Hybrid energy storage optimal configuration method and device considering economic wind curtailment

Abstract

The invention discloses a hybrid energy storage optimal configuration method and device considering economic wind curtailment, and relates to the technical field of electric power system planning, and the method comprises the steps: carrying out the wind curtailment scene simulation of an original wind power curve through employing a segmented test method, calculating the annual wind curtailment loss cost and the wind curtailment rate of each wind curtailment scene based on time-of-use electricity price data, and calculating the wind curtailment rate of each wind curtailment scene; simulating until the wind curtailment rate reaches the specified upper limit of the power grid; and traversing a power distribution scheme for each power curve after wind curtailment, determining power components of the supercapacitor and the storage battery through empirical mode decomposition, and further calculating rated power and rated capacity of the supercapacitor and the storage battery. And constructing a system operation constraint based on the rated power, the rated capacity and the upper limit of the wind curtailment rate, calculating the annual comprehensive cost of each wind curtailment scene and the power distribution scheme under the condition that the constraint condition is met, and selecting the scheme with the minimum cost as a final optimization configuration scheme. According to the method, economic balance among the wind curtailment loss cost, the energy storage cost and the operation and maintenance cost is realized, and excessive configuration of energy storage can be effectively avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, in particular to a mixed energy storage optimal configuration method and device considering economic curtailment of wind power. BACKGROUND

[0002] With the large-scale development of wind power industry, the intermittency and volatility of wind power output lead to the increasingly prominent problem of curtailment of wind power. In order to consume excess wind power, energy storage system becomes a key technical means, but single energy storage device is difficult to balance cost and response speed, and excessive configuration of energy storage will lead to a sharp increase in investment cost.

[0003] The existing mixed energy storage configuration technology has significant defects: on the one hand, most of them take "full consumption of wind power" or "zero curtailment" as the goal, and do not consider the economic curtailment demand. When the comprehensive cost (including investment, operation and maintenance, and life loss) of energy storage to consume excess wind power is higher than the wind power grid income, excessive configuration of energy storage will lead to a sharp increase in investment cost, and thus reduce the overall economy of the system. On the other hand, even if some schemes mention curtailment scheme, they mostly use fixed benchmark price to calculate curtailment cost, which cannot match the fluctuation characteristics of time-of-use electricity price in power market, cannot accurately judge the economy of curtailment, and leads to lack of scientific basis for energy storage configuration decision, and it is difficult to achieve the balance between energy storage cost and wind power consumption income. SUMMARY

[0004] The technical problem to be solved by the present application is to solve the problem that the existing mixed energy storage configuration method does not accurately calculate the curtailment cost, resulting in lack of economic basis for the energy storage configuration scheme. The purpose is to provide a mixed energy storage optimal configuration method and device considering economic curtailment, which solves the above problems.

[0005] The present application is realized by the following technical scheme:

[0006] In a first aspect, the present application provides a mixed energy storage optimal configuration method considering economic curtailment, comprising:

[0007] Based on the historical output data of the target wind farm, the original wind power curve under the planning scenario is determined;

[0008] The original wind power curve is simulated for multiple curtailment scenarios by using the piecewise trial method, and the curtailment power curve and the power curve after curtailment of each curtailment scenario are obtained;

[0009] Based on the curtailment power curve of each curtailment scenario and the time-of-use electricity price data of the power market, the annual curtailment loss cost and the curtailment rate of each curtailment scenario are obtained, until the curtailment rate is greater than or equal to the upper limit value of the curtailment rate specified by the power grid, and the simulation is stopped;

[0010] For the power curve after each wind curtailment scenario, traverse different power allocation schemes to determine the power component borne by the super capacitor and the battery in the hybrid energy storage system respectively;

[0011] Based on the power component, the rated power and the rated capacity of the super capacitor and the battery are calculated respectively;

[0012] Based on the rated power, the rated capacity and the upper limit of the wind curtailment rate, system operation constraints are constructed, and under the condition of meeting the system operation constraints, the annual comprehensive cost of each wind curtailment scenario and each power allocation scheme is calculated, and the wind curtailment scenario and the power allocation scheme with the minimum annual comprehensive cost are taken as the optimized configuration scheme of the hybrid energy storage system; the annual comprehensive cost is the sum of the annual cost of energy storage investment, the annual cost of energy storage operation and maintenance and the annual wind curtailment loss cost.

[0013] Optionally, based on the historical output data of the target wind farm, the original wind power curve under the planning scenario is determined, including:

[0014] Based on the historical output data of the target wind farm, the power fluctuation rate of each day is calculated;

[0015] The wind power curve corresponding to the day with the maximum power fluctuation rate is taken as the original wind power curve under the planning scenario.

[0016] Optionally, the piecewise trial method is used to simulate the original wind power curve multiple times to obtain the wind curtailment power curve and the power curve after wind curtailment of each wind curtailment scenario, including:

[0017] Starting from the maximum output value of the original wind power curve, the ideal grid-connected power upper limit is gradually reduced by a preset power step;

[0018] For each trial, the part of the original wind power curve that exceeds the current ideal grid-connected power upper limit is taken as the wind curtailment power curve of each wind curtailment scenario;

[0019] The part of the original wind power curve that does not exceed the current ideal grid-connected power upper limit is taken as the power curve after wind curtailment of each wind curtailment scenario.

[0020] Optionally, based on the wind curtailment power curve of each wind curtailment scenario and the time-of-use electricity price data of the electricity market, the annual wind curtailment loss cost and the wind curtailment rate of each wind curtailment scenario are obtained, including:

[0021] Based on the time-of-use electricity price data of the electricity market, the wind curtailment power of each period in the wind curtailment power curve is multiplied by the electricity price of the corresponding period and the period length to obtain the wind curtailment loss of each period;

[0022] The wind curtailment loss cost of all time periods in a day is accumulated to obtain a daily wind curtailment loss cost;

[0023] The daily wind curtailment loss cost is multiplied by the number of operation days in a year to obtain an annual wind curtailment loss cost of each wind curtailment scenario.

