Power distribution network electric vehicle charging multi-objective planning method based on chaos firefly algorithm

Abstract

The invention discloses a chaos firefly algorithm-based power distribution network electric vehicle charging multi-objective planning method, which comprises the following steps of: firstly, collecting basic data of a power network, a traffic network, electric vehicle use, a distributed power supply and the like, then establishing a path cost model, and considering battery capacity and charging station conditions so as to evaluate costs of different paths. And establishing a charging station evaluation model, and adopting a comprehensive evaluation index, a balance distance and satisfaction to optimize charging station site selection. A distributed power supply mathematical model is constructed, and wind power, photovoltaic power generation and energy storage devices are considered, so that the utilization of renewable energy sources is improved to the greatest extent. And finally, establishing a multi-objective optimization mathematical model which comprises the objectives of total cost minimization, network loss minimization, charging station evaluation index minimization and the like. A chaos firefly algorithm is adopted for optimization, and a globally optimal solution is found. The method can be applied to site selection and power system planning of the electric vehicle charging station so as to improve energy utilization efficiency and economical efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy power systems, and in particular relates to a multi-objective planning method for charging electric vehicles in a distribution network based on a chaotic firefly algorithm. Background Art

[0002] With the widespread use of electric vehicles, the rational planning and efficient layout of charging infrastructure have become crucial. The site selection optimization of electric vehicle charging piles is not only related to the convenience of users, but also has a profound impact on energy distribution, power network stability and environmental benefits. In the construction of future smart distribution networks, distributed generation (DG) and electric vehicle charging stations will play a key role, which requires considering their planning issues from a more comprehensive perspective. Effectively solving the site selection problem of electric vehicle charging piles can improve the accessibility of charging facilities, reduce energy waste, reduce carbon emissions, and promote the use of clean energy, thereby promoting sustainable development and a low-carbon economy.

[0003] Current research has made some progress in the optimization of the location of electric vehicle charging piles, but there are still some bottlenecks and limitations. First, the existing models usually consider the planning of electric vehicle charging stations or distributed power sources separately, and lack a comprehensive study that links the two. This leads to the possibility of incoordination in actual planning, affecting the stability of the power system and the convenience of charging electric vehicles. Secondly, most of the existing methods focus on solving local problems and ignore the optimization of the power system as a whole, so it is difficult to find the global optimal solution. In addition, the existing models fail to fully consider the impact of the transportation network on the location of electric vehicle charging stations, which is of great significance in urban transportation planning. Finally, the location selection problem of electric vehicle charging stations involves a large amount of data and complex spatial constraints, which requires more efficient and accurate optimization methods to solve.

[0004] In order to overcome the above problems, an innovative multi-objective planning method for electric vehicle charging in distribution network based on chaotic firefly algorithm is proposed. This method not only considers the planning problems of distributed power sources and electric vehicle charging stations, but also comprehensively considers the overall stability of the power system and the impact of the transportation network. By introducing the chaotic firefly algorithm, higher search randomness and diversity are achieved, which helps to overcome the problem of local optimal solution. This method can also flexibly deal with different constraints and complex spatial data to find a better site selection plan. This method is expected to provide strong support for the development of future smart distribution networks, promote the use of clean energy, improve urban transportation and environment, and promote the realization of sustainable development. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a multi-objective planning method for electric vehicle charging in a distribution network based on a chaotic firefly algorithm. The method takes into account the planning of distributed power sources and electric vehicle charging stations, the overall stability of the power system, and the impact of the transportation network. By introducing the chaotic firefly algorithm, higher search randomness and diversity are achieved, which helps to overcome the problem of local optimal solutions.

[0006] The technical solution adopted by the present invention is: a multi-objective planning method for charging electric vehicles in a distribution network based on a chaotic firefly algorithm, comprising the following steps:

[0007] S1. Collect basic data of application scenarios; these data include power network topology, traffic network data, electric vehicle usage, distributed power distribution information, etc.;

[0008] S2. Build a path cost model: Use the collected data to build a path cost model. The model takes into account the location of electric vehicle charging stations and the transportation network to determine the cost of different paths. The cost includes factors such as the charging cost of the electric vehicle, energy consumption, and travel time.

