Virtual power plant distribution scheduling method and system

Abstract

The invention discloses a virtual power plant distribution scheduling method and system, and belongs to the technical field of electric energy scheduling, and the method comprises the following steps: building an electric vehicle aggregation capacity model with the minimum parking lot node injection power, and calculating the equivalent output aggregation capacity of a parking lot and a virtual power plant; constructing a quotation curve model based on the electric vehicle and parking lot aggregation capacity value, and generating a quotation curve of a parking lot and virtual power plant level; and constructing a power grid dispatching model based on a quotation curve, and determining output values of each parking lot and each vehicle under the goal of lowest cost to realize dispatching distribution. According to the virtual power plant distribution scheduling method, feasibility aggregation from single electric vehicle constraint to group behavior is realized, accurate modeling in electric vehicle aggregation scheduling is realized, and efficient resource utilization is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy scheduling, and particularly relates to a virtual power plant allocation scheduling method and system. BACKGROUND

[0002] As an important means of realizing the source-grid-load-storage collaborative interaction in the new power system, the virtual power plant (VPP) has shown significant advantages in scheduling optimization and flexible load management. Existing researches are mostly focused on single-point optimization for distributed resources, such as simple models based on load response, hierarchical independent modeling methods, or master-slave game strategies for scheduling control. At the same time, electric vehicles can feedback power to the grid through vehicle-to-grid (V2G) technology. Therefore, carrying out power flow calculation on distribution networks containing distributed power sources and electric vehicles helps to better understand the operation state and optimize the scheduling of the power grid. These methods have improved the economy and scheduling efficiency to some extent, but generally have problems such as limited aggregation degree and low utilization rate of electric vehicles. In particular, the great potential of electric vehicles as flexible loads has not been systematically tapped, making it difficult to meet the response demand and operation constraints under complex conditions.

[0003] At present, the virtual power plant technology still has obvious deficiencies in the electric vehicle access scenario, mainly manifested as the scheduling model of electric vehicles being simplified, without detailed consideration of multiple actual constraints such as battery capacity, power change and travel time of the owner, resulting in a lack of feasibility of the scheduling scheme in actual execution. At the same time, existing researches are mostly focused on the control strategy of a single electric vehicle or a fixed load response model, lacking a systematic aggregation mechanism, making it difficult to effectively extract scheduling capacity from the group level. In addition, the bidding mechanism generally adopts static pricing or linear assumption, without dynamic optimization combined with the aggregated capacity of electric vehicle output, limiting the efficiency of resource allocation and the accuracy of response. SUMMARY

[0004] The present application aims to provide a virtual power plant allocation scheduling method and system, thereby solving the problems of rough modeling and low resource utilization in electric vehicle aggregation scheduling.

[0005] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows: In a first aspect, a virtual power plant allocation scheduling method comprises the following steps: An objective function is established with the minimum node injection power of the parking lot grid-connected node, an equivalent aggregated capacity acquisition model of electric vehicles is constructed at the parking lot level, and the aggregated capacity of electric vehicle output of the parking lot grid-connected node and the equivalent aggregated capacity of electric vehicle output of the virtual power plant response node are calculated. A pricing curve acquisition model is constructed. The pricing curve for each parking lot grid-connected node is obtained by optimizing the pricing based on the aggregated output capacity of electric vehicles at each parking lot grid-connected node. The pricing curve for the virtual power plant response node is obtained based on the pricing curve of each parking lot grid-connected node and the equivalent aggregated output capacity of electric vehicles at the virtual power plant response node. A power grid dispatch and allocation model is constructed. Based on the bidding curves of virtual power plant response nodes and the bidding curves of each parking lot grid-connected node, and according to the power grid dispatch instructions, the output value of each parking lot grid-connected node and the output value of each electric vehicle in each parking lot grid-connected node are obtained when the total power grid operating cost and the operating cost of the parking lot grid-connected node are minimized, so as to carry out power grid dispatch and allocation.

[0006] Optionally, constructing a model for obtaining the equivalent aggregate capacity of electric vehicles includes: Set the objective function and constraints of the electric vehicle equivalent aggregate capacity acquisition model, and optimize to obtain the electric vehicle output aggregate capacity of each parking lot grid node; The equivalent electric vehicle output aggregation capacity of the virtual power plant response node is obtained based on the electric vehicle output aggregation capacity of each parking lot grid-connected node.

[0007] Optionally, the objective function can be established by minimizing the node injection power of the parking lot grid-connected nodes, as follows: in, for Time of the first The node injection power of each parking lot grid-connected node, for Time of the first Active load of each parking lot grid connection node for Time of the first Aggregated electric vehicle output capacity at each parking lot grid-connected node; The constraints of the electric vehicle equivalent aggregate capacity acquisition model include: electric vehicle battery energy constraints, minimum battery energy constraints when the electric vehicle leaves, upper and lower limits of electric vehicle charging and discharging power constraints, and electric vehicle discharge degree of freedom constraints.

[0008] Optionally, constructing a pricing curve acquisition model includes: The electric vehicle costs of each parking lot grid-connected node are aggregated based on the aggregated electric vehicle output capacity of each parking lot grid-connected node to obtain the quotation curve of each parking lot grid-connected node; The parking costs of the virtual power plant response nodes are aggregated based on the price curve of each parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response nodes, thus obtaining the price curve of the virtual power plant response nodes.

