Virtual power plant allocation and scheduling method and system

Abstract

The application discloses a virtual power plant distribution scheduling method and system, and belongs to the technical field of electric energy scheduling, and comprises the following steps: establishing a target of minimizing parking lot node injection power, constructing an electric vehicle aggregation capacity model, and calculating the equivalent output aggregation capacity of the parking lot and the virtual power plant; constructing a bidding curve model based on the electric vehicle and the parking lot aggregation capacity value, generating a bidding curve at the level of the parking lot and the virtual power plant; constructing a power grid scheduling model based on the bidding curve, determining the output value of each parking lot and vehicle under the target of the lowest cost, and realizing scheduling distribution. The virtual power plant distribution scheduling method realizes the feasibility aggregation from single electric vehicle constraint to group behavior, and realizes accurate modeling in electric vehicle aggregation scheduling and efficient use of resources.

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 coordination 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 that the scheduling model of electric vehicles is mostly 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 regulation and control strategies of single electric vehicle or fixed load response model, lack systematic aggregation mechanism, and it is 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:

[0006] In a first aspect, a virtual power plant allocation scheduling method comprises the following steps:

[0007] 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.

[0008] The bidding curve acquisition 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.

[0009] The 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 the lowest and the parking lot grid-connected node operation cost is the lowest.

[0010] Optionally, the electric vehicle equivalent aggregation capacity acquisition model comprises:

[0011] The objective function and the constraint condition of the electric vehicle equivalent aggregation capacity acquisition model are set, and the electric vehicle output aggregation capacity of each parking lot grid-connected node is obtained by optimization.

[0012] 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.

[0013] Optionally, the objective function is established by taking the minimum node injection power of the parking lot grid-connected node, and is expressed as follows:

[0014]

[0015] wherein, is the node injection power of the i th parking lot grid-connected node at the t th moment, is the active load of the i th parking lot grid-connected node at the t th moment, is the electric vehicle output aggregation capacity of the i th parking lot grid-connected node at the t th moment.

[0016] The constraint condition of the electric vehicle equivalent aggregation capacity acquisition model 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.

[0017] Optionally, the bidding curve acquisition model comprises:

[0018] ​​​​​​aggregate the electric vehicle cost of each parking lot grid-connected node based on the electric vehicle output aggregation capacity of each parking lot grid-connected node to obtain a bidding curve of each parking lot grid-connected node;

[0019] aggregate the parking lot cost 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 to obtain a bidding curve of the virtual power plant response node.

[0020] Optionally, obtaining the bidding curve of each parking lot grid-connected node comprises: 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 equidistant 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, calculating the bidding optimization result of each parking lot grid-connected node with the lowest total electric vehicle output cost as the target; repeatedly performing the above 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.

[0021] Optionally, obtaining the bidding curve of the virtual power plant response node comprises: obtaining the bidding curve of each parking lot grid-connected node; taking a plurality of numerical points equidistant 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 grid operation cost as the target; repeatedly performing the above 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.

[0022] Optionally, constructing the grid dispatching and distribution model comprises:

[0023] establishing a target function with the lowest total grid operation cost, and 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 according to the grid dispatching instruction;

[0024] establishing a target function with the lowest parking lot grid-connected node operation cost, and 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 according to the grid dispatching instruction.

[0025] Optionally, the target function is established with the lowest total grid operation cost and is represented as follows:

[0026]

[0027] wherein, the cost of the virtual power plant response node, i.e. the total parking lot output cost; the cost of the i th parking lot grid-connected node, i.e. the total electric vehicle output cost of the i th parking lot grid-connected node. a bidding curve of the parking lot grid-connected node, an output value of the first parking lot grid-connected node;

[0028] a constraint condition is set for the target function of the lowest total cost of grid operation, including: a total output constraint of the parking lot grid-connected node and an output constraint of the single parking lot grid-connected node.

