]

[0032] In the formula, in the formula, t pj It represents the time from the pth power supply station to the jth user, t ij It represents the time from the i-th power station to the j-th user.

[0033] In addition, ω pij with b ij The following constraints exist:

[0034]

[0035] Here, M is a sufficiently large number.

[0036] Preferably, t ij The calculation process is as follows:

[0037] Establish a weighted directed graph G based on the transportation network T =(N T ,E T ,W T ), where NT For power outage users and power supply stations, E T represents the path set, W T represents the edge weight;

[0038] Construct matrix D; introduce time-related integer variable δ t (i, j), if the road between power supply station i and power-lost user j is damaged at time t, then δ t (i, j) = 0, D(i, j) = ∞, otherwise δ t (i,j)=1;

[0039] Assume that the shortest distance between power supply station i and power outage user j is D(i,j). For node k, verify whether D(i,k)+D(k,j)<D(i,j). If so, update D(i,j)=D(i,k)+D(k,j). Finally, traverse all nodes and obtain the shortest path matrix D at that moment. According to the road state variable δ t (i, j), find the shortest path matrix at each moment;

[0040] The driving time of the emergency power supply vehicle is:

[0041]

[0042] Where V avg is the average speed of the emergency power supply vehicle; D ij is the shortest distance from power supply station i to power-lost user j; Δt is the time step; is the rounding function.

[0043] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the emergency power supply scheduling method based on line fault prediction under typhoon as described above.

[0044] A processor is used to run a program, wherein when the program is run, the emergency power supply scheduling method based on line fault prediction under typhoon is executed as described above.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. This invention combines the prediction of line failures during typhoons with the dispatch of emergency power supply vehicles after disasters. The line failure probability model has the characteristics of low computational complexity and high computational speed, which is conducive to preparing emergency power supply dispatch plans in advance and effectively reducing losses caused by power outages.

[0047] 2. The present invention fully considers the impact of the traffic network on the operation of emergency power supply vehicles. Compared with the currently commonly used technical means, the present invention also takes into account the situation of road damage during typhoons and the possible existence of photovoltaic power generation at a certain node, making the emergency power supply optimization scheduling model more flexible.

[0048] 3. When the emergency power supply vehicle is dispatched, the present invention converts the traditional one-to-one power supply between the emergency power supply vehicle and the power-lost user into power supply to the isolated island, which can effectively reduce the power outage time of the power-lost user. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0050] Figure 1 is the line vulnerability curve;

[0051] Figure 2 This is the system function curve diagram of the distribution network under typhoon weather;

[0052] Figure 3 is the node transportation network graph;

[0053] Figure 4 This is the IEEE33 system diagram. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0056] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0057] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0058] In one embodiment of the present invention, a method for dispatching emergency power supply based on line fault prediction during typhoons is provided, comprising three parts: the first part is an analysis of the probability of line faults in the distribution network during typhoons; the second part is modeling of emergency power supply dispatch based on line faults; and the third part is solving the emergency power supply dispatch model. The first part calculates the probability of failure of the distribution network line using an improved Rankine model and typhoon data, making pre-disaster predictions for the subsequent formulation of emergency power supply dispatch plans; the second part establishes a multi-objective planning model with the minimum total power outage loss as the primary function and surplus capacity as the secondary function, taking into account the travel time of emergency power supply vehicles under actual traffic conditions and the presence of renewable energy power generation at some nodes; the third part includes the steps for solving the emergency power supply dispatch model, as well as improvements to the allocation of emergency power supply vehicles during the solution process.

[0059] 1. Analysis of the probability of distribution network line failure during typhoons

[0060] 1.1 Improved Rankine typhoon wind field model

[0061] The improved Rankine model assumes that wind speed will not be significantly reduced due to the influence of land topography and tropical cyclone boundaries, which simplifies the analysis of wind field characteristics to a certain extent. The specific model definition is as follows:

[0062]

[0063] Where V r is the typhoon wind speed of the area, r is the distance from the area to the typhoon center, T is a fixed value, which is a parameter for adjusting the wind speed distribution and is usually set to 0.5, R max is the typhoon's maximum wind speed radius, V max It is the maximum wind speed of a typhoon.

