Collaborative planning method and system for access of charging infrastructure to power grid

Abstract

The invention discloses a collaborative planning method and system for accessing a charging infrastructure to a power grid, and relates to the technical field of electric vehicle charging, and the method comprises the steps: building an upper optimization model based on the position and number of charging stations, building a lower optimization model based on the traffic flow and a user charging and discharging strategy, an improved particle swarm algorithm is constructed by dynamically adjusting an inertia weight factor and learning factor mechanism, and global search and local search are balanced; and based on an improved particle swarm algorithm, carrying out bilevel programming model solution, and formulating a charging station configuration optimization scheme. According to the method, an efficient and dynamic charging infrastructure planning method is jointly formed, efficient cooperation of the charging station and the power grid is achieved by comprehensively considering multiple factors, and the operation efficiency and economical efficiency of the whole electric vehicle charging system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to a collaborative planning method and system for connecting charging infrastructure to a power grid. Background Art

[0002] With the rapid development of the electric vehicle market, the construction and planning of charging infrastructure has become a key factor in promoting the optimization of energy structure and the popularization of electric vehicles. In this context, the collaborative planning method of charging infrastructure access to the power grid has received increasing attention. Early research mainly focused on the geographical location optimization of charging stations and the load management of a single power grid. Related technologies have achieved certain results in improving charging convenience and power grid operation efficiency. However, with the increase in the number of electric vehicles and the diversification of charging needs, the existing charging infrastructure planning technology has gradually exposed its limitations.

[0003] Existing charging infrastructure planning methods often fail to comprehensively consider the interaction between multiple factors such as the location and number of charging stations and vehicle flow, user charging and discharging strategies, etc., resulting in the following deficiencies in practical applications: the planning model lacks the ability to respond to dynamic changes in charging demand, and it is difficult to adapt to user charging needs during peak hours, resulting in waste or insufficient charging resources; traditional planning methods often use heuristic algorithms with fixed parameters in optimization algorithms, which are difficult to achieve an effective balance between global search and local search, resulting in limited optimization of charging station configuration schemes; existing technologies fail to fully consider the carrying capacity and stability requirements of the power grid side, resulting in the access of charging infrastructure may cause impacts on the power grid and affect the safe operation of the power grid; when solving the optimization model, the commonly used traditional algorithms in the existing technology are insufficient in the balance between global search and local search, making it difficult to achieve the optimization of charging station configuration. Therefore, how to build an efficient and practical collaborative planning method to achieve the optimal configuration of charging infrastructure and power grid has become an urgent problem to be solved. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing collaborative planning method for charging infrastructure access to the power grid has a single optimization model, insufficient response to dynamic changes in charging demand, poor balance between global and local searches of the optimization algorithm, and how to achieve efficient coordination and optimal configuration of the charging infrastructure and the power grid.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a collaborative planning method for charging infrastructure access to the power grid, comprising constructing an upper-level optimization model based on the location and number of charging stations, and constructing a lower-level optimization model based on vehicle flow and user charging and discharging strategies; by dynamically adjusting the inertia weight factor and the learning factor mechanism, an improved particle swarm algorithm is constructed to balance global search and local search, and a charging station configuration optimization plan is formulated based on the improved particle swarm algorithm and a two-level programming model is solved.

[0007] As a preferred solution of the collaborative planning method for connecting charging infrastructure to the power grid according to the present invention, the construction of the upper-level optimization model includes constructing the upper-level planning model objective function. When building a charging station, the cost of the charging station includes the construction cost, the operation and maintenance cost, and the electricity purchase cost. The planning objective of the EV charging station constructed based on the minimum comprehensive cost is expressed as:

[0008] F=min(C I +C B )

[0009] Among them, F is the comprehensive cost minimization target of charging station planning, C I is the average annual investment cost of the charging station, C B is the electricity purchase cost of the charging station; the average annual investment cost of the charging station is expressed as:

[0010]

[0011] Among them, K is the total number of charging stations to be built, C J,k and C M,k are the construction cost and operation and maintenance cost of the kth charging station respectively. The construction cost of the kth charging station is calculated as:

[0012]

[0013] Among them, r0 is the discount rate, n0 is the operating life of the charging station, c ch is the unit price of the charging pile, N ch,k , A k , and c f,k are the number of charging piles, floor space and rental cost per unit area of ​​the kth charging station respectively; the electricity purchase cost of the charging station is expressed as:

[0014]

[0015] Among them, n s Represents the number of charging stations, n j represents the power purchase cost weight factor of the jth charging station, p b is the electricity purchase price, P m,iis the charging power of node m in time period i; the upper optimization model constraints include the distance constraint between charging stations and the number of charging piles; the distance constraint between charging stations is expressed as:

[0016] d s ≥d min

[0017] Among them, d s is the distance between any two charging stations, d min is the shortest distance between charging stations; the number of charging piles is constrained as:

[0018]

[0019] Among them, P ch is the charging power of the charging pile, E de To meet charging needs.