[0024] Optionally, the post-wind-curtailment power curve of each wind curtailment scenario traverses different power distribution schemes to determine the power components borne by the supercapacitor and the battery in the hybrid energy storage system, including:

[0025] The post-wind-curtailment power curve of each wind curtailment scenario determines a fluctuating power sequence that needs to be smoothed by the hybrid energy storage system;

[0026] The fluctuating power sequence is decomposed into multiple IMF components and a residual term by using an empirical mode decomposition technique;

[0027] The multiple IMF components and the residual term are reconstructed into multiple sets of reconstruction results by traversing different demarcation points; each set of reconstruction results includes a high-frequency power component and a low-frequency power component;

[0028] For each set of reconstruction results, the high-frequency power component is allocated to the supercapacitor, and the low-frequency power component is allocated to the battery.

[0029] Optionally, the post-wind-curtailment power curve of each wind curtailment scenario determines a fluctuating power sequence that needs to be smoothed by the hybrid energy storage system, including:

[0030] The post-wind-curtailment power curve of each wind curtailment scenario is smoothed to obtain a smoothed power curve of the wind power grid connection node;

[0031] The difference between the post-wind-curtailment power curve and the smoothed power curve is calculated to obtain a fluctuating power sequence that needs to be smoothed by the hybrid energy storage system.

[0032] Optionally, the multiple IMF components and the residual term are reconstructed into multiple sets of reconstruction results by traversing different demarcation points, including:

[0033] A demarcation point j is set; where j is an integer from 1 to n, and n is the total number of the multiple IMF components;

[0034] The IMF components with serial numbers less than or equal to j are superimposed to reconstruct the high-frequency power component;

[0035] The IMF components with serial numbers greater than j and the residual term are superimposed to reconstruct the low-frequency power component.

[0036] Optionally, the rated power and the rated capacity of the supercapacitor and the battery are calculated based on the power components, respectively, including:

[0037] obtaining the output power of the super capacitor in each period based on the high-frequency power component and the charge-discharge efficiency of the super capacitor;

[0038] determining the maximum value of the absolute value of the output power of the super capacitor in each period as the rated power of the super capacitor;

[0039] time-integrating the output power of the super capacitor in each period to obtain the energy fluctuation of the super capacitor in each period;

[0040] calculating the rated capacity of the super capacitor based on the range of the energy fluctuation of the super capacitor in each period, the maximum allowable discharge depth of the super capacitor and a preset redundancy coefficient.

[0041] Optionally, the calculating the rated power and the rated capacity of the super capacitor and the battery based on the power component respectively comprises:

[0042] obtaining the output power of the battery in each period based on the low-frequency power component and the charge-discharge efficiency of the battery;

[0043] determining the maximum value of the absolute value of the output power of the battery in each period as the rated power of the battery;

[0044] time-integrating the output power of the battery in each period to obtain the energy fluctuation of the battery in each period;

[0045] calculating the rated capacity of the battery based on the range of the energy fluctuation of the battery in each period, the maximum allowable discharge depth of the battery and a preset redundancy coefficient.

[0046] In a second aspect, the present application provides a hybrid energy storage optimal configuration device considering economic curtailment of wind power, comprising:

[0047] a determination module configured to determine an original wind power curve in a planning scenario based on historical output data of a target wind farm;

[0048] a curtailment simulation module configured to simulate the original wind power curve multiple times to obtain a curtailment power curve and a post-curtailment power curve of each curtailment scenario by using a piecewise trial method, and to obtain an annual curtailment loss cost and a curtailment rate of each curtailment scenario based on the curtailment power curve of each curtailment scenario and time-of-use electricity price data of a power market until the curtailment rate is greater than or equal to an upper limit value of a grid-specified curtailment rate, and to stop the simulation;

[0049] A scheme traversal module is configured to traverse different power distribution schemes for a post-wind curtailment power curve of each wind curtailment scenario, and determine power components borne by the super capacitor and the battery in the hybrid energy storage system respectively;

[0050] A calculation module is configured to calculate rated power and rated capacity of the super capacitor and the battery respectively based on the power components, construct system operation constraints based on the rated power, the rated capacity and the upper limit of the wind curtailment rate, and calculate annual comprehensive cost under each wind curtailment scenario and each power distribution scheme under the condition of meeting the system operation constraints, and take the wind curtailment scenario and the power distribution scheme with the minimum annual comprehensive cost as the optimized configuration scheme of the hybrid energy storage system; the annual comprehensive cost is the sum of the annual energy storage investment cost, the annual energy storage operation and maintenance cost and the annual wind curtailment loss cost.

[0051] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0052] The present application provides a hybrid energy storage optimization configuration method considering economic wind curtailment, simulates different wind curtailment scenarios through a segmented trial method, finds an optimized configuration scheme with minimum annual comprehensive cost within the upper limit of the wind curtailment rate specified by the power grid, enables the hybrid energy storage system to accurately weigh the economic benefits between wind curtailment loss and energy storage investment and operation and maintenance, thereby avoiding energy storage capacity redundancy and investment waste caused by the consumption of a small amount of high-cost fluctuating power, and fundamentally improves the overall economy of the wind storage system. Compared with the traditional method of estimating wind curtailment loss based on a fixed electricity price, the method can accurately calculate the wind curtailment loss cost based on time-of-use electricity price data of the power market, accurately judge the economy of wind curtailment, and provide a scientific basis for energy storage configuration decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the example embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor. In the drawings:

[0054] Figure 1 A flowchart of the hybrid energy storage optimization configuration method considering economic wind curtailment provided by the embodiments of the present application;

[0055] Figure 2 A schematic diagram of the segmented trial method provided by the embodiments of the present application;

[0056] Figure 3 A schematic diagram of the overall flow of the hybrid energy storage optimization configuration method considering economic wind curtailment provided by the embodiments of the present application;

[0057] Figure 4 This is a schematic diagram of a hybrid energy storage optimization configuration device that takes into account economic wind curtailment, provided as an embodiment of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0059] To address the problem of inaccurate wind curtailment cost calculations in existing hybrid energy storage configuration methods, which leads to a lack of economic basis for energy storage configuration schemes, this application provides a hybrid energy storage optimization configuration method that considers economic wind curtailment. Please refer to... Figure 1 This is a schematic flowchart of a hybrid energy storage optimization configuration method considering economic wind curtailment provided in an embodiment of this application. The following is a description of... Figure 1 This paper introduces a hybrid energy storage optimization configuration method that takes into account economic wind curtailment.

[0060] S1. Based on the historical power output data of the target wind farm, determine the original wind power curve under the planning scenario.

[0061] In one possible implementation, the daily power fluctuation rate is calculated based on the historical power output data of the target wind farm; the wind power curve corresponding to the day with the largest power fluctuation rate is used as the original wind power curve under the planning scenario.

