A multi-scene-oriented offshore wind farm energy storage configuration method and terminal

Abstract

The application discloses a kind of offshore wind farm energy storage configuration methods and terminal for multi-scenario demand, respectively calculate the energy storage configuration capacity demand of power system under multiple scenarios, based on the energy storage configuration capacity demand under multiple scenarios and the characteristics of different types of energy storage Select the optimal energy storage type, under the first constraint condition, with the minimum annual comprehensive cost as the first objective function Build multi-scenario energy storage capacity and energy configuration model, and build multi-time scale production operation simulation model, according to two models Generate double-layer configuration model, and solve it to obtain energy storage configuration result, which comprehensively considers the demand of energy storage in inertia, primary frequency modulation, wind power prediction error and climbing event extreme scenario, and the model built can Ensure that effective energy storage configuration is realized while the system is economically and stably operated.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power technology, and in particular to an offshore wind farm energy storage configuration method and terminal for multi-scenario needs. Background Technology

[0002] Today, offshore wind power is entering a period of rapid development, and large-capacity offshore wind power will become the future development trend, which means that the proportion of new energy will continue to increase. This will bring new challenges to the system operation: (1) With the penetration of a high proportion of new energy, it is necessary to consider the system inertia level and frequency characteristics in order to determine whether the system meets the frequency stability requirements; (2) New energy (offshore wind power) needs to provide a certain primary frequency regulation capability; (3) Wind power output has randomness and volatility, and it is necessary to consider the prediction error of offshore wind power; (4) It is necessary to consider the impact of rapid ramp-up events of offshore wind power on power balance.

[0003] Configuring energy storage for offshore wind power is one of the important measures to solve these problems, but there is currently a lack of energy storage configuration technologies that comprehensively consider the above four needs. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an energy storage configuration method and terminal for offshore wind farms that meets the needs of multiple scenarios, and can comprehensively consider the energy storage requirements under extreme scenarios such as inertia, primary frequency regulation, wind power prediction error and ramping events, so as to achieve effective energy storage configuration.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios includes the following steps:

[0007] The energy storage configuration capacity requirements of the power system are calculated under multiple scenarios, including inertia support scenario, primary frequency regulation scenario, wind power prediction error scenario, and ramping event extreme scenario.

[0008] The optimal energy storage type is selected based on the energy storage configuration capacity requirements under the aforementioned multiple scenarios and the characteristics of different types of energy storage.

[0009] Under the first constraint, a multi-scenario energy storage capacity and energy configuration model is constructed with the minimum annual comprehensive cost as the first objective function, and a multi-timescale production and operation simulation model is also constructed.

[0010] A two-layer configuration model is generated based on the multi-scenario energy storage capacity and energy configuration model and the multi-timescale production and operation simulation model. The two-layer configuration model is then solved based on the energy storage configuration capacity requirements under the multi-scenario model and the optimal energy storage type to obtain the energy storage configuration results.

[0011] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0012] An offshore wind farm energy storage configuration terminal for multi-scenario needs includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0013] The energy storage configuration capacity requirements of the power system are calculated under multiple scenarios, including inertia support scenario, primary frequency regulation scenario, wind power prediction error scenario, and ramping event extreme scenario.

[0014] The optimal energy storage type is selected based on the energy storage configuration capacity requirements under the aforementioned multiple scenarios and the characteristics of different types of energy storage.

[0015] Under the first constraint, a multi-scenario energy storage capacity and energy configuration model is constructed with the minimum annual comprehensive cost as the first objective function, and a multi-timescale production and operation simulation model is also constructed.

[0016] A two-layer configuration model is generated based on the multi-scenario energy storage capacity and energy configuration model and the multi-timescale production and operation simulation model. The two-layer configuration model is then solved based on the energy storage configuration capacity requirements under the multi-scenario model and the optimal energy storage type to obtain the energy storage configuration results.

