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

Abstract

A hybrid energy supply optimization method and device based on energy storage vehicles and large power grid cooperation, the method comprising: constructing a hybrid energy supply system, which uses a large power grid and multiple energy storage vehicles to provide power for users within the coverage range of the large power grid, and deploys multiple energy storage vehicles to provide power for users beyond the coverage range of the large power grid; constructing a power distribution model, which maximizes the energy efficiency of the energy storage vehicles as the target, and optimizes the location of the energy storage vehicles, the power distribution of the large power grid, the associated scheduling between the users and the large power grid, and the associated scheduling between the users and the energy storage vehicles to meet the power demand of all users; converting the power distribution model into a mixed integer non-convex optimization problem, and using 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. The present application optimizes the cooperative work of the large power grid and the energy storage vehicles, realizes efficient power transmission, and ensures that users can obtain stable power supply in various complex scenarios.

Description

Technical Field

[0001] This 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 between energy storage vehicles and the main power grid. Background Technology

[0002] In some remote or power-deficient areas, the traditional single-grid power supply model can no longer meet users' electricity needs. By introducing mobile energy vehicles to collaborate with the main power grid to build a hybrid energy system, the reliability of power supply can be improved under flexible dispatch. However, how to efficiently allocate power between the main power grid and mobile energy vehicles, ensuring that the different needs of all users are met, while considering the efficiency of power transmission and reducing power loss, are the key challenges facing hybrid energy networks. Summary of the Invention

[0003] The purpose of this invention is to overcome the above-mentioned defects and problems in the prior art and provide a hybrid energy power supply optimization method and device based on the collaboration between energy storage vehicles and the power grid. By optimizing the collaborative work of the power grid and energy storage vehicles, efficient power transmission can be achieved, ensuring that users can obtain a stable power supply in various complex scenarios.

[0004] To achieve the above objectives, the technical solution of this invention is: a hybrid energy power supply optimization method based on the collaboration between energy storage vehicles and the power grid, comprising:

[0005] Construct a hybrid energy power supply system that utilizes a large power grid and multiple energy storage vehicles to provide power to users within the coverage area of ​​the large power grid, and deploys multiple energy storage vehicles to provide power to users outside the coverage area of ​​the large power grid;

[0006] A power distribution model is constructed with the goal of maximizing the energy efficiency of energy storage vehicles. This model optimizes the location of energy storage vehicles, the power distribution of the main power grid, the scheduling between users and the main power grid, and the scheduling between users and energy storage workshops to meet the power needs of all users.

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

[0008] The power distribution model is as follows:

[0009]

[0010]

[0011] In the formula, I n Location of the energy storage vehicle; δ a For users Inter-grid dispatch with the main power grid, if the main power grid dispatches to users within its coverage area Power supply, then δ a =1, otherwise δ a =0; For users In conjunction with energy storage workshops, dispatching energy storage vehicles to users within the coverage area of ​​the main power grid is possible. Power supply, otherwise For users and energy storage facilities, the scheduling is linked, especially for users located outside the coverage area of ​​the main power grid. Power supply, otherwise q a For users within the coverage area of ​​the large power grid The weights; N is the number of energy storage vehicles; P veh This represents the maximum power available from the battery in the energy storage vehicle. The path loss for power supply from the energy storage vehicle to the user; K is the number of users; M is the maximum number of users in the main power grid; Q is the maximum number of users that each energy storage vehicle can connect to; For users The power received by electricity; For users Total power required; For users The power received by electricity; For users The total power demand is: A is the number of users within the coverage area of ​​the main power grid; B is the number of users outside the coverage area of ​​the main 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 For the scheduling of energy storage vehicles and users; g k Let R be the location of the k-th user; R is the resistance coefficient.

[0015] The user Power received for:

[0016]

[0017] In the formula, P cen Power supplied to all users of the large power grid; For energy storage vehicles to users Path loss of power supply; The average power output of the energy storage vehicle to supply power to users;

[0018] The user Power received for:

[0019]

[0020] In the formula, For energy storage vehicles to users Path loss of power supply.

[0021] The method employs block coordinate descent and successive convex approximation 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, with {I} fixed in the first block. n q a Solving for the value of} Fixed in the second piece Value solution {I n q a The optimization problem of the two blocks is solved iteratively until convergence, thus obtaining the suboptimal solution of the objective.

