Layered and distributed electric vehicle charging and discharging scheduling method and system based on electricity price signal

Abstract

The invention discloses a layered and distributed electric vehicle charging and discharging scheduling method and system based on an electricity price signal. The method comprises the following steps: establishing a day-ahead electric vehicle charging and discharging scheduling model of an electric power system; converting the day-ahead electric vehicle charging and discharging scheduling model of the electric power system into a series of sub-problems to obtain a distributed charging and discharging scheduling mechanism based on price response; the convergence of solving the scheduling problem is improved; and aggregating the electric vehicles, and obtaining a layered and distributed electric vehicle scheduling mechanism. The system comprises a model building module, a model conversion module, an improved solving method module and an aggregation module. The invention provides two types of signals, namely an electricity price signal of system scheduling and a charge and discharge control signal of an aggregate, establishes an electric vehicle layered distributed scheduling method by separating target functions and constraints corresponding to a generator set and an electric vehicle, and forms a scheduling method conforming to a current electricity market construction trend. And a corresponding solving algorithm is provided, so that the convergence is effectively improved, and the operation cost of the power system is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of power system operation, and in particular relates to a hierarchical distributed electric vehicle charging and discharging scheduling method and system based on electricity price signals. Background Art

[0002] Besides their foreseeable large numbers, electric vehicles are also a highly representative type of controllable load resource. They can flexibly adjust electricity usage based on user travel plans, discharge power to the grid when necessary, and provide power support. They also possess a certain energy storage capacity. Therefore, further exploring the controllable characteristics of electric vehicles in the grid is crucial for the safe, economical, and stable operation of the power system.

[0003] How to coordinate the complex operational constraints and large number of electric vehicles is the key to solving the charging and discharging scheduling problem of regional power grids. Currently, centralized or distributed control methods are often used. After the electric vehicles are aggregated, control instructions or control signals are used to directly or indirectly regulate the charging and discharging process. The centralized method is simple to solve, but it usually makes the constraint aggregation process irreversible, resulting in an infeasible scheduling scheme. The distributed method ensures the feasibility of the scheduling scheme through top-level scheduling and bottom-level verification, providing a solution for solving large-scale electric vehicle scheduling problems. However, these methods are mainly applicable to regulated power systems and direct load control projects. Electric vehicle charging and discharging is different from traditional power system economic scheduling. In a market-oriented environment, the use of control instructions or control signals to regulate electric vehicle charging and discharging still has limitations.

[0004] Price is a core signal in a market-based environment and plays a key role in optimizing resource allocation. The source-load coordinated operation problem is essentially a configuration problem of controllable resources on both the source and load sides. The electricity price, when formulated, takes into account power system production characteristics such as unit operating constraints, power balance, and line flow constraints. It fully reflects the actual operation of the power system and takes into account the characteristics of the source, grid, and load. From this perspective, electricity price plays a crucial role. Furthermore, price iteration is a common distributed algorithm. The introduction of price signals will also facilitate the construction of distributed charging and discharging control architectures, thus providing insights into the load scheduling problem of large-scale electric vehicle charging and discharging. However, current research generally constructs "price" signals based on demand and achieves charge and discharge control coordination through price signal iteration. Although the specific forms vary, these price signals are all based on load levels during construction, ignoring the characteristics of the system's generation side and grid constraints. This leads to certain limitations, typically only enabling charge and discharge coordination among multiple electric vehicles within the load side. While these works have made it possible to consider electric vehicle constraints in regional charge and discharge control, they still cannot achieve coordinated scheduling of electric vehicles and generation resources.

[0005] In summary, it is urgent to fully consider the operating characteristics of generator sets and grid constraints in the research, based on the electricity price system, and carry out hierarchical distributed charging and discharging control based on electricity price signals to provide a basis and reference for the design of load dispatching mechanism in the power market environment. Summary of the Invention

[0006] The purpose of the present invention is to provide a hierarchical distributed electric vehicle charging and discharging scheduling method and system based on electricity price signals based on the current marketization status of the load side in the power system, targeting the massive load-side controllable resources in the power grid represented by electric vehicles, taking into account the operating characteristics of power generation resources, grid constraints and internal coordination of loads, so as to facilitate further research on the operation control issues of the power grid.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals includes the following steps: Step 1: Establish a day-ahead electric vehicle charging and discharging scheduling model for the power system; Step 2: Transform the day-ahead electric vehicle charging and discharging scheduling model of the power system into a series of sub-problems to obtain a distributed charging and discharging scheduling mechanism based on price response. Step 3: Based on the distributed charging and discharging scheduling mechanism, improve the solution method and enhance the convergence of the scheduling problem solution; Step 4: Based on the solution method in step 3, aggregate electric vehicles and obtain a hierarchical distributed electric vehicle scheduling mechanism.