[0064] The axisymmetric horizontal wind field generated by the improved Rankine model represents the gradient wind or average boundary layer wind on the surface. Therefore, in order to convert it into the axisymmetric wind speed corresponding to the standard altitude of 10 meters, the wind speed needs to be adjusted according to formula (2).

[0065] V 10 =K v ·V r (2)

[0066] Where K v is the correction factor for wind speed, which is 0.8.

[0067] In addition to the above-mentioned wind speed calculation, the typhoon wind direction α also needs to be calculated, which is calculated from the isobar inward according to formula (3).

[0068]

[0069] Because tropical cyclones are affected by both the Centripetal force and the geocentric force, they rotate counterclockwise in the Northern Hemisphere. Therefore, when studying and predicting tropical cyclones, the influence of this rotation effect needs to be considered. The specific influence is shown in Equation (4):

[0070]

[0071] Where V c is the typhoon's moving speed vector, V d is the axisymmetric wind speed V 10 Formula (3-4) ensures that the wind speed is affected by the typhoon's moving speed to a limited extent. At the typhoon center, V d is zero, the typhoon center to R max V d Gradually increases, R max V d It reaches its maximum value and then gradually decreases to zero radially outward.

[0072] Therefore, the wind speed vector after the above correction is formula (5):

[0073] V=V d +V 10 (5)

[0074] Where V is the wind speed vector.

[0075] Given the longitude and latitude of the earth, the distance between any two points L1 and L2 on the earth can be calculated as shown in formula (2-6), where east longitude and north latitude are positive and west longitude and south latitude are negative.

[0076] |L1L2|=R·arccos[siny1·siny2+cosy1·cosy2·cos(x1-x2)] (6)

[0077] Where R is the radius of the Earth, x is the longitude, and y is the latitude.

[0078] The wind speed and direction of the typhoon on the distribution network can be calculated from (1)-(6) above. Since the distribution network lines are short, it can be assumed that the typhoon wind speed impact on the distribution network lines is the same at the same time. However, because the typhoon affects each line in the distribution network in different directions, the effective wind speed impact on the line is also different. The effective wind speed of the typhoon on the line should be calculated according to formula (7).

[0079] V e = sinβ·V (7)

[0080] Where β is the angle between the typhoon wind direction and the line direction.

[0081] 1.2 Duration Model

[0082] Since the impact range of the entire trajectory of a typhoon disaster is much larger than the area of ​​the affected distribution network, assuming that the typhoon's moving speed in the affected area is constant, the duration of the typhoon's impact on the distribution network in the affected area can be determined as the typhoon path divided by the moving speed of the typhoon center, as shown in Equation (8).

[0083]

[0084] Where D wr is the trajectory of the typhoon in the affected distribution network area, v w is the moving speed of the typhoon center in this area.

[0085] 1.3 Typhoon Line Failure Probability Model

[0086] The present invention obtains the typhoon's path, direction, maximum wind speed radius and maximum wind speed from the typhoon database, calculates the effective wind speed of the typhoon on the distribution network line according to the above-mentioned improved Rankine typhoon model, and calculates the failure probability of the line using the line fault model formula (9). The topology of the post-disaster distribution network can be estimated more accurately, laying the foundation for subsequent emergency power supply scheduling.

[0087]

[0088] Where P is the probability of failure of the distribution network line due to wind speed, v is the effective wind speed that the distribution network line actually withstands, and V is the actual maximum wind speed that the distribution network line can withstand.

[0089] 1.4 Stochastic Model of Typhoon Weather

[0090] In order to randomly simulate extreme weather events, the present invention adopts the non-sequential Monte Carlo simulation method to sample the state of the distribution line under extreme weather conditions. Assuming that under extreme weather events, the state of the line is divided into fault state and normal operation state, the binary variable s is defined as i(t) is the state of line i at time t, if s i (t) = 1, the state of line i is normal operation, if s i If (t) = 0, the state of line i is fault. For each distribution line whose initial state at simulation time t is normal operation, the simulation process of the state is specifically shown in formula (10).

