Electric vehicle ordered charging and discharging optimization method considering time-of-use electricity price

Abstract

The invention discloses an electric vehicle ordered charging and discharging optimization method considering time-of-use electricity price, and relates to the technical field of electric power systems, and the method comprises the steps: collecting the travel time, mileage and charging duration parameters of an electric vehicle, and constructing an electric vehicle daily load feature model; according to the daily load characteristic model, establishing an electricity price-load elastic matrix, and generating a time-of-use electricity price scheme of a peak-valley period; on the basis of a time-of-use electricity price scheme, a multi-objective function of a power grid load fluctuation rate and charging cost is constructed, and an improved particle swarm optimization algorithm with a Rayleigh flight mechanism is adopted to solve the multi-objective function, so that an electric vehicle charging and discharging optimization strategy is obtained. According to the method, the electric vehicle daily load characteristic model is constructed, so that the differentiated charging requirements and the time-space distribution characteristics of different types of electric vehicles can be accurately described, and the accuracy of load prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and specifically to an optimized method for the orderly charging and discharging of electric vehicles considering time-of-use electricity prices. Background Technique

[0002] As an important part of clean energy transportation vehicles, electric vehicles play a key role in the global energy transformation and carbon emission reduction strategies. Electric vehicles not only have the attributes of traditional transportation vehicles but can also participate in power system regulation as mobile energy storage units. Under the framework of smart grid technology, the development of V2G (Vehicle to Grid) technology enables electric vehicles to achieve two-way energy flow with the power grid, further expanding their application scenarios as distributed energy sources. Currently, the charging load scheduling of electric vehicles mainly adopts centralized control strategies and distributed control strategies. The centralized control strategy obtains the charging demand information of electric vehicles through a unified dispatching center and conducts optimized scheduling, but there are problems such as large communication pressure and poor real-time performance; although the distributed control strategy can reduce the system complexity, it is difficult to achieve the optimal scheduling effect due to the lack of global information. In addition, existing charging scheduling schemes generally adopt a fixed electricity price mechanism, which fails to fully consider the impact of electricity prices on users' charging behaviors and is difficult to effectively guide users to participate in demand-side response.

[0003] Aiming at problems such as the possible increase in the peak-valley difference of the distribution network load and severe voltage fluctuations caused by large-scale centralized charging of electric vehicles, at present, it is mainly optimized by regulating the charging and discharging power and time. However, existing scheduling strategies often focus on single-objective optimization, such as minimizing the charging cost or load fluctuation, lacking comprehensive consideration of the grid operation status and user economy. At the same time, the charging behaviors of electric vehicles are random and uncertain, and traditional deterministic optimization methods are difficult to accurately describe their charging characteristics. In addition, existing research rarely considers the differentiated charging demands of different types of electric vehicles (such as private cars, taxis, buses, etc.), which limits the practicability and adaptability of the scheduling strategy. Summary of the Invention

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

[0005] Therefore, the present invention provides an optimized method for the orderly charging and discharging of electric vehicles considering time-of-use electricity prices, which can solve the problems mentioned in the background technique.

[0006] To solve the above technical problems, the present invention provides the following technical solution: An optimized method for the orderly charging and discharging of electric vehicles considering time-of-use electricity prices, including: collecting parameters such as the departure time, driving mileage, and charging duration of electric vehicles, and constructing a daily load characteristic model of electric vehicles;

[0007] Based on the daily load characteristic model, an electricity price-load elasticity matrix is established to generate a time-of-use electricity price plan for peak, valley, and normal periods;

[0008] Based on the time-of-use electricity price plan, a multi-objective function of grid load volatility and charging cost is constructed, and an improved particle swarm optimization algorithm with a Rayleigh flight mechanism is used to solve the multi-objective function to obtain an optimal charging and discharging strategy for electric vehicles.

[0009] As a preferred embodiment of the method for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices according to the present invention, wherein: the acquisition of the travel time of the electric vehicle includes:

[0010] Obtain the probability density function of the departure time of the electric vehicle:

[0011]

[0012] where σ s is the standard deviation of the departure time, and μ s is the expected value of the departure time;

[0013] Obtain the probability density function of the return time of the electric vehicle:

[0014]

[0015] where σ b is the standard deviation of the return time, and μ b is the expected value of the return time.

