Method and system for determining minimization of number of energy storage vehicles in distributed energy network

Abstract

The invention relates to an energy storage vehicle number minimization determination method and system in a distributed energy network, and the method comprises the steps: firstly constructing an energy storage vehicle power supply model, defining the network node position, user use, power supply demands and other conditions, and then constructing an energy storage vehicle number minimization model with the cost minimization as the target under the constraint condition of meeting the power demand, discretizing vehicle deployment positions, introducing a binary variable to optimize a minimum energy storage vehicle number model, finally constructing a multi-energy storage vehicle network topological graph, solving the model by adopting a greedy strategy, and outputting the minimum energy storage vehicle number and deployment positions; according to the invention, by optimizing the deployment positions of the energy storage vehicles and the scheduling of user power, constructing an integer linear programming model, and providing a low-complexity and high-efficiency algorithm based on a graph theory and a greedy algorithm for solving, the calculation complexity during path planning is reduced, the optimal deployment of the distributed energy network is ensured to be realized with the minimum number of energy storage vehicles, and the optimal deployment efficiency of the distributed energy network is improved. And the utilization efficiency of resources is obviously improved.

Description

Technical Field

[0001] The present invention relates to a method for determining the number of energy storage vehicles, belongs to the field of distributed energy networks, and in particular to a method and system for determining the minimum number of energy storage vehicles in a distributed energy network. Background Art

[0002] Distributed energy networks have the characteristics of distribution, modularity and easy expansion. They can provide power supply support for remote areas, areas with damaged infrastructure and areas with difficult grid access without relying on traditional large power grids. Among them, energy storage vehicles, as a flexible mobile energy storage device, can be flexibly deployed in different locations according to user needs and energy supply conditions. However, due to the limited number and capacity of energy storage vehicles, it is impossible to deploy energy storage vehicles for every user. Therefore, in order to maximize the effectiveness of energy storage vehicles, it is necessary to minimize the number of energy storage vehicles deployed while ensuring the user's power needs, so as to reduce the construction and operation and maintenance costs of the system. However, in existing technical means, linear programming problems are usually used to propose corresponding path planning solutions, but because it is a non-deterministic polynomial problem, the solution complexity is high, resulting in high uncertainty in path planning and low efficiency. Therefore, there is an urgent need for an accurate and efficient means to solve the above-mentioned defects in the prior art. 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 an accurate and efficient method and system for minimizing the number of energy storage vehicles in a distributed energy network.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for minimizing the number of energy storage vehicles in a distributed energy network, comprising:

[0005] S1. Based on the distributed energy network composed of mobile energy storage vehicles, a power supply model of energy storage vehicles is constructed; the power supply model of energy storage vehicles includes a distributed energy network composed of multiple energy storage vehicles providing power supply services to multiple users;

[0006] S2. Define the network node location conditions, user usage conditions and user power supply demand conditions in the energy storage vehicle power supply model;

[0007] The network node location conditions include the user location and the energy storage vehicle location; the user usage conditions include the association between the user and the energy storage vehicle; the user power supply demand conditions include the user's required power, the energy storage vehicle's supply power and the user's received power;

[0008] S3. Under the constraint of meeting the user's electricity demand, with the goal of minimizing the deployment cost of energy storage vehicles, a model for minimizing the number of energy storage vehicles is constructed by combining the above conditions;

[0009] S4. Based on the discretization method, the infinite number of possible energy storage vehicle deployment locations are decomposed into a finite number of feasible points, and a binary variable is introduced to describe whether the user establishes a connection with the energy storage vehicle at the candidate deployment location, so as to optimize the model of minimizing the number of energy storage vehicles;

[0010] S5. Construct a multi-energy storage vehicle network topology diagram based on graph theory methods to represent the connection relationship between the candidate locations of energy storage vehicles and users;

[0011] S6. Based on the multi-energy storage vehicle network topology diagram, a greedy strategy is used to solve the optimized minimum number of energy storage vehicles model, and the minimum number of energy storage vehicles and the deployment locations of energy storage vehicles that meet the needs of all users are output.

[0012] The network node location condition is: the location of the kth user is g k , the position of the mth energy storage vehicle is I m ;

[0013] The user usage conditions are: each user is associated with an energy storage vehicle, each energy storage vehicle is associated with N users; the binary association variable between an energy storage vehicle and a user is a m，k ∈{0, 1}; if the kth user is associated with energy storage vehicle m, then a m，k =1; otherwise a m，k =0;

[0014] The user power supply demand condition is: the minimum power required by each user is δ k , the maximum power supplied by the energy storage vehicle is P veh , and supplies power equally to each user; the power supplied by energy storage vehicle m to each user k is The power received by user k from energy storage vehicle m is in: is the path loss of the temporarily deployed energy storage vehicle; R is the resistance coefficient; if It means that the user's needs are met.