[0029] Optionally, a target function is established for the lowest running cost of the parking lot grid-connected node, and is expressed as follows:

[0030]

[0031] a constraint condition is set for the target function of the lowest running cost of the parking lot grid-connected node, including: a total output constraint of the electric vehicle, an upper and lower limit constraint of the battery energy of the electric vehicle, a lowest energy constraint of the battery when the electric vehicle leaves, and an upper and lower limit constraint of the charging and discharging power of the electric vehicle.

[0032] In a second aspect, a virtual power plant distribution scheduling system includes one or more processors, a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the virtual power plant scheduling distribution method as described in any one of the above.

[0033] The virtual power plant distribution scheduling method and system provided by the present application accurately calculates the equivalent output capacity of each grid-connected node of the electric vehicle by establishing an optimization model at the parking lot level with the constraints of charging and discharging power limitation, battery energy management and owner travel demand, and further optimizes the economy on this basis to generate a bidding curve reflecting the actual output cost, so that the grid can realize the optimal scheduling control of the electric vehicle group accordingly. The method realizes the feasibility aggregation from the single electric vehicle constraint to the group behavior, and realizes the precise modeling and efficient resource utilization in the aggregation scheduling of the electric vehicle.

[0034] 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

[0035] Figure 1 A flowchart of the virtual power plant distribution scheduling method provided by the present application.

[0036] Figure 2 A flowchart of the model for obtaining the equivalent aggregation capacity of the electric vehicle constructed in the present application.

[0037] Figure 3 A flowchart of the model for obtaining the bidding curve constructed in the present application.

[0038] Figure 4 The flow chart for constructing the power grid dispatching distribution model in the application. DETAILED DESCRIPTION

[0039] In order to make the purpose and technical scheme of the embodiments of the application clearer, the technical scheme of the embodiments of the application will be described clearly and completely below with reference to the drawings of the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the described embodiments of the application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of protection of the application.

[0040] In a specific embodiment of the application, please refer to Figure 1 , Figure 1 The flow chart of the virtual power plant distribution scheduling method provided by the application, the virtual power plant distribution scheduling method provided by the application comprises steps S1-S3.

[0041] Step S1: a target function is established with the minimum node injection power of the parking lot grid-connected node, an electric vehicle equivalent aggregated capacity acquisition model is constructed at the parking lot level, and the electric vehicle output aggregated capacity of the parking lot grid-connected node and the equivalent electric vehicle output aggregated capacity of the virtual power plant response node are calculated.

[0042] Step S2: a bidding curve acquisition model is constructed, the bidding curve of each parking lot grid-connected node is obtained through bidding optimization based on the electric vehicle output aggregated 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 aggregated capacity of the virtual power plant response node.

[0043] Step S3: a power grid dispatching 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 according to the power grid dispatching instruction, so as to perform power grid dispatching distribution when the total cost of power grid operation and the operation cost of the parking lot grid-connected node are the lowest.

[0044] In step S1, please refer to step S1 in Figure 1 , a target function is established with the minimum node injection power of each parking lot grid-connected node, an electric vehicle equivalent aggregated capacity acquisition model is constructed at the parking lot level, and the electric vehicle output aggregated capacity of the parking lot grid-connected node and the equivalent electric vehicle output aggregated capacity of the virtual power plant response node are calculated.

[0045] Specifically, the purpose of constructing the electric vehicle equivalent aggregate capacity acquisition model is to obtain the electric vehicle output of each parking lot grid-connected node while satisfying constraints such as the charging and discharging capacity of a single electric vehicle, the battery status of the electric vehicle, and the travel needs of the car owner, so as to realize the continuous upward aggregation of the electric vehicle group. The electric vehicle output aggregate capacity obtained from each parking lot grid-connected node can be used to obtain the equivalent electric vehicle output aggregate capacity of the virtual power plant response node.

[0046] In one embodiment of the present invention, please refer to Figure 2 , Figure 2 The flowchart for constructing the equivalent aggregate capacity acquisition model for electric vehicles includes steps S11 to S12.

[0047] 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.

[0048] 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.

[0049] 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:

[0050] (1)

[0051] 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.

[0052] 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:

[0053] (2)

[0054] where, is the power output of the i-th electric vehicle in the j-th parking lot grid-connected node at the k-th time, is a row vector.

