Distribution network resilience enhancement method and device based on charging load transfer and V2G technology

Abstract

A method and device for enhancing the resilience of a distribution network based on charging load transfer and V2G technology, the method comprising: constructing a transportation system operation model with the goal of minimizing the operating cost of the transportation system; establishing a charging station operation model that includes coupled operation constraints between the charging station and the transportation system and between the charging station and the distribution system; constructing an optimized operation model for the distribution system with the goal of minimizing the operating cost of the distribution system; establishing an optimized operation model for the distribution system that takes line disconnection into account under accidents; constructing a distribution system-transportation system collaborative optimized operation model to respond to accidents based on an electric vehicle emergency response strategy based on charging load transfer and V2G technology, and solving a distribution network resilience enhancement operation plan. The present invention utilizes the spatial transfer characteristics of the charging load and the mobile energy storage characteristics of electric vehicles under accidents to ensure the safe and stable operation of the distribution network and reduce the load shedding losses of the distribution system.

Description

Technical Field

[0001] The present invention relates to the field of improving the resilience of power systems, and in particular to a method and device for enhancing the resilience of distribution networks based on charging load transfer and V2G technology. Background Art

[0002] In the context of coupled distribution and transportation systems, vehicle-to-grid (V2G) technology can significantly enhance the resilience of distribution networks. In the event of a natural disaster or power line failure, electric vehicles with sufficient power can serve as mobile, dispatchable energy storage resources and may be willing to participate in emergency support through their V2G capabilities. Furthermore, rerouting and reselecting charging stations for underpowered electric vehicles can reduce the mismatch between power supply and charging demand, thereby improving the resilience of distribution networks. Currently, research on leveraging the shifting characteristics of charging loads and V2G technology is still in its early stages, and scholars have not yet explored the potential of using electric vehicle charging rescheduling and V2G-based emergency support to enhance the resilience of coupled transportation and power networks. Summary of the Invention

[0003] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a method and device for enhancing the resilience of a distribution network based on charging load transfer and V2G technology, which utilizes the spatial transfer characteristics of the charging load and the mobile energy storage characteristics of electric vehicles in the event of an accident to ensure the safe and stable operation of the distribution network and reduce the economic losses of the distribution system.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology, comprising:

[0005] With the goal of minimizing the operating costs of the transportation system, a transportation system operation model is constructed;

[0006] Establish a charging station operation model that includes the coupled operation constraints between the charging station and the transportation system, as well as the coupled operation constraints between the charging station and the power distribution system;

[0007] With the goal of minimizing the operating cost of the distribution system, an optimized operation model of the distribution system is constructed;

[0008] Establish an optimized operation model for the distribution system under unexpected events and taking line disconnection into account;

[0009] Based on the electric vehicle emergency response strategy of charging load transfer and V2G technology, with the goal of minimizing the operating costs of the transportation system and the distribution system, a distribution system-transportation system collaborative optimization operation model for responding to accidents is constructed, and a distribution network resilience enhancement operation plan is solved.

[0010] The objective function of the traffic system operation model is:

[0011] ;

[0012] ;

[0013] Where, represents the cost of operating the transportation system; 、 and OD pairs Traffic flow demands of Class A, Class B and Class C vehicles;

[0014] The constraints of the traffic system operation model are:

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] Where, Indicates OD pair No. Class A vehicle traffic flow on the paths; Indicates OD pair No. Traffic flows of Class B and Class C vehicles on the paths; Indicates road Traffic flow; Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient of Indicates road The actual travel time; Indicates road free passage time; Indicates road capacity; Indicates OD pair No. The travel cost of each path; represents the time economic parameter; Indicates road road congestion charges.

[0023] The coupled operation constraints of the charging station and the traffic system are:

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] Where, Indicates OD pair No. Choose a charging station on the route charging traffic flow; Indicates OD pair No. The traffic flow of Class A vehicles on the path, Class A vehicles are electric vehicles with insufficient power; Indicates OD pair No. Is the traffic flow on the path at the charging station? 0-1 variable for charging; Indicates charging station Total traffic volume; Indicates the charging power per unit traffic flow; Indicates charging station Charging power; Indicates the power upper limit of the charging unit; Indicates charging station The number of charging units in the

[0030] The coupled operation constraints of the charging station and the power distribution system are:

[0031] ;

[0032] Where, Represents a power distribution system node Charging power; Represents a connection node Charging stations collection.

