A method, system, device and medium for configuring shared energy storage capacity of a wind power station

Abstract

The present invention discloses a method, system, device and medium for configuring shared energy storage capacity of a wind power station, which relates to the field of shared energy storage technology, and includes the following steps: obtaining the daily operating cost of shared energy storage through the capacity cost and power cost of shared energy storage; jointly configuring shared energy storage for a wind power station alliance to obtain the economic benefits of the wind power station alliance; obtaining the optimal configuration of shared energy storage capacity through a two-stage stochastic programming problem, and distributing operating costs to multiple wind power stations through the optimal configuration. The joint optimal configuration scheme of shared energy storage capacity of multiple wind power stations finally obtained by the present invention can bring greater benefits than configuring energy storage separately, and comprehensively balances the life loss of energy storage equipment and the profitability of wind power stations.

Description

Technical Field

[0001] The present invention relates to the field of shared energy storage technology, and in particular to a method, system, equipment and medium for configuring shared energy storage capacity of a wind power station. Background Art

[0002] In order to get rid of dependence on fossil energy, my country is actively promoting energy revolution and vigorously developing clean and green renewable energy such as wind energy and solar energy. Renewable energy generation is developing rapidly, and energy storage has become an effective technology to promote the consumption of renewable energy and improve the flexibility of the energy system.

[0003] With the increasing energy interactions among different entities in the energy system and the successful progress of energy sharing, shared energy storage has higher benefits compared with traditional distributed energy storage frameworks and its advantages are gradually becoming more prominent.

[0004] The existing technology only considers shared energy storage among multiple wind power stations. While the capacity is fully utilized in energy storage sharing, charging and discharging will be more frequent compared to distributed personal energy storage, resulting in a reduction in energy storage life or sharing benefits. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, device and medium for configuring shared energy storage capacity of wind power stations in view of the above-mentioned deficiencies in the prior art, so as to solve the problem that the prior art only considers shared energy storage of multiple wind power stations, resulting in a reduction in energy storage life or shared benefits.

[0006] The present invention specifically provides the following technical solutions:

[0007] A method for configuring shared energy storage capacity of a wind power station comprises the following steps:

[0008] Obtain the daily operating cost of the shared energy storage through the capacity cost and power cost of the shared energy storage;

[0009] Jointly configure shared energy storage for a wind farm alliance to obtain economic benefits from the wind farm alliance, which includes multiple wind farms;

[0010] Obtaining an optimal configuration of shared energy storage capacity through a two-stage stochastic programming problem, and allocating operating costs for multiple wind power stations through the optimal configuration;

[0011] The optimal configuration of shared energy storage capacity is obtained through a two-stage stochastic programming problem, including:

[0012] The sum of the configuration cost and the minimum daily operating cost of shared energy storage under the condition of minimum economic benefit is obtained, as well as the constraints under the condition of minimum economic benefit;

[0013] The optimal solution of the sum of the configuration cost and the minimum daily operating cost of the shared energy storage is obtained according to the constraint conditions, and the optimal configuration of the shared energy storage capacity is obtained by using the optimal solution of the sum.

[0014] Preferably, the daily operation cost of the shared energy storage is obtained through the capacity cost and power cost of the shared energy storage, specifically:

[0015] Get the daily operating cost of shared energy storage , the specific expression is:

[0016] .

[0017] .

[0018] in, represents the capacity cost of shared energy storage, represents the power cost of shared energy storage, represents the investment cost coefficient of shared energy storage, r represents the discount rate, Indicates energy storage cycle life.

[0019] Preferably, before obtaining the daily operating cost of the shared energy storage through the capacity cost and power cost of the shared energy storage, the following steps are also included:

[0020] Get the energy storage cycle life, the specific expression is:

[0021] .

[0022] in, ; .

[0023] in, It represents the total number of cycles until the energy storage fails at 100% discharge depth, T represents the time, Indicates the equivalent number of cycles of energy storage at 100% discharge depth in one day, Indicates the energy storage cycle life, Indicates the depth of discharge DOD During continuous discharge j The cumulative increment within the discharge depth DOD During continuous discharge j Discharge depth at end time and discharge depth DOD During continuous discharge j Discharge depth at start time Difference .

[0024] Preferably, the step of jointly configuring the wind power plant alliance to share energy storage and obtain the economic benefits of the wind power plant alliance includes the following steps:

[0025] Obtain the sum of the electricity sales profits of multiple wind power stations in the wind power station alliance;

[0026] The economic benefit is obtained based on the difference between the sum of the profits from electricity sales and the configuration cost of shared energy storage and the total electricity bill of the wind power station alliance.