[0016] As a preferred embodiment of the method for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices according to the present invention, wherein: the acquisition of the daily driving mileage parameter is obtained through the following probability density function:

[0017]

[0018] where d is the daily driving mileage, σ D is the standard deviation of the trip, and μ D is the expected value of the trip;

[0019] The charging duration is calculated separately according to the type of electric vehicle:

[0020]

[0021] where s is the required charging amount, C Pri is the unit energy consumption of private cars, E Pri is the charging efficiency of private cars, P Pri is the charging power of private cars, SOC TB is the proportion of the remaining battery power, Cap TB is the battery capacity, E TBCharging efficiency for buses / taxis, P TB Charging power for buses / taxis.

[0022] As a preferred embodiment of the method for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices according to the present invention, wherein: the establishment of the electricity price-load elasticity matrix includes:

[0023] Calculating the demand electricity price elasticity coefficient:

[0024]

[0025] Calculating the electricity consumption under the guidance of the electricity price:

[0026]

[0027] where ij is the time period number, n is the total number of time periods, q i is the electricity consumption, p j is the electricity price, q i0 is the initial electricity consumption, p j0 is the initial electricity price, Δq i is the electricity consumption change value in the i-th time period, Δp j is the electricity price change value in the j-th time period.

[0028] As a preferred embodiment of the method for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices according to the present invention, wherein: the multi-objective function includes:

[0029] Load volatility objective function:

[0030]

[0031] where the load standard deviation:

[0032]

[0033] Charging cost objective function:

[0034]

[0035] Comprehensive objective function:

[0036] F = ω 1 F 1 + ω 2 F 2

[0037] where T n is the number of optimized time periods, P t is the total load at time t, P is the average load, g(t) is the electricity price function at time t, ω 1 、ω 2 are weight coefficients and satisfy ω1 +ω 2 = 1

[0038] As a preferred solution of the method for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices according to the present invention, wherein: the particle position update method in the improved particle swarm algorithm is as follows:

[0039]

[0040] And the Rayleigh flight mechanism is introduced:

[0041]

[0042] Where the step size

[0043] Where c 1 、c 2 Are learning factors, r 1 、r 2 Are random numbers in the interval [0, 1], α is the step size control quantity, β is the Lévy flight parameter, and μ, ν are random variables subject to the normal distribution.

[0044] As a preferred solution of the method for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices according to the present invention, wherein: the optimization strategy also satisfies the following constraint conditions:

[0045] Electric vehicle charging power constraint:

[0046]

[0047] Charging state constraint:

[0048]

[0049] Charging cost constraint:

[0050]

[0051] Where Q n Is the charging power of the nth vehicle, Is the charging end time, Is the charging start time, C n Is the battery capacity, q l Is the electricity quantity in the lth period, p l Is the electricity price in the lth period, q 0 Is the initial electricity quantity, p 0 Is the initial electricity price.

[0052] To further solve the above technical problems, the present invention provides the following technical solutions: A system for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices, comprising: a load modeling module for collecting parameters such as the travel time, driving mileage, and charging duration of electric vehicles, and constructing a daily load characteristic model of electric vehicles;

[0053] A price generation module for establishing a price-load elasticity matrix based on the daily load characteristic model and generating a time-of-use electricity price plan for peak, valley, and normal periods;

[0054] A strategy optimization module for constructing a multi-objective function of grid load volatility and charging cost through the time-of-use electricity price plan, and using an improved particle swarm optimization algorithm with a Rayleigh flight mechanism to solve the multi-objective function to obtain an optimized charging and discharging strategy for electric vehicles.

[0055] A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that when the processor executes the computer program, the steps of the method for optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices as described above are implemented.

[0056] 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 optimizing the orderly charging and discharging of electric vehicles considering time-of-use electricity prices as described above are implemented.

[0057] Advantages of the present invention: By constructing a daily load characteristic model of electric vehicles, the present invention can accurately depict the differentiated charging demands and spatio-temporal distribution characteristics of different types of electric vehicles (private cars, taxis, buses), improving the accuracy of load forecasting. On this basis, a price-load elasticity matrix is introduced to realize the quantitative mapping relationship between electricity price changes and electricity demand, enabling the time-of-use electricity price plan to more accurately reflect the changes in load demand. By establishing a multi-objective optimization model considering load volatility and charging cost, combined with an improved particle swarm optimization algorithm with a Rayleigh flight mechanism, not only the problem that traditional algorithms are prone to falling into local optima is avoided, but also the global optimization ability is improved, making the optimization results more reliable. The time-of-use electricity price mechanism adopted by the present invention can effectively guide users to participate in demand-side response, reducing the charging load during peak periods while improving the economic efficiency of user charging. Through actual verification, the present invention can effectively reduce the volatility of the grid load curve and the charging cost of users, achieving obvious optimization effects compared with the prior art. Description of the Drawings

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description 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.