[0009] Optionally, the bidding curve of each parking lot grid-connected node is obtained by: obtaining the bidding of the electric vehicle participating in the virtual power plant at each parking lot grid-connected node; taking a plurality of numerical points between the value 0 and the output aggregation capacity of the electric vehicle at the parking lot grid-connected node at the time t; for each selected numerical point, calculating the bidding optimization result of each parking lot grid-connected node with the lowest total cost of electric vehicle output as the target; repeating the bidding optimization calculation step until all selected numerical points are traversed; fitting the coordinate points obtained by all numerical points and corresponding bidding optimization results to obtain the bidding curve of each parking lot grid-connected node.

[0010] Optionally, the bidding curve of the virtual power plant response node is obtained by: obtaining the bidding curve of each parking lot grid-connected node; taking a plurality of numerical points between the value 0 and the equivalent output aggregation capacity of the virtual power plant response node at the time t; for each selected numerical point, calculating the bidding optimization result of the virtual power plant response node with the lowest total cost of grid operation as the target; repeating the bidding optimization calculation step until all selected numerical points are covered; fitting the coordinate points obtained by all numerical points and corresponding bidding optimization results to obtain the bidding curve of the virtual power plant response node.

[0011] Optionally, the grid dispatching distribution model is constructed by: establishing a target function with the lowest total cost of grid operation, calculating the parking lot output value at each parking lot grid-connected node based on the bidding curve of the virtual power plant response node and the grid dispatching instruction; establishing a target function with the lowest running cost of the parking lot grid-connected node, calculating the output value of each electric vehicle in each parking lot grid-connected node based on the bidding curve of each parking lot grid-connected node and the grid dispatching instruction.

[0012] Optionally, the target function with the lowest total cost of grid operation is established and represented as follows: Wherein, is the cost of the virtual power plant response node, that is, the total cost of the parking lot output; is the bidding curve of the i-th parking lot grid-connected node, is the output value of the i-th parking lot grid-connected node; is the output value of the i-th parking lot grid-connected node; is the output value of the i-th parking lot grid-connected node; The constraint condition of the target function with the lowest total cost of grid operation includes: total output constraint of the parking lot grid-connected node and output constraint of a single parking lot grid-connected node.

[0013] Optionally, the target function with the lowest running cost of the parking lot grid-connected node is established and represented as follows: The objective function of the minimum operating cost of the parking lot grid-connected node is constrained, including: total electric vehicle output constraint, electric vehicle battery energy upper and lower limit constraint, minimum energy constraint of the battery when the electric vehicle leaves, and charge and discharge power upper and lower limit constraint of the electric vehicle.

[0014] In a second aspect, a virtual power plant allocation and scheduling system includes one or more processors; a storage device storing one or more programs; and the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the virtual power plant scheduling allocation method of any one of the above.

[0015] The virtual power plant allocation and scheduling method and system provided by the application accurately calculates the equivalent output capacity of each grid-connected node of electric vehicles by establishing an optimization model with charge and discharge power limitation, battery energy management, and travel demand of the owner as constraints at the parking lot level, and further optimizes the economy on this basis to generate a quotation curve reflecting the actual output cost, so that the power grid can realize optimal scheduling control of the electric vehicle group accordingly. This method realizes the feasibility aggregation from single electric vehicle constraint to group behavior, and realizes precise modeling and efficient resource utilization in the aggregation and scheduling of electric vehicles.

[0016] In order to make the above features and advantages of the application more obvious and easy to understand, the following embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flowchart of the virtual power plant allocation and scheduling method provided by the application.

[0018] Figure 2 The flowchart of the model for obtaining the equivalent aggregation capacity of electric vehicles in the application.

[0019] Figure 3 The flowchart of the model for obtaining the quotation curve in the application.

[0020] Figure 4 The flowchart of the model for constructing the power grid scheduling allocation model in the application. DETAILED DESCRIPTION

[0021] In order to make the above features and advantages of the application more obvious and easy to understand, the following embodiments are described in detail below, and the accompanying drawings are described as follows.

[0022] In a specific embodiment of the present application, referring to Figure 1 , Figure 1 The flow chart of the virtual power plant allocation and scheduling method provided by the present application, the virtual power plant allocation and scheduling method provided by the present application, comprising: step S1 to step S3.

[0023] Step S1: establish a target function with the minimum node injection power of the parking lot grid-connected node, construct an electric vehicle equivalent aggregation capacity acquisition model at the parking lot level, and calculate the electric vehicle output aggregation capacity of the parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node.

[0024] Step S2: construct a bidding curve acquisition model, optimize the bidding based on the electric vehicle output aggregation capacity of each parking lot grid-connected node to obtain the bidding curve of each parking lot grid-connected node, and obtain the bidding curve of the virtual power plant response node based on the bidding curve of each parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node.

