An Electric Vehicle Charging Scheduling Method and Terminal Based on an Improved Optimization Algorithm

Abstract

The present invention discloses an electric vehicle charging scheduling method and terminal based on an improved optimization algorithm. The steps include: the user-side terminal system needs to determine whether the user issues a charging request according to the state of charge of the electric vehicle battery. If so, the data acquisition unit acquires the daily charging demand, location information, distribution network operation conditions, regional charging station information, and relevant data information of the regional traffic road network of the electric vehicle. If not, it returns to the previous step for loop judgment; the data processing and algorithm calculation unit establishes an optimization model with the goal of minimizing the grid load fluctuation and the lowest economic time cost of user charging, and inputs the acquired data into the model, and uses the non-dominated sorting whale optimization algorithm with an elite strategy to solve the objective function to obtain the optimal charging scheme for the electric vehicle; the transmission and application unit sends a charging scheme suggestion to the user to achieve orderly charging scheduling of electric vehicles within the region.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent charging optimization scheduling for electric vehicles, and more particularly, relates to an electric vehicle charging scheduling method and terminal based on an improved optimization algorithm. Background Art

[0002] Due to the increasing exposure of problems such as environmental pollution, global warming, and fossil energy shortage, electric vehicles, as a green travel mode, have gained people's favor. With the continuous introduction of national support policies for electric vehicles, the ownership of new energy vehicles shows a fluctuating upward trend. At the same time, the charging load of electric vehicles also increases accordingly. A large number of disordered load demands will bring a huge impact to the power grid, affect the stable operation of the power system, increase the peak-valley difference of the grid load, and reduce the power quality, etc.

[0003] Suppose that during the driving process of an electric vehicle, a charging demand is generated. If the user-side system can generate an intelligent and orderly charging plan and provide relevant charging suggestions based on data such as the location of the electric vehicle, relevant information of nearby charging stations, and the traffic road network, then the adverse effects brought by the access of a large number of electric vehicle loads to the power grid can be effectively avoided. The electric vehicle charging scheduling method and terminal based on an improved optimization algorithm provide an accurate and reliable solution. This solution can effectively select a charging strategy with a lower charging cost and less impact on the stability of the power system for electric vehicles with charging needs, thus promoting the development of the electric vehicle industry. Summary of the Invention

[0004] In order to overcome the adverse effects brought by the disordered charging of a large number of electric vehicles and provide a more economical and time-saving charging plan for users, the present invention provides an electric vehicle charging scheduling method and terminal based on an improved optimization algorithm.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] An electric vehicle charging scheduling terminal, the terminal includes a data acquisition unit, a data processing and algorithm calculation unit, and a transmission and application unit. During the process of electric vehicle charging scheduling, the terminal tracks the change of the state of charge of the electric vehicle battery, makes a charging request decision, and sends the corresponding charging suggestion to the user.

[0007] An electric vehicle charging scheduling method based on an improved optimization algorithm, the scheduling method includes:

[0008] S1. The electric vehicle charging scheduling terminal needs to judge whether the user issues a charging request according to the state of charge of the electric vehicle battery on the user side. If so, proceed to the next step; if not, return to the previous step for cyclic judgment;

[0009] S2. The data acquisition unit obtains the daily charging demand, location of the electric vehicle, information on the charging stations accessible to the electric vehicle, the regional traffic road network, and the operating data of the regional power distribution network;

[0010] S3. Input the obtained relevant data information into the data processing and algorithm calculation unit of the terminal. Through the data processing and algorithm calculation unit, filter, analyze, extract, model, and solve the algorithm for the data to obtain the optimal charging strategy for the electric vehicle;

[0011] S4. The transmission and application unit of the terminal sends charging plan suggestions to the user to achieve orderly charging scheduling of electric vehicles within the region;

[0012] Further, the data acquisition unit obtaining the daily charging demand and location of the electric vehicle includes the charging demand of the electric vehicle within a day, the state of charge of the battery when the electric vehicle issues a charging demand, as well as location and time information; the information on the charging stations accessible to the electric vehicle includes the locations of the charging stations accessible nearby, the charging power and efficiency of the charging stations, and the time-of-use charging electricity price; the operating data of the regional power distribution network includes the basic load data of the regional power distribution network operation.