[0033] The objective function of the distribution system optimization operation model is:

[0034] ;

[0035] ;

[0036] Where, represents the operating cost of the power distribution system; represents the price of electricity; Representation node Active power output of the substation;

[0037] The constraints of the distribution system optimization operation model are:

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] Where, Indicates line A 0-1 variable indicating whether to connect; and denote the number of nodes and charging stations respectively; Indicates line Virtual trends; represents a constant; Indicates that it is located at the node The active power output of the substation; Representation node Conventional active load; Representation node Charging load; Indicates line Active power transmission; Indicates that the starting point is a node The line collection of Indicates that the end is a node The line collection of Indicates that it is located at the node The reactive power output of the substation; Representation node Conventional reactive load; Indicates line Reactive power transmission; Indicates line Effective value of the voltage at the first terminal; Indicates line Effective value of terminal voltage; and Respectively represent lines resistance and reactance; Representation node voltage; Representation node The upper voltage limit; Representation node The lower voltage limit; Indicates that it is located at the node The capacity of the substation; Indicates line capacity.

[0048] The objective function of the distribution system optimization operation model taking line disconnection into account under the accident is:

[0049] ;

[0050] ;

[0051] Where, Represents the operating cost of the distribution system under unexpected events; represents the load shedding cost coefficient; Representation node Load shedding capacity; represents the price of electricity; Indicates that the node is located under the accident The active power output of the substation;

[0052] The constraints of the distribution system optimization operation model taking line disconnection into account under the accident are:

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] Where, Indicates line 0-1 variables for state; Indicates the line disconnection set; Representation node Conventional active load; Indicates the node under the accident Charging load; Indicates the node under the accident Active load shedding capacity; Indicates an accidental line drop Active power transmission; Indicates that the starting point is a node The line collection of Indicates that the end is a node The line collection of Indicates that the node is located under the accident The reactive power output of the substation; Representation node Conventional reactive load; Indicates an accident Reactive load shedding capacity; Indicates an accidental line drop Reactive power transmission; Indicates line A 0-1 variable indicating whether to connect; and denote the number of nodes and charging stations respectively; Indicates line Virtual trends; represents a constant; Indicates that it is located at the node The active power output of the substation; Representation node Charging load; Indicates line Active power transmission.

[0061] The objective function of the distribution system-transportation system collaborative optimization operation model is:

[0062] ;

[0063] ;

[0064] ;

[0065] Where, Indicates the operating cost of the transportation system in emergency situations; Represents the operating cost of the distribution system under unexpected events; represents the load shedding cost coefficient; Representation node Load shedding capacity; Indicates the charging power per unit traffic flow; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow, category B vehicles are electric vehicles with no shortage of electricity; represents the time economic parameter; represents the price of electricity; Indicates that the node is located under the accident The active power output of the substation; In the event of an accident, a Class A vehicle The minimum travel cost under In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be The minimum travel cost under In the event of an accident, a Class B vehicle providing emergency support will be The minimum travel cost under the above conditions: Category A vehicles are electric vehicles with insufficient power, and Category C vehicles are non-electric vehicles; 、 and OD pairs Traffic flow demands of Class A, Class B and Class C vehicles; Indicates the proportion of Class B vehicles involved in emergency response.

[0066] The operating constraints of the distribution system-transportation system collaborative optimization operation model are:

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] Where, Indicates the proportion of Class B vehicles providing emergency support, where Class B vehicles are electric vehicles with no power shortage; Indicates OD pair Traffic flow demand of Class B vehicles; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow; Indicates OD pair Middle, starting point There is insufficient energy supply at nearby charging stations; Indicates OD in emergency No. Traffic flow of Class B emergency vehicles along the routes; Indicates OD in emergency No. Traffic flow of Class A vehicles on the path, where Class A vehicles are electric vehicles with insufficient power; Indicates OD pair Traffic flow demand of Class A vehicles; Indicates OD in emergency No. Traffic flow of Class B non-emergency vehicles and Class C vehicles under the path, Class C vehicles are non-electric vehicles; Indicates OD pair Traffic flow demand of Category C vehicles; Indicates roads in emergency situations Traffic flow; Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient of Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient of Indicates roads in emergency situations The actual travel time; Indicates road free passage time; Indicates roads in emergency situations Traffic flow; Indicates road capacity; In the event of an accident, a Class A vehicle The next The travel cost of each path; Indicates charging station service fees; Indicates OD pair No. Whether the vehicle chooses a charging station on the path 0-1 variable for charging; represents the time economic parameter; Indicates road Additional congestion charges; In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be The next The travel cost of each path; In the event of an accident, a Class B vehicle providing emergency support will be The next The travel cost of each path; In the event of an accident, a Class A vehicle Minimum travel cost under In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be Minimum travel cost under In the event of an accident, a Class B vehicle providing emergency support will be The minimum travel cost under .