[0027] Preferably, obtaining the optimal solution of the sum of the configuration cost and the minimum daily operating cost of the shared energy storage according to the constraint conditions, and obtaining the optimal configuration of the shared energy storage capacity by using the optimal solution of the sum, comprises the following steps:

[0028] Accessing the configuration costs of shared energy storage , the specific expression is:

[0029] .

[0030] pass The objective function is obtained by summing up the minimum daily operating cost , the specific expression is:

[0031] .

[0032] The objective function and The constraints are:

[0033] .

[0034] .

[0035] .

[0036] .

[0037] In the formula, The Wind Farm Alliance, Represents shared energy storage capacity, including energy storage capacity and maximum charge and discharge power ; Indicates the scene number. Indicates the serial number is The scene, represents the set of all scenes, Representation scene Probability of occurrence; Indicates in the scene The operation strategy of any wind power plant in the event of occurrence; Indicates in the scene Wind power station i operational strategies, including charging and discharging of shared energy storage and electricity trading with the grid; Indicates the operation strategy The set of feasible domains; Indicates that shared storage t Energy storage capacity at the moment; and Represents shared storage in t Discharging power and charging power at the moment; Indicates the maximum time;

[0038] Obtaining the objective function under constraints The optimal solution, and the energy storage capacity in the optimal solution and maximum charge and discharge power As the optimal configuration of shared energy storage capacity.

[0039] Preferably, allocating operating costs for multiple wind power stations through the optimal configuration includes the following steps:

[0040] In the optimal configuration scenario Under this condition, the operating cost of the wind power plant alliance is obtained by the difference between the power purchase cost and the power sales income to the grid; the specific expression is:

[0041] .

[0042] The operating cost of each wind power station is allocated by the operating cost of the wind power station alliance; the specific expression is:

[0043] .

[0044] In the formula, Indicates in the scene In the event of a wind farm alliance, the operating costs incurred by and Represent the purchase price and sales price of electricity respectively, and Respectively in the scene In the case of wind power station i exist t The power purchased and sold by exchanging electricity with the external power grid at all times, represents the shared energy storage capacity, represents the cost of purchasing electricity, Represents the revenue from selling electricity to the grid.

[0045] Preferably, after allocating the operating cost of each wind power station using the operating cost of the wind power station alliance, the method further includes the following steps:

[0046] In wind power station i When using shared energy storage, obtain the charging and discharging rate limits; the specific expression is:

[0047] .

[0048] .

[0049] In the formula, and Wind power station i The electric power charged into and obtained from the shared energy storage; and Wind power station i The charging and discharging power limitations when using shared energy storage;

[0050] Wind power station i When buying and selling electricity with an external power grid, obtain the power limit of the transaction; the specific expression is:

[0051] .

[0052] .

[0053] In the formula, Indicates in the scene In the case of wind power station i Electric power traded.

[0054] The present invention provides a wind power station shared energy storage capacity configuration system, comprising:

[0055] An operation cost acquisition module, used for acquiring the daily operation cost of the shared energy storage through the capacity cost and power cost of the shared energy storage;

[0056] An economic benefit acquisition module is used to jointly configure shared energy storage for a wind power plant alliance to obtain the economic benefits of the wind power plant alliance, which includes multiple wind power plants;

[0057] A configuration acquisition allocation module is used to obtain the optimal configuration of the shared energy storage capacity through a two-stage stochastic programming problem, and to allocate operating costs for multiple wind power stations through the optimal configuration;

[0058] Among them, the configuration acquisition allocation module obtains the optimal configuration of the shared energy storage capacity through a two-stage stochastic programming problem, including:

[0059] A parameter acquisition module is used to obtain the sum of the configuration cost and the minimum daily operation cost of the shared energy storage under the condition of minimum economic benefit, and to obtain the constraint conditions under the condition of minimum economic benefit;

[0060] The optimal configuration module is used to obtain the optimal solution of the sum of the configuration cost and the minimum daily operating cost of the shared energy storage according to the constraint conditions, and obtain the optimal configuration of the shared energy storage capacity with the optimal solution of the sum.

[0061] The present invention provides a computer device, comprising a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of the above-mentioned method for configuring shared energy storage capacity of a wind power station.

