A distribution network optimization method, device, terminal equipment and storage medium under the influence of typhoon

Abstract

The present invention discloses a method, device, terminal device, and storage medium for optimizing a distribution network under the influence of a typhoon. The method includes: constructing a distribution network optimization model based on acquired node failure probability, node load power, node disaster state variables, distribution network basic data, and preset distribution network regional load weights with the goal of minimizing the load loss power of the distribution network to be optimized; solving the distribution network optimization model under power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints; and adjusting the load of the distribution network to be optimized based on the distribution network basic data and the solved distribution network line state data. By implementing the present invention, the load of the distribution network to be optimized can be adjusted under the influence of a typhoon.

Description

Technical Field

[0001] The present invention relates to the field of power systems and automation technologies, and in particular to a method, device, terminal equipment and storage medium for optimizing a distribution network under the influence of a typhoon. Background Art

[0002] With the increasing incidence of extreme weather events worldwide, natural disasters such as typhoons are having an increasingly severe impact on power grid systems. Typhoons severely test the stable operation of power systems, causing multiple equipment failures within the grid. Combined with power flow shifts and the failure or misoperation of protective devices, these failures can exacerbate power outages and even cause widespread blackouts. Resilience describes the ability of distribution networks to predict, adapt, and rapidly respond to natural disasters such as typhoons. Given the growing demand for renewable energy and microgrids, building a resilient distribution network system is crucial for ensuring power security. Existing systems that rely solely on distributed generation to form isolated islands to supply loads during typhoons are insufficient. They fail to fully account for the unique needs of extreme typhoon weather conditions, struggle to adapt to rapidly changing operating environments and complex constraints, and are unable to meet the need for timely restoration of normal power supply to distribution networks under typhoon conditions. Summary of the Invention

[0003] The embodiments of the present invention provide a distribution network optimization method, apparatus, terminal device, and storage medium under the influence of a typhoon, which can effectively solve the problem in the prior art that relying solely on distributed power sources to form isolated islands to supply power to loads when a typhoon disaster occurs is insufficient, and the capacity of distributed power sources and energy storage devices is limited, which cannot meet the demand for timely restoration of normal power supply of the distribution network under the influence of a typhoon.

[0004] An embodiment of the present invention provides a method for optimizing a distribution network under the influence of a typhoon, comprising:

[0005] Obtain node failure probability, node load power, node disaster state variables, basic distribution network data, and comprehensive energy data of the distribution network to be optimized when a typhoon arrives; the basic distribution network data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximums, line transmission power, and distribution network topology data; the comprehensive energy data includes photovoltaic power generation system power, wind turbine power, electric vehicle power, mobile diesel generator power, fixed energy storage station power, and mobile energy storage vehicle power;

[0006] According to the node failure probability, the node load power, the node disaster state variable, the distribution network basic data and the preset distribution network regional load weight, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized;

[0007] Under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints constructed according to the distribution network basic data and the comprehensive energy data, the distribution network optimization model is solved to generate distribution network line status data when the load loss power of the distribution network to be optimized is minimized;

[0008] The load of the distribution network to be optimized is adjusted according to the distribution network basic data and the distribution network line status data.

[0009] Furthermore, the construction of the distribution network optimization model includes:

[0010] Calculating the distribution line failure probability of the distribution network to be optimized based on the node failure probability and the number of distribution network lines in the distribution network basic data;

[0011] Determine the number of nodes affected by the typhoon in the distribution network to be optimized according to the node disaster state variable and the number of nodes in the basic data of the distribution network;

[0012] Calculate the load loss power of the distribution line of the distribution network to be optimized according to the node failure probability, the distribution line failure probability, the node load power, the node disaster state variable, the number of disaster-stricken nodes, and the number of distribution network lines in the distribution network basic data;

[0013] According to the load loss power of the distribution line, the number of distribution network areas in the distribution network basic data and the preset load weights of the distribution network areas, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized.

[0014] Furthermore, the power balance constraint is:

[0015]

[0016] Among them, P Gk The active power injected into the grid in region k; Q Gk The reactive power injected into the grid in area k; P PVkn is the active power output by the n-th photovoltaic power station in region k; Q PVkn is the reactive power output by the n-th photovoltaic power station in region k; P Wkn is the active power output by the n-th wind power station in region k; Q Wkn is the reactive power output by the n-th wind power station in region k; P EVkn The active power output by the nth electric vehicle in region k that can provide services to the grid; QEVkn P is the reactive power output by the nth electric vehicle in region k that can provide services to the grid; SVkn is the active power output by the nth mobile diesel generator in area k; Q SVkn is the reactive power output by the nth mobile diesel generator in area k; P FSEkn is the active power output by the nth fixed energy storage station in region k; Q FSEkn is the reactive power output by the nth fixed energy storage station in region k; P SSEkn is the active power output by the nth mobile energy storage vehicle in area k; Q SSEkn is the reactive power output by the nth mobile energy storage vehicle in area k; P Dk is the active power of the load still on the grid after the disaster in area k; Q Dk is the reactive power of the load still on the grid after the disaster in area k; P LLk Q is the active power of the distribution lines in the typhoon disaster area k that lose load due to typhoon failure; LLk N is the reactive power of the distribution lines in the typhoon disaster area k due to typhoon failure; PV is the number of photovoltaic power stations in the distribution network; N W is the number of wind power stations in the distribution network; N EV N is the number of electric vehicles in the distribution network that can provide services to the grid; SV is the number of mobile diesel generators in the distribution network; N FSE is the number of fixed energy storage stations in the distribution network; N SSE is the number of mobile energy storage vehicles in the distribution network;

[0017] The node voltage constraint is:

[0018] U i,min ≤U i ≤U i,max

[0019] Among them, U i,min is the lower limit of the voltage at node i; U i,max is the upper limit of the voltage at node i; U i is the actual voltage amplitude at node i;

[0020] The branch current constraint is:

[0021] 0≤I Li ≤I Li,max

[0022] Among them, I Li,max is the maximum allowable current value of line Li; I Li is the actual current value of line Li;

[0023] The line transmission quota constraints are:

[0024]