[0081] A distribution network resilience enhancement device based on charging load transfer and V2G technology, which is applied to the above-mentioned method, comprises:

[0082] The first model building module is used to build a transportation system operation model with the goal of minimizing transportation system operation costs; establish a charging station operation model that includes coupled operation constraints between the charging station and the transportation system and between the charging station and the power distribution system; and build an optimized operation model for the power distribution system with the goal of minimizing the power distribution system operation costs;

[0083] The second model building module is used to establish an optimized operation model of the power distribution system under accident conditions and considering line disconnection;

[0084] The operation plan acquisition module is used to develop an emergency response strategy for electric vehicles based on charging load transfer and V2G technology. With the goal of minimizing the operating costs of the transportation system and the distribution system, it constructs a distribution system-transportation system collaborative optimization operation model to deal with accidents, and solves the distribution network resilience enhancement operation plan.

[0085] A distribution network resilience enhancement device based on charging load transfer and V2G technology, including a memory and a processor;

[0086] The memory is configured to store computer program code and transmit the computer program code to the processor;

[0087] The processor is configured to execute the method according to the instructions in the computer program code.

[0088] A computer-readable storage medium stores a computer program, which implements the above-mentioned method when executed by a processor.

[0089] Compared with the prior art, the present invention has the following beneficial effects:

[0090] The present invention provides a method and device for enhancing distribution network resilience based on charging load transfer and V2G technology. The method can simulate the operation of a distribution system under accident conditions, including line disconnections, determine the amount of load shedding, and quantitatively assess the economic losses caused by the accident to the distribution system. Furthermore, the method can stimulate the regulatory potential of electric vehicles, fully utilizing the spatial transfer characteristics of charging loads and the mobile energy storage characteristics of electric vehicles in the event of an accident, ensuring the safe and stable operation of the distribution network, improving the resilience of the distribution system, and reducing the load shedding losses of the distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 This is a flow chart of a distribution network resilience enhancement method based on charging load transfer and V2G technology of the present invention.

[0092] Figure 2 It is a 12-node traffic system topology diagram of a distribution network resilience enhancement method based on charging load transfer and V2G technology in an embodiment of the present invention.

[0093] Figure 3 It is a 21-node distribution system topology diagram of a distribution network resilience enhancement method based on charging load transfer and V2G technology in an embodiment of the present invention.

[0094] Figure 4 It is a schematic diagram of the cost distribution and load shedding of the distribution-transportation system under accident conditions without and with the resilience enhancement strategy in an embodiment of the present invention.

[0095] Figure 5 This is a structural block diagram of a distribution network resilience enhancement device based on charging load transfer and V2G technology in the present invention.

[0096] Figure 6 This is a structural block diagram of a distribution network resilience enhancement device based on charging load transfer and V2G technology in the present invention. DETAILED DESCRIPTION

[0097] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0098] See also Figure 1 The present invention provides a method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology, comprising:

[0099] S1. With the goal of minimizing the operating cost of the transportation system, a transportation system operation model without emergency response strategy is constructed to simulate the traffic flow distribution under normal conditions;

[0100] Establish a charging station operation model that includes the coupled operation constraints of charging stations and transportation systems, as well as the coupled operation constraints of charging stations and power distribution systems. Simulate the charging load distribution based on the relationship between charging load and traffic flow.

[0101] With the goal of minimizing the operating cost of the distribution system, an optimal operation model of the distribution system under normal conditions is constructed to obtain the reference operating cost of the distribution system under normal conditions.

[0102] S2. Establish an optimized operation model for the distribution system under accidents and line disconnections, determine the unserved power load, and quantitatively assess the economic losses caused by accidents to the distribution system.

[0103] This paper uses the total load shedding of the distribution network multiplied by the economic coefficient to quantify the economic losses caused by an accident to the distribution system. First, an optimized operation model for the distribution system, taking line disconnections into account during an accident, is established. This model fully accounts for line disconnections in the distribution system during an accident and does not support the distribution system based on the flexible transfer characteristics of electric vehicle charging loads or the mobile energy storage characteristics of electric vehicles. This allows for the identification of the most severe load shedding scenarios in the distribution system and further estimates the economic losses.

[0104] S3. An electric vehicle emergency response strategy based on charging load transfer and V2G technology is constructed with the goal of minimizing the operating costs of the transportation system and the distribution system. A distribution system-transportation system collaborative optimization operation model for responding to accidents is constructed, and a distribution network resilience enhancement operation plan is solved.