[0062] The present invention provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for configuring shared energy storage capacity of a wind power station are implemented.

[0063] Compared with the prior art, the present invention has the following significant advantages:

[0064] The present invention obtains the daily operating cost of shared energy storage through the capacity cost and power cost of shared energy storage, jointly configures shared energy storage for the wind power station alliance, obtains the economic benefit of the wind power station alliance, considers various scenarios of wind power and load, adopts a two-stage random programming algorithm, obtains the sum of the configuration cost of shared energy storage with minimized economic benefit and the minimum daily operating cost, and obtains the optimal configuration of shared energy storage capacity by minimizing the economic benefit. The operating cost is allocated to multiple wind power stations through the optimal configuration, and the benefits of energy storage sharing and the cost of energy storage life loss are weighed. An ex post benefit distribution plan is formulated for the wind power station alliance to obtain the optimal operating strategy for utilizing shared energy storage. Compared with other wind power stations jointly configuring shared energy storage, the present invention can bring greater benefits than configuring energy storage alone. From the perspective of benefits, various entities tend to form an alliance to share energy storage, which improves the energy storage life and shared benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of a plurality of wind power stations jointly configured with shared energy storage in the present invention;

[0066] Figure 2 It is a wind power output curve diagram of the first wind power station in different scenarios in the present invention;

[0067] Figure 3 It is a wind power output curve diagram of the second wind power station in different scenarios in the present invention;

[0068] Figure 4 This is a wind power output curve diagram of the third wind power station in different scenarios in the present invention;

[0069] Figure 5 This is a load curve diagram of the first wind power station in different scenarios in the present invention;

[0070] Figure 6 The load curve diagram of the second wind power station in different scenarios in the present invention;

[0071] Figure 7 The load curve diagram of the third wind power station in different scenarios in the present invention;

[0072] Figure 8 This is a comparison chart of the benefits of configuring energy storage individually and configuring shared energy storage together. DETAILED DESCRIPTION

[0073] The following is a clear and complete description of the technical solutions of the embodiments of the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0074] The purpose of the present invention is to provide a method, system, device and medium for configuring shared energy storage capacity of a wind power station. Figure 1 , the present invention establishes an energy storage model that takes life loss into consideration based on the Peukert Lifetime Energy Throughput (PLET) theory and linearization method, and obtains the daily operating cost of shared energy storage. Next, based on the cooperative game theory, a model for wind power station alliance to plan shared energy storage capacity is established, and the value of the alliance is the economic benefit obtained by jointly configuring shared energy storage. Finally, based on the two-stage stochastic programming problem, the alliance value is characterized, the optimal configuration of shared energy storage capacity is solved, and the Sharpe column value method is used to allocate the optimal operating strategy for multiple wind power stations using shared energy storage. Subsequent example analysis also shows that jointly configuring shared energy storage with other wind power stations can bring greater benefits than configuring energy storage alone, and in the case of shared energy storage planning, the comprehensive trade-off between wind power station profitability and shared energy storage life loss costs should be considered, which proves the effectiveness of this method.

[0075] The present invention proposes a method for configuring shared energy storage capacity of a wind power station to solve the problems of the prior art, which specifically includes the following steps:

[0076] Step S1: Based on the Poker Lifetime Energy Throughput (PLET) theory and linearization method, an energy storage model considering life loss is established, and the daily operation cost of the shared energy storage is obtained through the capacity cost and power cost of the shared energy storage in the energy storage model.

[0077] During the operation of shared energy storage, if charging and discharging are too frequent, it will cause loss of energy storage life, thus generating certain energy storage life loss costs. Based on the Poker Life Energy Throughput (PLET) theory, a mathematical model considering energy storage life loss was established, which can achieve a comprehensive trade-off between wind power station profitability and shared energy storage life loss costs.

[0078] Specifically:

[0079] A PLET energy storage model considering life loss is established; the PLET energy storage model is expressed as:

[0080] .

[0081] In the formula, It represents the total number of cycles until the energy storage fails at 100% discharge depth. It is a constant parameter and can be obtained from the manufacturer. It represents the equivalent maximum cycle number of energy storage at 100% discharge depth. This variable represents the energy storage life loss in the PLET energy storage model. DOD is the discharge depth; Depth of discharge DOD Considering the influence of discharge behavior on energy storage life loss, the energy storage life loss from the beginning of the discharge period to the end of the discharge period is expressed as:

[0082] .

[0083] .

