Scheduling method for vehicle charging

Abstract

The invention discloses a scheduling method for vehicle charging, and relates to the technical field of electric vehicle energy management. The method comprises the following steps: on the basis of a StarColberg model, taking a charging party as a bottom follower, and taking a charging party minimization cost function determined according to electricity price and charging emission reduction awards as a target to determine a game charging amount of the charging party; determining the game electricity price of the power supply side by taking the power supply side as a middle-layer follower and taking a power supply side maximum income function determined according to the power supply emission reduction reward, the electricity price and the game charging amount as a target; determining a game charging emission reduction reward and a game power supply emission reduction reward of the carbon emission evaluation party by taking the carbon emission evaluation party as a leader and taking a maximum emission reduction income function determined according to the charging emission reduction reward, the power supply emission reduction reward and the game charging as a target; and the three layers are iterated repeatedly until the convergence condition is met, so that the power supply quantity of the power supply party in the target time period is obtained. According to the method, the power supply quantity meeting multiple targets in the target time period can be determined.

Description

Technical Field

[0001] This application relates to the technical field of electric vehicle energy management, and specifically to a scheduling method for vehicle charging. Background Art

[0002] With the acceleration of the global energy transition and the continuous enhancement of environmental protection awareness, low-carbon transportation has gradually become a key direction for the development of future transportation systems. As a low-carbon and environmentally friendly means of transportation, electric vehicles are receiving strong support and promotion from governments around the world. However, with the rapid growth of the number of electric vehicles, the demand for charging infrastructure is constantly rising. How to optimize the charging scheduling of electric vehicles has become an important research topic in the field of energy management. Traditional charging scheduling methods often rely on a single optimization goal and cannot fully consider the interest balance among multiple stakeholders and the overall system benefits. The construction and operation of charging stations involve multiple factors. It is necessary to meet user needs, maximize revenue, and at the same time, the government also hopes to achieve as good a carbon emission reduction effect as possible with as low a carbon emission reduction reward as possible. Especially in the context of the increasingly diverse charging behaviors of users, how to perform power scheduling for vehicles at charging stations to meet the needs of multiple parties has become a key problem to be solved urgently. Summary of the Invention

[0003] The purpose of the embodiments of this application is to provide a scheduling method for vehicle charging to solve the technical problem of how to perform power scheduling for vehicles at charging stations to meet the needs of multiple parties in the prior art.

[0004] To achieve the above purpose, the first aspect of this application provides a scheduling method for vehicle charging, and the scheduling method includes:

[0005] Obtain the electricity price of the clean energy power supplier, the charging emission reduction reward provided by the carbon emission assessment party for the charging party, and the power supply emission reduction reward provided by the carbon emission assessment party for the power supplier;

[0006] Based on the Stackelberg model, take the charging party as the bottom follower, and determine the game charging amount of the charging party with the goal of minimizing the cost function of the charging party determined according to the electricity price and the charging emission reduction reward;

[0007] Take the power supplier as the middle follower, and determine the game electricity price of the power supplier with the goal of maximizing the revenue function of the power supplier determined according to the power supply emission reduction reward, the electricity price, and the game charging amount;

[0008] Take the carbon emission assessment party as the leader, and determine the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party with the goal of maximizing the emission reduction revenue function determined according to the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the game charging amount;

[0009] Iterate the charging amount, game electricity price, game charging emission reduction reward, and power supply emission reduction reward in the Stackelberg model until the convergence condition is reached, so as to obtain the game charging amount in the Stackelberg equilibrium as the power supply amount of the power supply side during the target period.

[0010] In the embodiment of the present application, with the goal of minimizing the cost function of the charging side determined according to the electricity price and the charging emission reduction reward, the game charging amount of the charging side is determined, including: in the pre-constructed convenience cost function, determining the convenience cost of the charging side according to the target period, where the closer the target period is to the charging peak period, the lower the convenience cost; with the goal of minimizing the cost function of the charging side determined according to the electricity price, the charging emission reduction reward, and the convenience cost, determining the game charging amount of the charging side.

[0011] In the embodiment of the present application, the power supply side can provide clean energy and purchased energy, and the power supply side makes full use of clean energy for power supply; with the goal of maximizing the revenue function of the power supply side determined according to the power supply emission reduction reward, the electricity price, and the game charging amount, determining the game electricity price of the power supply side, including: determining the additional power supply cost of the power supply side according to the power supply amounts of clean energy and purchased energy for the game power supply amount, where the power supply amount of purchased energy is positively correlated with the additional power supply cost, and the power supply amount of clean energy is negatively correlated with the additional power supply cost; with the goal of maximizing the revenue function of the power supply side determined according to the power supply emission reduction reward, the electricity price, the game charging amount, and the additional power supply cost, determining the game electricity price of the power supply side.

[0012] In the embodiment of the present application, determining the additional power supply cost of the power supply side according to the power supply amounts of clean energy and purchased energy for the game power supply amount includes: in the case where the power supply amount of clean energy is less than the game charging amount, determining the additional power supply cost of the power supply side according to the power supply amount of purchased energy and the purchase price; in the case where the power supply amount of clean energy is greater than the game charging amount, determining the power supply surplus revenue of the power supply side according to the power supply surplus of clean energy and the external sale price of clean energy, and taking the power supply surplus revenue as the additional power supply cost.

