Power distribution network collaborative recovery method and system considering interconnected microgrid and network reconstruction

Abstract

The invention discloses a power distribution network collaborative recovery method and system considering an interconnected microgrid and network reconstruction, and belongs to the technical field of electrical engineering, and the method specifically comprises the steps: simulating and guiding an EV running on a traffic network to enter a nearby charging station for refuge in a pre-disaster stage, and participating in vehicle grid-connected reverse power transmission in a post-disaster stage; in the post-disaster stage, according to the sensed line fault condition, the EV information obtained through simulation and the information of the distributed resources, a mixed integer second-order cone model containing the scheduling position of a rush repair team, the output condition of the distributed resources, the power supply range of each micro-grid and the interconnection state is obtained through optimization, and post-disaster load rapid recovery and line repair are carried out; and solving the established mixed integer second-order cone model by adopting a commercial solver CPLEX to obtain the dynamic micro-grid region division and interconnection state, the line switch state and the output condition of each distributed resource at different moments in the fault recovery time period.

Description

Background Art

[0002] With the gradual advancement of carbon neutrality, the construction of a new power system with new energy as the main body is accelerating. Distributed energy resources (DERs), as a typical application of renewable energy, are an important way to build a new power system that is "clean, low-carbon, safe and efficient". At present, it is necessary to promote the development of distributed renewable energy and promote multi-energy synergy and comprehensive cascade utilization of energy in the field of terminal energy use. Microgrids (MGs), as localized small-scale power systems with enhanced control capabilities, are regarded as an effective solution to integrate and coordinate different types of distributed energy resources (e.g., diesel generators, wind turbines, photovoltaics, energy storage, etc.) to enhance the resilience of distribution networks.

[0003] At present, some scholars have studied the load recovery methods and resilience improvement strategies of distribution systems under the collaborative support of different types of distributed energy resources before and after disasters. However, existing research focuses on using distributed resources to form multiple independent microgrids after disasters and restoring load power supply by controlling switches to reconstruct the network. Although the electrical boundaries of these independent microgrids can change dynamically as the recovery process proceeds, the interconnected power supply between microgrids is not considered. At the same time, when affected by extreme natural disasters, the distribution network may enter island mode after the disaster. In this case, each isolated microgrid requires at least one DER for frequency and voltage control, and current research rarely considers frequency and voltage control in the post-disaster fault recovery process.

[0004] Therefore, to address this problem, it is urgent to find a multi-source collaborative recovery method for distribution networks that can take into account the formation of dynamic interconnected microgrids and network reconstruction, so as to optimize the recovery of distribution network post-disaster faults. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a distribution network collaborative recovery method and system taking into account interconnected microgrids and network reconstruction in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problem of how to collaboratively utilize network reconstruction technology and distributed resources in the distribution network to form a dynamic interconnected microgrid to achieve post-disaster fault recovery when an extreme event causes a failure of the upstream main network of the distribution network and insufficient power supply capacity occurs.

[0006] The present invention adopts the following technical solutions:

[0007] The coordinated restoration method of distribution network considering interconnected microgrids and network reconstruction includes the following steps:

[0008] In the post-disaster stage, based on the perceived line fault conditions and the simulated electric vehicle information and distributed resource information in the pre-disaster stage, the vehicle grid-connected reverse charging parameters, distributed resource coordination parameters, repair team dispatch parameters, flexible load management parameters, network reconstruction parameters, and dynamic interconnected microgrid formation parameters are optimized to obtain a mixed integer second-order cone model that includes the dispatch position of the repair team, the output of distributed resources, the power supply range of each microgrid, and the interconnection status.

[0009] The established mixed integer second-order cone model is solved to obtain the dynamic microgrid regional division and interconnection status, line switch status and the output of each distributed resource at different times during the fault recovery period. Based on the dynamic microgrid regional division and interconnection status, as well as the line switch status at different times during the fault recovery period, post-disaster load rapid recovery and line repair are carried out according to the output of each distributed resource.

[0010] Preferably, the simulated electric vehicle information in the pre-disaster stage is specifically:

[0011] In the pre-disaster stage, based on graph theory, the topological structure of the transportation network is modeled as an undirected graph G represented by a set of road network nodes V and a set of edges E that represent the connectivity between nodes. The coupling relationship between the road network and the power grid is equivalent to whether there are inter-layer node connections between the road network and the power grid. The road network and power grid models in the region are modeled.

[0012] Based on the regional road network and power grid model, the travel chain theory and real-time status model are used to describe the travel characteristics of electric vehicle users before the disaster. The simulation guides electric vehicles traveling on the traffic network to enter the nearest charging station for refuge and participate in the vehicle grid-connected reverse power transmission in the post-disaster stage. The travel willingness coefficient is introduced to measure the impact of disasters on users' travel willingness, and the spatiotemporal distribution status and remaining power information of electric vehicles are obtained.

[0013] Preferably, the set of coupling edges between the road network and the power grid is:

[0014] ξ={E αβ ∈V α ×V β}

[0015]

[0016] Among them, E αβ is an element in the set ξ; V α and V β They are the set of power grid nodes and the set of road network nodes respectively; is the i-th node in the power grid; is the jth node in the road network; is a 0-1 variable indicating whether the grid node i and the road network node j are coupled. There is a coupling relationship between the road network and the power grid.

[0017] Preferably, the travel chain set expression is:

[0018] TC={L 0 ,L f ,P 0f ,T 0 ,T f ,t 0f ,T s}

[0019] Among them, TC is the set of state variables of a section of the trip chain, including the starting point L 0 , end point L f 、Driving path P 0f , departure time T 0 , arrival time T f , driving time t 0f and dwell time T s ;

[0020] The driving time of the i-th road segment Arrival time and the departure time for the next leg of the journey It is expressed as follows:

[0021]

[0022] Among them, d i is the length of the i-th road section; v is the average speed of EV.

[0023] Preferably, the real-time state model of the electric vehicle is expressed as:

[0024] EVstatus={S rem ,W}

[0025] Among them, EVstatus is the collection of real-time status information of each electric vehicle, including the remaining power set S of the electric vehicle rem And the driving state set W.

[0026] Preferably, the number of electric vehicles N participating in the dispatch response road for:

[0027] N road =ρN EV

[0028] Among them, ρ is the travel ratio coefficient; N EV is the total number of electric vehicles.

[0029] Preferably, the objective function of the mixed integer second-order cone model is:

[0030] min f=ω 1 f 1 +ω 2 f 2 +ω 3 f 3 +c price f 4

[0031] Among them, ω 1 is the load shedding cost; 2 is the switch operation cost; 3 is the DER power generation cost; c price is the cost of the distribution network purchasing electricity from the upper level, f 1 , f 2 , f 3 , f 4 They are respectively minimizing the weighted power loss load, minimizing the switch operation cost during fault recovery, minimizing the cost of mobilizing distributed resource output and the cost of buying electricity from substations.

[0032] Preferably, the weighted power loss load is minimized, the switch operation cost during fault recovery is minimized, the cost of mobilizing distributed resource output and the cost of buying electricity from the substation are minimized. 1 , f 2 , f 3 , f 4 They are:

[0033]

[0034] Among them, N T Indicates the total number of fault time intervals; N bus Represents the total number of nodes in the network; Δt represents the length of each time interval during the fault period; represents the power loss of node i in time period t; i is the load weight of node i; is a 0-1 optimization variable, representing the switch state of the branch starting from node i and ending at node j during the fault recovery period t. Indicates that the switch is closed; N line Indicates the total number of branches in the network; represents the output power of distributed power generation at node i during period t; It means that node i buys electricity from the substation during period t.