[0009] S3. Establish a charging station evaluation model: A charging station evaluation model will be established, taking into account factors such as the location, capacity, and convenience of the charging station. The model may include factors such as user demand analysis, traffic flow simulation, and charging efficiency of the charging station;

[0010] S4. Establish a mathematical model of distributed power sources; Establish a mathematical model of distributed power sources, taking into account factors such as the timing characteristics and output curve of distributed power sources;

[0011] S5. Establish a multi-objective optimization mathematical model and constraints; Establish a multi-objective optimization mathematical model, considering multiple objectives such as minimizing the total system cost, minimizing the power grid loss, and maximizing traffic satisfaction. At the same time, it is necessary to formulate constraints, including system flow constraints, node voltage constraints, branch transmission power constraints, distributed power installation capacity constraints, charging station number constraints, and allocation rationality constraints;

[0012] S6. Use the Chaos Firefly Algorithm to optimize; introduce the Chaos Firefly Algorithm to optimize the multi-objective mathematical model. This algorithm will increase the randomness and diversity of the search, help overcome the problem of local optimal solutions, and thus find the global optimal charging station layout plan. Through continuous iteration, the algorithm will converge to one or more approximate optimal solutions, providing an optimization solution for the planning of smart distribution networks.

[0013] Specifically, the path cost model described in S2 specifically refers to establishing a reasonable model for the path selection problem in the transportation network while considering the endurance and charging requirements of electric vehicles. When an electric vehicle is traveling in a transportation network, it is necessary to consider the situation of reaching any destination other than the starting point from any starting point. If the electric vehicle can reach the destination without charging, the battery capacity after reaching the destination can be calculated by the following formula:

[0014] B D =B S -d S,D ·p k

[0015] Among them, B D Indicates the battery capacity when arriving at destination D, and is always greater than 0. B S The battery capacity that is allowed to be discharged safely from the starting point S. S,D is the distance from the starting point S to the end point D. k The power consumption per kilometer.

[0016] If the starting battery capacity is not enough to reach the destination, and there is a fast charging station, then the distance to the destination can be divided into two parts:

[0017] d S,D =d S,sta +d sta,D

[0018] Where sta represents the electric vehicle charging station. In this case, the remaining capacity of the battery B D It can be calculated by the following formula:

[0019] B D =B Full -d sta,D ·p k

[0020] Here, B Full Indicates the capacity of the battery when fully charged.

[0021] For the convenience of calculation, a weighted distance is used, taking into account the time required for driving. This is to ensure the convenience of charging for users, because the selection of charging stations should take into account not only the distance but also the driving time. Weighted distance d′ S,D It can be expressed as:

[0022] d′ S,D =d S,D +α(T S,sta +T sta,D +αT C )

[0023] Among them, T S,staIt represents the driving time of the car from the starting point to the charging station. sta,D represents the driving time of the car from the charging station to the destination. α is the time-to-distance coefficient. T C is the charging time. If charging is not required, then T C =0.

[0024] The concept of weighted distance takes into account the impact of time on route selection, ensuring that users have a convenient charging experience, as long driving time will indirectly increase driving distance. This model calculates the cost of the route selected by electric vehicle users, taking into account the comprehensive factors of charging demand and driving time, and finally converts it into weighted distance.

[0025] Specifically, the S3 charging station evaluation model evaluates the site selection performance through an evaluation index EVA, which represents traffic satisfaction. The calculation of EVA includes two parts: one considers the weighted distance of network demand, and the other considers the satisfaction of network demand. The specific expression is as follows:

[0026]

[0027] Among them, EVA is used to evaluate the site selection performance and represents traffic satisfaction. The goal of subsequent optimization is to minimize the value of EVA. ω is a trade-off factor with a value range of [0,1], which is used to balance the importance of weighted distance and satisfaction. i represents the flow demand at the starting point i. ij represents the distance between node i and node j. ij Indicates whether the facility exists between nodes i and j, 1 if it exists, and 0 if it does not. q Y represents the traffic flow of OD path q in the network. q is a binary variable, which is 1 if the requirements of path q are met, otherwise it is 0. In the calculation of EVA, the first half considers the weighted distance of network requirements, while the second half considers the satisfaction of network requirements.

