A method and system for determining the minimum number of energy storage vehicles in a distributed energy network

Abstract

A distributed energy storage vehicle number minimization determination method and system in an energy network, the method first constructs an energy storage vehicle power supply model, and defines the network node position, user use, power supply demand and other conditions, then under the constraint condition of meeting the power demand, a minimum energy storage vehicle number model is constructed with the minimum cost as the target, the vehicle deployment position is discretized and a binary variable is introduced to optimize the minimum energy storage vehicle number model, finally a multi-energy storage vehicle network topology graph is constructed and a greedy strategy is used to solve the model, and the minimum energy storage vehicle number and deployment position are output; the deployment position of the energy storage vehicle and the scheduling of the user power are optimized, an integer linear programming model is constructed, and a low complexity and high efficiency algorithm based on graph theory and greedy algorithm is proposed to solve, the calculation complexity in path planning is reduced, the optimal deployment of the distributed energy network is realized with the least number of energy storage vehicles, and the resource utilization efficiency is significantly improved.

Description

Technical Field

[0001] This invention relates to a method for determining the number of energy storage vehicles, belonging to the field of distributed energy networks, and particularly to a method and system for minimizing the number of energy storage vehicles in a distributed energy network. Background Technology

[0002] Distributed energy networks, characterized by their distribution, modularity, and scalability, can provide power supply support to remote areas, regions with damaged infrastructure, and areas with difficult grid access without relying on traditional large power grids. Among these, energy storage vehicles (EVs), as flexible mobile energy storage devices, can be flexibly deployed in different locations according to user needs and energy supply conditions. However, due to the limited number and capacity of EVs, it is impossible to deploy them for every user. Therefore, to maximize the effectiveness of EVs, it is necessary to minimize the number of EVs deployed while ensuring user power demand, thereby reducing system construction and operation costs. Current technologies typically employ linear programming to propose path planning solutions, but this is a nondeterministic polynomial problem with high solution complexity, leading to high uncertainty and low efficiency in path planning. Therefore, an accurate and efficient method is urgently needed to address the aforementioned shortcomings of existing technologies. Summary of the Invention

[0003] The purpose of this invention is to overcome the above-mentioned defects and problems 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 a distributed energy network composed of mobile energy storage vehicles, construct an energy storage vehicle power supply model; 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;

[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 supplied power, and the user's received power.

[0008] S3. Under the constraint of meeting users' electricity demand, with the goal of minimizing the deployment cost of energy storage vehicles, construct a model that minimizes the number of energy storage vehicles 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 and minimize the number of energy storage vehicles.

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

[0011] S6. Based on the network topology of multiple energy storage vehicles, a greedy strategy is used to solve the optimized model for minimizing the number of energy storage vehicles, and the output is the minimum number of energy storage vehicles and the deployment location of energy storage vehicles that meet the needs of all users.

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

[0013] The user usage conditions are as follows: each user is associated with one energy storage vehicle, and 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 k-th user is associated with the energy storage vehicle m, then a m，k =1; otherwise a m，k =0;

[0014] The user power supply requirements are as follows: the minimum power requirement for each user is δ. k The maximum power supply of the energy storage vehicle is P. veh And supply power equally to each user; the energy storage vehicle m supplies each user k with a power of The power received by user k from energy storage vehicle m is in: For the path loss of the temporarily deployed energy storage vehicle; R is the resistivity; if This indicates that the user's needs have been met.

[0015] Step S3 specifically refers to: under the constraint of meeting the user's electricity demand, deploying the joint energy storage vehicle at location I. m With user power dispatch a m，k Determine the minimum number of energy storage vehicles required for the energy network and construct a model that minimizes 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 that minimizes the number of energy storage vehicles are as follows:

[0019]

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

[0021] This means that the power received by the user must be greater than the minimum power requirement δ for each user. k .

[0022] Step S4 specifically includes:

[0023] The potential area for deployable energy storage vehicles is divided into J equal-sized regions, and a binary variable is introduced.

[0024] f j ∈{0,1}、β k，j The model for minimizing the number of energy storage vehicles is transformed into a discrete equivalent form ∈{0,1} to optimize the model for minimizing the number of energy storage vehicles.