[0062] In practical implementation, historical power output data refers to the power output data of the target wind farm within a preset historical period (e.g., one year). This power output data is analyzed to calculate the power volatility. Power volatility is the degree of fluctuation in wind power relative to a benchmark value over a certain period, and the calculation formula is as follows:

[0063]

[0064] in, P(t) represents the rated installed capacity of the target wind farm; P(t) represents the actual output power of the target wind farm at time t. Indicates the target wind farm is in The actual output power at any given time; The preset sampling interval is typically 1 minute or 10 minutes. Indicates from Power fluctuation rate from time t to time t.

[0065] Calculate the sampling intervals within each 24-hour period (e.g.) the maximum value of the power fluctuation rate of all sampling intervals in the day is taken as the power fluctuation rate of the day. By comparing the power fluctuation rates of all days in a preset historical period (such as one year), the day with the maximum power fluctuation rate is taken as the planning scenario.

[0066] In the embodiments of the present application, by actively selecting the "worst scenario" with the most severe power fluctuation as the basis for planning, the hybrid energy storage system configured based on the present application can ensure the ability to handle extreme fluctuations, thereby improving the robustness and engineering practicability of the optimized configuration scheme, and avoiding the risk of failure in actual operation due to the inability to cope with extreme cases under mild fluctuation scenarios.

[0067] S2, using a segmented trial method, a plurality of wind curtailment scenarios are simulated to obtain a wind curtailment power curve and a post-curtailment power curve of each wind curtailment scenario.

[0068] In a possible embodiment, starting from the maximum output value of the original wind power curve, the ideal grid-connected power upper limit is gradually reduced by a preset power step; for each trial, the part of the original wind power curve that exceeds the current ideal grid-connected power upper limit is taken as the wind curtailment power curve of each wind curtailment scenario; and the part of the original wind power curve that does not exceed the current ideal grid-connected power upper limit is taken as the post-curtailment power curve of each wind curtailment scenario.

[0069] In the specific implementation process, the core of the entire trial process is to gradually set and reduce an ideal grid-connected power upper limit, which defines the maximum value of the power allowed to be delivered by the wind farm to the grid in the simulated scenario, and the calculation formula is as follows:

[0070]

[0071] wherein, is the ideal grid-connected power upper limit set at the mth segment (i.e. the mth trial); is the maximum output value of the original wind power curve in the entire planning period (such as one day); is the preset power step, i.e. the variation amplitude of the wind curtailment power between adjacent two simulated scenarios; and m is the current set segment number.

[0072] Starting from m = 0, at this time corresponding to the zero wind curtailment scenario. Subsequently, the value of m is gradually increased by the preset step , thereby generating a series of simulated wind curtailment scenarios from top to bottom.

[0073] For the mth trial, the part of the original wind power curve that exceeds the current ideal grid-connected power upper limit the power part of the original wind power curve that is not greater than the power part of the wind power curve of the current curtailment scenario, as the curtailed power curve of each curtailment scenario. extracts and takes, as the post-curtailment power curve of each curtailment scenario, the power part of the original wind power curve that is not greater than the power part of the wind power curve of the current curtailment scenario.

[0074] Please refer to Figure 2 a schematic diagram of a segmented trial method provided for the embodiments of the present application. In the diagram, is a preset power step; is an ideal grid-connected power upper limit.

[0075] In the embodiments of the present application, considering that the peak output part of a distributed power source (DG) usually fluctuates greatly and mostly exceeds the grid accommodation capacity, which is not economical for grid accommodation and energy storage accommodation, therefore, by starting from the maximum output point and using a fixed power step to try from top to bottom, a complete scenario sequence from zero curtailment to satisfying the upper limit of the grid specified curtailment rate is simulated. By traversing all reasonable curtailment levels, a decision domain covering all feasible technical solutions is constructed for the optimization algorithm, which fundamentally avoids the problem of the optimization result falling into local optimum or producing decision bias caused by presetting a single curtailment scenario or selecting limited scenarios by experience in the traditional method. The method converts the continuous economic optimization problem into a series of discrete and quantifiable scenarios, each of which accurately corresponds to a specific curtailment amount and a certain curtailment loss cost, providing a data basis for subsequent energy storage optimization configuration.

[0076] S3, according to the curtailed power of each curtailment scenario and the time-of-use electricity price data of the electricity market, obtaining the curtailment loss cost and the curtailment rate of each curtailment scenario, until the curtailment rate is greater than or equal to the upper limit value of the grid specified curtailment rate, stopping the simulation.

[0077] In a possible embodiment, the calculation process of the curtailment loss cost includes:

[0078] Based on the time-of-use electricity price data of the electricity market, multiplying the curtailed power of each period in the curtailed power curve by the electricity price of the corresponding period and the length of the period to obtain the curtailed loss of each period; adding up the curtailed losses of all periods in a day to obtain the daily curtailed loss cost; multiplying the daily curtailed loss cost by the number of operating days in a year to obtain the annual curtailed loss cost of each curtailment scenario.

[0079] In the specific implementation process, the calculation formula of the annual curtailed loss cost is as follows:

[0080]

[0081] In the formula, T is the total number of periods in 1 day, is the curtailed power of period t in the curtailed power curve of a curtailment scenario, is the period interval, The market electricity price for time period t; This represents the annual cost of wind curtailment losses in this scenario.

[0082] Traditional methods often use fixed benchmark electricity prices to estimate wind curtailment losses, which can severely distort the true cost differences between different wind curtailment strategies. In this embodiment, the wind curtailment power curve and the time-of-use electricity price curve are precisely matched and calculated point by point on a time scale. This allows for a differentiated and accurate representation of the huge economic losses from wind curtailment during peak hours and the smaller economic losses from wind curtailment during off-peak hours, enabling subsequent energy storage optimization to be based on real market price signals.

[0083] In one possible embodiment, the calculation process for the wind curtailment rate includes:

[0084] Integrate the curtailment power curve for each curtailment scenario to obtain the curtailment amount for each scenario; integrate the original wind power curve to obtain the power generation of the target wind farm without considering curtailment; calculate the ratio between the curtailment amount for each curtailment scenario and the power generation of the target wind farm without considering curtailment to obtain the curtailment rate for each scenario.

