Double-layer economic dispatching method, device and equipment for satisfaction degree of electric vehicle and charging station and medium

Abstract

The invention relates to the technical field of electric vehicle participation vehicle network interaction optimization scheduling, in particular to a double-layer economic scheduling method, device and equipment for electric vehicle and charging station satisfaction and a medium, and the method comprises the steps: generating initial data of each electric vehicle according to Monte Carlo sampling; constructing a distance preference index, a time preference index and a total charging cost of the electric vehicle to the charging station to form a satisfaction index of the electric vehicle user; the profit is used as the satisfaction index of the charging station when the charging station is matched with the electric vehicle for charging; a final scheduling strategy giving consideration to the satisfaction degree of the electric vehicle and the charging station at the same time is obtained through bidirectional optimization; the upper-layer model considers that the satisfaction degree of the electric vehicle formed by a distance index, a time index and an economic index is highest, and the lower-layer model considers that the charging income of the electric vehicle charging station is maximum; and the charging prices of the charging stations at different moments in the lower-layer model are transmitted to the upper-layer model, so that an optimal power grid dispatching participation scheme of the electric vehicle users can be obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles participating in vehicle-grid interactive optimization scheduling, and in particular to a double-layer economic scheduling method, device, equipment and medium for electric vehicles and charging station satisfaction. Background Art

[0002] With the demand for green energy development, the proportion of flexible and controllable resources on the load side of the distribution network continues to increase, and the rapidly growing electric vehicle load has become an important means to promote energy reform. At present, many car owners are blind and random when choosing public charging stations for charging. The factor they consider is often the closest distance, ignoring the waiting time at the charging station, which leads to a decline in user charging service experience and a decline in charging station revenue. Existing electric vehicle charging strategies often ignore the comprehensive satisfaction of electric vehicles, only consider the charging cost of electric vehicle owners, and do not fully consider the preferences of electric vehicle users for charging stations. Therefore, the optimal charging strategy between electric vehicles and charging stations cannot be given.

[0003] The charging behavior of electric vehicle owners depends on the layout of charging stations in the area. Therefore, when electric vehicles go to charging stations, it is a bilateral choice. Considering a single electric vehicle user cannot reflect the true situation of charging strategy. With the popularization of electric vehicles and charging stations, the strategy of electric vehicle users to choose public charging stations for charging has become a key link in the future vehicle-grid interaction. At present, there are few patents and literature that consider the satisfaction of both electric vehicles and charging stations, so further exploration is needed.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a double-layer economic dispatch method, device, equipment and medium for electric vehicle and charging station satisfaction, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a double-layer economic dispatch method for electric vehicle and charging station satisfaction, comprising the following steps:

[0007] S10: generating initial data of each electric vehicle according to Monte Carlo sampling, wherein the initial data includes initial charging time, remaining power and daily mileage;

[0008] S20: Based on the initial data, the distance preference index, time preference index and total charging cost of the electric vehicle to the charging station are obtained to form the satisfaction index of the electric vehicle user. The satisfaction index of the electric vehicle user is used as the objective function of the upper model to construct an optimization model of the charging plan of the electric vehicle user, and solve the optimal charging power of each electric vehicle;

[0009] S30: Based on the profit of the charging station when matching the electric vehicle for charging as the satisfaction index of the charging station, the satisfaction index of the charging station is used as the objective function of the lower model, the optimal charging power is input into the lower model, and an optimization model for the charging station to obtain the maximum profit satisfaction is constructed, and the scheduling result of the electric vehicle is solved, and the charging electricity price of the charging station is adjusted at the same time;

[0010] S40: combining the optimal charging power of the electric vehicle and the charging electricity price of the charging station, and obtaining a final dispatching strategy that takes into account the satisfaction of both the electric vehicle and the charging station through two-way optimization.

[0011] Furthermore, in step S10, initial data of each electric vehicle is generated according to Monte Carlo sampling, and the steps include:

[0012] S11: The travel patterns of historical fuel-powered private cars are used to describe the travel patterns of electric private cars. The daily mileage of a single electric car is obtained by fitting the probability density distribution function of the daily mileage.

[0013] S12: according to the time when each electric vehicle ends its trip and arrives at the charging station, plus the waiting time of the electric vehicle at the charging station, an initial charging time is obtained, wherein the initial charging time approximately obeys a normal distribution and is fitted according to a probability density distribution function;

[0014] S13: by statistics on the driving and charging patterns of electric vehicles, the remaining power of the electric vehicles when they arrive at the charging station is obtained, and fitting is performed according to the probability density distribution function;

[0015] S14: Based on the charging habits of electric vehicles, a mathematical model is established using Monte Carlo random sampling. The total load of a large number of electric vehicles is generated by the superposition of the charging load of a single electric vehicle. The total charging load of electric vehicles is then Indicates that the charging time at the charging station is Indicates that the initial charge capacity is express;

[0016] Where P ev (t) is the charging power of a single electric vehicle in period t, S x is the daily mileage of a single electric vehicle, W 100is the power consumption of electric vehicles per 100 kilometers, C is the rated capacity of electric vehicle batteries, η c Charging efficiency for electric vehicles.