[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 j-th region (x j y j ) on; β k，j ∈{0,1} indicates whether user k is associated with the drone in the j-th region;

[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 the variable; This means that the user must connect to an energy storage vehicle; This means that the electricity needs of every user must be met; This means that an energy storage vehicle needs to be deployed in region j only when at least one user is connected.

[0031] Step S5 specifically includes:

[0032] S51. Create an empty graph that contains no nodes or edges. With empty list Where G is used to store the locations of candidate energy storage vehicles; U is used to store the set of ground users covered by each candidate location;

[0033] S52. For each candidate location j of the energy storage vehicle, create an empty set U. j , for use by users within its coverage area;

[0034] S53. For each candidate location j of the energy storage vehicle, iterate through all ground users k; and determine whether the candidate location j of the energy storage vehicle can provide user k with a service that meets the power requirements.

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

[0036] S54. Merge all candidate locations for energy storage vehicles to serve the user set U. j Obtain the list of end users and output the network topology G of the multi-energy storage vehicle and the user association set U.

[0037] Step S6 specifically includes:

[0038] S61. Initialize the multi-energy storage vehicle network topology graph G and the user association set U, assign all candidate locations of energy storage vehicles to set D, and create an empty set F;

[0039] S62. Select the candidate energy storage vehicle location u with the fewest current ground users and delete u to determine whether it affects user coverage;

[0040] If there are users who cannot be covered by the candidate energy storage vehicle location u, then add u to set F and remove all ground users covered by the candidate energy storage vehicle location u from the multi-energy storage vehicle network topology graph G.

[0041] If deleting the candidate energy storage vehicle location u will not affect user coverage, then remove u from set D and remove the set of ground users associated with energy storage vehicle location u from user association set U;

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

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

[0044] The power supply model construction module is used to construct a power supply model for energy storage vehicles based on a distributed energy network composed of mobile energy storage vehicles; the power supply model for energy storage vehicles 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 supplied power, and the user's received power.

[0047] The module for constructing a model that minimizes the number of energy storage vehicles is used to construct a model that minimizes the number of energy storage vehicles, under the constraint of meeting users' electricity demand and with the goal of minimizing the deployment cost of energy storage vehicles, in conjunction with the above conditions.

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

[0049] The network topology construction module is used to construct a multi-energy storage vehicle network topology graph representing the connection relationship between candidate locations of energy storage vehicles and users based on graph theory methods.

[0050] The module for solving the minimum number of energy storage vehicles is used to solve the optimized minimum number of energy storage vehicles model based on the network topology of multiple energy storage vehicles using a greedy strategy. It outputs the minimum number of energy storage vehicles and the deployment location of energy storage vehicles that meet the needs of all users.

[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 m-th energy storage vehicle is I. m ;

[0053] The user usage conditions are as follows: each user is associated with one energy storage vehicle, and 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 k-th user is associated with the energy storage vehicle m, then a m，k =1; otherwise a m，k =0;

[0054] The user power supply requirements are as follows: the minimum power requirement for each user is δ. k The maximum power supply of the energy storage vehicle is P.veh And supply power equally to each user; the energy storage vehicle m supplies each user k with a power of The power received by user k from energy storage vehicle m is in: For the path loss of the temporarily deployed energy storage vehicle; R is the resistivity; if This indicates that the user's needs have been met.

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

[0056] Under the constraint of meeting users' electricity demand, the joint energy storage vehicle is deployed at location I. m With user power dispatch a m，k Determine the minimum number of energy storage vehicles required for the energy network and construct a model that minimizes 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 that minimizes the number of energy storage vehicles are as follows:

[0060]

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

[0062] This means that the power received by the user must be greater than the minimum power requirement δ for each user. k .

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

[0064] The potential area for deployable energy storage vehicles is divided into J equal-sized regions, and a binary variable f is introduced. j ∈{0,1}、β k，j The model for minimizing the number of energy storage vehicles is transformed into a discrete equivalent form ∈{0,1} to optimize the model for minimizing the number of energy storage vehicles.