[0020] As a preferred solution of the collaborative planning method for connecting charging infrastructure to the power grid according to the present invention, the construction of the lower-level optimization model includes that the total cost of the user's entire charging process is the difference between the charging cost and the discharging benefit, expressed as:

[0021]

[0022] Among them, E C,k The total cost of the user's charging process, N is the number of charging users, n' is the index value of the electricity user, t is the charging time period, T k is the total number of charging time periods, p k,t is the average charge and discharge power, represents the rate of return during discharge, represents the cost rate during charging, Δt represents the time interval, The charge and discharge identifier has values ​​of -1, 0, and 1, which represent the vehicle being in the discharge, occupied, and charging states, respectively. The values ​​include 0 and 1. For vehicle-grid interactive users with V2G functions, the values ​​include -1, 0, and 1. For autonomous charging users who do not want to participate in scheduling, the values ​​include 1. The constraints of the lower-level optimization model include charging capacity constraints and state of charge constraints. The charging capacity constraints include analyzing the net charging capacity range of electric vehicles after arrival based on the state of charge SOC, the user-specified off-grid SOC interval, battery capacity, access time, and charging and discharging power, expressed as:

[0023] (SOC lel,k -SOC arr,k )E k ≤W C,k ≤(SOC leu,k -SOC arr,k )Ek

[0024] Among them, W C,k Indicates the net charge range of the electric vehicle after arrival, SOC arr,k is the SOC of the vehicle when it arrives, SOC lel,k Indicates the lower limit of the off-grid state of charge SOC specified by the user, SOC leu,k Indicates the upper limit of the off-grid state of charge SOC specified by the user, [SOC lel,k ,SOC leu,k ] is the off-grid SOC interval specified by the user, E k is the battery capacity of the vehicle; the state of charge constraint includes that the electric vehicle can only be in one of the charging, discharging, and occupying states in each period. The change of the state of charge during the charging and discharging process of the electric vehicle is expressed as:

[0025]

[0026] Among them, SOC t+1,k is the SOC of the vehicle at time t+1, SOC t,k are the SOC of the vehicle during period t respectively.

[0027] As a preferred solution of the collaborative planning method for connecting charging infrastructure to the power grid described in the present invention, the improved particle swarm algorithm includes characterizing each particle by a position vector and a velocity vector. The position vector symbolizes the candidate solution in the potential solution space, and the velocity vector reflects the direction and amplitude of the particle position adjustment in the solution space. The total number of particles is M, and the position X' of the nth particle in dimension d is n and speed V' n It is expressed as:

[0028]

[0029] By tracking the previous individual best position and group best position of each particle, the speed and position of each particle are adjusted, and the individual best position and group best position are expressed as:

[0030]

[0031] Among them, P' n is the individual optimal position of the nth particle, P' g is the optimal position of the group; the speed and position update formula of the particle swarm algorithm is expressed as:

[0032]

[0033] Among them, w' is the inertia weight factor. When performing global search, the inertia weight factor is increased, and when the algorithm converges, the inertia weight factor is reduced. c'1 and c'2 are learning factors, which represent the self-learning ability and social learning ability of the particle respectively. r'1 and r'2 are random numbers uniformly distributed in [0,1].

[0034] As a preferred solution of the collaborative planning method for connecting charging infrastructure to the power grid described in the present invention, the improved particle swarm algorithm is constructed by dynamically adjusting the inertia weight factor and the learning factor mechanism. The improved strategy includes population initialization, initializing the speed and position of all particles, and calculating the objective function of all particles; determining the individual optimum, initialization process, the individual optimum of each particle is the particle itself; updating the inertia weight, the inertia weight is related to the local search capability and the global search capability of the algorithm, and in each iteration, the inertia weight is updated according to the update function, which is expressed as:

[0035]

[0036] Among them, w is the inertia weight factor of the current iteration, IT is the current iteration number, MI is the total iteration number, and w s and w e are the initial and final values ​​of the inertia weight factor, c 1s and c 1e is the initial and stop value of c1, c 1s Greater than c 1e , c 2s and c 2e It is the initial value and stop value of c2; update the speed and position of the particle; calculate the fitness of the particle and update the individual optimum; determine whether the number of iterations is met, output the result if it is met, and return to update the inertia weight if it is not met.

