Charging hosting method and system

Abstract

The invention relates to the technical field of charging management, and provides a charging hosting method and system, and the method comprises the following steps: S1, obtaining charging hosting data; s2, considering balance of the wholesale price difference, the station service fee and the user charging fee, and constructing an objective function of a charging power regulation and control model; s3, constraint conditions of the charging power regulation and control model are established according to charging facility hardware limitation and user vehicle using requirements, and the user vehicle using requirements comprise charging requirements and available charging time of a user vehicle; and S4, solving the charging power regulation and control model based on the charging hosting data, and obtaining the optimal charging power of the user vehicle at each moment. According to the invention, the balance of virtual power plant income, charging operator income and user charging cost is realized; the supply and demand contradiction of the power grid is effectively relieved, the peak regulation pressure of the power grid is reduced, the operation efficiency and stability of the power grid are improved, friendly interaction between the power grid and the electric vehicle is promoted, and powerful support is provided for sustainable development of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of charging management, and particularly relates to a charging trusteeship method and system. Background Art

[0002] With the deepening development of the power market, the rapid expansion of the scale of electric vehicles, and the increasing improvement of charging infrastructure, the charging demand of electric vehicles has shown an explosive growth. In scenarios such as destination stations, community stations, and exclusive stations, most charging users have sufficient parking and charging time. However, when these users perform natural charging on their vehicles, they often aim to complete the charging demand as soon as possible with a relatively high charging power, without considering the real-time electricity price, resulting in obvious anti-peak shaving characteristics in the time and space distribution of charging behavior, and the parking and charging time not being fully utilized.

[0003] For users, starting charging during peak hours will lead to high charging costs; for the power grid, this anti-peak shaving characteristic further increases the pressure on the power grid's supply-demand balance and is likely to impact the safe and stable operation of the power grid; for charging operators, charging operators mainly rely on charging station service fees to make a profit, but due to factors such as fierce market competition and limited service fee pricing, the profit space is limited; for virtual power plants participating in the electricity energy market as power selling aggregators, since users often do not perform long-term charging during low electricity price periods, which are also periods with a large wholesale-retail price difference, the profit space of virtual power plants is also small. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a charging trusteeship method and system, which realizes the balance of the benefits of virtual power plants, charging operators, and user charging costs; effectively alleviates the supply-demand contradiction of the power grid, reduces the peak shaving pressure of the power grid, improves the operation efficiency and stability of the power grid, promotes the friendly interaction between the power grid and electric vehicles, and provides strong support for the sustainable development of the power grid.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A charging trusteeship method includes the following steps: S1, obtaining charging trusteeship data, where the charging trusteeship data includes price data, basic information data of the charging station, and user vehicle usage demand data; S2, considering the balance of the wholesale-retail price difference, charging station service fees, and user charging fees, establishing an objective function of a charging power regulation model; S3, considering the hardware limitations of charging facilities and user vehicle usage demands, establishing constraint conditions of the charging power regulation model, where the user vehicle usage demands include the charging demands and available charging times of user vehicles; S4, solving the charging power regulation model based on the charging trusteeship data to obtain the optimal charging power of the user vehicle at each moment.

[0007] In addition, the charging trusteeship method proposed by the present invention may further have the following additional technical features.

[0008] According to an embodiment of the present invention, the objective function is:

[0009]

[0010] Wherein, respectively represent the unit wholesale-retail price difference, unit station service fee, and unit user charging fee at the i-th moment, x is the charging power line vector, and x i is the charging power at the i-th moment, n represents the available charging time of the user's vehicle, and M1, M2, and M3 are the weights of the wholesale-retail price difference, station service fee, and user charging fee, respectively.

[0011] According to an embodiment of the present invention, the constraint conditions include charging constraints and demand power constraints. The charging constraints are linear equality constraints or cone constraints. The method further includes: constructing a relationship model between the SOC (State of Charge, the percentage of remaining battery capacity) and the demand power of the user's vehicle based on the historical charging data of the user; when determining the demand power constraint, calculating the demand power of the user's vehicle according to the SOC of the user's vehicle at each moment and the relationship model.

[0012] According to an embodiment of the present invention, the charging constraints include: starting charging constraints, continuous charging constraints, and power supply constraints constructed based on charging state variables; SOC recursive constraints and vehicle demand power constraints constructed based on the vehicle's SOC, wherein the charging state variables are Boolean variables.

