Hybrid energy power supply optimization method and device based on cooperation of energy storage vehicle and large power grid

Abstract

The invention discloses a hybrid energy power supply optimization method and device based on cooperation of energy storage vehicles and a large power grid, and the method comprises the steps: constructing a hybrid energy power supply system which uses the large power grid and a plurality of energy storage vehicles to provide power for users in the coverage range of the large power grid, and deploying the plurality of energy storage vehicles to provide power for users beyond the coverage range of the large power grid; a power distribution model is constructed, the model aims at maximizing the energy efficiency of the energy storage vehicle, and the power requirements of all users are met through optimization of the position of the energy storage vehicle, power distribution of a large power grid, association scheduling between the users and the large power grid and association scheduling between the users and energy storage workshops; and converting the power distribution model into a mixed integer non-convex optimization problem, and solving the power distribution model step by step by adopting a block coordinate descent method and a successive convex approximation method to obtain a suboptimal solution. By optimizing cooperative work of the large power grid and the energy storage vehicle, efficient power transmission is achieved, and it is ensured that a user can obtain stable power supply in various complex scenes.

Description

Technical Field

[0001] The present invention relates to the field of smart grids, and in particular to a hybrid energy power supply optimization method and device based on the collaboration of an energy storage vehicle and a large power grid. Background Art

[0002] In some remote areas or areas with insufficient power supply, the traditional single power grid power supply mode can no longer meet the power needs of users. By introducing mobile energy vehicles to collaborate with the large power grid to build a hybrid energy system, the reliability of power supply can be improved under flexible scheduling. However, how to efficiently distribute power between the large power grid and mobile energy vehicles to ensure that the different needs of all users can be met, while considering the efficiency of power transmission and reducing power losses, is a key challenge facing hybrid energy networks. Summary of the invention

[0003] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a hybrid energy power supply optimization method and device based on the collaboration of energy storage vehicles and large power grids, so as to achieve efficient power transmission by optimizing the collaborative work of large power grids and energy storage vehicles, and ensure that users can obtain stable power supply in various complex scenarios.

[0004] To achieve the above objectives, the technical solution of the present invention is: a hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids, comprising:

[0005] Build a hybrid energy power supply system that uses a large power grid and multiple energy storage vehicles to provide electricity to users within the coverage of the large power grid, and deploy multiple energy storage vehicles to provide electricity to users beyond the coverage of the large power grid;

[0006] Construct a power distribution model that aims to maximize the energy efficiency of energy storage vehicles and meet the power needs of all users by optimizing the location of energy storage vehicles, power distribution of the large power grid, the associated scheduling between users and the large power grid, and the associated scheduling between users and energy storage workshops;

[0007] The power distribution model is transformed into a mixed integer non-convex optimization problem, and the block coordinate descent method and successive convex approximation method are used to solve the power distribution model step by step to obtain a suboptimal solution.

[0008] The power distribution model is:

[0009]

[0010]

[0011] In the formula, I n is the position of the energy storage vehicle; δ a For users If the large power grid dispatches users within its coverage area, Power supply, then δ a =1, otherwise δ a =0; For users If the energy storage vehicle is dispatched to the user within the coverage area of ​​the large power grid, Power supply, then otherwise Associating users with energy storage workshops for scheduling, if the user is beyond the coverage of the large power grid Power supply, then otherwise q a For users within the coverage of the large power grid The weight of; N is the number of energy storage vehicles; P veh The maximum power available for the energy storage vehicle battery; is the path loss of the energy storage vehicle supplying power to users; K is the number of users; M is the maximum user load of the large power grid; Q is the maximum number of users that can be connected to each energy storage vehicle; For users The power of receiving electricity; For users The total power required; For users The power of receiving electricity; For users A is the number of users within the coverage of the large power grid; B is the number of users beyond the coverage of the large power grid.

[0012] The path loss of the energy storage vehicle supplying power to the user for:

[0013]

[0014] In the formula, ζ k，n It is the associated scheduling between energy storage vehicles and users; k is the kth user position; R is the resistivity.

[0015] The user Power received for:

[0016]

[0017] Where P cen Supply power to all users of the large power grid; For energy storage vehicles to users Power supply path loss; The average power supplied by the energy storage vehicle to users;

[0018] The user Power received for:

[0019]

[0020] In the formula, For energy storage vehicles to users Power supply path loss.