[0105] Charging load transfer: In unexpected situations, the distribution system-traffic system collaborative optimization operation model proposed in this invention can be used to formulate road congestion fees and charging station charging fees to guide the charging load of electric vehicles from charging stations with insufficient energy supply to charging stations with sufficient energy supply, thereby supporting the safe and stable operation of the power grid.

[0106] V2G technology: Under the framework proposed by this invention, when an unexpected situation occurs, electric vehicles that provide power support to charging stations do not need to pay road congestion fees; thereby encouraging electric vehicles that are not short of power to go to charging stations with insufficient power supply for reverse power supply, and reverse power supply needs to be achieved through V2G technology.

[0107] The present invention can utilize the spatial transfer characteristics of the charging load and the mobile energy storage characteristics of electric vehicles in the event of an accident to ensure the safe and stable operation of the distribution network and reduce the load shedding loss of the distribution system.

[0108] Furthermore, the objective function of the traffic system operation model is:

[0109] ;

[0110] ;

[0111] Where, represents the operating cost of the transportation system (i.e., the time cost of traffic congestion); 、 and OD pairs Traffic flow demands of Class A, Class B and Class C vehicles;

[0112] The constraints of the traffic system operation model are:

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] Where, Indicates OD pair No. Class A vehicle traffic flow on the paths; Indicates OD pair No. Traffic flows of Class B and Class C vehicles on the paths; Indicates road Traffic flow; Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient, 1 means passing, 0 means not passing; Indicates road The actual travel time; Indicates road free passage time; Indicates road capacity; Indicates OD pair No. The travel cost of each path; represents the time economic parameter; Indicates road The last two equations represent the equilibrium state of the traffic network, where any vehicle cannot further reduce its travel cost by unilaterally changing its route.

[0121] Category A vehicles are electric vehicles that require power and require recharging at fast-charging stations along the way. These vehicles can enhance the grid's resilience in the event of an emergency by rerouting and reselecting charging stations. Category B vehicles are electric vehicles that do not require power. Some of these vehicles can be mobilized in the event of an emergency to provide grid support via vehicle-to-grid (V2G). Category C vehicles, excluding Category A and Category B vehicles, actually refer to non-electric vehicles, such as fuel-powered vehicles.

[0122] Furthermore, the coupled operation constraints of the charging station and the transportation system are:

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] Where, Indicates OD pair No. Choose a charging station on the route charging traffic flow; Indicates OD pair No. The traffic flow of Class A vehicles on the path, Class A vehicles are electric vehicles with insufficient power; Indicates OD pair No. Is the traffic flow on the path at the charging station? A 0-1 variable for charging, where 1 indicates selection and 0 indicates non-selection; Indicates charging station Total traffic volume; Indicates the charging power per unit traffic flow; Indicates charging station Charging power; Indicates the power upper limit of the charging unit; Indicates charging station The number of charging units in the

[0129] The coupled operation constraints of the charging station and the power distribution system are:

[0130] ;

[0131] Where, Represents a power distribution system node Charging power; Represents a connection node Charging stations collection.

[0132] Furthermore, the objective function of the distribution system optimization operation model is:

[0133] ;

[0134] ;

[0135] Where, represents the operating cost of the power distribution system; represents the price of electricity; Representation node Active power output of the substation;

[0136] The constraints of the distribution system optimization operation model are:

[0137] ;

[0138] The above formula shows the relationship between the number of lines, the number of nodes, and the number of substations, ensuring that the distribution network topology is a radial network;

[0139] ;

[0140] ;

[0141] The above formula indicates that the virtual load "-1" of the node can be supplied by the virtual power flow, avoiding the node being in an island operation state;

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] Where, Indicates line A 0-1 variable indicating whether the connection is established, 1 indicates connected, 0 indicates disconnected; and denote the number of nodes and charging stations respectively; Indicates line Virtual trends; Indicates a constant, usually a large value, such as 10000; Indicates that it is located at the node The active power output of the substation; Representation node Conventional active load; Representation node Charging load; Indicates line Active power transmission; Indicates that the starting point is a node The line collection of Indicates that the end is a node The line collection of Indicates that it is located at the node The reactive power output of the substation; Representation node Conventional reactive load; Indicates line Reactive power transmission; Indicates line Effective value of the voltage at the first terminal; Indicates line Terminal voltage effective value; and Respectively represent lines resistance and reactance; Representation node voltage; Representation node The upper voltage limit; Representation node The lower voltage limit; Indicates that it is located at the node The capacity of the substation; Indicates line capacity.