[0013] According to an embodiment of the present invention, it further includes: S5, performing trusteeship charging on the user's vehicle based on the optimal charging power of the user's vehicle at each moment.

[0014] In addition, to achieve the above object, the present invention also proposes a charging trusteeship system.

[0015] A charging trusteeship system, comprising: an acquisition module, which is used to acquire charging trusteeship data, and the charging trusteeship data includes price data, basic information data of the charging station, and user vehicle usage demand data; a first modeling module, which is used to consider the balance of the wholesale-retail price difference, charging station service fee, and user charging cost, and establish an objective function of the charging power regulation model; a second modeling module, which is used to consider the hardware limitations of the charging facilities and the user vehicle usage demand, and establish the constraint conditions of the charging power regulation model, and the user vehicle usage demand includes the charging demand and available charging time of the user vehicle; an optimization module, which solves the charging power regulation model based on the charging trusteeship data to obtain the optimal charging power of the user vehicle at each moment.

[0016] In addition, the charging trusteeship system proposed by the present invention may further have the following additional technical features.

[0017] According to an embodiment of the present invention, the objective function is:

[0018]

[0019] Wherein, respectively represent the unit wholesale-retail price difference, unit charging station service fee, and unit user charging cost at the i-th moment, x is the charging power line vector, and x i is the charging power at the i-th moment, n represents the available charging time of the user vehicle, and M1, M2, and M3 are the weights of the wholesale-retail price difference, charging station service fee, and user charging cost respectively.

[0020] According to an embodiment of the present invention, the constraint conditions include charging constraints and demand power constraints, the charging constraints are linear equality constraints or cone constraints, and the system further includes: a third modeling module, which is used to construct a relationship model between the SOC and demand power of the user vehicle according to the historical charging data of the user; when determining the demand power constraint, the second modeling module calculates the demand power of the user vehicle according to the SOC of the user vehicle at each moment and the relationship model.

[0021] According to an embodiment of the present invention, the charging constraints include: starting charging constraints, continuous charging constraints, and power supply constraints constructed based on charging state variables; SOC recursive constraints and vehicle required power constraints constructed based on the vehicle SOC, wherein the charging state variables are Boolean variables.

[0022] According to an embodiment of the present invention, it further includes: a trusteeship charging module, which is used to perform trusteeship charging on the user vehicle based on the optimal charging power of the user vehicle at each moment.

[0023] The beneficial effects of the present invention:

[0024] The charging trusteeship method of the present invention takes into account the wholesale-retail price difference of the virtual power plant, the station service fee, and the user's charging cost when constructing the charging power regulation model, can obtain the optimal charging power of the user's vehicle at each moment, makes full use of the user's available charging time, realizes the balance of the virtual power plant's revenue, the charging operator's revenue, and the user's charging cost, can save the user's charging cost, and improve the user's charging experience and satisfaction; enables the charging operator to provide charging services for more vehicles during low electricity price periods, increasing the station service fee income; enables the virtual power plant to better play the aggregation advantage in the power market, enhancing the virtual power plant's market position and profitability; effectively alleviates the contradiction between power supply and demand of the power grid, reduces the peak regulation pressure of the power grid, improves the operation efficiency and stability of the power grid, promotes the friendly interaction between the power grid and electric vehicles, and provides strong support for the sustainable development of the power grid. Brief Description of the Drawings

[0025] Figure 1 It is a flowchart of the charging trusteeship method according to an embodiment of the present invention;

[0026] Figure 2 It is a comparison chart of the regulation results of natural charging and the charging trusteeship method of the present invention in a specific embodiment;

[0027] Figure 3 It is a schematic block diagram of the charging trusteeship system according to an embodiment of the present invention. Detailed Embodiment

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the 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.

[0029] As Figure 1 shown, the charging trusteeship method of the embodiment of the present invention includes the following steps:

[0030] S1, obtaining charging trusteeship data.

[0031] Specifically, the charging trusteeship data may include price data, station basic information data, and user vehicle usage demand data. Among them, the price data may include: the wholesale-retail price difference data of the virtual power plant acting as an agent for the station, the charging electricity fee data and station service fee data at each moment obtained from the station operation system of the charging operator. The station basic information data may include the rated charging power of the charging facilities, and the user vehicle usage demand data may include the available charging time provided by the user, the SOC of the user's vehicle, the battery capacity, etc.