[0037] As a preferred solution of the collaborative planning method for accessing the charging infrastructure to the power grid described in the present invention, wherein: the solving of the two-level planning model includes parameter initialization of the particle swarm algorithm to generate an initial population; the charging pile configuration scheme generated by the upper layer is passed to the lower layer as the initial condition of the lower layer, and the lower layer model determines the user's optimal charging cost based on the vehicle flow and the orderly charging and discharging strategy; the optimal strategy is passed to the lower layer, and the upper layer model directly updates the global particle speed and position based on the optimization scheme; it is determined whether the termination condition is met. If so, the global optimum is the optimal planning scheme. If not, the position and speed of the particles are updated, and the charging pile configuration scheme generated by the upper layer is passed to the lower layer again.

[0038] As a preferred solution of the collaborative planning method for connecting charging infrastructure to the power grid described in the present invention, the formulation of the charging station configuration optimization plan includes dividing the user group into three categories based on user preferences, including orderly charging behavior users, vehicle-grid interaction users with V2G functions, and autonomous charging users who are unwilling to participate in scheduling; when the user category is an orderly charging behavior user and a vehicle-grid interaction user with V2G function, the vehicle is allowed to start charging at any time point from connecting to the power grid to the preset off-grid time period, and ensure that the battery state of charge SOC expected by the user is reached at the expected off-grid time, and the control target is the user's minimum charging cost; when the user category is a vehicle-grid interaction user with V2G function, while meeting the charging demand, the vehicle is also allowed to flexibly choose to implement the discharge operation within a continuous time period between the user connecting to the power grid and the expected off-grid time; when the user category is an autonomous charging user who is unwilling to accept scheduling, the charging process is started immediately after connecting to the power grid until the battery is fully charged or the vehicle leaves the charging network.

[0039] Another object of the present invention is to provide a collaborative planning system for accessing charging infrastructure to the power grid, which can formulate a charging station configuration optimization plan based on an improved particle swarm algorithm and solve a two-layer programming model, thereby solving the problem of insufficient efficient coordination and optimized configuration of charging infrastructure and the power grid contained in the current collaborative planning technology for accessing electric vehicle charging infrastructure to the power grid.

[0040] As a preferred solution of the collaborative planning system for accessing the charging infrastructure to the power grid described in the present invention, it includes: a two-layer planning module, an algorithm improvement module, and a configuration optimization module; the two-layer planning module is used to construct an upper-layer optimization model based on the location and number of charging stations, and to construct a lower-layer optimization model based on vehicle flow and user charging and discharging strategies; the algorithm improvement module is used to construct an improved particle swarm algorithm by dynamically adjusting the inertia weight factor and the learning factor mechanism, and to balance the global search and the local search; the configuration optimization module is used to formulate a charging station configuration optimization plan based on the improved particle swarm algorithm and solving the two-layer planning model.

[0041] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a collaborative planning method for connecting charging infrastructure to a power grid.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a collaborative planning method for connecting charging infrastructure to a power grid.

[0043] The beneficial effects of the present invention are as follows: by constructing an upper optimization model based on the location and number of charging stations, and a lower optimization model based on vehicle flow and user charging and discharging strategies, a comprehensive consideration of the planning of charging infrastructure is achieved, the distribution of charging stations is optimized, the construction cost and operation and maintenance cost are reduced, and the operation stability of the power grid is improved; by dynamically adjusting the inertia weight factor and the learning factor mechanism, an improved particle swarm algorithm is constructed, a balance between global search and local search is achieved, the adaptability and efficiency of the algorithm in solving complex optimization problems are improved, and the solution accuracy and speed of the algorithm are also improved, the optimization of the charging station configuration scheme is ensured, and the risk of the algorithm falling into the local optimum is reduced; through the iterative optimization process, the optimal solution for the charging station configuration is found, and the configuration of the charging station can be dynamically adjusted according to real-time data and user behavior to adapt to the changing market demand, thereby improving the overall performance of the charging infrastructure, reducing the charging cost of users, and enhancing the flexibility and reliability of the power grid. The present invention has achieved better results in improving the operation stability of the power grid, realizing efficient coordination between charging stations and power grids, and improving the operation efficiency and economy of electric vehicle charging systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 An overall flow chart of a collaborative planning method for connecting charging infrastructure to a power grid provided in a first embodiment of the present invention.

