Optimization method and device for energy storage capacity configuration of wind storage power plant

Abstract

The invention provides a wind storage power plant energy storage capacity configuration optimization method and device, and the method comprises the steps: determining the rated power corresponding to a wind storage power plant based on the historical wind power prediction error of the wind storage power plant and the power compensation demand information of the wind power prediction error; according to the rated power, the energy storage operation age limit corresponding to the wind storage power plant and the compensation charging and discharging power corresponding to the wind storage power plant, the energy storage net income of the wind storage power plant is determined; based on the energy storage net income and the operation constraint corresponding to the wind storage power plant, the rated capacity corresponding to the wind storage power plant is determined; and optimizing the energy storage capacity configuration of the wind storage power plant based on the rated power and the rated capacity. The wind power prediction error can be reduced, the energy storage utilization rate is improved, and the operation economic benefits of the wind storage system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage-assisted wind power grid connection, and in particular to a method and device for optimizing the energy storage capacity configuration of a wind power plant. Background Art

[0002] Driven by energy transition and low-carbon demands, renewable energy storage has become a core component of building a new power system. Currently, the cumulative installed capacity of new energy storage projects has reached 44.44 million kilowatts, of which renewable energy storage accounts for 42.8%, making it the primary driver of growth. The average daily operating hours of renewable energy storage have increased from 2.05 hours to 3.74 hours, and the average daily equivalent charge and discharge times have increased from 0.31 to 0.50, equivalent to a complete charge and discharge every two days. This demonstrates the low utilization rate of energy storage.

[0003] Existing technologies offer energy storage capacity configuration solutions tailored to specific operating scenarios or typical days to improve energy storage utilization. However, in reality, weather conditions vary from day to day. Configuring energy storage based solely on specific power scenarios can yield results that are highly dependent on the power scenario. Furthermore, selecting typical days is difficult, and the resulting configuration may not be optimal. Therefore, an effective solution is urgently needed to address these issues. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a method and device for optimizing the energy storage capacity configuration of a wind power plant.

[0005] The present invention provides a method for optimizing the energy storage capacity configuration of a wind power plant, comprising: Determining a rated power corresponding to the wind storage power plant based on a historical wind power prediction error of the wind storage power plant and power compensation demand information for the wind power prediction error; Determining the net energy storage income of the wind storage power plant according to the rated power, the energy storage operation years corresponding to the wind storage power plant, and the compensation charge and discharge power corresponding to the wind storage power plant; Determining a rated capacity corresponding to the wind-storage power plant based on the net energy storage benefit and the operating constraints corresponding to the wind-storage power plant; Based on the rated power and the rated capacity, the energy storage capacity configuration of the wind power plant is optimized.

[0006] According to a method for optimizing the energy storage capacity configuration of a wind-storage power plant provided by the present invention, determining the net energy storage income of the wind-storage power plant based on the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensatory charge and discharge power corresponding to the wind-storage power plant includes: Determining the energy storage configuration cost of the wind-storage power plant according to the rated power and the energy storage operation years corresponding to the wind-storage power plant; Determining the energy storage income of the wind storage power plant according to the energy storage operation years and the compensation charge and discharge power corresponding to the wind storage power plant; The net energy storage income of the wind power plant is determined based on the energy storage income and the energy storage configuration cost.

[0007] According to a method for optimizing energy storage capacity configuration of a wind power plant provided by the present invention, the energy storage configuration cost is the full life cycle cost of energy storage; The determining of the energy storage configuration cost of the wind-storage power plant according to the rated power and the energy storage operation years corresponding to the wind-storage power plant includes: Determining the investment and construction cost and the operation and maintenance cost of the wind-storage power plant based on the rated power and the rated capacity to be determined; Determining the battery recovery cost of the wind power plant based on the investment and construction cost; The full life cycle cost of the energy storage is determined based on the energy storage operation years corresponding to the wind power plant, the investment and construction cost, the operation and maintenance cost, and the battery recovery cost.

[0008] According to the optimization method for energy storage capacity configuration of a wind power plant provided by the present invention, the energy storage benefit is the benefit of energy storage reduction assessment power; The determining of the energy storage income of the wind storage power plant according to the energy storage operation years and the compensation charge and discharge power corresponding to the wind storage power plant includes: Obtaining unit revenue for power prediction error compensation; The energy storage income of the wind power plant is determined based on the energy storage operation years, the compensated charging and discharging power, and the unit income.

[0009] According to a method for optimizing the energy storage capacity configuration of a wind-storage power plant provided by the present invention, before determining the net energy storage income of the wind-storage power plant based on the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensatory charge and discharge power corresponding to the wind-storage power plant, the method further includes: Obtaining the charge and discharge depth corresponding to the wind storage power plant; Determining a maximum number of cycles of the wind-storage power plant at the charge-discharge depth based on the charge-discharge depth and the maximum rated number of cycles of the wind-storage power plant at the rated charge-discharge depth; Based on the maximum number of cycles and the maximum rated number of cycles, the energy storage operation life corresponding to the wind-storage power plant is determined.

[0010] According to a method for optimizing the energy storage capacity configuration of a wind-storage power plant provided by the present invention, before obtaining the charge and discharge depth corresponding to the wind-storage power plant, the method further includes: Determining the power range of the actual power after compensation corresponding to the wind power plant based on the allowable error power range in the power compensation demand information; Based on the power range, an energy storage action strategy corresponding to the wind storage power plant is determined, the energy storage action strategy is used to determine the energy storage state of the wind storage power plant, and the energy storage state is used to determine the charge and discharge depth.

[0011] According to a method for optimizing the energy storage capacity configuration of a wind power plant provided by the present invention, the operation constraints include actual power constraints, rated power constraints and energy storage state constraints; Before determining the rated capacity corresponding to the wind-storage power plant based on the net energy storage income and the operation constraints corresponding to the wind-storage power plant, the method further includes: Establish actual power constraints based on the maximum power of the tie line; constructing a rated power constraint based on the rated power; Based on the upper and lower limits of the energy storage state, energy storage state constraints are constructed.