[0032] In an embodiment of the present invention, after step S1, data preprocessing can also be performed on the obtained charging trusteeship data, such as outlier judgment, data alignment, data merging, etc., to avoid missing or abnormal acquired data and facilitate subsequent data calculation.

[0033] Specifically, the outlier judgment may include the following steps:

[0034] a) When a certain item of data in the charging trusteeship data cannot be obtained, the user is informed that charging trusteeship is not supported and the trusteeship process ends.

[0035] b) When the data range in the charging trusteeship data cannot meet the entire charging trusteeship process (for example, the available charging time is too short to fully charge the battery), the user is informed that charging trusteeship is not supported and the trusteeship process ends.

[0036] c) When the data in the charging trusteeship data significantly exceeds the previously set range (for example, the available charging time is too long, the battery capacity is too large, etc.), an abnormal data alarm is directly triggered, the user is informed that charging trusteeship is not supported, and the trusteeship process ends.

[0037] The data alignment may include the following steps:

[0038] a) Align the obtained price data according to the time interval;

[0039] b) Uniformly process the price data into data with the smallest time interval (assuming that the wholesale-retail price difference is a time interval of 1 hour and the service fee is a time interval of 0.5 hour, then it needs to be uniformly set to the smallest time interval of 0.5 hour), and the processing method can be downward filling.

[0040] When performing data merging, the above heterogeneous data can be uniformly processed into a form that can be processed by the algorithm (such as DataFrame in Python or matrix in C++), etc., to facilitate subsequent calculation.

[0041] S2. Considering the balance of the wholesale-retail price difference, the station service fee, and the user's charging fee, establish the objective function of the charging power regulation model.

[0042] Specifically, the objective function can be:

[0043]

[0044] Among them, respectively represent the unit wholesale-retail price difference, the unit station service fee, and the unit user charging fee at the i-th moment, represents the n-dimensional real number space, where the user's charging fee may include charging electricity fee and station service fee, is the charging power line vector, x i$P_i$ is the charging power at the $i$-th moment, $n$ represents the available charging time of the user's vehicle, and $M_1$, $M_2$, and $M_3$ are the weights of the wholesale-retail price difference, the station service fee, and the user's charging cost respectively, which are used to balance the importance of different objectives.

[0045] It can be understood that the three terms of the objective function respectively represent maximizing the virtual power plant's wholesale-retail price difference revenue, maximizing the station service fee revenue, and minimizing the user's charging cost. In a specific embodiment of the present invention, $M_1 = 1$, $M_2 = 1$, and $M_3 = 1000$ can be taken. When optimizing the benefits of the three parties, the lowest user charging cost is given priority, reflecting the idea of giving priority to the interests of charging users. At this time, the objective function can be understood as maximizing the virtual power plant's revenue and the station's revenue as much as possible on the premise of giving priority to the lowest user charging cost.

[0046] S3. Considering the hardware limitations of the charging facilities and the user's vehicle usage requirements, establish the constraint conditions of the charging power regulation model. The user's vehicle usage requirements include the charging requirements and the available charging time of the user's vehicle.

[0047] In an embodiment of the present invention, the constraint conditions may include charging constraints and demand power constraints. Among them, the charging constraints are the hardware limitations of the charging facilities during the charging process or the constraint conditions of the vehicle's SOC, such as power supply constraints, SOC recursion constraints, vehicle power demand constraints, etc., all of which are linear equality constraints or cone constraints. The demand power constraint is the constraint condition of the vehicle for the charging power, indicating that the charging power at the current moment must be less than or equal to the vehicle's demand power at the current SOC of the user's vehicle. The formula is:

[0048] x i ≤f θ (SOC i )i = 0,..n

[0049] Among them, f θ (SOC i ) represents the relationship model between the SOC and the demand power of the user's vehicle, and θ is the parameter in the relationship model.

[0050] It can be understood that when optimizing the charging power, strictly following the charging constraints and the demand power constraints can avoid charging failures or equipment damage, make the charging strategy meet the user's expectations, and can be successfully implemented under the existing hardware conditions, with strong feasibility, bringing a convenient and reliable charging experience to users and ensuring the stable operation of the station.

[0051] The charging trusteeship method may further include: constructing a relationship model f θ (SOC)=P car-need between the SOC and the demand power of the user's vehicle according to the user's historical charging data, where P car-needRepresents the demand power of the user's vehicle. When determining the demand power constraint, the demand power of the user's vehicle is calculated according to the SOC of the user's vehicle at each moment and the relationship model.