[0055] As an example, the power output of the i-th electric vehicle in the j-th parking lot grid-connected node at the k-th time is a valid value only when the three conditions of the presence of the vehicle, the permission of discharging, and the lower limit of the power of the electric vehicle are all satisfied, otherwise the power output is 0, in which case, is the aggregation capacity of the power output of all electric vehicles in the j-th parking lot grid-connected node at the k-th time, which is represented by a matrix as follows:

[0056] (3)

[0057] where, K ; is the discharge freedom selection matrix of all electric vehicles in the j-th parking lot grid-connected node at the k-th time, is the parking time matrix of all electric vehicles in the j-th parking lot grid-connected node at the k-th time, is the lower limit of the charging and discharging power of the i-th electric vehicle in the j-th parking lot grid-connected node. In detail, the parking time matrix of all electric vehicles in the j-th parking lot grid-connected node at the k-th time

[0058] records the parking-in and parking-out times of all electric vehicles in the j-th parking lot grid-connected node, where is the time, and it is assumed that there are electric vehicles in the j-th parking lot grid-connected node, the row vector of the matrix is the parking time of an electric vehicle, and the values in the matrix are only 1 or 0. For example, it is assumed that the time is 10 hours, the i-th electric vehicle parks in the time from the 4th hour to the 7th hour, then the i-th row of the matrix is ​​​​​​​​​​​​​​​​​​​​​​​​Action 0001111000, parking time 4-7 is 1, and the rest is 0.

[0059] Further, the first parking lot grid-connected node selects the discharge freedom matrix of all electric vehicles at time points The discharge time of all electric vehicles of the first parking lot grid-connected node is recorded, The values in the time period are also only 1 or 0, indicating whether the electric vehicle is parked in the parking lot at the current time. The discharge time of the electric vehicle must be within the time period when the electric vehicle is parked in the parking lot, for example: the parking time of the first electric vehicle is the 4th-7th hour (0001111000), then The value of 1 in the first row must be within 4-7, that is, the position of 1 in “0001111000”.

[0060] As an example, for the first electric vehicle, in order to ensure that the battery energy does not exceed the limit value per hour, the electric vehicle battery energy constraint is set for the objective function , which is as follows:

[0061] (4)

[0062] Wherein, is the total charge and discharge power vector of the first electric vehicle in the first parking lot grid-connected node at time point, is the transpose symbol, is the initial electric quantity vector of the electric vehicle , is the upper limit vector of the battery capacity, is the lower limit vector of the battery capacity, is the initial electric quantity vector of the electric vehicle , is the upper limit vector of the battery capacity, is the power of the first electric vehicle in the first parking lot grid-connected node at time point when it is in the parking lot but does not participate in discharge, which is a row vector, and is as follows:

[0063] (5)

[0064] Wherein, is the time period when the first electric vehicle in the first parking lot grid-connected node is in the parking lot and allowed to discharge, is the 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.

[0065] 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.

[0066] 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:

[0067] (6)

[0068] in, For the first Initial battery level (%) of the electric vehicle. For the first The battery capacity of an electric vehicle This represents the cumulative charging and discharging energy of electric vehicles over all time periods.

[0069] As an example, regarding the objective function Set up electric vehicles Upper and lower limits of charging and discharging power constraints , means as follows:

[0070] (7)

[0071] 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.

[0072] 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:

[0073] (8)

[0074] 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:

[0075] (9).

[0076] 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:

[0077] (10)

[0078] 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.

[0079] Furthermore, the electric vehicle equivalent aggregate capacity acquisition model provides the aggregated power output capacity of the electric vehicle group.

[0080] The quantity provides the maximum output limit for cost calculation of the quotation curve acquisition model and power grid dispatch allocation model.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] Step S211: Obtain the first Electric vehicles at each parking lot grid connection node participate in the bidding for virtual power plants. .

[0087] Step S212: At time, at the value 0 and Time of the first Aggregated output capacity of electric vehicles at each parking lot grid connection 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 representation.