[0008] A further improvement of the present invention is that, in step 2, based on the analysis of the characteristics of the electric vehicle charging and discharging scheduling model of the power system in step 1, the generator set and the electric vehicle are decoupled to achieve the separation of the generator set constraints and the electric vehicle constraints and their objective functions, and the scheduling problem of electric vehicles in the power system is converted into a price response problem of a series of single generator sets and a series of single electric vehicles; The current electric vehicle charging and discharging scheduling model is: In the day-ahead load dispatch model, the objective function is to minimize the total cost of system operation; (1) Where, is the generator set number, is the generator set number, is the total number of time periods in the scheduling cycle, For generator sets The fuel cost function is For generator sets startup costs, For generator sets downtime costs, For generator sets exist The effort of every moment, It is a 0-1 variable, indicating the generator set exist The start and stop status of the time period, It is a 0-1 variable, indicating the generator set exist The starting action of the moment, It is a 0-1 variable, indicating the generator set exist The shutdown action at the moment; Under the market environment, each item in formula (1) corresponds to the quotation of the generator set; The constraints are: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) Where, Generator sets For The upward and downward spare capacity available during the period, Generator sets The maximum and minimum output, Generator sets Minimum continuous shutdown and startup time, Generator sets Upward and downward climbing rates, is the time interval length; The system is The upward and downward spare capacity requirements that can be provided during the period, for Fixed loads other than controllable loads in the time period system, For electric vehicles exist The net power drawn from the grid during the period, is the dual multiplier of the power balance constraint, corresponding to Time period electricity price, are the dual multipliers of the upward and downward reserve capacity constraints, corresponding to Upward and downward spare prices for the period, For nodes A collection of electric vehicles connected to the grid; Electric vehicles exist Time period charge and discharge power, Electric vehicles Maximum charge and discharge power, is a 0-1 variable, indicating the grid-connected status of the electric vehicle. For electric vehicles exist Energy storage level at all times, Electric vehicles Maximum and minimum energy storage levels, Electric vehicles Charging and discharging efficiency, is a parameter representing electric vehicles exist Energy consumed during driving during the period; where, equations (2)-(4) are the maximum and minimum output constraints of the unit, equations (5)-(8) are the continuous start-stop time constraints of the unit, and equations (9)-(10) are the climbing constraints of the unit; where (11) is the power balance constraint, and (12)-(13) are the spare capacity constraints; where (14) is the net power consumption constraint, (15)-(16) are the charge and discharge power constraints, (17) is the upper and lower limit constraints of the battery state of charge, and (18) is the state of charge change constraint; By using the dual factor and relaxing the constraints (11)-(13), we obtain the following Lagrange function: (19) Separate the parts related to generator sets and electric vehicles: (20) (twenty one) Combined generator set and electric vehicles The running constraints of , we can get the following sub-problems: (twenty two) (twenty three) 、 and Corresponding to the price signals in system operation, (22) and (23) correspond to the optimization problem of power generation cost of generator sets and the minimization problem of electric vehicle charging fees, respectively; through LR, the electric vehicle charging and discharging scheduling problem is transformed into a series of price response problems of single generator sets and a series of single electric vehicles.

[0009] A further improvement of the present invention is that, in step 2, based on the electricity price signal, the following distributed charge and discharge scheduling mechanism is designed: 1) System dispatch selects a set of initial electricity price signals as dispatch signals and sets the iteration index ; 2) Generators and electric vehicles respond to electricity price signals, solving the single-machine optimization problem (22) and the single-vehicle optimization problem (23), and reporting the power optimization results and corresponding costs to the system dispatcher; 3) System scheduling summarizes the optimization results of a single vehicle and a single unit, and calculates the objective function value of the dual problem and the original problem objective function value ; 4) System scheduling calculation relative duality gap

[0010] (twenty four) according to The value determines whether the problem converges. If it converges, turn to 5); otherwise, use the subgradient method to update the price and set , then turn to step 2); (25) (26) (27) Where, Used to mark the The variables related to the iteration, Adjust the step size for electricity prices; 5) The charging and discharging power of each vehicle is the optimal power, and a feasible power generation scheduling plan can be constructed based on the solution of the dual problem; Based on LR decomposition, a feasible price iteration mechanism was designed, that is, through the supply and demand imbalance of the system, the price signal is gradually adjusted to achieve coordination between power generation resources and the charging and discharging process of electric vehicles.

[0011] A further improvement of the present invention is that, in step 3, based on the distributed charging and discharging scheduling mechanism, the solution method is improved to improve the convergence of the solution to the scheduling problem, including: In order to improve the convergence of the solution to the scheduling problem, the augmented Lagrange function is obtained based on the auxiliary theorem and local linearization, which enhances the convexity of the problem and avoids severe oscillation in the iterative process: (28) Accordingly, the generator set is separated from the electric vehicle, resulting in two sub-problems: (29) (30).

[0012] A further improvement of the present invention is that, in step 4, to avoid the problem of directly dispatching the power and number of a single electric vehicle, the power of the electric vehicle aggregate is introduced through the following equation: (31) (32) Where, for Moment Electric Vehicle Aggregate Power drawn from the grid; According to the auxiliary problem theorem and with necessary adjustments, the generator group, electric vehicle aggregate, and electric vehicle terms can be obtained as follows: (33) (34) (35) Where, is the correlation coefficient, Aggregates The correlation coefficient, Aggregates Released The wheel charging control signal is obtained by the following formula: (36) Where, Electric vehicle aggregates The charge and discharge control parameters, For electric vehicles In the The charging control scheme reported by the wheel, Aggregates Charge and discharge power instructions obtained from system scheduling; In the charge and discharge control process, the corresponding problems of equations (33) and (34) are first solved; by solving equation (34) and equation (36), the aggregate is obtained. The internal charge and discharge control signals are transmitted to the electric vehicles under their jurisdiction; then, the electric vehicles arrange their own charging and discharging plans by solving the problem corresponding to Equation (35); after that, the aggregate summarizes the electric vehicle charging and discharging plans as the basis for linearization and problem solving in the next iteration.

[0013] A further improvement of the present invention is that in step 4, a three-layer distributed electric vehicle charging scheduling framework based on electricity price signals is proposed as follows: The framework consists of three layers: the system dispatch layer L1, the generator group and main electric vehicle cluster layer L2, and the electric vehicle layer L3. The main functions of each layer are as follows: The main body of the L1 layer is the system dispatching SO, which is responsible for coordinating the controllable resources on the power generation and load sides of the entire network; In the L2 layer, the generator sets and electric vehicle clusters optimize their own operations according to the instructions issued by the system dispatcher; In the L3 layer, electric vehicles are responsible for optimizing their own charging and discharging processes; Furthermore, a basic process for the coordination between power generation scheduling and electric vehicle charging and discharging control is proposed; 1) Before a new dispatch cycle begins, the system dispatcher releases the initial electricity price signal; 2) After receiving the price signal, the generator sets formulate their own optimal operation plans and report them to the system dispatcher; 3) Based on the price signal, the electric vehicle cluster optimizes charging and discharging, generates a charge and discharge control signal within the cluster, and sends it to the electric vehicles under its jurisdiction together with the electricity price signal and the charge and discharge power of the cluster in the previous round of charge and discharge control; 4) Electric vehicles optimize their own charging and discharging processes based on the price signals and charging and discharging control signals issued by the aggregate, and report the charging and discharging plans to the upper aggregate; 5) The cluster summarizes the charging and discharging plans of the electric vehicles under its jurisdiction, forms the cluster charging and discharging power curve, and reports it to the system dispatcher; 6) Based on the information reported by the aggregate and generator sets, the system dispatcher calculates the relative dual gap And judge whether it converges; 7) If convergence occurs, the scheduling process ends and the scheduling instructions are confirmed; otherwise, the price signal is updated and released to the generators and aggregates, and the process goes to step 2).