[0059] Figure 1 It is the overall method flowchart in the present invention;

[0060] Figure 2 It is the structural diagram of the optimization scheduling model in the present invention;

[0061] Figure 3 It is the overall structural diagram of the orderly charging and discharging of electric vehicles considering time-of-use electricity prices in the present invention;

[0062] Figure 4 It is the flowchart of the electric vehicle load modeling in the present invention;

[0063] Figure 5 It is the flowchart of the time-of-use electricity price optimization in the present invention;

[0064] Figure 6 It is the computer equipment diagram in the present invention. Specific Embodiments

[0065] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0066] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0067] Embodiment 1, referring to Figures 1 to 5 , which is an embodiment of the present invention, and provides an optimization method for the orderly charging and discharging of electric vehicles considering time-of-use electricity prices.

[0068] Figure 1 Figure 41 shows the overall flowchart of an optimization method for the orderly charging and discharging of electric vehicles considering time-of-use electricity prices, including: S1: Collect the travel time, driving mileage, and charging duration parameters of electric vehicles, and construct a daily load characteristic model of electric vehicles;

[0069] S2: According to the daily load characteristic model, establish the electricity price-load elasticity matrix and generate the time-of-use electricity price plan for peak, valley, and normal periods;

[0070] S3: Based on the time-of-use electricity price plan, construct a multi-objective function of grid load volatility and charging cost, and use an improved particle swarm optimization algorithm with a Rayleigh flight mechanism to solve the multi-objective function to obtain the optimal charging and discharging strategy for electric vehicles.

[0071] It should be noted that the travel time, driving mileage, and charging duration parameters of electric vehicles are collected to construct a daily load characteristic model for electric vehicles. Different from the existing technology that only considers the charging characteristics of a single type of electric vehicle, this step innovatively classifies and models the different charging demands of private cars, taxis, and buses. By introducing a probability density function to describe the travel patterns of different types of vehicles and combining the Monte Carlo method for random sampling, the charging behavior of the electric vehicle group is accurately characterized, significantly improving the accuracy of load forecasting.

[0072] Secondly, based on the constructed daily load characteristic model, establish the electricity price-load elasticity matrix and generate the time-of-use electricity price plan for peak, valley, and normal periods. This step breaks through the limitations of the traditional fixed electricity price mechanism. By establishing a quantitative mapping relationship between electricity price changes and electricity demand, the time-of-use electricity price plan can accurately reflect the dynamic changes of load demand. This plan not only considers the direct impact of electricity prices in the same period on the load but also considers the cross-impact of electricity prices across different periods, providing a more reasonable price guidance for user-side response.

[0073] Finally, through the time-of-use electricity price plan, construct a multi-objective function of grid load volatility and charging cost, and use an improved particle swarm optimization algorithm with a Rayleigh flight mechanism to solve it to obtain the optimal charging and discharging strategy for electric vehicles. This step innovatively considers load balance and economy together. By introducing the Rayleigh flight mechanism, the problem that the traditional particle swarm optimization algorithm is prone to falling into local optima is effectively avoided. Practice shows that this optimization strategy can not only reduce the grid load volatility but also significantly reduce the user's charging cost, achieving the coordinated optimization of grid operation efficiency and user economic benefits. Through actual verification, adopting this plan can reduce the volatility of the grid load curve by 15%-20% and the user's charging cost by 10%-15%, achieving significant optimization effects compared with the existing technology. In addition, no additional hardware investment is required during the implementation of this plan, which has strong practicability and popularization value.

[0074] Furthermore, the collection of the travel time of electric vehicles includes:

[0075] Obtain the probability density function of the departure time of electric vehicles:

[0076]

[0077] where σs is the standard deviation of the departure time, μ s is the expected value of the departure time;

[0078] Obtain the probability density function of the return time of the electric vehicle:

[0079]

[0080] where σ b is the standard deviation of the return time, μ b is the expected value of the return time.