[0084] In the formula, j Indicates the continuous discharge period, that is, the time period during which the energy storage is continuously discharged. This time period includes a continuous discharge period j , or includes multiple consecutive discharge periods, such as j,j+ 1 ，…n ; Indicates the depth of discharge DOD During continuous discharge j The cumulative increment within Indicates the depth of discharge DOD During continuous discharge j The discharge depth at the end time, Indicates the depth of discharge DOD During continuous discharge j The depth of discharge at the start time, Indicates the number of equivalent 100% discharge depth cycles when the energy storage is continuously discharged, representing the continuous discharge period j The loss of energy storage life.

[0085] Before obtaining the daily operating cost of shared energy storage through the capacity cost and power cost of shared energy storage, the following steps are also included:

[0086] The energy storage life loss of different continuous discharge periods can be linearly and independently accumulated to represent the total life loss of energy storage. Therefore, the energy storage cycle life is obtained by adding the energy storage life loss of all continuous discharge periods in a day. The specific expression is:

[0087] .

[0088] in, . .

[0089] in, It represents the total number of cycles until the energy storage fails at 100% discharge depth, T represents the time, Indicates the equivalent number of cycles of energy storage at 100% discharge depth in one day, Indicates energy storage cycle life.

[0090] Get the discharge depth increment of the energy storage during each operating time interval t The expression is:

[0091] .

[0092] in, Indicates that storage t The discharge depth at the moment, Indicates that storage t- The discharge depth at 1 moment, Indicates that storage t The capacity status at the moment, Indicates that storage t- Capacity status at moment 1.

[0093] The change in discharge depth of energy storage during the discharge period. It is believed that only continuous discharge behavior has an impact on the life of energy storage. , , cumulative discharge depth increment The specific expression is:

[0094] .

[0095] and The physical meanings of are the same, both indicating the cumulative discharge depth increment of energy storage during the continuous discharge period. The difference is It can be obtained by calculation.

[0096] in, is equal to 1, here t is the last period in the continuous discharge period; the cumulative discharge depth increment in the continuous discharge period The specific expression is:

[0097] .

[0098] .

[0099] .

[0100] Due to the cumulative discharge depth increment during the continuous discharge period In each period t All are calculated once, so in the period t Calculated Contains the previous period calculated . What is actually needed is only the last period of the continuous discharge period. , which is the cumulative discharge depth increment during the discharge period. Therefore, another 0-1 variable is introduced To find the last period in a continuous discharge period:

[0101] .

[0102] .

[0103] in, Indicates time t With the moment t -1 Discharge depth DOD difference, is a 0-1 variable, during the discharge period The value of is 1, otherwise The value of is 0, For the next moment t +1 for the 0-1 variable. Ensures Only during the discharge period , otherwise 0. Subsequently, a new 0-1 variable is introduced To represent the continuous discharge period, Calculate according to the following formula:

[0104] .

[0105] .

[0106] in, For the previous moment t -1, which ensures that the energy storage is discharged only when it is discharged for at least two consecutive periods. The value of is 1, and the current period is determined to be a continuous discharge period.

[0107] The large M value method is used for linearization to process the product of 0-1 variables and continuous variables; the specific expression is:

[0108] .

[0109] .

[0110] .

[0111] in, is a 0-1 variable, x and y is a continuous variable, and Respectively x The lower and upper bounds of .

[0112] Defining energy storage capacity , the specific expression is:

[0113] .

[0114] .

[0115] in, is a constant, indicating the k Energy storage capacity available for selection, For the Wind Farm Alliance, For the k A 0-1 variable of the energy storage capacity to be selected. K is the energy storage capacity available for selection. K Among the available energy storage capacities, at most only one energy storage capacity is adopted. On this basis, the PLET model is used to K The possible energy storage capacity can be calculated to avoid the situation where two continuous variables are divided. Correspondingly, a 0-1 variable is also introduced in the objective function. Multiply with the cost function. For the case of multiplying 0-1 variables with continuous variables, the large M value method can be used for linear processing.

[0116] For two 0-1 variables and The product of can be linearized using the following inequality:

[0117] .

[0118] .

[0119] .

[0120] .

[0121] In the formula, , and All are 0-1 variables.

[0122] Get the daily operating cost of shared energy storage , the specific expression is:

[0123] .

[0124] .

[0125] In the formula, represents the capacity cost of shared energy storage, represents the power cost of shared energy storage, represents the investment cost coefficient of shared energy storage, r Represents the discount rate.