[0085] In the specific implementation process, the formula for calculating the wind curtailment rate is as follows:

[0086]

[0087] In the formula, The amount of electricity wasted in a specific wind curtailment scenario; The target wind farm does not consider the power generation during periods of curtailment. This represents the wind curtailment rate for this wind curtailment scenario.

[0088] Calculate the wind curtailment rate in the current wind curtailment scenario. Afterwards, Compared with the upper limit of wind curtailment rate stipulated by the power grid In comparison, this upper limit for wind curtailment rate is usually set by the grid company based on the system's absorption capacity and policy objectives (e.g., 10%).

[0089] When the wind curtailment rate does not exceed the upper limit of the wind curtailment rate stipulated by the power grid ( When the current wind curtailment scenario is within the allowable range, the number of segments m is increased (i.e., the amount of wind curtailment is increased), and m = m + 1 is set to continue generating and evaluating the next more aggressive wind curtailment scenario.

[0090] When the wind curtailment rate exceeds the upper limit of the wind curtailment rate stipulated by the power grid ( When the value of the wind curtailment scenario reaches or exceeds the maximum wind curtailment limit allowed by the power grid, the simulation will stop.

[0091] S4. For each wind curtailment scenario, after the wind curtailment power curve, traverse different power allocation schemes to determine the power components undertaken by the supercapacitor and the battery in the hybrid energy storage system.

[0092] In one possible embodiment, the specific steps of S4 include:

[0093] For each wind curtailment scenario, the power curve after curtailment is determined, and the fluctuating power sequence that the hybrid energy storage system needs to smooth is identified. The fluctuating power sequence is decomposed into multiple IMF components and a residual term using empirical mode decomposition (EMD). By traversing different boundary points, the multiple IMF components and the residual term are reconstructed into multiple sets of reconstruction results. Each set of reconstruction results includes a high-frequency power component and a low-frequency power component. For each set of reconstruction results, the high-frequency power component is allocated to the supercapacitor, and the low-frequency power component is allocated to the battery.

[0094] In practical implementation, Empirical Mode Decomposition (EMD) is an adaptive method for decomposing nonstationary and nonlinear signals. Its core principle is to decompose complex fluctuating signals into several intrinsic mode functions (IMFs) and a residual trend term (EMF). The EMD algorithm is particularly suitable for processing time-varying fluctuation signals such as wind power. The specific steps of the EMD algorithm are as follows:

[0095] First, initialization:

[0096] Set the maximum number of IMF components (generally 5-8, sufficient to cover the frequency range of wind power fluctuations), and the screening stop threshold (e.g., standard deviation ≤ 0.2, to avoid over-decomposition); let the original signal be x(t), and the residual term... , IMF component counter i=1.

[0097] Secondly, screening IMF components:

[0098] Find All local maxima and local minima;

[0099] The upper envelope is obtained by fitting the maxima and minima using cubic spline interpolation. and lower envelope ;

[0100] Calculate the envelope mean ;

[0101] Subtracting the mean yields the candidate components. ;

[0102] verify Does it meet the IMF criteria? If so, then If not satisfied, let repeat the above screening process (usually 3-5 iterations).

[0103] Then, update the residual term: .

[0104] When the residual term is a monotonic function or a constant (no significant fluctuations), stop decomposition, at this time get n IMF components and 1 residual term.

[0105] Finally, the decomposition result verification:

[0106] All IMF components and residual terms satisfy the reconstruction relationship as follows:

[0107]

[0108] Where, is the reconstructed signal; is the i-th IMF component; is the residual term; n is the maximum number of IMF components.

[0109] Verify the error (such as mean square error MSE≤0.01) between the reconstructed signal and the original signal to ensure that the decomposition is distortionless.

[0110] In the embodiments of the present application, according to the scale of the wind farm and the wind power fluctuation characteristics, the hybrid energy storage system adopts a hybrid energy storage combination form of "battery + super capacitor". Through EMD adaptive decomposition and optimal boundary point selection, it is ensured that high-frequency, short-time, and high-power fluctuations (such as minute-level power impact) are borne by super capacitors with long cycle life and fast response, protecting the battery from such harmful impacts and prolonging its service life; while low-frequency, long-time, and large-energy fluctuations (such as daily cross-period peak shaving) are borne by batteries with high energy density and low unit capacity cost, avoiding unnecessary large-capacity configuration of super capacitors. This precise division of labor and responsibility based on fluctuation characteristics enables the hybrid energy storage system to complete the same smoothing task with lower overall cost and higher operational reliability.

[0111] In one possible embodiment, for the power curve after curtailment of each curtailment scenario, the fluctuating power sequence that the hybrid energy storage system needs to smooth is determined, including:

[0112] Smooth the power curve after curtailment of each curtailment scenario to obtain a smoothed power curve of the wind power grid connection node; calculate the difference between the power curve after curtailment and the smoothed power curve to obtain the fluctuating power sequence that the hybrid energy storage system needs to smooth.

[0113] In the implementation process, based on the power curve after wind curtailment, the sliding average method is used to obtain the smooth power curve of the wind power grid node, and then the original power curve after wind curtailment and the smooth power curve are subtracted point by point. The difference sequence obtained is the fluctuation power that needs to be absorbed or released by the hybrid energy storage system in real time to completely suppress.

[0114] In the embodiments of the present application, through smoothing processing, the low-frequency trend component is effectively stripped from the complex original power curve, and the remaining fluctuation power sequence accurately represents the total amount and dynamic characteristics of the power fluctuation that needs to be suppressed.

[0115] In a possible embodiment, by traversing different demarcation points, the plurality of IMF components and the residual term are reconstructed into a plurality of groups of reconstruction results, including:

[0116] Set the demarcation point j; j is an integer from 1 to n, n is the total number of the plurality of IMF components; the IMF components with serial numbers less than or equal to j are superimposed to reconstruct a high-frequency power component; the IMF components with serial numbers greater than j and the residual term are superimposed to reconstruct a low-frequency power component.

[0117] In the implementation process, the expression of reconstruction is as follows:

[0118]

[0119] In the formula, is the high-frequency power component reconstructed in the t period; is the absorption power of the supercapacitor in the t period; is the low-frequency power component reconstructed in the t period; is the absorption power of the battery in the t period; j is the demarcation point; is the i th IMF component; is the residual term; n is the maximum number of IMF components.

[0120] S5, based on the power component, the rated power and the rated capacity of the supercapacitor and the battery are calculated respectively.