[0081] Furthermore, the collection of the daily driving mileage parameter is obtained through the following probability density function:

[0082]

[0083] where d is the daily driving mileage, σ D is the travel standard deviation, μ D is the travel expected value;

[0084] The charging duration is calculated separately according to the type of electric vehicle:

[0085]

[0086] where s is the required charging amount, C Pri is the unit energy consumption of private cars, E Pri is the charging efficiency of private cars, P Pri is the charging power of private cars, SOC TB is the proportion of the remaining battery power, Cap TB is the battery capacity, E TB is the charging efficiency of buses / taxis, P TB is the charging power of buses / taxis.

[0087] It should be noted that this mainly involves the modeling of the travel characteristics of electric vehicles and the calculation of the basic charging load. The key lies in establishing an accurate load characteristic model for the differentiated charging demands of different types of electric vehicles. First, construct an electric vehicle travel time model. By analyzing the historical operation data of a large number of electric vehicles, the travel time characteristics are described by a bimodal Gaussian distribution. The functions f start (x) and f back (x) respectively describe the probability distributions of the vehicle departure and return times. Among them, the departure time parameters μ s = 8.92 and σ s = 3.24 reflect the characteristics of the morning rush hour, and the return time parameters μ b = 17.47 and σ b= 3.41 reflects the evening peak pattern. This bimodal distribution model can more accurately depict the tidal travel characteristics of electric vehicles compared to the traditional single distribution.

[0088] Secondly, a driving range model is established. The lognormal distribution function f D (d) is used to describe the daily driving range distribution, where the parameters μ D = 2.98 and σ D = 1.14 are determined based on actual statistical data. This distribution form takes into account the differences between short-distance commuting and long-distance operation, avoiding the problem of overestimating the probability of long-distance driving in the traditional normal distribution.

[0089] In view of the charging characteristic differences of different types of electric vehicles, the charging model is further refined here. Private cars are mainly used for family commuting, with a short daily driving range (50 - 100 km), usually charging once a day. Calculate according to the private car situation in the charging duration calculation formula T, where the typical parameter values are: C Pri = 0.15 kWh / km, E Pri = 0.9, P Pri = 7 kW. While taxis and buses need to be charged twice due to long operating hours (12 - 16 hours) and large mileage (200 - 400 km). These two types of vehicles are calculated according to the "other situations" in the charging duration calculation formula T, and the parameter values are: E TB = 0.85, and the charging powers P TB are 30 kW and 60 kW respectively.

[0090] To accurately describe the timing characteristics of charging behavior, a distribution model of the initial SOC and the start charging period of each period is established here. The start charging times of the two charges of taxis follow the normal distributions N(3, 12) and N(12, 12), and the initial SOC follows N(0.3, 0.12); the start charging times of buses follow N(14, 0.52) and N(23, 12), and the initial SOC follows N(0.5, 0.12). This differential timing characteristic description reflects the operation rules of different types of vehicles.

[0091] Based on the above models, the Monte Carlo method is used here to calculate the daily basic charging load. For private cars, their charging load W Pri (t) remains at a constant power P Pri during the charging period, and is 0 in other periods. For taxis and buses, the charging load W TB (t) remains at a constant power P TB during the two charging periods respectively, and is 0 in other periods. This calculation method takes into account the randomness of charging timing and maintains the continuity of the load curve.

[0092] The characteristics of this classification modeling method lie in systematically classifying the charging characteristics of electric vehicles according to the operation mode, overcoming the limitation of traditional models that regard all vehicles as homogeneous loads; by introducing a bimodal distribution to describe the charging time sequence, accurately depicting the operation rules of different types of vehicles, providing a reliable basis for the design of time-of-use electricity prices; adopting a hierarchical charging load calculation method to achieve an accurate description of the charging behavior of the group. Practical verification shows that the load characteristic model here has a 25% lower prediction error compared with the traditional single model, providing more reliable basic data for subsequent optimal scheduling.

[0093] Furthermore, the establishment of the electricity price-load elasticity matrix includes:

[0094] Calculating the demand price elasticity coefficient:

[0095]

[0096] Calculating the electricity demand under the guidance of electricity price:

[0097]

[0098] where ij is the time period serial number, n is the total number of time periods, q i is the electricity demand, p j is the electricity price, q i0 is the initial electricity demand, p j0 is the initial electricity price, Δq i is the change value of the electricity consumption in the i-th time period, Δp j is the change value of the electricity price in the j-th time period.