[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 j-th region (x j y j ) on; β k，j ∈{0,1} indicates whether user k is associated with the drone in the j-th region;

[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 the variable; This means that the user must connect to an energy storage vehicle; This means that the electricity needs of every user must be met; This means that an energy storage vehicle needs to be deployed in region j only when at least one user is connected.

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0072] This invention discloses a method and system for minimizing the number of energy storage vehicles in a distributed energy network. The method first constructs a power supply model for the energy storage vehicles and defines conditions such as network node locations, user usage, and power demand. Then, under the constraint of meeting power demand, a model minimizing the number of energy storage vehicles is constructed with the goal of minimizing cost. Next, the vehicle deployment locations are discretized, and a binary variable is introduced to optimize the model minimizing the number of energy storage vehicles. Finally, a multi-energy storage vehicle network topology graph is constructed, and a greedy strategy is used to solve the model, outputting the minimum number of energy storage vehicles and their deployment locations. In application, this design optimizes the deployment locations of energy storage vehicles and the scheduling of user power by constructing an integer linear programming model. A low-complexity, high-efficiency algorithm based on graph theory and a greedy algorithm is proposed for solving the model, reducing the computational complexity of path planning and ensuring optimal deployment of the distributed energy network with the minimum number of energy storage vehicles, significantly improving resource utilization efficiency. Attached Figure Description

[0073] Figure 1 This is a flowchart of the method steps of the present invention.

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

[0075] Figure 3 This is a schematic diagram of the multi-energy storage vehicle network topology in Embodiment 1 of the present invention.

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

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

[0078] In the diagram: 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 Implementation

[0079] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0080] Example 1:

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

[0082] S1. Based on a distributed energy network composed of mobile energy storage vehicles, construct a power supply model for energy storage vehicles; such as... 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 supplied 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 m-th energy storage vehicle is I. 川 ;

[0086] The user usage conditions are as follows: each user is associated with one energy storage vehicle, and 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 k-th user is associated with the energy storage vehicle m, then a m，k =l; otherwise a m，k =0;

[0087] The user power supply requirements are as follows: the minimum power requirement for each user is δ. k The maximum power supply of the energy storage vehicle is P. veh And supply power equally to each user; the energy storage vehicle m supplies each user k with a power of The power received by user k from energy storage vehicle m is in: For the path loss of the temporarily deployed energy storage vehicle; R is the resistivity; if This indicates that the user's needs have been met.

[0088] S3. Under the constraint of meeting users' electricity demand, with the goal of minimizing the deployment cost of energy storage vehicles, construct a model that minimizes the number of energy storage vehicles by combining the above conditions.

[0089] Furthermore, under the constraint of meeting users' electricity demand, the deployment location of the joint energy storage vehicle is I. m With user power dispatch a m，k Determine the minimum number of energy storage vehicles required for the energy network and construct a model that minimizes the number of energy storage vehicles;

[0090] Based on this, an integer linear programming problem model (P1) is proposed to minimize the number of energy storage vehicles by using reasonable deployment locations and user scheduling strategies, in order 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 that minimizes the number of energy storage vehicles are as follows:

[0093]

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

[0095] This means that the power received by the user must be greater than the minimum power requirement δ for each user. k .

[0096] Since (P1) is a nondeterministic polynomial-hard (NP-hard) problem, and no known polynomial-time algorithm can find the optimal solution in all cases, this proposal suggests an efficient algorithm that uses graph theory and a greedy strategy to find a suboptimal solution. To simplify the solution process, this proposal transforms problem (P1) into (P2). During the transformation, a binary variable is introduced to describe whether the user establishes a connection with the energy storage vehicle at the candidate location, in order 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 and minimize the number of energy storage vehicles.

[0098] Furthermore, the potential deployment area for energy storage vehicles is divided into J equal-sized regions, and a binary variable f is introduced. j ∈{0,1}、β k，j The model for minimizing the number of energy storage vehicles is transformed into a discrete equivalent form ∈{0,1} to optimize the model for minimizing the number of energy storage vehicles.