[0014] The hierarchical distributed electric vehicle charging and discharging scheduling system based on electricity price signals includes: Model building module, which builds the day-ahead electric vehicle charging and discharging scheduling model for the power system; The model conversion module converts the day-ahead electric vehicle charging and discharging scheduling model of the power system into a series of sub-problems, resulting in a distributed charging and discharging scheduling mechanism based on price response; Improved solution method module: Based on the distributed charging and discharging scheduling mechanism, the solution method is improved to improve the convergence of the scheduling problem solution; The aggregation module aggregates electric vehicles based on the solution method of the improved solution method module and obtains a hierarchical distributed electric vehicle scheduling mechanism.

[0015] Compared with the prior art, the present invention has at least the following beneficial technical effects: 1) A price-based charging scheduling approach is adopted to avoid the practical difficulties of direct load control, resulting in an electric vehicle charging and discharging scheduling method suitable for the current market environment. 2) A hierarchical distributed charging and discharging load scheduling mechanism is designed based on an improved LR method. First, a distributed charging and discharging control method is designed based on the traditional LR method. Augmentation terms are introduced and the algorithm is improved to establish a hierarchical distributed framework, effectively improving the convergence of the distributed problem solution. 3) Compared with existing distributed methods, the hierarchical distributed method proposed in this paper can further reduce system production costs, achieve coordinated scheduling of charging and discharging loads and power generation resources, and improve system economic benefits.

[0016] In summary, the present invention utilizes two types of signals: electricity price signals issued by the system dispatcher and effective across the entire network, and charge-discharge control signals generated by the aggregate and effective only within the corresponding aggregate. Furthermore, the present invention utilizes the separation of the objective functions and constraints corresponding to the generator set and the electric vehicle to achieve distributed problem solving. Furthermore, a three-layer distributed scheduling framework is proposed to effectively improve the convergence of the problem. This overcomes the drawbacks of traditional scheduling methods, such as the difficulty in implementing "direct load control" in charge-discharge control and the violent oscillations in the distributed solution process. This further reduces system production costs, achieves coordinated scheduling of charge-discharge loads and power generation resources, and effectively improves the economic benefits of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the method of the present invention; Figure 2 The system load curve under different charge and discharge control modes; Figure 3 Schematic diagram of the iterative process under different control frameworks; Figure 4 This is a schematic diagram of the hierarchical distributed charge and discharge control framework proposed by the present invention; Figure 5 This is a flowchart of the algorithm for hierarchical distributed charge and discharge control proposed by the present invention; Figure 6 This is a structural block diagram of the hierarchical distributed electric vehicle charging and discharging scheduling system based on electricity price signals of the present invention. DETAILED DESCRIPTION

[0018] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0019] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0020] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0022] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0023] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Example 1 The present invention provides a hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals, comprising the following steps: Step 1: Establish a day-ahead electric vehicle charging and discharging scheduling model for the power system; Step 2: Transform the day-ahead electric vehicle charging and discharging scheduling model of the power system into a series of sub-problems to obtain a distributed charging and discharging scheduling mechanism based on price response. Step 3: Based on the distributed charging and discharging scheduling mechanism, improve the solution method and enhance the convergence of the scheduling problem solution; Step 4: Based on the solution method in step 3, aggregate electric vehicles and obtain a hierarchical distributed electric vehicle scheduling mechanism.

[0025] Example 2 like Figure 1 As shown, the hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals provided by the present invention includes the following steps: Step 1: Establish a day-ahead electric vehicle charging and discharging scheduling model for the power system In the day-ahead load dispatch model, the objective function is to minimize the total cost of system operation.

[0026] (37) Where, is the generator set number, is the generator set number, is the total number of time periods in the scheduling cycle, For generator sets The fuel cost function is For generator sets startup costs, For generator sets downtime costs, For generator sets exist The effort of every moment, It is a 0-1 variable, indicating the generator set exist The start and stop status of the time period, It is a 0-1 variable, indicating the generator set exist The starting action of the moment, It is a 0-1 variable, indicating the generator set exist In the market environment, each term in Equation (37) corresponds to the quotation of the generator set.

[0027] Constraints mainly include the following three parts: ① Generator set constraints (38) (39) (40) (41) (42) (43) (44) (45) (46) Where, Generator sets For The upward and downward spare capacity available during the period, Generator sets The maximum and minimum output, Generator sets Minimum continuous shutdown and startup time, Generator sets Upward and downward climbing rates, is the time interval length. Among them, equations (38)-(40) are the maximum and minimum output constraints of the unit, equations (41)-(44) are the continuous start-up and shutdown time constraints of the unit, and equations (45)-(46) are the ramp constraints of the unit.

[0028] ② Network operation constraints (47) (48) (49) Where, The system is The upward and downward spare capacity requirements that can be provided during the period, for Fixed loads other than controllable loads in the time period system, For electric vehicles exist The net power drawn from the grid during the period, is the dual multiplier of the power balance constraint, corresponding to Time period electricity price, are the dual multipliers of the upward and downward reserve capacity constraints, corresponding to Upward and downward spare prices for the period, For nodes The set of electric vehicles connected to the grid. Among them, (47) is the power balance constraint, (48)-(49) are the reserve capacity constraints. my country's power system has a large construction capacity and small congestion, so the active transmission capacity constraint of the transmission line is not considered for the time being.

[0029] ③ Electric vehicle restrictions (50) (51) (52) (53) (54) Where, Electric vehicles exist Time period charge and discharge power, Electric vehicles Maximum charging and discharging power, is a 0-1 variable, indicating the grid-connected status of the electric vehicle. For electric vehicles exist Energy storage level at all times, Electric vehicles Maximum and minimum energy storage levels, Electric vehicles Charging and discharging efficiency, is a parameter representing electric vehicles exist The energy consumed during driving time.