[0025] Among them, P Li,min is the minimum active power of line Li; P Li,max is the maximum active power of line Li; Q Li,min is the minimum reactive power of line Li; Q Li,max is the maximum reactive power of line Li; It is a collection of distribution network lines;

[0026] The network topology constraints are:

[0027] z ab +z ba =Z

[0028] g e ∈G

[0029] Among them, z ab is the power flow direction from node a to node b; z ba is the power flow direction of the line from node b to node a; Z is the line connection state variable; g e is the network topology structure after the distribution network is adjusted; G is the set of topology structures that meet the normal operation of the network;

[0030] The power constraint of the photovoltaic power generation system is:

[0031]

[0032] in, P is the maximum allowable value of the active power output of the n-th photovoltaic power station in region k; PVkn is the minimum allowed value of the active power output of the n-th PV power station in region k; The maximum allowed value of reactive power output by the n-th PV power station in region k; Q PVkn P is the minimum allowable value of reactive power output by the n-th photovoltaic power station in region k; PVkn is the active power output by the n-th photovoltaic power station in region k; Q PVkn is the reactive power output by the n-th PV power station in region k;

[0033] The wind turbine power constraint is:

[0034]

[0035] in, is the maximum allowable value of the active power output of the n-th wind turbine in region k; P Wknis the minimum allowable value of the active power output of the n-th wind turbine in region k; is the maximum allowable value of reactive power output by the n-th wind turbine in region k; Q Wkn is the minimum allowable value of reactive power output by the n-th wind turbine in region k; P Wkn is the active power output by the n-th wind turbine in region k; Q Wkn is the reactive power output by the n-th wind turbine in region k;

[0036] The electric vehicle power constraint is:

[0037]

[0038] in, The maximum allowed value of the active power output by the nth electric vehicle in region k that can provide services to the grid; P EVkn The minimum allowed value of active power output by the nth electric vehicle in region k that can provide services to the grid; The maximum allowed value of reactive power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn The minimum allowed value of reactive power output by the nth electric vehicle in region k that can provide services to the grid; P EVkn The active power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn The reactive power output by the nth electric vehicle in region k that can provide services to the grid;

[0039] The power constraint of the mobile diesel generator is:

[0040]

[0041] in, is the maximum allowable value of the active power output of the nth mobile diesel generator in area k; P SVkn is the minimum allowable value of the active power output of the nth mobile diesel generator in area k; The maximum allowed value of reactive power output by the nth mobile diesel generator in area k; Q SVkn P is the minimum allowable value of reactive power output by the nth mobile diesel generator in area k; SVkn is the active power output by the nth mobile diesel generator in area k; Q SVkn is the reactive power output by the nth mobile diesel generator in area k;

[0042] The power constraint of the fixed energy storage station is:

[0043]

[0044] in, The maximum allowable value of the active power output of the nth fixed energy storage station in area k; P FSEkn The minimum allowed value of the active power output of the nth fixed energy storage station in region k; The maximum allowed value of reactive power output by the nth fixed energy storage station in region k; Q FAEkn P is the minimum allowable value of reactive power output by the nth fixed energy storage station in region k; FSEkn is the active power output by the nth fixed energy storage station in region k; Q FSEkn is the reactive power output by the nth fixed energy storage station in region k;

[0045] The power constraint of the mobile energy storage vehicle is:

[0046]

[0047] in, The maximum allowable value of the active power output of the nth mobile energy storage vehicle in area k; P SSEkn The minimum allowed value of the active power output of the nth mobile energy storage vehicle in area k; The maximum allowed value of reactive power output by the nth mobile energy storage vehicle in area k; Q SSEkn P is the minimum allowable value of reactive power output by the nth mobile energy storage vehicle in area k; SSEkn is the active power output by the nth mobile energy storage vehicle in area k; Q SSEkn is the reactive power output by the nth mobile energy storage vehicle in area k.

[0048] Furthermore, adjusting the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data includes:

[0049] Determining the changed distribution network topology data according to the distribution network basic data and the distribution network line status data;

[0050] According to the changed distribution network topology data, adjust the access power of the comprehensive energy in the distribution network to be optimized;

[0051] According to the access power of the adjusted comprehensive energy, the load of the distribution network to be optimized is adjusted.

[0052] Furthermore, the load loss power of the distribution line of the distribution network to be optimized is calculated by the following formula:

[0053]

[0054] Among them, P LL N is the distribution network to be optimized L The power loss load power of the distribution line; P LDij,t is the node load power of the jth affected node of the i-th distribution line where the power outage occurs in time period t; is the disaster-affected state variable of node j in the typhoon eye area; is the disaster state variable of node j in the cloud wall area; is the disaster state variable of node j in the spiral rain belt area; is the node failure probability of the i-th line in the typhoon eye area at time t; is the node failure probability of the i-th line in the cloud wall area at time t; is the node failure probability of the i-th line in the spiral rain belt area at time t; T T is the number of disaster-affected periods; N L is the number of distribution network lines; N LDi is the number of affected nodes.

[0055] As an improvement to the above solution, another embodiment of the present invention provides a distribution network optimization device under the influence of a typhoon, comprising:

[0056] The distribution network data acquisition module is used to obtain the node failure probability, node load power, node disaster state variables, distribution network basic data, and comprehensive energy data of the distribution network to be optimized when a typhoon arrives. The distribution network basic data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximum values, line transmission power, and distribution network topology data. The comprehensive energy data includes the power of photovoltaic power generation systems, wind turbine generator sets, electric vehicles, mobile diesel generators, fixed energy storage stations, and mobile energy storage vehicles.

[0057] a distribution network model construction module, configured to construct a distribution network optimization model based on the node failure probability, the node load power, the node disaster state variable, the distribution network basic data, and the preset distribution network regional load weight, with the goal of minimizing the load loss power of the distribution network to be optimized;

[0058] a distribution network model solving module, configured to solve the distribution network optimization model under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints constructed based on the distribution network basic data and the comprehensive energy data, and generate distribution network line state data when the load loss power of the distribution network to be optimized is minimized;

[0059] The distribution network optimization module is used to adjust the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data.