[0015] The step S3 specifically refers to: under the constraint condition of meeting the user's electricity demand, the combined energy storage vehicle is deployed at position I m Power dispatch with users m，k , determine the number of energy storage vehicles required to minimize the energy network, and build a model to minimize the number of energy storage vehicles;

[0016] The model for minimizing the number of energy storage vehicles is as follows:

[0017]

[0018] The constraints of the model for minimizing the number of energy storage vehicles are as follows:

[0019]

[0020] in: Indicates the number of users connected to each energy storage vehicle; a m，k ∈{0,1} represents a m，k is a binary variable of {0, 1}; It means that each user is powered by only one energy storage vehicle;

[0021] Indicates that the power received by the user must be greater than the minimum required power δ required by each user k .

[0022] The step S4 specifically includes:

[0023] The potential deployment area of ​​energy storage vehicles is divided into J equal-sized areas, and the binary variable

[0024] f j ∈{0,1},β k，j ∈{0, 1} converts the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model;

[0025] The optimized model for minimizing the number of energy storage vehicles is as follows:

[0026]

[0027] Where: f j ∈{0, 1} indicates whether the energy storage vehicle can be deployed in the jth area (x j ,y j ) on; β k，j ∈{0, 1} indicates whether user k is associated with the UAV in the jth area;

[0028] The constraints of the optimized model for minimizing the number of energy storage vehicles are as follows:

[0029]

[0030] Where: f j ∈{0,1},β k，j ∈{0, 1} represents a binary constraint on a variable; Indicates that the user must be connected to an energy storage vehicle; It means that the electricity demand of each user must be met; It means that the energy storage vehicle needs to be deployed in area j only when at least one user is connected.

[0031] The step S5 specifically includes:

[0032] S51. Create an empty graph that does not contain any nodes or edges With an empty list Where G is used to store candidate energy storage vehicle locations; U is used to store the set of ground users covered by each candidate location;

[0033] S52. For each energy storage vehicle candidate position j, create an empty set U j , for users stored within its coverage area;

[0034] S53, for each energy storage vehicle candidate position j, traverse all ground users k; and determine whether the energy storage vehicle candidate position j can provide services that meet the power requirements for user k;

[0035] If the energy storage vehicle candidate position j can serve any user, then add the energy storage vehicle candidate position j to the empty graph G, add an edge between it and all serviceable users in the graph, and add these users to the set U j ; If not, determine the next candidate position j for the energy storage vehicle;

[0036] S54: Merge all energy storage vehicle candidate locations to serve the user set U j , obtain the final user list, and output the multi-energy storage vehicle network topology G and the user association set U.

[0037] The step S6 specifically includes:

[0038] S61, initialize the multi-energy storage vehicle network topology G and the user association set U, assign all energy storage vehicle candidate positions to the set D, and create an empty set F;

[0039] S62, selecting a candidate energy storage vehicle position u that currently covers the least number of ground users, and deleting u to determine whether it affects user coverage;

[0040] If there are users that cannot be covered when the candidate energy storage vehicle position u is deleted, then u is added to the set F, and all ground users covered by the candidate energy storage vehicle position u are deleted from the multi-energy storage vehicle network topology graph G;

[0041] If deleting the candidate energy storage vehicle position u will not affect user coverage, then u is deleted from the set D, and the ground user set related to the energy storage vehicle position u is removed from the user association set U;

[0042] S63, repeat steps S61-S62 until the set D minus the set F is empty, then the loop ends and the set D is output; at this time, the set D contains the minimum number of energy storage vehicles and energy storage vehicle deployment locations that meet the needs of all users.

[0043] A system for minimizing the number of energy storage vehicles in a distributed energy network, the system comprising:

[0044] A power supply model construction module is used to construct a power supply model of an energy storage vehicle based on a distributed energy network composed of mobile energy storage vehicles; the energy storage vehicle power supply model includes a distributed energy network composed of multiple energy storage vehicles providing power supply services to multiple users;

[0045] The condition setting module is used to define the network node location conditions, user usage conditions and user power supply demand conditions in the energy storage vehicle power supply model;

[0046] The network node location conditions include the user location and the energy storage vehicle location; the user usage conditions include the association between the user and the energy storage vehicle; the user power supply demand conditions include the user's required power, the energy storage vehicle's supply power and the user's received power;

[0047] The vehicle number minimization model building module is used to build a vehicle number minimization model under the constraint of meeting the user's electricity demand and taking minimizing the deployment cost of energy storage vehicles as the goal.

[0048] The minimization vehicle number model optimization module is used to decompose the infinite number of possible energy storage vehicle deployment locations into a finite number of feasible points based on the discretization method, and introduce a binary variable to describe whether the user establishes a connection with the energy storage vehicle at the candidate deployment location, so as to optimize the minimization vehicle number model;

[0049] A network topology construction module is used to construct a multi-energy storage vehicle network topology diagram representing the relationship between the candidate locations of energy storage vehicles and user connections based on graph theory methods;

[0050] The minimum vehicle number model solving module is used to solve the optimized minimum energy storage vehicle number model based on the multi-energy storage vehicle network topology diagram using a greedy strategy, and output the minimum number of energy storage vehicles and energy storage vehicle deployment locations that meet all user needs.