[0091]

[0092] In the formula, the random variable rand i is a random number that follows uniform distribution U(0,1), which is generated by sampling the state of each line i; p i is the failure probability of line i, which can be calculated based on Figure 1 The fragility curve is obtained under a certain wind speed.

[0093] Typhoon wind speed determines the weather intensity. Assuming the maximum wind speed of the line is 30 kts, the vulnerability curve of the distribution network line can be calculated by formula (9), as follows: Figure 1 shown.

[0094] 1.5 Fault scenario selection based on system information entropy

[0095] The main impact of typhoons on distribution networks is a significant increase in component failure rates, which in turn increases the probability of large-scale multiple failures. In this scenario, the failure and recovery process is very complex. There are many components in the distribution network, and the number of multiple failure scenarios composed of different faulty components is huge. Therefore, it is necessary to analyze the failure scenarios that may be caused by extreme weather based on the probability and uncertainty of the scenarios and the failure rate of overhead lines. The system information entropy method is a method of selecting reasonable system status scenarios based on the probability of a single event. Entropy represents the degree of uncertainty of the system. The distribution network is an uncertain system that may fail at any moment. Its entropy value W is:

[0096]

[0097] Where T represents the time it takes for the typhoon to pass through the distribution network area; Ω B represents the distribution network line set; z i.,t Indicates whether line i has a fault at time t, 1 means a fault has occurred. Each resilience analysis scenario corresponds to a z i.,t Vector, corresponding to the entropy value of the system in this scenario.

[0098] Considering the uncertainty of the failure scenario, z i.,tThe value of should obey the distribution of the failure rate. The higher the failure rate of a line, the greater the probability of the occurrence of the uncertainty event of the line failure, and the more scenarios corresponding to z i.,t The value is 1. For example, if the failure rate of line i is 0, the uncertainty of the occurrence of the component failure event is infinite. In all scenarios, there must be z i.,t =0, so from the perspective of the possibility of actual scenarios, the reasonable resilience analysis scenario W value cannot be too large or too small, and must satisfy:

[0099]

[0100] 1.6 Distribution Network Resilience Assessment Model

[0101] Under typhoon weather conditions, the distribution network suffered large-scale failures, resulting in widespread power outages. After the typhoon passed, the distribution network gradually returned to its original normal operating state. Figure 2 This is a schematic diagram of the system function curve of the distribution network during the entire typhoon weather impact process.

[0102] The present invention uses the missing area of ​​system functions under extreme weather conditions to reflect the resilience of the distribution network, and uses the probability of occurrence of fault scenarios and the corresponding missing area of ​​load curves to calculate the distribution network resilience index.

[0103]

[0104] Where λ n is the probability of occurrence of scenario n; N is the number of selected fault scenarios; Im n is the power supply loss degree of scenario n; T0 represents the time when the distribution network is affected by extreme weather, including the time it takes for the typhoon to pass through the distribution network and the time it takes for the distribution network to restore normal power supply. Since the time required for fault recovery in each scenario is different, T0 should be much longer than the corresponding t4 in each scenario; L(t) represents the actual load curve when a large-scale fault occurs due to extreme weather; TL(t) represents the target load curve when the system is running without faults; RES n express Figure 2 The area between the actual curve and the target curve is represented as the missing area of ​​the load curve.

[0105] The probability of occurrence of multiple failure scenarios n λ h It can be calculated from the failure rate of a single component:

[0106]

[0107] v hi =p h p i (17)

[0108] v hij=p h p i p j (18)

[0109] v hijk =p h p i p j p k (19)

[0110] Where λ h represents the probability of occurrence of a scenario where only component h fails in the distribution network of the region, λ hi represents the probability of a scenario in which only component h and component i fail in the distribution network of the region, and so on; v h represents the probability of component failure, v hi represents the probability of simultaneous failure of component h and component i, and so on; p i is the failure rate of the corresponding line.