[0060] Furthermore, the distribution network model construction module is used to construct a distribution network optimization model based on the node failure probability, the node load power, the node disaster state variable, the distribution network basic data and the preset distribution network regional load weight, with the goal of minimizing the load loss power of the distribution network to be optimized;

[0061] The construction of the distribution network optimization model includes:

[0062] Calculating the distribution line failure probability of the distribution network to be optimized based on the node failure probability and the number of distribution network lines in the distribution network basic data;

[0063] Determine the number of nodes affected by the typhoon in the distribution network to be optimized according to the node disaster state variable and the number of nodes in the basic data of the distribution network;

[0064] Calculate the load loss power of the distribution line of the distribution network to be optimized according to the node failure probability, the distribution line failure probability, the node load power, the node disaster state variable, the number of disaster-stricken nodes, and the number of distribution network lines in the distribution network basic data;

[0065] According to the load loss power of the distribution line, the number of distribution network areas in the distribution network basic data and the preset load weights of the distribution network areas, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized.

[0066] Furthermore, the distribution network optimization module is used to adjust the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data;

[0067] Adjusting the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data includes:

[0068] Determining the changed distribution network topology data according to the distribution network basic data and the distribution network line status data;

[0069] According to the changed distribution network topology data, adjust the access power of the comprehensive energy in the distribution network to be optimized;

[0070] According to the access power of the adjusted comprehensive energy, the load of the distribution network to be optimized is adjusted.

[0071] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a distribution network optimization method under the influence of a typhoon as described in the above embodiment.

[0072] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network optimization method under the influence of a typhoon as described in the above embodiment.

[0073] By implementing the present invention, at least the following beneficial effects are achieved:

[0074] The present invention provides a method, device, terminal equipment and storage medium for optimizing a distribution network under the influence of a typhoon. The method can obtain the node failure probability, node load power, node disaster state variable, distribution network basic data and comprehensive energy data of the distribution network to be optimized when a typhoon comes; wherein the distribution network basic data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximum value, line transmission power and distribution network topology data; the comprehensive energy data includes photovoltaic power generation system power, wind turbine power, electric vehicle power, mobile diesel generator power, fixed energy storage station power and mobile energy storage vehicle power; according to the node failure probability, the node load power, the node disaster state variable, the distribution network basic data and comprehensive energy data, the comprehensive energy data includes the ... Based on the basic data of the distribution network and the preset regional load weights of the distribution network, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized; under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints and mobile energy storage vehicle power constraints constructed according to the basic data of the distribution network and the comprehensive energy data, the distribution network optimization model is solved to generate distribution network line status data when the load loss power of the distribution network to be optimized is minimized; and the load of the distribution network to be optimized is adjusted according to the basic data of the distribution network and the distribution network line status data. By considering the basic data of the distribution network under the influence of typhoons, a distribution network optimization model is constructed, which comprehensively considers comprehensive energy data such as photovoltaic power generation system power, wind turbine power, electric vehicle power, mobile diesel generator power and fixed energy storage station power. By solving the distribution network optimization model, the optimal distribution network line status data can be generated, which can effectively respond to the impact of typhoon disasters, adapt to the rapidly changing operating environment and complex constraints, adjust the load of the distribution network to be optimized, and improve the operating stability and reliability of the distribution network in emergency situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a flow chart of a method for optimizing a distribution network under the influence of a typhoon, provided by one embodiment of the present invention;

[0076] Figure 2 This is a structural diagram of a distribution network optimization device under the influence of a typhoon provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0077] 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 the embodiments. 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.

[0078] See also Figure 1 , is a flow chart of a method for optimizing a distribution network under the influence of a typhoon, provided by one embodiment of the present invention, including:

[0079] S1. Obtain node failure probability, node load power, node disaster state variables, distribution network basic data, and comprehensive energy data of the distribution network to be optimized when a typhoon arrives; wherein the distribution network basic data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximum values, line transmission power, and distribution network topology data; the comprehensive energy data includes photovoltaic power generation system power, wind turbine power, electric vehicle power, mobile diesel generator power, fixed energy storage station power, and mobile energy storage vehicle power;

[0080] S2. Constructing a distribution network optimization model based on the node failure probability, the node load power, the node disaster state variable, the distribution network basic data, and the preset distribution network regional load weight, with the goal of minimizing the load loss power of the distribution network to be optimized;

[0081] S3. Solve the distribution network optimization model under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints constructed based on the distribution network basic data and the comprehensive energy data, and generate distribution network line status data when the load loss power of the distribution network to be optimized is minimized;

[0082] S4. Adjust the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data.

[0083] In a preferred embodiment of the present invention, the node disaster state variable indicates whether the node in the distribution network to be optimized is affected by the typhoon, which can be represented by 0 and 1. When the node disaster state variable is 1, it indicates that the node is affected by the typhoon, and when it is 0, it indicates that the node has not been affected by the typhoon. First, the node failure probability, node load power, node disaster state variable, distribution network basic data and comprehensive energy data of the distribution network to be optimized when the typhoon comes are obtained; wherein, the distribution network basic data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximum value, line transmission power and distribution network topology data; the comprehensive energy data includes photovoltaic power generation system power, wind turbine power, electric vehicle power, mobile diesel generator power, fixed energy storage station power and mobile energy storage vehicle power; then, according to the node failure probability, the node load power, the node disaster state variable, the distribution network basic data and the preset distribution network area load weight, the node disaster state variable is used to determine the node to be optimized. The invention aims to minimize the load loss power of the distribution network to be optimized, and constructs a distribution network optimization model; then, under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints constructed according to the distribution network basic data and the comprehensive energy data, solves the distribution network optimization model to generate distribution network line status data when the load loss power of the distribution network to be optimized is minimized; finally, the load of the distribution network to be optimized is adjusted according to the distribution network basic data and the distribution network line status data.

[0084] Preferably, the construction of the distribution network optimization model includes:

[0085] Calculating the distribution line failure probability of the distribution network to be optimized based on the node failure probability and the number of distribution network lines in the distribution network basic data;

[0086] Determine the number of nodes affected by the typhoon in the distribution network to be optimized according to the node disaster state variable and the number of nodes in the basic data of the distribution network;

[0087] Calculate the load loss power of the distribution line of the distribution network to be optimized according to the node failure probability, the distribution line failure probability, the node load power, the node disaster state variable, the number of disaster-stricken nodes, and the number of distribution network lines in the distribution network basic data;

[0088] According to the load loss power of the distribution line, the number of distribution network areas in the distribution network basic data and the preset load weights of the distribution network areas, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized.