[0028] In the traffic network, by minimizing the value of EVA, that is, minimizing the weighted distance and maximizing satisfaction. To solve this problem, the weighted distance is used to calculate the distance d between any two nodes in the network. ij To determine Y q .

[0029] Specifically, the charging station evaluation model described in S4 includes describing the operation of wind power generation, photovoltaic power generation and energy storage devices. The seasonal complementarity and random fluctuations between different power sources are taken into account, and energy storage technology is introduced to stabilize the power supply of the power grid.

[0030] At node i, the distributed generation output at time t can be expressed according to the following formula:

[0031]

[0032] Among them, P wind (t) and P solar (t) represents the output of wind power and photovoltaic power generation, respectively, taking into account their timing characteristics and seasonal complementarity. battery (t) represents the output of the energy storage device at time t, which takes into account the efficiency of charging and discharging, respectively represented by η ch and η disch The output of the energy storage device depends on the difference between the power output and the load. The key to this model is to optimize the use of various distributed power sources to minimize the impact of power grid fluctuations. At the same time, considering the introduction of energy storage technology, the mismatch between power sources and loads can be balanced and the stability of the power grid can be improved.

[0033] Specifically, the multi-objective optimization mathematical model described in S5 needs to consider the three aspects of minimizing the total system cost, reducing network losses, and improving traffic satisfaction at the same time. The details are as follows:

[0034] Minimize total system cost Cost: This includes the investment cost of distributed power generation or electric vehicle charging stations Fuel costs required to operate the system The goal is to minimize these costs to reduce the overall economic burden on the power system:

[0035]

[0036] Minimize network loss P loss : This is achieved by minimizing the power losses in the grid, including the current I between nodes ij (t) and resistance R ij The goal is to reduce the loss of electrical energy during transmission to improve the efficiency of the system.

[0037]

[0038] Minimizing the charging station evaluation index EVA: This index measures the quality of the charging station location, and the goal is to minimize it. Provide better transportation services and reduce traffic congestion.

[0039]

[0040] This multi-objective optimization model integrates technical, economic and user experience factors to provide high-quality transportation services while ensuring the sustainability and efficiency of the power system. The goal is to find a set of decision variables that can optimize all three indicators.

[0041] Specifically, the external constraints described in S5 include: system power flow constraints, node voltage constraints, branch transmission power constraints, distributed power supply and charging station capacity constraints, and charging station number constraints, as follows:

[0042] System flow constraints: These constraints ensure that the current and power distribution in the power system are within acceptable limits. Specifically, they focus on the active power P of the nodes. is and reactive power Q is , these powers and node voltage amplitudes V i and the phase angle difference θ between the nodes ij This helps ensure that power flows are correctly transmitted in the network.

[0043]

[0044] Node voltage constraints: These constraints ensure that the voltage at each node remains within an acceptable range to maintain system stability. i must be within the predetermined upper and lower limits V i,min and V i,max between.

[0045] V i,min ≤V i ≤V i,max ,i∈N

[0046] Branch transmission power constraints: These constraints limit the transmission power of each line to avoid overload and loss. The transmission power of each branch S j Cannot exceed its upper limit S j,max .

[0047] S j ≤S j,max ,j∈R branch

[0048] Distributed generation and charging station capacity constraints: These constraints limit the installed capacity of distributed generation and electric vehicle charging stations. DG,i and the capacity of the electric vehicle charging station G CS,j Cannot exceed its maximum access capacity G DG，max and G CS，max This helps avoid exceeding system capacity.

[0049] G DG,i ≤G DG,max ,i∈N DG

[0050] G CS,j ≤G CS,max ,j∈N CS

[0051] Constraint on the number of charging stations: This constraint specifies the maximum number of electric vehicle charging stations that can be set up in the network. CS This ensures that the number of charging stations is properly controlled to meet traffic demand.