[0099] The optimized model for minimizing the number of energy storage vehicles (P2) is as follows:

[0100]

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

[0102] β k，j ∈{0,1} indicates whether user k is associated with the drone in the j-th region;

[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 the variable; This means that the user must connect to an energy storage vehicle; This means that the electricity needs of every user must be met; This means that an energy storage vehicle needs to be deployed in region j only when at least one user is connected.

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

[0107] Furthermore, step S5 specifically includes:

[0108] S51. Create an empty graph that contains no nodes or edges. With empty list Where G is used to store the locations of candidate energy storage vehicles; U is used to store the set of ground users covered by each candidate location;

[0109] S52. For each candidate location j of the energy storage vehicle, create an empty set U. j , for use by users within its coverage area;

[0110] S53. For each candidate location j of the energy storage vehicle, iterate through all ground users k; and determine whether the candidate location j of the energy storage vehicle can provide user k with a service that meets the power requirements.

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

[0112] S54. Merge all candidate locations for energy storage vehicles to serve the user set U. j The system obtains the end-user list and outputs a multi-energy storage vehicle network topology graph G and a user association set U. The multi-energy storage vehicle network topology graph is as follows: Figure 3 As shown.

[0113] S6. Based on the network topology of multiple energy storage vehicles, a greedy strategy is used to solve the optimized model for minimizing the number of energy storage vehicles, and the output is the minimum number of energy storage vehicles and the deployment location of energy storage vehicles that meet the needs of all users.

[0114] Furthermore, step S6 specifically includes:

[0115] S61. Initialize the multi-energy storage vehicle network topology graph G and the user association set U, assign all candidate locations of energy storage vehicles to set D, and create an empty set F;

[0116] S62. Select the candidate energy storage vehicle location u with the fewest current ground users and delete u to determine whether it affects user coverage;

[0117] If there are users who cannot be covered by the candidate energy storage vehicle location u, then add u to set F and remove all ground users covered by the candidate energy storage vehicle location u from the multi-energy storage vehicle network topology graph G.

[0118] If deleting the candidate energy storage vehicle location u will not affect user coverage, then remove u from set D and remove the set of ground users associated with energy storage vehicle location u from user association set U;

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

[0120] This solution addresses the problem of covering all users' electricity needs within a given area with the minimum number of energy storage vehicles by optimizing the deployment locations of energy storage vehicles and the scheduling of user power. This is modeled as an NP-hard integer linear programming problem, with a certain level of complexity in its solution. To effectively solve this problem, this solution proposes a low-complexity, high-efficiency algorithm based on graph theory and a greedy algorithm. First, the deployment area of ​​the energy storage vehicles is discretized using graph theory, establishing an energy storage vehicle network graph. Each node represents a candidate energy storage vehicle and a user location, and edges represent the connection between the energy storage vehicle and the covered users. Then, a greedy algorithm is used to progressively remove redundant nodes to minimize the number of energy storage vehicles. In each iteration, the algorithm prioritizes removing candidate location nodes that have a smaller impact on user coverage, determining whether they would lead to insufficient user coverage, until all redundant nodes are removed. The remaining energy storage vehicle locations represent the optimized deployment scheme.

[0121] Example 2:

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

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

[0124] Condition setting module 2 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;

[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 supplied 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 m-th energy storage vehicle is I. m ;

[0128] The user usage conditions are as follows: each user is associated with one energy storage vehicle, and 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 k-th user is associated with the energy storage vehicle m, then a m，k =1; otherwise a m，k =0;

[0129] The user power supply requirements are as follows: the minimum power requirement for each user is δ. k The maximum power supply of the energy storage vehicle is P. veh And supply power equally to each user; the energy storage vehicle m supplies each user k with a power of The power received by user k from energy storage vehicle m is in: For the path loss of the temporarily deployed energy storage vehicle; R is the resistivity; if This indicates that the user's needs have been met.

[0130] Minimize vehicle number model construction module 3 is used to construct a minimize energy storage vehicle number model under the constraint of meeting user electricity demand, with the goal of minimizing the deployment cost of energy storage vehicles, in conjunction with the above conditions.

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

[0132] Under the constraint of meeting users' electricity demand, the joint energy storage vehicle is deployed at location I. m With user power dispatch a m，k Determine the minimum number of energy storage vehicles required for the energy network and construct a model that minimizes 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 that minimizes the number of energy storage vehicles are as follows:

[0136]

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

[0138] This means that the power received by the user must be greater than the minimum power requirement δ for each user. k .