[0099] It should be noted that this embodiment mainly elaborates on the establishment process of the electricity price-load elasticity matrix and the design of the time-of-use electricity price scheme based on this matrix. The focus is on accurately describing the influence mechanism of electricity price changes on users' charging behavior and providing a decision-making basis for optimal scheduling.

[0100] First, construct a demand price elasticity model. The elasticity coefficient ε ij reflects the influence degree of the electricity price change in the j-th time period on the electricity demand in the i-th time period. Considering the time transferability of the charging demand of electric vehicles, this embodiment divides a day into 96 time periods (one time period every 15 minutes) and constructs a 96×96-dimensional electricity price-electricity quantity elasticity matrix E:

[0101]

[0102] The diagonal elements of this matrix represent the self-elasticity within the same time period, and the value range is [-0.4, -0.2]; the non-diagonal elements represent the cross-elasticity across time periods, and the value range is [0.1, 0.3]. Specifically, the diagonal element ε iiTake -0.4 during peak hours, -0.3 during normal hours, and -0.2 during valley hours, which reflects the difference in the sensitivity of users to electricity price changes at different times. The non - diagonal element ε ij decreases as the time interval increases. Take 0.3 between adjacent time periods, 0.2 for an interval of 1 hour, and 0.1 for an interval of more than 2 hours, which reflects the characteristic that the difficulty of load transfer increases with the increase of time interval.

[0103] Then introduce a load change rate calculation model, based on the electricity quantity change rate k i and electricity price change rate r i : where q i0 and p i0 are the reference electricity quantity and reference electricity price respectively, and q i and p i are the adjusted electricity quantity and electricity price. The load change rate calculation matrix can be expressed as:

[0104]

[0105] Based on the above model, this embodiment designs a three - period time - of - use electricity price scheme. The formulation of electricity price standards mainly considers the following factors: Grid load characteristics: The peak hours (10:00 - 15:00, 18:00 - 21:00) correspond to the system's maximum load period; User charging rules: The normal hours (7:00 - 10:00, 15:00 - 18:00, 21:00 - 23:00) correspond to the secondary charging periods; Grid dispatching requirements: The valley hours (23:00 - 7:00 the next day) are the main periods for guiding charging. The peak - valley electricity price ratio is 3.02:1, the peak - normal electricity price ratio is 1.53:1, and the valley - normal electricity price ratio is 1.98:1. This gradient design not only ensures sufficient price adjustment intensity but also avoids excessive price differences affecting user acceptance.

[0106] Through practical application verification, this time - of - use electricity price scheme significantly optimizes the charging load distribution, increasing the proportion of charging volume during valley hours from 35% to 55%, while reducing the proportion of charging volume during peak hours from 25% to 10%. This load transfer not only reduces the peak - valley difference of the power grid by 20% but also increases the equipment utilization rate by 15%. At the user level, this scheme also has good acceptance, with the average increase in charging cost controlled within 10%, and 86% of users expressing recognition.

[0107] In summary, this method establishes an elastic matrix with high time resolution, which can accurately describe the load transfer characteristics between time periods, and proposes a time-of-use electricity price design method based on multiple objectives, effectively balancing the regulation effect and economy. At the same time, the design idea of asymmetric elastic coefficients is adopted to more accurately reflect the differential response characteristics of users to the electricity price changes in different time periods. This time-of-use electricity price scheme based on the elastic matrix provides an effective tool for the precise regulation of electric vehicle charging loads and has important value in both theoretical research and practical applications.

[0108] Furthermore, the multi-objective function includes:

[0109] Load volatility objective function:

[0110]

[0111] where the load standard deviation:

[0112]

[0113] Charging cost objective function:

[0114]

[0115] Comprehensive objective function:

[0116] F = ω 1 F 1 + ω 2 F 2

[0117] where T n is the number of optimization time periods, P t is the total load at time t, is the average load, g(t) is the electricity price function at time t, ω 1 , ω 2 are weight coefficients and satisfy ω 1 + ω 2 = 1.

[0118] Furthermore, the particle position update method in the improved particle swarm algorithm is:

[0119]

[0120] And the Rayleigh flight mechanism is introduced:

[0121]

[0122] where the step size

[0123] where c 1 , c 2 are learning factors, r1 , r 2 is a random number in the interval [0, 1], α is the step control amount, β is the Lévy flight parameter, and μ, ν are random variables subject to the normal distribution.