[0051] The conditions set by the condition setting module are as follows:

[0052] The network node location condition is: the location of the kth user is g k , the position of the mth energy storage vehicle is I m ;

[0053] The user usage conditions are: each user is associated with an energy storage vehicle, each energy storage vehicle is associated with N users; the binary association variable between an energy storage vehicle and a user is a m，k ∈{0, 1}; if the kth user is associated with energy storage vehicle m, then a m，k =1; otherwise a m，k =0;

[0054] The user power supply demand condition is: the minimum power required by each user is δ k , the maximum power supplied by the energy storage vehicle is Pveh , and supplies power equally to each user; the power supplied by energy storage vehicle m to each user k is The power received by user k from energy storage vehicle m is in: is the path loss of the temporarily deployed energy storage vehicle; R is the resistance coefficient; if It means that the user's needs are met.

[0055] The minimization vehicle number model constructed by the minimization vehicle number model construction module is as follows:

[0056] Under the constraint of meeting the user's electricity demand, the combined energy storage vehicle is deployed at location I m Power dispatch with users m，k , determine the number of energy storage vehicles required to minimize the energy network, and build a model to minimize the number of energy storage vehicles;

[0057] The model for minimizing the number of energy storage vehicles is as follows:

[0058]

[0059] The constraints of the model for minimizing the number of energy storage vehicles are as follows:

[0060]

[0061] in: Indicates the number of users connected to each energy storage vehicle; a m，k ∈{0,1} represents a m，k is a binary variable of {0, 1}; It means that each user is powered by only one energy storage vehicle;

[0062] Indicates that the power received by the user must be greater than the minimum required power δ required by each user k .

[0063] The minimum vehicle number model optimization module is used to optimize the minimum vehicle number model according to the following method:

[0064] The potential deployment area of ​​energy storage vehicles is divided into J equal-sized areas, and a binary variable f is introduced j ∈{0,1},β k，j ∈{0, 1} converts the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model;

[0065] The optimized model for minimizing the number of energy storage vehicles is as follows:

[0066]

[0067] Where: fj ∈{0, 1} indicates whether the energy storage vehicle can be deployed in the jth area (x j ,y j ) on; β k，j ∈{0, 1} indicates whether user k is associated with the UAV in the jth area;

[0068] The constraints of the optimized model for minimizing the number of energy storage vehicles are as follows:

[0069]

[0070] Where: f j ∈{0,1},β k，j ∈{0, 1} represents a binary constraint on a variable; Indicates that the user must be connected to an energy storage vehicle; It means that the electricity demand of each user must be met; It means that the energy storage vehicle needs to be deployed in area j only when at least one user is connected.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention discloses a method and system for determining the minimum number of energy storage vehicles in a distributed energy network. The method first constructs a power supply model of an energy storage vehicle, and defines conditions such as network node locations, user usage, and power supply requirements. Then, under the constraint of satisfying the power demand, a model for minimizing the number of energy storage vehicles is constructed with the goal of minimizing costs. The vehicle deployment positions are discretized and binary variables are introduced to optimize the model for minimizing the number of energy storage vehicles. Finally, a network topology diagram of multiple energy storage vehicles is constructed and a greedy strategy is adopted to solve the model, and the minimum number of energy storage vehicles and deployment positions are output. In the application of the present design, an integer linear programming model is constructed by optimizing the deployment positions of energy storage vehicles and the scheduling of user power, and a low-complexity and efficient algorithm based on graph theory and a greedy algorithm is proposed for solving the problem, thereby reducing the computational complexity during path planning, ensuring that the optimal deployment of the distributed energy network is achieved with a minimum number of energy storage vehicles, and significantly improving the utilization efficiency of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flow chart of the method steps of the present invention.

[0074] Figure 2 It is a schematic diagram of the power supply model architecture of the energy storage vehicle in Example 1 of the present invention.

[0075] Figure 3 This is a schematic diagram of the network topology of multiple energy storage vehicles in Example 1 of the present invention.

[0076] Figure 4 It is a schematic diagram of the system structure of the present invention.

[0077] Figure 5 It is a schematic diagram of the device structure of the present invention.

[0078] In the figure: power supply model construction module 1, condition setting module 2, minimum vehicle number model construction module 3, minimum vehicle number model optimization module 4, network topology construction module 5, minimum vehicle number model solution module 6, processor 7, memory 8, computer program code 81. DETAILED DESCRIPTION

[0079] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0080] Embodiment 1:

[0081] See also Figure 1 , a method for minimizing the number of energy storage vehicles in a distributed energy network, comprising:

[0082] S1. Based on the distributed energy network composed of mobile energy storage vehicles, a power supply model for energy storage vehicles is constructed; Figure 2 As shown, the energy storage vehicle power supply model includes a distributed energy network composed of multiple energy storage vehicles providing power supply services to multiple users;

[0083] S2. Define the network node location conditions, user usage conditions and user power supply demand conditions in the energy storage vehicle power supply model;