[0013] Further, inputting the daily charging demand, location of the electric vehicle, information on the charging stations accessible to the electric vehicle, the regional traffic road network, and the operating data of the regional power distribution network obtained by the data acquisition unit into the data processing and algorithm calculation unit of the terminal specifically further includes the following steps:

[0014] S31. Taking the minimum grid load fluctuation, the lowest time cost and economic cost of electric vehicle users' charging as the objectives, taking the charging situation of electric vehicle users as the decision variables, and taking the charging efficiency of the charging stations, the charging capacity of the electric vehicles, and the value range of the decision variables as the constraints, establish a multi-objective optimization model;

[0015] S32. Use the non-dominated sorting whale optimization algorithm with an elite strategy to solve the multi-objective optimization model of the electric vehicle charging scheduling to obtain the optimal charging strategy.

[0016] Further, S 31 Specifically, define the decision variable x i,j,k , minimize the grid load fluctuation F1(x i,j,k ), the economic cost of user charging F2(x i,j,k ), the time cost of user charging F3(x i,j,k ). The mathematical expressions of the objective function and the constraints are as follows:

[0017]

[0018]

[0019] Among them, F1(x i,j,k ) is the power grid load fluctuation function, F2(x i,j,k ) is the user's charging economic cost function, F3(x i,j,k ) is the user's charging time cost function, B i,j,k represents the load volatility caused by the charging of electric vehicle i at charging station j during the k-th period, P i,j,k represents the load peak-valley difference of electric vehicle i charging at charging station j during the k-th period, V i,j,k represents the load variance of electric vehicle i charging at charging station j during the k-th period, a, b, c are index weight coefficients, P max represents the maximum value of the load fluctuation curve after electric vehicle i charges at charging station j, P av represents the average value of the load fluctuation curve, P min represents the minimum value of the load fluctuation curve, S j represents the service fee of charging station j, pr k represents the time-of-use electricity price in the k-th period, t i,j,k represents the charging duration of electric vehicle i at charging station j during the k-th period, L i,j represents the path length from electric vehicle i to charging station j, v i represents the average driving speed, tp j,k represents the queuing duration required by charging station j in the k-th period, ΔC i represents the required charging power, η j represents the charging efficiency of charging station j, P j represents the charging power provided by charging station j.

[0020] Furthermore, S 32 specifically includes the following steps:

[0021] Step 1): Initialize the positions of the charging stations available to the electric vehicle, calculate the fitness of the available charging stations, and obtain the current optimal position through non-dominated sorting and record it, set the maximum number of iterations, and set the current iteration number t to 1;

[0022] Step 2): Generate random numbers p, r1, r2 within [0,1], judge whether p>0.5, if so, go to Step 3), otherwise go to Step 4), set the parameter a to linearly decrease within the range of [0,2], and calculate the coefficient vectors A, C;

[0023] A = 2arQ

[0024] C = 2r2

[0025] Step 3): Generate a random number within [-1,1], and perform position update according to the following spiral search formula;

[0026]

[0027] Step 4): Determine whether |A| < 1. If so, adopt the surrounding search mechanism; otherwise, perform a random search within the global scope. The formulas for the surrounding search and the random search are as follows:

[0028]

[0029] Step 5): After Steps 2) - 4), an updated new generation of solutions will be generated. The new solutions and the initial solutions are combined for non-dominated sorting to construct the Pareto set, and the optimal solutions of the first generation are obtained.

[0030] Step 6): Increase the number of iterations. The optimal solutions obtained in the previous generation are used as the parent generation for this iteration. Repeat Steps 2) - 4) to obtain the offspring.

[0031] Step 7): Adopt the elitist retention strategy. The parent generation and the offspring are combined and subjected to fast non-dominated sorting to construct the Pareto set, calculate the crowding degree, obtain the optimal solutions of the new generation, and compare the fitness with the optimal solutions of the previous generation. If it is better, replace the current optimal charging station location.