[0126] For energy storage models, it is generally believed that the discharge depth DoD is the basic element for evaluating the loss of energy storage life. The capacity state and discharge depth of energy storage can be expressed as follows:

[0127] .

[0128] .

[0129] In the formula, Indicates that storage t The amount of power stored at any time, represents the upper limit of energy storage capacity, which is the decision variable in the first stage problem.

[0130] Step S2: jointly configure the wind power station alliance to share energy storage and obtain the economic benefits of the wind power station alliance, where the wind power station alliance includes multiple wind power stations.

[0131] Specifically:

[0132] A shared energy storage capacity planning model is established based on the wind power plant alliance. The wind power plant alliance in the shared energy storage capacity planning model jointly configures shared energy storage. That is, the problem of jointly configuring shared energy storage for multiple wind power plants can be reduced to a cooperative game model. To characterize each wind farm, N wind power plants, any non-empty subset of the wind power plant cluster that is jointly configured with shared energy storage As a wind farm alliance ;use Wind Farm Alliance Economic benefits gained from joint configuration of shared energy storage: wind power station alliances will jointly configure shared energy storage, and gain economic benefits through the difference between the wind power station's electricity sales profit and the total electricity bill and the configuration cost of shared energy storage.

[0133] Step S3: Characterize the value of the wind power plant alliance through a two-stage stochastic programming problem, obtain the sum of the configuration cost of the shared energy storage with minimized economic benefits and the minimum daily operating cost, and obtain the optimal configuration of the shared energy storage capacity by minimizing the economic benefits. Use the optimal configuration to allocate operating costs for multiple wind power plants.

[0134] Specifically:

[0135] Based on a two-stage stochastic programming problem, the alliance value is characterized, and the optimal configuration of shared energy storage capacity and the optimal operation strategy of multiple wind power stations using shared energy storage are solved.

[0136] Phase 1 objective function: Obtaining the configuration cost of shared energy storage , the specific expression is:

[0137] .

[0138] The second stage objective function: The objective function is obtained by summing up the minimum daily operating cost , the specific expression is:

[0139] .

[0140] The objective function and The constraints are:

[0141] .

[0142] .

[0143] .

[0144] .

[0145] In the formula, The Wind Farm Alliance, Represents shared energy storage capacity, including energy storage capacity and maximum charge and discharge power ; Indicates the scene number. Indicates the serial number is The scene, represents the set of all scenes, Representation scene Probability of occurrence; Indicates in the scene The operation strategy of any wind power plant in the event of occurrence; Indicates in the scene Wind power station i operational strategies, including charging and discharging of shared energy storage and electricity trading with the grid; Indicates the operation strategy The set of feasible domains; represents the energy storage capacity of shared energy storage at time t; and They represent the discharge power and charging power of the shared energy storage at time t respectively; Represents the maximum time. Through the first stage objective function Obtain the optimal configuration of shared energy storage capacity.

[0146] Obtaining the objective function under constraints The optimal solution, and the energy storage capacity in the optimal solution and maximum charge and discharge power As the optimal configuration of shared energy storage capacity.

[0147] The objective function of the first stage is to minimize the sum of the configuration cost and the operating cost of the shared energy storage, where the operating cost is the minimum operating cost of the wind power station, which depends not only on the decision variables of the first stage problem, i.e., the capacity of the shared energy storage and maximum charge and discharge power , but also depends on the actual scene , by formulating reasonable operation strategies To minimize operating costs, the operating cost is the difference between the cost of purchasing electricity and the income from selling electricity to the grid.

[0148] The optimal operation strategy of wind power station shared energy storage is obtained by allocating the operation cost for multiple wind power stations through the optimal configuration of shared energy storage capacity, including the following steps:

[0149] In the optimal configuration scenario Under this condition, the operating cost of the wind power plant alliance is obtained by the difference between the power purchase cost and the power sales income to the grid; the specific expression is:

[0150] .

[0151] The operating cost of each wind power station is allocated through the operating cost of the wind power station alliance; the specific expression is:

[0152] .

[0153] In the formula, Indicates in the scene In the event of a wind farm alliance, the operating costs incurred by Indicates the electricity purchase price, represents the electricity selling price, and Respectively in the scene In the case of wind power station i exist t The power purchased and sold by exchanging electricity with the external power grid at all times, represents the shared energy storage capacity, represents the cost of purchasing electricity, Represents the revenue from selling electricity to the grid.