[0017] Furthermore, in step S20, the objective function f of the upper model includes:

[0018]

[0019] In the formula, μ1, μ2, and μ3 are the weights of the distance index, time index, and total charging cost index, respectively. ij is the distance between electric vehicle i and target charging station j, t ij is the time it takes for electric vehicle i to reach the target charging station j, t w is the waiting time of electric vehicles at charging stations, is the total charging price at charging station j, is the basic parking fee for charging station j, t c,i is the charging time of electric vehicle i, t w,i is the queue time of electric vehicle i, t dis,i is the discharge time of electric vehicle i, C dis is the benefit of receiving discharge of electric vehicle i, P c,i is the average charging power, P dis,i is the average discharge power.

[0020] Furthermore, in step S20, an optimization model of the charging plan of the electric vehicle user is constructed, and the constraint conditions of the optimization model of the charging plan of the electric vehicle user are:

[0021] SOC min,i <SOC i,e <SOC max,i ;

[0022]

[0023] L cha,i (t)+L dis,i (t)≤1;

[0024] In the formula, SOC i,e is the charge termination capacity of electric vehicle i, SOC i is the battery capacity of electric vehicle i when it arrives at the charging station, Q max,i is the maximum battery capacity of electric vehicle i, P c,i is the average charging power, P dis,i is the average discharge power, η c,i is the charging efficiency of electric vehicle i; L cha,i (t) represents the charging state of electric vehicle i at time t, Ldis,i (t) represents the discharge state of electric vehicle i at time t, and both are 01 variables.

[0025] Furthermore, in step S30, the objective function F of the lower model includes:

[0026]

[0027] In the formula, Corresponding to the total charging price of charging pile j optimized by the lower model, P c,i is the average charging power, P ev,i is the charging power of electric vehicle i, C buy The price of electricity purchase.

[0028] Furthermore, in step S30, an optimization model for the charging station to obtain the maximum profit satisfaction is constructed, and the dispatch result of the electric vehicle is solved. The inequality constraint condition corresponding to the optimization model for the charging station to obtain the maximum profit satisfaction is:

[0029]

[0030] In the formula, is the total charging price of charging pile j optimized by the lower model, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, is the minimum total charging price, The maximum total charging price.

[0031] Furthermore, in step S40, a final dispatching strategy that takes into account the satisfaction of both electric vehicles and charging stations is obtained through bidirectional optimization, and the steps include:

[0032] S41: the upper layer transmits the charging capacity and the discharging capacity as inputs to the optimization model for the charging plan of the electric vehicle user, and transmits them to the optimization model for the power station to obtain the maximum profit satisfaction of the lower layer, so as to optimize the revenue of the charging station;

[0033] At the same time, the lower layer optimizes the charging price in real time, transmits the real-time total charging price of each charging station to the upper layer model, and updates the operating cost of electric vehicle users;

[0034] S42: Based on the strong duality theorem, the nonlinear term transformation of the electric vehicle charging price in the upper model is realized to simplify the model solving process;

[0035] S43: Substituting the converted electric vehicle charging price into the objective function of the upper model to represent the total charging cost of the electric vehicle.

[0036] Furthermore, in step S42, the nonlinear term conversion of the electric vehicle charging price in the upper model is realized based on the strong duality theorem, and the model includes:

[0037]

[0038] In the formula, is the total charging price of charging pile j optimized by the lower model, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, P ev,i is the charging capacity of electric vehicle i, C buy is the electricity purchase price, is the minimum total charging price, is the maximum total charging price, m is the total number of charging piles, and n is the total number of electric vehicles.

[0039] Further, in step S43, the converted electric vehicle charging price is substituted into the objective function of the upper model to characterize the total charging cost of the electric vehicle. The model includes:

[0040]

[0041] In the formula, μ1, μ2, and μ3 are the weights of the distance index, time index, and total charging cost index, respectively. ij is the distance index, t ij +t w is the time indicator, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, is the minimum total charging price, is the maximum total charging price, C buy is the electricity purchase price, P c,i is the average charging power, C dis is the benefit of receiving discharge of electric vehicle i, P dis,i is the average discharge power, t dis,i is the discharge time of electric vehicle i.