[0030] Step 2: Transform the day-ahead electric vehicle charging and discharging scheduling model of the power system into a series of sub-problems to obtain a distributed charging and discharging scheduling mechanism based on price response. By analyzing the characteristics of the scheduling model in step 1, we can find that the constraints can be divided not only into generator group constraints and electric vehicle constraints, but also further subdivided into single generator group constraints and single electric vehicle constraints. Furthermore, the objective function is also separable. The generator group problem and the large number of electric vehicle problems are linked together through the coupling constraints in Equations (47)-(49), forming a charging and discharging scheduling model. This problem structure is particularly suitable for constraint relaxation and problem decomposition using the LR method.

[0031] By using the dual factor and relaxing the constraints (47)-(49), we can obtain the following Lagrange function: (55) Separate the parts related to generator sets and electric vehicles: (56) (57) Combined generator set and electric vehicles The running constraints of , we can get the following sub-problems: (58) (59) 、 and Corresponding to the price signals in system operation, (58) and (59) correspond to the optimization problem of the power generation cost of the generator set and the minimization problem of the electric vehicle charging fee, respectively. Through LR, the electric vehicle charging and discharging scheduling problem is transformed into a price response problem of a series of single generator sets and a series of single electric vehicles. Furthermore, according to the basic process of the LR algorithm, the following distributed charging and discharging scheduling mechanism can be designed: 1) System dispatch selects a set of initial electricity price signals as dispatch signals and sets the iteration index ; 2) Generators and electric vehicles respond to electricity price signals, solve the single-machine optimization problem (58) and the single-vehicle optimization problem (59), and report the power optimization results and corresponding costs to the system dispatcher.

[0032] 3) System scheduling summarizes the optimization results of a single vehicle and a single unit, and calculates the objective function value of the dual problem and the original problem objective function value .

[0033] 4) System scheduling calculation relative dual gap (relative dual gap, ) (60) according to The value determines whether the problem converges. If it converges, turn to 5); otherwise, use the subgradient method to update the price and set , then go to step 2).

[0034] (61) (62) (63) Where, Used to mark the The variables related to the iteration, The step size for electricity price adjustment.

[0035] 5) The charging and discharging power of each vehicle is the optimal power, and a feasible power generation scheduling plan can be constructed based on the solution of the dual problem.

[0036] Based on LR decomposition, the above process provides a feasible price iteration mechanism, that is, through the imbalance of supply and demand (including electricity and reserve) of the system, the price signal is adjusted in a certain proportion and step size to achieve coordination between power generation resources and the charging and discharging process of electric vehicles.

[0037] Step 3: Further improve the solution method to improve the convergence of the scheduling problem solution In the EV charging and discharging load scheduling, since the EV subproblem in Equation (59) is linear, its optimal solution is always obtained at the extreme point. This non-strict convexity may exacerbate the oscillation phenomenon in price iteration. On the other hand, it also actually compresses the space for EV charging and discharging optimization, which is not conducive to the optimization of the objective function.

[0038] Incorporate the squared terms of the equality constraints into the Lagrange function and construct the augmented Lagrange function as shown below: (64) Where, is the penalty factor, which is a positive constant.

[0039] Although the introduction of the square penalty term helps to improve the problem properties, it also destroys the separability of the Lagrange function. According to the auxiliary problem principle, the augmented Lagrange function can be locally linearized near the last iterative solution, thus obtaining (65) Where, is the linear correlation coefficient, which is a positive constant. And when the following conditions are met, the auxiliary problem principle algorithm introduced in formula (65) is strictly convergent: (66) Where, is the cardinality of the set, i.e. the number of elements it contains, here the number of electric vehicles governed by the aggregate.

[0040] Accordingly, (67) (68) The linearized problem has the property of being separable. The subproblems in the iteration Since the objective function of the electric vehicle subproblem includes a square term, the convexity of the problem is enhanced, the range of the optimal solution is expanded, and it is also beneficial to avoid severe oscillation during the iteration process.

[0041] Step 4: Based on the solution method in step 3, aggregate electric vehicles and obtain a hierarchical distributed electric vehicle scheduling mechanism In the charging and discharging load scheduling problem, considering the huge number of electric vehicles, The value of must be very small, which will make the subproblems shown in equations (67) and (68) seriously pathological. At the same time, the charging and discharging power of electric vehicles is relatively small, and the form of the subproblem shown in equation (68) is not conducive to optimizing the charging and discharging power of a single vehicle. To this end, the power of the electric vehicle aggregate is introduced through the following equation: (69) (70) Where, for Moment Electric Vehicle Aggregate The power drawn from the grid. Since Equations (69) and (70) are essentially equivalent to Equation (47), they share the same dual multiplier. Using Equations (69) and (70) to replace Equation (47) in the augmented Lagrange function, according to the auxiliary problem theorem and with necessary adjustments, we can obtain the following generator group, electric vehicle aggregate, and electric vehicle terms: (71) (72) (73) Where, is the correlation coefficient, Aggregates The correlation coefficient, Aggregates Released The wheel charging control signal is obtained by the following formula: (74) Where, Electric vehicle aggregates The charge and discharge control parameters, For electric vehicles In the The charging control scheme reported by the wheel, Aggregates The charging and discharging power instructions are obtained from the system scheduling.

[0042] In the charge and discharge control process, the corresponding problems of equations (71) and (72) are solved first. By solving equation (72) and equation (74), the aggregate is obtained. The internal charge and discharge control signals are transmitted to the electric vehicles under their control. The electric vehicles then make their own charge and discharge plans by solving the problem corresponding to Equation (73). Afterwards, the aggregate summarizes the electric vehicle charge and discharge plans as the basis for linearization and problem solving in the next iteration.