[0046] Figure 2 An overall flow chart of a collaborative planning system for connecting charging infrastructure to a power grid provided in accordance with a third embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a collaborative planning method for connecting charging infrastructure to a power grid, comprising:

[0049] S1: The upper optimization model is constructed based on the location and number of charging stations, and the lower optimization model is constructed based on the vehicle flow and user charging and discharging strategies.

[0050] Furthermore, constructing the upper-level optimization model includes constructing the upper-level planning model objective function. When building a charging station, the cost of the charging station includes construction cost, operation and maintenance cost, and electricity purchase cost. The EV charging station planning objective based on the minimum comprehensive cost is expressed as:

[0051] F=min(C I +C B )

[0052] Among them, F is the comprehensive cost minimization target of charging station planning, C I is the average annual investment cost of the charging station, C B is the electricity purchase cost of the charging station; the average annual investment cost of the charging station is expressed as:

[0053]

[0054] Among them, K is the total number of charging stations to be built, C J,k and C M,k are the construction cost and operation and maintenance cost of the kth charging station respectively. The construction cost of the kth charging station is calculated as:

[0055]

[0056] Among them, r0 is the discount rate, n0 is the operating life of the charging station, c ch is the unit price of the charging pile, N ch,k , A k , and c f,k are the number of charging piles, floor space and rental cost per unit area of ​​the kth charging station respectively; the electricity purchase cost of the charging station is expressed as:

[0057]

[0058] Among them, n s Represents the number of charging stations, n j represents the power purchase cost weight factor of the jth charging station, p b is the electricity purchase price, P m,i is the charging power of node m in time period i; the upper optimization model constraints include the distance constraint between charging stations and the number of charging piles; the distance constraint between charging stations is expressed as:

[0059] d s ≥d min

[0060] Among them, d s is the distance between any two charging stations, dmin is the shortest distance between charging stations; the number of charging piles is constrained as:

[0061]

[0062] Among them, P ch is the charging power of the charging pile, E de To meet charging needs.

[0063] It should be noted that the total cost of constructing the lower-level optimization model including the user's charging process is the difference between the charging cost and the discharge benefit, expressed as:

[0064]

[0065] Among them, E C,k The total cost of the user's charging process, N is the number of charging users, n' is the index value of the electricity user, t is the charging time period, T k is the total number of charging time periods, p k,t is the average charge and discharge power, represents the rate of return during discharge, represents the cost rate during charging, Δt represents the time interval, The charge and discharge identifier has values ​​of -1, 0, and 1, which represent the vehicle being in the discharge, occupied, and charging states, respectively. The values ​​include 0 and 1. For vehicle-grid interactive users with V2G functions, the values ​​include -1, 0, and 1. For autonomous charging users who do not want to participate in scheduling, the values ​​include 1. The constraints of the lower-level optimization model include charging capacity constraints and state of charge constraints. The charging capacity constraints include analyzing the net charging capacity range of electric vehicles after arrival based on the state of charge SOC, the user-specified off-grid SOC interval, battery capacity, access time, and charging and discharging power, expressed as:

[0066] SOC lel,k -SOC arr,k )E k ≤W C,k ≤(SOC leu,k -SOC arr,k )E k

[0067] Among them, W C,k Indicates the net charge range of the electric vehicle after arrival, SOC arr,k is the SOC of the vehicle when it arrives, SOC lel,k Indicates the lower limit of the off-grid state of charge SOC specified by the user, SOC leu,k Indicates the upper limit of the off-grid state of charge SOC specified by the user, [SOC lel,k ,SOC leu,k] is the off-grid SOC interval specified by the user, E k is the battery capacity of the vehicle; the state of charge constraint includes that the electric vehicle can only be in one of the charging, discharging, and occupying states in each period. The change of the state of charge during the charging and discharging process of the electric vehicle is expressed as:

[0068]

[0069] Among them, SOC t+1,k is the SOC of the vehicle at time t+1, SOC t,k are the SOC of the vehicle during period t respectively.

[0070] It should also be noted that by constructing an upper-level optimization model based on the location and number of charging stations, and a lower-level optimization model based on vehicle flow and user charging and discharging strategies, comprehensive consideration of charging infrastructure planning is achieved, the distribution of charging stations is optimized, construction costs and operation and maintenance costs are reduced, and the operational stability of the power grid is improved.