[0017] The beneficial effects of this invention are as follows: It calculates the energy storage configuration capacity requirements of the power system under multiple scenarios, selects the optimal energy storage type based on the energy storage configuration capacity requirements under multiple scenarios and the characteristics of different types of energy storage, and constructs an energy storage capacity and energy configuration model for multiple scenarios with the minimum annual comprehensive cost as the first objective function under the first constraint. It also constructs a multi-timescale production and operation simulation model, generates a two-layer configuration model based on the two models, and solves it to obtain the energy storage configuration results. This comprehensively considers the energy storage requirements under extreme scenarios such as inertia, primary frequency regulation, wind power prediction errors, and ramp-up events. Furthermore, the constructed model ensures that the system operates economically and stably while achieving effective energy storage configuration. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of an offshore wind farm energy storage configuration method for multi-scenario needs according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of an offshore wind farm energy storage configuration terminal for multi-scenario needs according to an embodiment of the present invention;

[0020] Figure 3This is a flowchart illustrating the calculation of energy storage configuration capacity requirements of the power system under multiple scenarios in the offshore wind farm energy storage configuration method for multi-scenario needs according to an embodiment of the present invention.

[0021] Figure 4 This is a flowchart of the energy storage selection process in the offshore wind farm energy storage configuration method for multi-scenario needs according to an embodiment of the present invention.

[0022] Figure 5 This is a schematic diagram of a two-layer configuration model in the offshore wind farm energy storage configuration method for multi-scenario needs according to an embodiment of the present invention.

[0023] Figure 6 This is a flowchart illustrating the solution process of the two-layer configuration model in the offshore wind farm energy storage configuration method for multiple scenarios according to an embodiment of the present invention. Detailed Implementation

[0024] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0025] Please refer to Figure 1 A method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios includes the following steps:

[0026] The energy storage configuration capacity requirements of the power system are calculated under multiple scenarios, including inertia support scenario, primary frequency regulation scenario, wind power prediction error scenario, and ramping event extreme scenario.

[0027] The optimal energy storage type is selected based on the energy storage configuration capacity requirements under the aforementioned multiple scenarios and the characteristics of different types of energy storage.

[0028] Under the first constraint, a multi-scenario energy storage capacity and energy configuration model is constructed with the minimum annual comprehensive cost as the first objective function, and a multi-timescale production and operation simulation model is also constructed.

[0029] A two-layer configuration model is generated based on the multi-scenario energy storage capacity and energy configuration model and the multi-timescale production and operation simulation model. The two-layer configuration model is then solved based on the energy storage configuration capacity requirements under the multi-scenario model and the optimal energy storage type to obtain the energy storage configuration results.

[0030] As can be seen from the above description, the beneficial effects of the present invention are as follows: the energy storage configuration capacity requirements of the power system under multiple scenarios are calculated respectively; the optimal energy storage type is selected based on the energy storage configuration capacity requirements under multiple scenarios and the characteristics of different types of energy storage; under the first constraint, an energy storage capacity and energy configuration model for multiple scenarios is constructed with the minimum annual comprehensive cost as the first objective function; a multi-timescale production and operation simulation model is constructed; a two-layer configuration model is generated based on the two models; and the energy storage configuration result is obtained by solving the model. This comprehensively considers the energy storage requirements under extreme scenarios such as inertia, primary frequency regulation, wind power prediction error, and ramping events; and the constructed model can ensure that the system operates economically and stably while achieving effective energy storage configuration.

[0031] Furthermore, the calculation of energy storage configuration capacity requirements for the power system under multiple scenarios includes:

[0032] Calculate the first energy storage configuration capacity requirement of the power system under inertia support scenario;

[0033] Calculate the second energy storage configuration capacity requirement of the power system under the primary frequency regulation scenario;

[0034] Calculate the capacity requirement of the third energy storage configuration for the power system under the wind power prediction error scenario;

[0035] Calculate the capacity requirement of the fourth energy storage configuration for the power system under the extreme scenario of a ramp-up event.

[0036] As described above, the energy storage configuration capacity requirements are calculated under inertia support scenario, primary frequency regulation scenario, wind power prediction error scenario, and ramping event extreme scenario, respectively. Based on this, subsequent energy storage configuration is carried out to ensure that the energy storage configuration results can ensure the safe and stable operation of the system.

[0037] Furthermore, the first energy storage configuration capacity requirement of the computational power system in the inertia-supported scenario includes:

[0038] The equivalent inertial constant is calculated based on the power system's unit start-up and shutdown status, unit rated capacity, and the proportion of new energy sources;

[0039] Determine whether the equivalent inertia constant is greater than or equal to a preset value. If yes, it is determined that no power compensation for inertia is required through energy storage. If no, it is determined that power compensation for inertia is required through energy storage, and the minimum inertia to be compensated is calculated based on the equivalent inertia constant.

[0040] The power required for energy storage compensation is calculated based on the minimum inertia to be compensated, and the power required for energy storage compensation is taken as the first energy storage configuration capacity requirement in the inertia support scenario.