[0043] By introducing the aggregate subproblem, the convergence condition of the algorithm related to the auxiliary problem theorem changes to: (75) (76) The introduction of small parameters can be avoided by rational configuration of EV aggregates ( and ) values ​​and ill-conditioned sub-problems. In essence, ill-conditioned problems mainly come from optimizing generator sets and electric vehicles at the same level. The former has a large single-unit capacity but a limited number, while the latter has a small power and energy scale but a large number. After the introduction of electric vehicle clusters, they are isolated and optimized at different levels. The capacity scale of electric vehicle clusters can be set to be equivalent to that of generator sets. However, as required by equations (75) and (76), the number of electric vehicle clusters and the number of electric vehicles under the jurisdiction of the clusters should not be too large. To this end, multi-layer clusters can be set and expanded and developed according to the algorithm of the present invention. From the perspective of ease of implementation, electric vehicles that are geographically close and in the same area can be divided into the same cluster.

[0044] The hierarchical distributed charge and discharge control framework proposed in this invention is as follows Figure 4 shown.

[0045] Structurally, the framework consists of three main layers: the system dispatch layer (L1), the generator and primary EV cluster layer (L2), and the EV layer (L3). In future large-scale regional power systems, multiple cluster sublayers can be added to the primary and EV layers as needed to form a tree-like EV charging and discharging dispatch system, meeting the needs of large-scale and ultra-large-scale EV charging and discharging control. The main functions of each layer are listed below.

[0046] The main body of the L1 layer is the System Operations (SO), responsible for coordinating controllable resources on the generation and load sides of the entire network. During operation, the SO issues electricity price signals to generators and electric vehicle clusters, guiding their responses. Based on their responses, the SO continuously adjusts and updates the price signals until the problem converges and the iteration terminates.

[0047] At the L2 layer, generators and electric vehicle clusters optimize their operations according to instructions issued by the system dispatcher. Generators determine their start / stop status and output levels and report these to the system dispatcher. As an intermediary, the electric vehicle cluster not only coordinates with the generators through the system dispatcher but also coordinates the charging and discharging of the electric vehicles under its jurisdiction. After receiving the electricity price signal from the system dispatcher, the cluster solves optimization subproblems to generate charging and discharging control signals, which it then simultaneously transmits to the electric vehicles under its jurisdiction. The cluster also compiles the charging and discharging plans for the electric vehicles under its jurisdiction and reports them to the system dispatcher.

[0048] In Level 3, electric vehicles are responsible for optimizing their own charging and discharging processes. Based on the electricity price signals and charging and discharging control signals transmitted and issued by the cluster, they formulate charging and discharging plans based on travel needs and charging and discharging technical requirements, and then feed the plans back to the cluster.

[0049] Furthermore, the following process is designed to organically organize the above functions together to form the basic process of coordination between power generation scheduling and electric vehicle charging and discharging control.

[0050] The main process is as follows, and the relevant algorithm process can be found in Figure 5 shown.

[0051] 1) Before the start of a new dispatch cycle (usually 1 day), the system dispatcher releases the initial electricity price signal; considering the time-consuming price iteration process, there should be a certain lead time.

[0052] 2) After receiving the price signal, the generator set forms its own optimal operation plan (start and stop status, output level) and reports it to the system dispatcher.

[0053] 3) Based on the price signal, the electric vehicle cluster optimizes charging and discharging, generates a charging and discharging control signal within the cluster, and sends it together with the electricity price signal and the charging and discharging power of the cluster in the previous round of charging and discharging control to the electric vehicles under its jurisdiction.

[0054] 4) Electric vehicles optimize their own charging and discharging processes based on the price signals and charging and discharging control signals issued by the aggregate, and report the charging and discharging plans to the upper aggregate; 5) The cluster summarizes the charging and discharging plans of the electric vehicles under its jurisdiction, forms the cluster charging and discharging power curve, and reports it to the system dispatcher; 6) Based on the information reported by the aggregate and generator sets, the system dispatcher calculates the relative dual gap And determine whether it converges.

[0055] 7) If convergence occurs, the scheduling process ends and the scheduling instructions are confirmed; otherwise, the price signal is updated and released to the generators and aggregates, and the process goes to step 2).

[0056] The above process primarily involves two types of signals: electricity price signals, issued by the system dispatcher and effective across the entire network, and charge and discharge control signals, generated by the aggregate and effective only within that aggregate. For electric vehicles, the former corresponds to the charging (discharging) costs (benefits) at different times, guiding users to charge during periods of low prices and discharge during periods of high prices. The latter, on the other hand, reflects the aggregate's charge and discharge plans from the previous iteration, preventing large fluctuations in electric vehicle charge and discharge power that could cause significant oscillations during the iteration process. In the proposed framework, the electricity price signal is modified and updated by the system dispatcher based on the system's supply and demand balance, while the charge and discharge control signals are obtained by the aggregate by solving the aggregate's optimization subproblem.

[0057] Example 3 1) Analysis of the benefits of charging and discharging scheduling First, a comparative analysis was conducted between a precise centralized approach (Method 1), a distributed algorithm (Method 2), and the hierarchical distributed algorithm of our invention (Method 3). When the number of electric vehicles is large, the charge and discharge scheduling model becomes large, exceeding the solution capabilities of the precise centralized approach and potentially introducing ill-posed problems. For this comparison, the electric vehicles in the same cluster are assumed to have identical vehicle parameters and travel characteristics. This allows simulation analysis using Methods 1 and 2. The charge and discharge power of a single vehicle can be obtained by simply scaling down the resulting cluster charge and discharge power by a certain percentage. This section considers 50 clusters, each containing 1,000 electric vehicles with identical characteristics.

[0058] This example uses the adapted IEEE-RTS1979, with the maximum system load revised to 2550 MW. It assumes a total of 50,000 electric vehicles in the adapted IEEE-RTS; these vehicles are distributed across load nodes in the same proportion as the base electricity load. Table 1 lists the types of electric vehicles, their proportions, and the key parameters of each type. It assumes a charging efficiency of 0.95 and a discharging efficiency of 0.9 to maintain parameter consistency and facilitate constraint aggregation. User travel data and travel characteristics can be found in relevant survey reports. Vehicle 1 is selected for this example.