[0149] Furthermore, the objective function of the distribution system optimization operation model taking line disconnection into account under the accident is:

[0150] ;

[0151] ;

[0152] Where, Represents the operating cost of the distribution system under unexpected events; represents the load shedding cost coefficient; Representation node Load shedding capacity; represents the price of electricity; Indicates that the node is located under the accident The active power output of the substation;

[0153] The constraints of the distribution system optimization operation model taking line disconnection into account under the accident are:

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] Where, Indicates line A 0-1 variable representing the state, where 1 indicates a line connection and 0 indicates a line disconnection. Indicates the line disconnection set; Representation node Conventional active load; Indicates the node under the accident Charging load; Indicates the node under the accident Active load shedding capacity; Indicates an accidental line drop Active power transmission; Indicates that the starting point is a node The line collection of Indicates that the end is a node The line collection of Indicates that the node is located under the accident The reactive power output of the substation; Representation node Conventional reactive load; Indicates an accident Reactive load shedding capacity; Indicates an accidental line drop Reactive power transmission; Indicates line A 0-1 variable indicating whether to connect; and denote the number of nodes and charging stations respectively; Indicates line Virtual trends; represents a constant; Indicates that it is located at the node The active power output of the substation; Representation node Charging load; Indicates line Active power transmission.

[0162] Furthermore, the objective function of the power distribution system-transportation system collaborative optimization operation model is:

[0163] ;

[0164] ;

[0165] ;

[0166] Where, Indicates the operating cost of the transportation system in emergency situations; Represents the operating cost of the distribution system under unexpected events; represents the load shedding cost coefficient; Representation node Load shedding capacity; Indicates the charging power per unit traffic flow; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow, category B vehicles are electric vehicles with no shortage of electricity; represents the time economic parameter; represents the price of electricity; Indicates that the node is located under the accident The active power output of the substation; In the event of an accident, a Class A vehicle The minimum travel cost under In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be The minimum travel cost under In the event of an accident, a Class B vehicle providing emergency support will be The minimum travel cost under the above conditions: Category A vehicles are electric vehicles with insufficient power, and Category C vehicles are non-electric vehicles; 、 and OD pairs Traffic flow demands of Class A, Class B and Class C vehicles; Indicates the proportion of Class B vehicles involved in emergency response.

[0167] Furthermore, the operating constraints of the power distribution system-transportation system collaborative optimization operation model are:

[0168] ;

[0169] ;

[0170] ;

[0171] ;

[0172] ;

[0173] ;

[0174] ;

[0175] ;

[0176] ;

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] Where, Indicates the proportion of Class B vehicles providing emergency support, where Class B vehicles are electric vehicles with no power shortage; Indicates OD pair Traffic flow demand of Class B vehicles; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow; Indicates OD pair Middle, starting point There is insufficient energy supply at nearby charging stations; Indicates OD in emergency No. Traffic flow of Class B emergency vehicles along the routes; Indicates OD in emergency No. Traffic flow of Class A vehicles on the path, where Class A vehicles are electric vehicles with insufficient power; Indicates OD pair Traffic flow demand of Class A vehicles; Indicates OD in emergency No. Traffic flow of Class B non-emergency vehicles and Class C vehicles under the path, Class C vehicles are non-electric vehicles; Indicates OD pair Traffic flow demand of Category C vehicles; Indicates roads in emergency situations Traffic flow; Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient, 1 means passing, 0 means not passing; Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient, 1 means passing, 0 means not passing; Indicates roads in emergency situations The actual travel time; Indicates road free passage time; Indicates roads in emergency situations Traffic flow; Indicates road capacity; In the event of an accident, a Class A vehicle The next The travel cost of each path; Indicates charging station service fees; Indicates OD pair No. Whether the vehicle chooses a charging station on the path A 0-1 variable for charging, where 1 indicates selection and 0 indicates non-selection; represents the time economic parameter; Indicates road Additional congestion charges; In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be The next The travel cost of each path; In the event of an accident, a Class B vehicle providing emergency support will be The next The travel cost of each path; In the event of an accident, a Class A vehicle Minimum travel cost under In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be Minimum travel cost under In the event of an accident, a Class B vehicle providing emergency support will be The minimum travel cost under .

[0182] The operating constraints of the distribution system-transportation system collaborative optimization operation model also include the constraints of the distribution system optimization operation model taking into account line disconnection under accidents and other operating constraints of the distribution system and charging stations under emergency conditions. Other operating constraints are the same as those in normal conditions and will not be repeated here.