[0088] Step S213: Receive the x-coordinate of the numerical point obtained in step S212. With the objective function of minimizing the operating cost of parking lot grid-connected nodes, the optimization result of the price for each parking lot grid-connected node is calculated based on the power output of each electric vehicle. ,in, For arrays, including The corresponding values ​​are obtained by substituting them into the objective function.

[0089] Step S214: Receive the first The x-coordinate of the numerical points of each parking lot network node Optimization results of the pricing ,make Return to step S213 until... until.

[0090] Step S215: Fit all polynomials using a polynomial fitting method. coordinates point, to obtain the first Price curve for each parking lot network node .

[0091] In step S213, an objective function is established to minimize the operating cost of the parking lot grid connection node. , means as follows:

[0092] (11)

[0093] in, For the first The operating cost of a parking lot network node, through the first The total cost of electric vehicle output at each parking lot grid connection node is obtained; For the first The first parking lot network node Electric vehicles participate in the price reporting of the virtual power plant. For the first The first parking lot network node The discharge power of an electric vehicle.

[0094] 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:

[0095] (12)

[0096] in, For the first The first parking lot network node The discharge power of an electric vehicle.

[0097] 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:

[0098] (13)

[0099] 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:

[0100] (14)

[0101] in, For the first The first parking lot network node The charging power of an electric vehicle.

[0102] 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:

[0103] (15)

[0104] 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.

[0105] 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:

[0106] (16)

[0107] 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.

[0108] 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:

[0109] (17)

[0110] 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.

[0111] 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].

[0112] 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.

[0113] 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:

[0114] (18)

[0115] 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.

[0116] 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.

[0117] 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 representation.

[0118] 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.

[0119] Step S223: Receive the bid optimization results from the virtual power plant response node. ,make Return to step S222 until... until.

[0120] 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. .

[0121] In step S222, an objective function is established to minimize the total cost of power grid operation. , means as follows: (19)

[0122] 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; For the known number 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.

[0123] 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:

[0124] (20).

[0125] 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:

[0126] (twenty one).

[0127] 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 .

[0128] 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:

[0129] (twenty two)

[0130] 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.

[0131] 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). All values in [0, 10] can be traversed.

[0132] Further, the output aggregation capacity quotation curve acquisition model based on the equivalent aggregation capacity acquisition model of the electric vehicle calculates the cost corresponding to different outputs of the electric vehicle, that is, the quotation curve, converts the physical output into an economic index, and provides a decision basis for low-cost scheduling of the power grid scheduling allocation model.

[0133] In step S3, please refer to Figure 1 for step S3, construct a power grid scheduling allocation model, based on the electric vehicle output aggregation capacity of the first parking lot grid-connected node obtained by the equivalent aggregation capacity acquisition model of the electric vehicle, the quotation curve of the first parking lot grid-connected node obtained by the quotation curve acquisition model , and the quotation curve of the virtual power plant response node , realize the scheduling and allocation of the power grid to the underlying electric vehicle. Please refer to Figure 4 , Figure 4 for the flowchart of constructing the power grid scheduling allocation model, which includes steps S31-S32.

[0134] Step S31: establish a target function with the lowest total cost of power grid operation, calculate the output value of the first parking lot grid-connected node based on the quotation curve of the virtual power plant response node according to the power grid scheduling instruction.

[0135] Step S32: establish a target function with the lowest running cost of the parking lot grid-connected node, calculate the output value of each electric vehicle under each parking lot grid-connected node based on the quotation curve of each parking lot grid-connected node according to the power grid scheduling instruction.

[0136] In step S31, a target function is established with the lowest total cost of power grid operation , the output value of the first parking lot grid-connected node is calculated based on the quotation curve of the virtual power plant response node , which is represented as follows:

[0137] (23)

[0138] Wherein, is the sum of the output values of the electric vehicles of the first parking lot grid-connected node.

[0139] As an example, to ensure that the sum of the outputs of all parking lot grid-connected nodes is equal to the output of the power grid scheduling instruction, set the total output constraint of the parking lot grid-connected node for the target function ​, is expressed as follows:

[0140] (24)

[0141] wherein, is the grid dispatch instruction.