[0084] The network node location conditions include the user location and the energy storage vehicle location; the user usage conditions include the association between the user and the energy storage vehicle; the user power supply demand conditions include the user's required power, the energy storage vehicle's supply power and the user's received power;

[0085] Furthermore, the network node location condition is: the location of the kth user is g k , the position of the mth energy storage vehicle is I 川 ;

[0086] The user usage conditions are: each user is associated with an energy storage vehicle, each energy storage vehicle is associated with N users; the binary association variable between an energy storage vehicle and a user is a m，k ∈{0, 1}; if the kth user is associated with energy storage vehicle m, then a m，k = l; otherwise a m，k =0;

[0087] The user power supply demand condition is: the minimum power required by each user is δ k , the maximum power supplied by the energy storage vehicle is P veh , and supplies power equally to each user; the power supplied by energy storage vehicle m to each user k is The power received by user k from energy storage vehicle m is in: is the path loss of the temporarily deployed energy storage vehicle; R is the resistance coefficient; if It means that the user's needs are met.

[0088] S3. Under the constraint of meeting the user's electricity demand, with the goal of minimizing the deployment cost of energy storage vehicles, a model for minimizing the number of energy storage vehicles is constructed by combining the above conditions;

[0089] Furthermore, under the constraint of meeting the user's electricity demand, the combined energy storage vehicle is deployed at location I m Power dispatch with users m，k , determine the number of energy storage vehicles required to minimize the energy network, and build a model to minimize the number of energy storage vehicles;

[0090] On this basis, an integer linear programming problem model (P1) is proposed to minimize the number of energy storage vehicles through reasonable energy storage vehicle deployment locations and user scheduling strategies to meet the electricity needs of all users. That is, the model for minimizing the number of energy storage vehicles (P1) is as follows:

[0091]

[0092] The constraints of the model for minimizing the number of energy storage vehicles are as follows:

[0093]

[0094] in: Indicates the number of users connected to each energy storage vehicle; a m，k ∈{0,1} represents a m，k is a binary variable of {0, 1}; It means that each user is powered by only one energy storage vehicle;

[0095] Indicates that the power received by the user must be greater than the minimum required power δ required by each user k .

[0096] Since (P1) is a non-deterministic polynomial-hard (NP-hard) problem, there is no known polynomial-time algorithm that can find the optimal solution in all cases. Therefore, this solution proposes an effective algorithm to find suboptimal solutions through graph theory and greedy strategy. To simplify the solution process, this solution transforms problem (P1) into (P2). During the transformation process, a binary variable is introduced to describe whether the user establishes a connection with the energy storage vehicle at the candidate location to optimize model (P2), as follows:

[0097] S4. Based on the discretization method, the infinite number of possible energy storage vehicle deployment locations are decomposed into a finite number of feasible points, and a binary variable is introduced to describe whether the user establishes a connection with the energy storage vehicle at the candidate deployment location, so as to optimize the model of minimizing the number of energy storage vehicles;

[0098] Furthermore, the potential deployment area of ​​energy storage vehicles is divided into J areas of equal size, and a binary variable f is introduced j ∈{0,1},β k，j ∈{0, 1} converts the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model;

[0099] The optimized minimum energy storage vehicle number model (P2) is as follows:

[0100]

[0101] Where: f j ∈{0, 1} indicates whether the energy storage vehicle can be deployed in the jth area (x j ,y j )superior;

[0102] β k，j ∈{0, 1} indicates whether user k is associated with the UAV in the jth area;

[0103] The constraints of the optimized model for minimizing the number of energy storage vehicles are as follows:

[0104]

[0105] Where: f j ∈{0,1},β k，j ∈{0, 1} represents a binary constraint on a variable; Indicates that the user must be connected to an energy storage vehicle; It means that the electricity demand of each user must be met; It means that the energy storage vehicle needs to be deployed in area j only when at least one user is connected.

[0106] S5. Construct a multi-energy storage vehicle network topology diagram based on graph theory methods to represent the connection relationship between the candidate locations of energy storage vehicles and users;

[0107] Furthermore, the step S5 specifically includes:

[0108] S51. Create an empty graph that does not contain any nodes or edges With an empty list Where G is used to store candidate energy storage vehicle locations; U is used to store the set of ground users covered by each candidate location;

[0109] S52. For each energy storage vehicle candidate position j, create an empty set U j , for users stored within its coverage area;

[0110] S53, for each energy storage vehicle candidate position j, traverse all ground users k; and determine whether the energy storage vehicle candidate position j can provide services that meet the power requirements for user k;

[0111] If the energy storage vehicle candidate position j can serve any user, then add the energy storage vehicle candidate position j to the empty graph G, add an edge between it and all serviceable users in the graph, and add these users to the set U j ; If not, determine the next candidate position j for the energy storage vehicle;

[0112] S54: Merge all energy storage vehicle candidate locations to serve the user set U j , obtain the final user list, and output the multi-energy storage vehicle network topology G and the user association set U. The multi-energy storage vehicle network topology is as follows Figure 3 shown.