[0035] Preferably, the constraints of the mixed integer second-order cone model are as follows:

[0036] For nodes connected to distributed renewable energy, the constraints to be met are:

[0037]

[0038] Among them, N R is the set of nodes connected to DREs; and are the active and reactive outputs of DREs at node i at time t; P i,Rmin and P i,Rmax are the minimum and maximum active outputs of DREs at node i at time t; Q i,Rmin and Q i,Rmax are the minimum reactive power output and maximum reactive power output of DREs at node i at time t, respectively;

[0039] For nodes connected to energy storage, the constraints satisfied are:

[0040]

[0041] Among them, N E A collection of nodes connected to energy storage; is the state of charge of the energy storage at node i at time t; S i,E,max is the maximum value of the energy storage charge state at node i; and are the charging power and discharging power of the energy storage at node i at time t; P i,E,ch,max and P i,E,dis,max They represent the maximum charging and discharging power of the energy storage device connected to the i-node respectively; η ch and η dis are charging efficiency and discharging efficiency respectively; and are 0-1 decision variables for the charging and discharging states of the energy storage at node i at time t, respectively;

[0042] V2G reverse charging constraints:

[0043]

[0044] Among them, N V Represents a collection of V2G stations; is the actual power of the i-th V2G station at time t; P i,V2Gmax and P i,V2Gmin are the maximum and minimum output values ​​of the i-th V2G station respectively;

[0045] Load constraints:

[0046]

[0047] Among them, N bus,con and N bus,un They represent the set of curtailable loads and the set of non-curtailable loads respectively; and represents the active and reactive load shedding of node i at time t; and They represent the active power and reactive power requirements of node i at time t respectively; s t Indicates the switching of the load that cannot be cut at time t, s t =1 means load shedding, s t =0 means load access;

[0048] Repair team dispatch constraints:

[0049]

[0050] Where F represents the set of all faulty lines; T f The time required to repair a line, = t+T f The switch status of line (i, j) at the moment; r is the maximum number of faulty lines restored in the same time period;

[0051] Dynamically interconnected microgrid topology constraints:

[0052]

[0053] in, is the virtual output of the dominant node in region q at time t; is the virtual power on the line from node i to node j in region q at time t; is the virtual load of node i; A is a three-dimensional space composed of 0-1 variables, Indicates that node i is a node in island q at time t; N C is the sum of the number of DERs with droop control and the number of substations; is the power of the virtual branch from node j to node k in region q at time t; q is the node number of DER with droop control and substation; O is a matrix with 0-1 variables, the number of rows corresponds to the total number of fault time intervals, and the number of columns corresponds to the sum of the number of DER with droop control and the number of substations; It indicates that at time t, an island region exists with the q power source point as the dominant node;

[0054] The node power balance, node voltage and branch current constraints are expressed as:

[0055]

[0056] in, and They represent the active power and reactive power transmitted by branch (i, j) at time t respectively; r i,j and xi,j are the resistance and reactance of branch (i, j); is the square of the branch current modulus at time t; is the square of the voltage modulus of node i at time t; S max is the maximum capacity of the line transmission;

[0057] Microgrid frequency constraints:

[0058]

[0059] f i,min ≤f i,t ≤f i,max ,i∈N bus,con

[0060] (1-s t )f i,min -Ms t ≤f i,t ≤(1-s t )f i,max +Ms t , i∈N bus,un

[0061] Where M is a constant; is the switch state of line (i, j) at time t, Indicates that the switch is closed; f i,t is the frequency of node i at time t, f j,t is the frequency of node j at time t; N bus,con and N bus,un They represent the set of loads that can be cut and the set of loads that cannot be cut, respectively; f i,min , f i,max is the lower and upper limits of the node frequency; s t Indicates the switching of the load that cannot be cut at time t; s t =1 means load shedding, s t =0 means load is connected.

[0062] In a second aspect, an embodiment of the present invention provides a distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction, including:

[0063] The construction module, in the post-disaster stage, optimizes the mixed integer second-order cone model including the dispatching position of the repair team, the output of distributed resources, the power supply range of each microgrid and the interconnection status, based on the perceived line fault conditions and the simulated electric vehicle information and distributed resource information in the pre-disaster stage, the vehicle grid-connected reverse charging parameters, the distributed resource coordination parameters, the dispatching parameters of the repair team, the flexible load management parameters, the network reconstruction parameters and the dynamic interconnected microgrid formation parameters;

[0064] The recovery module solves the established mixed integer second-order cone model to obtain the dynamic microgrid regional division and interconnection status, line switch status and the output of each distributed resource at different times during the fault recovery period. Based on the dynamic microgrid regional division and interconnection status, as well as the line switch status at different times during the fault recovery period, post-disaster load rapid recovery and line repair are carried out according to the output of each distributed resource.

[0065] Preferably, the simulated electric vehicle information in the pre-disaster stage is specifically:

[0066] In the pre-disaster stage, based on graph theory, the topological structure of the transportation network is modeled as an undirected graph G represented by a set of road network nodes V and a set of edges E that represent the connectivity between nodes. The coupling relationship between the road network and the power grid is equivalent to whether there are inter-layer node connections between the road network and the power grid. The road network and power grid models in the region are modeled.

[0067] Based on the regional road network and power grid model, the travel chain theory and real-time status model are used to describe the travel characteristics of electric vehicle users before the disaster. The simulation guides electric vehicles traveling on the traffic network to enter the nearest charging station for refuge and participate in the vehicle grid-connected reverse power transmission in the post-disaster stage. The travel willingness coefficient is introduced to measure the impact of disasters on users' travel willingness, and the spatiotemporal distribution status and remaining power information of electric vehicles are obtained.

[0068] Preferably, the set of coupling edges between the road network and the power grid is:

[0069] ξ={E αβ ∈V α ×V β}

[0070]

[0071] Among them, E αβ is an element in the set ξ; V α and V β They are the set of power grid nodes and the set of road network nodes respectively; is the i-th node in the power grid; is the jth node in the road network; is a 0-1 variable indicating whether the grid node i and the road network node j are coupled. There is a coupling relationship between the road network and the power grid;

[0072] The travel chain set expression is:

[0073] TC={L 0 ,L f ,P 0f ,T 0 ,T f ,t 0f ,Ts}

[0074] Among them, TC is the set of state variables of a section of the trip chain, including the starting point L 0 , end point L f 、Driving path P 0f , departure time T 0 , arrival time T f , driving time t 0f and dwell time T s ;

[0075] The driving time of the i-th road segment Arrival time and the departure time for the next leg of the journey It is expressed as follows:

[0076]

[0077] Among them, d i is the length of the i-th road section; v is the average speed of the EV;

[0078] The real-time state model of electric vehicles is expressed as:

[0079] EVstatus={S rem ,W}

[0080] Among them, EVstatus is the collection of real-time status information of each electric vehicle, including the remaining power set S of the electric vehicle rem And the driving state set W.

[0081] Preferably, the number of electric vehicles N participating in the dispatch response road for:

[0082] N road =ρN EV

[0083] Among them, ρ is the travel ratio coefficient; N EV is the total number of electric vehicles.

[0084] Preferably, the objective function of the mixed integer second-order cone model is:

[0085] min f=ω 1 f 1 +ω 2 f 2 +ω 3 f 3 +c price f 4

[0086] Among them, ω 1 is the load shedding cost; 2is the switch operation cost; 3 is the DER power generation cost; c price is the cost of the distribution network purchasing electricity from the upper level, f 1 , f 2 , f 3 , f 4 They are respectively minimizing the weighted power loss load, minimizing the switch operation cost during fault recovery, minimizing the cost of mobilizing distributed resource output and the cost of buying electricity from substations.