[0021] The block coordinate descent method and the successive convex approximation method are used to solve the power distribution model step by step to obtain a suboptimal solution, including: dividing the variables into {I n ,q a}Two groups, in the first block, fixed {I n ,q a} to find the value Fixed in the second block Value Solver n ,q a}, by iteratively solving the optimization problem of the two blocks until convergence, the suboptimal solution of the objective is obtained.

[0022] To solve Convert the power distribution model to:

[0023]

[0024]

[0025] In the formula, β 0 is the auxiliary variable introduced to replace the objective function; The coordinates of users within the coverage area of ​​the large power grid; is the coordinate of the user beyond the coverage of the large power grid; ρ is the penalty factor; γ n , γ a，n , γ b，n is an auxiliary variable, replacing the first-order Taylor expansion; They are δ a , At a given local point in the rth iteration; is the auxiliary variable introduced to replace the first-order Taylor expansion.

[0026] To solve the variable {I n ,q a}, and transform the power distribution model into:

[0027]

[0028] In the formula, is the auxiliary variable introduced to replace the first-order Taylor expansion; ζ k，n It is the associated scheduling between energy storage vehicles and users; k is the user coordinate; τ k，n , is the auxiliary variable introduced to replace the first-order Taylor expansion; P cen Supply power to all users of the large power grid; For energy storage vehicles to users Power supply path loss; is the auxiliary variable introduced to replace the first-order Taylor expansion; For energy storage vehicles to users Power supply path loss; is the auxiliary variable introduced to replace the first-order Taylor expansion.

[0029] A hybrid energy power supply optimization device based on the coordination of an energy storage vehicle and a large power grid, the device is applied to the above-mentioned method, and the device comprises:

[0030] A hybrid energy power supply system building module, which is used to build a hybrid energy power supply system, which uses a large power grid and multiple energy storage vehicles to provide electricity to users within the coverage of the large power grid, and deploys multiple energy storage vehicles to provide electricity to users beyond the coverage of the large power grid;

[0031] The power distribution model building module is used to build a power distribution model. The model aims to maximize the energy efficiency of energy storage vehicles and meet the power needs of all users by optimizing the location of energy storage vehicles, power distribution of the large power grid, the associated scheduling between users and the large power grid, and the associated scheduling between users and energy storage workshops.

[0032] The power distribution model solving module is used to transform the power distribution model into a mixed integer non-convex optimization problem, and adopts the block coordinate descent method and the successive convex approximation method to solve the power distribution model step by step to obtain a suboptimal solution.

[0033] A hybrid energy power supply optimization device based on the coordination of energy storage vehicles and large power grids, including a memory and a processor;

[0034] The memory is used to store computer program code and transmit the computer program code to the processor;

[0035] The processor is used to execute the method according to the instructions in the computer program code.

[0036] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] In the hybrid energy power supply optimization method and device based on the collaboration of energy storage vehicles and large power grids of the present invention, the energy efficiency of energy storage vehicles is maximized by optimizing the power distribution of large power grids, the associated scheduling of users and large power grids, the associated scheduling of users and energy storage vehicles, and the distribution location of energy storage vehicles. At the same time, the problem is modeled as a mixed integer non-convex optimization problem, and is effectively solved using block coordinate descent technology, continuous convex approximation technology, etc. Ultimately, the model achieves efficient transmission of electricity by continuously optimizing the collaborative work of large power grids and energy storage vehicles, ensuring that users can obtain stable power supply in various complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of a hybrid energy power supply optimization method based on the collaboration between energy storage vehicles and large power grids of the present invention.

[0040] Figure 2 It is a schematic diagram of a hybrid energy power supply system in which an energy storage vehicle cooperates with a large power grid in an embodiment of the present invention.

[0041] Figure 3 It is a flow chart of a method for allocating electric energy sources to different users in an embodiment of the present invention.

[0042] Figure 4 It is a structural block diagram of a hybrid energy power supply optimization device based on the coordination of an energy storage vehicle and a large power grid according to the present invention.

[0043] Figure 5 It is a structural block diagram of a hybrid energy power supply optimization device based on the collaboration between an energy storage vehicle and a large power grid according to the present invention. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0045] See also Figure 1 The present invention provides a hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids, comprising:

[0046] S1. Build a hybrid energy power supply system that uses a large power grid and multiple energy storage vehicles to provide electricity to users within the coverage of the large power grid, and deploys multiple energy storage vehicles to provide electricity to users beyond the coverage of the large power grid.