[0089] Specifically, each node in the distribution network forms a line in series. Therefore, when any node in any line fails, the entire line will be shut down and the line is considered to be faulty. The failure probability of the distribution line of the distribution network to be optimized is calculated using the following formula:

[0090]

[0091] in, are the failure probabilities of the i-th distribution line at time t in the typhoon eye area, cloud wall area, and spiral rain belt area, respectively. The calculation formula adopts the calculation formula given by the public research results:

[0092]

[0093] Where, are the distribution network line sets of the i-th line in the typhoon eye area, cloud wall area, and spiral rain belt area respectively; are the probabilities of failure of the kth node of the i-th line at time t in the typhoon eye area, cloud wall area, and spiral rain belt area, respectively.

[0094] Then, the number of nodes affected by the typhoon in the distribution network to be optimized is determined based on the node affected state variable and the number of nodes in the basic data of the distribution network. When a power outage occurs on a distribution line in the typhoon disaster area, the load of the affected nodes on the line will be lost. Assuming that the number of affected nodes N on the i-th distribution line with a power outage is LDi , then N in the typhoon disaster area L The power loss of distribution lines due to typhoon is P LL It can be calculated by the following formula:

[0095]

[0096] Among them, P LL N is the distribution network to be optimized L The power loss load power of the distribution line; P LDij,t is the node load power of the jth affected node of the i-th distribution line where the power outage occurs in time period t; is the disaster-affected state variable of node j in the typhoon eye area; is the disaster status variable of node j in the cloud wall area, which is 1 when it indicates disaster, and 0 when it indicates no disaster; is the disaster state variable of node j in the spiral rain belt area; is the node failure probability of the i-th line in the typhoon eye area at time t; is the node failure probability of the i-th line in the cloud wall area at time t; is the node failure probability of the i-th line in the spiral rain belt area at time t; TT is the number of disaster-affected periods; N L is the number of distribution network lines; N LDi is the number of affected nodes.

[0097] Finally, based on the load loss power of the distribution line, the number of distribution network areas in the basic data of the distribution network, and the preset load weights of the distribution network areas, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized. Where, P LLk is the load loss power of the distribution lines in the typhoon disaster area k due to typhoon failure, ω i is the load weight of the distribution network in region k, N Q is the number of distribution network areas.

[0098] Schematically, the distribution network system needs to meet certain active power and reactive power balance constraints during operation. The power balance constraints are:

[0099]

[0100] Among them, P Gk The active power injected into the grid in region k; Q Gk The reactive power injected into the grid in area k; P PVkn is the active power output by the n-th photovoltaic power station in region k; Q PVkn is the reactive power output by the n-th photovoltaic power station in region k; P Wkn is the active power output by the n-th wind power station in region k; Q Wkn is the reactive power output by the n-th wind power station in region k; P EVkn The active power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn P is the reactive power output by the nth electric vehicle in region k that can provide services to the grid; SVkn is the active power output by the nth mobile diesel generator in area k; Q SVkn is the reactive power output by the nth mobile diesel generator in area k; P FSEkn is the active power output by the nth fixed energy storage station in region k; Q FSEkn is the reactive power output by the nth fixed energy storage station in region k; P SSEkn is the active power output by the nth mobile energy storage vehicle in area k; Q SSEkn is the reactive power output by the nth mobile energy storage vehicle in area k; P Dk is the active power of the load still on the grid after the disaster in area k; Q Dk is the reactive power of the load still on the grid after the disaster in area k; P LLkQ is the active power of the distribution lines in the typhoon disaster area k that lose load due to typhoon failure; LLk N is the reactive power of the distribution lines in the typhoon disaster area k due to typhoon failure; PV is the number of photovoltaic power stations in the distribution network; N W is the number of wind power stations in the distribution network; N EV N is the number of electric vehicles in the distribution network that can provide services to the grid; SV is the number of mobile diesel generators in the distribution network; N FSE is the number of fixed energy storage stations in the distribution network; N SSE is the number of mobile energy storage vehicles in the distribution network;

[0101] After the distribution network is reconfigured and optimized, the node voltage must be within the allowable range, that is, the node voltage must not exceed the upper and lower limits. The node voltage constraint is:

[0102] U i,min ≤U i ≤U i,max

[0103] Among them, U i,min is the lower limit of the voltage at node i; U i,max is the upper limit of the voltage at node i; U i is the actual voltage amplitude at node i;

[0104] After the distribution network is reconfigured and optimized, the branch current must be within the allowed range, that is, the branch current does not exceed the upper limit. The branch current constraint is:

[0105] 0≤I Li ≤I Li,max

[0106] Among them, I Li,max is the maximum allowable current value of line Li; I Li is the actual current value of line Li;

[0107] The distribution network lines need to meet certain transmission capacity constraints, that is, the transmission power of each line must be within the range of the line's allowed transmission capacity. The line transmission limit constraints are:

[0108]

[0109] Among them, P Li,min is the minimum active power of line Li; P Li,max is the maximum active power of line Li; Q Li,min is the minimum reactive power of line Li; Q Li,max is the maximum reactive power of line Li; It is a collection of distribution network lines;

[0110] The distribution network is reconfigured by changing the switch state of the distribution network line. The distribution network needs to meet the open-loop operation structure, that is, there is no loop in the distribution network. The network topology constraint is:

[0111] z ab +z ba =Z

[0112] g e ∈G

[0113] Among them, z ab is the power flow direction from node a to node b; z ba is the power flow direction from node b to node a, z ab =1 means the power on the line flows from node a to node b, z ba Similarly, Z is the line connection state variable. When its value is 1, it means that the line is closed, otherwise, it means that the line is disconnected. e is the network topology structure after the distribution network is adjusted; G is the set of topology structures that meet the normal operation of the network;