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

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

[0141] The potential area for deployable energy storage vehicles is divided into J equal-sized regions, and a binary variable f is introduced. j ∈{0,1}、β k，j The model for minimizing the number of energy storage vehicles is transformed into a discrete equivalent form ∈{0,1} to optimize the model for minimizing the number of energy storage vehicles.

[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 j-th region (x j y j )superior;

[0145] β k，j ∈{0,1} indicates whether user k is associated with the drone in the j-th region;

[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 the variable; This means that the user must connect to an energy storage vehicle; This means that the electricity needs of every user must be met; This means that an energy storage vehicle needs to be deployed in region j only when at least one user is connected.

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

[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 contains no nodes or edges. With empty list Where G is used to store the locations of candidate energy storage vehicles; U is used to store the set of ground users covered by each candidate location;

[0152] S52. For each candidate location j of the energy storage vehicle, create an empty set U. j, for use by users within its coverage area;

[0153] S53. For each candidate location j of the energy storage vehicle, iterate through all ground users k; and determine whether the candidate location j of the energy storage vehicle can provide user k with a service that meets the power requirements.

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

[0155] S54. Merge all candidate locations for energy storage vehicles to serve the user set U. j Obtain the list of end users and output the network topology G of the multi-energy storage vehicle and the user association set U.

[0156] Minimize vehicle number model solution module 6 is used to solve the optimized minimize energy storage vehicle number model based on the multi-energy storage vehicle network topology using a greedy strategy, and outputs the minimum number of energy storage vehicles and the deployment location of energy storage vehicles that meet the needs of all users.

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

[0158] S61. Initialize the multi-energy storage vehicle network topology graph G and the user association set U, assign all candidate locations of energy storage vehicles to set D, and create an empty set F;

[0159] S62. Select the candidate energy storage vehicle location u with the fewest current ground users and delete u to determine whether it affects user coverage;

[0160] If there are users who cannot be covered by the candidate energy storage vehicle location u, then add u to set F and remove all ground users covered by the candidate energy storage vehicle location u from the multi-energy storage vehicle network topology graph G.

[0161] If deleting the candidate energy storage vehicle location u will not affect user coverage, then remove u from set D and remove the set of ground users associated with energy storage vehicle location u from user association set U;

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

[0163] Example 3:

[0164] See Figure 5A 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 computer program code 81 and to 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 the distributed energy network described in Embodiment 1 according to the instructions in the computer program code 81.

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

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

[0169] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EKROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0170] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smarttalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, 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 a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or to an external computer (e.g., via the Internet using an Internet service provider) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0171] The aforementioned equipment and non-transitory computer-readable storage media can be found in the detailed description of a 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 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. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for determining energy storage vehicle number minimization in a distributed energy network, characterized in that, The method comprises the following steps: S1, constructing a mobile energy storage vehicle power supply model based on a distributed energy network composed of mobile energy storage vehicles; the mobile energy storage vehicle power supply model comprises a distributed energy network composed of a plurality of mobile energy storage vehicles providing power supply services for a plurality of users; S2, defining network node position conditions, user use conditions and user power supply demand conditions in the mobile energy storage vehicle power supply model; the network node position conditions comprise user positions and mobile energy storage vehicle positions; the user use conditions comprise associations between users and mobile energy storage vehicles; and the user power supply demand conditions comprise user demand powers, mobile energy storage vehicle supply powers and user receiving powers; S3, constructing a mobile energy storage vehicle number minimization model under the constraint condition of meeting user power demand and taking minimization of mobile energy storage vehicle deployment cost as a target, in combination with the above conditions; S4, based on a discretization method, decomposing an infinite number of possible mobile energy storage vehicle deployment positions into a finite number of feasible points, and introducing binary variables to describe whether a user is connected with a mobile energy storage vehicle at a candidate deployment position, so as to optimize the mobile energy storage vehicle number minimization model; S5, constructing a multi-mobile energy storage vehicle network topology graph representing a connection relationship between candidate mobile energy storage vehicle positions and users based on a graph theory method; S6, based on the multi-mobile energy storage vehicle network topology graph, solving the optimized mobile energy storage vehicle number minimization model by using a greedy strategy, and outputting a minimum number of mobile energy storage vehicles and mobile energy storage vehicle deployment positions meeting all user demands; The step S3 specifically refers to: under the constraint condition of meeting the user's electricity demand, jointly deploying the positions of the energy storage vehicles With user power dispatch Determine the minimum number of energy storage vehicles required for the energy network, and construct a minimum energy storage vehicle number model; the mobile energy storage vehicle number minimization model is as follows: ； constraint conditions of the mobile energy storage vehicle number minimization model are as follows: ； wherein: represents the number of users connected to each energy storage vehicle; represents is a binary variable; represents that each user is powered by only one energy storage vehicle; represents that the power received by the user must be greater than the minimum demand power required by each user .