[0047] S2. Build a power distribution model based on the power demand of users. The model aims to maximize the energy efficiency of energy storage vehicles and meet the power demand of all users by optimizing the location of energy storage vehicles, power distribution of large power grids, related scheduling between users and large power grids, and related scheduling between users and energy storage workshops.

[0048] S3. The power distribution model is transformed into a mixed integer non-convex optimization problem, and the block coordinate descent method and the successive convex approximation method are used to solve the power distribution model step by step to obtain a suboptimal solution.

[0049] When the infrastructure is damaged or there is a temporary large electricity demand, the existing large power grid may experience a short-term power shortage. At this time, mobile energy storage vehicles can be deployed to areas where load demand is concentrated to provide flexible and reliable power support. For different user groups, the scheduling variables and constraints of the large power grid and the energy storage workshop are defined, and the power demand of each user is met by optimizing the location of the energy storage vehicle and the weight distribution of the large power grid; binary decision variables are used to represent the power relationship between the user and the large power grid or the energy storage vehicle, ensuring that each user obtains the required power within the coverage of the large power grid or through the energy storage vehicle; by iteratively solving the scheduling problem of the large power grid and the energy storage vehicle, the overall energy utilization rate of the system is gradually improved. The present invention optimizes the collaborative work of the large power grid and the mobile energy vehicle to achieve efficient power transmission and ensure that users can obtain a stable power supply in various complex scenarios.

[0050] like Figure 2 The hybrid energy power supply system shown in the figure meets the power needs of K power users through N energy storage vehicles and a traditional large power grid. The large power grid can cover an area with a radius of d. cen The circular area can be divided into two categories according to whether they are within the coverage area. and The corresponding coordinate positions are expressed as and use represents the set of mobile energy storage vehicles, I n ={x n ,y n} represents energy storage vehicle v n It is known that the total power required by each user is and Users within the coverage area can be powered by the large power grid and energy storage vehicles; while users outside the coverage area can only be powered by energy storage vehicles.

[0051] Assume that the maximum number of user loads in the large power grid is M. Considering that the large power grid is connected to multiple stable energy sources, it is assumed that its energy supply is unlimited and can supply all users with a total of P cen The power of . Define the binary variable δ a ∈{0,1} represents user The relationship between the power grid and the large power grid. Energy supply, then δ a =1, otherwise δ a =0, at the same time:

[0052]

[0053] With its powerful computing power, the large power grid can flexibly allocate power to each user. a For users The power shared by the user in the large power grid can be expressed as P cen q a δ a ,and:

[0054]

[0055] Assume that each energy storage vehicle can connect to a maximum of Q users, and the maximum power available from the battery is P veh .definition express and energy storage vehicle v n If the relationship between By v n Energy supply, then otherwise Similarly, definition And there are:

[0056]

[0057] Limited by its own computing power, the energy storage vehicle outputs equal power to each user. The average power transmitted by the nth energy storage vehicle to each user can be calculated by Considering that the temporarily deployed energy storage vehicle is not fully configured and does not have the low-loss high-voltage transmission capability of the large power grid, it will experience non-negligible path loss. The path loss of the energy storage vehicle supplying power to the user is Among them, k，n is the associated dispatching of energy storage vehicles and users (including users within and outside the large power grid), g k is the kth user position, and R is the resistance coefficient. Then, the power received by the kth user from all energy storage vehicles can be obtained by Calculation. Considering the limited capacity of the on-board battery, it cannot continuously provide large-scale electricity like the large power grid. Therefore, how to improve energy utilization efficiency and ensure that every unit of electricity can be effectively used is the key to improving the power supply capacity of energy storage vehicles. The energy efficiency of energy storage vehicles can be expressed as:

[0058]

[0059] Furthermore, the user Power received for:

[0060]

[0061] Where P cenSupply power to all users of the large power grid; For energy storage vehicles to users Power supply path loss; The average power supplied by the energy storage vehicle to users;

[0062] The user Power received for:

[0063]

[0064] In the formula, For energy storage vehicles to users Power supply path loss.

[0065] To meet all user needs, and For ease of understanding, Figure 3 The figure depicts the hybrid energy power supply system that supplies power to users within and outside the coverage of the large power grid.

[0066] Furthermore, in order to provide energy-saving normal services to all users, the present invention aims to maximize the energy efficiency of all energy storage vehicles by jointly optimizing the positions of energy storage vehicles. n}、Associated dispatch between users and large power grids {δ a}、Association and scheduling between users and energy storage workshops Large power grid user weight {q a}, ensuring the power needs of all users.