[0111] 2. Emergency Power Supply Dispatch Modeling Based on Line Faults

[0112] Assume that there are m power supply stations in a city, l types of mobile emergency power supplies, and n important power outage users. Among them, the power outage power of the jth (j=1,2,3,...,n) important user is d j The power outage loss per unit power and per unit time of the jth important user is c j , the capacity of the kth (k=1,2,3,...,l) type of mobile emergency power supply is P k The time required from the power outage to the time when the city power supply station receives the dispatching instruction for the mobile emergency power supply is t', and the time required for the emergency power supply to travel from the i-th (i=1,2,3,...,m) power supply station to the j-th important user is t ij Suppose the number of k-th emergency power supplies provided by the i-th power supply station to the j-th important user is x ijk , x ijk For the emergency power supply scheduling problem, the mobile emergency power supply that the city's power supply station should have should, in principle, meet the electricity demand of all important users in the city.

[0113] The higher the importance of the power outage user, the greater the power outage loss per unit power and per unit time, so the priority of restoring its power supply is higher. For the jth power outage user, its importance I j It can be calculated using formula (20):

[0114] I j =ω α α j +ω β β j +ωγ γ j (20)

[0115] Among them, α j ,β j ,γ j are the impact factors of power outage on life safety, economy and particularity of the j-th user respectively; ω α ,ω β ,ω γ represents the weights of life safety, economic benefits, and uniqueness in the user's importance evaluation, and ω α +ω β +ω γ = 1. Finally, according to the importance of the user and the unit power and unit time power outage loss value c j 'To determine the final power outage loss value c of important user j j , as shown in formula (21):

[0116] c j =I j c j ' (twenty one)

[0117] Considering that some nodes have photovoltaic power generation equipment, based on the current research results on photovoltaic power generation, the Beta distribution is generally used to simulate light intensity. The probability density function of the Beta distribution is as follows:

[0118]

[0119] Where, s is the light intensity; s m is the maximum light intensity; Γ is the gamma function; α and β are Beta parameters; μ is the average output of photovoltaic power; σ is the standard deviation output of photovoltaic power.

[0120]

[0121] Where, P PV The actual output of photovoltaic power generation; P STC is the maximum output power of photovoltaic power generation under the most suitable conditions; L is the actual local light intensity (w / m2); L STC is the light intensity under the most suitable conditions (kW / m2); K PT is the temperature coefficient (ppm / ℃); T is the actual temperature; T r The optimum temperature is 25°C.

[0122] When calculating the power shortage power d of power-lost users j When , the power required by the node with photovoltaic power generation is corrected to formula (26),

[0123] d j '=d j -P PV (26)

[0124] If d j '≤0, it is considered that the user does not need to provide emergency power supply. If d j '>0, then emergency power supply vehicle is still needed to supply d j =d j 's capacity. During actual typhoon disasters, the weather conditions are often overcast, and the output of photovoltaic power generation is relatively low. Generally, emergency power supply is still required. After the typhoon passes, the output of photovoltaic power generation increases. At this time, the power outage users at that node will be supplied by both emergency power supply and photovoltaic power generation. If the photovoltaic power generation exceeds the power demand of a user at a certain location, the emergency power supply vehicle at that location will be released to provide power to other nodes.

[0125] When using emergency power supply vehicles to restore power, the present invention considers the impact of the actual traffic network on route planning and selects the route with the shortest travel time based on this. The traffic network is established as a weighted directed graph G T =(N T ,E T ,W T ), where N T For power outage users and power supply stations, E T represents the edge, is the path set, W T Represents the edge weight. A typical simplified 4-node transportation network is as follows Figure 2 shown.