[0042] The present invention also includes a double-layer economic dispatch device for electric vehicle and charging station satisfaction, using the above method, including:

[0043] A data generating unit, used to generate initial data of each electric vehicle according to Monte Carlo sampling, wherein the initial data includes initial charging time, remaining power and daily mileage;

[0044] An electric vehicle satisfaction calculation unit is used to obtain, based on the initial data, a distance preference index, a time preference index and a total charging cost of the electric vehicle to the charging station to form a satisfaction index of the electric vehicle user, as the objective function of the upper model, to construct an optimization model of the charging plan of the electric vehicle user, and solve the optimal charging power of each electric vehicle;

[0045] A charging station satisfaction calculation unit is used to use the profit of the charging station when matching electric vehicles for charging as a satisfaction index of the charging station and as an objective function of the lower model, input the optimal charging power into the lower model, construct an optimization model for the charging station to obtain the maximum profit satisfaction, and solve the scheduling result of the electric vehicle, and adjust the charging electricity price of the charging station;

[0046] The bidirectional optimization unit is used to combine the optimal charging power of the electric vehicle and the charging electricity price of the charging station, and obtain a final scheduling strategy that takes into account the satisfaction of both the electric vehicle and the charging station through bidirectional optimization.

[0047] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0048] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0049] The beneficial effects of the present invention are:

[0050] The present invention comprehensively considers the economic optimization process of the respective interests of electric vehicles and charging stations. It fully considers the impact of charging queue time, charging fees and excessively high service fees that need to be considered in the charging satisfaction of electric vehicles, and also considers the economic satisfaction index of charging stations as separate individuals for scheduling. An economic scheduling plan that takes into account the satisfaction of both parties is comprehensively formulated, thereby achieving maximum interests and efficient scheduling for both parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 Flowchart of the two-tier economic dispatch method for electric vehicles and charging station satisfaction;

[0053] Figure 2Schematic diagram of the two-tier economic dispatch model for electric vehicles and charging station satisfaction;

[0054] Figure 3 A two-tier economic dispatch framework diagram for electric vehicles and charging station satisfaction;

[0055] Figure 4 This is the main architecture diagram of the electric vehicle and charging station dispatching system;

[0056] Figure 5 The schematic diagram of the structure of the two-tier economic dispatch device for electric vehicle and charging station satisfaction;

[0057] Figure 6 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0059] like Figures 1 to 4 As shown: A two-layer economic dispatch method for electric vehicle and charging station satisfaction includes the following steps:

[0060] S10: generating initial data of each electric vehicle according to Monte Carlo sampling, the initial data including initial charging time, remaining power and daily mileage;

[0061] S20: Based on the initial data, the distance preference index, time preference index and total charging cost of the electric vehicle to the charging station are obtained to constitute the satisfaction index of the electric vehicle user. The satisfaction index of the electric vehicle user is used as the objective function of the upper model to construct an optimization model of the charging plan of the electric vehicle user, and solve the optimal charging power of each electric vehicle;

[0062] S30: Based on the profit of the charging station when matching the electric vehicle for charging as the satisfaction index of the charging station, the satisfaction index of the charging station is used as the objective function of the lower model, the optimal charging power is input into the lower model, and an optimization model for the charging station to obtain the maximum profit satisfaction is constructed, and the scheduling result of the electric vehicle is solved, and the charging electricity price of the charging station is adjusted at the same time; ensure that the charging station achieves the optimization of economy while meeting the charging demand of the electric vehicle;

[0063] S40: Combine the optimal charging power of electric vehicles and the charging electricity price of charging stations, and through two-way optimization, obtain the final scheduling strategy that takes into account the satisfaction of both electric vehicles and charging stations.

[0064] The present invention comprehensively considers the economic optimization process of the respective interests of electric vehicles and charging stations. It fully considers the impact of charging queue time, charging fees and excessively high service fees that need to be considered in the charging satisfaction of electric vehicles, and also considers the economic satisfaction index of charging stations as separate individuals for scheduling. An economic scheduling plan that takes into account the satisfaction of both parties is comprehensively formulated, thereby achieving maximum interests and efficient scheduling for both parties.

[0065] The upper model considers that electric vehicles have the highest satisfaction level based on distance indicators, time indicators and economic indicators, and the lower model considers that the charging benefits of electric vehicle charging stations are the largest. Then, the Karush-Kuhn-Tucker condition and strong duality theory are used to realize the transformation from a double-layer model to a single-layer model. The charging prices of charging stations at different times in the lower model are transmitted to the upper model to obtain the optimal participation plan for electric vehicle users in power grid dispatching.

[0066] By calculating the distance preference, time preference and total charging cost of electric vehicle users, a comprehensive user satisfaction model is constructed to help users make the best charging choice. This solves the blindness and randomness problems of users in choosing charging stations mentioned in the background technology. It not only pays attention to the distance, but also considers the waiting time and total cost of the charging station, making the charging experience of electric vehicle users more satisfactory and efficient.