[0154] In wind power station i When using shared energy storage, charging and discharging rate limits need to be met:

[0155] .

[0156] .

[0157] In the formula, and Wind power station i The electric power charged into and obtained from the shared energy storage; and Wind power station i The charging and discharging power limit when using shared energy storage is that each wind power station i The charging and discharging power cannot exceed a limit.

[0158] Each wind farm i Both need to meet the power supply and demand balance:

[0159] .

[0160] In the formula, and They respectively indicate that in a certain scenario, the wind power station i At the moment t The power load and wind power output value.

[0161] When a wind power station conducts power purchase and sale transactions with an external power grid, the power traded cannot exceed the upper limit:

[0162] .

[0163] .

[0164] In the formula, Indicates in the scene In the case of wind power stationi Electric power traded.

[0165] Wind power station i The charging and discharging process cannot be carried out at the same time:

[0166] .

[0167] Wind power station i The electricity purchase and sales process cannot be carried out at the same time:

[0168] .

[0169] The following constraints must be met during energy storage operation:

[0170] .

[0171] .

[0172] .

[0173] .

[0174] .

[0175] .

[0176] .

[0177] In the formula, Wind power station i At the moment t Net power exchange with shared energy storage, and Represents shared storage in t The discharge power and charging power at the moment, Indicates that shared storage is at time t Net power exchange with all wind power plants. In addition, shared energy storage must meet the constraints of the upper limit of charging and discharging power, and cannot be charged and discharged at the same time.

[0178] This two-stage stochastic programming problem has a minimize-minimize structure and can be converted into a one-stage convex problem and finally solved directly using the solver.

[0179] Experimental process:

[0180] Combined with the analysis in Table 1, the effectiveness of the joint configuration method of shared energy storage capacity of multiple wind power stations based on cooperative game theory is verified.

[0181] Table 1 Time-of-use electricity price data

[0182]

[0183] Consider multiple scenarios for planning runs: see Figure 2 , Figure 3 and Figure 4 , is the wind power output curve of each wind power station in different scenarios. It can be seen that the present invention meets the requirements of multi-wind power entity shared energy storage planning considering multiple scenarios. Figure 2 , Figure 3 and Figure 4 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 represent scenario 1, scenario 2, scenario 3, scenario 4, scenario 5, scenario 6, scenario 7, scenario 8, scenario 9 and scenario 10.

[0184] Consider the load demand in the wind power station: see Figure 5 and Figure 6 ,as well as Figure 7 , are the in-station load curves of each wind power station under different scenarios. It can be seen that the present invention meets the shared energy storage planning of multiple wind power entities taking into account the in-station load demand. Figure 5 and Figure 6 ,as well as Figure 7 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 represent scenario 1, scenario 2, scenario 3, scenario 4, scenario 5, scenario 6, scenario 7, scenario 8, scenario 9 and scenario 10.

[0185] Economic benefits and operation strategies of multiple wind power entities: See Table 2, where the game mode refers to the way in which wind power stations form alliances. For example, game modes {1}, {2}, and {3} indicate that each wind power station does not form any alliance and configures energy storage separately; game modes {1, 2}, and {3} indicate that wind power station 1 and wind power station 2 form an alliance, while wind power station 3 configures energy storage separately; game mode {1, 2, 3} indicates that three wind power stations form a large alliance and jointly configure energy storage; game modes {2, 3} and {1} indicate that wind power station 2 and wind power station 3 form an alliance, while wind power station 1 configures energy storage separately; game modes {1, 3} and {2} indicate that wind power station 1 and wind power station 3 form an alliance, while wind power station 2 configures energy storage separately; it can be seen that for any wind power station, forming an alliance with other entities and jointly planning shared energy storage always makes more profit than planning energy storage alone.

[0186] Table 2 Energy storage capacity and profit under different game modes

[0187]

[0188] See also Figure 8, the profit distribution plan of each wind power station calculated by the Sharpe value method. It can be seen that after using the Sharpe value method for profit distribution, the profits allocated to each wind power station are 1148.5 yuan, 2340.2 yuan and 2394.6 yuan respectively; and when each entity plans the energy storage capacity separately, the profits obtained are 1006.4 yuan, 2198.1 yuan and 2288.8 yuan respectively. After forming an alliance, the daily economic benefits of each entity increased by 142.1 yuan, 142.1 yuan and 105.8 yuan respectively. The profits of each wind power entity have increased, and the alliance is stable.