[0126] During a typhoon disaster, the road network status changes, but the basic Floyd algorithm assumes that the road network structure is fixed during the solution process. This paper makes improvements based on the basic Floyd algorithm. First, according to the traffic network G T Construct matrix D; introduce time-related integer variable δ t (i, j), if the road between power supply station i and power-lost user j is damaged at time t, then δ t (i, j) = 0, D(i, j) = ∞, otherwise δ t (i, j) = 1. Then assume that the shortest distance between power supply station i and power-off user j is D(i, j). For node k, verify whether D(i, k) + D(k, j) < D(i, j). If so, update D(i, j) = D(i, k) + D(k, j). Finally, traverse all nodes and obtain the shortest path matrix D at that moment. According to the road state variable δ t (i,j), find the shortest path matrix at each moment.

[0127] In the present invention, the improved Floyd algorithm is used to obtain the shortest path matrix P and the shortest distance matrix D between all nodes. The travel time of the emergency power supply vehicle is:

[0128]

[0129] Where V avg is the average speed of the emergency power supply vehicle; D ij The shortest distance from power station i to power outage user j is obtained by improving the Floyd algorithm; Δt is the time step; is the rounding function.

[0130] 1. Objective function establishment

[0131] Based on the above calculation of the power outage loss per user unit and the shortest travel time, the main objective of the emergency power supply optimization scheduling problem is established to minimize the total power outage loss. The main objective function is described by Equation (28).

[0132]

[0133] Among them, z ij is a 0-1 variable, indicating that if the i-th power supply station provides at least one emergency power supply to the j-th power-lost user, then z ij =1, otherwise z ij =0; t' is the emergency response time, which is a constant.

[0134] The emergency power supply provided by the power supply station to the user cannot just meet the power shortage of the user. The excess capacity is surplus capacity. In order to avoid the waste of emergency power supply capacity and to supply power to as many users as possible, it is necessary to consider matching the emergency power supply with the user's required power as much as possible. Therefore, the optimization scheduling problem needs to add a secondary objective, that is, minimizing the surplus capacity. The secondary objective function is shown in formula (29):

[0135]

[0136] 2. Constraints

[0137] (1) Power demand limit for power outage users. The power supply station must ensure that the total emergency power supply capacity dispatched to the jth power outage user can meet its power demand, as shown in formula (30).

[0138]

[0139] For the total number of emergency power supplies provided by the power supply station to the jth user, if any one of them is removed, the power demand of the user cannot be met. This constraint can be expressed by equations (31) and (32):

[0140]

[0141] Among them, f sgn (x) is a sign function, k = 1, 2, 3, ..., l; j = 1, 2, 3, ..., n.

[0142] (2) The number of mobile emergency power supplies for each power supply station is limited. The types and quantities of emergency power supplies for each power supply station are limited. The number of the kth type of emergency power supplies provided by the i-th power supply station cannot exceed the number of the kth type of emergency power supplies owned by the power supply station. As shown in formula (33):

[0143]

[0144] Where y ik is the number of the kth type of emergency power supply owned by the i-th power supply station.

[0145] 3. Improvement and simplification of sign function and maximum function

[0146] The main objective function of formula (28) contains a maximum function. In order to simplify the calculation, this section introduces a 0-1 variable b ij , remove the maximum function. The surplus capacity can only be provided by one emergency power supply. If the surplus capacity of the jth power outage user is provided by the i-th power supply, then b ij =1, otherwise it is equal to 0. Combined with the above statement, b ij Need to meet:

[0147]

[0148] To determine b ij To determine the value of , we need to find the emergency power supply that reaches user j the latest, and introduce the comparison variable ω pij Describe the results after pairwise comparison:

[0149]

[0150] Where, t pj It represents the time from the pth power supply station to the jth user, t ij It represents the time from the i-th power supply station to the j-th user. pij with b ij There is a constraint relationship between

[0151]

[0152] Where: i = 1, 2, 3, ..., m; j = 1, 2, 3, ..., n; M is a sufficiently large number.

[0153] Through two variables ω pij with bij With the introduction of , the objective function (28) is simplified to (38):

[0154]

[0155] Similarly, the above method is also adopted for the sign function, as shown in Equation (39) to Equation (42),

[0156]

[0157] v qk is a 0-1 variable, p q Indicates the capacity of the qth emergency power supply, p k represents the kth emergency power supply capacity, p q and p k The result of the size comparison is v qk express.