[0013] In the embodiment of the present application, with the goal of maximizing the revenue function of the power supply side determined according to the power supply emission reduction reward, the electricity price, and the game charging amount, determining the game electricity price of the power supply side, including: determining the load imbalance cost of the power supply side according to the charging change amount of the game charging amount during the target period; with the goal of maximizing the revenue function of the power supply side determined according to the power supply emission reduction reward, the electricity price, the game charging amount, and the load imbalance cost, determining the game electricity price of the power supply side.

[0014] In the embodiments of the present application, with the goal of maximizing the emission reduction income function determined by the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the game charging amount, determining the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party includes: obtaining the unit carbon emission price; determining the total emission reduction price of the power supply party according to the unit carbon emission price and the game charging amount; with the goal of maximizing the emission reduction income function determined by the difference between the total emission reduction price and the product, determining the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party.

[0015] In the embodiments of the present application, the power supply party supplies power through clean energy and purchased energy, and the power supply party makes full use of clean energy for power supply. In the case where the power supply party uses purchased energy for power supply, the carbon emission amount increases; with the goal of maximizing the emission reduction income function determined by the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the game charging amount, determining the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party includes: obtaining the unit carbon emission price, the benchmark total carbon emission price, and the clean energy power supply amount of the power supply party; determining the purchased energy game power supply amount of the power supply party according to the game charging amount and the clean energy power supply amount; determining the total emission reduction price of the power supply party according to the product of the difference between the benchmark total carbon emission price and the purchased energy game power supply amount and the unit carbon emission; determining the purchased energy game total carbon emission price of the power supply party according to the purchased energy game power supply amount and the unit carbon emission price; with the goal of maximizing the emission reduction income function determined by the difference between the game total carbon emission price and the product, determining the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party.

[0016] In the embodiments of the present application, the power supply party can supply power through clean energy and purchased energy, and the power supply party makes full use of clean energy for power supply; with the goal of maximizing the emission reduction income function determined by the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the game charging amount, determining the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party includes: obtaining the maximum clean energy power supply amount of the power supply party; in the case where the game charging amount is less than or equal to the low-demand charging amount threshold and the game charging amount is greater than or equal to the maximum clean energy power supply amount, the determined game power supply emission reduction reward is greater than or equal to the game charging emission reduction reward; in the case where the game charging amount is less than or equal to the high-demand charging amount threshold and the game charging amount is less than or equal to the maximum clean energy power supply amount, the determined game charging emission reduction reward is greater than or equal to the game power supply emission reduction reward; in the case where the game charging amount is greater than the high-demand charging amount threshold, the determined game power supply emission reduction reward is greater than or equal to the game charging emission reduction reward.

[0017] In the embodiments of the present application, to determine the game charging amount of the charging party with the goal of minimizing the cost function, it includes: in a single-round iteration of the Stackelberg model, taking the minimizing cost function as the objective function of the genetic algorithm, and determining the game charging amount of the current iteration round through the genetic algorithm; and / or, to determine the game electricity price of the power supply party with the goal of maximizing the revenue function, it includes: in a single-round iteration of the Stackelberg model, taking the maximizing revenue function as the objective function of the genetic algorithm, and determining the game electricity price of the current iteration round through the genetic algorithm; and / or, to determine the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party with the goal of maximizing the emission reduction revenue function, it includes: in a single-round iteration of the Stackelberg model, taking the maximizing emission reduction revenue function as the objective function of the genetic algorithm, and determining the game charging emission reduction reward and the game power supply emission reduction reward of the current iteration round through the genetic algorithm.

[0018] In the embodiments of the present application, the scheduling method further includes: according to the game electricity price, the game charging emission reduction reward, and the game power supply emission reduction reward in the target time period under the Stackelberg equilibrium, respectively serving as the electricity price, the charging emission reduction reward, and the power supply emission reduction reward in the next time period, to determine the power supply amount of the power supply party in the next time period based on the Stackelberg model.

[0019] The above technical solution constructs a three-layer game framework for the charging party, the power supply party, and the carbon emission assessment party based on the Stackelberg model. In this framework, the carbon emission assessment party serves as the top leader of the game, guiding the market behaviors of the power supply party and the charging party by setting the charging emission reduction reward and the power supply emission reduction reward; the power supply party serves as the middle follower, formulating the electricity price based on the supply-demand relationship of electricity and the power supply emission reduction reward in the above market behaviors while meeting the charging amount required by the charging party; electric vehicle users serve as the bottom followers, adjusting their charging behaviors according to the electricity price and the charging emission reduction reward; by solving the optimal strategies of all parties layer by layer in the bottom-up order from the bottom follower to the leader, and iterating repeatedly until the above three parties finally reach the Stackelberg equilibrium state, the game power supply amount that can achieve the goals of multiple parties in the target time period can be determined.