[0114] The output power value of the photovoltaic power generation system must be between the upper and lower values ​​of its allowed output. The power constraint of the photovoltaic power generation system is:

[0115]

[0116] in, is the maximum allowable value of the active power output of the n-th PV power station in region k; P PVkn is the minimum allowed value of the active power output of the n-th PV power station in region k; The maximum allowed value of reactive power output by the n-th PV power station in region k; Q PVkn P is the minimum allowable value of reactive power output by the n-th photovoltaic power station in region k; PVkn is the active power output by the n-th photovoltaic power station in region k; Q PVkn is the reactive power output by the n-th PV power station in region k;

[0117] The output power value of the wind turbine must be between the upper and lower limits of its allowed output. The wind turbine power constraint is:

[0118]

[0119] in, is the maximum allowable value of the active power output of the n-th wind turbine in region k; P Wknis the minimum allowable value of the active power output of the n-th wind turbine in region k; is the maximum allowable value of reactive power output by the n-th wind turbine in region k; Q Wkn is the minimum allowable value of reactive power output by the n-th wind turbine in region k; P Wkn is the active power output by the n-th wind turbine in region k; Q Wkn is the reactive power output by the n-th wind turbine in region k;

[0120] The output power value of electric vehicles that can provide services to the power grid must be between the upper and lower limits of their allowed output. The electric vehicle power constraint is:

[0121]

[0122] in, The maximum allowed value of the active power output by the nth electric vehicle in region k that can provide services to the grid; P EVkn The minimum allowed value of active power output by the nth electric vehicle in region k that can provide services to the grid; The maximum allowed value of reactive power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn The minimum allowed value of reactive power output by the nth electric vehicle in region k that can provide services to the grid; P EVkn The active power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn The reactive power output by the nth electric vehicle in region k that can provide services to the grid;

[0123] The output power value of the mobile diesel generator must be between the upper and lower limits of its allowed output. The power constraint of the mobile diesel generator is:

[0124]

[0125] in, is the maximum allowable value of the active power output of the nth mobile diesel generator in area k; P SVkn is the minimum allowable value of the active power output of the nth mobile diesel generator in area k; The maximum allowed value of reactive power output by the nth mobile diesel generator in area k; Q SVkn P is the minimum allowable value of reactive power output by the nth mobile diesel generator in area k; SVkn is the active power output by the nth mobile diesel generator in area k; Q SVknis the reactive power output by the nth mobile diesel generator in area k;

[0126] The output power value of a fixed energy storage station must be between the upper and lower limits of its allowed output. The power constraint of the fixed energy storage station is:

[0127]

[0128]

[0129] in, The maximum allowable value of the active power output of the nth fixed energy storage station in area k; P FSEkn The minimum allowed value of the active power output of the nth fixed energy storage station in region k; The maximum allowed value of reactive power output by the nth fixed energy storage station in region k; Q FAEkn P is the minimum allowable value of reactive power output by the nth fixed energy storage station in region k; FSEkn is the active power output by the nth fixed energy storage station in region k; Q FSEkn is the reactive power output by the nth fixed energy storage station in region k;

[0130] The output power value of the mobile energy storage vehicle must be between the upper and lower values ​​of its allowed output. The power constraint of the mobile energy storage vehicle is:

[0131]

[0132] in, The maximum allowable value of the active power output of the nth mobile energy storage vehicle in area k; P SSEkn The minimum allowed value of the active power output of the nth mobile energy storage vehicle in area k; The maximum allowed value of reactive power output by the nth mobile energy storage vehicle in area k; Q SSEkn P is the minimum allowable value of reactive power output by the nth mobile energy storage vehicle in area k; SSEkn is the active power output by the nth mobile energy storage vehicle in area k; Q SSEkn is the reactive power output by the nth mobile energy storage vehicle in area k.

[0133] Specifically, adjusting the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data includes:

[0134] Determining the changed distribution network topology data according to the distribution network basic data and the distribution network line status data;

[0135] According to the changed distribution network topology data, adjust the access power of the comprehensive energy in the distribution network to be optimized;

[0136] According to the access power of the adjusted comprehensive energy, the load of the distribution network to be optimized is adjusted.

[0137] By changing the state of the distribution network lines, changing the distribution network topology and making decision adjustments on the power of photovoltaic, wind power and mobile diesel vehicles connected to the distribution network, the power loss of the distribution network can be minimized.

[0138] Preferably, the load loss power of the distribution line of the distribution network to be optimized is calculated by the following formula:

[0139]

[0140] Among them, P LL N is the distribution network to be optimized L The power loss load power of the distribution line; P LDij,t is the node load power of the jth affected node of the i-th distribution line where the power outage occurs in time period t; is the disaster-affected state variable of node j in the typhoon eye area; is the disaster state variable of node j in the cloud wall area; is the disaster state variable of node j in the spiral rain belt area; is the node failure probability of the i-th line in the typhoon eye area at time t; is the node failure probability of the i-th line in the cloud wall area at time t; is the node failure probability of the i-th line in the spiral rain belt area at time t; T T is the number of disaster-affected periods; N L is the number of distribution network lines; N LDi is the number of affected nodes.

[0141] In a preferred embodiment of the present invention, a distribution network optimization model is solved based on an optimization problem solving method of a robust extreme learning machine.

[0142] (1) Given N groups of samples (x i ,y i )∈R n ×R m .

[0143] (2) Objective function construction

[0144]

[0145] Among them, e is the training residual; R is the regularization coefficient.

[0146] (3) Construction of Lagrangian function

[0147]

[0148] Among them, H is the hidden layer output matrix, μ is the penalty term coefficient, λ is the Lagrange multiplier; β is the output weight, and its calculation formula is:

[0149]

[0150] When the number of input training samples is less than the number of hidden neurons, the output weight β is calculated as follows:

[0151]

[0152] Penalty coefficient μ value

[0153] μ=2N / ||y||1

[0154] The Lagrangian function is used to iteratively calculate and determine β, e, and λ.