[0012] According to a method for optimizing the energy storage capacity configuration of a wind power storage plant provided by the present invention, the method determines the rated power corresponding to the wind power storage plant based on the historical wind power forecast error of the wind power storage plant and the power compensation demand information for the wind power forecast error, including: Performing non-parametric kernel density estimation on the historical wind power forecast error of the wind storage power plant to determine the probability density corresponding to the historical wind power forecast error; Determining a cumulative probability distribution corresponding to the historical wind power forecast error based on the probability density; Based on the cumulative probability distribution and the power compensation demand information, a rated power corresponding to the wind-storage power plant is determined.

[0013] According to a method for optimizing the energy storage capacity configuration of a wind-storage power plant provided by the present invention, before determining the rated power corresponding to the wind-storage power plant based on the historical wind power prediction error of the wind-storage power plant and the power compensation demand information for the wind power prediction error, the method further includes: Obtaining the historical day-ahead wind power forecast and historical actual wind power of the wind storage power plant; The historical wind power prediction error is determined based on the historical wind power day-ahead predicted power and the historical wind power actual power.

[0014] The present invention also provides an optimization device for configuring the energy storage capacity of a wind power plant, comprising the following modules: A first determining module is configured to determine a rated power corresponding to the wind power storage power plant based on a historical wind power prediction error of the wind power storage power plant and power compensation demand information for the wind power prediction error; A second determining module is configured to determine the net energy storage income of the wind storage power plant according to the rated power, the energy storage operation years corresponding to the wind storage power plant, and the compensatory charge and discharge power corresponding to the wind storage power plant; A third determination module is configured to determine a rated capacity corresponding to the wind-storage power plant based on the net energy storage income and the operation constraints corresponding to the wind-storage power plant; The optimization module is configured to optimize the energy storage capacity configuration of the wind power plant based on the rated power and the rated capacity.

[0015] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for optimizing the energy storage capacity configuration of a wind power plant as described in any one of the above is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing the energy storage capacity configuration of a wind power plant as described in any one of the above.

[0017] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for optimizing the energy storage capacity configuration of a wind power plant.

[0018] The present invention provides a method and device for optimizing the energy storage capacity configuration of a wind storage power plant. The method and device determine the rated power corresponding to the wind storage power plant based on the historical wind power prediction error of the wind storage power plant and the power compensation demand information for the wind power prediction error; determine the net energy storage income of the wind storage power plant based on the rated power, the energy storage operation years corresponding to the wind storage power plant, and the compensated charging and discharging power corresponding to the wind storage power plant; determine the rated capacity corresponding to the wind storage power plant based on the net energy storage income and the operating constraints corresponding to the wind storage power plant; and optimize the energy storage capacity configuration of the wind storage power plant based on the rated power and the rated capacity, thereby ultimately reducing the configuration cost required for energy storage to compensate for the wind power prediction error, improving the energy storage utilization rate, and the economic efficiency of the wind storage system operation. That is, the wind power prediction error can be reduced, the energy storage utilization rate can be improved, and the economic efficiency of the wind storage system operation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1It is a flow chart of the method for optimizing the energy storage capacity configuration of a wind power plant provided by the present invention.

[0021] Figure 2 It is a schematic diagram of the energy storage action strategy provided by the present invention; Figure 3 It is a schematic diagram of dividing error days provided by the present invention; Figure 4 Schematic diagram of the cumulative probability distribution curve of the prediction error considering the allowable deviation range of the prediction error provided by the present invention; Figure 5 This is a diagram showing the effect of energy storage provided by the present invention on compensating wind power prediction errors; Figure 6 It is a structural schematic diagram of the device for optimizing the energy storage capacity configuration of a wind power plant provided by the present invention.

[0022] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0024] The following combination Figure 1-Figure 7 The present invention describes a method and device for optimizing the energy storage capacity configuration of a wind power plant.

[0025] Figure 1 This is a flow chart of the method for optimizing the energy storage capacity configuration of a wind power plant provided by the present invention, such as Figure 1 As shown, the method includes steps 101 to 104.

[0026] Step 101: Based on the historical wind power prediction error of the wind power storage power plant and the power compensation demand information for the wind power prediction error, the rated power corresponding to the wind power storage power plant is determined.

[0027] Specifically, wind power prediction error refers to the difference between the predicted power and actual power of wind power storage equipment. Power compensation demand information refers to the power demand required to economically meet energy storage construction and compensate for prediction error.

[0028] In practical applications, the capacity and power of energy storage devices are closely coupled. However, for scenarios involving wind power forecast error compensation, the rated power of the energy storage device determines the maximum error that the storage can compensate, while the rated capacity determines how long the storage can continue to operate in this scenario. The power and capacity of the energy storage device correspond to the power and electricity scenarios of wind power forecast error, respectively. Power demand, compared to electricity demand, has no significant temporal coupling. Therefore, the rated power of the required energy storage configuration can be determined through statistical methods.

[0029] The historical wind power forecast errors of a wind-storage power plant can be calculated. Here, the historical wind power forecast errors refer to multiple historical wind power forecast errors, not just a single historical wind power forecast error. Furthermore, based on the historical wind power forecast errors, the allowable error power range is determined by comprehensively considering the economics of energy storage construction and the power required to compensate for the forecast errors. Furthermore, the confidence level for power outside the allowable error power range is used as the rated power to be configured for the energy storage device, i.e., the corresponding rated power of the wind-storage power plant. The confidence level ranges from 0 to 1.

[0030] Step 102: Determine the net energy storage income of the wind-storage power plant according to the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensation charge and discharge power corresponding to the wind-storage power plant.

[0031] Specifically, the energy storage operating life refers to the length of time that wind power generation energy storage equipment can be put into use. The net benefit of energy storage is the difference between the energy storage benefit and the energy storage configuration cost.