[0071] S2: By dynamically adjusting the inertia weight factor and learning factor mechanism, an improved particle swarm algorithm is constructed to balance global search and local search.

[0072] Furthermore, the construction of the improved particle swarm algorithm includes characterizing each particle by a position vector and a velocity vector. The position vector represents the candidate solution in the potential solution space, and the velocity vector reflects the direction and amplitude of the adjustment of the particle position in the solution space. The total number of particles is M, and the position X' of the nth particle in dimension d is n and speed V' n It is expressed as:

[0073]

[0074] By tracking the previous individual best position and group best position of each particle, the speed and position of each particle are adjusted, and the individual best position and group best position are expressed as:

[0075]

[0076] Among them, P' n is the individual optimal position of the nth particle, P' g is the optimal position of the group; the speed and position update formula of the particle swarm algorithm is expressed as:

[0077]

[0078] Among them, w' is the inertia weight factor. When performing global search, the inertia weight factor is increased, and when the algorithm converges, the inertia weight factor is reduced. c'1 and c'2 are learning factors, which represent the self-learning ability and social learning ability of the particle respectively. r'1 and r'2 are random numbers uniformly distributed in [0,1].

[0079] It should be noted that in order to alleviate the phenomenon of unbalanced power grid load caused by the overlap of peak hours of electric vehicle charging and peak hours of household electricity consumption, the present invention makes full use of the bidirectionality of electric vehicle energy, optimizes the layout of electric vehicle charging facilities, and formulates reasonable charging and discharging strategies for electric vehicle users, so that they can charge during peak and valley periods and discharge during peak periods, thereby achieving the goal of smoothing the peak and valley difference, improving power grid stability, and reducing the construction cost of charging power stations and the charging cost of users; constructing an improved particle swarm algorithm also includes constructing an improved particle swarm algorithm by dynamically adjusting the inertia weight factor and the learning factor mechanism. The improved strategy includes population initialization, initializing the speed and position of all particles, and calculating the objective function of all particles; determining the individual optimum, initialization process, the individual optimum of each particle is the particle itself; updating the inertia weight, the inertia weight is related to the local search capability and global search capability of the algorithm, and at each iteration, the inertia weight is updated according to the update function, expressed as:

[0080]

[0081] Among them, w is the inertia weight factor of the current iteration, IT is the current iteration number, MI is the total iteration number, and w s and w e are the initial and final values ​​of the inertia weight factor, c 1s and c 1e is the initial and stop value of c1, c 1s Greater than c 1e , c 2s and c 2e It is the initial value and stop value of c2; update the speed and position of the particle; calculate the fitness of the particle and update the individual optimum; determine whether the number of iterations is met, output the result if it is met, and return to update the inertia weight if it is not met.

[0082] It should also be noted that by dynamically adjusting the inertia weight factor and the learning factor mechanism, an improved particle swarm algorithm was constructed to achieve a balance between global search and local search, improve the adaptability and efficiency of the algorithm in solving complex optimization problems, and also improve the algorithm's solution accuracy and speed, ensuring the optimization of the charging station configuration plan while reducing the risk of the algorithm falling into local optimality.

[0083] S3: Based on the improved particle swarm algorithm and solving the two-level programming model, an optimization plan for charging station configuration is formulated.

[0084] Furthermore, solving the two-level programming model includes parameter initialization of the particle swarm algorithm to generate the initial population; passing the charging pile configuration scheme generated by the upper layer to the lower layer as the initial condition of the lower layer, and the lower layer model determines the user's optimal charging cost based on the vehicle flow and orderly charging and discharging strategy; passing the optimal strategy to the lower layer, and the upper layer model directly updates the global particle speed and position based on the optimization scheme; judging whether the termination condition is met, if so, the global optimum is the optimal planning scheme, if not, the particle position and speed are updated, and the charging pile configuration scheme generated by the upper layer is passed to the lower layer again.