[0052] It should be noted that the relationship model can be established before step S1, or can be established when the user conducts charging trusteeship. After the user's vehicle completes charging, the relationship model can also be re-modeled according to the updated historical charging data to update the parameters in the relationship model. This embodiment does not impose any restrictions.

[0053] It can be understood that, generally, the relationship model f θ (SOC) between the SOC of the user's vehicle and the demand power is a convex function, and other constraint conditions in the charging power regulation model are linear equality constraints or cone constraints. Therefore, solving the charging power regulation model is a cone programming problem, and its local optimal solution is the global optimal solution, avoiding the common local optimal trap in non-convex optimization and ensuring the global optimality of the solution result; moreover, there are mature algorithms for solving cone programming problems in the prior art, such as the interior point method, etc., which can efficiently solve cone programming problems, and users do not need to wait for a long time for the solution result, and the user experience is better.

[0054] In an embodiment of the present invention, the charging constraints can be constructed based on the charging state variable or the vehicle SOC, including: the starting charging constraint, continuous charging constraint, and power supply constraint constructed based on the charging state variable; the SOC recursive constraint and vehicle demand power constraint constructed based on the vehicle SOC, where the charging state variable b is a Boolean variable, 1 represents charging, and 0 represents stopping.

[0055] Specifically, the starting charging constraint means that charging starts after the user starts charging trusteeship, and its formula is:

[0056] b0 = 1, b i ∈{0, 1}

[0057] where b0 represents the starting charging state, and b i is the charging state variable at the i-th moment, and b i takes the value of 1 when charging and 0 when not charging.

[0058] The continuous charging constraint means that it is always in the charging state before the user's charging demand is met, and its formula is:

[0059] b i ≤b i-1 i = 1,..n

[0060] By setting the continuous charging constraint, the frequent start and stop of orders during the charging trusteeship process can be avoided, and the risk of charging failure can be reduced.

[0061] The power supply constraint indicates that the charging power must be greater than the minimum power supply of the charging facility, and its formula is:

[0062] m1·b i ≤x i ≤m2·b i i = 0,..n

[0063] Where m1 and m2 represent the minimum and maximum power supplies of the charging facility respectively; in some embodiments of the present invention, m2 can also be 2000 or any value greater than the maximum charging power of a general charging facility, and this embodiment does not impose any restrictions.

[0064] The SOC recursive constraint represents the recursive relationship between the current vehicle SOC and the previous vehicle SOC, and its formula is:

[0065]

[0066] This constraint is a standard cone constraint, where SOC i represents the percentage of the remaining battery power of the user's vehicle at the i-th moment (the percentage of the remaining power in the battery capacity), freq represents the charging regulation frequency, and Cap represents the battery capacity of the user's vehicle. When the charging power is adjusted every minute, freq = 60.

[0067] The vehicle power demand constraint indicates that the entrusted charging amount is equal to the vehicle power demand, that is, it meets the user's charging demand for the vehicle's power, and its formula is:

[0068]

[0069] Where SOC0 represents the percentage of the remaining battery power of the user's vehicle at the starting moment, and SOC n represents the target percentage of the remaining battery power of the user's vehicle after charging trusteeship. It can be understood that SOC n can be 1 (i.e., 100%), and at this time the user's charging demand is reflected in the vehicle being fully charged. In some other embodiments of the present invention, the user can also require the vehicle to be charged to a specified power, such as charging the vehicle to 80% of the power. At this time, the formula for the vehicle power demand constraint is:

[0070]

[0071] In this embodiment, since the Boolean variable b is also an integer variable, the charging power regulation model is a cone integer programming problem, and existing open-source solvers can solve it in a relatively short time, further improving the user experience.

[0072] S4. Solve the charging power regulation model based on the charging trusteeship data to obtain the optimal solution of the charging power regulation model, i.e., the optimal charging power of the user's vehicle at each moment.

[0073] In an embodiment of the present invention, the charging trusteeship method further includes:

[0074] S5. Perform trusteeship charging on the user's vehicle based on the optimal charging power of the user's vehicle at each moment.