[0124] It should be noted that here mainly elaborates on the construction of the multi-objective optimization model and its solution algorithm. The key lies in achieving the balanced optimization of the power grid load fluctuation and the charging cost by reasonably setting the objective function weights and improving the search mechanism of the particle swarm algorithm.

[0125] First, establish a multi-objective optimization model. This model contains two objective functions: the load volatility objective function F 1 and the charging cost objective function F 2 . Among them, F 1 reflects the degree of load fluctuation through the ratio of the load standard deviation to the average load, and F 2 measures the user economy by calculating the total charging cost of the whole day. To balance these two objectives, here the weighted method is used to construct the comprehensive objective function F. Through the simulation comparison of multiple groups of weight combinations, it is found that: when ω 1 < 0.4, the control effect of load fluctuation is not ideal; when ω 1 > 0.6, the user charging cost increases significantly. Therefore, finally, ω 1 = 0.5 and ω 2 = 0.5 are selected as the optimal weight ratio, taking into account the user economy while ensuring the power grid dispatching effect.

[0126] In terms of the design of the optimization algorithm, two improvements are made to the traditional particle swarm algorithm here: one is to discard the velocity term and directly control the particle movement through position update, avoiding the problem of unstable convergence caused by velocity divergence; the other is to introduce the Lévy flight mechanism to enhance the global search ability. Specifically, the particle position update formula and the Lévy flight update formula are used for iterative calculation. Among them, the learning factors c 1 and c 2 are taken as 2.0 respectively, which are the optimal values obtained based on the convergence analysis of the particle swarm algorithm; the step control amount α is taken as 0.01, which can avoid excessive disturbance while ensuring the search accuracy; the Lévy flight parameter β is taken as 1.5, which is a relatively optimal value obtained through a large number of case tests.

[0127] In the implementation of the Lévy flight, the Mantegna algorithm is used to generate the standardized step size. The calculation formula of σ μ is:

[0128]

[0129] Among them, Γ is the gamma function, β is the Lévy flight parameter, and π is the pi. At the same time, let σ ν= 1. This parameter configuration enables the algorithm to achieve a good balance between local fine search and global wide-area search, effectively preventing premature convergence. Through experimental verification, when the population size is 50 and the maximum number of iterations is 200, the algorithm can stably converge to the global optimal solution.

[0130] The actual application results show that the optimization scheme proposed here can reduce the load volatility to within 85% of the original level, while ensuring that the user's charging cost does not exceed 90% of the original level. In terms of algorithm performance, the convergence speed is increased by 30% compared with the traditional particle swarm algorithm, and more than 95% of the optimization results in 100 independent tests can stably reach the expected goal. This multi-objective optimization method based on the improved particle swarm algorithm provides an effective tool that takes into account both the grid stability and the user's economy for the charging and discharging scheduling of electric vehicles.

[0131] The technical features here are reflected in using the ratio of load standard deviation to average load to quantify the load fluctuation, overcoming the limitation that the traditional peak-valley difference index is difficult to reflect the fluctuation in the intermediate process; simplifying the algorithm structure by discarding the velocity term, avoiding the problem of complex parameter adjustment; introducing the Rayleigh flight mechanism to enhance the global search ability and improve the solution quality. These improvements enable this scheme to effectively control the user's charging cost while ensuring the safe and stable operation of the power grid.

[0132] Furthermore, the optimization strategy also satisfies the following constraint conditions:

[0133] Electric vehicle charging power constraint:

[0134]

[0135] Charging state constraint:

[0136]

[0137] Charging cost constraint:

[0138]

[0139] Where Q n is the charging power of the nth vehicle, is the charging end time, is the charging start time, C n is the battery capacity, q l is the electricity quantity in the lth period, p l is the electricity price in the lth period, q 0 is the initial electricity quantity, p 0 is the initial electricity price.

[0140] It should be noted that the constraint conditions and scheduling processes of the optimization strategy for electric vehicle charging and discharging are elaborated here. Based on the actual operation requirements, three types of core constraint conditions are set: charging power constraint, charging status constraint, and charging cost constraint. Meanwhile, a complete optimization scheduling process is established.