[0087] Preferably, the weighted power loss load is minimized, the switch operation cost during fault recovery is minimized, the cost of mobilizing distributed resource output and the cost of buying electricity from the substation are minimized. 1 , f 2 , f 3 , f 4 They are:

[0088]

[0089] Among them, N T Indicates the total number of fault time intervals; N bus Represents the total number of nodes in the network; Δt represents the length of each time interval during the fault period; represents the power loss of node i in time period t; i is the load weight of node i; is a 0-1 optimization variable, representing the switch state of the branch starting from node i and ending at node j during the fault recovery period t. Indicates that the switch is closed; N line Indicates the total number of branches in the network; represents the output power of distributed power generation at node i during period t; It means that node i buys power from the substation during period t;

[0090] The constraints of the mixed integer second-order cone model are as follows:

[0091] For nodes connected to distributed renewable energy, the constraints to be met are:

[0092]

[0093] Among them, N R is the set of nodes connected to DREs; and are the active and reactive outputs of DREs at node i at time t; P i,Rmin and P i,Rmax are the minimum and maximum active outputs of DREs at node i at time t; Q i,Rmin and Q i,Rmaxare the minimum reactive power output and maximum reactive power output of DREs at node i at time t, respectively;

[0094] For nodes connected to energy storage, the constraints satisfied are:

[0095]

[0096] Among them, N E A collection of nodes connected to energy storage; is the state of charge of the energy storage at node i at time t; S i,E,max is the maximum value of the energy storage charge state at node i; and are the charging power and discharging power of the energy storage at node i at time t; P i,E,ch,max and P i,E,dis,max They represent the maximum charging and discharging power of the energy storage device connected to the i-node respectively; η ch and η dis are charging efficiency and discharging efficiency respectively; and are 0-1 decision variables for the charging and discharging states of the energy storage at node i at time t, respectively;

[0097] V2G reverse charging constraints:

[0098]

[0099] Among them, N V Represents a collection of V2G stations; is the actual power of the i-th V2G station at time t; P i,V2Gmax and P i,V2Gmin are the maximum and minimum output values ​​of the i-th V2G station respectively;

[0100] Load constraints:

[0101]

[0102] Among them, N bus,con and N bus,un They represent the set of curtailable loads and the set of non-curtailable loads respectively; and represents the active and reactive load shedding of node i at time t; and They represent the active power and reactive power requirements of node i at time t respectively; s t Indicates the switching of the load that cannot be cut at time t, s t =1 means load shedding, s t =0 means load access;

[0103] Repair team dispatch constraints:

[0104]

[0105] Where F represents the set of all faulty lines; T f The time required to repair a line, = t+T f The switch status of line (i, j) at the moment; r is the maximum number of faulty lines restored in the same time period;

[0106] Dynamically interconnected microgrid topology constraints:

[0107]

[0108] in, is the virtual output of the dominant node in region q at time t; is the virtual power on the line from node i to node j in region q at time t; is the virtual load of node i; A is a three-dimensional space composed of 0-1 variables, Indicates that node i is a node in island q at time t; N C is the sum of the number of DERs with droop control and the number of substations; is the power of the virtual branch from node j to node k in region q at time t; q is the node number of DER with droop control and substation; O is a matrix with 0-1 variables, the number of rows corresponds to the total number of fault time intervals, and the number of columns corresponds to the sum of the number of DER with droop control and the number of substations; It indicates that at time t, an island region exists with the q power source point as the dominant node;

[0109] The node power balance, node voltage and branch current constraints are expressed as:

[0110]

[0111] in, and They represent the active power and reactive power transmitted by branch (i, j) at time t respectively; r i,j and x i,j are the resistance and reactance of branch (i, j); is the square of the branch current modulus at time t; is the square of the voltage modulus of node i at time t; S max is the maximum capacity of the line transmission;

[0112] Microgrid frequency constraints:

[0113]

[0114] f i,min ≤f i,t ≤f i,max ,i∈N bus,con

[0115] (1-s t )f i,min -Ms t ≤f i,t ≤(1-s t )f i,max +Ms t , i∈N bus,un

[0116] Where M is a constant; is the switch state of line (i, j) at time t, Indicates that the switch is closed; f i,t is the frequency of node i at time t, f j,t is the frequency of node j at time t; N bus,con and N bus,un They represent the set of loads that can be cut and the set of loads that cannot be cut, respectively; f i,min , f i,max is the lower and upper limits of the node frequency; s t Indicates the switching of the load that cannot be cut at time t; s t =1 means load shedding, s t =0 means load is connected.

[0117] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction are implemented.

[0118] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned distribution network collaborative restoration method taking into account interconnected microgrids and network reconstruction.

[0119] Compared with the prior art, the present invention has at least the following beneficial effects:

[0120] A coordinated recovery method for distribution networks taking into account interconnected microgrids and network reconstruction. In the post-disaster stage, the dispatch center considers issues such as reverse charging of electric vehicles (EVs), coordinated distributed resources, dispatch of repair teams, flexible load management, network reconstruction, and formation of dynamic interconnected microgrids based on the perceived line fault conditions and information on distributed resources. The dispatch center optimizes the dispatch position of the repair team, the output of distributed resources, the power supply range and interconnection status of each microgrid, and performs rapid post-disaster load recovery and line repair. A dynamic interconnected microgrid formation method considering droop control is proposed. The static active-frequency and reactive-voltage characteristics of the droop-controlled distributed power sources are considered as analytical constraints. The number of post-disaster microgrid divisions is set as the decision variable to be optimized. An optimization model for dynamic microgrid division is proposed. It not only takes into account that the microgrid boundaries can change dynamically during the fault recovery process, but also takes into account that microgrids can be interconnected for power supply.

[0121] Furthermore, considering the travel characteristics of EVs, the road network and the coupling relationship between the road network and the power grid are first modeled. Considering that the road network can be modeled as a special network, the topological structure of the road network can be modeled based on graph theory knowledge; then the travel chain theory and real-time state model are used to describe the travel characteristics of EV users before the disaster, where the travel chain means that driven by the travel intention, the user starts from the starting point, passes through multiple target locations in chronological order, and finally reaches the end point to complete the entire travel process. By considering the travel characteristics of EV users, the distribution status and remaining power status of EVs in the road network during the pre-disaster scheduling period can be effectively simulated; finally, the travel intention coefficient is introduced to measure the impact of disasters on users' travel intentions, and the spatiotemporal distribution status and remaining power information of EVs are obtained by simulating the pre-disaster preparation model, providing information on EV reverse charging for the post-disaster recovery model.

[0122] Furthermore, considering that the post-disaster power supply restoration of the distribution network under the elastic category places more emphasis on giving play to the role of various elastic resources to restore as many important loads as possible. In addition, the power supply restoration cost is controlled on the basis of ensuring that the load power supply is restored as much as possible. Therefore, the optimization goal of the power supply restoration model of the present invention is divided into 5 parts. The first part is to minimize the weighted power loss load, which accounts for the highest proportion of the entire goal. The second part is to minimize the switch operation cost during the power supply restoration process. The third and fourth parts are to minimize the cost of mobilizing distributed power output and the cost of buying electricity from substations, respectively. The fifth part is the cost of using electric vehicles that participate in charging through V2G technology during the power restoration process.

[0123] Furthermore, considering the fault recovery constraints in the post-disaster stage, at this stage, steady-state optimization can be used to adjust the output of distributed power sources, control the disconnection of flexible controllable loads, V2G reverse charging and other operational measures. At the same time, network reconstruction methods and microgrid interconnection strategies with droop control can be considered to maximize the post-disaster power supply load of the new distribution system when some nodes lose power supply from the main grid, and minimize the power supply restoration cost while ensuring power supply.

[0124] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0125] In summary, the method of the present invention collaboratively utilizes different types of distributed resources to dynamically form multiple interconnected microgrids within the power supply restoration cycle, thereby achieving rapid recovery of loads during faults; at the same time, by considering the droop control of frequency and voltage constraints during the formation of microgrids after disasters, the load's demand for frequency and voltage safety can be guaranteed during the power supply restoration process, thereby avoiding power supply restoration based solely on load weights, and providing an important reference for power supply restoration of distribution networks under extreme events.

[0126] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0127] Figure 1 This is a flow chart of multi-source coordinated fault recovery after a distribution network disaster;

[0128] Figure 2 This is a schematic diagram of the improved PG&E 69 node distribution network;

[0129] Figure 3 This is a schematic diagram of the fault recovery process;

[0130] Figure 4 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0131] Figure 5 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0132] The present invention provides a distribution network collaborative recovery method taking into account interconnected microgrids and network reconstruction, which takes microgrid frequency and voltage control and the formation of dynamically interconnected microgrids into consideration in post-disaster multi-source collaborative fault recovery. While ensuring the feasibility of the recovery strategy in the actual distribution network, it maximizes the use of network reconstruction and dynamic interconnected microgrid methods to coordinate various distributed energy resources for fault recovery.