[0067] The power distribution model is:

[0068]

[0069] In the formula, I n is the position of the energy storage vehicle; δ a For users If the large power grid dispatches users within its coverage area, Power supply, then δ a =1, otherwise δ a =0; For users If the energy storage vehicle is dispatched to the user within the coverage area of ​​the large power grid, Power supply, then otherwise Associating users with energy storage workshops for scheduling, if the user is beyond the coverage of the large power grid Power supply, then otherwise q a For users within the coverage of the large power grid The weight of; N is the number of energy storage vehicles; P veh The maximum power available for the energy storage vehicle battery; is the path loss of the energy storage vehicle supplying power to users; K is the number of users; M is the maximum user load of the large power grid; Q is the maximum number of users that can be connected to each energy storage vehicle; For users The power of receiving electricity; For users The total power required; For users The power of receiving electricity; For users A is the number of users within the coverage of the large power grid; B is the number of users beyond the coverage of the large power grid.

[0070] Furthermore, the present invention establishes a power distribution model with the goal of maximizing the energy efficiency of the energy storage vehicle. To solve the mathematical model, the following steps are adopted:

[0071] Sub-model decomposition: The original optimization problem (P1) is decomposed into two sub-problems (P2) and (P4), which are the large power grid and energy storage vehicle scheduling optimization sub-model, and the energy storage vehicle location and large power grid power distribution optimization sub-model. By using convex optimization technology in each sub-problem, the optimal solution of each sub-model is gradually solved, and the two sub-problems are optimized by alternating iterations to achieve convergence of suboptimal solutions.

[0072] Introduction of penalty term: For a large number of binary variables in the first sub-problem (P2), the present invention converts the original optimization problem model into an equivalent form with penalty terms, and introduces a penalty parameter greater than zero, so that the part that does not meet the equality constraint condition is penalized to improve the accuracy of the solution. This step converts the optimization problem model into the second penalty problem model (P3).

[0073] Iterative loop algorithm: Each loop will alternately solve the transformed problem models (P3) and (P5) under the condition of given penalty parameters using block coordinate descent and successive convex approximation techniques and solver tools such as CVX, and finally obtain the suboptimal solution to the problem. The loop will go through multiple rounds of iterations until the increase ratio of the objective function value is lower than the preset threshold. In this way, the energy utilization and power transmission efficiency of the energy storage vehicle can be maximized without sacrificing system constraints, ensuring that the power needs of all users are met.

[0074] In the power distribution model (P1), the constraint and is a convex constraint, and the optimization objective and other constraints are non-convex. To solve the problem effectively, we will use the block coordinate descent (BCD) method and the successive convex approximation method to solve the power distribution model step by step to obtain a suboptimal solution. The block coordinate descent technique is used to divide the variables into different blocks for solving to reduce the complexity of the problem. Specifically, the variables are divided into {I n ,q a}Two groups, in the first block, fixed {I n ,q a} to find the value Fixed in the second block Value Solver n ,q a}, by iteratively solving the optimization problem of the two blocks until convergence, the suboptimal solution of the objective function is obtained.

[0075] Furthermore, to solve Convert the power distribution model to:

[0076]

[0077] All three variables are binary variables, and the constraints and Contains nonlinear terms. To facilitate the solution, when other variables are fixed, the objective function can be transformed into:

[0078]

[0079] To solve the constraints and The nonlinear term in the introduction of auxiliary variables S n , and order:

[0080]

[0081] constraint The left side of can be expressed as:

[0082]

[0083] Although there is The non-concave term of , but by giving a local point and Using the first-order Taylor expansion approximation, we can get the following linear approximate expression:

[0084]

[0085] Therefore, the constraint The left side of can be approximated by its linear lower bound function:

[0086]

[0087] Similarly, the constraint The left side of is expressed as:

[0088]

[0089] For binary constraints and It can be converted into the following equivalent form:

[0090]

[0091] The three nonlinear constraints after conversion can be added to the objective function as penalty terms, that is, if the values ​​of these three variables are not 0 or 1, a penalty is imposed on the objective function to increase the function value. The positive parameter ρ is introduced as the penalty factor, and the converted objective function is:

[0092]

[0093] Although the three newly added penalty terms are all non-convex functions, they can also be expanded using the first-order Taylor approximation to obtain the upper bound linear expression:

[0094]

[0095]

[0096] Based on this, the upper bound of the objective function β can be obtained:

[0097]

[0098] In summary, the converted power distribution model (P2) is approximated as:

[0099]

[0100] In the formula, β 0 is the auxiliary variable introduced to replace the objective function; The coordinates of users within the coverage area of ​​the large power grid; is the coordinate of the user beyond the coverage of the large power grid; ρ is the penalty factor; γ n , γ a，n , γ b，n is an auxiliary variable, replacing the first-order Taylor expansion; They are δ a , At a given local point in the rth iteration; is the auxiliary variable introduced to replace the first-order Taylor expansion.