2. The method according to claim 1, characterized in that: The network node location condition is: The first The location of each user is , No. The location of the energy storage vehicle is ; The user usage condition is that each user is associated with one energy storage vehicle, and each energy storage vehicle is associated with one user The binary association variable between one energy storage vehicle and one user is If the first user is associated with the energy storage vehicle , then ; otherwise ; The user power supply demand condition is that the minimum demand power required by each user is , the maximum supply power of the energy storage vehicle is , and power is equally supplied to each user; the energy storage vehicle supplies power to each user , and the power is ; the user receives power from the energy storage vehicle , and the power is ; wherein: is the path loss of the temporarily deployed energy storage vehicle; is the resistance coefficient; if , it indicates that the demand of the user is met.

3. The method according to claim 1, characterized in that: the step S4 specifically comprises: dividing the potential deployable energy storage vehicle area into equal-sized regions and introducing binary variables , converting the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model; the optimized mobile energy storage vehicle number minimization model is as follows: ； wherein: represents whether the energy storage vehicle can be deployed on the first region ; represents whether the user has an association with the first region's drone; constraint conditions of the optimized mobile energy storage vehicle number minimization model are as follows: ； wherein: , represents a binary constraint on a variable; represents that a user must be connected to an energy storage vehicle; represents that the power demand of each user must be satisfied; represents that an energy storage vehicle is only needed in the area if at least one user is connected.

4. The method according to claim 1, characterized in that: the step S5 specifically comprises: S51, create an empty graph that does not contain any nodes and edges with an empty list ; wherein for storing candidate energy storage vehicle locations; for storing a set of ground users covered by each candidate location; S52, for each energy storage vehicle candidate location , create an empty set , for storing users within its coverage range; S53, for each energy storage vehicle candidate location , iterate through all ground users ; and determine whether the energy storage vehicle candidate location can provide service to the users that meet the power requirements; If the energy storage vehicle candidate position can serve any user, then the energy storage vehicle candidate position is added to the empty graph and an edge between it and all the users it can serve is added to the graph, while these users are added to the set ; if not, the next energy storage vehicle candidate position is judged. S54, merge all the candidate locations of the energy storage vehicles that can serve the user set , obtain the final user list, and output the multi-energy storage vehicle network topology graph and the user association set .