[0041] As can be seen from the above description, considering the uncertainty of the output of high proportion of new energy sources, calculating the equivalent inertia constant can effectively and accurately determine whether the system needs to compensate for inertia.

[0042] Furthermore, the calculation of the second energy storage configuration capacity requirement of the power system under the primary frequency regulation scenario includes:

[0043] The primary frequency regulation power capacity and primary frequency regulation energy capacity are determined based on the frequency of the power system, and the primary frequency regulation power capacity and primary frequency regulation energy capacity are used as the second energy storage configuration capacity requirement under the primary frequency regulation scenario.

[0044] As described above, the primary frequency regulation power capacity is determined based on the power system frequency, and the primary frequency regulation power capacity is used as the second energy storage configuration capacity requirement in the primary frequency regulation scenario, so that energy storage can better participate in the primary frequency regulation of the power system.

[0045] Furthermore, the calculation of the third energy storage configuration capacity requirement of the power system under the wind power prediction error scenario includes:

[0046] The average error power of the sampling period is calculated based on the actual power and predicted power of the wind farm within the sampling period.

[0047] The capacity status of the energy storage system is calculated based on the average error power of the sampling period.

[0048] The power requirement of the energy storage configuration is determined based on the average error power of the sampling period, and the energy requirement of the energy storage configuration is determined based on the capacity status of the energy storage system.

[0049] The power requirement and energy requirement of the energy storage configuration are used as the third energy storage configuration capacity requirement under the wind power prediction error scenario.

[0050] As described above, the power requirement of the energy storage configuration is determined based on the average error power of the sampling period, and the energy requirement of the energy storage configuration is determined based on the capacity status of the energy storage system, thereby determining the offshore wind power prediction error scenario requirement of the energy storage configuration.

[0051] Furthermore, the calculation of the fourth energy storage configuration capacity requirement of the power system under the extreme scenario of a ramp event includes:

[0052] Based on the multi-parameter segmentation algorithm, typical ramping scenarios at different time scales are obtained, and energy storage configuration targets corresponding to the typical ramping scenarios at different time scales are determined.

[0053] Calculate the amount of wind power variation that needs to be smoothed out;

[0054] Calculate the dynamic uphill capability of the power system when a downhill climb event occurs and the dynamic downhill capability when an uphill climb event occurs;

[0055] The required power capacity for energy storage compensation is calculated based on the amount of wind power fluctuation that needs to be smoothed, the dynamic uphill climbing capability, and the dynamic downhill climbing capability.

[0056] Calculate the energy capacity requirement for energy storage compensation based on the power capacity requirement that needs to be compensated.

[0057] The power capacity requirement and the energy capacity requirement that need energy storage compensation are taken as the fourth energy storage configuration capacity requirement under the extreme scenario of ramping event.

[0058] As described above, the required power capacity for energy storage compensation is calculated based on the amount of wind power fluctuations that need to be smoothed, the dynamic uphill and downhill capabilities, and the required energy capacity for energy storage compensation is calculated based on the required power capacity for energy storage compensation. This is to control the fluctuation range of offshore wind power in adjacent moments within a certain range.

[0059] Furthermore, the selection of the optimal energy storage type based on the energy storage configuration capacity requirements under the multiple scenarios and the characteristics of different types of energy storage includes:

[0060] Based on the energy storage configuration capacity requirements in the aforementioned multiple scenarios and the characteristics of different types of energy storage, a mapping matching method is used to select the optimal energy storage type.

[0061] As described above, the optimal energy storage type is selected by using a mapping matching method based on the energy storage configuration capacity requirements under multiple scenarios and the characteristics of different types of energy storage. By using the mapping matching method to accurately select the optimal energy storage type, it is ensured that effective energy storage configuration results can be obtained subsequently.

[0062] Furthermore, the first objective function is:

[0063]

[0064] In the formula, C1 represents the annual comprehensive cost, and r Y Ω represents the discount rate, Y represents the investment period, and Ω represents the investment period. ESS c represents a collection of energy storage devices. ε,P Let P represent the unit power cost of the ε-th type of energy storage device. ε,inv c represents the power capacity of the ε-th type of energy storage device configuration. ε,E E represents the unit energy cost of the ε-th type of energy storage device. ε,inv T represents the energy capacity of the ε-th type of energy storage device configuration. y D represents the number of typical daily scenes. sC represents the number of days corresponding to a typical daily scenario. op,ty,d This represents the operating cost on the ty-th typical day.