[0155] (4) Iterative calculation

[0156] The iterative calculation is performed through the following formula:

[0157] β k+1 =(H T H+2 / RμI) -1 H T (ye k +λ k / μ)

[0158] μ=2N / ||y||1e k+1 =shink(Hβ k+1 +λ k / μ,1 / μ)

[0159] λ k+1 =λ k +μ(y-Hβ k+1 -e k+1 )

[0160] The robust extreme learning machine iteratively updates β, e, and λ through the above three formulas, and takes the parameters that meet the convergence conditions or expected errors as the final model forming parameters.

[0161] (5) Construction of robust extreme learning machine model

[0162] The robust extreme learning machine model is constructed using the parameters input weight w, bias value b and output weight β.

[0163] (6) Extreme Learning Machine Optimization and Solution

[0164] Extreme learning machines can be optimized using particle swarm optimization and genetic algorithms. Both methods optimize the input weights w and hidden layer biases b of the extreme learning machine, using model training accuracy as the individual fitness to find a set of parameters that achieve high accuracy and stability. PSO simulates the collective behavior of bird flocks in nature, such as foraging or migrating. Individuals in the flock are treated as particles in the solution space, and the position information carried by the particles represents the candidate solutions to the problem. By calculating the updated position information of the flock in the solution space and incorporating a competition mechanism, the optimal position of each individual (individual best Pbest) and the optimal position of the group (global best Pgbest) are retained in each evolution. This evolutionary process is the process of searching for the optimal solution. Genetic algorithms mimic the natural laws of biological evolution, encoding the optimization parameters (w, b) into chromosomes, represented as binary mathematical vectors. Through crossover, mutation, and selection, chromosomes are promoted to move in the solution space. Similarly, a competition mechanism is used to continuously move chromosomes closer to the optimal solution position until the optimal solution is found.

[0165] The N sets of samples represent data related to the state and operation of the distribution network, including the current topology of the distribution network, load data under different fault conditions, voltage levels at each node, current levels in branches, and other data, reflecting the operational status of the distribution network. The hidden layer output matrix represents the input features after nonlinear transformation of the input data. These features can better capture the complex state and dynamic changes of the distribution network, thereby supporting more robust and efficient reconstruction decisions and improving the accuracy and reliability of decisions.

[0166] By implementing this embodiment, the node failure probability, node load power, node disaster state variable, distribution network basic data and comprehensive energy data of the distribution network to be optimized can be obtained when a typhoon comes; wherein the distribution network basic data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, the node voltage limit, the maximum branch current, the line transmission power and the distribution network topology data; the comprehensive energy data includes the power of the photovoltaic power generation system, the power of the wind turbine, the power of the electric vehicle, the power of the mobile diesel generator, the power of the fixed energy storage station and the power of the mobile energy storage vehicle; according to the node failure probability, the node load power, the node disaster state variable, the distribution network basic data and the preset distribution network area load The weight is set, and a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized; under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints and mobile energy storage vehicle power constraints constructed according to the distribution network basic data and the comprehensive energy data, the distribution network optimization model is solved to generate distribution network line status data when the load loss power of the distribution network to be optimized is minimized; and the load of the distribution network to be optimized is adjusted according to the distribution network basic data and the distribution network line status data. By considering the basic data of the distribution network under the influence of typhoons, a distribution network optimization model is constructed, which comprehensively considers comprehensive energy data such as photovoltaic power generation system power, wind turbine power, electric vehicle power, mobile diesel generator power and fixed energy storage station power. By solving the distribution network optimization model, the optimal distribution network line status data can be generated, which can effectively respond to the impact of typhoon disasters, adapt to the rapidly changing operating environment and complex constraints, adjust the load of the distribution network to be optimized, and improve the operating stability and reliability of the distribution network in emergency situations.

[0167] See also Figure 2 , is a schematic structural diagram of a distribution network optimization device under the influence of a typhoon provided by one embodiment of the present invention, comprising:

[0168] The distribution network data acquisition module is used to obtain the node failure probability, node load power, node disaster state variables, distribution network basic data, and comprehensive energy data of the distribution network to be optimized when a typhoon arrives. The distribution network basic data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximum values, line transmission power, and distribution network topology data. The comprehensive energy data includes the power of photovoltaic power generation systems, wind turbine generator sets, electric vehicles, mobile diesel generators, fixed energy storage stations, and mobile energy storage vehicles.

[0169] a distribution network model construction module, configured to construct a distribution network optimization model based on the node failure probability, the node load power, the node disaster state variable, the distribution network basic data, and the preset distribution network regional load weight, with the goal of minimizing the load loss power of the distribution network to be optimized;

[0170] a distribution network model solving module, configured to solve the distribution network optimization model under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints constructed based on the distribution network basic data and the comprehensive energy data, and generate distribution network line state data when the load loss power of the distribution network to be optimized is minimized;

[0171] The distribution network optimization module is used to adjust the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data.

[0172] Preferably, the distribution network model construction module is used to construct a distribution network optimization model based on the node failure probability, the node load power, the node disaster state variable, the distribution network basic data and the preset distribution network regional load weight, with the goal of minimizing the load loss power of the distribution network to be optimized;

[0173] The construction of the distribution network optimization model includes:

[0174] Calculating the distribution line failure probability of the distribution network to be optimized based on the node failure probability and the number of distribution network lines in the distribution network basic data;

[0175] Determine the number of nodes affected by the typhoon in the distribution network to be optimized according to the node disaster state variable and the number of nodes in the basic data of the distribution network;

[0176] Calculate the load loss power of the distribution line of the distribution network to be optimized according to the node failure probability, the distribution line failure probability, the node load power, the node disaster state variable, the number of disaster-stricken nodes, and the number of distribution network lines in the distribution network basic data;

[0177] According to the load loss power of the distribution line, the number of distribution network areas in the distribution network basic data and the preset load weights of the distribution network areas, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized.

[0178] Specifically, the distribution network optimization module is used to adjust the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data;

[0179] Adjusting the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data includes:

[0180] Determining the changed distribution network topology data according to the distribution network basic data and the distribution network line status data;

[0181] According to the changed distribution network topology data, adjust the access power of the comprehensive energy in the distribution network to be optimized;

[0182] According to the access power of the adjusted comprehensive energy, the load of the distribution network to be optimized is adjusted.