[0075] It can be understood that after solving the optimal solution of the charging power regulation model, the user's charging cost for trusteeship charging based on the optimal solution can be calculated according to the price data for the user's reference. In some other embodiments of the present invention, the user can adjust the vehicle usage demand again according to the initially obtained user charging cost, such as extending the available charging time, reducing the target remaining battery power, etc., to achieve the adjustment of the charging cost, and then determine whether to perform trusteeship charging on the vehicle.

[0076] Figure 2 For a specific embodiment, it is a comparison chart of the regulation results of natural charging and the charging trusteeship method of the present invention. As Figure 2 can be seen, the charging trusteeship method of the present invention will use a lower charging power to charge at moments when the charging cost is high, the service fee is low, and the wholesale-retail price difference is low, and will use a higher charging power to charge at moments when the charging cost is low, the service fee is high, and the wholesale-retail price difference is high, which can make full use of the user's available charging time, help the user effectively save the charging cost, and improve the economic benefits of the charging operator and the virtual power plant.

[0077] Let a certain charging station operate according to the charging trusteeship method of the present invention, and the actual cost-saving situations for all parties are shown in the following table:

[0078]

[0079] Among them, the total number of orders refers to the number of orders participating in the experiment, and the cost-saving amount is defined as the total cost-saving amount of these orders participating in the experiment. The definitions of the user cost-saving ratio, the charging station revenue increase ratio, and the virtual power plant revenue increase ratio are as follows:

[0080]

[0081] As can be seen from the above table, the charging trusteeship method of the present invention can better achieve the purposes of saving costs for users, increasing revenue for the virtual power plant, and increasing revenue for the charging operator's charging station. Since the peak-valley-flat electricity price of the charging station reflects the supply and demand pressure of the power grid at each time period, the charging trusteeship indirectly realizes friendly interaction with the power grid through flexible power control and transfers the charging power to lower electricity price time periods.

[0082] According to the charging trusteeship method of the embodiments of the present invention, when constructing the charging power regulation model, the wholesale-retail price difference, the station service fee, and the user charging cost are considered, and the optimal charging power of the user's vehicle can be obtained at each moment, making full use of the user's available charging time and achieving the balance of the virtual power plant revenue, the charging operator revenue, and the user charging cost.

[0083] For users, this method optimizes the charging power at each moment, reasonably distributes the charging power to the periods with lower electricity prices, thus significantly reducing the user's charging cost. While users enjoy the convenient charging service, they can also save a large amount of expenses, improving the user's charging experience and satisfaction.

[0084] For the virtual power plant, this method comprehensively considers factors such as the wholesale-retail price difference, the station service fee, and the user charging price, and maximizes the wholesale-retail price difference revenue of the virtual power plant as much as possible, enabling it to better play the aggregation advantage in the power market, achieving stable growth of revenue, and enhancing the market position and profitability of the virtual power plant.

[0085] For the charging operator, this system optimizes the charging power, enabling the charging operator to provide charging services for more vehicles during the periods with lower electricity prices, thereby increasing the station service fee income, bringing more profit opportunities for the charging operator, and enhancing its competitiveness in the market.

[0086] Regarding the power grid supply and demand, this method will distribute the charging power to the periods with lower electricity prices, and the power grid supply and demand pressure is also relatively small during the periods with lower electricity prices. Reasonably distributing the charging power to the periods with relatively small power grid supply and demand pressure effectively alleviates the power grid supply and demand contradiction, enables the regulation ability of the virtual power plant on the user side to be fully exerted, reduces the power grid peak shaving pressure, improves the operation efficiency and stability of the power grid, promotes the friendly interaction between the power grid and electric vehicles, and provides strong support for the sustainable development of the power grid.

[0087] In addition, to achieve the above object, the present invention also proposes a charging trusteeship system.

[0088] Such as Figure 3As shown in the figure, the charging trusteeship system according to the embodiment of the present invention includes: an acquisition module 10, a first modeling module 20, a second modeling module 30, and an optimization module 40. Among them, the acquisition module 10 is used to acquire charging trusteeship data; the first modeling module 20 is used to establish an objective function of the charging power regulation model by considering the balance of the wholesale-retail price difference, the station service fee, and the user's charging cost; the second modeling module 30 is used to establish the constraint conditions of the charging power regulation model by considering the hardware limitations of the charging facilities and the user's vehicle usage requirements, and the user's vehicle usage requirements include the charging requirements and the available charging time of the user's vehicle; the optimization module 40 solves the charging power regulation model based on the charging trusteeship data to obtain the optimal solution of the charging power regulation model, that is, the optimal charging power of the user's vehicle at each moment.