[0067] In the lower-level model of this method, the profitability and equipment utilization of charging stations are used as the core indicators of charging station satisfaction, and the charging pricing strategy of charging stations is optimized to maximize the profitability of charging stations. This solves the problem of declining charging station revenue mentioned in the background technology, and at the same time improves the utilization efficiency of charging facilities through reasonable scheduling.

[0068] This method optimizes the user's decision to choose a charging station, allowing users to choose a charging station based on different preferences (such as distance, time, and cost), avoiding the long waiting time caused by focusing on the nearest charging station, thereby improving the overall scheduling efficiency and reducing the queuing phenomenon of electric vehicles.

[0069] Charging stations can dynamically adjust charging prices according to the charging needs of electric vehicles, realizing a flexible pricing mechanism. This not only helps to improve the economic benefits of charging stations, but also influences the charging behavior of electric vehicle users through price signals, guides users to reasonably allocate charging needs, and further balances supply and demand.

[0070] As a preferred embodiment of the above, in step S10, initial data of each electric vehicle is generated according to Monte Carlo sampling, and the steps include:

[0071] S11: As the travel patterns of car owners have not changed, the travel patterns of historical fuel-powered private cars are used to describe the travel patterns of electric private cars. The daily mileage of a single electric car is obtained by fitting the probability density distribution function of the daily mileage.

[0072]

[0073] Where x is the daily mileage of a single car, and its value is x∈(0,200], μ D is the expected daily mileage, σ D is the standard deviation of daily mileage;

[0074] S12: The initial charging time is obtained based on the time when each electric vehicle ends its trip and arrives at the charging station, and the waiting time of the electric vehicle at the charging station is added. When the electric vehicle is charged in an unordered manner, the initial charging time approximately obeys a normal distribution and is fitted according to a probability density distribution function;

[0075]

[0076] Where b is the time it takes for the electric vehicle to reach the charging station, t w is the waiting time of electric vehicles at charging stations, μ T is the expected time for the electric vehicle to reach the charging station, σ T is the standard deviation of the time it takes for electric vehicles to reach the charging station;

[0077] S13: by statistics on the driving and charging patterns of electric vehicles, the remaining power of the electric vehicles when they arrive at the charging station is obtained, and fitting is performed according to the probability density distribution function;

[0078]

[0079] In the formula, SOC s is the remaining power of the electric vehicle when it arrives at the charging station, μ v is the expected value of the remaining power, σ v is the standard deviation of the remaining power.

[0080] S14: Based on the charging habits of electric vehicles, a mathematical model is established using Monte Carlo random sampling. The total load of a large number of electric vehicles is generated by the superposition of the charging load of a single electric vehicle. The total charging load of electric vehicles is then Indicates that the charging time at the charging station is Indicates that the initial charge capacity is express;

[0081] Where P ev (t) is the charging power of a single electric vehicle in period t, S xis the daily mileage of a single electric vehicle, W 100 is the power consumption of electric vehicles per 100 kilometers, C is the rated capacity of electric vehicle batteries, η c is the charging efficiency of the electric vehicle. The above steps can be used to obtain the load curve of the electric vehicles under disordered charging whose behaviors are independent of each other, and then the total charging cost of the electric vehicle owner can be obtained.

[0082] By using Monte Carlo sampling and probability distribution models to fit key data such as daily mileage, charging time, and remaining power of electric vehicles, the charging demand of electric vehicles can be predicted more accurately. This data-driven model can capture the uncertainty in vehicle travel and charging behavior, and provide reliable basic data for subsequent scheduling optimization; the diversified data generated by Monte Carlo sampling can reflect the charging behavior of electric vehicles in different scenarios. These random samples simulate the diversity and uncertainty of vehicles, ensuring that the scheduling model of electric vehicles and charging stations still has strong robustness and adaptability in the face of uncertainty and randomness; by counting the time and waiting time of electric vehicles arriving at charging stations, the resource allocation of charging stations can be more reasonably optimized, avoiding the queuing problem caused by electric vehicles arriving at charging stations in peak hours. This can effectively reduce the waiting time of electric vehicles and improve charging efficiency.

[0083] Wherein, step S20 specifically includes the following steps:

[0084] S21: When choosing a charging station for electric vehicle users, the distance, time and charging cost are considered as the satisfaction index, where the distance index is d ij , the time index is t ij +t w The total charging cost is

[0085] Among them, d ij is the distance between electric vehicle i and target charging station j, t ij is the time it takes for electric vehicle i to reach the target charging station j, is the total charging price of charging station j (including basic charging unit price and basic service fee), is the basic parking fee for charging station j, t c,i is the charging time of electric vehicle i, C dis is the income of electric vehicle i from accepting discharge, for P ev (t), which is given by P c,i -P dis,i Composition, P c,i is the average charging power, P dis,i is the average discharge power.