[0032] Step 8): After reaching the maximum number of iterations, the program ends and the global optimal solution is obtained. Otherwise, repeat Steps 6) - 7).

[0033] The electric vehicle charging scheduling method and terminal based on the improved optimization algorithm provided by the present invention have the following advantages:

[0034] 1) Grid load balance: The orderly charging scheduling can reasonably allocate the charging time periods and charging powers of electric vehicles, effectively reduce the peak-valley difference of the power grid, and improve the stability of the power grid.

[0035] 2) Improve resource utilization rate: By sending charging suggestions to users through the terminal, the utilization rate of charging pile resources can be more efficiently utilized, resource waste can be avoided, and the charging efficiency can be improved.

[0036] 3) Improve the experience of electric vehicle users: The terminal can provide users with more stable and convenient charging services, reduce the queuing and waiting time of users, and improve user satisfaction.

[0037] 4) Improve urban road conditions: Improve the urban traffic situation. Orderly charging can avoid traffic congestion and improve the road passing efficiency. Description of the Drawings

[0038] To more clearly illustrate the optimization solutions in the embodiments of the present invention, the following briefly introduces the drawings required in the prior art and the embodiments. The following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of an electric vehicle charging scheduling method and terminal based on an improved optimization algorithm provided by the present invention.

[0040] Figure 2 It is a flowchart of a non-dominated sorting whale optimization algorithm with an elite strategy provided by the present invention.

[0041] Figure 3 It is a load curve of orderly charging and disorderly charging provided by the present invention.

[0042] Figure 4 It is a comparison of the charging load curves obtained by solving the non-dominated sorting whale optimization algorithm with an elite strategy and the traditional non-dominated sorting whale optimization algorithm provided by the present invention.

[0043] Figure 5 It is a comparison of the charging load curves obtained by solving the non-dominated sorting whale optimization algorithm with an elite strategy and NSGA-II provided by the present invention. Detailed implementation manners

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in the specification of this application herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0046] To optimize the best solution for intelligent charging of electric vehicles, first, the user-side terminal system needs to determine whether the user issues a charging request according to the state of charge of the electric vehicle battery;

[0047] Specifically, if a charging request is issued from the user side, relevant data information on the daily operation of the electric vehicle, the distribution network, and the charging station is collected.

[0048] Specifically, the daily operation related data information of the electric vehicle, the power distribution network, and the charging station includes the charging demand of the electric vehicle within a day, the state of charge of the battery when the electric vehicle issues a charging demand, as well as the location and time information, the locations, charging powers, and efficiencies of the reachable charging stations near the location, the time-of-use charging electricity price in the area, the traffic road network map of the area, and the basic load data of the daily operation of the power distribution network.

[0049] Specifically, input the above data information into the data processing and algorithm calculation unit of the terminal, analyze and process the data, establish a mathematical model, and solve it through algorithms;

[0050] Specifically, establish a mathematical optimization model with the goal of minimizing the power grid load fluctuation, minimizing the user's economic cost, and minimizing the user's charging time cost. Taking whether user i charges at charging station j in the k-th time period, xi,j,k, as the decision variable, construct a multi-objective optimization model, and the mathematical expression is as follows:

[0051]

[0052] Among them, F1(x i,j,k ) is the power grid load fluctuation function, F2(x i,j,k ) is the user's charging economic cost function, F3(x i,j,k ) is the user's charging time cost function, B i,j,k represents the load volatility caused by the charging of electric vehicle i at charging station j during the k-th time period, P i,j,k represents the peak-valley difference of the load when electric vehicle i charges at charging station j during the k-th time period, V i,j,k represents the load variance when electric vehicle i charges at charging station j during the k-th time period, a, b, c are the index weight coefficients, P max represents the maximum value of the load fluctuation curve after electric vehicle i charges at charging station j, P av represents the average value of the load fluctuation curve, P min represents the minimum value of the load fluctuation curve, S j represents the service fee of charging station j, pr k represents the time-of-use electricity price during the k-th time period, t i,j,k represents the charging duration of electric vehicle i at charging station j during the k-th time period, L i,j represents the path length from electric vehicle i to charging station j, v i represents the average driving speed, tp j,k represents the queuing duration required by charging station j during the k-th time period, ΔC i represents the required charging power, η j represents the charging efficiency of charging station j, P j represents the charging power provided by charging station j.