[0089] According to the charging trusteeship system of the embodiment of the present invention, the first modeling module 20 and the second modeling module 30 consider the wholesale-retail price difference, the station service fee, and the user's charging cost when constructing the charging power regulation model, can obtain the optimal charging power of the user's vehicle at each moment, make full use of the available charging time of the user, and achieve the balance of the virtual power plant revenue, the charging operator revenue, and the user's charging cost; can improve the utilization efficiency of the charging infrastructure, enhance the user experience, promote the healthy development of the electric vehicle charging market, and provide technical support for the stable operation of the power grid and the efficient utilization of new energy.

[0090] In an embodiment of the present invention, the charging trusteeship data may include price data, station basic information data, and user vehicle usage requirement data. Among them, the price data may include: the wholesale-retail price difference data of the virtual power plant acting as the station, the charging electricity fee data and the charging service fee data at each moment obtained from the station operation system of the charging operator. The station basic information data may include the rated charging power of the charging facilities. The user vehicle usage requirement data may include the available charging time provided by the user, the SOC of the user's vehicle, the battery capacity, etc.

[0091] In an embodiment of the present invention, the acquisition module 10 may also perform data preprocessing on the acquired charging trusteeship data, such as outlier judgment, data alignment, data merging, etc., to avoid missing and abnormal acquired data and facilitate subsequent data calculation. The specific data preprocessing methods have been described above and will not be elaborated here.

[0092] In an embodiment of the present invention, the objective function is:

[0093]

[0094] Wherein, respectively represent the unit wholesale-retail price difference, the unit station service fee, and the unit user charging cost at the i-th moment, represents the n-dimensional real number space. Among them, the user's charging cost may include the charging electricity fee and the charging service fee. is the charging power line vector, x i is the charging power at the i-th moment, n represents the available charging time of the user's vehicle, and M1, M2, and M3 are the weights of the wholesale-retail price difference, station service fee, and user charging cost respectively, which are used to balance the importance of different objectives.

[0095] It can be understood that the three items of the objective function respectively represent maximizing the virtual power plant's wholesale-retail price difference revenue, maximizing the station service fee revenue, and minimizing the user's charging cost. In a specific embodiment of the present invention, M1 = 1, M2 = 1, and M3 = 1000 can be taken. When optimizing the benefits of the three parties, the lowest user charging cost is given priority, reflecting the idea of putting the interests of charging users first. At this time, the objective function can be understood as maximizing the virtual power plant revenue and station revenue as much as possible on the premise of giving priority to the lowest user charging cost.

[0096] In an embodiment of the present invention, the constraint conditions may include charging constraints and demand power constraints. Among them, the charging constraints are the hardware limitations of the charging facilities during the charging process or the constraint conditions of the vehicle's SOC, such as power supply constraints, SOC recursion constraints, vehicle power demand constraints, etc., all of which are linear equality constraints or cone constraints. The demand power constraint is the constraint condition of the vehicle for the charging power, indicating that the charging power at the current moment must be less than or equal to the vehicle's demand power under the current SOC of the user's vehicle. The formula is:

[0097] x i ≤ f θ (SOC i ) i = 0,..n

[0098] where f θ (SOC i ) represents the relationship model between the SOC and demand power of the user's vehicle, and θ is the parameter in the relationship model.

[0099] It can be understood that when optimizing the charging power, strictly following the charging constraints and demand power constraints can avoid charging failures or equipment damage, making the charging strategy meet the user's expectations and be smoothly implemented under the existing hardware conditions, with strong feasibility, bringing a convenient and reliable charging experience to users, and ensuring the stable operation of the station.

[0100] The charging trusteeship system may further include: a third modeling module for constructing a relationship model f between the SOC and demand power of the user's vehicle according to the user's historical charging data θ (SOC) = P car-need ; The second modeling module 30 calculates the demand power of the user's vehicle according to the SOC of the user's vehicle at each moment and the relationship model when determining the demand power constraint.

[0101] It should be noted that the third modeling module can establish a relationship model based on the user's historical charging data before the acquisition module 10 acquires the charging trusteeship data, or can establish it based on the user's historical charging data acquired by the acquisition module 10 when the user conducts charging trusteeship. After the user's vehicle completes charging, the third modeling module can also re-model based on the updated historical charging data and update the parameters in the relationship model. This embodiment does not impose any restrictions.