[0113] S6. Based on the multi-energy storage vehicle network topology diagram, a greedy strategy is used to solve the optimized minimum number of energy storage vehicles model, and the minimum number of energy storage vehicles and the deployment locations of energy storage vehicles that meet the needs of all users are output.

[0114] Furthermore, the step S6 specifically includes:

[0115] S61, initialize the multi-energy storage vehicle network topology G and the user association set U, assign all energy storage vehicle candidate positions to the set D, and create an empty set F;

[0116] S62, selecting a candidate energy storage vehicle position u that currently covers the least number of ground users, and deleting u to determine whether it affects user coverage;

[0117] If there are users that cannot be covered when the candidate energy storage vehicle position u is deleted, then u is added to the set F, and all ground users covered by the candidate energy storage vehicle position u are deleted from the multi-energy storage vehicle network topology graph G;

[0118] If deleting the candidate energy storage vehicle position u will not affect user coverage, then u is deleted from the set D, and the ground user set related to the energy storage vehicle position u is removed from the user association set U;

[0119] S63, repeat steps S61-S62 until the set D minus the set F is empty, then the loop ends and the set D is output; at this time, the set D contains the minimum number of energy storage vehicles and energy storage vehicle deployment locations that meet the needs of all users.

[0120] In this scheme, the deployment location of energy storage vehicles and the dispatch of user electricity are optimized to solve the problem of how to cover the electricity needs of all users in a given area with the least number of energy storage vehicles. This is modeled as an NP-hard integer linear programming problem, and its solution has a certain complexity; to effectively solve the problem, this scheme proposes a low-complexity and efficient algorithm based on graph theory and greedy algorithm. First, the deployment area of ​​the energy storage vehicle is discretized using graph theory methods to establish an energy storage vehicle network graph. Each node represents a candidate energy storage vehicle and user location, and the edge represents the connection between the energy storage vehicle and the covered user; then, the greedy algorithm is used to gradually delete redundant nodes to minimize the number of energy storage vehicles. In each iteration of the algorithm, the candidate location nodes with less impact on user coverage are deleted first, and it is judged whether they will cause insufficient user coverage until all redundant nodes are removed. The energy storage vehicle position that is finally retained is the optimized deployment plan.

[0121] Embodiment 2:

[0122] See also Figure 4 , a system for minimizing the number of energy storage vehicles in a distributed energy network, the system comprising:

[0123] A power supply model construction module 1 is used to construct a power supply model of an energy storage vehicle based on a distributed energy network composed of mobile energy storage vehicles; the power supply model of the energy storage vehicle includes a distributed energy network composed of multiple energy storage vehicles providing power supply services to multiple users;

[0124] Condition setting module 2, used to define network node location conditions, user usage conditions and user power supply demand conditions in the energy storage vehicle power supply model;

[0125] The network node location conditions include the user location and the energy storage vehicle location; the user usage conditions include the association between the user and the energy storage vehicle; the user power supply demand conditions include the user's required power, the energy storage vehicle's supply power and the user's received power;

[0126] Furthermore, the conditions set by the condition setting module 2 are as follows:

[0127] The network node location condition is: the location of the kth user is g k , the position of the mth energy storage vehicle is I m ;

[0128] The user usage conditions are: each user is associated with an energy storage vehicle, each energy storage vehicle is associated with N users; the binary association variable between an energy storage vehicle and a user is a m，k ∈{0, 1}; if the kth user is associated with energy storage vehicle m, then a m，k =1; otherwise a m，k =0;

[0129] The user power supply demand condition is: the minimum power required by each user is δ k , the maximum power supplied by the energy storage vehicle is P veh , and supplies power equally to each user; the power supplied by energy storage vehicle m to each user k is The power received by user k from energy storage vehicle m is in: is the path loss of the temporarily deployed energy storage vehicle; R is the resistance coefficient; if It means that the user's needs are met.

[0130] The minimization vehicle number model construction module 3 is used to construct the minimization energy storage vehicle number model under the constraint of meeting the user's electricity demand and taking minimization of the energy storage vehicle deployment cost as the goal, combining the above conditions to build the minimization energy storage vehicle number model;

[0131] Furthermore, the minimization vehicle number model constructed by the minimization vehicle number model construction module 3 is as follows:

[0132] Under the constraint of meeting the user's electricity demand, the combined energy storage vehicle is deployed at location I m Power dispatch with users m，k , determine the number of energy storage vehicles required to minimize the energy network, and build a model to minimize the number of energy storage vehicles;

[0133] The model for minimizing the number of energy storage vehicles is as follows:

[0134]

[0135] The constraints of the model for minimizing the number of energy storage vehicles are as follows:

[0136]

[0137] in: Indicates the number of users connected to each energy storage vehicle; a m， k∈{0,1} represents a m，k is a binary variable of {0, 1}; It means that each user is powered by only one energy storage vehicle;

[0138] Indicates that the power received by the user must be greater than the minimum required power δ required by each user k .