[0065] As described above, the first objective function can optimize and obtain the comprehensive cost for each year within the investment period, ensuring the economic efficiency of energy storage configuration.

[0066] Furthermore, the construction of the multi-timescale production operation simulation model includes:

[0067] Establish a second objective function for minimizing medium- and long-term costs and a second constraint condition corresponding to the second objective function;

[0068] Establish a third objective function for minimizing ultra-short-term costs and a third constraint condition corresponding to the third objective function;

[0069] A multi-timescale production operation simulation model is constructed based on the second objective function, the second constraint, the third objective function, and the third constraint.

[0070] As described above, the multi-timescale production operation simulation model is divided into medium- and long-term and ultra-short-term production simulations according to different time scales of different scenarios, which effectively improves the economic benefits of energy storage configuration.

[0071] Please refer to Figure 2 Another embodiment of the present invention provides an offshore wind farm energy storage configuration terminal for multi-scenario needs, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements each step of the above-described offshore wind farm energy storage configuration method for multi-scenario needs.

[0072] The offshore wind farm energy storage configuration method and terminal described above for multi-scenario needs are applicable to power systems with a high proportion of renewable energy, i.e., power systems with renewable energy accounting for 30%. The following is a detailed description of the specific implementation methods:

[0073] Please refer to Figure 1 , Figures 3-6 Embodiment 1 of the present invention is as follows:

[0074] A method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios includes the following steps:

[0075] S1. Calculate the energy storage configuration capacity requirements of the power system under multiple scenarios, including inertia support scenario, primary frequency regulation scenario, wind power prediction error scenario, and ramp-up event extreme scenario, such as... Figure 3 As shown, specifically including S11-S14:

[0076] S11, Calculate the first energy storage configuration capacity requirement of the power system under the inertia support scenario, specifically including S111-S112:

[0077] S111. Calculate the equivalent inertia constant based on the power system's unit start-up and shutdown status, rated unit capacity, and the proportion of renewable energy sources. Specifically:

[0078]

[0079] In the formula, H' sys β represents the equivalent inertial constant. new Indicates the proportion of new energy sources, 0 < β new <1, N represents the total number of units, x i This indicates the unit's on / off status, and is a 0-1 variable, where 0 represents off and 1 represents on. (H) i S represents the inertial constant of unit i. N,i This indicates the rated capacity of unit i.

[0080] S112. Determine whether the equivalent inertia constant is greater than or equal to a preset value. If yes, determine that no power compensation for inertia is required through energy storage. If no, determine that power compensation for inertia is required through energy storage, and calculate the minimum inertia to be compensated based on the equivalent inertia constant. Calculate the power to be compensated through energy storage based on the minimum inertia to be compensated, and use the power to be compensated through energy storage as the first energy storage configuration capacity requirement in the inertia support scenario.

[0081] In one optional implementation, the preset value is 4 seconds. When the equivalent inertial constant is less than 4 seconds, the smaller the equivalent inertial constant, the more necessary it is to use energy storage to compensate for inertia.

[0082] Wherein, the minimum inertia to be compensated calculated based on the equivalent inertial constant is:

[0083] ΔH=4-H' sys ,0<ΔH<4;

[0084] In the formula, ΔH represents the minimum inertia that needs to be compensated;

[0085] The calculation of the power to be compensated by energy storage based on the minimum inertia to be compensated specifically includes:

[0086] (1) According to the system frequency response formula:

[0087]

[0088] In the formula, H represents the system's equivalent inertial constant, and f sThe system's nominal frequency is represented by df / dt, the system's rate of change of frequency (RoCof) is represented by df / dt, and the system's rated capacity (including both generating and non-generating power) is represented by S.

[0089] (2) Take the system nominal frequency f s The system's rated frequency f N ;

[0090] (3) Taking the average value of the initial RoCof (i.e., the maximum RoCof) of the system instead of the above formula, we get:

[0091]

[0092] In the formula, f 0 This represents the initial value of the system frequency before the change in the calculation time. Δf represents the average rate of change of the initial system frequency, where Δf is the frequency f. 0 The difference between the frequency f and the frequency after the change, P k Represents the load curve, 0 + Represents the load curve P k The instant of change to the next measurement moment.