[0121] In a possible embodiment, the calculation process of the rated power and the rated capacity of the battery is as follows:

[0122] S5.11, based on the low-frequency power component and the charge-discharge efficiency of the battery, the output power of the battery in each period is obtained.

[0123]

[0124] In the formula, is the output power of the battery in the t period; is the charging efficiency of the battery; discharge efficiency of the battery; absorbed power of the battery at the t-th time interval, whose value is equal to the low-frequency power component .

[0125] S5.12, the maximum value of the absolute value of the output power of the battery at each time interval is determined as the rated power of the battery.

[0126]

[0127] wherein, the rated power of the battery; represents the maximum value of the absolute value of

[0128] S5.13, the time integral of the output power of the battery at each time interval is obtained as the energy fluctuation of the battery at each time interval.

[0129]

[0130] wherein, the energy fluctuation of the battery at the t-th time interval; the output power of the battery at the t-th time interval; T is the total time interval of a complete operation cycle.

[0131] S5.14, based on the range of the energy fluctuation of the battery at each time interval, the maximum allowable discharge depth of the battery and the preset redundancy coefficient, the rated capacity of the battery is calculated.

[0132]

[0133]

[0134] wherein, the rated capacity of the battery; the energy fluctuation of the battery at the t-th time interval; the maximum value of the minimum value of k is a preset redundancy coefficient, and the value range is 1.05-1.1; the maximum allowable discharge depth of the battery; the upper limit value of the SOC of the battery, the lower limit value of the SOC of the battery.

[0135] In a possible embodiment, the calculation process of the rated power and the rated capacity of the super capacitor is as follows:

[0136] S5.21, based on the high-frequency power component and the charge-discharge efficiency of the super capacitor, the output power of the super capacitor at each time interval is obtained.​

[0137]

[0138] wherein, P(t) is the output power of the supercapacitor at the tth time period; ηc is the charging efficiency of the supercapacitor; ηd is the discharging efficiency of the supercapacitor; Pabs(t) is the absorption power of the supercapacitor at the tth time period, which is equal to the high-frequency power component .

[0139] S5.22, the maximum value of the absolute value of the output power of the supercapacitor at each time period is determined as the rated power of the supercapacitor.

[0140]

[0141] wherein, Pmax is the rated power of the supercapacitor; represents the maximum value of the absolute value of .

[0142] S5.23, the time integral of the output power of the supercapacitor at each time period is obtained as the energy fluctuation of the supercapacitor at each time period.

[0143]

[0144] wherein, ΔE(t) is the energy fluctuation of the supercapacitor at the tth time period; P(t) is the output power of the supercapacitor at the tth time period; T is the total time period of a complete operation cycle.

[0145] S5.24, based on the range of the energy fluctuation of the supercapacitor at each time period, the maximum allowable discharge depth of the supercapacitor, and the preset redundancy coefficient, the rated capacity of the supercapacitor is calculated.

[0146]

[0147]

[0148] wherein, Cmax is the rated capacity of the supercapacitor; ΔE(t) is the energy fluctuation of the supercapacitor at the tth time period; is the maximum value of ; is the minimum value of ; k is a preset redundancy coefficient, and the value range is 1.05-1.1; Dmax is the maximum allowable discharge depth of the supercapacitor; a lower limit value of the SOC of the super capacitor; an upper limit value of the SOC of the super capacitor.

[0149] S6, based on the rated power, the rated capacity and the upper limit value of the wind curtailment rate, constructing system operation constraints, and under the condition of meeting the system operation constraints, calculating the annual comprehensive cost under each wind curtailment scenario and each power distribution scheme, and taking the wind curtailment scenario and the power distribution scheme with the minimum annual comprehensive cost as the optimized configuration scheme of the hybrid energy storage system.

[0150] In the specific implementation process, the optimization target of the present application is to minimize the annual comprehensive cost of the hybrid energy storage system in the whole life cycle, and the annual comprehensive cost is the sum of the energy storage investment annual cost, the energy storage operation and maintenance annual cost and the annual wind curtailment loss cost. Therefore, the objective function is constructed as follows:

[0151]

[0152] In the formula, is the annual comprehensive cost; is the energy storage investment annual cost; is the operation and maintenance annual cost; is the annual wind curtailment loss cost; and min represents the minimum value.

[0153] (1) Energy storage investment annual cost:

[0154]

[0155]

[0156] Among them, is the energy storage investment annual cost, is the investment cost of the battery; is the investment cost of the super capacitor, is the rated power of the battery; is the rated capacity of the battery; is the rated power of the super capacitor; is the rated capacity of the super capacitor; is the unit power cost of the battery; is the unit capacity cost of the battery; is the unit power cost of the super capacitor; is the unit capacity cost of the super capacitor; is the service life of the battery; is the service life of the super capacitor; and r is the discount rate.

[0157] (2) Energy storage operation and maintenance annual cost:

[0158]

[0159] wherein, is the annual operation and maintenance cost of the energy storage; is the annual operation and maintenance unit cost of the battery; is the rated capacity of the battery; is the annual operation and maintenance unit cost of the supercapacitor; is the rated capacity of the supercapacitor.

[0160] (3) Annual wind curtailment loss cost: the specific calculation method is referred to the content discussed in S3, which is not repeated here.

[0161] In order to ensure the safe operation of the hybrid energy storage system, the optimization scheme needs to meet the following system safe operation constraints:

[0162] ① Energy storage charge and discharge power constraint:

[0163] The output power of the hybrid energy storage system at any t period of time cannot exceed its rated power.

[0164]

[0165] wherein, is the output power of the battery at the t period of time; is the rated power of the battery; is the output power of the supercapacitor at the t period of time; is the rated power of the supercapacitor.

[0166] ② State of charge (SOC) upper and lower limit constraints:

[0167] The remaining capacity (or energy corresponding to the state of charge) of the hybrid energy storage system at any t period of time must be within the safe range.

[0168]

[0169] wherein, is the upper limit value of the SOC of the battery, is the lower limit value of the SOC of the battery; is the rated capacity of the battery; is the remaining capacity of the battery at the t period of time; is the lower limit value of the SOC of the supercapacitor; is the upper limit value of the SOC of the supercapacitor; is the rated capacity of the supercapacitor; is the remaining capacity of the supercapacitor at the t period of time.

[0170] ③ Wind curtailment constraint:

[0171] The wind curtailment strategy corresponding to the optimization scheme must meet the requirements of grid dispatching.