[0085] It should be noted that the formulation of the charging station configuration optimization plan includes dividing the user groups into three categories based on user preferences, including users with orderly charging behavior, vehicle-grid interaction users with V2G functions, and autonomous charging users who are unwilling to participate in scheduling; when the user category is a user with orderly charging behavior and a user with V2G function, the vehicle is allowed to start charging at any time from connecting to the grid to the preset off-grid time period, and ensure that the battery state of charge SOC expected by the user is reached at the expected off-grid time, and the control target is the user's minimum charging cost; when the user category is a vehicle-grid interaction user with V2G function, while meeting the charging demand, the vehicle is also allowed to flexibly choose to implement discharge operations within a continuous time period between the user's connection to the grid and the expected off-grid time; when the user category is an autonomous charging user who is unwilling to accept scheduling, the charging process is started immediately after connecting to the grid until the battery is fully charged or the vehicle leaves the charging network.

[0086] It should also be noted that the traditional particle swarm algorithm uses fixed inertia weight coefficients and learning factors, which may cause the system to easily converge to the local optimal solution rather than the global optimal solution. In order to effectively coordinate the performance of the algorithm between the global search range and local fine mining, and improve the overall optimization efficiency and the ability to effectively break through local extreme values, when the particle swarm algorithm faces a local optimum, it is urgent to jump out of the local optimal position rate with a higher probability. Therefore, the present invention proposes an improved particle swarm optimization algorithm, which aims to ensure that the algorithm can continue to show superior search efficiency and robustness throughout the optimization evolution process by dynamically adjusting the inertia weight coefficient and the learning factor mechanism; through the iterative optimization process, the optimal solution for the charging station configuration is found, and the configuration of the charging station can be dynamically adjusted according to real-time data and user behavior to adapt to the changing market demand, thereby improving the overall performance of the charging infrastructure, reducing the charging cost of users, and enhancing the flexibility and reliability of the power grid.

[0087] Embodiment 2 is an embodiment of the present invention, which provides a collaborative planning method for connecting charging infrastructure to the power grid. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0088] First, relevant data is collected, including city maps, traffic flow data, user charging habits and grid load data. These data are used to build upper-level optimization models and lower-level optimization models. The upper optimization model is constructed based on the location and number of charging stations, as well as the cost of charging stations (including construction cost, operation and maintenance cost, and electricity purchase cost). The average annual investment cost and electricity purchase cost of charging stations are used to represent the comprehensive cost minimization goal of charging station planning. When calculating the average annual investment cost, the number of charging piles, floor space, and unit area rental cost of the charging station are fully considered. The electricity purchase cost is calculated based on the weight factor of the electricity purchase cost and the charging power of the charging station. The lower optimization model is constructed based on the vehicle flow and user charging and discharging strategy, and the total cost of the user's charging process is fully considered, including charging cost and discharge income. According to the user's charging habits and grid load, charging strategies are formulated for users with orderly charging behavior, users with V2G function and autonomous charging users who are unwilling to participate in scheduling. In order to solve the two-level planning model, an improved particle swarm algorithm is used to achieve a balance between global search and local search by dynamically adjusting the inertia weight factor and learning factor mechanism. The algorithm finds the optimal solution for the configuration of charging stations through an iterative optimization process. The experimental data are recorded and analyzed with reference to Table 1.

[0089] Table 1 Experimental data record table

[0090]

[0091] By comparing the data in the table, it can be seen that the use of the proposed collaborative planning method and system can effectively reduce the comprehensive cost of charging stations and improve the stability of the power grid. Specifically, in terms of the number of charging stations and vehicle flow, the data of experiments 1, 2, 3, 4, 5 and 6 show that with the increase in the number of charging stations and the increase in vehicle flow, the comprehensive cost of charging stations gradually decreases, which shows that the present invention can adapt to cities of different sizes and traffic flows. In terms of user charging and discharging strategies, the data of experiments 1, 4 and 6 show that orderly charging behavior and vehicle-grid interactive users can reduce charging costs by optimizing charging strategies. This shows that the method can adapt to different types of user needs. In terms of charging station costs and electricity purchase costs, the data of experiments 1, 2, 3, 4, 5 and 6 show that by optimizing the layout of charging stations and charging strategies, the comprehensive cost of charging stations can be effectively reduced, which shows that the present invention can improve the stability of the power grid.

[0092] Example 3, reference Figure 2 , as an embodiment of the present invention, provides a collaborative planning system for charging infrastructure access to a power grid, including a two-layer planning module, an algorithm improvement module, and a configuration optimization module.

[0093] The two-layer planning module is used to construct an upper-level optimization model based on the location and number of charging stations, and to construct a lower-level optimization model based on vehicle flow and user charging and discharging strategies; the algorithm improvement module is used to construct an improved particle swarm algorithm by dynamically adjusting the inertia weight factor and the learning factor mechanism, and to balance global search and local search; the configuration optimization module is used to formulate a charging station configuration optimization plan based on the improved particle swarm algorithm and solving the two-layer planning model.