[0133] See also Figure 1The present invention provides a distribution network coordinated restoration method taking into account interconnected microgrids and network reconstruction, comprising the following steps:

[0134] S1. In the pre-disaster stage, the travel characteristics of electric vehicle (EV) users are considered, and EVs traveling on the traffic network are guided to take refuge in the nearest charging station, and participate in the post-disaster vehicle to grid (V2G) reverse power transmission;

[0135] S101, firstly, model the regional road network and power grid;

[0136] Based on graph theory, the topological structure of the transportation network is modeled as an undirected graph G represented by a set of road network nodes V and a set of edges E that connect the nodes, as follows:

[0137] G=(V,E)

[0138] The coupling relationship between the road network and the power grid is equivalent to whether there are inter-layer node connections between the road network and the power grid.

[0139] The set of coupling edges between the road network and the power grid is:

[0140] ξ={E αβ ∈V α ×V β}

[0141]

[0142] Among them, E αβ is an element in the set ξ; V α and V β They are the set of power grid nodes and the set of road network nodes respectively; is the i-th node in the power grid; is the jth node in the road network; is a 0-1 variable indicating whether the grid node i and the road network node j are coupled. There is a coupling relationship between the two networks. In terms of energy transmission, the V2G station on the road network can transmit electrical energy to the corresponding node of the power grid.

[0143] S102. Based on the road network and power grid model, the travel chain theory and real-time status model are used to describe the travel characteristics of EV users before the disaster, and the travel willingness coefficient is introduced to measure the impact of the disaster on the user's travel willingness, so as to obtain the spatiotemporal distribution status and remaining power information of EV.

[0144] The travel chain theory believes that people's travel is regarded as a series of travel chains, each chain represents the beginning and end of a travel activity; by considering the travel characteristics of EV users, the distribution status and remaining power status of EVs in the road network during the pre-disaster scheduling period can be effectively simulated, thereby providing information for reverse charging of V2G stations after the disaster.

[0145] The travel chain set expression is:

[0146] TC={L 0 ,L f ,P 0f ,T 0 ,T f ,t 0f ,T s}

[0147] Among them, TC is the set of state variables of a section of the trip chain, including the starting point L 0 , end point L f 、Driving path P 0f , departure time T 0 , arrival time T f , driving time t 0f and dwell time T s .

[0148] The driving time of the i-th road segment Arrival time and the departure time for the next leg of the journey It is expressed as follows:

[0149]

[0150]

[0151] Among them, d i is the length of the i-th road section; v is the average speed of EV.

[0152] The EV real-time status model is expressed as:

[0153] EVstatus={S rem ,W}

[0154] EVstatus is the set of real-time status information of each EV, including the EV remaining power set S rem And the driving state set W.

[0155] The EV travel intention model believes that extreme events will affect the travel behavior of EV users to a certain extent. In order to reflect the impact of disasters on users' travel intention, the travel proportion coefficient ρ is introduced to quantify the change in the total travel intention of the EV group.

[0156] N road=ρN EV

[0157] Among them, N road N is the number of EVs participating in the dispatch response; EV is the total number of EVs.

[0158] By simulating the pre-disaster preparation model, the number of EVs participating in V2G reverse charging after the disaster and their remaining power were obtained.

[0159] S2. In the post-disaster stage, based on the perceived line fault conditions and the EV information and distributed resource information obtained by simulation in step S1, issues such as V2G reverse charging, distributed resource coordination, dispatch of repair teams, flexible load management, network reconstruction, and formation of dynamic interconnected microgrids are considered to optimize the dispatch position of the repair team, the output of distributed resources, the power supply range and interconnection status of each microgrid, and perform rapid post-disaster load recovery and line repair;

[0160] S201. The optimization objectives of the post-disaster fault recovery model are divided into four parts: the first part is to minimize the weighted power loss load, which accounts for the highest proportion of the entire objective; the second part is to minimize the switch operation cost during the fault recovery process; the third and fourth parts are to minimize the cost of mobilizing distributed resource output and the cost of buying electricity from the substation respectively;

[0161]

[0162] Among them, N T Indicates the total number of fault time intervals; N bus Represents the total number of nodes in the network; Δt represents the length of each time interval during the fault period; represents the power loss of node i in time period t; i is the load weight of node i; It is a 0-1 optimization variable, representing the switch state of the branch starting from node i and ending at node j during the fault recovery period t. Indicates that the switch is closed; N line Indicates the total number of branches in the network; represents the output power of distributed power generation at node i during period t; It means that node i buys electricity from the substation during period t.

[0163] According to the primary and secondary relationship of the objective function, the objective functions of the four parts are combined through the weight coefficient:

[0164] minf=ω 1 f 1 +ω 2 f 2 +ω 3 f 3+c price f 4

[0165] Among them, ω 1 represents the load shedding cost; ω 2 represents the switching operation cost; ω 3 represents the DER power generation cost; c price It represents the cost of the distribution network purchasing electricity from the upper level.

[0166] S202: The constraints considered in the post-disaster recovery model are as follows:

[0167] 1) Various types of DER operation constraints

[0168] For nodes connected to droop control DER, first set their power reference values:

[0169]

[0170] Among them, N D is the set of nodes connected to the droop control DER; and is a continuous decision variable, which represents the reference value of the active and reactive outputs given by the droop control DER at node i at time t; P i,Dmin and P i,Dmax are the minimum and maximum active output of the droop-controlled DER at node i, respectively; Q i,Dmin and Q i,Dmax are the minimum and maximum output reactive powers of the DER, respectively. Once the reference value is set, the actual power output is calculated according to the droop control equation:

[0171]

[0172] in, and are the actual active and reactive outputs of the droop-controlled DER at node i at time t; f ref is the system rated frequency; is the reference voltage of node i; f i,t and U i,t are the frequency and voltage of node i at time t; K i,DP and K i,DQ are the active power-frequency and reactive power-voltage droop control coefficients of the droop control DER at node i, respectively.

[0173] For nodes connected to distributed renewable energy, the constraints to be met are:

[0174]

[0175] Among them, NR is the set of nodes connected to DREs; and are the active and reactive outputs of DREs at node i at time t; P i,Rmin and P i,Rmax are the minimum and maximum active outputs of DREs at node i at time t; Q i,Rmin and Q i,Rmax are the minimum reactive power output and maximum reactive power output of DREs at node i at time t, respectively.

[0176] For nodes connected to energy storage, the constraints satisfied are:

[0177]

[0178] Among them, N E A collection of nodes connected to energy storage; is the state of charge of the energy storage at node i at time t; S i,E,max is the maximum value of the energy storage charge state at node i; and are the charging power and discharging power of the energy storage at node i at time t; P i,E,ch,max and P i,E,dis,max They represent the maximum charging and discharging power of the energy storage device connected to the i-node respectively; η ch and η dis are charging efficiency and discharging efficiency respectively; and are 0-1 decision variables for the charging and discharging states of the energy storage at node i at time t, respectively;

[0179] 2) V2G reverse charging constraints

[0180]

[0181] Among them, N V Represents a collection of V2G stations; is the actual power of the i-th V2G station at time t; P i,V2Gmax and P i,V2Gmin are the maximum and minimum output values ​​of the i-th V2G station respectively.

[0182] 3) Load constraints

[0183]

[0184] Among them, N bus,con and N bus,un They represent the set of curtailable loads and the set of non-curtailable loads respectively; and represents the active and reactive load shedding of node i at time t; and They represent the active power and reactive power requirements of node i at time t respectively; s t Indicates the switching of the load that cannot be cut at time t, s t =1 means load shedding, s t =0 means load is connected.

[0185] 4) Repair team dispatch constraints

[0186]

[0187] Where F represents the set of all faulty lines; T f The time required to repair a line.

[0188] 5) Dynamically interconnected microgrid topology constraints

[0189]

[0190] Among them, O is a matrix whose elements are 0-1 variables, the number of rows corresponds to the total number of fault time intervals, and the number of columns corresponds to the sum of the number of DERs with droop control and the number of substations. Indicates that at time t, there is an island region dominated by the q power point; A is a three-dimensional space composed of 0-1 variables, Indicates that node i at time t is a node in island q. The above formula shows that each node belongs to a unique island, and the island may be an island with multiple droop control DERs formed by the interconnection of multiple microgrids. At the same time, the above formula ensures that there are no isolated nodes in each island and that each power dominant node is always located in its corresponding island partition.