[0020] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation part. Description of the Drawings

[0021] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings:

[0022] Figure 1 Schematically shows a flowchart of a scheduling method for vehicle charging according to an embodiment of the present application;

[0023] Figure 2 Schematically shows a schematic flowchart of another scheduling method for vehicle charging according to an embodiment of the present application;

[0024] Figure 3 Schematically shows a schematic diagram of the optimization result of the load curve by the scheduling method for vehicle charging according to an embodiment of the present application. Detailed implementation manners

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the specific implementation manners described herein are only for explaining and illustrating the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0026] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solutions of the present application all comply with the relevant regulations of national laws and regulations. In the embodiments of the present application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solutions of the present application, but it does not mean that the applicant has already or necessarily used this solution.

[0027] If there are descriptions involving "first", "second", etc. in the embodiments of the present application, such descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement it. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present application.

[0028] Currently, although there are already some solutions that focus on the optimization problem of charging scheduling, most of the existing solutions lack consideration of environmental factors and do not establish an effective incentive mechanism to guide users to charge during low-carbon periods. In addition, due to the competitive and cooperative relationships among the various participants within the charging system, traditional optimization methods are difficult to accurately describe and solve the problem of multi-party interest games.

[0029] Specifically, in the vehicle charging scenario, the charging times of multiple users as the charging parties are different. The distribution system operator as the power supply party needs to meet the charging demands of users at different times as much as possible. At the same time, in order to maximize revenue, the power supply party also needs to avoid power waste and increase the power supply cost. Therefore, the power supply party will pursue that its power supply quantity exactly meets the charging demands of users. Based on the relationship between the above-mentioned charging party and the power supply party, there is also a carbon emission assessment party such as a government agency that pursues reducing carbon dioxide emissions. As a carbon emission assessment party, the government agency will provide rewards to the charging party and the power supply party by providing subsidies to them, so as to achieve the goal of carbon emission reduction. Thus, the rewards provided by the carbon emission assessment party will further affect the charging behavior of the charging party and correspondingly also affect the power supply behavior of the power supply party. Therefore, power energy scheduling needs to be carried out under the condition of meeting the demands of the above-mentioned multiple parties as much as possible. Based on the above analysis, the embodiment of the present application provides a scheduling method for vehicle charging, and determines the power supply quantity in the target period through a Stackelberg model established based on the demands of the above-mentioned multiple parties and the influence relationship between them, so as to meet the demands of the above-mentioned multiple parties.

[0030] Figure 1 Schematically shows a flowchart of a scheduling method for vehicle charging according to an embodiment of the present application. As Figure 1 shown, the embodiment of the present application provides a scheduling method for vehicle charging, and the method may include the following steps:

[0031] S102. Obtain the electricity price of the clean energy power supply party, the charging emission reduction reward provided by the carbon emission assessment party for the charging party, and the power supply emission reduction reward provided by the carbon emission assessment party for the power supply party;

[0032] S104. Based on the Stackelberg model, take the charging party as the bottom-level follower, and determine the game charging quantity of the charging party with the goal of minimizing the cost function of the charging party determined according to the electricity price and the charging emission reduction reward;

[0033] S106. Take the power supply party as the middle-level follower, and determine the game electricity price of the power supply party with the goal of maximizing the revenue function of the power supply party determined according to the power supply emission reduction reward, the electricity price, and the game charging quantity;

[0034] S108. Take the carbon emission assessment party as the leader, and determine the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party with the goal of maximizing the emission reduction revenue function determined according to the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the game charging quantity;

[0035] S110. Iteratively game the charging amount, game electricity price, game charging emission reduction reward, and power supply emission reduction reward in the Stackelberg model until the convergence condition is reached, so as to obtain the game charging amount in the case of Stackelberg equilibrium as the power supply amount of the power supply side during the target period.

[0036] The scheduling method for vehicle charging provided in the embodiments of the present application is based on the Stackelberg model, constructs a three-layer game framework for the charging side, the power supply side, and the carbon emission assessment side. In this framework, the carbon emission assessment side is the top leader of the game, and guides the market behaviors of the power supply side and the charging side by setting the charging emission reduction reward and the power supply emission reduction reward. The power supply side is the middle follower. While meeting the charging amount required by the charging side, it formulates the electricity price based on the supply-demand relationship of electricity and the power supply emission reduction reward in the above market behavior. The electric vehicle user is the bottom follower and adjusts the charging behavior according to the electricity price and the charging emission reduction reward. This method solves the optimal strategies of all parties layer by layer in the order from the bottom follower to the leader. Through repeated iteration, the above three parties finally reach the Stackelberg equilibrium state where none of them can achieve target optimization by unilaterally changing the strategy. This hierarchical decision-making mechanism finally reaches the Stackelberg equilibrium through the dynamic game of all parties, so as to determine the power supply amount that can achieve the multi-party goals, that is, the power supply amount after the game of multi-party demands, during the target period.