[0093] (4) The rated capacity S of the system is the capacity S added to participate in the frequency response. ESS The subsequent system power generation capacity S sys ,Right now:

[0094]

[0095] In the formula, S Σ ΔP represents the system's power generation capacity. ESS,ine This indicates the power that needs to be compensated by energy storage devices;

[0096] (5) Load curve P k Take the net load fluctuation curve ΔP NL ;

[0097] (6) The quantity to be calculated is ΔP = ΔP ESS The "H" in the corresponding formula is ΔH, and then the power ΔP that needs to be compensated by energy storage is calculated. ESS,ine ,for:

[0098]

[0099] The derived formula yields the power ΔP that needs to be compensated by energy storage. ESS,ine for:

[0100]

[0101]

[0102] In the formula, γ represents S sys The preceding coefficient.

[0103] S12. Calculate the second energy storage configuration capacity requirement of the power system under the primary frequency regulation scenario.

[0104] Specifically, the primary frequency regulation power capacity and primary frequency regulation energy capacity are determined based on the frequency of the power system, and the primary frequency regulation power capacity and primary frequency regulation energy capacity are used as the second energy storage configuration capacity requirement under the primary frequency regulation scenario.

[0105] In one alternative implementation, the dead zone habit value for primary frequency modulation is set to ±0.03Hz.

[0106] Energy storage configured for offshore wind farms should have the capability to participate in the primary frequency regulation of the power system, and the change in active power ΔP of the wind farm should be provided by the energy storage. Furthermore, it should satisfy the following condition: when the frequency of the power system is greater than 50.03Hz, the wind farm should reduce its active power output according to the primary frequency regulation curve, but the wind farm's output should not decrease, and the configured energy storage should charge ΔP. ESS,frstd It is advisable to use 10% P. t When the frequency of the power system is less than 49.97Hz, the energy storage discharge ΔP ESS,frstd The appropriate value is 6% P. t Therefore, the primary frequency modulation power capacity ΔP ESS,frstd for:

[0107]

[0108] In the formula, P t This represents the active power of the wind farm at time t, in MW.

[0109] Meanwhile, the primary frequency modulation energy capacity ΔE ESS,frstd for:

[0110]

[0111] S13. Calculate the capacity requirement of the third energy storage configuration of the power system under the wind power prediction error scenario, specifically including S131-S134:

[0112] S131. Calculate the average error power of the sampling period based on the actual power and predicted power of the wind farm within the sampling period, specifically as follows:

[0113]

[0114] In the formula, P K Indicates the sampling period t K The average error power, P Pi This indicates that the wind farm is in the sampling period t KPlanned power within, P Mi This indicates that the wind farm is in the sampling period t K The actual power within, where n represents the number of predicted samples.

[0115] S132. Calculate the capacity status of the energy storage system based on the average error power of the sampling period, specifically as follows:

[0116]

[0117] In the formula, W represents the capacity state of the energy storage system, and t K Indicates the sampling period.

[0118] S133. Determine the power requirement of the energy storage configuration based on the average error power of the sampling period, and determine the energy requirement of the energy storage configuration based on the capacity status of the energy storage system.

[0119] Specifically, when P K When P > 0, it indicates that the energy storage system needs charging. K When the value is less than 0, it indicates that the energy storage system needs to discharge. The initial state of charge of the energy storage system is set to 0.5, and the compensation capacity is accumulated for each sampling period. The energy storage capacity required to accurately predict wind power is the maximum absolute value of W during the accumulation process; the minimum energy storage capacity required to accurately predict wind power over the entire prediction period is P. K The maximum absolute value, that is:

[0120]

[0121] In the formula, ΔP ESS,w This represents the power demand of the energy storage configuration considering wind power forecasting errors, ΔE. ESS,w This represents the energy demand of the energy storage configuration taking into account wind power forecasting errors.

[0122] By altering the distribution of wind power forecasting errors, the required energy storage configuration can be effectively reduced.

[0123] S134. The power requirement and energy requirement of the energy storage configuration are taken as the third energy storage configuration capacity requirement under the wind power prediction error scenario.

[0124] S14. Calculate the capacity requirement of the fourth energy storage configuration for the power system under the extreme scenario of a ramp-up event, specifically including S141-S146:

[0125] S141. Based on the multi-parameter segmentation algorithm, typical climbing scenarios at different time scales are obtained, and the energy storage configuration targets corresponding to the typical climbing scenarios at different time scales are determined.