[0022] To solve Transform the power distribution model into:

[0023]

[0024]

[0025] 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; The coordinates of users outside the coverage area of ​​the main power grid; ρ is the penalty factor; γ n γ a，n γ b，n As an auxiliary variable, it replaces the first-order Taylor expansion; δ a , At a given local point in the r-th iteration; The auxiliary variable introduced replaces the first-order Taylor expansion.

[0026] To solve for variable {I n q a The power distribution model is transformed into:

[0027]

[0028] In the formula, The auxiliary variable introduced replaces the first-order Taylor expansion; ζk，n For the scheduling of energy storage vehicles and users; g k For user coordinates; τ k，n , The auxiliary variable introduced replaces the first-order Taylor expansion; P cen Power supplied to all users of the large power grid; For energy storage vehicles to users Path loss of power supply; The auxiliary variable is introduced to replace the first-order Taylor expansion; For energy storage vehicles to users Path loss of power supply; The auxiliary variable introduced replaces the first-order Taylor expansion.

[0029] A hybrid energy power supply optimization device based on the collaboration between energy storage vehicles and the main power grid, wherein the device is applied to the method described above, the device comprising:

[0030] The hybrid energy power supply system construction module is used to build a hybrid energy power supply system that uses a large power grid and multiple energy storage vehicles to provide power to users within the coverage area of ​​the large power grid, and deploys multiple energy storage vehicles to provide power to users outside the coverage area of ​​the large power grid;

[0031] The power distribution model construction module is used to build a power distribution model that aims to maximize the energy efficiency of energy storage vehicles. This model optimizes the location of energy storage vehicles, the power distribution of the main power grid, the scheduling between users and the main power grid, and the scheduling between users and energy storage workshops to meet the power needs of all users.

[0032] The power distribution model solution module is used to transform the power distribution model into a mixed integer non-convex optimization problem, and uses 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 collaboration between energy storage vehicles and the power grid, 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 configured to execute the method described above according to instructions in the computer program code.

[0036] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention discloses a hybrid energy supply optimization method and device based on the collaboration between energy storage vehicles and the power grid. By optimizing power distribution within the power grid, user-grid interaction scheduling, user-energy storage vehicle interaction scheduling, and energy storage vehicle distribution, the energy efficiency of the energy storage vehicles is maximized. Simultaneously, the problem is modeled as a mixed-integer non-convex optimization problem and solved effectively using block coordinate descent and continuous convex approximation techniques. Ultimately, by continuously optimizing the collaborative work of the power grid and energy storage vehicles, this model achieves efficient power transmission, ensuring users can obtain a stable power supply under various complex scenarios. Attached Figure Description

[0039] Figure 1 This is a flowchart of a hybrid energy power supply optimization method based on the collaboration between energy storage vehicles and the power grid, according to the present invention.

[0040] Figure 2 This is a schematic diagram of a hybrid energy power supply system in which an energy storage vehicle and a large power grid work together, according to an embodiment of the present invention.

[0041] Figure 3 This is a flowchart illustrating the method for allocating power sources to different users in an embodiment of the present invention.

[0042] Figure 4 This is a structural block diagram of a hybrid energy power supply optimization device based on the collaboration between energy storage vehicles and the power grid according to the present invention.

[0043] Figure 5 This is a structural block diagram of a hybrid energy power supply optimization device based on the collaboration between energy storage vehicles and the power grid according to the present invention. Detailed Implementation

[0044] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] See Figure 1 This invention provides a hybrid energy power supply optimization method based on the collaboration between energy storage vehicles and the power grid, comprising:

[0046] S1. Construct a hybrid energy power supply system that utilizes a large power grid and multiple energy storage vehicles to provide power to users within the coverage area of ​​the large power grid, and deploys multiple energy storage vehicles to provide power to users outside the coverage area of ​​the large power grid.

[0047] S2. Based on the users' power demand, construct a power allocation model. This model aims to maximize the energy efficiency of energy storage vehicles by optimizing the location of energy storage vehicles, power allocation of the main power grid, the scheduling between users and the main power grid, and the scheduling between users and energy storage workshops to meet the power demand of all users.

[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 the suboptimal solution.