[0032] In practical applications, in order to balance the contradictory relationship between the cost of configuring energy storage and the effect of compensating for power forecast errors, a multi-objective optimization problem can be solved with the goals of maximizing the benefit of compensating for power forecast errors (energy storage benefits) and minimizing the energy storage configuration cost. That is, the objective function is constructed with the goal of maximizing the net benefit of energy storage, that is, the objective function is constructed with the goal of minimizing the opposite of the net benefit of energy storage. The objective function is shown in Formula (1).

[0033] min F = min( f 1 – f 2 ) (1) in, F is the opposite of the net benefit of energy storage, f 1 The cost of energy storage configuration, f 2 For energy storage benefits.

[0034] Step 103: Determine the rated capacity corresponding to the wind-storage power plant based on the net energy storage income and the operation constraints corresponding to the wind-storage power plant.

[0035] In practical applications, after constructing the objective function, the constraints corresponding to the wind-storage power plant, i.e., the operating constraints, are pre-constructed and the objective function is solved to obtain the rated capacity corresponding to the wind-storage power plant.

[0036] Step 104: Optimize the energy storage capacity configuration of the wind-storage power plant based on the rated power and the rated capacity.

[0037] After the rated power and rated capacity are determined, the energy storage capacity of the wind power plant can be optimized based on the rated power and rated capacity.

[0038] The present invention provides an optimization method for the configuration of energy storage capacity of a wind storage power plant. The method determines the rated power corresponding to the wind storage power plant based on the historical wind power prediction error of the wind storage power plant and the power compensation demand information for the wind power prediction error; determines the net energy storage income of the wind storage power plant according to the rated power, the energy storage operation years corresponding to the wind storage power plant and the compensated charging and discharging power corresponding to the wind storage power plant; determines the rated capacity corresponding to the wind storage power plant based on the net energy storage income and the operation constraints corresponding to the wind storage power plant; and optimizes the energy storage capacity configuration of the wind storage power plant based on the rated power and the rated capacity, thereby ultimately reducing the configuration cost required for energy storage to compensate for the wind power prediction error, improving the energy storage utilization rate and the economic efficiency of the wind storage system operation, that is, reducing the wind power prediction error, improving the energy storage utilization rate, and improving the economic efficiency of the wind storage system operation.

[0039] In one or more optional embodiments of the present invention, before determining the rated power corresponding to the wind power storage power plant based on the historical wind power prediction error of the wind power storage power plant and the power compensation demand information for the wind power prediction error, the method further includes: Obtaining the historical day-ahead wind power forecast and historical actual wind power of the wind storage power plant; The historical wind power prediction error is determined based on the historical wind power day-ahead predicted power and the historical wind power actual power.

[0040] In practical applications, a wind power prediction error model can be established, and then the historical wind power day-ahead prediction power and the historical wind power actual power can be substituted into the wind power prediction error model to obtain the historical wind power prediction error.

[0041] Specifically, the wind power prediction error model can be t The wind power forecast error at the moment can be expressed as the difference between the wind power forecasted a day ago and the actual wind power, as shown in formula (2).

[0042] (2) in, fort The actual wind power prediction error at the moment is, for t The day-ahead wind power forecast at the time; for t The actual wind power at the moment.

[0043] Due to the bidirectional power flow characteristics of energy storage equipment, there is no need to consider the positive and negative error powers separately, only the absolute error power needs to be considered. In this way, the wind power prediction error can be expressed as the absolute wind power prediction error. It is represented as shown in formula (3).

[0044] (3) Furthermore, the historical wind power forecast power obtained is used as the wind power forecast power, and the historical wind power actual power is used as the wind power actual power. Substituting them into formulas (2) and (3), the historical wind power forecast error can be obtained. In this way, the accuracy and speed of the historical wind power forecast error can be guaranteed.

[0045] In one or more optional embodiments of the present invention, The determining of the rated power corresponding to the wind power storage power plant based on the historical wind power prediction error of the wind power storage power plant and the power compensation demand information for the wind power prediction error includes: Performing non-parametric kernel density estimation on the historical wind power forecast error of the wind storage power plant to determine the probability density corresponding to the historical wind power forecast error; Determining a cumulative probability distribution corresponding to the historical wind power forecast error based on the probability density; Based on the cumulative probability distribution and the power compensation demand information, a rated power corresponding to the wind-storage power plant is determined.

[0046] Specifically, nonparametric kernel density estimation is a statistical method that aims to infer the form of a probability density function from known sample data.

[0047] In practical applications, a nonparametric kernel density estimation is performed on a given wind power error sample (historical wind power forecast error) to obtain a probability density. Specifically, a nonparametric kernel density estimation can be performed on the wind power error sample according to the probability density function to obtain a probability density, as shown in Formula (4).

[0048] (4) in, f ( ) is the probability density function, g ( ) is the kernel function in this paper, that is, the non-parametric kernel density estimation function, which can be a Gaussian kernel function; Nis the total number of samples, i.e., the number of historical wind power forecast errors; h is the bandwidth, which is determined by cross-validation method; is the mth historical wind power forecast error; x is a variable.

[0049] Then, the probability density is integrated to obtain the cumulative probability distribution. Specifically, the probability density can be integrated according to the cumulative probability distribution function to obtain the cumulative probability distribution. The cumulative probability distribution function is shown in formula (5): (5) in, F ( ) is the cumulative probability distribution function.

[0050] Furthermore, by comprehensively considering the economic efficiency of energy storage construction and the power demand for compensating for prediction errors, the allowable error power range is determined, and the confidence level exceeding the allowable error power range is taken as the rated power that the energy storage equipment needs to be configured with, that is, the rated power corresponding to the wind storage power plant, as shown in Formula (6).

[0051] (6) Where: is the inverse function of the cumulative probability distribution function, k is the confidence level, P bn is the rated power.