[0126] The goal of allocating storage for extreme offshore wind power scenarios is to control the fluctuation range of adjacent extreme offshore wind power scenarios within a certain range.

[0127] The energy storage configuration targets corresponding to typical ramping scenarios at different time scales include:

[0128] Energy storage configuration targets for ramp-up event type I:

[0129]

[0130] Energy storage configuration targets for ramp-up event type II:

[0131]

[0132] Energy storage configuration targets for ramp-up event type III:

[0133]

[0134] In the formula, p ti Indicates the starting point of different types of climbing events. ΔP represents the endpoint of different types of climbing events. wli P represents the fluctuation limit for different types of climbing events, i = 1, 2, 3. GN This indicates the rated capacity of the wind farm.

[0135] S142. Calculate the amount of wind power change that needs to be smoothed out, specifically:

[0136]

[0137] In the formula, ΔP i This indicates the amount of wind power fluctuation that needs to be smoothed out.

[0138] To respond to wind power fluctuations at different time scales, conventional units need to provide ramp-up and ramp-down capabilities: when a ramp-down event occurs, resulting in insufficient power generation, the system ramps up to adjust the power balance; when a ramp-up event occurs, resulting in power generation exceeding the load, the system ramps down to adjust the power balance, as described below.

[0139] S143. Calculate the dynamic uphill capability of the power system when a downhill climb event occurs and the dynamic downhill capability when an uphill climb event occurs, specifically as follows:

[0140]

[0141] In the formula, R sys,up R represents the system's dynamic uphill capability when a downhill event occurs. sys,down R represents the system's dynamic downhill climbing capability when an uphill event occurs.i,up R represents the upper limit of the power ramp-up of conventional generating units. i,down This indicates the upper limit of the ramp-up capability under conventional unit power, u i The first 0-1 variable, v, represents the start-up and shutdown status of a conventional generating unit. i The second 0-1 variable, w, represents the start-up and shutdown status of a conventional unit. i The third 0-1 variable represents the start-up and shutdown status of the conventional generating unit; a value of 1 indicates operation, and a value of 0 indicates shutdown. (T) SU Indicates the boot time, T SD P represents the downtime. t SU Indicates the boot sequence, P t SD This indicates the shutdown trajectory.

[0142] S144. Calculate the required energy storage capacity based on the wind power fluctuations that need to be smoothed, the dynamic ramp-up capability, and the dynamic ramp-down capability. Specifically:

[0143]

[0144] In the formula, ΔP ESS,dis,r Indicates the discharge power capacity requirement, ΔP ESS,ch,r This indicates the charging power capacity requirement.

[0145] S145. Calculate the energy capacity requirement for energy storage compensation based on the power capacity requirement that needs to be compensated, specifically as follows:

[0146]

[0147] In the formula, ΔE ESS,dis,r Indicates the discharge power capacity requirement, ΔE ESS,ch,r This indicates the charging power capacity requirement, expressed in MWh.

[0148] Therefore, if conventional units lack sufficient ramping capability to mitigate wind power ramping events, energy storage can be used for power and energy compensation.

[0149] S146. The power capacity requirement requiring energy storage compensation and the energy capacity requirement requiring energy storage compensation are taken as the fourth energy storage configuration capacity requirement under the extreme scenario of the ramping event.

[0150] S2. Select the optimal energy storage type based on the energy storage configuration capacity requirements under the multiple scenarios and the characteristics of different types of energy storage.

[0151] Specifically, based on the energy storage configuration capacity requirements under the aforementioned multiple scenarios and the characteristics of different types of energy storage, a mapping matching method is used to select the optimal energy storage type, such as... Figure 4As shown, specifically including (1)-(2):

[0152] (1) Determine the selection scenario based on the multiple scenarios, and determine the requirements under the selection scenario based on the energy storage configuration capacity requirements under the multiple scenarios. The selection scenarios include frequency regulation inertia scenario, wind power prediction error scenario and ramping event extreme scenario. The requirements include technical requirements, economic requirements and other requirements.

[0153] For example, for the selection scenario of frequency regulation inertia scenario, it corresponds to inertia support scenario and primary frequency regulation scenario. From a technical point of view only, the response time requirement of inertia support scenario is almost immediate response, and the response time of primary frequency regulation scenario is 0 to 2 seconds. Their respective support times are 0 to 5 seconds and 2 to 15 seconds. Therefore, under this selection scenario, the minimum technical requirements for energy storage are that it can respond almost immediately and the support time is at least 15 seconds.