[0049] When infrastructure is damaged or there is a temporary surge in electricity demand, the existing power grid may experience short-term power shortages. In such cases, mobile energy storage vehicles can be deployed to areas with concentrated load demand to provide flexible and reliable power support. For different user groups, scheduling variables and constraints for the power grid and energy storage vehicles are defined. By optimizing the location of the energy storage vehicles and the weight allocation of the power grid, the power needs of each user are guaranteed. A binary decision variable is used to represent the power relationship between users and the power grid or energy storage vehicles, ensuring that each user obtains the required power within the coverage area of ​​the power grid or through the energy storage vehicle. By iteratively solving the scheduling problem between the power grid and energy storage vehicles, the overall energy utilization rate of the system is gradually improved. This invention achieves efficient power transmission by optimizing the collaborative work of the power grid and mobile energy vehicles, ensuring that users can obtain a stable power supply in various complex scenarios.

[0050] like Figure 2 The hybrid energy power supply system shown meets the electricity needs of K 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 used to divide users into two categories based on whether they are within the coverage area, which can be represented by a set. and The corresponding coordinate positions are represented as follows: and use I represents a collection of mobile energy storage vehicles. n ={x n y n} represents the energy storage vehicle v n The location. The total power requirement for each user is known to be... and Users within the coverage area can be powered by the main power grid and energy storage vehicles; while users outside the coverage area can only be powered by energy storage vehicles.

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

[0052]

[0053] Leveraging its powerful computing capabilities, a large power grid can flexibly allocate power to each user. Define q. a For users If the weight of the user is determined, then the power allocated to that user in the main power grid can be expressed as P. cen q a δ a ,and:

[0054]

[0055] Assume each energy storage vehicle can connect to a maximum of Q users, and the maximum power supplied by the battery is P. veh .definition express and energy storage vehicle v n The relationship between them, if 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 determined by... Calculation. Considering that the temporarily deployed energy storage vehicle is not fully configured and lacks the low-loss, high-voltage transmission capability of the main power grid, it will experience significant path losses. The path loss for the energy storage vehicle to supply power to users is... Where, ζ k，n For the coordinated dispatch of energy storage vehicles and users (including users within and outside the main power grid), g k Let R be the resistance coefficient of the k-th user location. Then, the power received by the k-th user from all the energy storage vehicles can be calculated from... Calculations. Considering the limited capacity of onboard batteries, they cannot continuously provide large-scale power like a large power grid. Therefore, improving energy efficiency and ensuring that every unit of electrical energy is effectively utilized is key to enhancing the power supply capacity of energy storage vehicles. The energy efficiency of an energy storage vehicle can be expressed as:

[0058]

[0059] Furthermore, the user Power received for:

[0060]

[0061] In the formula, P cenPower supplied to all users of the large power grid; For energy storage vehicles to users Path loss of power supply; The average power output of the energy storage vehicle to supply power to users;

[0062] The user Power received for:

[0063]

[0064] In the formula, For energy storage vehicles to users Path loss of power supply.

[0065] To meet the needs of all users, the requirements are as follows: and For ease of understanding, in Figure 3 The text describes a hybrid energy power supply system as a way to supply power to users within and outside the coverage area of ​​a large power grid.

[0066] Furthermore, to provide energy-efficient normal service to all users, this invention aims to maximize the energy efficiency of all energy storage vehicles by jointly optimizing the location of energy storage vehicles {I}. n}、Inter-user and large power grid related scheduling {δ a User-storage workshop linkage scheduling Large power grid user weights {q a This ensures the power needs of all users.

[0067] The power distribution model is as follows:

[0068]

[0069] In the formula, I n Location of the energy storage vehicle; δ a For users Inter-grid dispatch with the main power grid, if the main power grid dispatches to users within its coverage area Power supply, then δ a =1, otherwise δ a =0; For users In conjunction with energy storage workshops, dispatching energy storage vehicles to users within the coverage area of ​​the main power grid is possible. Power supply, otherwise For users and energy storage facilities, the scheduling is linked, especially for users located outside the coverage area of ​​the main power grid. Power supply, otherwise q a For users within the coverage area of ​​the large power grid The weights; N is the number of energy storage vehicles; P veh This represents the maximum power available from the battery in the energy storage vehicle. The path loss for power supply from the energy storage vehicle to the user; K is the number of users; M is the maximum number of users in the main power grid; Q is the maximum number of users that each energy storage vehicle can connect to; For users The power received by electricity; For users Total power required; For users The power received by electricity; For users The total power demand is: A is the number of users within the coverage area of ​​the main power grid; B is the number of users outside the coverage area of ​​the main power grid.