[0139] The minimization vehicle number model optimization module 4 is used to decompose the infinite number of possible energy storage vehicle deployment locations into a finite number of feasible points based on a discretization method, and introduce a binary variable to describe whether the user establishes a connection with the energy storage vehicle at the candidate deployment location, so as to optimize the minimization vehicle number model;

[0140] Furthermore, the minimization vehicle number model optimization module 4 is used to optimize the minimization vehicle number model according to the following method:

[0141] The potential deployment area of ​​energy storage vehicles is divided into J equal-sized areas, and a binary variable f is introduced j ∈{0,1},β k，j ∈{0, 1} converts the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model;

[0142] The optimized model for minimizing the number of energy storage vehicles is as follows:

[0143]

[0144] Where: f j ∈{0, 1} indicates whether the energy storage vehicle can be deployed in the jth area (x j ,y j )superior;

[0145] β k，j ∈{0, 1} indicates whether user k is associated with the UAV in the jth area;

[0146] The constraints of the optimized model for minimizing the number of energy storage vehicles are as follows:

[0147]

[0148] Where: f j ∈{0,1},β k，j ∈{0, 1} represents a binary constraint on a variable; Indicates that the user must be connected to an energy storage vehicle; It means that the electricity demand of each user must be met; It means that the energy storage vehicle needs to be deployed in area j only when at least one user is connected.

[0149] A network topology construction module 5 is used to construct a multi-energy storage vehicle network topology diagram representing the connection relationship between the candidate locations of the energy storage vehicles and the users based on a graph theory method;

[0150] Furthermore, the network topology construction module 5 is used to construct a multi-energy storage vehicle network topology diagram according to the following steps:

[0151] S51. Create an empty graph that does not contain any nodes or edges With an empty list Where G is used to store candidate energy storage vehicle locations; U is used to store the set of ground users covered by each candidate location;

[0152] S52. For each energy storage vehicle candidate position j, create an empty set U j, for users stored within its coverage area;

[0153] S53, for each energy storage vehicle candidate position j, traverse all ground users k; and determine whether the energy storage vehicle candidate position j can provide services that meet the power requirements for user k;

[0154] If the energy storage vehicle candidate position j can serve any user, then add the energy storage vehicle candidate position j to the empty graph G, add an edge between it and all serviceable users in the graph, and add these users to the set U j ; If not, determine the next candidate position j for the energy storage vehicle;

[0155] S54: Merge all energy storage vehicle candidate locations to serve the user set U j , obtain the final user list, and output the multi-energy storage vehicle network topology G and the user association set U.

[0156] The minimum vehicle number model solving module 6 is used to solve the optimized minimum energy storage vehicle number model based on the multi-energy storage vehicle network topology diagram using a greedy strategy, and output the minimum number of energy storage vehicles and energy storage vehicle deployment locations that meet all user needs.

[0157] Furthermore, the minimum vehicle number model solving module 6 is used to solve the minimum vehicle number model according to the following steps:

[0158] S61, initialize the multi-energy storage vehicle network topology G and the user association set U, assign all energy storage vehicle candidate positions to the set D, and create an empty set F;

[0159] S62, selecting a candidate energy storage vehicle position u that currently covers the least number of ground users, and deleting u to determine whether it affects user coverage;

[0160] If there are users that cannot be covered when the candidate energy storage vehicle position u is deleted, then u is added to the set F, and all ground users covered by the candidate energy storage vehicle position u are deleted from the multi-energy storage vehicle network topology graph G;

[0161] If deleting the candidate energy storage vehicle position u will not affect user coverage, then u is deleted from the set D, and the ground user set related to the energy storage vehicle position u is removed from the user association set U;

[0162] S63, repeat steps S61-S62 until the set D minus the set F is empty, then the loop ends and the set D is output; at this time, the set D contains the minimum number of energy storage vehicles and energy storage vehicle deployment locations that meet the needs of all users.

[0163] Embodiment 3:

[0164] See also Figure 5, a device for minimizing the number of energy storage vehicles in a distributed energy network, the device comprising a processor 7 and a memory 8;

[0165] The memory 8 is used to store the computer program code 81 and transmit the computer program code 81 to the processor 7;

[0166] The processor 7 is used to execute the method for minimizing the number of energy storage vehicles in a distributed energy network described in Example 1 according to the instructions in the computer program code 81.

[0167] This embodiment also includes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed on a computer, the method for minimizing the number of energy storage vehicles in a distributed energy network described in Example 1 is implemented.

[0168] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0169] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with 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 may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0170] Computer program code for performing the operation 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 suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent 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 or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0171] The above-mentioned device and non-temporary computer-readable storage medium can be found in the detailed description of the method for minimizing the number of energy storage vehicles in a distributed energy network and its beneficial effects, which will not be repeated here.