[0052] The embodiments of the present invention can effectively handle the complexity of data distribution through non-parametric kernel density estimation without assuming the specific distribution form of the data in advance. By utilizing historical wind power forecast error data, it can generate a probabilistic forecast of wind power and reveal the error fluctuation characteristics of wind power forecast output, thereby improving the accuracy and reliability of rated power.

[0053] In one or more optional embodiments of the present invention, determining the net energy storage income of the wind-storage power plant according to the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensatory charge and discharge power corresponding to the wind-storage power plant includes: Determining the energy storage configuration cost of the wind-storage power plant according to the rated power and the energy storage operation years corresponding to the wind-storage power plant; Determining the energy storage income of the wind storage power plant according to the energy storage operation years and the compensation charge and discharge power corresponding to the wind storage power plant; The net energy storage income of the wind power plant is determined based on the energy storage income and the energy storage configuration cost.

[0054] In practical applications, the energy storage configuration cost can be determined according to the set energy storage configuration cost determination strategy, and the energy storage operating years corresponding to the rated power and the wind storage power plant can be processed to obtain the energy storage configuration cost of the wind storage power plant. In addition, the energy storage operating years and the compensating charge and discharge power corresponding to the wind storage power plant can be processed according to the set energy storage benefit determination strategy to obtain the energy storage benefit of the wind storage power plant. Furthermore, the difference between the energy storage benefit and the energy storage configuration cost can be determined as the net energy storage benefit of the wind storage power plant.

[0055] In this way, determining the net benefit of energy storage from the two levels of energy storage income and energy storage configuration cost can improve the accuracy of the net benefit of energy storage.

[0056] In one or more optional embodiments of the present invention, before determining the net energy storage income of the wind-storage power plant based on the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensatory charge and discharge power corresponding to the wind-storage power plant, the method further includes: Obtaining the charge and discharge depth corresponding to the wind storage power plant; Determining a maximum number of cycles of the wind-storage power plant at the charge-discharge depth based on the charge-discharge depth and the maximum rated number of cycles of the wind-storage power plant at the rated charge-discharge depth; Based on the maximum number of cycles and the maximum rated number of cycles, the energy storage operation life corresponding to the wind-storage power plant is determined.

[0057] In practical applications, the life of energy storage equipment, that is, the number of years of energy storage operation, is closely related to the charge and discharge behavior of the energy storage equipment. That is, the energy storage equipment is continuously charged for a period of time, and the state of charge (SOC) rises from SOC0 to SOC1, and then continuously discharged, and the state of charge drops from SOC1 to SOC0, which is a complete charge and discharge (cycle) depth. The calculation process is shown in formula (7).

[0058] (7) in, for t Energy storage charge state at all times; for t+ Energy storage charge state at moment 1; The energy storage power in the charge and discharge cycle is negative when the energy storage is charging and positive when the energy storage is discharging; Charging efficiency for energy storage; is the rated capacity of the wind-storage power plant to be constructed.

[0059] Based on the obtained charge and discharge depth, the maximum rated cycle number of the wind storage power plant at the rated charge and discharge depth is determined. Specifically, the number of cycles in the entire life cycle of the energy storage (the wind power plant is at the depth of charge and discharge) Maximum number of cycles under The relationship is shown in formula (8).

[0060] (8) in, i Indicates the i or i Second-rate; For the i The depth of charge and discharge during the first cycle; For The maximum number of cycles of the battery at the depth of charge and discharge; is the rated charge and discharge depth, generally 1; The maximum number of energy storage cycles when cycling at the rated charge and discharge depth, that is, the maximum rated cycle number, can be taken as 4500 times here.

[0061] Furthermore, we define the coefficient Depth of charge and discharge The loss of battery life during one cycle is equivalent to the loss of battery life during a cycle at the rated charge and discharge depth, that is, the equivalent charge and discharge depth. The calculation process is shown in formula (9).

[0062] (9) Finally, the rain flow counting method is used to count the energy storage charging and discharging behaviors within one year, and the energy storage operating life is shown in formula (10).

[0063] (10) in, The first i Equivalent charge and discharge depth; The operating life of the energy storage system; y It is the total number of charge and discharge cycles of the energy storage device.

[0064] In one or more optional embodiments of the present invention, before obtaining the charge and discharge depth corresponding to the wind-storage power plant, the method further includes: Determining the power range of the actual power after compensation corresponding to the wind power plant based on the allowable error power range in the power compensation demand information; Based on the power range, an energy storage action strategy corresponding to the wind storage power plant is determined, the energy storage action strategy is used to determine the energy storage state of the wind storage power plant, and the energy storage state is used to determine the charge and discharge depth.

[0065] In practical applications, in order to explore the optimal configuration of energy storage, the allowable error power range of the wind power prediction error can be combined to obtain the power range of the actual power after energy storage compensation, including the upper and lower limits, as shown in formula (11).

[0066] (11) in, The upper limit of actual power after energy storage compensation; The lower limit of actual power after energy storage compensation; To allow for an error power range, 20% of the rated capacity corresponding to the wind-storage power plant can be taken.

[0067] Furthermore, based on the power range of the actual power after energy storage compensation, the energy storage action strategy is determined. Figure 2 , Figure 2 This diagram illustrates the energy storage operation strategy provided by the present invention: When the actual power exceeds the upper limit (the upper limit of the error band), the energy storage device absorbs excess wind power, i.e., energy storage charging. When the actual power is between the upper limit and the lower limit (the lower limit of the error band), the energy storage device remains inactive. When the actual power is less than the lower limit, the energy storage device generates power to make up for the shortfall between the actual power and the predicted power, i.e., energy storage discharging. The horizontal axis represents time, and the vertical axis represents power.

[0068] Since the forecast accuracy is assessed on a daily basis, the annual power data is divided into normal days (days when the wind power forecast error is within the allowable error range) and error days (days when the wind power forecast error exceeds the allowable error range). The energy storage demand is analyzed on a rolling basis using the day as the time window.