[0086] S22: Charging time t for electric vehicle i c,i It can be further expressed as

[0087] Among them, SOC i,e is the charging end capacity of electric vehicle i, Q max,i is the maximum battery capacity of electric vehicle i.

[0088] S23: Establish a satisfaction index consisting of the above three indicators.

[0089] As a preference of the above embodiment, in step S20, the objective function f of the upper model includes:

[0090]

[0091] In the formula, μ1, μ2, and μ3 are the weights of the distance index, time index, and total charging cost index, respectively. ij is the distance between electric vehicle i and target charging station j, t ij is the time it takes for electric vehicle i to reach the target charging station j, t w is the waiting time of electric vehicles at charging stations, is the total charging price of charging station j (including basic charging unit price and basic service fee), is the basic parking fee for charging station j, t c,i is the charging time of electric vehicle i, t w,i is the queue time of electric vehicle i, t dis,i is the discharge time of electric vehicle i, C dis is the income of electric vehicle i from accepting discharge, for P ev (t), which is given by P c,i -P dis,i Composition, P c,i is the average charging power, P dis,i is the average discharge power.

[0092] The combination of distance, time and charging cost can fully reflect the actual needs of electric vehicle users when choosing a charging station. This comprehensive consideration avoids the selection error of a single factor (such as the shortest distance) and improves the rationality of the user's charging choice. By modeling the waiting time and charging time, electric vehicle users can more clearly understand the total time required to choose a charging station, thereby avoiding the negative experience caused by long waiting times.

[0093] As a preference of the above embodiment, in step S20, an optimization model of the charging plan of the electric vehicle user is constructed, and the constraint conditions of the optimization model of the charging plan of the electric vehicle user are:

[0094] SOCmin,i <SOC i,e <SOC max,i ;

[0095]

[0096] L cha,i (t)+L dis,i (t)≤1;

[0097] In the formula, SOC i,e is the charge termination capacity of electric vehicle i, SOC i is the battery capacity of electric vehicle i when it arrives at the charging station, Q max,i is the maximum battery capacity of electric vehicle i, P c,i is the average charging power, P dis,i is the average discharge power, η c,i is the charging efficiency of electric vehicle i; L cha,i (t) represents the charging state of electric vehicle i at time t, L dis,i (t) represents the discharge state of electric vehicle i at time t, and both are 01 variables.

[0098] By setting the charging termination capacity of the electric vehicle to the minimum and maximum capacity values ​​of the battery, overcharging is effectively prevented, the battery life is protected, and battery loss is reduced; using the 01 variable to control the charging or discharging time of the electric vehicle to ensure that charging and discharging operations cannot be performed at the same time, avoiding conflicts and invalid operations in the system scheduling process and improving scheduling efficiency.

[0099] In this embodiment, in step S30, the objective function F of the lower model includes:

[0100]

[0101] In the formula, Corresponding to the total charging price of charging pile j optimized by the lower model, P c,i is the average charging power, P ev,i is the charging power of electric vehicle i, C buy The price of electricity purchase.

[0102] By taking the total charging price as the core optimization variable of the lower-level model, charging stations can dynamically adjust prices to ensure optimal profits under different demand conditions. This solution enables charging stations to have the ability to adjust prices in the market environment, respond to changes in charging demand, and optimize profits.

[0103] Among them, in step S30, an optimization model for the charging station to obtain the maximum profit satisfaction is constructed, and the scheduling result of the electric vehicle is solved. The inequality constraint condition corresponding to the lower model is:

[0104]

[0105] In the formula, is the total charging price of charging pile j optimized by the lower model, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, is the minimum total charging price, The maximum total charging price.

[0106] Setting the minimum and maximum total charging prices can effectively prevent excessive fluctuations in charging prices. When demand is high, the charging price will not be too high, ensuring that users will not refuse to use charging services due to high prices. When demand is low, the price will not be too low, thereby ensuring the basic profitability of charging stations. This price constraint enhances market stability and avoids the adverse effects of extreme pricing. The introduction of dual multipliers can flexibly handle inequality constraints and balance the impact of various constraints during the price adjustment process, so that the optimization algorithm can effectively manage the constraints of upper and lower limits on pricing strategies while maximizing the revenue of charging stations. The dual multiplier provides the system with greater flexibility in price optimization and can make more accurate adjustments to pricing strategies based on actual market conditions.