[0189] Balancing the profitability of wind power stations and the life-loss costs of shared energy storage: See Table 3. When planning shared energy storage with the life-loss of energy storage in mind, the charging and discharging of shared energy storage is reduced, and the benefits brought by sharing are reduced, but a certain amount of life-loss costs of energy storage are saved. When planning without considering the life-loss of energy storage, although the benefits of energy storage sharing increase, the life of energy storage is reduced.

[0190] Table 3 Benefit comparison before and after considering energy storage life loss

[0191]

[0192] Based on the above method, the present invention provides a wind power station shared energy storage capacity configuration system, including: an operation cost acquisition module, an economic benefit acquisition module and an operation strategy acquisition module.

[0193] Among them, the operating cost acquisition module is used to obtain the daily operating cost of shared energy storage through the capacity cost and power cost of shared energy storage; the economic benefit acquisition module is used to jointly configure the shared energy storage of the wind power station alliance to obtain the economic benefits of the wind power station alliance, and the wind power station alliance includes multiple wind power stations; the configuration acquisition allocation module is used to obtain the optimal configuration of the shared energy storage capacity through a two-stage random programming problem, and to allocate the operating costs for multiple wind power stations through the optimal configuration. Among them, when the configuration acquisition allocation module obtains the optimal configuration of the shared energy storage capacity through a two-stage random programming problem, it includes a parameter acquisition module and an optimal configuration module: the parameter acquisition module is used to obtain the sum of the configuration cost and the minimum daily operating cost of the shared energy storage under the condition of minimum economic benefit, and to obtain the constraint conditions under the condition of minimum economic benefit; the optimal configuration module is used to obtain the optimal solution of the sum of the configuration cost and the minimum daily operating cost of the shared energy storage according to the constraint conditions, and to obtain the optimal configuration of the shared energy storage capacity with the sum of the optimal solution.

[0194] The present invention also provides a computer device, including a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for configuring shared energy storage capacity of a wind power station.

[0195] According to the disclosed embodiments, a computing device may communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth communications, etc.), or with any device (e.g., routers, modems, etc.) that enables a computing device to communicate with one or more other computing devices.

[0196] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for configuring shared energy storage capacity of a wind power station is implemented.