[0052]

[0053] Specifically, the chaotic firefly algorithm described in S6 is an optimization algorithm combined with chaotic mapping, which can increase the randomness and diversity of the algorithm, thereby helping to avoid falling into local extreme values. The following is a detailed chaotic firefly algorithm process, including the formula and explanation of each step:

[0054] S6.1: Initialize parameters; initialize the number of firefly populations N, and initialize the maximum number of iterations Iter max ; Initialize the initial position X of the firefly individual i , where i ranges from 1 to N, and the position represents a solution to the problem; initialize the brightness (objective function value) I i , where i ranges from 1 to N, and the initial brightness is the target function value:

[0055] S6.2: Calculate brightness and attractiveness; for each firefly i, calculate its brightness I i , which is the objective function value; for each pair of fireflies i and j, calculate their attraction A ij :

[0056]

[0057] Among them, α is a parameter that controls the strength of attraction, ||X i -X j || 2 Represents X i and X j The Euclidean distance between .

[0058] S6.3: Chaotic map generates random perturbations; for each firefly i, generate a chaotic map C i , used to generate random disturbances; the chaotic map can be various known chaotic maps, such as Logistic map or Tent map, used to generate random values ​​r i , usually in the range [0,1]:

[0059] The present invention adopts Tent mapping, and the expression is as follows:

[0060]

[0061] Among them, r i is the current state, r i+1is the next state, p is the parameter of the mapping, which usually takes values ​​in the interval [0,2].

[0062] S6.4: Update the firefly position; for each firefly i, update its position X i The new position X′ i :

[0063] X′ i =X i +β·(X j -X i )+r i

[0064] Among them, β is the parameter of attraction strength, X j Indicates being X j A randomly selected individual among the attracted fireflies;

[0065] S6.5: Update brightness and optimal solution; for each firefly i, calculate the brightness I′ at its new position i If I′ i Better than the current brightness Ii (smaller, because it is a minimization problem), then update the brightness and position:

[0066] X i =X′ i

[0067] I i =I′ i

[0068] Record the global optimal solution X best and the corresponding brightness I best , that is, find the position of minimum brightness;

[0069] S6.6: Determine the end condition; if the maximum number of iterations Iter is reached max Or if the stopping condition is met (for example, the brightness is small enough), the iteration ends, otherwise the process returns to step S6.2 to continue the iteration.

[0070] S6.7: Output results; output the optimal solution X best and the corresponding minimum brightness I best as the optimal solution to the optimization problem.

[0071] Beneficial effects of the present invention: The beneficial effects of the present invention are that it provides a multi-objective planning method for electric vehicle charging in a distribution network based on a chaotic firefly algorithm, which comprehensively considers the planning issues of distributed power sources and electric vehicle charging stations, the overall stability of the power system, and the impact of the traffic network. By introducing the chaotic firefly algorithm, the present invention achieves higher search randomness and diversity, thereby overcoming the problem of local optimal solutions and providing an optimization solution for the planning of smart distribution networks. Through this method, the total cost of the power system can be effectively reduced, network losses can be reduced, traffic satisfaction can be improved, the intelligent layout of electric vehicle charging stations can be realized, and the effective use of distributed power sources can be ensured, providing important support for the development of future smart distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of the main steps of the present invention;

[0073] Figure 2 The chaotic firefly algorithm flow chart of the present invention;

[0074] Figure 3 IEEE33 node topology diagram (solid line) and traffic road topology diagram (dashed line) in an embodiment of the present invention;

[0075] Figure 4 It is a graph of objective function value-iteration number in the optimization process of the chaotic firefly algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention, and are specifically described in combination with the embodiments below.

[0077] like Figure 1 and 2 As shown, the present invention comprises the following steps:

[0078] S1. Collect basic data of application scenarios; the basic data collected mainly include: power network topology data, recording the connection relationship between nodes, line parameters, etc.; traffic network data, including road connection conditions, road length, speed limit and other parameters; electric vehicle usage data, statistics of travel demand in different time periods; distributed power source data, including location, type and maximum output power, etc.;

[0079] S2. Establish a path cost model; use the collected data to establish a path cost model. The path cost model calculates the cost of different paths for electric vehicles, as follows:

[0080] If the electric vehicle can reach the destination without charging, the battery capacity after reaching the destination is calculated by the following formula:

[0081] B D =B S -d S,D ·p k

[0082] Among them, B D Indicates the battery capacity when arriving at destination D, and is always greater than 0. B S The battery capacity that is allowed to be discharged safely from the starting point S. S,D is the distance from the starting point S to the end point D. k The power consumption per kilometer.