[0094] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0095] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0096] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0097] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A collaborative planning method for charging infrastructure access to a power grid, characterized in that: include: The upper optimization model is built based on the location and number of charging stations, and the lower optimization model is built based on the traffic flow and user charging and discharging strategies; By dynamically adjusting the inertia weight factor and learning factor mechanism, an improved particle swarm algorithm is constructed to balance global search and local search; Based on the improved particle swarm algorithm and solving the two-level programming model, an optimization plan for charging station configuration is formulated.

2. The collaborative planning method for connecting charging infrastructure to a power grid as claimed in claim 1, characterized in that: The construction of the upper optimization model includes constructing the upper planning model objective function. When building a charging station, the cost of the charging station includes construction cost, operation and maintenance cost, and electricity purchase cost. The planning objective of the EV charging station based on the minimum comprehensive cost is expressed as: F=min(C I +C B ) Among them, F is the comprehensive cost minimization target of charging station planning, C I is the average annual investment cost of the charging station, C B The cost of purchasing electricity for the charging stations; The average annual investment cost of the charging station is calculated as: Among them, K is the total number of charging stations to be built, C J,k and C M,k are the construction cost and operation and maintenance cost of the kth charging station respectively. The construction cost of the kth charging station is calculated as: Among them, r0 is the discount rate, n0 is the operating life of the charging station, c ch is the unit price of the charging pile, N ch,k , A k , and c f,k are the number of charging piles, floor space and rental cost per unit area of ​​the kth charging station respectively; The electricity purchase cost of the charging station is calculated as: Among them, n s Represents the number of charging stations, n j represents the power purchase cost weight factor of the jth charging station, p b is the electricity purchase price, P m,i is the charging power of node m in time period i; The upper optimization model constraints include the distance constraint between charging stations and the number of charging piles; The distance constraint between charging stations is expressed as: d s ≥d min Among them, d s is the distance between any two charging stations, d min is the shortest distance between charging stations; The constraint on the number of charging piles is expressed as: Among them, P ch is the charging power of the charging pile, E de To meet charging needs.

3. The collaborative planning method for connecting charging infrastructure to the power grid as claimed in claim 2, characterized in that: The construction of the lower optimization model includes that the total cost of the user's entire charging process is the difference between the charging cost and the discharging benefit, expressed as: Among them, E C,k The total cost of the user's charging process, N is the number of charging users, n' is the index value of the electricity user, t is the charging time period, T k is the total number of charging time periods, p k,t is the average charge and discharge power, represents the rate of return during discharge, represents the cost rate during charging, Δt represents the time interval, The charge and discharge identifier has values ​​of -1, 0, and 1, which represent the vehicle being in the discharge, occupied, and charging states, respectively. The value includes 0 and 1. For vehicle-grid interaction users with V2G function, the value includes -1, 0, 1. For autonomous charging users who do not want to participate in scheduling, the value includes 1. The constraints of the lower optimization model include charge capacity constraints and state of charge constraints; The charging capacity constraint includes the analysis of the net charging capacity range of the electric vehicle after arrival based on the grid-connected state of charge SOC, the user-specified off-grid SOC interval, the battery capacity, the access time and the charging and discharging power, which can be expressed as: (SOC lel,k -SOC arr,k )IN k ≤W C,k ≤(SOC leu,k -SOC arr,k )IN k Among them, W C,k Indicates the net charge range of the electric vehicle after arrival, SOC arr,k is the SOC of the vehicle when it arrives, SOC lel,k Indicates the lower limit of the off-grid state of charge SOC specified by the user, SOC leu,k Indicates the upper limit of the off-grid state of charge SOC specified by the user, [SOC lel,k ,SOC leu,k ] is the off-grid SOC interval specified by the user, E k is the battery capacity of the vehicle; The state of charge constraint includes that the electric vehicle can only be in one of the following states: charging, discharging, or occupying in each period. The change of the state of charge during the charging and discharging process of the electric vehicle is expressed as: Among them, SOC t+1,k is the SOC of the vehicle at time t+1, SOC t,k are the SOC of the vehicle during period t respectively.