[0154] (2) For each of the selected scenarios, iterate through all types of energy storage;

[0155] The types mentioned include power-type and energy-type. Power-type energy storage includes supercapacitors, flywheel energy storage, and electrochemical energy storage, while energy-type energy storage includes electrochemical energy storage, compressed air energy storage, pumped hydro storage, and hydrogen storage. Electrochemical energy storage is used in a general sense here, mainly including lithium-ion batteries, sodium-sulfur batteries, and flow batteries, which can be divided into power-type or energy-type based on their technological support time.

[0156] (2-1) For each target energy storage that has been traversed, calculate the feature matching value between each characteristic of the target energy storage and each requirement of the selection scenario;

[0157] The characteristics mentioned above include technical characteristics, economic characteristics, and other characteristics. The feature matching value is the matching value of each characteristic.

[0158] Specifically, 1) For non-quantifiable technical requirements, 0-1 variables are used as feature matching values: if the characteristics of the target energy storage meet the technical requirements, the feature matching value is 1, otherwise it is 0; 2) For quantifiable technical requirements, a linear dimensionless normalization method is used for normalization, and the feature matching values ​​of the target energy storage characteristics that meet the technical requirements are distributed in the range of [0.51,1], and the feature matching value of the target energy storage characteristics that do not meet the technical requirements is 0.

[0159] (2-2) Calculate the response time matching value and normalized matching value of each characteristic of the target energy storage and each requirement of the selection scenario;

[0160] (2-3) The feature matching value, the response time matching value, and the normalized matching value are weighted and summed to obtain the comprehensive matching value;

[0161] (2-4) If the comprehensive matching value is greater than the preset matching value, it is determined that the target energy storage is fully compatible with the selected scenario and can participate in the hybrid energy storage adapted to the selected scenario, and participate in the energy storage configuration in the subsequent configuration process. If the comprehensive matching value is not greater than the preset matching value, it is determined that the target energy storage is not compatible with the selected scenario and will not participate in the hybrid energy storage adapted to the selected scenario.

[0162] The above mapping and matching method is existing technology, and can be found in Section 2 of the paper "Active Distribution Network Hierarchical Distributed Coordinated Optimization Scheduling Considering Frequency Regulation Reserve Benefits".

[0163] In one alternative implementation, the preset matching value is 4.

[0164] S3. Under the first constraint, construct a multi-scenario energy storage capacity and energy configuration model with the minimum annual comprehensive cost as the first objective function, and construct a multi-timescale production and operation simulation model, such as... Figure 5 As shown, specifically including S31-S34:

[0165] S31. Under the first constraint, construct an energy storage capacity and energy configuration model for multiple scenarios with the minimum annual comprehensive cost as the first objective function.

[0166] The first objective function is:

[0167]

[0168] In the formula, C1 represents the annual comprehensive cost, and r Y Ω represents the discount rate, Y represents the investment period, and Ω represents the investment period. ESS c represents a collection of energy storage devices. ε,P Let P represent the unit power cost of the ε-th type of energy storage device. ε,inv c represents the power capacity of the ε-th type of energy storage device configuration. ε,E E represents the unit energy cost of the ε-th type of energy storage device. ε,inv T represents the energy capacity of the ε-th type of energy storage device configuration. y D represents the number of typical daily scenes. s C represents the number of days corresponding to a typical daily scenario. op,ty,d This represents the operating cost on the ty-th typical day.

[0169] The first constraint includes power-type energy storage constraints, energy-type energy storage constraints, and the combined constraints of power-type energy storage and energy-type energy storage;

[0170] The first constraint includes the demand constraint for the selected energy storage configuration. The selected energy storage includes power-type energy storage and energy-type energy storage. Power-type energy storage should prioritize meeting the inertia and primary frequency regulation scenarios, while energy-type energy storage should prioritize meeting the power prediction error and ramp-up event scenarios. Therefore, the constraint for power-type energy storage is:

[0171]

[0172] In the formula, Ω ESS,P This indicates the selected power-type energy storage set; the formula indicates that the configuration capacity of power-type energy storage should prioritize the inertia and primary frequency regulation scenarios: the lower limit of the total power capacity to be configured should at least meet the minimum power capacity requirements in the inertia and primary frequency regulation scenarios, and the energy requirements to be configured should meet the minimum energy requirements of primary frequency regulation.