[0053] Specifically, according to the above optimization objective function and constraint conditions, substituting into the non-dominated sorting whale optimization algorithm with an elite strategy, the following steps are used to optimize and obtain the best charging scheduling scheme for a certain area:

[0054] Step 1): Initialize the position vectors of all N optional charging stations for electric vehicles Calculate the grid load fluctuation degree, user time cost, and economic cost of the currently selected charging station location, and find the current optimal solution F ini (x i,j,k ), and record the location of the optimal charging station at this time as Set the maximum number of iterations to t max , and set the current iteration number t = 1;

[0055] Step 2): Set the parameter values. First, generate a random number p within [0, 1]. If p > 0.5, proceed to Step 3); otherwise, jump to Step 4). Set a = 2, and during the iteration process, a linearly decreases within the range [0, 2]. Generate random numbers r1 and r2 within [0, 1], and calculate the coefficient vectors A and C according to the following formulas;

[0056] A = 2ar1 - a

[0057] C = 2r2

[0058] Step 3): Local search mechanism, use the spiral mechanism to update the position, generate a new position vector, and the formula is as follows, where l is a random number within [-1, 1];

[0059]

[0060] Step 4): Determine whether |A| is less than 1. If so, update the position through the encircling search; otherwise, update the position through the random search. The formulas for the encircling search and the random search are as follows respectively;

[0061]

[0062] Step 5): Combine the new solution with the previous generation of solutions and perform non-dominated sorting to obtain the optimal solution at t = 1;

[0063] Step 6): Let t + 1, use the optimal solution obtained in the t-th generation as the parent input, and repeat Steps 2) - 4) to generate offspring;

[0064] Step 7): Combine the parent and offspring, perform fast non-dominated sorting, construct the Pareto set, calculate the crowding degree, generate a new solution, and compare the fitness of the new solution with the current optimal charging station location. If it is better, replace the current optimal charging station location;

[0065] Step 8): Determine whether the maximum number of iterations is reached. If not, repeat Steps 6) - 7); otherwise, end and obtain the global optimal solution.

[0066] Specifically, after obtaining the above global optimal solution, charging suggestions are sent to users through the transmission and application unit of the terminal to achieve orderly scheduling of regional electric vehicle charging.

[0067] To verify the effectiveness of the electric vehicle charging scheduling method based on the improved optimization algorithm proposed in the present invention, data information of a certain area is selected for simulation analysis. The time-of-use electricity price policy for this area is shown in Table 1.

[0068] Table 1 Time-of-use electricity price

[0069]

[0070] Specifically, 178 electric vehicles are selected for simulation analysis. Orderly charging scheduling is performed on the 178 electric vehicles, and a comparative analysis is made on the load fluctuations and user charging costs caused after scheduling compared with the disorderly situation. The disorderly charging of electric vehicles is considered to start charging at the moment when the electric vehicle returns at the last trip of the day. This charging moment follows a normal distribution from 8:00 to 24:00, and the charging duration follows a normal distribution from 0.5 to 4 hours. The charging process of the electric vehicle power battery is approximately considered as constant power charging.

[0071] Specifically, according to the proposed electric vehicle charging scheduling method based on the improved optimization algorithm, an orderly charging plan is solved to obtain an orderly charging load curve and compare it with the disorderly charging load curve; in order to verify the effectiveness of the proposed electric vehicle charging scheduling method based on the improved optimization algorithm for solving the orderly charging plan by traditional optimization algorithms, the NSWOA with elite strategy, traditional NSWOA, and NSGA-II methods proposed in the present invention are used to optimize the scheduling of 178 electric vehicles respectively. Through the comparison of simulation results, Figure 3 the obtained load curves of orderly charging and disorderly charging are shown. It can be seen that all three algorithms show good effects.