[0158]

[0159] Therefore, constraint (31) is simplified to (43),

[0160]

[0161] 4. Processing of multi-objective functions

[0162] When processing multi-objective functions, the present invention uses the efficiency coefficient method to convert them into a single objective function. First, the optimal and worst-case solutions for each objective function must be solved; then normalization calculations are performed to obtain the efficiency coefficient for each objective; finally, each efficiency coefficient is weighted to convert multiple objectives into a single objective. The mathematical model for the solution is shown below:

[0163] min{f1(x),f2(x),......,f n (x),}(44)

[0164] f jmin =minf j (x) (45)

[0165] f jmax =maxf j (x) (46)

[0166]

[0167] Among them, f jmax 、f jmin are the maximum and minimum values ​​that the j-th objective function can take; O j is the normalized efficacy coefficient of the jth objective function; μ jis the weight of the j-th target, also called the weight coefficient.

[0168] 3. Solution of emergency power supply dispatch model

[0169] According to formula (20)-formula (48), the multi-objective function can be converted into a single-objective function through the efficiency coefficient method, and the optimization solution is obtained through the gams multi-objective nonlinear integer hybrid programming minimum cost model. For the convenience of description, IEEE33 nodes are used for description. The broken lines calculated under the typhoon in the first part are 11-12, 14-15, 19-20, and 29-30. Photovoltaic power generation equipment is set at nodes 22 and 18, as shown in the following example: Figure 3 The specific steps for solving the emergency power supply vehicle dispatching are as follows:

[0170] Step 1: Abstract the emergency power dispatch problem into a mathematical problem, and determine the importance of each power-off user by formula (10): j , and then determine the power outage loss per unit time c according to formula (11) j ;

[0171] Step 2: Determine the power shortage power d based on the load capacity and self-provided emergency power capacity of important users and the photovoltaic output, as shown in equations (22) to (26). j ;

[0172] Step 3: From formula (17), we can get the travel time t of the emergency power supply vehicle under actual traffic conditions. ij In the solution process, the present invention reduces the travel time and scheduling difficulty of the emergency power supply vehicle, combines the actual line disconnection situation, and can be divided into the following categories according to the disconnected lines: Figure 3 In the present invention, the four islands A, B, C, and D are powered by a conventional emergency power supply vehicle and a one-to-one power supply to the power-lost user, which is converted into power supply from the emergency power supply vehicle to the island. Taking island A as an example, island A includes nodes 20, 21, and 22. When calculating the travel time of the emergency power supply vehicle, it is necessary to compare the shortest travel time from power station i to each node in the island, and set this time as t ij , and participate in subsequent calculations.

[0173] Step 4: Determine the time comparison variable ω according to equations (26) and (31) pij and capacity comparison variable v qk .

[0174] Step 5: Establish a multi-objective emergency power supply scheduling model, normalize it using the efficiency coefficient method, transform the multi-objective function into a single-objective function, obtain multiple groups of solutions based on different weights, and finally decide on a suitable emergency power supply scheduling solution based on actual conditions.

[0175] In summary, the present invention is an emergency power supply scheduling method based on line fault prediction during typhoons, which mainly consists of two steps. First, the improved Rankine model and line fault probability model in the first part are used to predict the lines that will fail after the disaster. Then, based on the actual traffic conditions and new energy power generation conditions after the circuit breaker, an emergency power supply optimization scheduling model is established to make emergency power supply optimization scheduling plans in a timely manner to reduce the losses caused by power outages.