[0083] If the starting battery capacity is not enough to reach the destination, and there is a fast charging station, then the distance to the destination is divided into two parts:

[0084] d S,D =d S,sta +d sta,D

[0085] Where sta represents the electric vehicle charging station. In this case, the remaining capacity of the battery B D Calculated by the following formula:

[0086] B D =B Full -d sta,D ·p k

[0087] B Full Indicates the capacity of the battery when fully charged.

[0088] Weighted distance d′ S,D It can be expressed as:

[0089] d′ S,D =d S,D +α(T S,sta +T sta,D +αT C )

[0090] Among them, T S,sta It represents the driving time of the car from the starting point to the charging station. sta,D represents the driving time of the car from the charging station to the destination. α is the time-to-distance coefficient. T C is the charging time. If charging is not required, then T C =0.

[0091] S3. Establish a charging station evaluation model; the charging station evaluation model uses the evaluation index EVA, and the calculation method is as follows:

[0092]

[0093] Among them, EVA is used to evaluate the site selection performance and represents traffic satisfaction. The goal of subsequent optimization is to minimize the value of EVA. ω is a trade-off factor with a value range of [0,1], which is used to balance the importance of weighted distance and satisfaction. i represents the flow demand at the starting point i. ij represents the distance between node i and node j. ij Indicates whether the facility exists between nodes i and j, 1 if it exists, and 0 if it does not. q Y represents the traffic flow of OD path q in the network. q is a binary variable that equals 1 if the requirements of path q are met and 0 otherwise.

[0094] S4. Establish a mathematical model of distributed power supply;

[0095] At node i, the distributed generation output at time t can be expressed according to the following formula:

[0096]

[0097] Among them, P wind (t) and P solar (t) represents the output of wind power and photovoltaic power generation, respectively, taking into account their timing characteristics and seasonal complementarity. battery (t) represents the output of the energy storage device at time t, which takes into account the efficiency of charging and discharging, respectively represented by η ch and η disch The output of the energy storage device depends on the difference between the power output and the load.

[0098] S5. Establish multi-objective optimization mathematical model and constraints;

[0099] Minimize total system cost Cost: including the investment cost of distributed power generation or electric vehicle charging stations Fuel costs required to operate the system The goal is to minimize these costs to reduce the overall economic burden on the power system:

[0100]

[0101] Minimize network loss P loss :Calculate the current I between nodes ij (t) and resistance R ij The goal is to reduce the loss of electrical energy during transmission to improve the efficiency of the system.

[0102]

[0103] Charging station evaluation index minimizes EVA:

[0104]

[0105] Application system power flow constraints, node voltage constraints, branch transmission power constraints, distributed power and charging station capacity constraints, and charging station number constraints are as follows:

[0106] System flow constraints: power and node voltage amplitude V i and the phase angle difference θ between the nodes ij The relationship between:

[0107]

[0108] Node voltage constraint: voltage V i must be within the predetermined upper and lower limits V i,min and V i,max between.

[0109] V i,min ≤V i ≤V i,max ,i∈N

[0110] Branch transmission power constraint: The transmission power S of each branch j Cannot exceed its upper limit S j,max .

[0111] S j ≤S j,max ,j∈R branch

[0112] Distributed power generation and charging station capacity constraints: Distributed power generation capacity G DG,i and the capacity of electric vehicle charging stations G CS,j Cannot exceed its maximum access capacity G DG,max and G CS,max .

[0113] G DG,i ≤G DG,max ,i∈N DG

[0114] G CS,j ≤G CS,max ,j∈N CS

[0115] Constraints on the number of charging stations:

[0116]

[0117] S6. Optimize using the chaotic firefly algorithm;

[0118] S6.1: Initialize parameters; initialize the number of firefly populations N, and initialize the maximum number of iterations Iter max ; Initialize the initial position X of the firefly individual i , where i ranges from 1 to N, and the position represents a solution to the problem; initialize the brightness (objective function value) I i , where i ranges from 1 to N, and the initial brightness is the target function value:

[0119] S6.2: Calculate brightness and attractiveness; for each firefly i, calculate its brightness I i , which is the objective function value; for each pair of fireflies i and j, calculate their attraction A ij :

[0120]

[0121] Among them, α is a parameter that controls the strength of attraction, ||X i -X j || 2 Represents X i and X j The Euclidean distance between .