[0037] The scheduling method for vehicle charging provided in the embodiments of the present application can not only provide a strategy for the power supply side to formulate the power supply amount during the target period, but also use the game electricity price in the case of Stackelberg equilibrium as a strategy for formulating the electricity price during the target period. Correspondingly, since there are also corresponding game charging emission reduction rewards and game power supply emission reduction rewards in the case of Stackelberg equilibrium, the scheduling method for vehicle charging provided in the embodiments of the present application can also be used to provide a strategy for the carbon emission assessment side to formulate the charging emission reduction reward and the power supply emission reduction reward during the target period.

[0038] Understandably, the power supplier in step S102 can provide clean energy for power supply, and will obtain power supply emission reduction rewards when the power supplier provides clean energy for power supply. In step S104, the cost function minimized by the charger can include the result of the product of the electricity price and the game charging amount minus the product of the charging emission reduction reward and the game charging amount. The revenue function maximized in step S106 can include the result of the product of the sum of the power supply emission reduction reward and the electricity price and the game charging amount. The emission reduction revenue function maximized in step S108 can include the result of the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the game charging amount. The game electricity price in the case of reaching the Stackelberg equilibrium in step S110 can be used as the electricity price of the power supplier during the target period. The corresponding game charging emission reduction reward and game power supply emission reduction reward can be used to provide reward references for the charger and the power supplier, and can be used as the charging emission reduction reward and power supply emission reduction reward provided by the carbon emission assessment party to the charger and the power supplier respectively during the target period. The convergence conditions in step S110 can include: the game charging emission reduction reward and the power supply emission reduction reward converge, or the number of iterations of the Stackelberg model reaches the preset upper limit of the number of iterations.

[0039] Specifically, in steps S104, S106, and S108, the game charging amount, game electricity price, game charging emission reduction reward, and game power supply emission reduction reward obtained respectively can be determined in each level of a single iteration through heuristic algorithms such as the genetic algorithm and the particle swarm optimization algorithm.

[0040] Taking the genetic algorithm as an example, in some embodiments of the present application, to determine the game charging amount of the charger with the minimized cost function as the goal, it can include:

[0041] In a single iteration of the Stackelberg model, taking the minimized cost function as the objective function of the genetic algorithm, and determining the game charging amount of the current iteration round through the genetic algorithm;

[0042] And / or, to determine the game electricity price of the power supplier with the maximized revenue function as the goal, it can include:

[0043] In a single iteration of the Stackelberg model, taking the maximized revenue function as the objective function of the genetic algorithm, and determining the game electricity price of the current iteration round through the genetic algorithm;

[0044] And / or, to determine the game charging emission reduction reward and game power supply emission reduction reward of the carbon emission assessment party with the maximized emission reduction revenue function as the goal, it can include:

[0045] In a single iteration of the Stackelberg model, taking the maximized emission reduction revenue function as the objective function of the genetic algorithm, and determining the game charging emission reduction reward and game power supply emission reduction reward of the current iteration round through the genetic algorithm.

[0046] Based on the three - layer game framework of the Stackelberg model, the genetic algorithm can make the process of determining the game results at each layer converge as fast as possible, and can make the game results obtained at each layer reach the optimal in the current layer of the current iteration round, that is, can satisfy its objective requirements as much as possible.

[0047] It can be understood that for any power supply party providing charging services to the charging party, the charging party may include one to multiple users who need to charge their vehicles. Therefore, in the case where the charging party includes multiple users, taking the minimization cost function as the objective function of the genetic algorithm and determining the game charging amount of the current iteration round through the genetic algorithm may include:

[0048] Taking the minimization cost function as the objective function of the genetic algorithm, and determining the respective game charging amounts of multiple users of the charging party in the current iteration round through the genetic algorithm.

[0049] As Figure 2 shown, in some embodiments of the present application, to determine the power supply amounts of the power supply party at multiple different time periods, the scheduling method for vehicle charging provided by the embodiments of the present application may further include:

[0050] According to the game electricity price, game charging emission reduction reward, and game power supply emission reduction reward in the target time period under the Stackelberg equilibrium, respectively, as the electricity price, charging emission reduction reward, and power supply emission reduction reward in the next time period, to determine the power supply amount of the power supply party in the next time period based on the Stackelberg model.

[0051] Since the behaviors of the charging party, power supply party, and carbon emission assessment party may change over time, based on the above steps, the Stackelberg model in the embodiments of the present application can be used in multiple cycles, so as to determine the power supply amounts at multiple different time periods, and further, the formulation of the power supply strategy of the power supply party for multiple time periods or even the entire time period can be realized.

[0052] In some embodiments of the present application, to more accurately measure the minimization cost function of the charging party, taking the charging party minimization cost function determined according to the electricity price and charging emission reduction reward in step S104 as the target, and determining the game charging amount of the charging party may include:

[0053] In the pre - constructed convenience cost function, determine the convenience cost of the charging party according to the target time period, where the closer the target time period is to the charging peak period, the lower the convenience cost;

[0054] Taking the charging party minimization cost function determined according to the electricity price, charging emission reduction reward, and convenience cost as the target, and determining the game charging amount of the charging party.