[0183] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0184] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0185] Another embodiment of the present invention provides a terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for optimizing a distribution network under the influence of a typhoon as described in the above embodiment. The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0186] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0187] The memory can be used to store the computer program, and the processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0188] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network optimization method under the influence of a typhoon as described in the above embodiment.

[0189] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0190] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing a distribution network under the influence of a typhoon, characterized in that: include: Obtain node failure probability, node load power, node disaster state variables, basic distribution network data, and comprehensive energy data of the distribution network to be optimized when a typhoon arrives; the basic distribution network data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximums, line transmission power, and distribution network topology data; the comprehensive energy data includes photovoltaic power generation system power, wind turbine power, electric vehicle power, mobile diesel generator power, fixed energy storage station power, and mobile energy storage vehicle power; According to the node failure probability, the node load power, the node disaster state variable, the distribution network basic data and the preset distribution network regional load weight, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized; Under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints constructed according to the distribution network basic data and the comprehensive energy data, the distribution network optimization model is solved to generate distribution network line status data when the load loss power of the distribution network to be optimized is minimized; Adjusting the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data; The construction of the distribution network optimization model includes: Calculating the distribution line failure probability of the distribution network to be optimized based on the node failure probability and the number of distribution network lines in the distribution network basic data; Determine the number of nodes affected by the typhoon in the distribution network to be optimized according to the node disaster state variable and the number of nodes in the basic data of the distribution network; Calculate the load loss power of the distribution line of the distribution network to be optimized according to the node failure probability, the distribution line failure probability, the node load power, the node disaster state variable, the number of disaster-stricken nodes, and the number of distribution network lines in the distribution network basic data; According to the load loss power of the distribution line, the number of distribution network areas in the distribution network basic data, and the preset load weights of the distribution network areas, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized; The load loss power of the distribution line of the distribution network to be optimized is calculated by the following formula: Among them, P LL N is the distribution network to be optimized L The power loss load power of the distribution line; P LDij,t is the node load power of the jth affected node of the i-th distribution line where the power outage occurs in time period t; is the disaster-affected state variable of node j in the typhoon eye area; is the disaster state variable of node j in the cloud wall area; is the disaster state variable of node j in the spiral rain belt area; is the node failure probability of the i-th line in the typhoon eye area at time t; is the node failure probability of the i-th line in the cloud wall area at time t; is the node failure probability of the i-th line in the spiral rain belt area at time t; T T is the number of disaster-affected periods; N L is the number of distribution network lines; N LDi is the number of affected nodes.

2. The method for optimizing a distribution network under the influence of a typhoon according to claim 1, wherein: The power balance constraint is: Among them, P Gk The active power injected into the grid in region k; Q Gk The reactive power injected into the grid in area k; P PVkn is the active power output by the n-th photovoltaic power station in region k; Q PVkn is the reactive power output by the n-th photovoltaic power station in region k; P Wkn is the active power output by the n-th wind power station in region k; Q Wkn is the reactive power output by the n-th wind power station in region k; P EVkn The active power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn P is the reactive power output by the nth electric vehicle in region k that can provide services to the grid; SVkn is the active power output by the nth mobile diesel generator in area k; Q SVkn is the reactive power output by the nth mobile diesel generator in area k; P FSEkn is the active power output by the nth fixed energy storage station in region k; Q FSEkn is the reactive power output by the nth fixed energy storage station in region k; P SSEkn is the active power output by the nth mobile energy storage vehicle in area k; Q SSEkn is the reactive power output by the nth mobile energy storage vehicle in area k; P Dk is the active power of the load still on the grid after the disaster in area k; Q Dk is the reactive power of the load still on the grid after the disaster in area k; P LLk Q is the active power of the distribution lines in the typhoon disaster area k that lose load due to typhoon failure; LLk N is the reactive power of the distribution lines in the typhoon disaster area k due to typhoon failure; PV is the number of photovoltaic power stations in the distribution network; N W is the number of wind power stations in the distribution network; N EV N is the number of electric vehicles in the distribution network that can provide services to the grid; SV is the number of mobile diesel generators in the distribution network; N FSE is the number of fixed energy storage stations in the distribution network; N SSE is the number of mobile energy storage vehicles in the distribution network; The node voltage constraint is: IN i,min ≤U i ≤U i,max Among them, U i,min is the lower limit of the voltage at node i; U i,max is the upper limit of the voltage at node i; U i is the actual voltage amplitude at node i; The branch current constraint is: 0≤I Li ≤I Li,max Among them, I Li,max is the maximum allowable current value of line Li; I Li is the actual current value of line Li; The line transmission quota constraints are: Among them, P Li,min is the minimum active power of line Li; P Li,max is the maximum active power of line Li; Q Li,min is the minimum reactive power of line Li; Q Li,max is the maximum reactive power of line Li; It is a collection of distribution network lines; The network topology constraints are: With ab +with ba =Z g e ∈G Among them, z ab is the power flow direction from node a to node b; z ba is the power flow direction of the line from node b to node a; Z is the line connection state variable; g e is the network topology structure after the distribution network is adjusted; G is the set of topology structures that meet the normal operation of the network; The power constraint of the photovoltaic power generation system is: in, is the maximum allowable value of the active power output of the n-th PV power station in region k; P PVkn is the minimum allowed value of the active power output of the n-th PV power station in region k; The maximum allowed value of reactive power output by the n-th PV power station in region k; Q PVkn P is the minimum allowable value of reactive power output by the n-th photovoltaic power station in region k; PVkn is the active power output by the n-th photovoltaic power station in region k; Q PVkn is the reactive power output by the n-th PV power station in region k; The wind turbine power constraint is: in, is the maximum allowable value of the active power output of the n-th wind turbine in region k; P Wkn is the minimum allowable value of the active power output of the n-th wind turbine in region k; is the maximum allowable value of reactive power output by the n-th wind turbine in region k; Q Wkn is the minimum allowable value of reactive power output by the n-th wind turbine in region k; P Wkn is the active power output by the n-th wind turbine in region k; Q Wkn is the reactive power output by the n-th wind turbine in region k; The electric vehicle power constraint is: in, The maximum allowed value of the active power output by the nth electric vehicle in region k that can provide services to the grid; P EVkn The minimum allowed value of active power output by the nth electric vehicle in region k that can provide services to the grid; The maximum allowed value of reactive power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn The minimum allowed value of reactive power output by the nth electric vehicle in region k that can provide services to the grid; P EVkn The active power output by the nth electric vehicle in region k that can provide services to the grid; Q EVkn The reactive power output by the nth electric vehicle in region k that can provide services to the grid; The power constraint of the mobile diesel generator is: in, is the maximum allowable value of the active power output of the nth mobile diesel generator in area k; P SVkn is the minimum allowable value of the active power output of the nth mobile diesel generator in area k; The maximum allowed value of reactive power output by the nth mobile diesel generator in area k; Q SVkn P is the minimum allowable value of reactive power output by the nth mobile diesel generator in area k; SVkn is the active power output by the nth mobile diesel generator in area k; Q SVkn is the reactive power output by the nth mobile diesel generator in area k; The power constraint of the fixed energy storage station is: in, The maximum allowable value of the active power output of the nth fixed energy storage station in area k; P FSEkn The minimum allowed value of the active power output of the nth fixed energy storage station in region k; The maximum allowed value of reactive power output by the nth fixed energy storage station in region k; Q FAEkn P is the minimum allowable value of reactive power output by the nth fixed energy storage station in region k; FSEkn is the active power output by the nth fixed energy storage station in region k; Q FSEkn is the reactive power output by the nth fixed energy storage station in region k; The power constraint of the mobile energy storage vehicle is: in, The maximum allowable value of the active power output of the nth mobile energy storage vehicle in area k; P SSEkn The minimum allowed value of the active power output of the nth mobile energy storage vehicle in area k; The maximum allowed value of reactive power output by the nth mobile energy storage vehicle in area k; Q SSEkn P is the minimum allowable value of reactive power output by the nth mobile energy storage vehicle in area k; SSEkn is the active power output by the nth mobile energy storage vehicle in area k; Q SSEkn is the reactive power output by the nth mobile energy storage vehicle in area k.