[0070] Furthermore, this invention establishes a power allocation model aimed at maximizing the energy efficiency of energy storage vehicles. The following steps are employed to solve this mathematical model:

[0071] Sub-model decomposition: The original optimization problem (P1) is decomposed into two sub-problems (P2) and (P4), namely, the sub-model of power grid and energy storage vehicle scheduling optimization, and the sub-model of energy storage vehicle location and power grid power distribution optimization. By employing convex optimization techniques in each sub-problem, the optimal solution of each sub-model is solved step by step, and the convergence of the suboptimal solution is achieved by iteratively optimizing these two sub-problems alternately.

[0072] Penalty Term Introduction: For the numerous binary variables in the first subproblem (P2), this invention transforms the original optimization problem model into an equivalent form with a penalty term, introducing a penalty parameter greater than zero. This penalizes the parts that do not satisfy the equality constraints, thereby improving the accuracy of the solution. This step transforms the optimization problem model into a second-penalty problem model (P3).

[0073] The iterative loop algorithm, under a given penalty parameter, utilizes techniques such as block coordinate descent and successive convex approximation, along with solver tools like CVX, to alternately solve the transformed problem model (P3) and (P5), ultimately obtaining a suboptimal solution. The loop iterates multiple times until the increase in the objective function value falls below a preset threshold. This maximizes the energy utilization and power transmission efficiency of the energy storage vehicle without sacrificing system constraints, ensuring that the power needs of all users are met.

[0074] In the power distribution model (P1), constraints and As the constraints are convex, the optimization objective and other constraints are non-convex. To solve the problem efficiently, we will employ Block Coordinate Descent (BCD) and successive convex approximation to solve the power distribution model step by step to obtain a suboptimal solution. The BCD technique is used to divide the variables into different blocks for solution, reducing the complexity of the problem. Specifically, the variables are divided into... {I n q a Two groups, with {I} fixed in the first block. n q a Solving for the value of} Fixed in the second piece Value solution {I n q a The optimization problem of the two blocks is solved iteratively until convergence, and the suboptimal solution of the objective function is obtained.

[0075] Furthermore, in order to solve Transform the power distribution model into:

[0076]

[0077] All three variables are binary variables, and there are constraints. and Including nonlinear terms, for ease of solution, the objective function can be transformed into:

[0078]

[0079] To resolve constraints and The nonlinear term in the text introduces an auxiliary variable S. n And order:

[0080]

[0081] constraint The left-hand side can be represented as:

[0082]

[0083] Although the above exists The non-concave term, but through a given local point and Using a first-order Taylor expansion, we can obtain the following linear approximation:

[0084]

[0085] Therefore, constraints The left-hand side term can be approximated by its linear lower bound function:

[0086]

[0087] Similarly, constraints The left-hand side is represented as:

[0088]

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

[0090]

[0091] The three transformed nonlinear constraints can be added as penalty terms to the objective function. That is, if the values ​​of these three variables are not 0 or 1, a penalty is applied to the objective function to increase its value. Introducing a positive parameter ρ as a penalty factor, the transformed objective function is:

[0092]

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

[0094]

[0095]

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

[0097]

[0098] In summary, the transformed power distribution model (P2) can be approximated as follows:

[0099]

[0100] 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; The coordinates of users outside the coverage area of ​​the main power grid; ρ is the penalty factor; γ n γ a，n γ b，n As an auxiliary variable, it replaces the first-order Taylor expansion; δ a , At a given local point in the r-th iteration; The auxiliary variable introduced replaces the first-order Taylor expansion.