[0102] It can be understood that, generally, the relationship model f θ (SOC) between the SOC of the user's vehicle and the demand power is a convex function, and other constraint conditions in the charging power regulation model are linear equality constraints or cone constraints. Therefore, solving the charging power regulation model is a cone programming problem, and its local optimal solution is the global optimal solution, avoiding the common local optimal traps in non-convex optimization and ensuring the global optimality of the solution result; the optimization module 40 can adopt a mature cone programming problem solving algorithm in the prior art, such as the interior point method, etc., to efficiently solve the cone programming problem, and the user does not need to wait for a long time for the solution result, and the user experience is better.

[0103] In an embodiment of the present invention, the second modeling module 30 can construct charging constraints based on the charging state variable or the vehicle SOC. Specifically, the charging constraints can include: the starting charging constraint, the continuous charging constraint, and the power supply constraint constructed based on the charging state variable; the SOC recursive constraint and the vehicle demand power constraint constructed based on the vehicle SOC, where the charging state variable b is a Boolean variable, 1 represents charging, and 0 represents stopping.

[0104] Specifically, the starting charging constraint means that charging starts after the user starts charging trusteeship, and its formula is:

[0105] b0 = 1, b i ∈{0, 1}

[0106] where, b0 represents the starting charging state, and b i is the charging state variable at the i-th moment, and b i takes the value of 1 when charging and 0 when not charging.

[0107] The continuous charging constraint means that it is always in the charging state before the user's charging demand is met, and its formula is:

[0108] b i ≤b i-1 i = 1,..n

[0109] By setting the continuous charging constraint, it is possible to avoid frequent start and stop orders during the charging trusteeship process and reduce the risk of charging failure.

[0110] The power supply constraint indicates that the charging power must be greater than the minimum power supply of the charging facility, and its formula is:

[0111] m1·b i ≤x i ≤m2·b i i = 0,..n

[0112] Where m1 and m2 represent the minimum and maximum power supplies of the charging facility respectively; in some embodiments of the present invention, m2 can also be 2000 or any value greater than the maximum charging power of the general charging facility, and this embodiment is not limited.

[0113] The SOC recursive constraint represents the recursive relationship between the current vehicle SOC and the previous vehicle SOC, and its formula is:

[0114]

[0115] Where SOC i represents the percentage of the remaining battery power of the user's vehicle at the i-th moment, freq represents the charging regulation frequency, and Cap represents the battery capacity of the user's vehicle. When the charging power is adjusted every minute, freq = 60.

[0116] The vehicle power demand constraint indicates that the entrusted charging amount is equal to the vehicle power demand, that is, it meets the user's charging demand for the vehicle power, and its formula is:

[0117]

[0118] Where SOC0 represents the percentage of the remaining battery power of the user's vehicle at the starting moment, and SOC n represents the target percentage of the remaining battery power of the user's vehicle after charging trusteeship. It can be understood that SOC n can be 1 (i.e., 100%), and at this time the user's charging demand is reflected as the vehicle being fully charged. In some other embodiments of the present invention, the user can also require the vehicle to be charged to a specified power, such as charging the vehicle to 80% of the power. At this time, the formula for the vehicle power demand constraint is:

[0119]

[0120] In this embodiment, since the Boolean variable b is also an integer variable, therefore, solving the charging power regulation model is a conic integer programming problem, and the optimization module 40 can use the existing open-source solver to solve it in a relatively short time, further improving the user experience.

[0121] In one embodiment of the present invention, the charging trusteeship system further includes: a trusteeship charging module, configured to perform trusteeship charging on a user vehicle based on the optimal charging power of the user vehicle at each moment.

[0122] It can be understood that after the optimization module 40 solves the optimal solution of the charging power regulation model, the user charging cost for trusteeship charging based on the optimal solution can be calculated according to the price data for the user's reference. In some other embodiments of the present invention, the user can adjust the vehicle usage requirements again based on the initially obtained user charging cost, such as extending the available charging time, reducing the target remaining battery power, etc., and then provide it to the optimization module 40 for re-solving to adjust the charging cost and then determine whether to perform trusteeship charging on the vehicle.