[0172]

[0173] In the formula, is the wind curtailment rate; is the upper limit of the wind curtailment rate specified by the power grid, such as 10%.

[0174] Under the conditions of meeting the energy storage charge and discharge power constraints, the upper and lower limits of the state of charge (SOC) and the wind curtailment constraints, the annual comprehensive cost of each wind curtailment scenario and each power distribution scheme is calculated, and the wind curtailment scenario and the power distribution scheme with the minimum annual comprehensive cost are selected from all the wind curtailment scenarios and all the power distribution schemes as the optimal configuration scheme of the hybrid energy storage system.

[0175] Please refer to Figure 3 , which is the overall flowchart of the hybrid energy storage optimal configuration method considering economic wind curtailment provided by the embodiments of the present application. The following introduces Figure 3 The hybrid energy storage optimal configuration method considering economic wind curtailment provided by the present application is introduced.

[0176] Step (1), in combination with the historical output curve of wind power, a day with the maximum power fluctuation rate is selected as the worst scenario to carry out optimization planning.

[0177] Step (2), initialization: let the segment number m=0, and let the division point j=1.

[0178] Step (3), using the segment trial method to measure and calculate segment by segment: based on the wind power curve , starting from the maximum output of wind power , a certain power step is used to reduce the power from top to bottom, and the ideal grid-connected power upper limit of the mth segment is At this time, the curtailment curve is the part higher than the ideal grid-connected power upper limit, based on which the wind curtailment rate and the curtailment loss cost are calculated, and the power curve after the curtailment curve segment is obtained.

[0179] Step (4), based on the power curve after curtailment, the sliding average method is used to calculate the smoothed power curve of the wind power grid node, and the fluctuation power that needs to be smoothed by the hybrid energy storage system can be obtained by subtracting the smoothed power curve from the power curve after curtailment.

[0180] Step (5), the empirical mode decomposition is used to obtain high-frequency components and low-frequency components, and each submodal component is reconstructed into low-frequency power components and high-frequency power components. The super capacitor undertakes the task of high-frequency power smoothing less than or equal to j, and the battery undertakes the task of low-frequency power smoothing greater than the division point j.

[0181] Step (6), the energy storage power and capacity configuration calculation is carried out, and then the annual comprehensive cost of the hybrid energy storage system is calculated according to the objective function.

[0182] Step (7), check whether the boundary point j reaches the maximum number of IMF components, if yes, go to step (8), otherwise, let j = j + 1, return to step (5).

[0183] Step (8), check whether the mth segment wind curtailment rate reaches the upper limit of wind curtailment rate , if yes, go to step (9), otherwise, let m = m + 1, return to step (3).

[0184] Step (9) selects the energy storage configuration with the minimum annual comprehensive cost as the optimal configuration scheme.

[0185] In summary, the application provides a hybrid energy storage optimal configuration method considering economic wind curtailment, which has the following beneficial effects:

[0186] 1. Reduce the system comprehensive cost:

[0187] By using the piecewise trial method to actively optimize the wind curtailment strategy, part of the wind power consumption is actively abandoned when it is not economical, which effectively avoids the over-provisioning of energy storage system for consuming a small amount of high-cost electricity. This method comprehensively considers the investment, operation and maintenance, and dynamic wind curtailment loss, and realizes the optimization of the system's full life cycle comprehensive cost.

[0188] 2. Improve the adaptability of energy storage:

[0189] Using the "battery + super capacitor" hybrid architecture, combined with signal processing technologies such as empirical mode decomposition, the fluctuating power of wind power is adaptively decomposed into high-frequency and low-frequency components, and is respectively allocated to super capacitors and batteries to bear, realizing precise matching and efficient collaboration of different energy storage devices and fluctuation characteristics.

[0190] 3. Enhance the scientific nature of optimization decision:

[0191] Based on the time-of-use electricity price data of the electricity market, the wind curtailment loss is calculated in detail and by time period, which truly reflects the economic cost of wind curtailment in different time periods, provides quantitative and scientific basis for energy storage configuration, and improves the practicality and economic benefit of the scheme.

[0192] Based on the same inventive concept, please refer to Figure 4 The application also provides a hybrid energy storage optimal configuration considering economic wind curtailment, which comprises:

[0193] A determination module is configured to determine an original wind power curve under a planning scenario based on historical output data of a target wind farm.

[0194] The wind curtailment simulation module is configured to simulate multiple wind curtailment scenarios by using a piecewise trial method, to obtain a wind curtailment power curve and a post-wind curtailment power curve of each wind curtailment scenario, and to obtain an annual wind curtailment loss cost and a wind curtailment rate of each wind curtailment scenario based on the wind curtailment power curve of each wind curtailment scenario and time-of-use electricity price data of the electricity market, until the wind curtailment rate is greater than or equal to an upper limit of a wind curtailment rate specified by the power grid, and the simulation is stopped;

[0195] The scheme traversal module is configured to traverse different power distribution schemes for the post-wind curtailment power curve of each wind curtailment scenario, and to determine power components respectively assumed by the supercapacitor and the battery in the hybrid energy storage system.

[0196] The calculation module is configured to calculate a rated power and a rated capacity of the supercapacitor and the battery, respectively, based on the power components, to construct system operation constraints based on the rated power, the rated capacity, and the upper limit of the wind curtailment rate, and to calculate an annual comprehensive cost under each wind curtailment scenario and each power distribution scheme in a condition of satisfying the system operation constraints, and to take a wind curtailment scenario and a power distribution scheme with a minimum annual comprehensive cost as an optimized configuration scheme of the hybrid energy storage system, where the annual comprehensive cost is a sum of an energy storage investment annual cost, an energy storage operation and maintenance annual cost, and the annual wind curtailment loss cost.

[0197] Optionally, the determination module is specifically configured to:

[0198] The power fluctuation rate of each day is calculated based on historical output data of the target wind farm;

[0199] The wind power curve corresponding to a day with the maximum power fluctuation rate is taken as the original wind power curve under the planning scenario.

[0200] Optionally, the wind curtailment simulation module is specifically configured to:

[0201] Starting from a maximum output value of the original wind power curve, the ideal grid-connected power upper limit is gradually reduced by a preset power step;

[0202] For each trial, a part of the original wind power curve that exceeds the current ideal grid-connected power upper limit is taken as the wind curtailment power curve of each wind curtailment scenario;

[0203] A part of the original wind power curve that does not exceed the current ideal grid-connected power upper limit is taken as the post-wind curtailment power curve of each wind curtailment scenario.