[0059] Table 1 Distribution of electric vehicle types and main technical parameters

[0060] Table 2 shows the system production costs for different charging and discharging scheduling methods. As shown, Method 1 has the lowest production cost due to precise unified optimization, while Methods 2 and 3 have relatively high production costs. However, centralized methods have significant disadvantages in handling large-scale discrete-parameter electric vehicles and protecting user privacy.

[0061] Table 2 System production costs under different charging and discharging scheduling methods

[0062] Because it considers generator unit operating constraints and directly optimizes system production costs, Method 3 achieves a lower-cost charge-discharge scheduling scheme than Method 2, both in pure charging control mode and under V2G control. Conventional generator units are subject to numerous inter-period constraints, such as ramp rate and continuous on / off time. This makes system production costs dependent not only on fuel costs per period but also on generator start / stop costs and power adjustments during consecutive periods. Method 2 focuses on optimizing fuel costs but ignores the interrelationship between generator start / stop costs and production operation modes within each period. When ignoring inter-period operating constraints, Method 2 achieves a system production cost of $677,937.8, while Method 3 achieves a very similar $677,938.1. In fact, when there are a sufficient number of electric vehicles and the load curve can be completely flattened through charge and discharge control, Methods 2 and 3 are equally effective in optimizing system generation costs.

[0063] 2) Simulation analysis of layered distributed method The case study includes 50,000 electric vehicles with various parameters and characteristics (see Table 1), divided into 50 electric vehicle clusters. The total electricity demand for electric vehicle travel is approximately 950MWh. A comparative study was conducted under the following three scenarios: fast charging (Case I), pure charging control (Case II), and V2G control (Case III); Figure 2 Table 3 compares the system load curve and production costs under different charge and discharge control modes. It is clear that system production costs are reduced in Cases II and III. Both pure charge control and V2G control not only smooth the load curve and reduce the number of unit starts and stops, but also improve the operating efficiency of conventional generator sets and reduce the unit energy consumption of power generation.

[0064] Table 3 System production costs under different charge and discharge modes

[0065] At the same time, the iterative process of the hierarchical distributed method (improved LR method) and the distributed method (traditional LR method) is compared. Figure 3 It can be seen that no matter whether the simple charging control or the V2G control mode is adopted, the iterative process has oscillation phenomenon under the distributed charging and discharging control framework. In contrast, under the hierarchical distributed framework, as the iteration proceeds, the relative duality gap It can maintain a nearly monotonic decrease. The introduction of relatively complex aggregates helps avoid oscillations during iterations and accelerates the convergence of the problem, playing an indispensable role in the scheduling mechanism.

[0066] Based on the above analysis, the following conclusions can be drawn: 1) The hierarchical distributed method proposed in this paper is suitable for the charging and discharging control problems of large-scale discrete-parameter electric vehicle groups, and has good applicability to both simple charging control and V2G modes; 2) Compared with the distributed electric vehicle scheduling mechanism, the iterative process of the hierarchical distributed mechanism proposed in this paper is relatively stable, in which the setting of electric vehicle clusters is indispensable.

[0067] Example 4 like Figure 6 As shown, the hierarchical distributed electric vehicle charging and discharging scheduling system based on electricity price signals provided by the present invention includes: Model building module, which builds the day-ahead electric vehicle charging and discharging scheduling model for the power system; The model conversion module converts the day-ahead electric vehicle charging and discharging scheduling model of the power system into a series of sub-problems, resulting in a distributed charging and discharging scheduling mechanism based on price response; Improved solution method module: Based on the distributed charging and discharging scheduling mechanism, the solution method is improved to improve the convergence of the scheduling problem solution; The aggregation module aggregates electric vehicles based on the solution method of the improved solution method module and obtains a hierarchical distributed electric vehicle scheduling mechanism.

[0068] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0069] In addition, it should be understood that although this specification describes the embodiments, not every embodiment contains only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for the purpose of illustrating the technical concept of the present invention and cannot be used to limit the scope of protection of the present invention. Any changes made based on the technical solution in accordance with the technical concept proposed by the present invention fall within the scope of protection of the claims of the present invention.

Claims

1. A hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals, characterized in that: The following steps are involved: Step 1: Establish a day-ahead electric vehicle charging and discharging scheduling model for the power system; Step 2: Convert the day-ahead electric vehicle charging and discharging scheduling model of the power system into a series of sub-problems to obtain a distributed charging and discharging scheduling mechanism based on price response; Step 3: Based on the distributed charging and discharging scheduling mechanism, improve the solution method and improve the convergence of the scheduling problem solution; Step 4: Based on the solution method in step 3, aggregate electric vehicles and obtain a hierarchical distributed electric vehicle scheduling mechanism.