[0055] Since the charging party represents the user who charges the vehicle or the electric vehicle aggregator that integrates and manages users with consistent charging behaviors, and users generally tend to charge during time periods such as evenings and mornings, thus forming a statistically significant charging peak period. If the user chooses to charge during a non-charging peak period, this behavior means that vehicle charging may occupy the user's time for other behaviors, thereby increasing the user's charging cost. Therefore, based on the above convenience cost function, the user's tendency to charge during the charging peak period can be included in the minimization cost function, making the game result more in line with the user's actual behavior.

[0056] Understandably, the independent variable of the convenience cost function may include a charging preference function that depends on time, and this charging preference function reflects the charging time preferred by the charging party. The charging preference function can be, for example, a normal distribution function, a bimodal function, or a superposition of different functions, etc., to reflect the charging time preferred by the charging party.

[0057] As an example, the minimization cost function of the charging party can be, for example:

[0058]

[0059] where, min Q U EV is the minimization cost function, Q is the charging amount of the charging party during the target period, T is the target period, C ch (t) is the charging cost determined according to the charging amount and the electricity price, C u (t) is the convenience cost function, I inc,ev (t) is the charging emission reduction income determined according to the charging amount and the charging emission reduction reward.

[0060] As an example, the charging cost in formula (1) can be, for example:

[0061] C ch (t) = p(t) * Q(t)Δt; (2)

[0062] where, p(t) is the electricity price at time t, Q(t) is the charging power at time t, and Δt is the time step.

[0063] As an example, the convenience cost function in formula (1) can be, for example:

[0064] C u (t) = α u (1 - φ u (t))Q(t) 2 Δt; (3)

[0065] where, α u is the convenience cost coefficient, φu (t) is the charging preference function of the charging party. In the charging preference function, the closer the time t is to the charging peak period, the greater the value of the charging preference function.

[0066] As an example, the charging emission reduction income in formula (1) can be, for example:

[0067] I inc,ev (t) = r ev (t)Q(t)Δt; (4)

[0068] Where r ev (t) is the charging emission reduction reward at time t.

[0069] According to the above formulas (1) to (4), the minimum cost of the power supply party can be determined, and the power supply party can include multiple users charging vehicles. The minimum cost of each user can be determined based on formulas (1) to (4).

[0070] In addition, the following constraints can be imposed on the charging power Q(t) at time t, so as to incorporate the behavior of the charging party to protect the vehicle battery during the game process.

[0071] As an example, continuing from the above, for any user in the charging party, the charging power Q(t) at time t satisfies the following constraint conditions:

[0072] S(t) = S(t - 1) + ηQ(t), t ≥ 2; (5)

[0073]

[0074] Where S(t) is the SOC (state of charge, charge state) of the vehicle battery at time t, η is the charging efficiency coefficient, S min is the minimum battery SOC threshold, S max is the maximum battery SOC threshold, Q max is the maximum charging power, Q initial (t) is the initial charging power without game at time t.

[0075] To protect the electric vehicle battery, the system also sets upper and lower limits on the state of charge. Users choose the optimal charging time period and charging power by weighing these factors.

[0076] In some embodiments of the present application, considering that during periods such as peak charging hours, the clean energy provided by the power supply party may not fully meet the charging needs of the charging party. Therefore, the power supply party may need to purchase external energy for power supply, thereby increasing the power supply cost of the power supply party. Therefore, when the power supply party can provide clean energy and external purchased energy, and the power supply party fully uses clean energy for power supply, taking the power supply party's maximum revenue function determined according to the power supply emission reduction reward, electricity price, and game charging amount as the target to determine the game electricity price of the power supply party may include:

[0077] Determine the additional power supply cost of the power supply party according to the power supply amounts of clean energy and external purchased energy for the game power supply amount, wherein the power supply amount of external purchased energy is positively correlated with the additional power supply cost, and the power supply amount of clean energy is negatively correlated with the additional power supply cost;

[0078] Taking the power supply party's maximum revenue function determined according to the power supply emission reduction reward, electricity price, game charging amount, and additional power supply cost as the target, determine the game electricity price of the power supply party.

[0079] In the above game process, the power supply party further has the ability to adjust the electricity price to reduce the volatility of the game charging amount, that is, reduce the charging demand of the charging party during peak charging hours, and increase the charging demand during valley charging hours, thereby avoiding the increase in the power supply cost of the power supply party. Therefore, based on the above steps, the game process of the power supply party can be made more in line with the actual situation.

[0080] It can be understood that when the game power supply amount is known, the power supply party will preferentially supply power to the charging party through clean energy, and supply power to the power supply party through external purchased energy when the supply of clean energy is insufficient.

[0081] Specifically, determining the additional power supply cost of the power supply party according to the power supply amounts of clean energy and external purchased energy for the game power supply amount may include:

[0082] When the power supply amount of clean energy is less than the game charging amount, determine the additional power supply cost of the power supply party according to the power supply amount of external purchased energy and the external purchase price;

[0083] When the power supply amount of clean energy is greater than the game charging amount, determine the power supply surplus revenue of the power supply party according to the power supply surplus of clean energy and the external selling price of clean energy, and use the power supply surplus revenue as the additional power supply cost.