[0204] Optionally, the wind curtailment simulation module is specifically configured to:

[0205] Based on the time-of-use electricity price data of the electricity market, the wind curtailment loss of each time period is obtained by multiplying the wind curtailment power of each time period in the wind curtailment power curve, the electricity price of the corresponding time period, and the time period length;

[0206] The wind curtailment loss cost of all time periods in a day is accumulated to obtain a daily wind curtailment loss cost;

[0207] The daily wind curtailment loss cost is multiplied by the number of operation days in a year to obtain an annual wind curtailment loss cost of each wind curtailment scenario.

[0208] Optionally, the scheme traversal module is specifically configured to:

[0209] For the fluctuation power sequence that the hybrid energy storage system needs to smooth, the power curve after wind curtailment of each wind curtailment scenario is determined.

[0210] The fluctuation power sequence is decomposed into a plurality of IMF components and a residual term by using an empirical mode decomposition technology;

[0211] By traversing different demarcation points, the plurality of IMF components and the residual term are reconstructed into a plurality of reconstruction results; each reconstruction result includes a high-frequency power component and a low-frequency power component;

[0212] For each reconstruction result, the high-frequency power component is allocated to the super capacitor, and the low-frequency power component is allocated to the battery.

[0213] Optionally, the scheme traversal module is specifically configured to:

[0214] The power curve after wind curtailment is smoothed to obtain a smoothed power curve of the wind power grid connection node for each wind curtailment scenario.

[0215] The difference between the power curve after wind curtailment and the smoothed power curve is calculated to obtain the fluctuation power sequence that the hybrid energy storage system needs to smooth.

[0216] Optionally, the scheme traversal module is specifically configured to:

[0217] The demarcation point j is set; wherein j is an integer from 1 to n, and n is the total number of the plurality of IMF components;

[0218] The IMF components with serial numbers less than or equal to j are superimposed to reconstruct a high-frequency power component;

[0219] The IMF components with serial numbers greater than j and the residual term are superimposed to reconstruct a low-frequency power component.

[0220] Optionally, the scheme traversal module is specifically configured to:

[0221] Based on the high-frequency power component and the charge-discharge efficiency of the super capacitor, the output power of the super capacitor at each time period is obtained;

[0222] The maximum value of the absolute value of the output power of the super capacitor at each time period is determined as the rated power of the super capacitor;

[0223] The output power of the super capacitor at each time period is time-integrated to obtain energy fluctuation of the super capacitor at each time period;

[0224] Based on the range of the energy fluctuation of the super capacitor at each time period, the maximum allowable discharge depth of the super capacitor, and a preset redundancy coefficient, a rated capacity of the super capacitor is calculated.

[0225] Optionally, the scheme traversal module is specifically configured to:

[0226] Based on the low-frequency power component and the charge-discharge efficiency of the battery, the output power of the battery at each time period is obtained;

[0227] The maximum value of the absolute value of the output power of the battery at each time period is determined as the rated power of the battery;

[0228] The output power of the battery at each time period is time-integrated to obtain energy fluctuation of the battery at each time period;

[0229] Based on the range of the energy fluctuation of the battery at each time period, the maximum allowable discharge depth of the battery, and a preset redundancy coefficient, a rated capacity of the battery is calculated.

[0230] It should be noted that the modules in the mixed energy storage optimal configuration device considering economic curtailment of wind power in the embodiment correspond one by one to the steps in the mixed energy storage optimal configuration method considering economic curtailment of wind power in the foregoing embodiment, and therefore, the specific embodiments of the embodiment can refer to the embodiments of the foregoing mixed energy storage optimal configuration method considering economic curtailment of wind power, which will not be described herein again.

[0231] Based on the same inventive concept, the present application further provides a computer device, which comprises a processor, a memory, and a computer program stored in the memory, and the computer program is run by the processor to implement the foregoing mixed energy storage optimal configuration method considering economic curtailment of wind power.

[0232] Based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is run by the processor to implement the foregoing mixed energy storage optimal configuration method considering economic curtailment of wind power.

[0233] In some embodiments, the computer readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; or various devices comprising one or any combination of the above memories. The computer can be various computing devices including smart terminals and servers.

[0234] In some embodiments, the executable instructions can take the form of programs, software, software modules, scripts, or code, written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages; and they can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0235] By way of example, the executable instructions can, but need not, correspond to a file in a file system, can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub programs, or portions of code.

[0236] By way of example, the executable instructions can be deployed to be executed on one computer, or on multiple computers of a distributed computing environment, or on multiple computers of a location- independent distributed computing environment.

[0237] It should be noted that, in the present document, the terms "comprising", "comprises" or any other variations thereof are intended to cover the non-exclusive inclusions, so that a process, method, article, or system that includes a series of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article, or system. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of another identical element in the process, method, article, or system that includes the element.

[0238] The above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0239] The above detailed description sets forth the purposes, technical solutions, and advantages of the present application. It should be understood that the above is only a specific implementation of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for optimizing configuration of hybrid energy storage considering economic curtailment, characterized in that, The method comprises the following steps: Based on the historical output data of the target wind farm, the original wind power curve under the planning scenario is determined; Using the piecewise trial method, the original wind power curve is simulated for multiple wind curtailment scenarios to obtain the wind curtailment power curve and the post-curtailment power curve of each wind curtailment scenario; Based on the wind curtailment power curve of each wind curtailment scenario and the time-of-use electricity price data of the electricity market, the annual wind curtailment loss cost and the wind curtailment rate of each wind curtailment scenario are obtained, until the wind curtailment rate is greater than or equal to the upper limit of the wind curtailment rate specified by the grid, and the simulation is stopped; For the post-curtailment power curve of each wind curtailment scenario, different power distribution schemes are traversed to determine the power components borne by the supercapacitor and the battery in the hybrid energy storage system respectively; Based on the power components, the rated power and the rated capacity of the supercapacitor and the battery are calculated respectively; Based on the rated power, the rated capacity and the upper limit of the wind curtailment rate, system operation constraints are constructed, and under the condition of meeting the system operation constraints, the annual comprehensive cost under each wind curtailment scenario and each power distribution scheme is calculated, and the wind curtailment scenario and the power distribution scheme with the minimum annual comprehensive cost are taken as the optimal configuration scheme of the hybrid energy storage system; the annual comprehensive cost is the sum of the annual cost of energy storage investment, the annual cost of energy storage operation and maintenance and the annual wind curtailment loss cost.