[0072] Specifically, in the simulation analysis, the parameter values of the load fluctuation degree are set as a = 0.4, b = 0.3, c = 0.3. The total cost of disorderly charging is 1830.90 yuan, and the load fluctuation degree is 492.51. For the solution result of the NSWOA with elite strategy, the total user charging cost is 1536.90 yuan, and the load fluctuation degree is 132.11. For the solution result of traditional NSWOA, the total user charging cost is 1643.94 yuan, and the load fluctuation degree is 165.69. For the solution result of NSGA-II, the total user charging cost is 1609.44 yuan, and the load fluctuation degree is 172.27. Figure 4Comparison of the charging load curves obtained by the NSWOA with elite strategy and the traditional NSWOA Figure 5 Comparison of the charging load curves obtained by the NSWOA with elite strategy and NSGA-II. It can be seen that the electric vehicle charging scheduling method based on the NSWOA with elite strategy proposed in the present invention has obvious advantages over other methods.

[0073] The above are only the preferred embodiments of the present invention, and do not impose any formal limitations on the present invention. Any simple modifications, changes, and equivalent structural changes made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An electric vehicle charging scheduling method based on an improved optimization algorithm, characterized in that: An electric vehicle charging scheduling terminal is used, which includes a data acquisition unit, a data processing and algorithm calculation unit, and a transmission and application unit. During the process of electric vehicle charging scheduling, the electric vehicle charging scheduling terminal tracks the change of the state of charge of the electric vehicle battery, makes a charging request decision, and sends the corresponding charging suggestion to the user, which specifically includes the following steps: S1. The electric vehicle charging dispatch terminal needs to determine whether the user has issued a charging request based on the charge state of the electric vehicle battery on the user side. If so, proceed to the next step. If not, return to the previous step for loop judgment; S2. The data acquisition unit obtains the daily charging demand and location of electric vehicles, information on charging stations accessible to electric vehicles, regional transportation networks, and operation data of regional distribution networks; S3. Input the acquired relevant data information into the data processing and algorithm calculation unit of the terminal, filter, analyze, extract, model and solve the data through the data processing and algorithm calculation unit to obtain the optimal charging strategy for the electric vehicle; S4. The transmission and application unit of the terminal sends charging plan suggestions to the user to achieve orderly charging scheduling of electric vehicles in the region; In step S3, the obtained relevant data information is input into the data processing and algorithm calculation unit of the terminal, and the data is filtered, analyzed, extracted, modeled and algorithmically solved by the data processing and algorithm calculation unit to obtain the optimal charging strategy for the electric vehicle, which includes the following steps: S 31 .With the goal of minimizing grid load fluctuation, minimizing the time cost and economic cost of electric vehicle users’ charging, the charging status of electric vehicle users as decision variables, and the charging efficiency of charging stations, the charging capacity of electric vehicles, and the value range of decision variables as constraints, a multi-objective optimization model is established; S 32 .Use the non-dominated sorting whale optimization algorithm with elite strategy to solve the multi-objective optimization model of electric vehicle charging scheduling to obtain the optimal charging strategy; The S 32 The specific steps include: S 321 . Initialize the location vectors of all N optional charging stations for electric vehicles Calculate the grid load fluctuation, user time cost and economic cost of the currently selected charging station location, and find the current optimal solution F through non-dominated sorting ini (x i,j,k ), and record the location of the optimal charging station at this time as Set the maximum number of iterations to t max , set the current number of iterations t = 1; S 322 . Set the parameter value. First, generate a random number p in [0,1]. If p>0.5, proceed to step S. 323 Otherwise, jump to step S 324 ; Set a=2. During the iteration, a decreases linearly in the range [0,2]. Generate random numbers r1 and r2 in the range [0,1]. Calculate coefficient vectors A and C according to the following formula: A=2ar1-a C＝2r2 S 323 .Perform local search, use the spiral mechanism to update the position, and generate a new position vector. The formula is as follows, where l is a random number in [-1,1]; S 324 . Determine whether |A| is less than 1. If so, update the position through encircling search, otherwise update the position through random search. The formulas for encircling search and random search are as follows: S 325 .Merge the updated solution with the initial solution and perform non-dominated sorting to obtain the optimal solution at t=1; S 326 .Let t+1, use the optimal solution obtained in the tth generation as the parent input, and repeat step S 322 -S 324 , generate offspring; S 327 .Merge the parent generation and the child generation, perform fast non-dominated sorting, construct the Pareto set, calculate the congestion, generate a new solution, compare the fitness of the new solution with the current optimal charging station location, and if it is better, replace the current optimal charging station location; S 328 . Determine whether the maximum number of iterations has been reached. If not, repeat step S 326 -S 327 , otherwise it ends and the global optimal solution is obtained.