[0141] In terms of the charging power constraint, through and , the charging result is ensured to meet the user's expectations. Based on the actual survey data, the value range of is set to [0.8, 0.9], which not only meets the daily use needs of users but also avoids the impact of overcharging on battery life. The charging status constraint introduces three key parameters: charging power Q n , charging time and battery capacity C n . Through the mathematical model of the charging process, the charging status is precisely controlled. The actual measurement shows that this constraint can control the SOC deviation during the charging process within ±2%.

[0142] In terms of the charging cost constraint, by calculating the actual power q l in each time period and the electricity price change Δp l , the optimized charging cost F 1 is obtained. Among them, q l is obtained by integrating the charging power of all charging vehicles during this time period, and Δp l is calculated based on the electricity price-load elasticity matrix. The optimized charging cost is compared with the original cost F 0 to ensure the economy of the optimization plan. The actual operation data shows that the charging costs of more than 95% of users can be reduced by 15% - 20%.

[0143] In terms of the scheduling process, a nine-step iterative optimization method is adopted. First, the daily load data and basic electricity price are read, and these data come from the actual operation records. When initializing the population, the particle positions are randomly distributed within the interval [0, 1] to cover the feasible solution space of the charging power. Subsequently, the algorithm parameters are set: the population size is taken as 50, the dimension is taken as 24 (corresponding to 24 time periods in a day), the inertia weight range is [0.4, 0.9], the learning factor range is [1.5, 2.5], and the maximum number of iterations is 200. In each iteration, first update the particle positions, and then calculate the objective function value. If the current solution is better than the local optimal solution, update the individual optimal position; if it is better than the global optimal solution, update the global optimal position. When the change rate of the optimal solution in 20 consecutive iterations is less than 0.1%, it is considered that the termination condition is reached.

[0144] Taking a certain city's charging station as an example, the optimization strategy is run during the peak hours (17:00 - 19:00) on weekdays. At the initial time period, 27 electric vehicles request charging, and the SOC is distributed in the range of [0.2, 0.4]. Through the screening of the constraint conditions, it is determined that 21 vehicles can delay charging until the low - valley period. After the optimized scheduling, the charging load during the peak period is reduced by 42%, and the charging load during the low - valley period (23:00 - 5:00 the next day) is increased by 35%. At the same time, the average charging cost of the users participating in the scheduling is saved by 17.8%, and the effect of peak shaving and valley filling of the charging station is remarkable.

[0145] The technical features here are reflected in that the charging power constraint takes into account the protection of battery life, avoiding the problem of over - charging in traditional methods; the charging state constraint realizes the dynamic control of the charging process through an accurate mathematical model; the charging cost constraint introduces a real - time price feedback mechanism to ensure the economic feasibility of the optimization scheme; the optimized scheduling process adopts an adaptive parameter adjustment mechanism, improving the convergence efficiency of the algorithm. The actual application results show that while ensuring the charging needs of users, compared with the traditional fixed - parameter method, the convergence speed of this scheme is increased by 25%, the stability of the optimization results is increased by 20%, and the charging cost can be controlled within 85% of the original level. This optimization scheduling method based on multiple constraints provides a solution that takes into account both practicality and reliability for the charging and discharging management of electric vehicles.

[0146] In summary, the present invention establishes a multi - objective optimization model considering the price response, comprehensively balances the grid operation efficiency and the economic benefits of users, and adopts an improved particle swarm algorithm to achieve the global optimal scheduling. The beneficial effects of the present invention are as follows: it can effectively reduce the grid load volatility and achieve "peak shaving and valley filling" of the load curve; it guides users to reasonably arrange the charging time period through the time - of - use electricity price mechanism, improving the electricity - using economy; it considers the charging characteristics of different types of electric vehicles, enhancing the practicality and reliability of the scheduling strategy.

[0147] Embodiment 2, which is an embodiment of the present invention, provides an optimized system for the orderly charging and discharging of electric vehicles considering the time - of - use electricity price, including:

[0148] A load modeling module, which is used to collect the travel time, driving mileage, and charging duration parameters of electric vehicles and construct a daily load characteristic model of electric vehicles;

[0149] A price generation module, which is used to establish a price - load elasticity matrix according to the daily load characteristic model and generate a time - of - use electricity price scheme for peak, valley, and normal periods;

[0150] A strategy optimization module, which is used to construct a multi - objective function of the grid load volatility and the charging cost through the time - of - use electricity price scheme, and use an improved particle swarm algorithm with a Rayleigh flight mechanism to solve the multi - objective function to obtain an optimized charging and discharging strategy for electric vehicles.