[0084] In some embodiments of the present application, the load imbalance cost may be further introduced into the power supply party's maximum revenue function. Correspondingly, taking the power supply party's maximum revenue function determined according to the power supply emission reduction reward, electricity price, and game charging amount as the target to determine the game electricity price of the power supply party may include:

[0085] Determine the load imbalance cost of the power supply side according to the charging change amount of the game charging amount in the target period;

[0086] Taking the power supply side's maximized revenue function determined according to the power supply emission reduction reward, electricity price, game charging amount, and load imbalance cost as the goal, determine the game electricity price of the power supply side.

[0087] The load imbalance cost represents the cost expenditure brought to the power supply side by the continuous change of the charging demand of the charging side in the target period. This enables the power supply side to also consider the change in the charging demand of the charging side during the game process, making the game process more in line with the actual needs of the power supply side to obtain a more reasonable power supply volume.

[0088] As an example, the maximized revenue function in step S106 may include: power supply emission reduction reward, electricity price, game charging amount, load imbalance cost, and additional power supply cost. The maximized revenue function may be, for example:

[0089]

[0090] where max p U CSO is the maximized revenue function, p is the electricity price, I sell is the power supply revenue, I inc,cso is the power supply emission reduction revenue, C e is the additional power supply cost, C b is the load imbalance cost.

[0091] As an example, the power supply revenue in formula (9) may be, for example:

[0092] I sell (t) = p(i)Q(t)Δt; (10)

[0093] As an example, the power supply emission reduction revenue in formula (9) may be, for example:

[0094] I inc,cso (t) = r cso (t)Q(t)Δt; (11)

[0095] where r cso is the power supply emission reduction reward.

[0096] As an example, the additional power supply cost in formula (9) may be, for example:

[0097]

[0098] where is the clean energy power supply capacity, c e,b is the price of externally purchased energy, c e,sFor the selling price of clean energy.

[0099] As an example, the load imbalance cost in formula (9) can be, for example:

[0100] C b (t) = γ[Q(t + 1) - Q(t)] 2 Δt; (13)

[0101] Where γ is the load imbalance cost coefficient.

[0102] In addition, the electricity price p(t) at time t can follow the following constraints:

[0103]

[0104] Where p min is the lower limit of the electricity price, and p max is the upper limit of the electricity price, is the un - gamed externally purchased energy, is the externally purchased energy after gaming.

[0105] In some embodiments of the present application, with the goal of the maximized emission reduction income function determined by the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the gaming charging amount in step S108, determining the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessment party may include:

[0106] Obtain the unit carbon emission price;

[0107] Determine the total emission reduction price of the power supply party according to the unit carbon emission price and the gaming charging amount;

[0108] With the goal of the maximized emission reduction income function determined by the difference between the total emission reduction price and the product, determine the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessment party.

[0109] In some other embodiments of the present application, the power supply party supplies power through clean energy and externally purchased energy, and the power supply party fully uses clean energy for power supply. In the case where the power supply party uses externally purchased energy for power supply, the carbon emission increases; correspondingly, with the goal of the maximized emission reduction income function determined by the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the gaming charging amount in step S108, determining the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessment party may include:

[0110] Obtain the unit carbon emission price, the benchmark total carbon emission price, and the clean energy power supply amount of the power supply party;

[0111] Determine the externally purchased energy gaming power supply amount of the power supply party according to the gaming charging amount and the clean energy power supply amount;

[0112] Determine the total emission reduction price of the power supply side based on the product of the difference between the total price of benchmark carbon emissions and the power supply quantity in the game of purchased energy and the unit carbon emission;

[0113] Determine the total carbon emission price of the purchased energy in the game of the power supply side based on the power supply quantity in the game of purchased energy and the unit carbon emission price;

[0114] With the maximized emission reduction income function determined based on the difference between the total game carbon emission price and the product as the goal, determine the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment side.

[0115] Through the above steps, in the game process, the carbon emission assessment side will adjust the power supply emission reduction reward according to the purchased energy of the power supply side, so that the obtained game power supply emission reduction reward is more in line with the needs of the carbon emission assessment side, and thus the power supply quantity and the purchased energy quantity of the power supply side obtained from the game are more in line with the actual situation.

[0116] As an example, the maximized emission reduction income function can be, for example:

[0117]

[0118] Among them, is the maximized emission reduction income function, r CSO is the power supply emission reduction reward formulated by the carbon emission assessment side, r ev is the charging emission reduction reward formulated by the carbon emission assessment side, λ1 is the first calibration coefficient, λ2 is the second calibration coefficient, ΔE is the emission reduction amount, κ is the carbon pollution control cost, C gov is the total emission reduction reward.

[0119] As an example, the emission reduction amount can be, for example:

[0120]

[0121] Among them, E base is the baseline carbon emission amount, E op is the carbon emission amount after the game.

[0122] As an example, formula (18) can be further refined as:

[0123]

[0124] Among them, is the amount of purchased energy under the condition of no game, is the amount of purchased energy after the game.