[0183] Furthermore, the nonlinear constraints in the above model are linearized using techniques such as the Big-M method, piecewise linearization, and binary expansion to facilitate model solution. This includes linearizing the empirical function of road travel time on road traffic flow and linearizing product-form nonlinear constraints such as user balancing criteria and charging selection constraints.

[0184] The steps for linearizing the empirical function of road travel time with respect to road traffic flow are as follows:

[0185] A. On the road The horizontal coordinate intervals on the travel time function curve are points, whose coordinates are , split the curve into part;

[0186] B. Based on the endpoint coordinates and weight distribution, the curve is approximated by a piecewise function, as shown in the following formula:

[0187] ;

[0188] in, For the The weight of each point is limited by the following formula:

[0189] ;

[0190] C. Describe which section of the curve the coordinate point is located on, as shown in the following formula:

[0191] ;

[0192] Among them, the binary variable Indicates whether it is in the segment (1 means located, 0 means not located);

[0193] D. Establish weights and binary variables The relationship is as shown below, ensuring that when the coordinate point is located at During this period, only and Can take value, which is equivalent to using Endpoint of the segment and Describes the line segment;

[0194] ;

[0195] .

[0196] The linearization steps for product-form nonlinear constraints such as user balancing criteria and charging selection constraints are as follows:

[0197] E. Using Big-M method, linearization , binary variable is an auxiliary variable, when hour, ,when hour, , as shown below:

[0198] ;

[0199] ;

[0200] Similarly, it can be linearized 、 、 、 ;

[0201] F. Using Big-M method, linearization ,when hour, ,when hour, , as shown below:

[0202] ;

[0203] ;

[0204] Similarly, it can be linearized in ;

[0205] G. Use binary expansion method to convert continuous variables Discretization, is the introduced binary variable, Indicates the total number of segments, Indicates the length of each segment, as shown below:

[0206] ;

[0207] H. Substitute the above formula into The following formula is obtained:

[0208] ;

[0209] I. Replace the above formula The overall replacement is , we get the following formula:

[0210] ;

[0211] J. Establishment of the Big-M method and The linear correlation between hour, ,when hour, , as shown below:

[0212] ;

[0213] .

[0214] This application is based on the distribution network resilience enhancement method of charging load transfer and V2G technology. It was tested in a distribution-transportation system consisting of a 12-node transportation system and a 21-node distribution system. The topology diagram of the 12-21 node distribution-transportation system is as follows: Figure 2 and Figure 3As shown. The 12-node transportation system includes 7 charging stations, which are located at transportation network nodes 1, 2, 4, 6, 7, 9 and 12 respectively; the 21-node distribution system includes 1 substation, which is located at distribution network node 21. In the event of an accident, the power lines B10-B13, B10-B9, and B21-B14 are disconnected. After the network is reconstructed, the interconnection lines B2-B16, B9-B19, and B8-B18 are connected. In order to illustrate the effectiveness of the present invention, the cost distribution and load shedding of the distribution-transportation system without the resilience enhancement method under accidents are compared with the cost distribution and load shedding of the distribution-transportation system with the distribution network resilience enhancement method based on charging load transfer and V2G technology under accidents. The comparison results are shown in Figure 2. Figure 4 shown.

[0215] See also Figure 5 The present invention also provides a distribution network resilience enhancement device based on charging load transfer and V2G technology, which is applied to the above-mentioned distribution network resilience enhancement method based on charging load transfer and V2G technology, and the device includes:

[0216] The first model building module is used to build a transportation system operation model with the goal of minimizing transportation system operation costs; establish a charging station operation model that includes coupled operation constraints between the charging station and the transportation system and between the charging station and the power distribution system; and build an optimized operation model for the power distribution system with the goal of minimizing the power distribution system operation costs;

[0217] The second model building module is used to establish an optimized operation model of the power distribution system under accident conditions and considering line disconnection;

[0218] The operation plan acquisition module is used to develop an emergency response strategy for electric vehicles based on charging load transfer and V2G technology. With the goal of minimizing the operating costs of the transportation system and the distribution system, it constructs a distribution system-transportation system collaborative optimization operation model to deal with accidents, and solves the distribution network resilience enhancement operation plan.

[0219] See also Figure 6 , the present invention also provides a distribution network resilience enhancement device based on charging load transfer and V2G technology, including a memory and a processor;

[0220] The memory is configured to store computer program code and transmit the computer program code to the processor;

[0221] The processor is used to execute the above-mentioned method for enhancing distribution network resilience based on charging load transfer and V2G technology according to the instructions in the computer program code.