[0123] In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0124] The execution order of the steps shown in the flowchart is a preferred implementation manner. In other embodiments of the present invention, it can also be adjusted according to the functions involved in each step. For example, they can be executed simultaneously or in the reverse order.

[0125] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an 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 transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0126] Those of ordinary skill in the art in this technical field can understand that all or part of the steps carried by the methods in the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0127] In addition, in each embodiment of the present invention, the functional units can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

Claims

1. A charging trusteeship method, characterized in that: The following steps are involved: S1, obtaining charging trusteeship data, wherein the charging trusteeship data includes price data, basic information data of stations, and user vehicle demand data; S2, considering the balance between wholesale and retail price difference, station service fee and user charging cost, establish the objective function of the charging power control model; S3, establishing constraint conditions of the charging power control model by considering hardware limitations of charging facilities and user vehicle use requirements, wherein the user vehicle use requirements include charging requirements and available charging time of the user vehicle; S4, solving the charging power control model based on the charging trusteeship data to obtain the optimal charging power of the user's vehicle at each moment.

2. The charging trusteeship method according to claim 1, characterized in that: The objective function is: Among them, P i agg , They represent the unit wholesale-retail price difference, unit station service fee and unit user charging fee at the i-th moment, x is the charging power line vector, x i is the charging power at the i-th moment, n represents the available charging time of the user's vehicle, M1, M2, and M3 are the weights of the wholesale-retail price difference, the terminal service fee, and the user's charging fee, respectively.

3. The charging trusteeship method according to claim 1, characterized in that: The constraint condition includes a charging constraint and a demand power constraint, the charging constraint is a linear equality constraint or a cone constraint, and the method further includes: Constructing a relationship model between the SOC and required power of the user's vehicle based on the user's historical charging data; When determining the demand power constraint, the demand power of the user vehicle is calculated based on the SOC of the user vehicle at each moment and the relationship model.

4. The charging trusteeship method according to claim 3, characterized in that: The charging constraints include: initial charging constraints, continuous charging constraints, and power supply constraints constructed based on charging state variables; SOC recursive constraints and vehicle power requirement constraints constructed based on vehicle SOC, wherein the charging state variable is a Boolean variable.

5. The charging trusteeship method according to claim 1, characterized in that: Also includes: S5, based on the optimal charging power of the user vehicle at each moment, the user vehicle is charged in trust.

6. A charging trusteeship system, characterized in that: include: An acquisition module, the acquisition module is used to acquire charging trusteeship data, the charging trusteeship data includes price data, station basic information data, and user vehicle demand data; A first modeling module, wherein the first modeling module is used to consider the balance between wholesale and retail price difference, station service fee and user charging fee, and establish an objective function of the charging power control model; a second modeling module, the second modeling module being used to establish constraint conditions of the charging power regulation model by taking into account hardware limitations of charging facilities and user vehicle use requirements, wherein the user vehicle use requirements include charging requirements and available charging time of the user vehicle; An optimization module, wherein the optimization module solves the charging power control model based on the charging trusteeship data to obtain the optimal charging power of the user's vehicle at each moment.

7. The charging trusteeship system according to claim 6, characterized in that: The objective function is: Among them, P i agg , They represent the unit wholesale-retail price difference, unit station service fee and unit user charging fee at the i-th moment, x is the charging power line vector, x i is the charging power at the i-th moment, n represents the available charging time of the user's vehicle, M1, M2, and M3 are the weights of the wholesale-retail price difference, the terminal service fee, and the user's charging fee, respectively.

8. The charging trusteeship system according to claim 6, characterized in that: The constraint condition includes a charging constraint and a demand power constraint, the charging constraint is a linear equality constraint or a cone constraint, and the system further includes: A third modeling module, the third modeling module is used to construct a relationship model between the SOC and the required power of the user's vehicle according to the user's historical charging data; When determining the demand power constraint, the second modeling module calculates the demand power of the user vehicle according to the SOC of the user vehicle at each moment and the relationship model.

9. The charging trusteeship system according to claim 8, characterized in that: The charging constraints include: initial charging constraints, continuous charging constraints, and power supply constraints constructed based on charging state variables; SOC recursive constraints and vehicle power requirement constraints constructed based on vehicle SOC, wherein the charging state variable is a Boolean variable.

10. The charging trusteeship system according to claim 9, characterized in that: Also includes: A managed charging module is used to perform managed charging for the user's vehicle based on the optimal charging power of the user's vehicle at each moment.

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