5. The method according to claim 4, characterized in that: the step S6 specifically comprises: S61, initialize multi-energy vehicle network topology graph and user association set , assign all energy storage vehicle candidate positions to the set , and create an empty set ; S62, selecting the candidate energy storage vehicle position with the least number of currently covered ground users and deleting determining whether it affects user coverage; If the candidate energy storage vehicle location is deleted If the user cannot be covered, then Join the set And delete all ground users covered by the candidate energy storage vehicle location From the multi-energy storage vehicle network topology map ​ If the candidate energy storage vehicle location is deleted without affecting the user coverage, then is deleted from the set and the set of ground users associated with the energy storage vehicle location is removed from the user association set ; S63, repeat steps S61-S62 until the set Subtract the set If empty, the loop ends and the set is output ; At this time the set contains the minimum number of energy storage vehicles and the deployment locations of the energy storage vehicles that meet all user needs. 6.A system for energy storage vehicle number minimization determination in a distributed energy network, characterized in that, The system comprises: a power supply model construction module (1) configured to construct a mobile energy storage vehicle power supply model based on a distributed energy network composed of mobile energy storage vehicles; the mobile energy storage vehicle power supply model comprises a distributed energy network composed of a plurality of mobile energy storage vehicles providing power supply services for a plurality of users; a condition setting module (2) configured to define network node position conditions, user use conditions and user power supply demand conditions in the mobile energy storage vehicle power supply model; the network node position conditions comprise user positions and mobile energy storage vehicle positions; the user use conditions comprise associations between users and mobile energy storage vehicles; and the user power supply demand conditions comprise user demand powers, mobile energy storage vehicle supply powers and user receiving powers; a mobile energy storage vehicle number minimization model construction module (3) configured to construct a mobile energy storage vehicle number minimization model under the constraint condition of meeting user power demand and taking minimization of mobile energy storage vehicle deployment cost as a target, in combination with the above conditions; The minimum number of vehicles model optimization module (4) is configured to decompose an infinite number of possible energy storage vehicle deployment locations into a finite number of feasible points based on a discretization method, and introduce binary variables to describe whether a user is connected to an energy storage vehicle at a candidate deployment location, so as to optimize the minimum number of energy storage vehicles model; The network topology construction module (5) is configured to construct a multi-energy storage vehicle network topology graph representing the connection relationship between the candidate locations of the energy storage vehicles and the users based on a graph theory method; The minimum number of vehicles model solving module (6) is configured to solve the optimized minimum number of energy storage vehicles model based on the multi-energy storage vehicle network topology graph by using a greedy strategy, and output the minimum number of energy storage vehicles and the deployment locations of the energy storage vehicles that meet all the user demands. The minimum number of vehicles model constructed by the minimum number of vehicles model construction module (3) is as follows: Under the constraint condition of meeting the user power demand, the joint energy storage vehicle deployment position With user power dispatch , determine the minimum number of energy storage vehicles required for the energy network, and construct a minimum energy storage vehicle number model; The minimum number of energy storage vehicles model is as follows: ； The constraint conditions of the minimum number of energy storage vehicles model are as follows: ； wherein: represents the number of users connected per energy storage vehicle; represents is a binary variable; represents that each user is powered by only one energy storage vehicle; represents that the power received by the user must be greater than the minimum demand power required by each user .

7. The system for determining the minimum number of energy storage vehicles in a distributed energy network according to claim 6, wherein: The conditions set by the condition setting module (2) are as follows: The network node position condition is that the position of the first user is , and the position of the first energy storage vehicle is . , and the position of the first energy storage vehicle is . The user usage condition is: each user is associated with one energy storage vehicle, each energy storage vehicle is associated with one user The binary association variable between one energy storage vehicle and one user is If the first user is associated with the energy storage vehicle , then ; otherwise ; The user power supply demand condition is that the minimum demand power required by each user is , the maximum supply power of the energy storage vehicle is , and power is equally supplied to each user; the energy storage vehicle supplies power to each user , and the power is ; the user receives power from the energy storage vehicle , and the power is ; wherein: is the path loss of the temporarily deployed energy storage vehicle; is the resistance coefficient; if , it indicates that the demand of the user is met.

8. The system for determining the minimum number of energy storage vehicles in a distributed energy network according to claim 7, wherein: The minimum number of vehicles model optimization module (4) is configured to optimize the minimum number of vehicles model according to the following method: dividing a potential deployable energy storage vehicle area into equal-sized regions and introducing binary variables , converting the minimum energy storage vehicle number model into a discrete equivalent form to optimize the minimum energy storage vehicle number model; The optimized minimum number of energy storage vehicles model is as follows: ； wherein: represents whether the energy storage vehicle can be deployed on the first region ; represents whether the user has an association with the first region's drone; The constraint conditions of the optimized minimum number of energy storage vehicles model are as follows: ； wherein: , denotes a binary constraint on a variable; denotes that a user must be connected to an energy storage vehicle; denotes that the power demand of each user must be satisfied; denotes that an energy storage vehicle is only needed in the area if at least one user is connected.

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