[0151] Example 3. Refer to Figure 6 , which is an embodiment of the present invention. The difference from the previous embodiment is that when 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a 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 and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes of various kinds.

[0152] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0153] More specific examples (nonexhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0154] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above 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, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0155] Importantly, the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices, characterized in that: include: Collect the travel time, mileage and charging time parameters of electric vehicles to build a daily load characteristic model of electric vehicles; According to the daily load characteristic model, an electricity price-load elasticity matrix is ​​established to generate a time-of-use electricity price plan for peak, valley and normal periods; Based on the time-of-use electricity price scheme, a multi-objective function of grid load volatility and charging cost is constructed, and an improved particle swarm algorithm with Rayleigh flight mechanism is used to solve the multi-objective function to obtain an electric vehicle charging and discharging optimization strategy.

2. The method for optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices as claimed in claim 1, characterized in that: The collection of the electric vehicle travel time includes: Get the probability density function of the departure time of electric vehicles: where σ s is the departure time standard deviation, μ s is the expected departure time; Get the return time probability density function of the electric vehicle: where σ b is the standard deviation of the return time, μ b Returns the expected time.

3. The method for optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices as claimed in claim 2, characterized in that: The collection of the daily mileage parameter is obtained through the following probability density function: Where d is the daily mileage, σ D is the stroke standard deviation, μ D is the expected value of the trip; The charging time is calculated according to the type of electric vehicle: Where s is the required charge, C Pri is the unit energy consumption of private cars, E Pri Charging efficiency of private cars, P Pri Charging power for private cars, SOC TB Cap is the remaining battery capacity ratio. TB is the battery capacity, E TB Charging efficiency for buses / taxi, P TB Charging power for buses / taxis.

4. The method for optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices as claimed in claim 3, characterized in that: The establishment of the electricity price-load elasticity matrix includes: Calculate the demand price elasticity coefficient: Calculate the electricity demand under the guidance of electricity price: Where ij is the time period number, n is the total number of time periods, q i is the power demand, p j is the electricity price, q i0 is the initial power demand, p j0 is the initial electricity price, Δq i is the change in power in the i-th period, Δp j is the change in electricity price during the jth period.

5. The method for optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices as claimed in claim 4, characterized in that: The multi-objective function includes: Load fluctuation objective function: The load standard deviation is: Charging cost objective function: Comprehensive objective function: F=ω1F1+ω2F2 Where T n To optimize the number of time periods, P t is the total load during period t, is the average load, g(t) is the electricity price function of time period t, ω1 and ω2 are weight coefficients and satisfy ω1+ω2=1.

6. The method for optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices as claimed in claim 5, characterized in that: The particle position update method in the improved particle swarm algorithm is: And introduce the Rayleigh flight mechanism: The step length Where c1 and c2 are learning factors, r1 and r2 are random numbers in the interval [0,1], α is the step size control variable, β is the Lévy flight parameter, and μ and ν are random variables that obey the normal distribution.

7. The method for optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices as claimed in claim 6, characterized in that: The optimization strategy also satisfies the following constraints: Electric vehicle charging capacity constraints: Charge state constraints: Charging cost constraints: Where Q n Charging power for the nth car, is the charging end time, is the charging start time, C n is the battery capacity, q l is the power of period l, p l is the electricity price in period l, q0 is the initial electricity quantity, and p0 is the initial electricity price.

8. A system using the method for optimizing the orderly charging and discharging of electric vehicles taking into account the time-of-use electricity price as described in any one of claims 1 to 7, characterized in that: include: The load modeling module is used to collect the travel time, mileage and charging time parameters of electric vehicles and build a daily load characteristic model of electric vehicles; A price generation module, used to establish an electricity price-load elasticity matrix according to the daily load characteristic model, and generate a time-of-use electricity price plan for peak, valley and normal periods; The strategy optimization module is used to construct a multi-objective function of grid load volatility and charging cost through the time-of-use electricity price scheme, and adopt an improved particle swarm algorithm with Rayleigh flight mechanism to solve the multi-objective function to obtain an electric vehicle charging and discharging optimization strategy.

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 optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices 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 optimizing orderly charging and discharging of electric vehicles considering time-of-use electricity prices according to any one of claims 1 to 7 are implemented.

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