[0101] The above problem model (P3) is a standard convex optimization problem that can be efficiently solved using the CVX tool. By replacing the above approximate form, any feasible solution of the approximate form (P3) is always a feasible solution of the above converted power distribution model (P2).

[0102] Furthermore, to solve the variable {I n ,q a}, the power distribution model is converted into a sub-problem (P4), which is:

[0103]

[0104] Objective Function and Constraints The non-convexity in the function ||g k -I n || 2 non-concave. Similarly, by k -I n || 2 At the point Performing a first-order Taylor expansion, we can obtain the following inequality, which is:

[0105]

[0106] Applying the above lower bounds, we can get a series of lower bound linear expressions:

[0107]

[0108] Therefore, the power distribution model after the above conversion can be approximated as:

[0109]

[0110] In the formula, is the auxiliary variable introduced to replace the first-order Taylor expansion; ζ k，n It is the associated scheduling between energy storage vehicles and users; k is the user coordinate; τ k，n , is the auxiliary variable introduced to replace the first-order Taylor expansion; P cen Supply power to all users of the large power grid; For energy storage vehicles to users Power supply path loss; is the auxiliary variable introduced to replace the first-order Taylor expansion; For energy storage vehicles to users Power supply path loss; is the auxiliary variable introduced to replace the first-order Taylor expansion.

[0111] if and The lower bound If both are greater than the threshold, and must also satisfy the threshold. In addition, because The minimum performance can be ensured by maximizing the lower bound of the original objective function. Since the problem model (P5) is also a convex problem, it can be solved efficiently using solvers such as CVX.

[0112] Based on the above results, an iterative algorithm to solve problem (P1) is proposed by iteratively alternating optimization problems (P3) and (P5), specifically:

[0113] initialization And set the number of inner loop iterations r = 0;

[0114] At a given local point Next, solve the objective function get

[0115] At a given local point Next, solve the objective function Obtained

[0116] Set r = r + 1;

[0117] Repeat the above steps until convergence.

[0118] See also Figure 4 The present invention also provides a hybrid energy power supply optimization device based on the coordination of energy storage vehicles and large power grids. The device is applied to the above-mentioned hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids. The device includes:

[0119] A hybrid energy power supply system building module, which is used to build a hybrid energy power supply system, which uses a large power grid and multiple energy storage vehicles to provide electricity to users within the coverage of the large power grid, and deploys multiple energy storage vehicles to provide electricity to users beyond the coverage of the large power grid;

[0120] The power distribution model building module is used to build a power distribution model. The model aims to maximize the energy efficiency of energy storage vehicles and meet the power needs of all users by optimizing the location of energy storage vehicles, power distribution of the large power grid, the associated scheduling between users and the large power grid, and the associated scheduling between users and energy storage workshops.

[0121] The power distribution model solving module is used to transform the power distribution model into a mixed integer non-convex optimization problem, and adopts the block coordinate descent method and the successive convex approximation method to solve the power distribution model step by step to obtain a suboptimal solution.

[0122] See also Figure 5, the present invention also provides a hybrid energy power supply optimization device based on the coordination of energy storage vehicles and large power grids, including a memory and a processor;

[0123] The memory is used to store computer program code and transmit the computer program code to the processor;

[0124] The processor is used to execute the above-mentioned hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids according to the instructions in the computer program code.

[0125] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned hybrid energy power supply optimization method based on the collaboration between an energy storage vehicle and a large power grid is implemented.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] The above-mentioned devices and non-temporary computer-readable storage media can be found in the specific description of a hybrid energy power supply optimization method based on the collaboration between energy storage vehicles and large power grids and its beneficial effects, which will not be repeated here.

[0130] 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 hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids, characterized in that: include: Build a hybrid energy power supply system that uses a large power grid and multiple energy storage vehicles to provide electricity to users within the coverage of the large power grid, and deploy multiple energy storage vehicles to provide electricity to users beyond the coverage of the large power grid; Construct a power distribution model that aims to maximize the energy efficiency of energy storage vehicles and meet the power needs of all users by optimizing the location of energy storage vehicles, power distribution of the large power grid, the associated scheduling between users and the large power grid, and the associated scheduling between users and energy storage workshops; The power distribution model is transformed into a mixed integer non-convex optimization problem, and the block coordinate descent method and successive convex approximation method are used to solve the power distribution model step by step to obtain a suboptimal solution.