[0191] In order to meet the radial topology constraints of the distribution network, the virtual power flow method is used to constrain:

[0192]

[0193] in, is the virtual output of the dominant node in region q at time t; is the virtual power on the line from node i to node j in region q at time t; is the virtual load of node i. The above formula ensures the connectivity of each island area.

[0194] 6) Power flow constraints

[0195] According to the DistFlow model, the node power balance, node voltage and branch current constraints are expressed as:

[0196]

[0197] in, and They represent the active power and reactive power transmitted by branch (i, j) at time t respectively; r i,j and x i,j are the resistance and reactance of branch (i, j); is the square of the branch current modulus at time t; is the square of the voltage modulus of node i at time t; S max is the maximum capacity of the line transmission; M is a large constant. The quadratic equality constraint causes the model to be non-convex, and a second-order cone relaxation is required:

[0198]

[0199] In addition, since the voltage term U is introduced in the lower constraint i,t ,and It is approximated by piecewise linearization.

[0200] 7) Microgrid frequency constraints

[0201] In order to ensure that the frequency in each microgrid is equal and the frequency between microgrids is independent, and to ensure that the frequency of the load during the recovery process is within its safe range, the frequency constraint is added as follows:

[0202]

[0203] f i,min ≤f i,t ≤f i,max ,i∈N bus,con

[0204] (1-s t )f i,min -Ms t ≤f i,t ≤(1-s t )f i,max +Ms t , i∈N bus,un

[0205] S3. Use the commercial solver CPLEX to solve the mixed-integer second-order cone programming (MISOCP) model established in step S2 to obtain the dynamic microgrid area division and interconnection status, line switch status, and output of each distributed resource at different times during the fault recovery period.

[0206] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "platform".

[0207] In yet another embodiment of the present invention, a distribution network collaborative restoration system taking into account interconnected microgrids and network reconstruction is provided. The system can be used to implement the above-mentioned distribution network collaborative restoration method taking into account interconnected microgrids and network reconstruction. Specifically, the distribution network collaborative restoration system taking into account interconnected microgrids and network reconstruction includes a construction module and a restoration module.

[0208] Among them, the construction module, in the post-disaster stage, optimizes the mixed integer second-order cone model including the dispatching position of the repair team, the output of distributed resources, the power supply range of each microgrid and the interconnection status according to the vehicle grid-connected reverse charging parameters, distributed resource coordination parameters, dispatching parameters of the repair team, flexible load management parameters, network reconstruction and dynamic interconnected microgrid formation parameters based on the perceived line fault conditions and the simulated pre-disaster electric vehicle information and distributed resource information;

[0209] The specific information of electric vehicles obtained in the pre-disaster stage by simulation is as follows:

[0210] In the pre-disaster stage, based on graph theory, the topological structure of the transportation network is modeled as an undirected graph G represented by a set of road network nodes V and a set of edges E that represent the connectivity between nodes. The coupling relationship between the road network and the power grid is equivalent to whether there are inter-layer node connections between the road network and the power grid. The road network and power grid models in the region are modeled.

[0211] Based on the regional road network and power grid model, the travel chain theory and real-time status model are used to describe the travel characteristics of electric vehicle users before the disaster. The simulation guides electric vehicles traveling on the traffic network to enter the nearest charging station for refuge and participate in the vehicle grid-connected reverse power transmission in the post-disaster stage. The travel willingness coefficient is introduced to measure the impact of disasters on users' travel willingness, and the spatiotemporal distribution status and remaining power information of electric vehicles are obtained.

[0212] The set of coupling edges between the road network and the power grid is:

[0213] ξ={E αβ ∈V α ×V β}

[0214]

[0215] Among them, E αβ is an element in the set ξ; V αand V β They are the set of power grid nodes and the set of road network nodes respectively; is the i-th node in the power grid; is the jth node in the road network; is a 0-1 variable indicating whether the grid node i and the road network node j are coupled. There is a coupling relationship between the road network and the power grid.

[0216] The travel chain set expression is:

[0217] TC={L 0 ,L f ,P 0f ,T 0 ,T f ,t 0f ,T s}

[0218] Among them, TC is the set of state variables of a section of the trip chain, including the starting point L 0 , end point L f 、Driving path P 0f , departure time T 0 , arrival time T f , driving time t 0f and dwell time T s ;

[0219] The driving time of the i-th road segment Arrival time and the departure time for the next leg of the journey It is expressed as follows:

[0220]

[0221] Among them, d i is the length of the i-th road section; v is the average speed of EV.

[0222] The real-time state model of electric vehicles is expressed as:

[0223] EVstatus={S rem ,W}

[0224] Among them, EVstatus is the collection of real-time status information of each electric vehicle, including the remaining power set S of the electric vehicle rem And the driving state set W.

[0225] The number of electric vehicles participating in the dispatch response N road for:

[0226] N road =ρN EV

[0227] Among them, ρ is the travel ratio coefficient; N EV is the total number of electric vehicles.

[0228] The objective function of the mixed integer second-order cone model is:

[0229] min f=ω 1 f 1 +ω 2 f 2 +ω 3 f 3 +c price f 4

[0230] Among them, ω 1 is the load shedding cost; 2 is the switch operation cost; 3 is the DER power generation cost; c price is the cost of the distribution network purchasing electricity from the upper level, f 1 , f 2 , f 3 , f 4 They are to minimize the weighted power loss load, minimize the switch operation cost during fault recovery, minimize the cost of mobilizing distributed resource output and the cost of buying electricity from the substation;

[0231] Minimize the weighted power loss load, minimize the switch operation cost during fault recovery, minimize the cost of mobilizing distributed resource output and the cost of buying electricity from substations 1 , f 2 , f 3 , f 4 They are:

[0232]

[0233] Among them, N T Indicates the total number of fault time intervals; N bus Represents the total number of nodes in the network; Δt represents the length of each time interval during the fault period; represents the power loss of node i in time period t; i is the load weight of node i; is a 0-1 optimization variable, representing the switch state of the branch starting from node i and ending at node j during the fault recovery period t. Indicates that the switch is closed; N line Indicates the total number of branches in the network; represents the output power of distributed power generation at node i during period t; It means that node i buys power from the substation during period t;

[0234] The constraints of the mixed integer second-order cone model are as follows:

[0235] For nodes connected to distributed renewable energy, the constraints to be met are:

[0236]

[0237] Among them, N R is the set of nodes connected to DREs; and are the active and reactive outputs of DREs at node i at time t; P i,Rmin and P i,Rmax are the minimum and maximum active outputs of DREs at node i at time t; Q i,Rmin and Q i,Rmax are the minimum reactive power output and maximum reactive power output of DREs at node i at time t, respectively;

[0238] For nodes connected to energy storage, the constraints satisfied are:

[0239]

[0240]

[0241] Among them, N E A collection of nodes connected to energy storage; is the state of charge of the energy storage at node i at time t; S i,E,max is the maximum value of the energy storage charge state at node i; and are the charging power and discharging power of the energy storage at node i at time t; P i,E,ch,max and P i,E,dis,max They represent the maximum charging and discharging power of the energy storage device connected to the i-node respectively; η ch and η dis are charging efficiency and discharging efficiency respectively; and are 0-1 decision variables for the charging and discharging states of the energy storage at node i at time t, respectively;

[0242] V2G reverse charging constraints:

[0243]

[0244] Among them, N V Represents a collection of V2G stations; is the actual power of the i-th V2G station at time t; P i,V2Gmax and P i,V2Gmin are the maximum and minimum output values ​​of the i-th V2G station respectively;

[0245] Load constraints:

[0246]

[0247] Among them, N bus,con and N bus,un They represent the set of curtailable loads and the set of non-curtailable loads respectively; and represents the active and reactive load shedding of node i at time t; and They represent the active power and reactive power requirements of node i at time t respectively; s t Indicates the switching of the load that cannot be cut at time t, s t =1 means load shedding, s t =0 means load access;

[0248] Repair team dispatch constraints:

[0249]