2. The method of claim 1, wherein, Based on the historical output data of the target wind farm, the original wind power curve under the planning scenario is determined, which comprises the following steps: Based on the historical output data of the target wind farm, the power fluctuation rate of each day is calculated; The wind power curve corresponding to the day with the maximum power fluctuation rate is taken as the original wind power curve under the planning scenario.

3. The method of claim 1, wherein, Using the piecewise trial method, the original wind power curve is simulated for multiple wind curtailment scenarios to obtain the wind curtailment power curve and the post-curtailment power curve of each wind curtailment scenario, which comprises the following steps: Starting from the maximum output value of the original wind power curve, the ideal grid-connected power upper limit is gradually reduced by a preset power step; For each trial, the part of the original wind power curve that exceeds the current ideal grid-connected power upper limit is taken as the wind curtailment power curve of each wind curtailment scenario; The part of the original wind power curve that does not exceed the current ideal grid-connected power upper limit is taken as the post-curtailment power curve of each wind curtailment scenario.

4. The method of claim 1, wherein, Based on the wind curtailment power curve of each wind curtailment scenario and the time-of-use electricity price data of the electricity market, the annual wind curtailment loss cost and the wind curtailment rate of each wind curtailment scenario are obtained, which comprises the following steps: Based on the time-of-use electricity price data of the electricity market, the wind curtailment power of each time period in the wind curtailment power curve is multiplied by the electricity price of the corresponding time period and the time period length to obtain the wind curtailment loss of each time period; The wind curtailment losses of all time periods in a day are accumulated to obtain the daily wind curtailment loss cost; The daily wind curtailment loss cost is multiplied by the number of operating days in a year to obtain the annual wind curtailment loss cost of each wind curtailment scenario.

5. The method of claim 1, wherein, For the post-curtailment power curve of each wind curtailment scenario, different power distribution schemes are traversed to determine the power components borne by the supercapacitor and the battery in the hybrid energy storage system respectively, which comprises the following steps: For the post-curtailment power curve of each wind curtailment scenario, the fluctuating power sequence that needs to be smoothed by the hybrid energy storage system is determined; The fluctuating power sequence is decomposed into multiple IMF components and a residual term by using an empirical mode decomposition technique; The multiple IMF components and the residual term are reconstructed into multiple groups of reconstruction results by traversing different demarcation points; each group of reconstruction results includes a high-frequency power component and a low-frequency power component; For each group of reconstruction results, the high-frequency power component is allocated to the super capacitor, and the low-frequency power component is allocated to the battery.

6. The method of claim 5, wherein, The fluctuating power sequence that the hybrid energy storage system needs to smooth is determined based on the post-wind curtailment power curve of each wind curtailment scenario, including: The post-wind curtailment power curve of each wind curtailment scenario is smoothed to obtain a smoothed power curve of the wind power grid connection node; The difference between the post-wind curtailment power curve and the smoothed power curve is calculated to obtain the fluctuating power sequence that the hybrid energy storage system needs to smooth.

7. The method of claim 5, wherein, The multiple IMF components and the residual term are reconstructed into multiple groups of reconstruction results by traversing different demarcation points, including: Set the demarcation point j; where j is an integer from 1 to n, and n is the total number of multiple IMF components; The IMF components with serial numbers less than or equal to j are superimposed to reconstruct the high-frequency power component; The IMF components with serial numbers greater than j and the residual term are superimposed to reconstruct the low-frequency power component. 8.The method of claim 5, wherein, The rated power and the rated capacity of the super capacitor and the battery are respectively calculated based on the power component, including: Based on the high-frequency power component and the charge-discharge efficiency of the super capacitor, the output power of the super capacitor at each time period is obtained; The maximum value of the absolute value of the output power of the super capacitor at each time period is determined as the rated power of the super capacitor; The energy fluctuation of the super capacitor at each time period is obtained by time integration of the output power of the super capacitor at each time period; Based on the range of the energy fluctuation of the super capacitor at each time period, the maximum allowable discharge depth of the super capacitor, and a preset redundancy coefficient, the rated capacity of the super capacitor is calculated. 9.The method of claim 5, wherein, The rated power and the rated capacity of the super capacitor and the battery are respectively calculated based on the power component, including: Based on the low-frequency power component and the charge-discharge efficiency of the battery, the output power of the battery at each time period is obtained; The maximum value of the absolute value of the output power of the battery at each time period is determined as the rated power of the battery; The energy fluctuation of the battery at each time period is obtained by time integration of the output power of the battery at each time period; Based on the range of the energy fluctuation of the battery at each time period, the maximum allowable discharge depth of the battery, and a preset redundancy coefficient, the rated capacity of the battery is calculated.

10. A hybrid energy storage optimization configuration device considering economic curtailment, characterized in that, It includes: The determination module is configured to determine an original wind power curve under a planning scenario based on historical output data of a target wind farm; The wind curtailment simulation module is configured to simulate multiple wind curtailment scenarios by using a piecewise trial method, to obtain a wind curtailment power curve and a post-wind curtailment power curve for each wind curtailment scenario, and to obtain an annual wind curtailment loss cost and a wind curtailment rate for each wind curtailment scenario based on the wind curtailment power curve and time-of-use electricity price data of a power market until the wind curtailment rate is greater than or equal to an upper limit value of a wind curtailment rate specified by a power grid, and the simulation is stopped; The scheme traversal module is configured to traverse different power distribution schemes for the post-wind curtailment power curve of each wind curtailment scenario, and to determine power components respectively assumed by a super capacitor and a battery in the hybrid energy storage system; The calculation module is configured to calculate a rated power and a rated capacity of the super capacitor and the battery, respectively, based on the power components, to construct system operation constraints based on the rated power, the rated capacity, and the upper limit value of the wind curtailment rate, and to calculate an annual comprehensive cost under each wind curtailment scenario and each power distribution scheme under the condition that the system operation constraints are satisfied, and to take a wind curtailment scenario and a power distribution scheme with a minimum annual comprehensive cost as an optimal configuration scheme of the hybrid energy storage system, wherein the annual comprehensive cost is a sum of an energy storage investment annual cost, an energy storage operation and maintenance annual cost, and the annual wind curtailment loss cost.

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