[0122] S6.3: Chaotic map generates random perturbations; for each firefly i, generate a chaotic map C i , used to generate random disturbances; the present invention adopts Tent mapping, and the expression is as follows:

[0123]

[0124] Among them, r i is the current state, r i+1 is the next state, p is the parameter of the mapping, and its value is in the interval [0,2].

[0125] S6.4: Update the firefly position; for each firefly i, update its position X i The new position X′ i :

[0126] X′ i =X i +β·(X j -X i )+r i

[0127] Among them, β is the parameter of attraction strength, X j Indicates being X j A randomly selected individual among the attracted fireflies;

[0128] S6.5: Update brightness and optimal solution; for each firefly i, calculate the brightness I′ at its new position i If I′ i Brightness I i Better (smaller, because it is a minimization problem), then update the brightness and position:

[0129] X i =X′ i

[0130] I i =I′ i

[0131] Record the global optimal solution X best and the corresponding brightness I best , that is, find the position of minimum brightness;

[0132] S6.6: Determine the end condition; if the maximum number of iterations Iter is reached max Or if the stopping condition is met (for example, the brightness is small enough), the iteration ends; otherwise, the process returns to step S6.2 to continue the iteration.

[0133] S6.7: Output results; output the optimal solution X best and the corresponding minimum brightness I best as the optimal solution to the optimization problem.

[0134] The effectiveness of the present invention is verified as follows: an IEEE 33-node distribution network is used as a simulation example. First, a complete IEEE 33-node distribution network model is constructed. Figure 3As shown in the figure, the solid black line represents the electrical connection between nodes, and the dotted line represents the road connection. Wind turbines are set at nodes 4, 7, 10, and 15, photovoltaic generators are set at nodes 6, 12, 22, and 27, and 7 fast charging stations are randomly set. The driving routes of electric vehicles are randomly generated, the running converted distance is calculated, and a path selection model considering charging cost and time cost is established. Then, by setting charging stations for each candidate node, a charging station evaluation function is established, and its coverage demand and traffic flow are calculated to obtain the evaluation value EVA. Next, a distributed power output model describing the randomness of wind and photovoltaic power generation is established, and energy storage devices are added for smoothing. A multi-objective planning model with the goals of minimizing system cost, network loss and maximizing traffic satisfaction is constructed, various constraints are added, and the chaotic firefly algorithm is used for iterative solution. The algorithm is implemented in Python. Coding method: Each batch of populations is the location of 7 charging stations, which are initially randomly distributed at each road node. Population size: The maximum number of iterations is 100. The firefly algorithm uses α=0.3, β=0.2, and the Tent mapping parameter p=0.3. The algorithm is run 20 times, and the objective function value, number of iterations, running time, and distribution of the objective function solution are recorded for each run. The simulation results show that Figure 4 As shown in the figure, the system cost function keeps decreasing and stabilizes at a low level. The designed path selection model, charging station evaluation model, distributed power supply model and chaotic firefly algorithm can effectively solve the problem of electric vehicle charging station planning in the distribution network, achieve the technical effect of reducing system cost and network loss, and verify the effectiveness of the method.

[0135] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and scope of the elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A multi-objective planning method for electric vehicle charging in distribution network based on chaotic firefly algorithm, characterized by , including the following steps: S1. Collect basic data of application scenarios; S2. Establishing a path cost model; the path cost model takes into account the charging demand and driving time of electric vehicles, and converts the path selection cost into a weighted distance; S3. Establish a charging station evaluation model; the charging station evaluation model uses the evaluation index EVA, taking into account the weighted distance and satisfaction of network demand; S4. Establishing a mathematical model of distributed power supply; the mathematical model of distributed power supply takes into account the operation of wind power generation, photovoltaic power generation and energy storage devices; S5. Establish a multi-objective optimization mathematical model and constraints; the multi-objective optimization model simultaneously considers minimization of system total cost, minimization of network loss and maximization of traffic satisfaction, and sets constraints; S6. Optimize using the chaotic firefly algorithm; the chaotic firefly algorithm uses chaotic mapping to increase the randomness of the search.