2. The hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals according to claim 1 is characterized in that: In step 2, according to the analysis of the characteristics of the day-ahead electric vehicle charging and discharging scheduling model of the power system in step 1, the generator set is decoupled from the electric vehicle, the constraints of the generator set and the constraints of the electric vehicle and their objective functions are separated, and the scheduling problem of electric vehicles in the power system is converted into a series of single generator sets and a series of single electric vehicle price response problems; The current electric vehicle charging and discharging scheduling model is: In the day-ahead load dispatch model, the objective function is to minimize the total system operation cost; (1) In the formula, is the generator set number, is the generator set number, is the total number of time periods in the scheduling cycle, For generator sets The fuel cost function is For generator sets The startup costs, For generator sets The downtime cost, For generator sets exist The effort of the moment, It is a 0-1 variable, indicating the generator set exist The start and stop status of the time period, It is a 0-1 variable, indicating the generator set exist The starting action of the moment, It is a 0-1 variable, indicating the generator set exist The shutdown action at the moment; Under the market environment, each item in formula (1) corresponds to the quotation of the generator set; The constraints are: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) In the formula, Generator sets For The upward and downward spare capacity available during the period, Generator sets The maximum and minimum output, Generator sets Minimum continuous downtime and start-up time, Generator sets Upward and downward climbing rates, is the time interval length; The system is The upward and downward spare capacity requirements that can be provided during the period, for Fixed loads other than controllable loads in the time period system. For electric vehicles exist The net power drawn from the grid during the period, is the dual multiplier of the power balance constraint, corresponding to Electricity price during the period, are the dual multipliers of the upward and downward reserve capacity constraints, corresponding to The time period has alternate prices up and down, For Node A collection of electric vehicles connected to the grid; Electric vehicles exist The charging and discharging power during the period, Electric vehicles Maximum charging and discharging power, is a 0-1 variable, indicating the grid-connected status of the electric vehicle. For electric vehicles exist Energy storage level at all times, Electric vehicles Maximum and minimum energy storage levels, Electric vehicles Charging and discharging efficiency, is a parameter, indicating that electric vehicles exist Energy consumed during driving during the period; where (2)-(4) are the maximum and minimum output constraints of the unit, (5)-(8) are the continuous start-stop time constraints of the unit, and (9)-(10) are the climbing constraints of the unit; where (11) is the power balance constraint, (12)-(13) are the spare capacity constraints; where (14) is the net power consumption constraint, (15)-(16) are the charging and discharging power constraints, (17) are the upper and lower limit constraints of the battery state of charge, and (18) is the state of charge change constraint; By using the dual factor and relaxing constraints (11)-(13), we obtain the following Lagrange function: (19) Separate the parts related to generator sets and electric vehicles: (20) (21) Combined with generator set and electric vehicles The running constraints of , we can get the following sub-problems: (22) (23) , and Corresponding to the price signals in system operation, (22) and (23) correspond to the optimization problem of power generation cost of generator sets and the minimization problem of electric vehicle charging fee, respectively; through LR, the electric vehicle charging and discharging scheduling problem is transformed into a series of single generator sets and a series of single electric vehicles. Price response problem.

3. The hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals according to claim 2 is characterized in that: In step 2, based on the electricity price signal, the following distributed charging and discharging scheduling mechanism is designed: 1) System dispatch selects a set of initial electricity price signals as dispatch signals and sets the iteration index ; 2) Generators and electric vehicles respond to electricity price signals, solve single-machine optimization problems (22) and single-vehicle optimization problems (23), and report power optimization results and corresponding costs to the system dispatcher; 3) System dispatch summarizes the optimization results of a single vehicle and a single unit, and calculates the objective function value of the dual problem and the original problem objective function value ; 4) System scheduling calculation relative duality gap (24) according to The value determines whether the problem converges. If it converges, turn to 5); otherwise, use the subgradient method to update the price and set , then turn to step 2); (25) (26) (27) In the formula, Used to mark the The variables related to the iteration, Adjust the step size for electricity prices; 5) The charging and discharging power of each vehicle is the optimal power, and a feasible power generation scheduling plan can be constructed based on the solution of the dual problem; Based on LR decomposition, a feasible price iteration mechanism is designed, that is, through the imbalance of supply and demand in the system, the price signal is gradually adjusted to achieve the coordination of power generation resources and the charging and discharging process of electric vehicles.

4. The hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals according to claim 3 is characterized in that: In step 3, based on the distributed charging and discharging scheduling mechanism, the solution method is improved to improve the convergence of the scheduling problem solution, including: In order to improve the convergence of the solution to the scheduling problem, the augmented Lagrange function is obtained based on the auxiliary theorem and local linearization, which enhances the convexity of the problem and avoids severe oscillation in the iterative process: (28) Accordingly, the generator set is separated from the electric vehicle, resulting in two sub-problems: (29) (30)。 5. The hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals according to claim 4 is characterized in that: In step 4, in order to avoid the problem of directly dispatching the power and number of individual electric vehicles, the power of the electric vehicle aggregate is introduced through the following equation: (31) (32) In the formula, for Moment Electric Vehicle Aggregate The power drawn from the grid; According to the auxiliary problem theorem and necessary adjustments, the generator set, electric vehicle cluster and electric vehicle terms can be obtained as shown below: (33) (34) (35) In the formula, is the correlation coefficient, For aggregate The correlation coefficient, For aggregate Released The round charging control signal is obtained by the following formula: (36) In the formula, Electric vehicle aggregates The charge and discharge control parameters, For electric vehicles In the The charging control scheme reported by the round, For aggregate The charging and discharging power instructions obtained from system scheduling; In the process of charge and discharge control, the corresponding problems of equations (33) and (34) are first solved; by solving equation (34) and equation (36), the aggregate is obtained. The internal charge and discharge control signals are transmitted to the electric vehicles under their jurisdiction; then, the electric vehicles arrange their own charging and discharging plans by solving the problem corresponding to equation (35); after that, the aggregate summarizes the charging and discharging plans of the electric vehicles as the basis for linearization and problem solving in the next iteration.

6. The hierarchical distributed electric vehicle charging and discharging scheduling method based on electricity price signals according to claim 5 is characterized in that: In step 4, the proposed three-layer distributed electric vehicle charging scheduling framework based on electricity price signals is as follows: The framework consists of three layers, namely the system dispatch layer L1, the generator set and main electric vehicle aggregation layer L2, and the electric vehicle layer L3; the main functions of each layer are as follows; The main body of the L1 layer is the system dispatching SO, which is responsible for the coordination of controllable resources on the power generation and load sides of the entire network; In the L2 layer, the generator sets and electric vehicle clusters optimize their own operations according to the instructions issued by the system dispatch; In the L3 layer, electric vehicles are responsible for optimizing their own charging and discharging processes; Furthermore, the basic process of coordination between power generation dispatch and electric vehicle charging and discharging control is proposed; 1) Before a new dispatch cycle begins, the system dispatcher releases the initial electricity price signal; 2) After receiving the price signal, the generator sets form their own optimal operation plan and report it to the system dispatcher; 3) Based on the price signal, the electric vehicle cluster optimizes charging and discharging, generates a charging and discharging control signal within the cluster, and sends it together with the electricity price signal and the charging and discharging power of the cluster in the previous round of charging and discharging control to the electric vehicles under its jurisdiction; 4) Electric vehicles optimize their own charging and discharging process according to the price signals and charging and discharging control signals issued by the aggregate, and report the charging and discharging plan to the upper aggregate; 5) The aggregate summarizes the charging and discharging plans of electric vehicles, forms the aggregate charging and discharging power curve, and reports it to the system dispatcher; 6) Based on the information reported by the aggregate and generator sets, the system dispatcher calculates the relative dual gap And determine whether it converges; 7) If converged, the dispatch process ends and the dispatch instruction is confirmed; otherwise, the price signal is updated and released to the generators and aggregates, and then go to step 2).