[0125] As an example, the total emission reduction reward can be, for example:

[0126]

[0127] In some embodiments of the present application, the power supply party can supply power through clean energy and purchased energy, and the power supply party makes full use of clean energy for power supply. To reduce the charging fluctuation of the charging party over time through charging emission reduction rewards and power supply emission reduction rewards, and to reduce the amount of purchased energy of the power supply party, taking the maximization emission reduction income function determined by the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the game charging amount in step S108 as the objective, determining the game charging emission reduction reward and the game power supply emission reduction reward of the carbon emission assessment party may include:

[0128] Obtain the maximum power supply amount of clean energy of the power supply party;

[0129] When the game charging amount is less than or equal to the low-demand charging amount threshold and greater than or equal to the maximum power supply amount of clean energy, the determined game power supply emission reduction reward is greater than or equal to the game charging emission reduction reward;

[0130] When the game charging amount is less than or equal to the high-demand charging amount threshold and less than or equal to the maximum power supply amount of clean energy, the determined game charging emission reduction reward is greater than or equal to the game power supply emission reduction reward;

[0131] When the game charging amount is greater than the high-demand charging amount threshold, the determined game power supply emission reduction reward is greater than or equal to the game charging emission reduction reward.

[0132] As an example, the inequality relationship between the game power supply emission reduction reward and the game charging emission reduction reward may follow the following formula, for example:

[0133]

[0134] where T low is the charging low period, and T peak is the charging high period; T res is the clean energy high supply period, that is, the power supply amount of clean energy in the current period is greater than or equal to the charging demand.

[0135] Based on the vehicle charging scheduling method provided in the above embodiments, the charging load fluctuation can be effectively reduced, the load curve of the power supply party can be smoothed, the utilization efficiency of the charging facilities can be improved, and the amount of purchased energy of the power supply party can be reduced to reduce the carbon emissions. For example, the vehicle charging scheduling method provided in the above embodiments can obtain as Figure 3The optimization result of the shown load curve, where the horizontal axis represents time, and the vertical axis represents load and load shifting amount respectively. The units of load and load shifting amount are kilowatts. It can be determined from the figure that compared with the pre-optimized load curve, the change of the post-optimized load curve in the time direction is smaller, the curve is smoother, and the change amount of the curve is represented by the load shifting amount.

[0136] An embodiment of the present application also provides a processing device for working condition data, including: a memory configured to store instructions; and a processor configured to call instructions from the memory and capable of implementing the scheduling method for vehicle charging provided in any one of the above embodiments when executing the instructions.

[0137] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the scheduling method for vehicle charging provided in any one of the above embodiments.

[0138] An embodiment of the present application also provides a computer program product, including a computer program, and the computer program implements the scheduling method for vehicle charging provided in any one of the above embodiments when executed by a processor.

[0139] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0141] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction means that implements the functions specified in one or more of the procedures Figure 1 or more procedures and / or blocks Figure 1 or more blocks.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the procedures Figure 1 or more procedures and / or blocks Figure 1 or more blocks.

[0143] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0144] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0145] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0146] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0147] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A scheduling method for vehicle charging, characterized in that: The scheduling method comprises: Obtain the electricity price of the clean energy power supplier, the charging emission reduction reward provided by the carbon emission assessor to the charger, and the power supply emission reduction reward provided by the carbon emission assessor to the power supplier; Based on the Stackelberg model, the charging party is taken as the bottom follower, and the charging party's game charging amount is determined with the goal of minimizing the cost function determined by the electricity price and the charging emission reduction reward; Taking the power supplier as a middle-level follower, and taking the power supplier's revenue maximization function determined according to the power supply emission reduction reward, the electricity price and the game charging amount as the goal, determining the game electricity price of the power supplier; The carbon emission assessor is taken as the leader, and the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessor are determined with the goal of maximizing the emission reduction income function determined by the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the gaming charging amount; The game charging amount, the game electricity price, the game charging emission reduction reward and the power supply emission reduction reward are iterated in the Stackelberg model until the convergence condition is reached to obtain the game charging amount under the Stackelberg equilibrium as the power supply of the power supplier in the target period.

2. The scheduling method according to claim 1, characterized in that: The minimization cost function of the charging party determined according to the electricity price and the charging emission reduction reward is taken as the goal, and the game charging amount of the charging party is determined, including: In the pre-constructed convenience cost function, the convenience cost of the charger is determined according to the target time period, wherein the closer the target time period is to the charging peak period, the lower the convenience cost; The charging amount of the charging party is determined by minimizing the cost function of the charging party determined according to the electricity price, the charging emission reduction reward and the convenience cost.

3. The scheduling method according to claim 1, characterized in that: The power supplier can provide clean energy and purchased energy, and the power supplier fully utilizes the clean energy to supply electricity; The goal of determining the gaming electricity price of the power supplier based on the power supply emission reduction reward, the electricity price and the gaming charging amount is to maximize the income function of the power supplier, including: Determine the additional power supply cost of the power supplier according to the power supply of the clean energy and the purchased energy to the game power supply, wherein the power supply of the purchased energy is positively correlated with the additional power supply cost, and the power supply of the clean energy is negatively correlated with the additional power supply cost; The gaming electricity price of the power supplier is determined based on the power supply emission reduction reward, the electricity price, the gaming charging amount and the additional power supply cost to maximize the power supplier's income function.