[0025] Step S3: construct a power grid scheduling and distribution model, based on the bidding curve of the virtual power plant response node and the bidding curve of each parking lot grid-connected node, according to the power grid scheduling instruction, obtain the output value of each parking lot grid-connected node and the output value of each electric vehicle in each parking lot grid-connected node when the total cost of power grid operation is the lowest and the operation cost of parking lot grid-connected node is the lowest, and thus perform power grid scheduling and distribution.

[0026] In step S1, referring to Figure 1 Step S1 in the above, establish a target function with the minimum node injection power of each parking lot grid-connected node, construct an electric vehicle equivalent aggregation capacity acquisition model at the parking lot level, and calculate the electric vehicle output aggregation capacity of the parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node.

[0027] Specifically, the purpose of constructing the electric vehicle equivalent aggregation capacity acquisition model is to obtain the electric vehicle output of each parking lot grid-connected node while meeting the constraints of single electric vehicle charging and discharging capacity, electric vehicle battery state, and owner travel demand, and to realize the continuous aggregation of electric vehicle groups, wherein the electric vehicle output aggregation capacity obtained at each parking lot grid-connected node can obtain the equivalent electric vehicle output aggregation capacity of the virtual power plant response node.

[0028] In an embodiment of the present application, referring to Figure 2 , Figure 2 The flow chart for constructing the electric vehicle equivalent aggregation capacity acquisition model, the electric vehicle equivalent aggregation capacity acquisition model comprises steps S11 to S12.

[0029] Step S11: Set the objective function and constraints of the electric vehicle equivalent aggregate capacity acquisition model, and optimize to obtain the electric vehicle output aggregate capacity of each parking lot grid-connected node.

[0030] Step S12: Obtain the equivalent electric vehicle output aggregation capacity of the virtual power plant response node based on the electric vehicle output aggregation capacity of each parking lot grid-connected node.

[0031] In step S11, the objective function of the electric vehicle equivalent aggregate capacity acquisition model is set. The minimum power injection for the parking lot grid connection node is represented as follows: (1) in, for Time of the first The node injection power of each parking lot grid-connected node, for Time of the first Active load of each parking lot grid connection node for Time of the first The aggregated output capacity of electric vehicles at each parking lot grid-connected node; Among the parking lot network nodes This is the node where the parking lot is connected to the power grid.

[0032] As an example, For time T, the first The aggregated power output capacity of electric vehicles at each parking lot grid node is equal to ,for Row vectors are represented as follows: (2) in, For the first Within the first parking lot network node electric vehicles The effort at that moment, for Row vectors.

[0033] As an example, the output power at a given moment is only valid when all three conditions are met simultaneously: vehicle presence, permitted discharge, and the lower limit of electric vehicle power. Otherwise, the output power is zero. At the [time]th moment The aggregated output capacity of all electric vehicles at each parking lot grid-connected node is used The matrix representation is as follows: (3) Among them, the Within the network node of each parking lot, includingK A car, ; For the first Each parking lot network node is in All of the moments The discharge degree-of-freedom selection matrix for an electric vehicle. For the first Each parking lot network node is in All of the moments Dwell time matrix of electric vehicles For the first The first parking lot network node The lower limit of charging and discharging power for electric vehicles.

[0034] Specifically, no. Each parking lot network node is in All of the moments Dwell time matrix of electric vehicles Recorded the first The parking and departure times of all electric vehicles at each parking lot network node, among which For time, assume the parking lot network node Above A car, The row vectors of the matrix represent the parking time of an electric car, and The values ​​within are either 1 or 0. For example: assuming time... For 10 hours, the first If the electric vehicle is parked between the 4th and 7th hour, then... The first of the matrix The behavior is 0001111000. The stop time is 1 for 4-7, and 0 for the rest of the time.

[0035] Furthermore, the first Each parking lot network node is in All of the moments Discharge degree of freedom selection matrix for electric vehicles Recorded the first The discharge time of all electric vehicles at each parking lot grid connection node The value within the range is either 1 or 0, indicating whether the electric vehicle is currently parked in the parking lot. The electric vehicle's discharge time must occur within the time period during which the electric vehicle was parked in the parking lot. For example: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] If the electric vehicle is parked between the 4th and 7th hour (0001111000), then... The The value of 1 in the row must be between 4 and 7, which is the position of 1 in "0001111000".

[0036] As an example, for the first For an electric vehicle, to ensure that the battery energy per hour does not exceed a limit, the objective function is... Setting energy constraints for electric vehicle batteries , means as follows: (4) in, For the first Within the first parking lot network node electric vehicles The total charge and discharge power vector at each moment, It is the transpose symbol. For electric vehicles The initial charge vector, Let the upper limit vector of battery capacity be denoted as . This is the lower bound vector for battery capacity. For the first Within the first parking lot network node electric vehicles The power at a given moment when the object is in the field but not participating in the discharge is: Row vectors are represented as follows: (5) in, For the first The first parking lot network node During the period when electric vehicles are present and discharge is permitted, For the first The first parking lot network node electric vehicles The duration of stay within a given moment For the first The first parking lot network node electric vehicles The discharge degrees of freedom within a given moment For the first The charging and discharging power of an electric vehicle.