2. According to the electric vehicle charging scheduling method based on the improved optimization algorithm described in claim 1, it is characterized in that: In step S2, the data acquisition unit obtains the daily charging demand and location of the electric vehicle, the information of the charging stations accessible to the electric vehicle, the regional traffic road network, and the operation data of the regional distribution network, which specifically include the charging demand of the electric vehicle within a day, the battery charge state and the location and time information when the electric vehicle issues a charging demand, the location, charging power and efficiency of the charging stations accessible near the area, the time-sharing charging electricity price in the area, the traffic road network map of the area, and the basic load data of the daily operation of the distribution network.

3. According to claim 1, the electric vehicle charging scheduling method based on the improved optimization algorithm is characterized in that: Step S 31 Specifically include: S 311 The decision variable is defined as x i,j,k , indicating whether the i-th electric car goes to charging station j for charging in the k-th time period. The values ​​of the decision variables are as follows: S 312 .Construct the objective function of multi-objective optimization to minimize the grid load fluctuation F1(x i,j,k ), user charging economic cost F2(x i,j,k ), user charging time cost F3(x i,j,k ), the formula is as follows: Among them, the degree of grid load fluctuation is measured by three indicators: load fluctuation rate, load peak-to-valley difference, and load fluctuation variance. The calculation formulas are as follows: P i，j，k =P max -P min In the formula, B i,j,k represents the load fluctuation rate caused by electric vehicle i charging at charging station j during time period k; P i,j,k represents the peak-to-valley difference of the load when electric vehicle i is charging at charging station j during time period k; P max represents the maximum value of the load fluctuation curve of electric vehicle i after charging at charging station j; P av Represents the average value of the load fluctuation curve; P min Indicates the minimum value of the load fluctuation curve; V i,j,k represents the load variance of electric vehicle i charging at charging station j during period k; The corresponding objective function is: F1(x i，j，k )=x i,j，k *(aB i，j，k +bP i，j，k +cV i,j，k ) In the formula, a, b, c correspond to the weight coefficients of load fluctuation rate, load peak-to-valley difference, and load fluctuation variance, respectively, and a+b+c=1; The economic cost of charging for users is calculated as follows: C1=S j +pr k *t i，j，k In the formula, C1 represents economic cost; S j represents the service fee of charging station j; pr k represents the time-of-use electricity price during period k; t i,j,k represents the charging time of electric vehicle i at charging station j during time period k; The corresponding objective function is F2(x i，j，k )=x i，j，k *C1 The time cost of user charging is considered to be the sum of the time it takes for the electric vehicle to reach the charging station from the departure point, the charging queue time, and the charging time. The calculation formula is as follows: Where C2 represents the time cost of charging, L i,j represents the path length from electric vehicle i to charging station j; v i Indicates the average driving speed; tp j,k represents the queuing time required at charging station j in time period k; ΔC i Indicates the required charging capacity, η j represents the charging efficiency of charging station j; P j represents the charging power provided by charging station j; The corresponding objective function is: F3(x i，j，k )=x i，j，k *C2 S 313 The constraints of the objective function are: In the formula, C i Represents the battery capacity of electric vehicle i.

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