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

[0102] Furthermore, in order to solve for the variable {I} n q a The power distribution model is then transformed into a subproblem (P4), which is:

[0103]

[0104] Objective function and constraints The nonconvexity in the function ||g| all originates from the function ||g| ... k -I n || 2 Non-concave. Similarly, by considering ||g k -I n || 2 At point Performing a first-order Taylor expansion, we obtain the following inequality:

[0105]

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

[0107]

[0108] Therefore, the transformed power distribution model can be approximated as:

[0109]

[0110] In the formula, The auxiliary variable introduced replaces the first-order Taylor expansion; ζ k，n For the scheduling of energy storage vehicles and users; g k For user coordinates; τ k，n , The auxiliary variable introduced replaces the first-order Taylor expansion; P cen Power supplied to all users of the large power grid; For energy storage vehicles to users Path loss of power supply; The auxiliary variable is introduced to replace the first-order Taylor expansion; For energy storage vehicles to users Path loss of power supply; The auxiliary variable introduced replaces the first-order Taylor expansion.

[0111] if and lower bound If all are greater than the threshold, then and It must also meet the threshold. Furthermore, because... Minimum performance can be ensured by maximizing the lower bound of the original objective function. Because 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 for solving problem (P1) is proposed by iteratively optimizing problems (P3) and (P5), specifically as follows:

[0113] initialization And set the inner loop iteration count 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. Received

[0116] Set r = r + 1;

[0117] Repeat the above steps until convergence.

[0118] See Figure 4 The present invention also provides a hybrid energy power supply optimization device based on the collaboration between energy storage vehicles and the power grid. This device is applied to the hybrid energy power supply optimization method based on the collaboration between energy storage vehicles and the power grid described above. The device includes:

[0119] The hybrid energy power supply system construction module is used to build a hybrid energy power supply system that uses a large power grid and multiple energy storage vehicles to provide power to users within the coverage area of ​​the large power grid, and deploys multiple energy storage vehicles to provide power to users outside the coverage area of ​​the large power grid;

[0120] The power distribution model construction module is used to build a power distribution model that aims to maximize the energy efficiency of energy storage vehicles. This model optimizes the location of energy storage vehicles, the power distribution of the main power grid, the scheduling between users and the main power grid, and the scheduling between users and energy storage workshops to meet the power needs of all users.

[0121] The power distribution model solution module is used to transform the power distribution model into a mixed integer non-convex optimization problem, and uses 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 Figure 5The present invention also provides a hybrid energy power supply optimization device based on the collaboration between energy storage vehicle and large power grid, 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 aforementioned hybrid energy power supply optimization method based on the coordination of energy storage vehicles and the power grid, according to the instructions in the computer program code.

[0125] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for optimizing hybrid energy supply based on the coordination of energy storage vehicles and the power grid.

[0126] 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.

[0127] 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.

[0128] 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, Smalltalk, 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 such as TensorFlow and PyTorch can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a 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).

[0129] For details regarding the aforementioned equipment and non-transitory computer-readable storage media, please refer to 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; these details will not be repeated here.

[0130] 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 hybrid energy supply optimization method based on energy storage vehicles and large grid cooperation, characterized in that, The method comprises: constructing a hybrid energy power supply system that utilizes a large power grid and a plurality of energy storage vehicles to provide power for users within the coverage range of the large power grid, and deploying a plurality of energy storage vehicles to provide power for users beyond the coverage range of the large power grid; constructing a power distribution model that aims to maximize the energy efficiency of the energy storage vehicles, and satisfies the power demand of all users by optimizing the positions of the energy storage vehicles, the power distribution of the large power grid, the associated scheduling between the users and the large power grid, and the associated scheduling between the users and the energy storage vehicles; transforming the power distribution model into a mixed integer non-convex optimization problem, and solving the power distribution model step by step by using the block coordinate descent method and the successive convex approximation method to obtain a suboptimal solution.

2. The hybrid energy supply optimization method based on the coordination between the energy storage vehicle and the power grid according to claim 1, characterized in that, The power distribution model is: ； ； ； ； ； ； ； ； ； In the formula, is the position of the energy storage vehicle; is the user associated with the dispatch of the large power grid, if the large power grid supplies power to the user within the coverage range of the large power grid, then , otherwise ; is the user associated with the dispatch of the energy storage vehicle, if the energy storage vehicle supplies power to the user within the coverage range of the large power grid, then , otherwise ; is the user associated with the dispatch between the user and the energy storage vehicle, if the user is supplied with power beyond the coverage range of the large power grid, then , otherwise ; is the weight of the user within the coverage range of the large power grid; is the number of energy storage vehicles; is the maximum available power of the energy storage vehicle battery; is the path loss of the energy storage vehicle supplying power to the user; is the number of users; is the maximum user load of the large power grid; is the maximum number of connectable users per energy storage vehicle; is the power accepted by the user ; is the total demand power of the user ; is the power accepted by the user ; is the total demand power of the user ; is the number of users within the coverage range of the large power grid; is the number of users beyond the coverage range of the large power grid.