[0250] Where F represents the set of all faulty lines; T f The time required to repair a line, = t+T f The switch status of line (i, j) at the moment; r is the maximum number of faulty lines restored in the same time period;

[0251] Dynamically interconnected microgrid topology constraints:

[0252]

[0253] in, is the virtual output of the dominant node in region q at time t; is the virtual power on the line from node i to node j in region q at time t; is the virtual load of node i; A is a three-dimensional space composed of 0-1 variables, Indicates that node i is a node in island q at time t; N C is the sum of the number of DERs with droop control and the number of substations; is the power of the virtual branch from node j to node k in region q at time t; q is the node number of DER with droop control and substation; O is a matrix with 0-1 variables, the number of rows corresponds to the total number of fault time intervals, and the number of columns corresponds to the sum of the number of DER with droop control and the number of substations; It indicates that at time t, an island region exists with the q power source point as the dominant node;

[0254] The node power balance, node voltage and branch current constraints are expressed as:

[0255]

[0256] in, and They represent the active power and reactive power transmitted by branch (i, j) at time t respectively; r i,j and x i,j are the resistance and reactance of branch (i, j); is the square of the branch current modulus at time t; is the square of the voltage modulus of node i at time t; S max is the maximum capacity of the line transmission;

[0257] Microgrid frequency constraints:

[0258]

[0259] f i,min ≤f i,t ≤f i,max ,i∈N bus,con

[0260] (1-s t )f i,min -Ms t ≤f i,t ≤(1-s t )f i,max +Ms t , i∈N bus,un

[0261] Where M is a constant; is the switch state of line (i, j) at time t, Indicates that the switch is closed; f i,t is the frequency of node i at time t, f j,t is the frequency of node j at time t; N bus,con and N bus,un They represent the set of loads that can be cut and the set of loads that cannot be cut, respectively; f i,min , f i,max is the lower and upper limits of the node frequency; s t Indicates the switching of the load that cannot be cut at time t; s t =1 means load shedding, s t =0 means load access

[0262] The recovery module solves the established mixed integer second-order cone model to obtain the dynamic microgrid regional division and interconnection status, line switch status and the output of each distributed resource at different times during the fault recovery period. Based on the dynamic microgrid regional division and interconnection status, as well as the line switch status at different times during the fault recovery period, post-disaster load rapid recovery and line repair are carried out according to the output of each distributed resource.

[0263] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the distribution network collaborative restoration method taking into account the interconnected microgrid and network reconstruction, including:

[0264] In the post-disaster stage, according to the perceived line fault conditions and the simulated electric vehicle information and distributed resource information in the pre-disaster stage, the vehicle grid-connected reverse charging parameters, distributed resource coordination parameters, dispatching parameters of the repair team, flexible load management parameters, network reconstruction parameters and dynamic interconnected microgrid formation parameters are optimized to obtain a mixed integer second-order cone model including the dispatching position of the repair team, the output of distributed resources, the power supply range and interconnection status of each microgrid; the established mixed integer second-order cone model is solved to obtain the dynamic microgrid area division and interconnection status, line switch status and the output of each distributed resource at different times within the fault recovery period; based on the obtained dynamic microgrid area division and interconnection status at different times within the fault recovery period, as well as the line switch status, post-disaster load rapid recovery and line repair are carried out according to the output of each distributed resource.

[0265] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0266] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0267] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0268] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for coordinated restoration of a distribution network taking into account interconnected microgrids and network reconstruction in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by a processor as follows:

[0269] In the post-disaster stage, according to the perceived line fault conditions and the simulated electric vehicle information and distributed resource information in the pre-disaster stage, the vehicle grid-connected reverse charging parameters, distributed resource coordination parameters, dispatching parameters of the repair team, flexible load management parameters, network reconstruction parameters and dynamic interconnected microgrid formation parameters are optimized to obtain a mixed integer second-order cone model including the dispatching position of the repair team, the output of distributed resources, the power supply range and interconnection status of each microgrid; the established mixed integer second-order cone model is solved to obtain the dynamic microgrid area division and interconnection status, line switch status and the output of each distributed resource at different times within the fault recovery period; based on the obtained dynamic microgrid area division and interconnection status at different times within the fault recovery period, as well as the line switch status, post-disaster load rapid recovery and line repair are carried out according to the output of each distributed resource.

[0270] See also Figure 4 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the fluid composition in the reservoir transformation wellbore in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0271] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 4 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0272] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0273] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0274] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0275] Any reference to memory, database or other media used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0276] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0277] See also Figure 5 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0278] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0279] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0280] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0281] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0282] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0283] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0284] Considering that the fault duration is 4 hours, it is assumed that the EV user travel willingness coefficient is ρ = 0.8. Each V2G station has 5 charging piles, and the charging and discharging power of the charging piles is 6kW. The power generation costs of gas turbines, wind turbines, and photovoltaics in distributed resources are set to 0.71, 0.32, and 0.21 yuan / (kW·h), respectively. The compensation cost for load reduction is 0.8 yuan / (kW·h).

[0285] Appropriate modifications are made to the PG&E 69-node distribution system, adding multiple types of distributed resources, flexible loads, and V2G sites, and using this as an example. The schematic diagram is shown in Figure 2 The parameter information of the nodes is shown in Table 1, the output of distributed resources is shown in Table 2, the data of energy storage and V2G charging obtained through simulation is shown in Table 3, and the time-of-use electricity price of the power grid is shown in Table 4.

[0286] Table 1 PG&E 69 node parameters

[0287]

[0288]

[0289] Table 2 Output parameters of multiple types of distributed resources

[0290]

[0291] Table 3 Energy storage and V2G site parameters

[0292]

[0293] Table 4 Grid Time-of-Use Electricity Price

[0294]

[0295] In order to analyze the impact of introducing dynamic interconnected microgrid technology and considering the collaborative support of different resources for power supply on the post-disaster fault recovery of the distribution network, three different schemes are set for the fault scenarios considered in the present invention to solve the fault recovery problem, and the results are compared and analyzed:

[0296] 1) Solution 1. The dynamic interconnected microgrid technology and network reconstruction method described in the present invention are used to optimize the repair order of the emergency repair team and to coordinate and cooperate with multiple resources to perform fault recovery analysis.

[0297] 2) Scheme 2. Only the independent microgrid division and network reconstruction methods are considered, the repair order of the repair team is optimized, and multiple resources are used to coordinate and cooperate for fault recovery analysis.

[0298] 3) Solution 3: Using the dynamic interconnected microgrid technology and network reconstruction method described in the present invention, the repair order of the repair team is randomly determined, and multiple resources are used to coordinate and cooperate to perform fault recovery analysis.

[0299] The fault recovery results obtained using three different schemes are shown in Table 5. The dynamic recovery process of the distribution network after a disaster is shown in Table 5. Figure 3 shown.

[0300] Table 5 Fault recovery results obtained by three different scheme strategies

[0301]

[0302] like Figure 3 As shown in (a), the line fault leads to the separation of five isolated microgrids, each equipped with a micro turbine (MT) with frequency and voltage stabilization functions. After the first fault period, three faulty lines have been repaired according to the optimization model. Although there are still unrepaired faulty lines, each load node can obtain power from distributed energy resources or substations, and there is no load shedding. By comparison Figure 3 (c) and Figure 3 (d) It is found that by using tie switches to reconfigure the network, each load can get the required power supply even if there is a faulty line. Therefore, considering the cost of repairing the faulty lines, no further repairs were made to these lines. In addition, comparison Figure 3 (a) and Figure 3 (b) It is found that the interconnection between MT3 and MT4 forms an interconnected microgrid, which promotes the utilization of distributed energy.

[0303] Compared with other strategies, the proposed method achieves the minimum load shedding and the lowest cost during the entire fault period. This shows that the interconnection of microgrids and the optimization of the repair sequence of the emergency repair team can accelerate the load recovery speed and save costs. In terms of the utilization of distributed energy, the present invention pays more attention to the absorption of DERs, thereby enhancing its penetration. The results show that a unified mathematical model considering network reconstruction and the formation of dynamically interconnected microgrids can effectively reduce load shedding during faults and improve the resilience of the distribution network under disasters.