[0037] Specifically, no. Charging and discharging power of electric vehicles A value of positive indicates charging, while a value of negative indicates discharging. The lower limit of charging power = the lower limit of discharging power = 0, indicating that the electric vehicle is not charging or discharging, but simply remains stationary. The upper limit of discharging power = the lower limit of charging and discharging power, which is negative. The upper limit of charging power = the upper limit of charging and discharging power, which is positive.

[0038] As an example, for the first For each electric vehicle, to ensure it meets the owner's travel needs after leaving the factory, the objective function is... Set a minimum energy constraint for the battery when the electric vehicle leaves. , means as follows: (6) in, For the first Initial battery level (%) of the electric vehicle. For the first The battery capacity of an electric vehicle This refers to the cumulative charging and discharging energy of electric vehicles over all time periods.

[0039] As an example, regarding the objective function Set up electric vehicles Upper and lower limits of charging and discharging power constraints , means as follows: (7) in, For the first The charging and discharging power of an electric vehicle For the first The upper limit of the charging and discharging power of an electric vehicle. For the first The lower limit of the charging and discharging power of an electric vehicle.

[0040] As an example, to ensure the first The selection of the discharge time of the electric vehicle is continuous, which affects the objective function. Setting constraints on the degree of freedom of electric vehicle charging and discharging , means as follows: (8) in, For the first The first parking lot network node electric vehicles The discharge degrees of freedom within a given moment The coefficient matrix is ​​represented as follows: (9).

[0041] In step S12, the objective function of the model is obtained from the equivalent aggregate capacity of the electric vehicle. get Time of the first Aggregated output capacity of electric vehicles at each parking lot grid connection node Furthermore, in At any given time, the equivalent aggregated output capacity of electric vehicles at the virtual power plant response node is the sum of the aggregated output capacities of electric vehicles at each parking lot grid-connected node, as shown below: (10) in, for The equivalent aggregated output capacity of electric vehicles at each virtual power plant response node. This is the sum of the combined power output capacity of electric vehicles at each parking lot's grid connection node.

[0042] Furthermore, the electric vehicle equivalent aggregate capacity acquisition model provides the aggregated power output capacity of the electric vehicle group. The quantity provides the maximum output limit for cost calculation of the quotation curve acquisition model and power grid dispatch allocation model.

[0043] In step S2, please refer to Figure 1 In step S2, a pricing curve acquisition model is constructed to optimize the cost of equivalent power generation of electric vehicles while satisfying constraints. The essence of this optimization is that, under the premise of satisfying constraints, the virtual power plant prioritizes the group of electric vehicles with lower prices for equivalent power generation.

[0044] Furthermore, the pricing curve acquisition model is constructed based on the electric vehicle output aggregation capacity of the parking lot grid-connected nodes and the equivalent electric vehicle output aggregation capacity of the virtual power plant response nodes, output by the electric vehicle equivalent aggregation capacity acquisition model. This optimizes the pricing of electric vehicles at the parking lot grid-connected nodes and plots the pricing curve for the parking lot grid-connected nodes. Furthermore, based on the equivalent electric vehicle output aggregation capacity of the virtual power plant response nodes and the pricing curve of the parking lot grid-connected nodes, a pricing curve for the virtual power plant response nodes is plotted. Please refer to [link / reference]. Figure 3 , Figure 3 The flowchart for constructing the price quote curve acquisition model includes steps S21 to S22.

[0045] Step S21: Aggregate the electric vehicle costs of each parking lot grid-connected node based on the aggregated electric vehicle output capacity of each parking lot grid-connected node to obtain the quotation curve of each parking lot grid-connected node.

[0046] Step S22: Aggregate the parking costs of the virtual power plant response nodes based on the price curve of each parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node to obtain the price curve of the virtual power plant response node.

[0047] As an example, in step S21, the aggregated output capacity of electric vehicles at each parking lot grid-connected node is used to determine the first... The electric vehicle costs of each parking lot grid node are aggregated to obtain the quotation curve for each parking lot grid node, including steps S211 to S215.

[0048] Step S211: Obtain the first Electric vehicles at each parking lot grid connection node participate in the bidding for virtual power plants. .

[0049] Step S212: at the time point between the numerical value 0 and at the time point the aggregation capacity of the electric vehicle output of the th parking lot grid-connected node , take the numerical value points at equal intervals, let the coordinate of each numerical value point be , wherein is the interval number, ensuring that each interval point can be calculated cost; is the total number of intervals, and , The larger the value is, the more accurate the curve is.

[0050] Step S213: receive the abscissa of the numerical value point obtained in step S212 , taking the minimum running cost of the parking lot grid-connected node as the objective function, calculating the bidding optimization result of each parking lot grid-connected node based on the output of each electric vehicle , wherein is an array, including the corresponding numerical value obtained by substituting into the objective function.

[0051] Step S214: receive the bidding optimization result of the numerical value point of the th parking lot grid-connected node at the abscissa , let , return to step S213 until .

[0052] Step S215: fit all coordinate points by a polynomial fitting method to obtain the bidding curve of the th parking lot grid-connected node .