[0069] Specifically, see Figure 3 , Figure 3 This is a schematic diagram of dividing error days provided by the present invention: Figure 3 The blank area represents a normal day, and the shaded area represents an error day. Consider the energy storage device's initial SOC of 0.5 (D1). When error days occur consecutively [D2, D3], the energy storage device's SOC at the end of the previous day's time period is set as the initial SOC for the next day. When error days alternate with normal days [D3, D4, D5], to ensure the energy storage device's ability to adjust its power absorption and output, it is assumed that the energy storage device can obtain / release power from the grid or wind power on the normal day D4, bringing the energy storage device's SOC back to 0.5. This continues in this manner until Dn.

[0070] The embodiment of the present invention formulates a charging and discharging strategy and an energy storage action strategy for energy storage equipment based on the allowable deviation range of wind power prediction error and the error time series coupling relationship, thereby improving the accuracy and reliability of the energy storage action strategy.

[0071] In one or more optional embodiments of the present invention, the energy storage configuration cost is the full life cycle cost of the energy storage; accordingly, determining the energy storage configuration cost of the wind-storage power plant based on the rated power and the energy storage operation years corresponding to the wind-storage power plant includes: Determining the investment and construction cost and the operation and maintenance cost of the wind-storage power plant based on the rated power and the rated capacity to be determined; Determining the battery recovery cost of the wind power plant based on the investment and construction cost; The full life cycle cost of the energy storage is determined based on the energy storage operation years corresponding to the wind power plant, the investment and construction cost, the operation and maintenance cost, and the battery recovery cost.

[0072] Specifically, the full life cycle cost of energy storage, that is, the full life cycle cost of energy storage equipment, refers to all costs incurred by the energy storage equipment during its life cycle, including direct costs, indirect costs, derived costs and non-derivative costs; the main components are investment costs, operation and maintenance costs, and recycling and processing costs.

[0073] In actual applications, the full life cycle cost of energy storage mainly consists of three parts: investment and construction cost, operation and maintenance cost, and battery recycling cost.

[0074] Investment and construction costs refer to the fixed capital invested in the initial stage of energy storage equipment construction, which is usually used to purchase battery containers, converters, transformers and other major equipment. The calculation process is shown in formula (12).

[0075] (12) in, Cost coefficient per unit power for building battery energy storage; is the rated power corresponding to the wind storage power plant to be constructed; Cost coefficient per unit capacity for building battery energy storage; is the rated capacity of the wind-storage power plant to be constructed.

[0076] Operation and maintenance costs refer to the funds invested dynamically to ensure the normal operation of the energy storage system during its life cycle, usually including the costs of testing, installation, loss, outage, manpower, inspection and repair of the energy storage system, and are calculated on an annual basis. The calculation process is shown in formula (13).

[0077] (13) in, is the annual operation and maintenance cost coefficient per unit power of energy storage; is the annual operation and maintenance cost coefficient per unit capacity of energy storage.

[0078] Battery recycling costs refer to the costs incurred during the harmless treatment and reuse of retired batteries. Batteries are rich in precious metals such as lithium, nickel, cobalt, and manganese, and are highly valuable resources. Their economic value is high due to their recycling value. The recycling value varies depending on the type of battery, generally ranging from 3% to 40% of the initial investment, so the battery recycling cost is generally negative. The calculation process is shown in formula (14).

[0079] (14) in, is the residual value coefficient of the energy storage battery.

[0080] Furthermore, the life cycle cost of energy storage f 1 The calculation process is shown in formula (15).

[0081] (15) in, r is the net present value rate.

[0082] The embodiments of the present invention determine the full life cycle cost of energy storage in a fine-grained manner from three levels: investment and construction cost, operation and maintenance cost, and battery recovery cost. This not only ensures the comprehensiveness of the full life cycle cost of energy storage, but also improves the accuracy of the full life cycle cost of energy storage.

[0083] In one or more optional embodiments of the present invention, the energy storage benefit is a benefit of energy storage reduction assessment power; accordingly, determining the energy storage benefit of the wind storage power plant based on the energy storage operation years and the compensatory charge and discharge power corresponding to the wind storage power plant includes: Obtaining unit revenue for power prediction error compensation; The energy storage income of the wind power plant is determined based on the energy storage operation years, the compensated charging and discharging power, and the unit income.

[0084] In practical applications, energy storage equipment is used to compensate for wind power forecast errors, which can reduce the forecast error assessment power and weaken the randomness of wind power. The power integral of the actual power exceeding the upper and lower limits after compensation by energy storage is defined as the assessment power. The energy storage is used to compensate for the forecast error within the normal operation period, that is, the energy storage reduces the assessment power benefits. f 2 The calculation process is shown in formula (16).

[0085] (16) in, For energy storage tThe charging and discharging power used to compensate for the wind power forecast error to within the allowable error range and avoid penalties; for t The unit benefit of energy storage at a given moment for forecast error compensation; n is the total number of times, which is 96; is the time interval, which is 15 minutes.

[0086] In one or more optional embodiments of the present invention, the operation constraints include actual power constraints, rated power constraints, and energy storage state constraints; Before determining the rated capacity corresponding to the wind-storage power plant based on the net energy storage income and the operation constraints corresponding to the wind-storage power plant, the method further includes: Establish actual power constraints based on the maximum power of the tie line; constructing a rated power constraint based on the rated power; Based on the upper and lower limits of the energy storage state, energy storage state constraints are constructed.

[0087] Specifically, the actual power constraint means that the power of wind power during the actual grid connection process must not exceed the maximum power of the tie line. , as shown in formula (17).

[0088] (17) Rated power constraint refers to the energy storage power during the optimization process The selected rated power must not be exceeded, as shown in formula (18).

[0089] (18) Energy storage state constraint refers to the energy storage state at each moment to ensure the safe operation of energy storage. The limit cannot be exceeded, as shown in formula (19).

[0090] (19) in, is the upper limit of the energy storage state, is the lower limit of the energy storage state.

[0091] Among them, the energy storage state at each moment The calculation process is shown in formula (20).