[0176] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0177] In the embodiments provided by the present invention, it should be understood that the division of units is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0178] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0179] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for dispatching emergency power supply based on line fault prediction during typhoon, characterized in that: The following steps are involved: Obtain the typhoon's path, direction, maximum wind speed radius, and maximum wind speed, calculate the effective wind speed of the typhoon on the distribution network lines, and calculate the failure probability of the lines; The objective function is constructed based on the power outage loss of the power-losing user unit and the shortest travel time as follows: Where c j represents the power outage loss value of the jth important user, d j represents the power shortage power of the jth important user, t' represents the time required from the power outage to the time when the city power supply station receives the dispatching instruction for the mobile emergency power supply to start, t ij P represents the time it takes for the emergency power supply to reach the jth important user from the i-th power supply station. k represents the capacity of the kth type of mobile emergency power supply, z ij is a 0-1 variable. If the i-th power supply station provides at least one emergency power supply to the j-th power-off user, the value is 1, otherwise it is 0. t' is the emergency response time, x ijk It represents the number of k-th emergency power supplies provided by the i-th power supply station to the j-th important user; Solve the objective function and obtain the optimal emergency power supply scheduling strategy.

2. The method for dispatching emergency power supply based on line fault prediction during typhoon according to claim 1, characterized in that: The following steps are also included: Add a secondary objective function as follows:

3. The method for dispatching emergency power supply based on line fault prediction during typhoon according to claim 2, characterized in that: When dealing with multi-objective functions, the efficiency coefficient method is used to convert them into a single objective function. First, the optimal and worst solutions of each objective function are solved, and then normalization calculations are performed to obtain the efficiency coefficient of each objective. Finally, each efficiency coefficient is weighted to convert multiple objectives into one objective.

4. The method for dispatching emergency power supply based on line fault prediction during typhoon according to any one of claims 1 to 3, characterized in that: The following steps are also included: Set constraints, which at least include the power demand constraints of power outage users, as shown below: Where, f sgn (x) is the sign function.

5. The method for dispatching emergency power supply based on line fault prediction during typhoon according to any one of claims 1 to 3, characterized in that: The following steps are also included: Set constraints, which include at least the number of mobile emergency power supplies for each power supply station, as shown below: Where y ik is the number of the kth type of emergency power supply owned by the i-th power supply station.

6. The method for dispatching emergency power supply based on line fault prediction during typhoon according to claim 1, characterized in that: By introducing a 0-1 variable b ij , remove the maximum function, as follows: If the spare capacity of the jth power outage user is provided by the i-th power supply, then b ij =1, otherwise b ij =0, satisfying the following formula: Based on variable b ij , the value of the objective function is simplified as follows:

7. The method for dispatching emergency power supply based on line fault prediction during typhoon according to claim 6, characterized in that: variable b ij The value determination process is as follows: Introduce the comparison variable w pij The results of the pairwise comparison are described as follows: Where, t pj It represents the time from the pth power supply station to the jth user, t ij It represents the time from the i-th power supply station to the j-th user; In addition, ω pij with b ij The following constraints exist: Here, M is a sufficiently large number.

8. The method for dispatching emergency power supply based on line fault prediction during typhoon according to claim 1, characterized in that: t ij The calculation process is as follows: Establish a weighted directed graph G based on the transportation network T =(N T ,E T ,W T ), where N T For power outage users and power supply stations, E T represents the path set, W T represents the edge weight; Construct matrix D; introduce time-related integer variable δ t (i, j), if the road between power supply station i and power-lost user j is damaged at time t, then δ t (i, j) = 0, D(i, j) = ∞, otherwise δ t (i,j)=1; Assume that the shortest distance between power supply station i and power outage user j is D(i,j). For node k, verify whether D(i,k)+D(k,j)<D(i,j). If so, update D(i,j)=D(i,k)+D(k,j). Finally, traverse all nodes and obtain the shortest path matrix D at the current moment. According to the road state variable δ t (i, j), find the shortest path matrix at each moment; The driving time of the emergency power supply vehicle is: Where V avg is the average speed of the emergency power supply vehicle; D ij is the shortest distance from power supply station i to power-lost user j; Δt is the time step; is the rounding function.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the emergency power supply scheduling method based on line fault prediction under typhoon according to claim 1.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the emergency power supply scheduling method based on line fault prediction during a typhoon as described in claim 1.

Images