[0053] In step S213, the objective function is established by taking the minimum running cost of the parking lot grid-connected node , which is represented as follows: (11) , wherein is the running cost of the th parking lot grid-connected node, which is obtained by the total cost of the electric vehicle output of the th parking lot grid-connected node; is the reported price of the th electric vehicle participating in the virtual power plant of the th parking lot grid-connected node, is the ​The first parking lot network node The discharge power of an electric vehicle.

[0054] As an example, to ensure the first The total power output of electric vehicles at each parking lot grid-connected node equals the required power output, according to the objective function. Set the total output balance constraint for electric vehicles , means as follows: (12) in, For the first The first parking lot network node The discharge power of an electric vehicle.

[0055] As an example, for the first For an electric vehicle, to ensure that the battery energy per hour does not exceed a limit, the objective function is... Setting energy constraints for electric vehicle batteries , represents the following: (13) in, For the first Within the first parking lot network node electric vehicles The total charge / discharge power vector in the price optimization at each time point. It is the transpose symbol. For electric vehicles The initial charge vector, Let the upper limit vector of battery capacity be denoted as . This is the lower limit vector for battery capacity; For the first Within the first parking lot network node electric vehicles The charging power vector at each time point in the non-scheduled state is represented as follows: (14) in, For the first The first parking lot network node The charging power of an electric vehicle.

[0056] As an example, for the first To ensure that electric vehicles meet the needs of owners after they leave the factory, the objective function is... Set a minimum energy constraint for the battery when the electric vehicle leaves. , means as follows: (15) in, For the first Initial battery level (%) of the electric vehicle. For the first The battery capacity of an electric vehicle The cumulative charging and discharging energy of electric vehicles across all time periods is used to optimize the pricing.

[0057] As an example, regarding the objective function Set up electric vehicles Upper and lower limits are set for charging and discharging power. , means as follows: (16) in, For the first The first parking lot network node The upper limit of the charging and discharging power of an electric vehicle. For the first The first parking lot network node The lower limit of the charging and discharging power of an electric vehicle.

[0058] In step S215, all polynomial fitting methods are used to fit the data. Based on the coordinates, electric vehicles are ranked sequentially according to their reported prices in the virtual power plant. The electric vehicle with the lowest reported price is numbered 1, the second lowest is numbered 2, and so on, until the 1st electric vehicle is obtained. Price curve for each parking lot network node , means as follows: (17) in, For the first The price curve for each parking lot grid connection node indicates the power required for the parking lot grid connection node. In the case of , the cost corresponding to the kth cheapest electric vehicle; For the sorted number The first parking lot network node A cheap electric vehicle participates in the virtual power plant's price reporting; For the first The output value of the kth cheap electric vehicle at each parking lot network node. For the sorted number The lower limit of the charging and discharging power of the kth cheap electric vehicle at a parking lot grid node.

[0059] Specifically, It involves taking a finite number of points within a finite range of the horizontal coordinate. For example, suppose... Time of the first Aggregated output capacity of electric vehicles at each parking lot grid connection node The value is 10, that is The x-axis ranges from [0, 10]. Then you can take all values ​​in [0, 10]; assuming ,but ,So The possible values ​​are 2, 4, 6, 8, and 10 (n = 1, 2, 3, 4, 5). It can take all values ​​in the range [0, 10].

[0060] Specifically, the numbering of electric vehicles based on their reported prices for participation in the virtual power plant is only valid in step S215; otherwise, the arrangement is normal.

[0061] Specifically, suppose there are three electric vehicles in the i-th parking lot: electric vehicle A, electric vehicle B, and electric vehicle C. Their bidding and output in the virtual power plant are as follows: Electric vehicle A bids 3 yuan and outputs 4 MW; electric vehicle B bids 5 yuan and outputs 5 MW; electric vehicle C bids 7 yuan and outputs 6 MW. After sorting by bid, electric vehicle A has the cheapest bid, so it outputs 4 MW first. Electric vehicle B has the second cheapest bid, so it outputs 5 MW second. Electric vehicle C has the third cheapest bid, so it outputs 6 MW third. The bidding curve for this parking lot is shown below: (18) Where 3, 5, and 7 represent the reported prices of electric vehicles A, B, and C participating in the virtual power plant, respectively, and x represents the output value of the k-th cheaper electric vehicle. According to the... Price curve for each parking lot network node When the cost of a parking lot grid connection node is minimized, the power output distribution among the electric vehicles in that node is as follows: For example, when the parking lot grid connection node needs to output power... When the output is 6, electric vehicle A needs an output of 4, electric vehicle B needs an output of 2, and electric vehicle C needs an output of 0; when the parking lot grid connection node needs to output... When the value is 10, the required output value of electric vehicle A is 4, the required output value of electric vehicle B is 5, and the required output value of electric vehicle C is 1.

[0062] As an example, in step S22, the price curve of the virtual power plant response node is obtained based on the price curve of each parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node, including steps S221 to S224.

[0063] Step S221: At time 0, the equivalent aggregated output capacity of electric vehicles at the virtual power plant response node. Between, with To take numerical points at equal intervals, let the coordinates of each numerical point be... ,in, The number of intervals is used to ensure that the cost can be calculated for each interval point; The total number of intervals, and , The larger the value, the more accurate the curve depiction.