[0222] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology.

[0223] Generally speaking, computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media except for signals that are temporarily propagating.

[0224] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM 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 thereof. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0225] Computer program code for performing the operations of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. In particular, Python, which is suitable for neural network computing, and platform frameworks such as TensorFlow and PyTorch can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or to an external computer (for example, through the Internet using an Internet service provider).

[0226] The above-mentioned devices and non-temporary computer-readable storage media can be found in the detailed description of a distribution network resilience enhancement method based on charging load transfer and V2G technology and its beneficial effects, which will not be repeated here.

[0227] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology, characterized in that: include: With the goal of minimizing the operating costs of the transportation system, a transportation system operation model is constructed; Establish a charging station operation model that includes the coupled operation constraints between the charging station and the transportation system, as well as the coupled operation constraints between the charging station and the power distribution system; With the goal of minimizing the operating cost of the distribution system, an optimized operation model of the distribution system is constructed; Establish an optimized operation model for the distribution system under unexpected events and taking line disconnection into account; Based on the electric vehicle emergency response strategy of charging load transfer and V2G technology, with the goal of minimizing the operating costs of the transportation system and the distribution system, a distribution system-transportation system collaborative optimization operation model for responding to accidents is constructed, and a distribution network resilience enhancement operation plan is solved. The objective function of the distribution system-transportation system collaborative optimization operation model is: ； ； ； Where, Indicates the operating cost of the transportation system in emergency situations; Represents the operating cost of the distribution system under unexpected events; represents the load shedding cost coefficient; Representation node Load shedding capacity; Indicates the charging power per unit traffic flow; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow, category B vehicles are electric vehicles with no shortage of electricity; represents the time economic parameter; represents the price of electricity; Indicates that the node is located under the accident The active power output of the substation; In the event of an accident, a Class A vehicle The minimum travel cost under In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be The minimum travel cost under In the event of an accident, a Class B vehicle providing emergency support will be The minimum travel cost under the above conditions: Category A vehicles are electric vehicles with insufficient power, and Category C vehicles are non-electric vehicles; 、 and OD pairs Traffic flow demands of Class A, Class B and Class C vehicles; Indicates the proportion of Class B vehicles involved in emergency response.

2. The method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology according to claim 1 is characterized in that: The objective function of the traffic system operation model is: ； ； Where, represents the cost of operating the transportation system; 、 and OD pairs Traffic flow demands of Class A, Class B and Class C vehicles; The constraints of the traffic system operation model are: ； ； ； ； ； ； ； Where, Indicates OD pair No. Class A vehicle traffic flow on the paths; Indicates OD pair No. Traffic flows of Class B and Class C vehicles on the paths; Indicates road Traffic flow; Indicates OD pair No. Whether the path passes through a road 0-1 coefficient of; Indicates road The actual travel time; Indicates road free passage time; Indicates road capacity; Indicates OD pair No. The travel cost of each path; represents the time economic parameter; Indicates road road congestion charges.

3. The method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology according to claim 1 is characterized in that: The coupled operation constraints of the charging station and the traffic system are: ； ； ； ； ； Where, Indicates OD pair No. Choose a charging station on the route charging traffic flow; Indicates OD pair No. The traffic flow of Class A vehicles on the path, Class A vehicles are electric vehicles with insufficient power; Indicates OD pair No. Is the traffic flow on the path at the charging station? 0-1 variable for charging; Indicates charging station Total traffic volume; Indicates the charging power per unit traffic flow; Indicates charging station Charging power; Indicates the power upper limit of the charging unit; Indicates charging station The number of charging units in the The coupled operation constraints of the charging station and the power distribution system are: ； Where, Represents a power distribution system node Charging power; Represents a connection node Charging stations collection.

4. The method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology according to claim 1, characterized in that: The objective function of the distribution system optimization operation model is: ； ； Where, represents the operating cost of the power distribution system; represents the price of electricity; Representation node Active power output of the substation; The constraints of the distribution system optimization operation model are: ； ； ； ； ； ； ； ； ； Where, Indicates line A 0-1 variable indicating whether to connect; and denote the number of nodes and charging stations respectively; Indicates line Virtual trends; represents a constant; Indicates that it is located at the node The active power output of the substation; Representation node Conventional active load; Representation node Charging load; Indicates line Active power transmission; Indicates that the starting point is a node The line collection of Indicates that the end is a node The line collection of Indicates that it is located at the node The reactive power output of the substation; Representation node Conventional reactive load; Indicates line Reactive power transmission; Indicates line Effective value of the voltage at the first terminal; Indicates line Effective value of terminal voltage; and Respectively represent lines resistance and reactance; Representation node voltage; Representation node The upper voltage limit; Representation node The lower voltage limit; Indicates that it is located at the node The capacity of the substation; Indicates line capacity.