4. The collaborative planning method for connecting charging infrastructure to a power grid as claimed in claim 3, characterized in that: The improved particle swarm algorithm includes characterizing each particle by a position vector and a velocity vector. The position vector represents a candidate solution in the potential solution space, and the velocity vector reflects the direction and amplitude of the particle position adjustment in the solution space. The total number of particles is M, and the position X' of the nth particle in dimension d is n and speed V' n It is expressed as: By tracking the previous individual best position and group best position of each particle, the speed and position of each particle are adjusted, and the individual best position and group best position are expressed as: Among them, P' n is the individual optimal position of the nth particle, P' g is the optimal position of the group; The speed and position update formula of the particle swarm algorithm is expressed as: Among them, w' is the inertia weight factor. When performing global search, the inertia weight factor is increased, and when the algorithm converges, the inertia weight factor is reduced. c'1 and c'2 are learning factors, which represent the self-learning ability and social learning ability of the particle respectively. r'1 and r'2 are random numbers uniformly distributed in [0,1].

5. The collaborative planning method for connecting charging infrastructure to the power grid as claimed in claim 4, characterized in that: The improved particle swarm algorithm also includes constructing an improved particle swarm algorithm by dynamically adjusting the inertia weight factor and the learning factor mechanism, wherein the improved strategy includes population initialization, initializing the speed and position of all particles, and calculating the objective function of all particles; Determine the individual optimum and initialize the process. The individual optimum of each particle is the particle itself. Update the inertia weight. The inertia weight is related to the local search capability and global search capability of the algorithm. In each iteration, the inertia weight is updated according to the update function, which is expressed as: Among them, w is the inertia weight factor of the current iteration, IT is the current iteration number, MI is the total iteration number, and w s and w e are the initial and final values ​​of the inertia weight factor, c 1s and c 1e is the initial and stop value of c1, c 1s Greater than c 1e , c 2s and c 2e are the initial and stop values ​​of c2; Update the particle's velocity and position; Calculate the fitness of particles and update the individual optimum; Determine whether the number of iterations is met. If it is met, output the result. If not, return to update the inertia weight.

6. The collaborative planning method for connecting charging infrastructure to a power grid as claimed in claim 5, characterized in that: The solving of the bi-level programming model includes parameter initialization of the particle swarm algorithm to generate an initial population; The charging pile configuration scheme generated by the upper layer is passed to the lower layer as the initial condition of the lower layer. The lower layer model determines the optimal charging cost for users based on the vehicle flow and orderly charging and discharging strategy; The optimal strategy is passed to the lower layer, and the upper model directly updates the global particle speed and position based on the optimization solution; Determine whether the termination conditions are met. If so, the global optimum is the optimal planning scheme. If not, update the position and velocity of the particle, and pass the charging pile configuration scheme generated by the upper layer to the lower layer again.

7. The collaborative planning method for connecting charging infrastructure to a power grid as claimed in claim 6, characterized in that: The formulation of the charging station configuration optimization plan includes dividing the user group into three categories based on user preferences, including orderly charging behavior users, vehicle-grid interaction users with V2G functions, and autonomous charging users who are unwilling to participate in scheduling; When the user category is an orderly charging behavior user or a vehicle-grid interaction user with V2G function, the vehicle is allowed to start charging at any time from the time of connecting to the grid to the preset off-grid time, and ensure that the battery state of charge (SOC) expected by the user is reached at the expected off-grid time, and the control target is the user's minimum charging cost; When the user category is a vehicle-grid interactive user with V2G function, while meeting the charging demand, the vehicle is also allowed to flexibly choose to implement discharge operation in a continuous period between the user's access to the grid and the expected off-grid period; When the user category is an autonomous charging user who is unwilling to accept scheduling, the charging process is started immediately after connecting to the power grid until the battery is fully charged or the vehicle leaves the charging network.

8. A system using the collaborative planning method for connecting charging infrastructure to a power grid as claimed in any one of claims 1 to 7, characterized in that: Includes two-layer planning module, algorithm improvement module, and configuration optimization module; The two-layer planning module is used to construct an upper-layer optimization model based on the location and number of charging stations, and to construct a lower-layer optimization model based on vehicle flow and user charging and discharging strategies; The algorithm improvement module is used to construct an improved particle swarm algorithm by dynamically adjusting the inertia weight factor and the learning factor mechanism to balance the global search and the local search; The configuration optimization module is used to formulate a charging station configuration optimization plan based on an improved particle swarm algorithm and solving a double-layer programming model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for collaborative planning of charging infrastructure access to a power grid according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for collaborative planning of charging infrastructure access to a power grid according to any one of claims 1 to 7 are implemented.

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