[0064] Step S222: Receive the x-coordinate of the numerical point obtained in step S221. Taking the minimum total operating cost of the power grid as the objective function, based on the first... Price curve for parking lot grid connection nodes The optimized pricing results for the virtual power plant response nodes were calculated. ,in, For arrays, including The corresponding values ​​are obtained by substituting them into the objective function.

[0065] Step S223: Receive the bid optimization results from the virtual power plant response node. ,make Return to step S222 until... until.

[0066] Step S224: Fit all polynomials using a polynomial fitting method. The coordinate points are used to obtain the price curve of the virtual power plant response node. .

[0067] In step S222, an objective function is established to minimize the total cost of power grid operation. , means as follows: (19) in, The cost of the virtual power plant response node, i.e. the total cost of grid operation, is obtained through the total cost of parking lot output. For the first The price curve for each parking lot network node; Given the first When the cost of each parking lot network node is minimized, the first... The output value of each electric vehicle at each parking lot grid node.

[0068] As an example, to ensure that the total output of each parking lot's grid-connected nodes equals the current output value in each optimization process, the objective function is... Set the total output constraint for the parking lot grid connection node. The expression is as follows: (20).

[0069] As an example, to ensure the first The output value of each electric vehicle when the cost of each parking lot grid connection node is minimized No more than the number obtained in step S1 Aggregated output capacity of electric vehicles at each parking lot grid connection node For the objective function Set output constraints for individual parking lot grid connection nodes , means as follows: (twenty one).

[0070] Specifically, when For the first The output value of each electric vehicle when the cost of each parking lot grid connection node is minimized. The output value of the electric vehicles that are not contributing power in the grid-connected nodes of the parking lot is 0. At this time, the output value of the first electric vehicle is 0. The sum of the output values ​​of all electric vehicles at each parking lot grid connection node is: That is, the first The output value of each parking lot grid-connected node is .

[0071] In step S224, all polynomial fitting methods are used to fit the data. The coordinate points are used to obtain the price curve of the virtual power plant response node. , means as follows: (twenty two) in, The price curve for a virtual power plant response node represents the power output required by that node. In this case, the cost of the corresponding parking lot network node; For the first Output value of each parking lot grid node; It is a constant.

[0072] Specifically, Substitute into the objective function middle, It involves taking a finite number of points within a finite range of the horizontal coordinate. For example, suppose... Equivalent electric vehicle output aggregation capacity of virtual power plant response nodes at any time The value is 10, that is The x-axis ranges from [0, 10]. Then you can take all values ​​in [0, 10]; assuming ,but ,So The possible values ​​are 2, 4, 6, 8, and 10 (n = 1, 2, 3, 4, 5). It can take all values ​​in the range [0, 10].

[0073] Furthermore, based on the equivalent aggregated capacity acquisition model of electric vehicles, the output aggregated capacity pricing curve acquisition model calculates the cost corresponding to different outputs of electric vehicles, i.e., the pricing curve, transforming physical output into economic indicators, and providing a decision-making basis for low-cost scheduling of the power grid dispatching and allocation model.

[0074] In step S3, please refer to Figure 1 In step S3, a power grid dispatch and allocation model is constructed, and the first step is obtained based on the equivalent aggregate capacity acquisition model of electric vehicles. The model for obtaining the aggregated output capacity of electric vehicles at each parking lot grid node and the pricing curve is the first... Price curve for each parking lot network node Price curves for virtual power plant response nodes This enables the power grid to schedule and allocate power to the underlying electric vehicles. Please refer to [link / reference]. Figure 4 , Figure 4 The flowchart for constructing the power grid dispatch and allocation model includes steps S31 to S32.

[0075] Step S31: Establish the objective function to minimize the total cost of power grid operation. Based on the grid dispatch instructions and the bidding curves of the virtual power plant response nodes, calculate the... The output value of each parking lot's grid-connected node.

[0076] Step S32: Establish an objective function based on the lowest operating cost of the parking lot grid connection node, and calculate the output value of each electric vehicle under each parking lot grid connection node based on the bidding curve of each parking lot grid connection node according to the grid dispatch instructions.

[0077] In step S31, an objective function is established to minimize the total cost of power grid operation. Price curve based on virtual power plant response nodes Calculate the first Output value of each parking lot grid node , means as follows: (twenty three) in, For the first The sum of the output values ​​of electric vehicles at each parking lot grid-connected node.

[0078] As an example, to ensure that the sum of the outputs of all parking lot grid-connected nodes equals the output of the power grid dispatch command, the objective function is... Setting total power output constraint of parking lot grid-connected node , is expressed as follows: (24) wherein, is the grid dispatching instruction.

[0079] As an example, to ensure that the power output value of the first parking lot grid-connected node does not exceed the electric vehicle power output aggregation capacity of the first parking lot grid-connected node obtained in step S1, , the objective function is set with the power output value constraint of the single parking lot grid-connected node, which is expressed as follows: (25).

[0080] Further, to establish the objective function with the lowest total grid operation cost, the power output value of the first parking lot grid-connected node is obtained, , and is taken as the dispatching instruction of the electric vehicle accessing the first parking lot grid-connected node.