[0172] 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 limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for minimizing the number of energy storage vehicles in a distributed energy network, characterized in that: include: S1. Based on the distributed energy network composed of mobile energy storage vehicles, a power supply model of energy storage vehicles is constructed; the power supply model of energy storage vehicles includes a distributed energy network composed of multiple energy storage vehicles providing power supply services to multiple users; S2. Define the network node location conditions, user usage conditions and user power supply demand conditions in the energy storage vehicle power supply model; The network node location conditions include the user location and the energy storage vehicle location; the user usage conditions include the association between the user and the energy storage vehicle; the user power supply demand conditions include the user's required power, the energy storage vehicle's supply power and the user's received power; S3. Under the constraint of meeting the user's electricity demand, with the goal of minimizing the deployment cost of energy storage vehicles, a model for minimizing the number of energy storage vehicles is constructed by combining the above conditions; S4. Based on the discretization method, the infinite number of possible energy storage vehicle deployment locations are decomposed into a finite number of feasible points, and a binary variable is introduced to describe whether the user establishes a connection with the energy storage vehicle at the candidate deployment location, so as to optimize the model of minimizing the number of energy storage vehicles; S5. Construct a multi-energy storage vehicle network topology diagram based on graph theory methods to represent the connection relationship between the candidate locations of energy storage vehicles and users; S6. Based on the multi-energy storage vehicle network topology diagram, a greedy strategy is used to solve the optimized minimum number of energy storage vehicles model, and the minimum number of energy storage vehicles and the deployment locations of energy storage vehicles that meet the needs of all users are output.

2. The method for minimizing the number of energy storage vehicles in a distributed energy network according to claim 1, characterized in that: The network node location condition is: the location of the kth user is g k , the position of the mth energy storage vehicle is I m ; The user usage conditions are: each user is associated with an energy storage vehicle, each energy storage vehicle is associated with N users; the binary association variable between an energy storage vehicle and a user is a m，k ∈{0, 1}; if the kth user is associated with energy storage vehicle m, then a m，k =1; otherwise a m，k =0; The user power supply demand condition is: the minimum power required by each user is δ k , the maximum power supplied by the energy storage vehicle is P v，eh , and supplies power equally to each user; the power supplied by energy storage vehicle m to each user k is The power received by user k from energy storage vehicle m is in: is the path loss of the temporarily deployed energy storage vehicle; R is the resistance coefficient; if It means that the user's needs are met.

3. The method for minimizing the number of energy storage vehicles in a distributed energy network according to claim 2, characterized in that: The step S3 specifically refers to: under the constraint condition of meeting the user's electricity demand, the combined energy storage vehicle is deployed at position I m Power dispatch with users m，k , determine the number of energy storage vehicles required to minimize the energy network, and build a model to minimize the number of energy storage vehicles; The model for minimizing the number of energy storage vehicles is as follows: The constraints of the model for minimizing the number of energy storage vehicles are as follows: in: Indicates the number of users connected to each energy storage vehicle; a m，k ∈{0,1} represents a m，k is a binary variable of {0, 1}; It means that each user is powered by only one energy storage vehicle; Indicates that the power received by the user must be greater than the minimum required power δ required by each user k .

4. The method for minimizing the number of energy storage vehicles in a distributed energy network according to claim 3, characterized in that: The step S4 specifically includes: The potential deployment area of ​​energy storage vehicles is divided into J equal-sized areas, and a binary variable f is introduced j ∈{0,1},β k，j ∈{0, 1} converts the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model; The optimized model for minimizing the number of energy storage vehicles is as follows: Where: f j ∈{0, 1} indicates whether the energy storage vehicle can be deployed in the jth area (x j ,y j ) on; β k，j ∈{0, 1} indicates whether user k is associated with the UAV in the jth area; The constraints of the optimized model for minimizing the number of energy storage vehicles are as follows: Where: f j ∈{0,1},β k，j ∈{0, 1} represents a binary constraint on a variable; Indicates that the user must be connected to an energy storage vehicle; It means that the electricity demand of each user must be met; It means that the energy storage vehicle needs to be deployed in area j only when at least one user is connected.

5. The method for minimizing the number of energy storage vehicles in a distributed energy network according to claim 1, characterized in that: The step S5 specifically includes: S51. Create an empty graph that does not contain any nodes or edges With an empty list Where G is used to store candidate energy storage vehicle locations; U is used to store the set of ground users covered by each candidate location; S52. For each energy storage vehicle candidate position j, create an empty set U j , for users stored within its coverage area; S53, for each energy storage vehicle candidate position j, traverse all ground users k; and determine whether the energy storage vehicle candidate position j can provide services that meet the power requirements for user k; If the energy storage vehicle candidate position j can serve any user, then add the energy storage vehicle candidate position j to the empty graph G, add an edge between it and all serviceable users in the graph, and add these users to the set U j ; If not, determine the next candidate position j for the energy storage vehicle; S54: Merge all energy storage vehicle candidate locations to serve the user set U j , obtain the final user list, and output the multi-energy storage vehicle network topology G and the user association set U.