3. The method for optimizing a distribution network under the influence of a typhoon according to claim 1, wherein: Adjusting the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data includes: Determining the changed distribution network topology data according to the distribution network basic data and the distribution network line status data; According to the changed distribution network topology data, adjust the access power of the comprehensive energy in the distribution network to be optimized; According to the access power of the adjusted comprehensive energy, the load of the distribution network to be optimized is adjusted.

4. A distribution network optimization device under the influence of typhoon, characterized in that: include: The distribution network data acquisition module is used to obtain the node failure probability, node load power, node disaster state variables, distribution network basic data, and comprehensive energy data of the distribution network to be optimized when a typhoon arrives. The distribution network basic data includes the number of distribution network areas, the number of distribution network lines, the number of nodes, node voltage limits, branch current maximum values, line transmission power, and distribution network topology data. The comprehensive energy data includes the power of photovoltaic power generation systems, wind turbine generator sets, electric vehicles, mobile diesel generators, fixed energy storage stations, and mobile energy storage vehicles. a distribution network model construction module, configured to construct a distribution network optimization model based on the node failure probability, the node load power, the node disaster state variable, the distribution network basic data, and the preset distribution network regional load weight, with the goal of minimizing the load loss power of the distribution network to be optimized; a distribution network model solving module, configured to solve the distribution network optimization model under the power balance constraints, node voltage constraints, branch current constraints, line transmission limit constraints, network topology constraints, photovoltaic power generation system power constraints, wind turbine power constraints, electric vehicle power constraints, mobile diesel generator power constraints, fixed energy storage station power constraints, and mobile energy storage vehicle power constraints constructed based on the distribution network basic data and the comprehensive energy data, and generate distribution network line state data when the load loss power of the distribution network to be optimized is minimized; A distribution network optimization module, configured to adjust the load of the distribution network to be optimized based on the distribution network basic data and the distribution network line status data; The distribution network model construction module is used to construct a distribution network optimization model, including: Calculating the distribution line failure probability of the distribution network to be optimized based on the node failure probability and the number of distribution network lines in the distribution network basic data; Determine the number of nodes affected by the typhoon in the distribution network to be optimized according to the node disaster state variable and the number of nodes in the basic data of the distribution network; Calculate the load loss power of the distribution line of the distribution network to be optimized according to the node failure probability, the distribution line failure probability, the node load power, the node disaster state variable, the number of disaster-stricken nodes, and the number of distribution network lines in the distribution network basic data; According to the load loss power of the distribution line, the number of distribution network areas in the distribution network basic data, and the preset load weights of the distribution network areas, a distribution network optimization model is constructed with the goal of minimizing the load loss power of the distribution network to be optimized; The load loss power of the distribution line of the distribution network to be optimized is calculated by the following formula: Among them, P LL N is the distribution network to be optimized L The power loss load power of the distribution line; P LDij,t is the node load power of the jth affected node of the i-th distribution line where the power outage occurs in time period t; is the disaster-affected state variable of node j in the typhoon eye area; is the disaster state variable of node j in the cloud wall area; is the disaster state variable of node j in the spiral rain belt area; is the node failure probability of the i-th line in the typhoon eye area at time t; is the node failure probability of the i-th line in the cloud wall area at time t; is the node failure probability of the i-th line in the spiral rain belt area at time t; T T is the number of disaster-affected periods; N L is the number of distribution network lines; N LDi is the number of affected nodes.

5. The device for optimizing a distribution network under the influence of a typhoon according to claim 4, characterized in that: The distribution network optimization module is configured to adjust the load of the distribution network to be optimized based on the distribution network basic data and the distribution network line status data; Adjusting the load of the distribution network to be optimized according to the distribution network basic data and the distribution network line status data includes: Determining the changed distribution network topology data according to the distribution network basic data and the distribution network line status data; According to the changed distribution network topology data, adjust the access power of the comprehensive energy in the distribution network to be optimized; According to the access power of the adjusted comprehensive energy, the load of the distribution network to be optimized is adjusted.

6. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for optimizing a distribution network under the influence of a typhoon as claimed in any one of claims 1 to 3 is implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network optimization method under the influence of typhoon according to any one of claims 1 to 3.

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