[0081] In step S32, the objective function is established with the lowest parking lot grid-connected node operation cost, and the power output value of each electric vehicle under the first parking lot grid-connected node is calculated based on the bidding curve of each parking lot grid-connected node, which is expressed as follows: (26).

[0082] As an example, to ensure that the power output sum of all electric vehicles is equal to the parking lot grid-connected node dispatching instruction power output, the objective function is set with the total electric vehicle power output constraint, which is expressed as follows: (27).

[0083] As an example, for the first electric vehicle, to ensure that the battery energy per hour does not exceed the limit value, the objective function is set with the upper and lower limits of the electric vehicle battery energy constraint, which is expressed as follows: (28) wherein, is the first electric vehicle in the first parking lot grid-connected node, ​​​​​​​​electric vehicles The total charge / discharge power vector in the price optimization at each time point. For electric vehicles The initial charge vector, Let the upper limit vector of battery capacity be denoted as . This is the lower limit vector for battery capacity.

[0084] As an example, for the first For each electric vehicle, to ensure it meets the owner's travel needs after leaving the factory, the objective function is... Set a minimum energy constraint for the battery when the electric vehicle leaves. , means as follows: (29) in, For the first Initial battery level (%) of the electric vehicle. For the first The battery capacity of an electric vehicle The cumulative charging and discharging energy of electric vehicles across all time periods is used to optimize the pricing.

[0085] As an example, regarding the objective function Set upper and lower limits for the charging and discharging power of electric vehicles. , means as follows: (30) in, For the first The first parking lot network node The charging power of an electric vehicle For the first The first parking lot network node The discharge power of an electric vehicle For the first The first parking lot network node The upper limit of the charging and discharging power of an electric vehicle. For the first The first parking lot network node The lower limit of the charging and discharging power of an electric vehicle.

[0086] As an example, the power grid dispatch allocation model uses the electric vehicle output aggregation capacity of each parking lot grid-connected node in the electric vehicle equivalent aggregation capacity acquisition model as the upper limit of output as a constraint, and the bidding curve of each parking lot grid-connected node and the bidding curve of the virtual power plant response node in the bidding curve acquisition model as an economic guide. It decomposes the power grid dispatch instructions to specific parking lots and electric vehicles, completes the implementation from power grid demand to individual execution, and at the same time verifies the rationality of the first two models.

[0087] The virtual power plant distribution scheduling method provided by the application forms real-time dynamic optimization through the above-mentioned model optimization, i.e., through the electric vehicle equivalent aggregation capacity acquisition model, the offer curve acquisition model and the power grid scheduling distribution model, on the aggregation modeling method of the electric vehicle group. In the overall optimization, the accuracy of the electric vehicle group charging and discharging output strategy selection is improved, and the optimal cooperative control of the power purchase cost is realized. The application first establishes an aggregation modeling method for the electric vehicle group. On the basis of considering the key factors such as the charging and discharging power of each electric vehicle, the battery capacity, the power limit and the travel time of the vehicle owner, the actual adjustable output of the electric vehicle is accurately extracted, and the consistency of the scheduling model and the actual operation condition is significantly improved. Secondly, the application constructs an aggregation mechanism with offer optimization as the core. By introducing the price-output curve form, the optimal output cost of the electric vehicle group under different load demands is expressed, which provides executable dynamic scheduling basis for the power grid and enhances the economic control ability of the system to the flexible resource of the electric vehicle. Finally, the method completes the closed-loop design from overall scheduling to individual output with the parking lot as the grid-connected node, ensures that the scheduling process meets the premise of electric vehicle operation constraints and user demand, and realizes the minimization of system cost.

[0088] In yet another embodiment, the application also provides a virtual power plant distribution scheduling system, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors realize the virtual power plant distribution scheduling method as described in any one of the above embodiments.

[0089] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0090] Although the application has been disclosed as above with embodiments, it is not intended to limit the application, and anyone with ordinary knowledge in the art can make some changes and modifications without departing from the spirit and scope of the application, therefore the protection scope of the application shall be subject to the appended patent claim scope.

Claims

1. A virtual power plant allocation scheduling method, characterized in that, The method comprises the following steps: A target function is established based on the minimum node injection power of the parking lot grid-connected node, an equivalent aggregation capacity obtaining model of the electric vehicle is constructed at the parking lot level, and the electric vehicle output aggregation capacity of the parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node are calculated; A bidding curve obtaining model is constructed, the bidding curve of each parking lot grid-connected node is obtained based on the electric vehicle output aggregation capacity of each parking lot grid-connected node, and the bidding curve of the virtual power plant response node is obtained based on the bidding curve of each parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node; A grid dispatching and distribution model is constructed, the output value of each parking lot grid-connected node and the output value of each electric vehicle in each parking lot grid-connected node are obtained based on the bidding curve of the virtual power plant response node and the bidding curve of each parking lot grid-connected node, and the grid dispatching and distribution are performed according to the grid dispatching instruction, so that the total grid operation cost is minimized and the operation cost of each parking lot grid-connected node is minimized.