[0092] (20) in, is the discharge efficiency of the energy storage device.

[0093] The optimization method for the energy storage capacity configuration of the wind power plant provided by the present invention is tested below.

[0094] The simulation analysis is carried out using the short-term actual / forecast power of a 200MW wind farm with a time resolution of 15 minutes throughout the year.

[0095] See also Figure 4 , Figure 4 This is a schematic diagram of the cumulative probability distribution curve of the prediction error provided by the present invention, taking into account the allowable deviation range of the prediction error: It can be seen that for nearly 90% of the time throughout the year, the wind power prediction error is within the allowable error range specified in the "Two Detailed Rules". Energy storage mainly compensates for the errors that exceed the allowable error range in the figure, and this part of the error as a whole shows a substitution characteristic in which the probability of occurrence gradually decreases as the error power increases. That is, when the configured energy storage capacity is 40MW, it can meet the power requirements of more than 90% of the error power scenarios. Among them, the horizontal axis is the error power unit in MW (megawatt), the vertical axis is the probability, the dotted line represents the overall absolute error, and the solid line represents the error outside the allowable range (allowable error range).

[0096] Through simulation analysis, it is concluded that the economic efficiency for compensating wind power forecast errors is optimal when the rated power of energy storage is 45MW and the rated capacity of energy storage is 26MWh, as shown in Table 1 below.

[0097] Table 1 Energy storage economic configuration

[0098] See also Figure 5 , Figure 5 This diagram shows the effect of energy storage provided by the present invention on compensating for wind power forecast errors: It can be intuitively seen that energy storage can effectively compensate for wind power forecast error power (forecast power) to within the allowable deviation range. After energy storage operation, the maximum absolute error is reduced from 57MW to the allowable 40MW; after energy storage operation, the root mean square error is reduced from the original 0.1484 to 0.1348, and the sum of the absolute error power exceeding the upper and lower limits of the allowable error is reduced from 40.5MWh to 0MWh, which is a significant effect in compensating for forecast errors. After energy storage compensation, the number of normal days increased from 127 to 183 days. The reason for not increasing to 365 days is that, on the one hand, from a statistical perspective, the rated power of energy storage cannot fully cover the power forecast error scenarios, and on the other hand, from an economic perspective, the energy storage configuration cost is too high. Therefore, the capacity should not be too large to avoid reducing the economic efficiency of system operation. Among them, the horizontal axis is time, the unit is 15 minutes, the vertical axis is power, the unit is MW, the gray part represents the allowable error range, the combination of rectangle and horizontal line is the predicted power, the combination of triangle and horizontal line is the actual power, and the combination of circle and horizontal line is the combined wind and storage power (compensated power).

[0099] To fully demonstrate the superiority of this proposed capacity configuration, we set up different scenarios for horizontal comparison. Scheme 1: Traditional capacity configuration, with the energy storage rated power set at 20% of the wind turbine installed capacity and operating for 2 hours; Scheme 2: Maximum power capacity configuration, with the energy storage rated power set at the maximum absolute error power value and the rated capacity set at the maximum error power value; and Scheme 3: Optimized configuration that considers error power scenarios and operational constraints. The specific energy storage configurations are shown in Table 2, and the lifetime economic benefit indicators for the energy storage system under different configurations are shown in Table 3.

[0100] Table 2 Energy storage configuration of different solutions

[0101] Table 3 System economic benefit indicators under different configurations

[0102] As can be seen from the table, although the energy storage capacity of Scheme 1 is almost three times that of Scheme 3, its benefit in compensating for prediction errors and reducing error penalties is only 2.13 times that of Scheme 1. Even Scheme 2, which takes into account error power and continuous error power, cannot completely compensate for the prediction error. This is because, although the energy storage capacity is large enough, there is not enough power to charge the energy storage. The three configuration schemes have little difference in energy storage life, and the years required for Schemes 1 and 2 to recover the cost are far longer than the service life of the energy storage. Blindly increasing the energy storage capacity leads to a sharp decline in the economic efficiency of energy storage operation. The scheme provided by the present invention is significantly economical in reducing prediction errors and lowering assessment penalty costs. At the same time, due to the smaller energy storage capacity, the energy storage utilization rate of Scheme 3 is also much greater than that of the other schemes.

[0103] In summary, the method provided by the present invention is effective and has obvious advantages over other comparative strategies, has a higher input-output ratio, and has higher energy storage economic benefits.

[0104] The following describes the device for optimizing the energy storage capacity configuration of a wind storage power plant provided by the present invention. The device for optimizing the energy storage capacity configuration of a wind storage power plant described below and the method for optimizing the energy storage capacity configuration of a wind storage power plant described above can refer to each other.

[0105] Figure 6 This is a schematic diagram of the structure of the device for optimizing the energy storage capacity configuration of a wind power plant provided by the present invention. Figure 6 As shown, the device includes: The first determining module 601 is configured to determine the rated power corresponding to the wind power storage power plant based on the historical wind power prediction error of the wind power storage power plant and the power compensation demand information for the wind power prediction error; The second determining module 602 is configured to determine the net energy storage income of the wind-storage power plant according to the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensation charge and discharge power corresponding to the wind-storage power plant; A third determining module 603 is configured to determine a rated capacity corresponding to the wind-storage power plant based on the net energy storage income and the operating constraints corresponding to the wind-storage power plant; The optimization module 604 is configured to optimize the energy storage capacity configuration of the wind power plant based on the rated power and the rated capacity.