2. According to claim 1, a hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids is characterized in that: The power distribution model is: In the formula, I n is the position of the energy storage vehicle; δ a For users If the large power grid dispatches users within its coverage area, Power supply, then δ a =1, otherwise δ a =0; For users If the energy storage vehicle is dispatched to the user within the coverage area of ​​the large power grid, Power supply, then otherwise Associating users with energy storage workshops for scheduling, if the user is beyond the coverage of the large power grid Power supply, then otherwise q a For users within the coverage of the large power grid The weight of; N is the number of energy storage vehicles; P veh The maximum power available for the energy storage vehicle battery; is the path loss of the energy storage vehicle supplying power to users; K is the number of users; M is the maximum user load of the large power grid; Q is the maximum number of users that can be connected to each energy storage vehicle; For users The power of receiving electricity; For users The total power required; For users The power of receiving electricity; For users A is the number of users within the coverage of the large power grid; B is the number of users beyond the coverage of the large power grid.

3. A hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids according to claim 2, characterized in that: The path loss of the energy storage vehicle supplying power to the user for: In the formula, It is the associated scheduling between energy storage vehicles and users; k is the kth user position; R is the resistivity.

4. A hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids according to claim 2, characterized in that: The user Power received for: Where P cen Supply power to all users of the large power grid; For energy storage vehicles to users Power supply path loss; The average power supplied by the energy storage vehicle to users; The user Power received for: In the formula, For energy storage vehicles to users Power supply path loss.

5. A hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids according to claim 2, characterized in that: The block coordinate descent method and the successive convex approximation method are used to solve the power distribution model step by step to obtain a suboptimal solution, including: dividing the variables into {I n ,q a }Two groups, in the first block, fixed {I n ,q a } to find the value Fixed in the second block Value Solver n ,q a }, by iteratively solving the optimization problem of the two blocks until convergence, the suboptimal solution of the objective is obtained.

6. A hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids according to claim 5, characterized in that: To solve Convert the power distribution model to: In the formula, β0 is an auxiliary variable introduced to replace the objective function; The coordinates of users within the coverage area of ​​the large power grid; is the coordinate of the user beyond the coverage of the large power grid; ρ is the penalty factor; γ n , γ a，n , γ b，n is an auxiliary variable, replacing the first-order Taylor expansion; They are δ a , At a given local point in the rth iteration; is the auxiliary variable introduced to replace the first-order Taylor expansion.

7. The hybrid energy power supply optimization method based on the coordination of energy storage vehicles and large power grids according to claim 5 is characterized in that: To solve the variable {I n ,q a }, and transform the power distribution model into: In the formula, is the auxiliary variable introduced to replace the first-order Taylor expansion; It is the associated scheduling between energy storage vehicles and users; k is the user coordinate; τ k，n , is the auxiliary variable introduced to replace the first-order Taylor expansion; P cen Supply power to all users of the large power grid; For energy storage vehicles to users Power supply path loss; is the auxiliary variable introduced to replace the first-order Taylor expansion; For energy storage vehicles to users Power supply path loss; is the auxiliary variable introduced to replace the first-order Taylor expansion.

8. A hybrid energy power supply optimization device based on the coordination of energy storage vehicles and large power grids, characterized in that: The device is applied to the method described in any one of claims 1 to 7, and the device comprises: A hybrid energy power supply system building module, which is used to build a hybrid energy power supply system, which uses a large power grid and multiple energy storage vehicles to provide electricity to users within the coverage of the large power grid, and deploys multiple energy storage vehicles to provide electricity to users beyond the coverage of the large power grid; The power distribution model building module is used to build a power distribution model. The model aims to maximize the energy efficiency of energy storage vehicles and meet the power needs of all users by optimizing the location of energy storage vehicles, power distribution of the large power grid, the associated scheduling between users and the large power grid, and the associated scheduling between users and energy storage workshops. The power distribution model solving module is used to transform the power distribution model into a mixed integer non-convex optimization problem, and adopts the block coordinate descent method and the successive convex approximation method to solve the power distribution model step by step to obtain a suboptimal solution.

9. A hybrid energy power supply optimization device based on the coordination of energy storage vehicles and large power grids, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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