2. The virtual power plant dispatching scheduling method of claim 1, wherein, The equivalent aggregation capacity obtaining model of the electric vehicle comprises: The target function and the constraint condition of the equivalent aggregation capacity obtaining model of the electric vehicle are set, and the electric vehicle output aggregation capacity of each parking lot grid-connected node is obtained by optimization; The equivalent electric vehicle output aggregation capacity of the virtual power plant response node is obtained according to the electric vehicle output aggregation capacity of each parking lot grid-connected node.

3. The virtual power plant dispatching scheduling method of claim 2, wherein, The target function is established based on the minimum node injection power of the parking lot grid-connected node, and is expressed as follows: wherein, is the node injection power of the nth parking lot grid-connected node at the moment, is the active load of the nth parking lot grid-connected node at the moment, is the electric vehicle output aggregation capacity of the nth parking lot grid-connected node at the moment, is the electric vehicle output aggregation capacity of the nth parking lot grid-connected node at the moment, is The constraint condition of the equivalent aggregation capacity obtaining model of the electric vehicle comprises: the electric vehicle battery energy constraint, the minimum energy constraint of the battery when the electric vehicle leaves, the upper and lower limit constraint of the electric vehicle charging and discharging power, and the electric vehicle discharging freedom constraint.

4. The virtual power plant dispatching scheduling method of claim 3, wherein, The bidding curve obtaining model comprises: The electric vehicle cost of each parking lot grid-connected node is aggregated based on the electric vehicle output aggregation capacity of each parking lot grid-connected node, and the bidding curve of each parking lot grid-connected node is obtained; The parking lot cost of the virtual power plant response node is aggregated based on the bidding curve of each parking lot grid-connected node and the equivalent electric vehicle output aggregation capacity of the virtual power plant response node, and the bidding curve of the virtual power plant response node is obtained.

5. The virtual power plant dispatching scheduling method of claim 4, wherein, The bidding curve of each parking lot grid-connected node is obtained, comprising: obtaining the bid of the electric vehicle participating in the virtual power plant at each parking lot grid-connected node; taking a plurality of numerical points between the value 0 and the electric vehicle output aggregation capacity of the parking lot grid-connected node at the time t; for the selected numerical points, taking the minimum total electric vehicle cost as the target, calculating the bidding optimization result of each parking lot grid-connected node; repeating the above bidding optimization calculation steps until all selected numerical points are traversed; fitting the coordinate points obtained by all numerical points and corresponding bidding optimization results to obtain the bidding curve of each parking lot grid-connected node.

6. The virtual power plant dispatching scheduling method of claim 5, wherein, The bidding curve of the virtual power plant response node is obtained, including: obtaining the bidding curve of each parking lot grid-connected node; taking a plurality of numerical points between the numerical value 0 and the equivalent output aggregation capacity of the virtual power plant response node at the time t; for each selected numerical point, taking the minimum total cost of power grid operation as the target, calculating the bidding optimization result of the virtual power plant response node; repeating the above bidding optimization calculation steps until all selected numerical points are covered; fitting all numerical points and the corresponding coordinate points obtained by the bidding optimization result to obtain the bidding curve of the virtual power plant response node.

7. The virtual power plant dispatching scheduling method of claim 4, wherein, The power grid dispatching distribution model is constructed, including: A target function is established to minimize the total cost of power grid operation, and the parking lot output value at each parking lot grid-connected node is calculated based on the bidding curve of the virtual power plant response node according to the power grid dispatching instruction. A target function is established to minimize the running cost of the parking lot grid-connected node, and the output value of each electric vehicle in each parking lot grid-connected node is calculated based on the bidding curve of each parking lot grid-connected node according to the power grid dispatching instruction.

8. The virtual power plant dispatching scheduling method of claim 7, wherein, The target function to minimize the total cost of power grid operation is established, and is expressed as follows: wherein, is the cost of the virtual power plant response node, i.e. the total cost of the parking lot output; is the offer curve of the th parking lot grid-connected node, is the output value of the th parking lot grid-connected node; The constraint conditions of the target function to minimize the total cost of power grid operation are set, including: total output constraint of the parking lot grid-connected node and output constraint of a single parking lot grid-connected node. 9.The virtual power plant allocation scheduling method of claim 7, wherein, A target function is established to minimize the running cost of the parking lot grid-connected node, and is expressed as follows: wherein, is the total cost of the power output of the electric vehicle of the nth parking lot grid-connected node, is the total cost of the power output of the electric vehicle of the nth parking lot grid-connected node, is the reported price of the mth electric vehicle participating in the virtual power plant of the nth parking lot grid-connected node, is the power output value of each electric vehicle under the nth parking lot grid-connected node, is the power output value of each electric vehicle under the nth parking lot grid-connected node, is the discharge freedom of the mth electric vehicle of the nth parking lot grid-connected node within the nth time period.​​​​​ The constraint conditions of the target function to minimize the running cost of the parking lot grid-connected node are set, including: total output constraint of the electric vehicle, upper and lower limits of the battery energy of the electric vehicle, minimum energy constraint of the battery when the electric vehicle leaves, and upper and lower limits of the charging and discharging power of the electric vehicle.

10. A virtual power plant dispatching system, characterized by, The virtual power plant distribution and dispatching system includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors realize the virtual power plant dispatching distribution method according to any one of claims 1-9.

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