[0106] The device for optimizing the energy storage capacity configuration of a wind storage power plant provided by the present invention is configured, through a first determination module, to determine the rated power corresponding to the wind storage power plant based on the historical wind power prediction error of the wind storage power plant and the power compensation demand information for the wind power prediction error; the second determination module is configured to determine the net energy storage income of the wind storage power plant based on the rated power, the energy storage operation years corresponding to the wind storage power plant and the compensated charging and discharging power corresponding to the wind storage power plant; the third determination module is configured to determine the rated capacity corresponding to the wind storage power plant based on the net energy storage income and the operating constraints corresponding to the wind storage power plant; the optimization module is configured to optimize the energy storage capacity configuration of the wind storage power plant based on the rated power and the rated capacity, and ultimately achieve the reduction of the configuration cost required for energy storage to compensate for the wind power prediction error, the improvement of energy storage utilization and the economic efficiency of wind storage system operation, that is, the wind power prediction error can be reduced, the energy storage utilization rate can be improved, and the economic efficiency of wind storage system operation can be improved.

[0107] Optionally, the second determining module 602 is specifically configured to: Determining the energy storage configuration cost of the wind-storage power plant according to the rated power and the energy storage operation years corresponding to the wind-storage power plant; Determining the energy storage income of the wind storage power plant according to the energy storage operation years and the compensation charge and discharge power corresponding to the wind storage power plant; The net energy storage income of the wind power plant is determined based on the energy storage income and the energy storage configuration cost.

[0108] Optionally, the energy storage configuration cost is the full life cycle cost of the energy storage; The second determining module 602 is specifically configured to: Determining the investment and construction cost and the operation and maintenance cost of the wind-storage power plant based on the rated power and the rated capacity to be determined; Determining the battery recovery cost of the wind power plant based on the investment and construction cost; The full life cycle cost of the energy storage is determined based on the energy storage operation years corresponding to the wind power plant, the investment and construction cost, the operation and maintenance cost, and the battery recovery cost.

[0109] Optionally, the energy storage benefit is the benefit of reducing the assessed electricity volume through energy storage; The second determining module 602 is specifically configured to: Obtaining unit revenue for power prediction error compensation; The energy storage income of the wind power plant is determined based on the energy storage operation years, the compensated charging and discharging power, and the unit income.

[0110] Optionally, the apparatus further includes a fourth determining module configured to: Obtaining the charge and discharge depth corresponding to the wind storage power plant; Determining a maximum number of cycles of the wind-storage power plant at the charge-discharge depth based on the charge-discharge depth and the maximum rated number of cycles of the wind-storage power plant at the rated charge-discharge depth; Based on the maximum number of cycles and the maximum rated number of cycles, the energy storage operation life corresponding to the wind-storage power plant is determined.

[0111] Optionally, the apparatus further includes a fifth determining module configured to: Determining the power range of the actual power after compensation corresponding to the wind power plant based on the allowable error power range in the power compensation demand information; Based on the power range, an energy storage action strategy corresponding to the wind storage power plant is determined, the energy storage action strategy is used to determine the energy storage state of the wind storage power plant, and the energy storage state is used to determine the charge and discharge depth.

[0112] Optionally, the operation constraints include actual power constraints, rated power constraints and energy storage state constraints; The apparatus further comprises a building block configured to: Establish actual power constraints based on the maximum power of the tie line; constructing a rated power constraint based on the rated power; Based on the upper and lower limits of the energy storage state, energy storage state constraints are constructed.

[0113] Optionally, the first determining module 601 is specifically configured to: Performing non-parametric kernel density estimation on the historical wind power forecast error of the wind storage power plant to determine the probability density corresponding to the historical wind power forecast error; Determining a cumulative probability distribution corresponding to the historical wind power forecast error based on the probability density; Based on the cumulative probability distribution and the power compensation demand information, a rated power corresponding to the wind-storage power plant is determined.

[0114] Optionally, the apparatus further includes a sixth determining module configured to: Obtaining the historical day-ahead wind power forecast and historical actual wind power of the wind storage power plant; The historical wind power prediction error is determined based on the historical wind power day-ahead predicted power and the historical wind power actual power.

[0115] Figure 7 An example of a physical structure diagram of an electronic device is shown below. Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communications bus 740, wherein the processor 710, the communications interface 720, and the memory 730 communicate with each other via the communications bus 740. The processor 710 may call logic instructions in the memory 730 to execute a method for optimizing the energy storage capacity configuration of a wind storage power plant. The method includes: determining a rated power corresponding to the wind storage power plant based on the wind storage power plant's historical wind power forecast error and power compensation demand information for the wind power forecast error; determining a net energy storage benefit of the wind storage power plant based on the rated power, the energy storage operation years corresponding to the wind storage power plant, and the compensated charge and discharge power corresponding to the wind storage power plant; determining a rated capacity corresponding to the wind storage power plant based on the net energy storage benefit and the operating constraints corresponding to the wind storage power plant; and optimizing the energy storage capacity configuration of the wind storage power plant based on the rated power and the rated capacity.

[0116] Furthermore, the logic instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0117] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the optimization method for the energy storage capacity configuration of the wind storage power plant provided by the above methods, the method including: determining the rated power corresponding to the wind storage power plant based on the historical wind power prediction error of the wind storage power plant and the power compensation demand information for the wind power prediction error; determining the net energy storage income of the wind storage power plant based on the rated power, the energy storage operation years corresponding to the wind storage power plant and the compensated charging and discharging power corresponding to the wind storage power plant; determining the rated capacity corresponding to the wind storage power plant based on the net energy storage income and the operating constraints corresponding to the wind storage power plant; and optimizing the energy storage capacity configuration of the wind storage power plant based on the rated power and the rated capacity.

[0118] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing the energy storage capacity configuration of a wind storage power plant provided by the above-mentioned methods, the method comprising: determining the rated power corresponding to the wind storage power plant based on the historical wind power prediction error of the wind storage power plant and the power compensation demand information for the wind power prediction error; determining the net energy storage income of the wind storage power plant based on the rated power, the energy storage operation years corresponding to the wind storage power plant and the compensated charging and discharging power corresponding to the wind storage power plant; determining the rated capacity corresponding to the wind storage power plant based on the net energy storage income and the operating constraints corresponding to the wind storage power plant; and optimizing the energy storage capacity configuration of the wind storage power plant based on the rated power and the rated capacity.