[0197] According to the disclosed embodiments, the storage medium may be a non-volatile computer-readable storage medium, such as but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0198] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0199] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for configuring shared energy storage capacity of a wind power station, It is characterized in that include: Obtain the daily operating cost of the shared energy storage through the capacity cost and power cost of the shared energy storage; Jointly configure shared energy storage for a wind farm alliance to obtain economic benefits from the wind farm alliance, which includes multiple wind farms; Obtaining an optimal configuration of shared energy storage capacity through a two-stage stochastic programming problem, and allocating operating costs for multiple wind power stations through the optimal configuration; The optimal configuration of shared energy storage capacity is obtained through a two-stage stochastic programming problem, including: The sum of the configuration cost and the minimum daily operating cost of shared energy storage under the condition of minimum economic benefit is obtained, as well as the constraints under the condition of minimum economic benefit; Obtaining the optimal solution of the sum of the configuration cost and the minimum daily operating cost of the shared energy storage according to the constraint conditions, and obtaining the optimal configuration of the shared energy storage capacity with the optimal solution of the sum; The daily operating cost of shared energy storage is obtained through the capacity cost and power cost of shared energy storage, which is: Get the daily operating cost of shared energy storage The specific expression is: in, represents the capacity cost of shared energy storage, represents the power cost of shared energy storage, CRF represents the investment cost coefficient of shared energy storage, r represents the discount rate, T Cycle Indicates energy storage cycle life; Before obtaining the daily operating cost of shared energy storage through the capacity cost and power cost of shared energy storage, the following steps are also included: Get the energy storage cycle life, the specific expression is: Among them, ΔDoD j = DoD j,end -DoD j,start ; in, It represents the total number of cycles until the energy storage fails at 100% discharge depth, T represents the time, Indicates the equivalent number of cycles in one day at 100% discharge depth, T Cycle Indicates the energy storage cycle life, ΔDoD j Indicates the cumulative increment of the discharge depth DOD in the continuous discharge period j; the discharge depth DoD at the end of the continuous discharge period j is calculated by the discharge depth DOD j,end The discharge depth DoD at the start time of the continuous discharge period j j,start Difference ΔDoD j ; The method of jointly configuring shared energy storage for the wind power plant alliance to obtain the economic benefits of the wind power plant alliance includes the following steps: Obtain the sum of the electricity sales profits of multiple wind power stations in the wind power station alliance; The economic benefits are obtained by the difference between the sum of the profits from electricity sales and the configuration cost of shared energy storage and the total electricity bill of the wind power plant alliance. The step of obtaining the optimal solution of the sum of the configuration cost and the minimum daily operating cost of the shared energy storage according to the constraint conditions, and obtaining the optimal configuration of the shared energy storage capacity by using the optimal solution of the sum, comprises the following steps: Accessing the configuration costs of shared energy storage The specific expression is: pass The objective function is obtained by summing up the minimum daily operating cost The specific expression is: The objective function and The constraints are: In the formula, The Wind Farm Alliance, Represents shared energy storage capacity, including energy storage capacity and maximum charge and discharge power ω represents the scene number, ζ ω represents the scene with sequence number ω, Ω represents the set of all scenes, ρ ω represents the probability of scene ω appearing; y ω represents the operation strategy of any wind power station when scenario ω occurs; represents the operation strategy of wind power station i when scenario ω occurs, including the charging and discharging of shared energy storage and the electricity transaction with the grid; Indicates the operation strategy The set of feasible domains; represents the energy storage capacity of shared energy storage at time t; and They represent the discharge power and charging power of the shared energy storage at time t respectively; Indicates the maximum time; Obtaining the objective function under constraints The optimal solution, and the energy storage capacity in the optimal solution and maximum charge and discharge power As the optimal configuration of shared energy storage capacity; Allocating operating costs for multiple wind power plants through the optimal configuration includes the following steps: In the optimal configuration scenario, the operating cost of the wind power plant alliance is obtained by the difference between the power purchase cost and the power sales revenue to the grid; the specific expression is: The operating cost of each wind power station is allocated by the operating cost of the wind power station alliance; the specific expression is: In the formula, g(x,y ω ,ζ ω ) represents the operating cost of the wind power plant alliance when scenario ω occurs, Indicates the electricity purchase price, represents the electricity selling price, and They represent the purchased power and sold power generated by wind power station i exchanging power with the external grid at time t when scenario ω occurs. x represents the shared energy storage capacity. represents the cost of purchasing electricity, Represents the revenue from selling electricity to the grid; After the operating cost of each wind power station is allocated according to the operating cost of the wind power station alliance, the following steps are also included: When wind power station i uses shared energy storage, the charging and discharging rate limits are obtained; the specific expression is: In the formula, and They represent the electric power charged into the shared energy storage by wind power station i and the electric power obtained from the shared energy storage; p ch,max and p dis,max They respectively represent the charging and discharging power limits of wind power station i when using shared energy storage; When wind power station i conducts power purchase and sale transactions with the external power grid, it obtains the power limit of the transaction; the specific expression is: In the formula, It represents the traded electric power of wind farm i when scenario ω occurs.

2. A system applied to the method for configuring shared energy storage capacity of a wind power station according to claim 1, It is characterized in that include: An operation cost acquisition module, used for acquiring the daily operation cost of the shared energy storage through the capacity cost and power cost of the shared energy storage; An economic benefit acquisition module is used to jointly configure shared energy storage for a wind power plant alliance to obtain the economic benefits of the wind power plant alliance, which includes multiple wind power plants; A configuration acquisition allocation module is used to obtain the optimal configuration of the shared energy storage capacity through a two-stage stochastic programming problem, and to allocate operating costs for multiple wind power stations through the optimal configuration; Among them, the configuration acquisition allocation module obtains the optimal configuration of the shared energy storage capacity through a two-stage stochastic programming problem, including: A parameter acquisition module is used to obtain the sum of the configuration cost and the minimum daily operation cost of the shared energy storage under the condition of minimum economic benefit, and to obtain the constraint conditions under the condition of minimum economic benefit; The optimal configuration module is used to obtain the optimal solution of the sum of the configuration cost and the minimum daily operating cost of the shared energy storage according to the constraint conditions, and obtain the optimal configuration of the shared energy storage capacity with the optimal solution of the sum.

3. A computer device, It is characterized in that It comprises a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for configuring shared energy storage capacity of a wind power station as claimed in claim 1.

4. A storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of a method for configuring shared energy storage capacity of a wind power station described in claim 1 are implemented.

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