[0107] As a preferred embodiment of the above, in step S40, a final dispatching strategy that takes into account both the satisfaction of electric vehicles and charging stations is obtained through bidirectional optimization, and the steps include:

[0108] S41: The upper layer optimization model for the charging plan of electric vehicle users passes the charging capacity and the discharging capacity as inputs to the optimization model of the power station at the lower layer to obtain the maximum profit satisfaction, which is used to optimize the revenue of the charging station;

[0109] At the same time, the lower layer optimizes the charging price in real time, transmits the real-time total charging price of each charging station to the upper layer model, and updates the operating cost of electric vehicle users;

[0110] S42: Based on the strong duality theorem, the nonlinear term transformation of the electric vehicle charging price in the upper model is realized to simplify the model solution process;

[0111] S43: Substitute the converted electric vehicle charging price into the objective function of the upper model to characterize the total charging cost of the electric vehicle. This has a direct impact on its decision, and ultimately completes the two-way optimization of electric vehicles and charging stations.

[0112] The two-way optimization mechanism enables interaction between electric vehicle users and charging stations. The charging needs of electric vehicle users affect the pricing strategy of charging stations. Conversely, the price adjustments of charging stations are fed back to the cost accounting of electric vehicle users. The two-way feedback mechanism ensures the maximization of the interests of both parties, balances user satisfaction and charging station revenue, and improves the overall efficiency of the system.

[0113] By introducing the strong duality theorem, the nonlinear charging price term in the upper model is converted into a linear term, which simplifies the complex nonlinear optimization problem. This simplification greatly improves the efficiency of model solution, especially in the interaction process between large-scale electric vehicles and charging stations, which can effectively reduce computing time and resource consumption.

[0114] The revenue optimization model of the lower-level charging stations can dynamically adjust the charging price according to the real-time charging demand and market conditions. This means that the charging stations can flexibly respond to changes in demand during peak and trough periods, while ensuring the charging experience of electric vehicle users and maximizing their own profitability. This real-time price adjustment mechanism helps to improve the adaptability and competitiveness of charging stations in different market environments.

[0115] By optimizing the charging price in the upper model, electric vehicle users can obtain more reasonable charging cost calculations. Users can dynamically adjust charging decisions according to price changes at charging stations, thereby optimizing their charging costs. This mechanism takes into account factors such as distance and time, and introduces price factors to help users make the best choice under a variety of conditions. This dynamic price feedback mechanism ensures that users do not overpay during peak hours, and can adjust their charging behavior according to their own circumstances, thereby improving user satisfaction.

[0116] In this embodiment, in step S42, the nonlinear term conversion of the electric vehicle charging price in the upper model is implemented based on the strong duality theorem, and the model includes:

[0117]

[0118] In the formula, is the total charging price of charging pile j optimized by the lower model, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, P ev,i is the charging capacity of electric vehicle i, C buy is the electricity purchase price, is the minimum total charging price, is the maximum total charging price, m is the total number of charging piles, and n is the total number of electric vehicles.

[0119] The strong duality theorem is used to convert the nonlinear terms of electric vehicle charging prices into linear forms, so that complex nonlinear optimization problems can be simplified into linear optimization problems. This can greatly reduce the computational complexity and improve the efficiency of the solution, especially in large-scale scenarios of electric vehicles and charging stations interacting with each other, which can significantly shorten the calculation time and improve the overall performance of the system.

[0120] Transform the above model into:

[0121] As a preferred embodiment of the above, in step S43, the converted electric vehicle charging price is substituted into the objective function of the upper model to characterize the total charging cost of the electric vehicle. The model includes:

[0122]

[0123] In the formula, μ1, μ2, and μ3 are the weights of the distance index, time index, and total charging cost index, respectively. ij is the distance index, t ij +t w is the time indicator, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, is the minimum total charging price, is the maximum total charging price, C buy is the electricity purchase price, P c,i is the average charging power, C dis is the benefit of receiving discharge of electric vehicle i, P dis,i is the average discharge power, t dis,i is the discharge time of electric vehicle i.

[0124] By introducing the three core factors of distance, time and charging cost into the objective function, electric vehicle users can make fully optimized charging decisions. The model not only considers the convenience of distance, but also pays attention to waiting time and charging costs, improving the user's overall charging experience. This multi-objective optimization method helps users make more rational judgments in the selection of charging stations, avoids the influence of a single factor on decision-making, and enables users to achieve a balance in comprehensive cost, time and distance.

[0125] The upper and lower limit dual multipliers introduced in the model constrain the upper and lower limits of charging prices, ensuring that electric vehicle users will not bear additional costs due to excessively high prices, while also avoiding the impact of too low prices on the economic benefits of charging stations. This mechanism provides double protection for users and charging stations. The price control range is reasonable, users can enjoy services at a fair price, and charging stations can ensure the profitability of operations.