[0119] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0120] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing the energy storage capacity configuration of a wind power plant, characterized in that: include: Determining a rated power corresponding to the wind storage power plant based on a historical wind power prediction error of the wind storage power plant and power compensation demand information for the wind power prediction error; Determining the net energy storage income of the wind storage power plant according to the rated power, the energy storage operation years corresponding to the wind storage power plant, and the compensation charge and discharge power corresponding to the wind storage power plant; Determining a rated capacity corresponding to the wind-storage power plant based on the net energy storage benefit and the operating constraints corresponding to the wind-storage power plant; Based on the rated power and the rated capacity, the energy storage capacity configuration of the wind power plant is optimized.

2. The method for optimizing the energy storage capacity configuration of a wind power plant according to claim 1, characterized in that: The determining of the net energy storage income of the wind-storage power plant according to the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensatory charge and discharge power corresponding to the wind-storage power plant includes: Determining the energy storage configuration cost of the wind-storage power plant according to the rated power and the energy storage operation years corresponding to the wind-storage power plant; Determining the energy storage income of the wind storage power plant according to the energy storage operation years and the compensation charge and discharge power corresponding to the wind storage power plant; The net energy storage income of the wind power plant is determined based on the energy storage income and the energy storage configuration cost.

3. The method for optimizing the energy storage capacity configuration of a wind power plant according to claim 2, characterized in that: The energy storage configuration cost is the full life cycle cost of energy storage; The determining of the energy storage configuration cost of the wind-storage power plant according to the rated power and the energy storage operation years corresponding to the wind-storage power plant includes: Determining the investment and construction cost and the operation and maintenance cost of the wind-storage power plant based on the rated power and the rated capacity to be determined; Determining the battery recovery cost of the wind power plant based on the investment and construction cost; The full life cycle cost of the energy storage is determined based on the energy storage operation years corresponding to the wind power plant, the investment and construction cost, the operation and maintenance cost, and the battery recovery cost.

4. The method for optimizing the energy storage capacity configuration of a wind power plant according to claim 2, characterized in that: The energy storage benefit is the benefit of reducing the assessed electricity volume through energy storage; The determining of the energy storage income of the wind storage power plant according to the energy storage operation years and the compensation charge and discharge power corresponding to the wind storage power plant includes: Obtaining unit revenue for power prediction error compensation; The energy storage income of the wind power plant is determined based on the energy storage operation years, the compensated charging and discharging power, and the unit income.

5. The method for optimizing the energy storage capacity configuration of a wind power plant according to claim 1, characterized in that: Before determining the net energy storage income of the wind-storage power plant based on the rated power, the energy storage operation years corresponding to the wind-storage power plant, and the compensatory charge and discharge power corresponding to the wind-storage power plant, the method further includes: Obtaining the charge and discharge depth corresponding to the wind storage power plant; Determining a maximum number of cycles of the wind-storage power plant at the charge-discharge depth based on the charge-discharge depth and the maximum rated number of cycles of the wind-storage power plant at the rated charge-discharge depth; Based on the maximum number of cycles and the maximum rated number of cycles, the energy storage operation life corresponding to the wind-storage power plant is determined.

6. The method for optimizing the energy storage capacity configuration of a wind power plant according to claim 5, characterized in that: Before obtaining the charge and discharge depth corresponding to the wind storage power plant, the method further includes: Determining the power range of the actual power after compensation corresponding to the wind power plant based on the allowable error power range in the power compensation demand information; Based on the power range, an energy storage action strategy corresponding to the wind storage power plant is determined, the energy storage action strategy is used to determine the energy storage state of the wind storage power plant, and the energy storage state is used to determine the charge and discharge depth.

7. The method for optimizing the energy storage capacity configuration of a wind power plant according to claim 1, characterized in that: The operation constraints include actual power constraints, rated power constraints and energy storage state constraints; Before determining the rated capacity corresponding to the wind-storage power plant based on the net energy storage income and the operation constraints corresponding to the wind-storage power plant, the method further includes: Establish actual power constraints based on the maximum power of the tie line; constructing a rated power constraint based on the rated power; Based on the upper and lower limits of the energy storage state, energy storage state constraints are constructed.

8. The method for optimizing the energy storage capacity configuration of a wind power plant according to claim 1, characterized in that: The determining of the rated power corresponding to the wind power storage power plant based on the historical wind power prediction error of the wind power storage power plant and the power compensation demand information for the wind power prediction error includes: Performing non-parametric kernel density estimation on the historical wind power forecast error of the wind storage power plant to determine the probability density corresponding to the historical wind power forecast error; Determining a cumulative probability distribution corresponding to the historical wind power forecast error based on the probability density; Based on the cumulative probability distribution and the power compensation demand information, a rated power corresponding to the wind-storage power plant is determined.

9. The method for optimizing the energy storage capacity configuration of a wind power plant according to any one of claims 1 to 8, characterized in that: Before determining the rated power corresponding to the wind power storage power plant based on the historical wind power prediction error of the wind power storage power plant and the power compensation demand information for the wind power prediction error, the method further includes: Obtaining the historical day-ahead wind power forecast and historical actual wind power of the wind storage power plant; The historical wind power prediction error is determined based on the historical wind power day-ahead predicted power and the historical wind power actual power.

10. A device for optimizing the energy storage capacity configuration of a wind power plant, characterized in that: include: A first determining module is configured to determine a rated power corresponding to the wind power storage power plant based on a historical wind power prediction error of the wind power storage power plant and power compensation demand information for the wind power prediction error; A second determining module is configured to determine the net energy storage income of the wind storage power plant according to the rated power, the energy storage operation years corresponding to the wind storage power plant, and the compensatory charge and discharge power corresponding to the wind storage power plant; A third determination module is configured to determine a rated capacity corresponding to the wind-storage power plant based on the net energy storage income and the operation constraints corresponding to the wind-storage power plant; The optimization module is configured to optimize the energy storage capacity configuration of the wind power plant based on the rated power and the rated capacity.

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