2. The multi-objective planning method for electric vehicle charging in distribution network based on chaotic firefly algorithm according to claim 1 is characterized in that ,The weighted distance calculation formula of the path cost model in step S2 is: d′ S,D =d S,D +α(T S,sta +T sta,D +αT C ) Where, d′ S,D is the distance from the starting point S to the end point D, T S,sta represents the driving time from the starting point to the charging station, T sta,D represents the driving time from the charging station to the destination, α is the coefficient of time-to-distance conversion, T C For charging time.

3. The multi-objective planning method for electric vehicle charging in distribution network based on chaotic firefly algorithm according to claim 1 is characterized in that ,The calculation formula of the evaluation index EVA of the charging station evaluation model in step S3 is: Among them, P i is the flow demand of starting point i, d ij is the distance between nodes i and j, X ij Indicates whether there is a facility between nodes i and j, f q is the traffic flow of path q, Y q Indicates whether the requirements of path q are met.

4. The multi-objective planning method for electric vehicle charging in distribution network based on chaotic firefly algorithm according to claim 1 is characterized in that ,The calculation formula of the distributed power mathematical model in step S4 is: Among them, P wind (t), P solar (t) represent the output of wind power and photovoltaic power generation, P battery (t) represents the charge and discharge output of the energy storage device.

5. The multi-objective planning method for charging electric vehicles in a distribution network based on a chaotic firefly algorithm according to claim 1 is characterized in that ,The optimization objectives of the multi-objective optimization mathematical model in step S5 include: (1) Minimize the total system cost: Minimize network loss P loss : This is achieved by minimizing the power losses in the grid, including the current I between nodes ij (t) and resistance R ij ;The goal is to reduce the loss of electrical energy during transmission to improve the efficiency of the system; (2) Minimize network loss: Minimizing the charging station evaluation index EVA: This index measures the quality of the charging station location, and the goal is to minimize it; provide better transportation services and reduce traffic congestion; (3) Minimize the charging station evaluation index EVA:

6. The multi-objective planning method for charging electric vehicles in a distribution network based on a chaotic firefly algorithm according to claim 1 is characterized in that ,The constraints of the multi-objective optimization mathematical model in step S5 include: (1) Minimize the total system cost: (2) Network loss minimization P loss :Calculate the current I between nodes ij (t) and resistance R ij ;The goal is to reduce the loss of electrical energy during transmission to improve the efficiency of the system; (3) Charging station evaluation index minimizes EVA: Application system power flow constraints, node voltage constraints, branch transmission power constraints, distributed power and charging station capacity constraints, and charging station number constraints are as follows: (1) System flow constraints: power and node voltage amplitude V i and the phase angle difference θ between the nodes ij The relationship between: (2) Node voltage constraint: voltage V i must be within the predetermined upper and lower limits V i,min and V i,max between; In i,min ≤V i ≤V i,max ,i∈N (3) Branch transmission power constraint: The transmission power S of each branch j Cannot exceed its upper limit S j,max ; S j ≤S j,max ,j∈R branch (4) Distributed power generation and charging station capacity constraints: The capacity of distributed power generation G DG,i and the capacity of the electric vehicle charging station G CS,j Cannot exceed its maximum access capacity G DG,max and G CS,max ; G DG,i ≤G DG,max ,i∈N DG G CS,j ≤G CS,max ,j∈N CS (5) Constraints on the number of charging stations:

7. The multi-objective planning method for electric vehicle charging in distribution network based on chaotic firefly algorithm according to claim 1 is characterized in that ,The chaotic mapping used by the chaotic firefly algorithm in step S6 adopts Tent mapping, and the expression is as follows: Among them, r i is the current state, r i+1 is the next state, p is the parameter of the mapping, and its value is in the interval [0,2].

8. The multi-objective planning method for charging electric vehicles in a distribution network based on a chaotic firefly algorithm according to claim 1 is characterized in that ,The iterative update formula of the chaotic firefly algorithm in step S6 is: X′ i =X i +β·(X j -X i )+r i Among them, X′ i is the new position, β is the attraction parameter, X j is a randomly selected individual, r i Random numbers generated for the chaos map.

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