[0126] The model also takes into account the discharge benefits and discharge duration of electric vehicles, providing an additional source of income for electric vehicle users, which means that users can not only obtain services through charging, but also further reduce the total charging cost by participating in discharge transactions.

[0127] The present invention also includes a double-layer economic dispatch device for electric vehicle and charging station satisfaction, using the above method, such as Figure 5 As shown, including:

[0128] A data generation unit, used to generate initial data of each electric vehicle according to Monte Carlo sampling, the initial data including initial charging time, remaining power and daily mileage;

[0129] The electric vehicle satisfaction calculation unit is used to obtain the electric vehicle user satisfaction index based on the initial data, including the distance preference index, time preference index and total charging cost of the electric vehicle to the charging station, as the objective function of the upper model, to build an optimization model for the charging plan of the electric vehicle user, and to solve the optimal charging power of each electric vehicle;

[0130] The charging station satisfaction calculation unit is used to take the profit of the charging station when matching electric vehicles for charging as the satisfaction index of the charging station and as the objective function of the lower model, input the optimal charging power into the lower model, build an optimization model for the charging station to obtain the maximum profit satisfaction, and solve the scheduling results of the electric vehicles, and adjust the charging electricity price of the charging station;

[0131] The bidirectional optimization unit is used to combine the optimal charging power of electric vehicles and the charging electricity price of charging stations. Through bidirectional optimization, the final scheduling strategy that takes into account the satisfaction of both electric vehicles and charging stations is obtained.

[0132] See also Figure 6 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0133] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0134] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0135] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0136] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0137] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0138] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0139] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0140] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0141] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0142] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A two-tier economic dispatch method for electric vehicle and charging station satisfaction, characterized in that: The steps include: S10: generating initial data of each electric vehicle according to Monte Carlo sampling, wherein the initial data includes initial charging time, remaining power and daily mileage; S20: Based on the initial data, the distance preference index, time preference index and total charging cost of the electric vehicle to the charging station are obtained to form the satisfaction index of the electric vehicle user. The satisfaction index of the electric vehicle user is used as the objective function of the upper model to construct an optimization model of the charging plan of the electric vehicle user, and solve the optimal charging power of each electric vehicle; S30: Based on the profit of the charging station when matching the electric vehicle for charging as the satisfaction index of the charging station, the satisfaction index of the charging station is used as the objective function of the lower model, the optimal charging power is input into the lower model, and an optimization model for the charging station to obtain the maximum profit satisfaction is constructed, and the scheduling result of the electric vehicle is solved, and the charging electricity price of the charging station is adjusted at the same time; S40: combining the optimal charging power of the electric vehicle and the charging electricity price of the charging station, and obtaining a final dispatching strategy that takes into account the satisfaction of both the electric vehicle and the charging station through two-way optimization.

2. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 1 is characterized in that: In step S10, initial data of each electric vehicle is generated according to Monte Carlo sampling, and the steps include: S11: The travel patterns of historical fuel-powered private cars are used to describe the travel patterns of electric private cars. The daily mileage of a single electric car is obtained by fitting the probability density distribution function of the daily mileage. S12: according to the time when each electric vehicle ends its trip and arrives at the charging station, plus the waiting time of the electric vehicle at the charging station, an initial charging time is obtained, wherein the initial charging time approximately obeys a normal distribution and is fitted according to a probability density distribution function; S13: by statistics on the driving and charging patterns of electric vehicles, the remaining power of the electric vehicles when they arrive at the charging station is obtained, and fitting is performed according to the probability density distribution function; S14: Based on the charging habits of electric vehicles, a mathematical model is established using Monte Carlo random sampling. The total load of a large number of electric vehicles is generated by the superposition of the charging load of a single electric vehicle. The total charging load of electric vehicles is then Indicates that the charging time at the charging station is Indicates that the initial charge capacity is express; Where P ev (t) is the charging power of a single electric vehicle in period t, S x is the daily mileage of a single electric vehicle, W 100 is the power consumption of electric vehicles per 100 kilometers, C is the rated capacity of electric vehicle batteries, η c Charging efficiency for electric vehicles.

3. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 1 is characterized in that: In step S20, the objective function f of the upper model includes: In the formula, μ1, μ2, and μ3 are the weights of the distance index, time index, and total charging cost index, respectively. ij is the distance between electric vehicle i and target charging station j, t ij is the time it takes for electric vehicle i to reach the target charging station j, t w is the waiting time of electric vehicles at charging stations, is the total charging price at charging station j, is the basic parking fee for charging station j, t c,i is the charging time of electric vehicle i, t w,i is the queue time of electric vehicle i, t dis,i is the discharge time of electric vehicle i, C dis is the benefit of receiving discharge of electric vehicle i, P c,i is the average charging power, P dis,i is the average discharge power.

4. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 1 is characterized in that: In step S20, an optimization model of the charging plan of the electric vehicle user is constructed, and the constraint conditions of the optimization model of the charging plan of the electric vehicle user are: SOCIETY min,i <SOC i,e <SOC max,i ; L cha,i (t)+L dis,i (t)≤1; In the formula, SOC i,e is the charge termination capacity of electric vehicle i, SOC i is the battery capacity of electric vehicle i when it arrives at the charging station, Q max,i is the maximum battery capacity of electric vehicle i, P c,i is the average charging power, P dis,i is the average discharge power, η c,i is the charging efficiency of electric vehicle i; L cha,i (t) represents the charging state of electric vehicle i at time t, L dis,i (t) represents the discharge state of electric vehicle i at time t, and both are 01 variables.

5. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 1 is characterized in that: In step S30, the objective function F of the lower model includes: In the formula, Corresponding to the total charging price of charging pile j optimized by the lower model, P c,i is the average charging power, P ev,i is the charging power of electric vehicle i, C buy The price of electricity purchase.

6. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 1 is characterized in that: In step S30, an optimization model for the charging station to obtain the maximum profit satisfaction is constructed, and the dispatch result of the electric vehicle is solved. The inequality constraint condition corresponding to the optimization model for the charging station to obtain the maximum profit satisfaction is: In the formula, is the total charging price of charging pile j optimized by the lower model, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, is the minimum total charging price, The maximum total charging price.

7. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 1 is characterized in that: In step S40, a final dispatching strategy that takes into account the satisfaction of both electric vehicles and charging stations is obtained through bidirectional optimization, and the steps include: S41: the upper layer transmits the charging capacity and the discharging capacity as inputs to the optimization model for the charging plan of the electric vehicle user, and transmits them to the optimization model for the power station to obtain the maximum profit satisfaction of the lower layer, so as to optimize the revenue of the charging station; At the same time, the lower layer optimizes the charging price in real time, transmits the real-time total charging price of each charging station to the upper layer model, and updates the operating cost of electric vehicle users; S42: Based on the strong duality theorem, the nonlinear term transformation of the electric vehicle charging price in the upper model is realized to simplify the model solving process; S43: Substituting the converted electric vehicle charging price into the objective function of the upper model to represent the total charging cost of the electric vehicle.

8. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 7 is characterized in that: In step S42, the nonlinear term conversion of the electric vehicle charging price in the upper model is implemented based on the strong duality theorem, and the model includes: In the formula, is the total charging price of charging pile j optimized by the lower model, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, P ev,i is the charging capacity of electric vehicle i, C buy is the electricity purchase price, is the minimum total charging price, is the maximum total charging price, m is the total number of charging piles, and n is the total number of electric vehicles.

9. The double-layer economic dispatch method for electric vehicle and charging station satisfaction according to claim 7, characterized in that: In step S43, the converted electric vehicle charging price is substituted into the objective function of the upper model to characterize the total charging cost of the electric vehicle. The model includes: In the formula, μ1, μ2, and μ3 are the weights of the distance index, time index, and total charging cost index, respectively. ij is the distance index, t ij +t w is the time indicator, are the dual multipliers of the lower and upper bounds of the lower inequality constraints, is the minimum total charging price, is the maximum total charging price, C buy is the electricity purchase price, P c,i is the average charging power, C dis is the benefit of receiving discharge of electric vehicle i, P dis,i is the average discharge power, t dis,i is the discharge time of electric vehicle i.

10. A double-layer economic dispatching device for electric vehicle and charging station satisfaction, characterized in that: Use of the method according to any one of claims 1 to 9, comprising: A data generating unit, used to generate initial data of each electric vehicle according to Monte Carlo sampling, wherein the initial data includes initial charging time, remaining power and daily mileage; An electric vehicle satisfaction calculation unit is used to obtain, based on the initial data, a distance preference index, a time preference index and a total charging cost of the electric vehicle to the charging station to form a satisfaction index of the electric vehicle user, as the objective function of the upper model, to construct an optimization model of the charging plan of the electric vehicle user, and solve the optimal charging power of each electric vehicle; A charging station satisfaction calculation unit is used to use the profit of the charging station when matching electric vehicles for charging as a satisfaction index of the charging station and as an objective function of the lower model, input the optimal charging power into the lower model, construct an optimization model for the charging station to obtain the maximum profit satisfaction, and solve the scheduling result of the electric vehicle, and adjust the charging electricity price of the charging station; The bidirectional optimization unit is used to combine the optimal charging power of the electric vehicle and the charging electricity price of the charging station, and obtain a final scheduling strategy that takes into account the satisfaction of both the electric vehicle and the charging station through bidirectional optimization.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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