[0304] In summary, the present invention provides a method and system for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction, which achieves rapid recovery of loads during faults while ensuring the quality requirements of loads for frequency and voltage, and provides an important reference for power supply restoration of distribution networks under extreme events.

Claims

1. A distribution network coordinated restoration method taking into account interconnected microgrids and network reconstruction, characterized in that: The following steps are involved: In the post-disaster stage, based on the perceived line fault conditions and the simulated electric vehicle information and distributed resource information in the pre-disaster stage, the vehicle grid-connected reverse charging parameters, distributed resource coordination parameters, repair team dispatch parameters, flexible load management parameters, network reconstruction parameters, and dynamic interconnected microgrid formation parameters are optimized to obtain a mixed integer second-order cone model that includes the dispatch position of the repair team, the output of distributed resources, the power supply range of each microgrid, and the interconnection status. The established mixed integer second-order cone model is solved to obtain the dynamic microgrid regional division and interconnection status, line switch status and the output of each distributed resource at different times during the fault recovery period. Based on the dynamic microgrid regional division and interconnection status, as well as the line switch status at different times during the fault recovery period, post-disaster load rapid recovery and line repair are carried out according to the output of each distributed resource.

2. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 1, characterized in that: The specific information of electric vehicles obtained in the pre-disaster stage by simulation is as follows: In the pre-disaster stage, based on graph theory, the topological structure of the transportation network is modeled as an undirected graph G represented by a set of road network nodes V and a set of edges E that represent the connectivity between nodes. The coupling relationship between the road network and the power grid is equivalent to whether there are inter-layer node connections between the road network and the power grid. The road network and power grid models in the region are modeled. Based on the regional road network and power grid model, the travel chain theory and real-time status model are used to describe the travel characteristics of electric vehicle users before the disaster. The simulation guides electric vehicles traveling on the traffic network to enter the nearest charging station for refuge and participate in the vehicle grid-connected reverse power transmission in the post-disaster stage. The travel willingness coefficient is introduced to measure the impact of disasters on users' travel willingness, and the spatiotemporal distribution status and remaining power information of electric vehicles are obtained.

3. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 2, characterized in that: The set of coupling edges between the road network and the power grid is: ξ={E αβ ∈V α ×V β } Among them, E αβ is an element in the set ξ; V α and V β They are the set of power grid nodes and the set of road network nodes respectively; is the i-th node in the power grid; is the jth node in the road network; is a 0-1 variable indicating whether the grid node i and the road network node j are coupled. There is a coupling relationship between the road network and the power grid.

4. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 2, characterized in that: The travel chain set expression is: TC={L0,L f ,P 0f ,T0,T f ,t 0f ,T s } Among them, TC is the state variable set of a section of the trip chain, including the starting point L0, the end point L f 、Driving path P 0f , departure time T0, arrival time T f , driving time t 0f and dwell time T s ; The driving time of the i-th road segment Arrival time and the departure time for the next leg of the journey It is expressed as follows: Among them, d i is the length of the i-th road section; v is the average speed of EV.

5. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 2, characterized in that: The real-time state model of electric vehicles is expressed as: EVstatus={S rem ,W} in, EVstatus is a collection of real-time status information of each electric vehicle, including the remaining power set S of the electric vehicle rem And the driving state set W.

6. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 5, characterized in that: The number of electric vehicles participating in the dispatch response N road for: N road =ρN EV Among them, ρ is the travel ratio coefficient; N EV is the total number of electric vehicles.

7. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 1, characterized in that: The objective function of the mixed integer second-order cone model is: min f=ω1f1+ω2f2+ω3f3+c price f4 Where ω1 is the load shedding cost; ω2 is the switch operation cost; ω3 is the DER power generation cost; c price is the cost of the distribution network purchasing electricity from the upper level, f1, f2, f3, and f4 are respectively minimizing the weighted power loss load, minimizing the switch operation cost during fault recovery, minimizing the cost of mobilizing distributed resource output, and minimizing the cost of purchasing electricity from the substation.

8. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 7, characterized in that: Minimize the weighted power loss load, minimize the switch operation cost during fault recovery, minimize the cost of mobilizing distributed resource output and the cost of buying electricity from the substation. f1, f2, f3, and f4 are: Among them, N T Indicates the total number of fault time intervals; N bus Represents the total number of nodes in the network; Δt represents the length of each time interval during the fault period; represents the power loss of node i in time period t; i is the load weight of node i; is a 0-1 optimization variable, representing the switch state of the branch starting from node i and ending at node j during the fault recovery period t. Indicates that the switch is closed; N line Indicates the total number of branches in the network; represents the output power of distributed power generation at node i during period t; It means that node i buys electricity from the substation during period t.

9. The method for coordinated restoration of distribution networks taking into account interconnected microgrids and network reconstruction according to claim 7, characterized in that: The constraints of the mixed integer second-order cone model are as follows: For nodes connected to distributed renewable energy, the constraints to be met are: Among them, N R is the set of nodes connected to DREs; and are the active and reactive outputs of DREs at node i at time t; P i,Rmin and P i,Rmax are the minimum and maximum active outputs of DREs at node i at time t; Q i,Rmin and Q i,Rmax are the minimum reactive power output and maximum reactive power output of DREs at node i at time t, respectively; For nodes connected to energy storage, the constraints satisfied are: Among them, N E A collection of nodes connected to energy storage; is the state of charge of the energy storage at node i at time t; S i,E,max is the maximum value of the energy storage charge state at node i; and are the charging power and discharging power of the energy storage at node i at time t; P i,E,ch,max and P i,E,dis,max They represent the maximum charging and discharging power of the energy storage device connected to the i-node respectively; η ch and η dis are charging efficiency and discharging efficiency respectively; and are 0-1 decision variables for the charging and discharging states of the energy storage at node i at time t, respectively; V2G reverse charging constraints: Among them, N V Represents a collection of V2G stations; is the actual power of the i-th V2G station at time t; P i,V2Gmax and P i,V2Gmin are the maximum and minimum output values ​​of the i-th V2G station respectively; Load constraints: Among them, N bus,con and N bus,un They represent the set of curtailable loads and the set of non-curtailable loads respectively; and represents the active and reactive load shedding of node i at time t; and They represent the active power and reactive power requirements of node i at time t respectively; s t Indicates the switching of the load that cannot be cut at time t, s t =1 means load shedding, s t =0 means load access; Repair team dispatch constraints: Where F represents the set of all faulty lines; T f The time required to repair a line, = t+T f The switch status of line (i, j) at the moment; r is the maximum number of faulty lines restored in the same time period; Dynamically interconnected microgrid topology constraints: in, is the virtual output of the dominant node in region q at time t; is the virtual power on the line from node i to node j in region q at time t; is the virtual load of node i; A is a three-dimensional space composed of 0-1 variables, Indicates that node i is a node in island q at time t; N C is the sum of the number of DERs with droop control and the number of substations; is the power of the virtual branch from node j to node k in region q at time t; q is the node number of DER with droop control and substation; O is a matrix with 0-1 variables, the number of rows corresponds to the total number of fault time intervals, and the number of columns corresponds to the sum of the number of DER with droop control and the number of substations; It indicates that at time t, an island region exists with the q power source point as the dominant node; The node power balance, node voltage and branch current constraints are expressed as: in, and They represent the active power and reactive power transmitted by branch (i, j) at time t respectively; r i,j and x i,j are the resistance and reactance of branch (i, j); is the square of the branch current modulus at time t; is the square of the voltage modulus of node i at time t; S max is the maximum capacity of the line transmission; Microgrid frequency constraints: f i,min ≤f i,t ≤f i,max ,i∈N bus,con (1-s t )f i,min -Ms t ≤f i,t ≤(1-s t )f i,max +Ms t ,i∈N bus,un Where M is a constant; is the switch state of line (i, j) at time t, Indicates that the switch is closed; f i,t is the frequency of node i at time t, f j,t is the frequency of node j at time t; N bus,con and N bus,un They represent the set of loads that can be cut and the set of loads that cannot be cut, respectively; f i,min , f i,max is the lower and upper limits of the node frequency; s t Indicates the switching of the load that cannot be cut at time t; s t =1 means load shedding, s t =0 means load is connected.