3. The hybrid energy supply optimization method based on the coordination of the energy storage vehicle and the power grid according to claim 2, characterized in that, The path loss of the energy storage vehicle providing power to the user Is: ； In the formula, scheduling of the energy storage vehicle and the user; a first user location; a resistivity coefficient.

4. The hybrid energy power supply optimization method based on the cooperation between the energy storage vehicles and the large power grid according to claim 2, characterized in that The user Power Is: ； ； wherein the power supplied to all users by the large grid; the power supplied to users by the energy storage vehicle path loss of the power supply; the average power supplied to users by the energy storage vehicle The user Power Is: ； In the formula, To provide energy storage vehicles to users The path loss of power supply.

5. The hybrid energy supply optimization method based on energy storage vehicle and power grid cooperation according to claim 2, characterized in that, The method employs block coordinate descent and successive convex approximation to solve the power distribution model step by step to obtain a suboptimal solution, including: dividing the variables into... , Two sets, fixed in the first piece. Solve for the value Fixed in the second piece Value solution The suboptimal solution to the objective is obtained by iteratively solving the optimization problem of the two blocks until convergence.

6. The hybrid energy supply optimization method based on energy storage vehicle and power grid cooperation according to claim 5, characterized in that, To solve the power distribution model is converted to: ； ； ； ； ； ； ； ； ； ； ； ； ； In the formula, is an auxiliary variable introduced instead of the objective function formula; is the coordinate of the user within the coverage of the large power grid; is the coordinate of the user beyond the coverage of the large power grid; is a penalty factor; , , is an auxiliary variable introduced instead of the first-order Taylor expansion formula; , , are respectively , , at the given local point of the first iteration; , is an auxiliary variable introduced instead of the first-order Taylor expansion formula; is the resistance coefficient.

7. The hybrid energy supply optimization method based on energy storage vehicle and power grid cooperation according to claim 5, characterized in that, To solve for the variables The power distribution model is converted to: ； ； ； ； ； ； ； wherein is an introduced auxiliary variable, replacing the first order Taylor expansion; is the associated schedule of the energy storage vehicle with the user; is the user coordinate; , is an introduced auxiliary variable, replacing the first order Taylor expansion; is the power supplied by the main grid to all users; is the path loss for the energy storage vehicle to supply power to the user ; , is an introduced auxiliary variable, replacing the first order Taylor expansion; is the path loss for the energy storage vehicle to supply power to the user ; is an introduced auxiliary variable, replacing the first order Taylor expansion; is the resistance coefficient; is the average power supplied by the energy storage vehicle to the user.

8. A hybrid energy supply optimization device based on energy storage vehicles and large grid cooperation, characterized in that, The device is applied to the method according to any one of claims 1-7, and the device comprises: a hybrid energy power supply system construction module configured to construct a hybrid energy power supply system that utilizes a large power grid and a plurality of energy storage vehicles to provide power for users within the coverage range of the large power grid, and deploy a plurality of energy storage vehicles to provide power for users beyond the coverage range of the large power grid; a power distribution model construction module configured to construct a power distribution model that aims to maximize the energy efficiency of the energy storage vehicles, and satisfies the power demand of all users by optimizing the positions of the energy storage vehicles, the power distribution of the large power grid, the associated scheduling between the users and the large power grid, and the associated scheduling between the users and the energy storage vehicles; a power distribution model solving module configured to transform the power distribution model into a mixed integer non-convex optimization problem, and solve the power distribution model step by step by using the block coordinate descent method and the successive convex approximation method to obtain a suboptimal solution.

9. A hybrid energy power supply optimization device based on the cooperation between the energy storage vehicles and the large power grid, characterized in that comprising a memory and a processor; the memory is configured to store computer program codes and transmit the computer program codes to the processor; the processor is configured to execute the method according to any one of claims 1-7 according to the instructions in the computer program codes.

10. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium, and the computer program is executed by the processor to implement the method according to any one of claims 1-7.

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