7. A hierarchical distributed electric vehicle charging and discharging scheduling system based on electricity price signals, characterized in that: include: Model building module, building a day-ahead electric vehicle charging and discharging scheduling model for the power system; The model conversion module converts the day-ahead electric vehicle charging and discharging scheduling model of the power system into a series of sub-problems, and obtains a distributed charging and discharging scheduling mechanism based on price response; Improve the solution method module, based on the distributed charging and discharging scheduling mechanism, improve the solution method and improve the convergence of the scheduling problem solution; The aggregation module aggregates electric vehicles based on the solution method of the improved solution method module and obtains a hierarchical distributed electric vehicle scheduling mechanism.

8. The hierarchical distributed electric vehicle charging and discharging scheduling system based on electricity price signals according to claim 7 is characterized in that: In the model conversion module, based on the analysis of the characteristics of the electric vehicle charging and discharging scheduling model of the power system in the model establishment module, the generator set and the electric vehicle are decoupled to separate the generator set constraints and the electric vehicle constraints and their objective functions, and the scheduling problem of electric vehicles in the power system is converted into a series of single generator sets and a series of single electric vehicle price response problems; The current electric vehicle charging and discharging scheduling model is: In the day-ahead load dispatch model, the objective function is to minimize the total system operation cost; (1) In the formula, is the generator set number, is the generator set number, is the total number of time periods in the scheduling cycle, For generator sets The fuel cost function is For generator sets The startup costs, For generator sets The downtime cost, For generator sets exist The effort of the moment, It is a 0-1 variable, indicating the generator set exist The start and stop status of the time period, It is a 0-1 variable, indicating the generator set exist The starting action of the moment, It is a 0-1 variable, indicating the generator set exist The shutdown action at the moment; Under the market environment, each item in formula (1) corresponds to the quotation of the generator set; The constraints are: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) In the formula, Generator sets For The upward and downward spare capacity available during the period, Generator sets The maximum and minimum output, Generator sets Minimum continuous downtime and start-up time, Generator sets Upward and downward climbing rates, is the time interval length; The system is The upward and downward spare capacity requirements that can be provided during the period, for Fixed loads other than controllable loads in the time period system. For electric vehicles exist The net power drawn from the grid during the period, is the dual multiplier of the power balance constraint, corresponding to Electricity price during the period, are the dual multipliers of the upward and downward reserve capacity constraints, corresponding to The time period has alternate prices up and down, For Node A collection of electric vehicles connected to the grid; Electric vehicles exist The charging and discharging power during the period, Electric vehicles Maximum charging and discharging power, is a 0-1 variable, indicating the grid-connected status of the electric vehicle. For electric vehicles exist Energy storage level at all times, Electric vehicles Maximum and minimum energy storage levels, Electric vehicles Charging and discharging efficiency, is a parameter, indicating that electric vehicles exist Energy consumed during driving during the period; where (2)-(4) are the maximum and minimum output constraints of the unit, (5)-(8) are the continuous start-stop time constraints of the unit, and (9)-(10) are the climbing constraints of the unit; where (11) is the power balance constraint, (12)-(13) are the spare capacity constraints; where (14) is the net power consumption constraint, (15)-(16) are the charging and discharging power constraints, (17) are the upper and lower limit constraints of the battery state of charge, and (18) is the state of charge change constraint; By using the dual factor and relaxing constraints (11)-(13), we obtain the following Lagrange function: (19) Separate the parts related to generator sets and electric vehicles: (20) (21) Combined with generator set and electric vehicles The running constraints of , we can get the following sub-problems: (22) (23) , and Corresponding to the price signals in system operation, (22) and (23) correspond to the optimization problem of power generation cost of generator sets and the minimization problem of electric vehicle charging fee, respectively; through LR, the electric vehicle charging and discharging scheduling problem is transformed into a series of single generator sets and a series of single electric vehicles. Price response problem.

9. The hierarchical distributed electric vehicle charging and discharging scheduling system based on electricity price signals according to claim 8 is characterized in that: In the model conversion module, based on the electricity price signal, the following distributed charging and discharging scheduling mechanism is designed: 1) System dispatch selects a set of initial electricity price signals as dispatch signals and sets the iteration index ; 2) Generators and electric vehicles respond to electricity price signals, solve single-machine optimization problems (22) and single-vehicle optimization problems (23), and report power optimization results and corresponding costs to the system dispatcher; 3) System dispatch summarizes the optimization results of a single vehicle and a single unit, and calculates the objective function value of the dual problem and the original problem objective function value ; 4) System scheduling calculation relative duality gap (24) according to The value determines whether the problem converges. If it converges, turn to 5); otherwise, use the subgradient method to update the price and set , then turn to step 2); (25) (26) (27) In the formula, Used to mark the The variables related to the iteration, Adjust the step size for electricity prices; 5) The charging and discharging power of each vehicle is the optimal power, and a feasible power generation scheduling plan can be constructed based on the solution of the dual problem; Based on LR decomposition, a feasible price iteration mechanism is designed, that is, through the imbalance of supply and demand in the system, the price signal is gradually adjusted to achieve the coordination of power generation resources and the charging and discharging process of electric vehicles.

10. The hierarchical distributed electric vehicle charging and discharging scheduling system based on electricity price signals according to claim 9 is characterized in that: In the improved solution method module, based on the distributed charging and discharging scheduling mechanism, the solution method is improved to improve the convergence of the scheduling problem solution, including: In order to improve the convergence of the solution to the scheduling problem, the augmented Lagrange function is obtained based on the auxiliary theorem and local linearization, which enhances the convexity of the problem and avoids severe oscillation in the iterative process: (28) Accordingly, the generator set is separated from the electric vehicle, resulting in two sub-problems: (29) (30)。

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