[0142] As an example, to ensure that the output value of the first parking lot grid-connected node does not exceed the electric vehicle output aggregation capacity of the first parking lot grid-connected node obtained in step S1 , the target function is set with the output value constraint of the single parking lot grid-connected node, which is expressed as follows: , wherein, is the output value of the first parking lot grid-connected node. , the target function is set with the output value constraint of the single parking lot grid-connected node, which is expressed as follows:

[0143]

[0144] Further, to establish the target function with the lowest total grid operation cost, the output value of the first parking lot grid-connected node is obtained , and is taken as the dispatch instruction of the electric vehicle accessing the first parking lot grid-connected node. In step S32, the target function is established with the lowest parking lot grid-connected node operation cost, and the 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

[0145] , which is expressed as follows: , wherein,

[0146] (26)

[0147] As an example, to ensure that the output of all electric vehicles is equal to the parking lot grid-connected node dispatch instruction output, the total electric vehicle output constraint is set to the target function , which is expressed as follows:

[0148] (27)

[0149] As an example, for the first electric vehicle, to ensure that the battery energy per hour does not exceed the limit value, the upper and lower limit constraints of the electric vehicle battery energy are set to the target function :

[0150] (28) ​​​​​

[0151] wherein, is the initial state-of-charge of the i-th electric vehicle, is the upper limit of the battery capacity of the i-th electric vehicle, is the lower limit of the battery capacity of the i-th electric vehicle.

[0152] As an example, for the i-th electric vehicle, to ensure that the minimum energy of the battery after leaving the parking lot meets the owner's travel demand, the objective function is set with the minimum energy constraint of the battery when the electric vehicle leaves the parking lot , which is expressed as follows:

[0153] (29)

[0154] wherein, is the initial state-of-charge of the i-th electric vehicle, is the battery capacity of the i-th electric vehicle, is the cumulative charging and discharging energy of the i-th electric vehicle in all time periods in the bidding optimization. As an example, the charging and discharging power upper and lower limit constraint of the electric vehicle is set for the objective function

[0155] , which is expressed as follows:

[0156] (30)

[0157] wherein, is the charging power of the i-th electric vehicle of the j-th parking lot grid-connected node, is the discharging power of the i-th electric vehicle of the j-th parking lot grid-connected node, is the upper limit of the charging and discharging power of the i-th electric vehicle of the j-th parking lot grid-connected node, is the lower limit of the charging and discharging power of the i-th electric vehicle of the j-th parking lot grid-connected node.

[0158] ​​​​​​​​​​​​​​​​As an example, the power grid scheduling allocation model takes the electric vehicle output aggregation capacity of each parking lot grid-connected node obtained by the electric vehicle equivalent aggregation capacity obtaining model as the output upper limit, and takes the bidding curve of each parking lot grid-connected node and the bidding curve of the virtual power plant response node obtained by the bidding curve obtaining model as the economic guide, decomposes the power grid scheduling instruction to the specific parking lot and electric vehicle, completes the landing from the power grid demand to the individual execution, and verifies the rationality of the first two models in reverse.

[0159] The virtual power plant allocation scheduling method provided by the application forms real-time dynamic optimization through the above-mentioned models, i.e., through the electric vehicle equivalent aggregation capacity obtaining model, the bidding curve obtaining model and the power grid scheduling allocation model. 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 achieved. 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 bidding 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, an executable dynamic scheduling basis is provided for the power grid, and the economic regulation and control ability of the system to the flexible resource of the electric vehicle is enhanced. Finally, the method takes the parking lot as the grid-connected node, completes the closed-loop design from overall scheduling to individual output, and ensures that the scheduling process meets the premise of the electric vehicle operation constraint and the user demand to realize the minimization of the system cost.

[0160] In yet another embodiment, the application also provides a virtual power plant allocation 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 implement the virtual power plant allocation scheduling method as described in any of the above embodiments.

[0161] 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, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0162] 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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