4. The scheduling method according to claim 3, characterized in that: According to the power supply of the clean energy and the purchased energy to the game power supply, the additional power supply cost of the power supplier is determined, including: In the case where the power supply of the clean energy is less than the gaming charging amount, the additional power supply cost of the power supplier is determined according to the power supply of the purchased energy and the purchase price; When the clean energy supply is greater than the gaming charge, the power supply surplus income of the power supplier is determined according to the clean energy supply surplus and the selling price of the clean energy, and the power supply surplus income is used as the additional power supply cost.

5. The scheduling method according to claim 1, characterized in that: The goal of determining the gaming electricity price of the power supplier based on the power supply emission reduction reward, the electricity price and the gaming charging amount is to maximize the income function of the power supplier, including: Determine the load imbalance cost of the power supplier according to the charging change amount of the game charging amount in the target time period; The gaming electricity price of the power supplier is determined based on the power supply emission reduction reward, the electricity price, the gaming charging amount and the load imbalance cost to maximize the power supplier's revenue function.

6. The scheduling method according to claim 1, characterized in that: The objective of the maximum emission reduction income function determined based on the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the gaming charging amount is to determine the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessor, including: Get the unit carbon emission price; Determining the total emission reduction price of the power supplier according to the unit carbon emission price and the game charging amount; The gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessor are determined based on the maximum emission reduction income function determined by the difference between the total emission reduction price and the product.

7. The scheduling method according to claim 1, characterized in that: The power supplier supplies electricity through clean energy and purchased energy, and the power supplier fully uses the clean energy to supply electricity. When the power supplier uses the purchased energy to supply electricity, carbon emissions increase; The objective of the maximum emission reduction income function determined based on the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the gaming charging amount is to determine the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessor, including: Obtaining the unit carbon emission price, the benchmark carbon emission total price and the clean energy power supply of the power supplier; Determine the purchased energy gaming power supply amount of the power supplier according to the gaming charging amount and the clean energy power supply amount; Determine the total emission reduction price of the power supplier according to the product of the difference between the total price of the benchmark carbon emission and the power supply of the purchased energy game and the unit carbon emission; Determining the total carbon emission price of the purchased energy game of the power supplier according to the purchased energy game power supply amount and the unit carbon emission price; The gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessor are determined based on the maximum emission reduction income function determined by the difference between the gaming carbon emission total price and the product.

8. The scheduling method according to claim 1, characterized in that: The power supplier can supply electricity through clean energy and purchased energy, and the power supplier fully utilizes the clean energy to supply electricity; The objective of the maximum emission reduction income function determined based on the product of the sum of the charging emission reduction reward and the power supply emission reduction reward and the gaming charging amount is to determine the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessor, including: Obtaining the maximum clean energy supply of the power supplier; When the gaming charge amount is less than or equal to the low demand charge amount threshold, and the gaming charge amount is greater than or equal to the maximum clean energy supply, the determined gaming power supply emission reduction reward is greater than or equal to the gaming charging emission reduction reward; When the gaming charge amount is less than or equal to the high demand charge amount threshold, and the gaming charge amount is less than or equal to the maximum clean energy supply, the determined gaming charge emission reduction reward is greater than or equal to the gaming power supply emission reduction reward; In a case where the gaming charge amount is greater than the high-demand charge amount threshold, the determined gaming power supply emission reduction reward is greater than or equal to the gaming charging emission reduction reward.

9. The scheduling method according to claim 1, characterized in that: The step of determining the gaming charging amount of the charger according to the minimization cost function as a goal includes: In a single round of iteration of the Stackelberg model, the minimized cost function is used as the objective function of the genetic algorithm, and the game charging amount of the current iteration round is determined by the genetic algorithm; And / or, determining the gaming electricity price of the power supplier based on the maximization revenue function as a goal, includes: In a single round of iteration of the Stackelberg model, the maximized income function is used as the objective function of the genetic algorithm, and the gaming electricity price of the current iteration round is determined by the genetic algorithm; And / or, the determining of the gaming charging emission reduction reward and the gaming power supply emission reduction reward of the carbon emission assessor based on the maximization of emission reduction income function as the goal includes: In a single round of iteration of the Stackelberg model, the maximum emission reduction income function is used as the objective function of the genetic algorithm, and the game charging emission reduction reward and the game power supply emission reduction reward of the current iteration round are determined by the genetic algorithm.

10. The scheduling method according to claim 1, characterized in that: The scheduling method further includes: According to the Stackelberg equilibrium, the game electricity price, the game charging emission reduction reward and the game power supply emission reduction reward of the target time period are respectively used as the electricity price, the charging emission reduction reward, and the power supply emission reduction reward of the next time period to determine the power supply of the power supplier in the next time period based on the Stackelberg model.

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