6. The method for minimizing the number of energy storage vehicles in a distributed energy network according to claim 5, characterized in that: The step S6 specifically includes: s61, initialize the multi-energy storage vehicle network topology G and the user association set U, assign all energy storage vehicle candidate locations to the set D, and create an empty set F; S62, selecting a candidate energy storage vehicle position u that currently covers the least number of ground users, and deleting u to determine whether it affects user coverage; If there are users that cannot be covered when the candidate energy storage vehicle position u is deleted, then u is added to the set F, and all ground users covered by the candidate energy storage vehicle position u are deleted from the multi-energy storage vehicle network topology graph G; If deleting the candidate energy storage vehicle position u will not affect user coverage, then u is deleted from the set D, and the ground user set related to the energy storage vehicle position u is removed from the user association set U; S63, repeat steps S61-S62 until the set D minus the set F is empty, then the loop ends and the set D is output; at this time, the set D contains the minimum number of energy storage vehicles and energy storage vehicle deployment locations that meet the needs of all users.

7. A system for minimizing the number of energy storage vehicles in a distributed energy network, characterized in that: The system comprises: A power supply model construction module (1) is used to construct an energy storage vehicle power supply model based on a distributed energy network composed of mobile energy storage vehicles; the energy storage vehicle power supply model includes a distributed energy network composed of multiple energy storage vehicles providing power supply services to multiple users; A condition setting module (2), used to define network node location conditions, user usage conditions and user power supply demand conditions in the energy storage vehicle power supply model; The network node location conditions include the user location and the energy storage vehicle location; the user usage conditions include the association between the user and the energy storage vehicle; the user power supply demand conditions include the user's required power, the energy storage vehicle's supply power and the user's received power; A vehicle number minimization model building module (3) is used to build a vehicle number minimization model under the constraint of satisfying the user's electricity demand and taking minimizing the energy storage vehicle deployment cost as the goal, combining the above conditions to build a vehicle number minimization model; The minimization vehicle number model optimization module (4) is used to decompose the infinite number of possible energy storage vehicle deployment locations into a finite number of feasible points based on a discretization method, and introduce a binary variable to describe whether the user establishes a connection with the energy storage vehicle at the candidate deployment location, so as to optimize the minimization vehicle number model; A network topology construction module (5) is used to construct a multi-energy storage vehicle network topology diagram representing the relationship between the candidate locations of the energy storage vehicles and the user connections based on a graph theory method; The minimum vehicle number model solving module (6) is used to solve the optimized minimum energy storage vehicle number model based on the multi-energy storage vehicle network topology diagram using a greedy strategy, and output the minimum number of energy storage vehicles and energy storage vehicle deployment locations that meet all user needs.

8. The system for minimizing the number of energy storage vehicles in a distributed energy network according to claim 7, characterized in that: The conditions set by the condition setting module (2) are as follows: The network node location condition is: the location of the kth user is g k , the position of the mth energy storage vehicle is I m ; The user usage conditions are: each user is associated with an energy storage vehicle, each energy storage vehicle is associated with N users; the binary association variable between an energy storage vehicle and a user is a m，k ∈{0, 1}; if the kth user is associated with energy storage vehicle m, then a m，k =1; otherwise a m，k =0; The user power supply demand condition is: the minimum power required by each user is δ k , the maximum power supplied by the energy storage vehicle is P veh , and supplies power equally to each user; the power supplied by energy storage vehicle m to each user k is The power received by user k from energy storage vehicle m is in: is the path loss of the temporarily deployed energy storage vehicle; R is the resistance coefficient; if It means that the user's needs are met.

9. The system for minimizing the number of energy storage vehicles in a distributed energy network according to claim 8, characterized in that: The minimization vehicle number model constructed by the minimization vehicle number model construction module (3) is as follows: Under the constraint of meeting the user's electricity demand, the combined energy storage vehicle is deployed at location I m Power dispatch with users m，k , determine the number of energy storage vehicles required to minimize the energy network, and build a model to minimize the number of energy storage vehicles; The model for minimizing the number of energy storage vehicles is as follows: The constraints of the model for minimizing the number of energy storage vehicles are as follows: in: Indicates the number of users connected to each energy storage vehicle; a m，k ∈{0,1} represents a m，k is a binary variable of {0, 1}; It means that each user is powered by only one energy storage vehicle; Indicates that the power received by the user must be greater than the minimum required power δ required by each user k .

10. The system for minimizing the number of energy storage vehicles in a distributed energy network according to claim 9, characterized in that: The minimization vehicle number model optimization module (4) is used to optimize the minimization vehicle number model according to the following method: The potential deployment area of ​​energy storage vehicles is divided into J equal-sized areas, and a binary variable f is introduced j ∈{0,1},β k，j ∈{0, 1} converts the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model; The optimized model for minimizing the number of energy storage vehicles is as follows: Where: f j ∈{0, 1} indicates whether the energy storage vehicle can be deployed in the jth area (x j ,y j ) on; β k，j ∈{0, 1} indicates whether user k is associated with the UAV in the jth area; The constraints of the optimized model for minimizing the number of energy storage vehicles are as follows: Where: f j ∈{0,1},β k，j ∈{0, 1} represents a binary constraint on a variable; Indicates that the user must be connected to an energy storage vehicle; It means that the electricity demand of each user must be met; It means that the energy storage vehicle needs to be deployed in area j only when at least one user is connected.

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