5. The method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology according to claim 1, characterized in that: The objective function of the distribution system optimization operation model taking line disconnection into account under the accident is: ； ； Where, Represents the operating cost of the distribution system under unexpected events; represents the load shedding cost coefficient; Representation node Load shedding capacity; represents the price of electricity; Indicates that the node is located under the accident The active power output of the substation; The constraints of the distribution system optimization operation model taking line disconnection into account under the accident are: ； ； ； ； ； ； ； Where, Indicates line 0-1 variables for state; Indicates the line disconnection set; Representation node Conventional active load; Indicates the node under the accident Charging load; Indicates the node under the accident Active load shedding capacity; Indicates an accidental line drop Active power transmission; Indicates that the starting point is a node The line collection of Indicates that the end is a node The line collection of Indicates that the node is located under the accident The reactive power output of the substation; Representation node Conventional reactive load; Indicates an accident Reactive load shedding capacity; Indicates an accidental line drop Reactive power transmission; Indicates line A 0-1 variable indicating whether to connect; and denote the number of nodes and charging stations respectively; Indicates line Virtual trends; represents a constant; Indicates that it is located at the node The active power output of the substation; Representation node Charging load; Indicates line Active power transmission.

6. The method for enhancing the resilience of a distribution network based on charging load transfer and V2G technology according to claim 1, characterized in that: The operating constraints of the distribution system-transportation system collaborative optimization operation model are: ； ； ； ； ； ； ； ； ； ； ； ； ； Where, Indicates the proportion of Class B vehicles providing emergency support, where Class B vehicles are electric vehicles with no power shortage; Indicates OD pair Traffic flow demand of Class B vehicles; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow; Indicates that in emergency, Class B vehicles are Transfer to OD pair Traffic flow; Indicates OD pair Middle, starting point There is insufficient energy supply at nearby charging stations; Indicates OD in emergency No. Traffic flow of Class B emergency vehicles along the routes; Indicates OD in emergency No. Traffic flow of Class A vehicles on the path, where Class A vehicles are electric vehicles with insufficient power; Indicates OD pair Traffic flow demand of Class A vehicles; Indicates OD in emergency No. Traffic flow of Class B non-emergency vehicles and Class C vehicles under the path, Class C vehicles are non-electric vehicles; Indicates OD pair Traffic flow demand of Category C vehicles; Indicates roads in emergency situations Traffic flow; Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient of Indicates OD pair No. Whether the path passes through a road The 0-1 coefficient of Indicates roads in emergency situations The actual travel time; Indicates road free passage time; Indicates roads in emergency situations Traffic flow; Indicates road capacity; In the event of an accident, a Class A vehicle The next The travel cost of each path; Indicates charging station service fees; Indicates OD pair No. Whether the vehicle chooses a charging station on the path 0-1 variable for charging; represents the time economic parameter; Indicates road Additional congestion charges; In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be The next The travel cost of each path; In the event of an accident, a Class B vehicle providing emergency support will be The next The travel cost of each path; In the event of an accident, a Class A vehicle Minimum travel cost under In the event of an accident, the B-type vehicles and C-type vehicles that do not provide emergency support will be Minimum travel cost under In the event of an accident, a Class B vehicle providing emergency support will be The minimum travel cost under .

7. A distribution network resilience enhancement device based on charging load transfer and V2G technology, characterized in that: The device is applied to the method according to any one of claims 1 to 6, and the device comprises: The first model building module is used to build a transportation system operation model with the goal of minimizing transportation system operation costs; establish a charging station operation model that includes coupled operation constraints between the charging station and the transportation system and between the charging station and the power distribution system; and build an optimized operation model for the power distribution system with the goal of minimizing the power distribution system operation costs; The second model building module is used to establish an optimized operation model of the power distribution system under accident conditions and considering line disconnection; The operation plan acquisition module is used to develop an emergency response strategy for electric vehicles based on charging load transfer and V2G technology. With the goal of minimizing the operating costs of the transportation system and the distribution system, it constructs a distribution system-transportation system collaborative optimization operation model to deal with accidents, and solves the distribution network resilience enhancement operation plan.

8. A distribution network resilience enhancement device based on charging load transfer and V2G technology, characterized in that: including memory and processor; The memory is configured to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 6 according to instructions in the computer program code.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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