10. A distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction, characterized in that: include: The construction module, in the post-disaster stage, optimizes the mixed integer second-order cone model including the dispatching position of the repair team, the output of distributed resources, the power supply range of each microgrid and the interconnection status, based on the perceived line fault conditions and the simulated electric vehicle information and distributed resource information in the pre-disaster stage, the vehicle grid-connected reverse charging parameters, the distributed resource coordination parameters, the dispatching parameters of the repair team, the flexible load management parameters, the network reconstruction parameters and the dynamic interconnected microgrid formation parameters; The recovery module solves the established mixed integer second-order cone model to obtain the dynamic microgrid regional division and interconnection status, line switch status and the output of each distributed resource at different times during the fault recovery period. Based on the dynamic microgrid regional division and interconnection status, as well as the line switch status at different times during the fault recovery period, post-disaster load rapid recovery and line repair are carried out according to the output of each distributed resource.

11. The distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction according to claim 10, characterized in that: The specific information of electric vehicles obtained in the pre-disaster stage by simulation is as follows: In the pre-disaster stage, based on graph theory, the topological structure of the transportation network is modeled as an undirected graph G represented by a set of road network nodes V and a set of edges E that represent the connectivity between nodes. The coupling relationship between the road network and the power grid is equivalent to whether there are inter-layer node connections between the road network and the power grid. The road network and power grid models in the region are modeled. Based on the regional road network and power grid model, the travel chain theory and real-time status model are used to describe the travel characteristics of electric vehicle users before the disaster. The simulation guides electric vehicles traveling on the traffic network to enter the nearest charging station for refuge and participate in the vehicle grid-connected reverse power transmission in the post-disaster stage. The travel willingness coefficient is introduced to measure the impact of disasters on users' travel willingness, and the spatiotemporal distribution status and remaining power information of electric vehicles are obtained.

12. The distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction according to claim 11, characterized in that: The set of coupling edges between the road network and the power grid is: ξ={E αβ ∈V α ×V β } Among them, E αβ is an element in the set ξ; V α and V β They are the set of power grid nodes and the set of road network nodes respectively; is the i-th node in the power grid; is the jth node in the road network; is a 0-1 variable indicating whether the grid node i and the road network node j are coupled. There is a coupling relationship between the road network and the power grid; The travel chain set expression is: TC={L0,L f ,P 0f ,T0,T f ,t 0f ,T s } Among them, TC is the state variable set of a section of the trip chain, including the starting point L0, the end point L f 、Driving path P 0f , departure time T0, arrival time T f , driving time t 0f and dwell time T s ; The driving time of the i-th road segment Arrival time and the departure time for the next leg of the journey It is expressed as follows: Among them, d i is the length of the i-th road section; v is the average speed of the EV; The real-time state model of electric vehicles is expressed as: EVstatus={S rem ,W} Among them, EVstatus is the collection of real-time status information of each electric vehicle, including the remaining power set S of the electric vehicle rem And the driving state set W.

13. The distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction according to claim 12, characterized in that: The number of electric vehicles participating in the dispatch response N road for: N road =ρN EV Among them, ρ is the travel ratio coefficient; N EV is the total number of electric vehicles.

14. The distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction according to claim 10, characterized in that: The objective function of the mixed integer second-order cone model is: minf=ω1f1+ω2f2+ω3f3+c price f4 Where ω1 is the load shedding cost; ω2 is the switch operation cost; ω3 is the DER power generation cost; c price is the cost of the distribution network purchasing electricity from the upper level, f1, f2, f3, and f4 are respectively minimizing the weighted power loss load, minimizing the switch operation cost during fault recovery, minimizing the cost of mobilizing distributed resource output, and minimizing the cost of purchasing electricity from the substation.

15. The distribution network coordinated restoration system taking into account interconnected microgrids and network reconstruction according to claim 14, characterized in that: Minimize the weighted power loss load, minimize the switch operation cost during fault recovery, minimize the cost of mobilizing distributed resource output and the cost of buying electricity from the substation. f1, f2, f3, and f4 are: Among them, N T Indicates the total number of fault time intervals; N bus Represents the total number of nodes in the network; Δt represents the length of each time interval during the fault period; represents the power loss of node i in time period t; i is the load weight of node i; is a 0-1 optimization variable, representing the switch state of the branch starting from node i and ending at node j during the fault recovery period t. Indicates that the switch is closed; N line Indicates the total number of branches in the network; represents the output power of distributed power generation at node i during period t; It means that node i buys power from the substation during period t; The constraints of the mixed integer second-order cone model are as follows: For nodes connected to distributed renewable energy, the constraints to be met are: Among them, N R is the set of nodes connected to DREs; and are the active and reactive outputs of DREs at node i at time t; P i,Rmin and P i,Rmax are the minimum and maximum active outputs of DREs at node i at time t; Q i,Rmin and Q i,Rmax are the minimum reactive power output and maximum reactive power output of DREs at node i at time t, respectively; For nodes connected to energy storage, the constraints satisfied are: Among them, N E A collection of nodes connected to energy storage; is the state of charge of the energy storage at node i at time t; S i,E,max is the maximum value of the energy storage charge state at node i; and are the charging power and discharging power of the energy storage at node i at time t; P i,E,ch,max and P i,E,dis,max They represent the maximum charging and discharging power of the energy storage device connected to the i-node respectively; η ch and η dis are charging efficiency and discharging efficiency respectively; and are 0-1 decision variables for the charging and discharging states of the energy storage at node i at time t, respectively; V2G reverse charging constraints: Among them, N V Represents a collection of V2G stations; is the actual power of the i-th V2G station at time t; P i,V2Gmax and P i,V2Gmin are the maximum and minimum output values ​​of the i-th V2G station respectively; Load constraints: Among them, N bus,con and N bus,un They represent the set of curtailable loads and the set of non-curtailable loads respectively; and represents the active and reactive load shedding of node i at time t; and They represent the active power and reactive power requirements of node i at time t respectively; s t Indicates the switching of the load that cannot be cut at time t, s t =1 means load shedding, s t =0 means load access; Repair team dispatch constraints: Where F represents the set of all faulty lines; T f The time required to repair a line, = t+T f The switch status of line (i, j) at the moment; r is the maximum number of faulty lines restored in the same time period; Dynamically interconnected microgrid topology constraints: in, is the virtual output of the dominant node in region q at time t; is the virtual power on the line from node i to node j in region q at time t; is the virtual load of node i; A is a three-dimensional space composed of 0-1 variables, Indicates that node i is a node in island q at time t; N C is the sum of the number of DERs with droop control and the number of substations; is the power of the virtual branch from node j to node k in region q at time t; q is the node number of DER with droop control and substation; O is a matrix with 0-1 variables, the number of rows corresponds to the total number of fault time intervals, and the number of columns corresponds to the sum of the number of DER with droop control and the number of substations; It indicates that at time t, an island region exists with the q power source point as the dominant node; The node power balance, node voltage and branch current constraints are expressed as: in, and They represent the active power and reactive power transmitted by branch (i, j) at time t respectively; r i,j and x i,j are the resistance and reactance of branch (i, j); is the square of the branch current modulus at time t; is the square of the voltage modulus of node i at time t; S max is the maximum capacity of the line transmission; Microgrid frequency constraints: f i,min ≤f i,t ≤f i,max ,i∈N bus,con (1-s t )f i,min -Ms t ≤f i,t ≤(1-s t )f i,max +Ms t ,i∈N bus,un Where M is a constant; is the switch state of line (i, j) at time t, Indicates that the switch is closed; f i,t is the frequency of node i at time t, f j,t is the frequency of node j at time t; N bus,con and N bus,un They represent the set of loads that can be cut and the set of loads that cannot be cut, respectively; f i,min , f i,max is the lower and upper limits of the node frequency; s t Indicates the switching of the load that cannot be cut at time t; s t =1 means load shedding, s t =0 means load is connected.

16. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 9.

17. A computing device, characterized in that include: One or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method described in any one of claims 1 to 9.

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