[0173] The energy storage constraint is:

[0174]

[0175] In the formula, Ω ESS,E This represents the selected energy storage set. The formula indicates that energy storage should prioritize meeting power prediction errors and ramp-up event scenarios: the minimum required total energy capacity should at least meet the minimum energy capacity requirements for each type of ramp-up event and wind power prediction error, and the minimum required total power capacity should at least meet the minimum power requirements for each type of ramp-up event and wind power prediction error.

[0176] The synergistic constraint between power-type energy storage and energy-type energy storage is:

[0177]

[0178] This constraint takes into account the synergy between power-type energy storage and energy-type energy storage: the total power capacity configured by all energy storage in the hybrid energy storage system should meet the maximum power capacity requirement in all scenarios when working together; similarly, the energy capacity configured by all energy storage should meet the maximum energy capacity requirement in all scenarios.

[0179] S32. Establish a second objective function for minimizing medium- and long-term costs and a second constraint condition corresponding to the second objective function.

[0180] The second objective function, calculated using a 24-hour, 96-time-series production and operation simulation, calculates the minimum daily integrated operating cost for conventional generating units, wind curtailment, load shedding, and energy storage, as follows:

[0181]

[0182] In the formula, C ml,d C represents medium- to long-term costs. th,d,t C represents the operating cost of thermal power units.susd,d,t C represents the start-up and shutdown cost of thermal power units. w,d,t C represents the operating cost of wind power. w,ab,d,t C represents the cost of wind curtailment. ε,d,t Let C represent the operating cost of the ε-th type of energy storage. LS,d,t C represents the load shedding cost. g,others,d,t This represents the operating cost of other power sources in the system at time t on day d, with the value at each time point being a constant.

[0183]

[0184] C susd,d,t =c su,d,t x su,d,t +c sd,d,t x sd,d,t ;

[0185] C w,d,t =c w,d,t (P w,d,t -P w,ab,d,t )Δt;

[0186] C w,ab,d,t =c w,ab,d,t P w,ab,d,t Δt;

[0187] C ε,d,t =(c ε,ch,d,t P ε,ch,d,t +c ε,dis,d,t P ε,dis,d,t )Δt;

[0188] C LS,d,t =c LS,d,t P LS,d,t Δt;

[0189] In the formula, a0 represents the first coefficient (i.e., the constant term coefficient) of the cost function of a conventional unit, a1 represents the second coefficient (i.e., the linear term coefficient) of the cost function of a conventional unit, a2 represents the third coefficient (i.e., the quadratic term coefficient) of the cost function of a conventional unit, and P th,d,t This represents the output of the thermal power unit at time t on day d, where Δt represents the time step, and c su,d,t c represents the startup cost of a thermal power unit. sd,d,t x represents the shutdown cost of a thermal power unit. su,d,t Indicates the startup behavior, x sd,d,t Indicates shutdown action, c w,d,t P represents the cost coefficient of the wind turbine unit during time period t on day d. w,d,t P represents the wind power output at time t on day d. w,ab,d,t c represents the wind curtailment power at time t on day d. w,ab,d,t c represents the wind curtailment penalty coefficient at time t on day d. ε,ch,d,tc represents the charging cost of the ω-th energy storage type at time t on day d. ε,dis,d,t P represents the discharge cost of the ω-th type of energy storage at time t on day d. ε,ch,d,t P represents the charging power of the ε-th type of energy storage at time t on day d. ε,dis,d,t c represents the discharge power of the ε-th type of energy storage at time t on day d. LS,d,t P represents the load shedding penalty coefficient at time t on day d. LS,d,t This indicates the load shedding power.

[0190] The second set of constraints includes active power balance constraints, wind curtailment constraints, load shedding constraints, thermal power unit start-up and shutdown constraints, thermal power unit output upper and lower limit constraints, thermal power unit ramping constraints, energy storage charging and discharging power constraints, and energy storage SOC constraints.

[0191] The active power balance constraint is:

[0192]

[0193] In the formula, P g,others,d,t P represents the total output of other power sources in the system at time t on day d. L,d,t This represents the load at time t on day d;

[0194] The wind curtailment constraint is:

[0195] 0≤P w,ab,d,t ≤P w,d,t ;

[0196] The load shearing constraint is:

[0197] 0≤P LS,d,t ≤P L,d,t ;

[0198] The start-stop constraints for the thermal power units are as follows:

[0199]

[0200] In the formula, u th,d,t This represents the start-up and shutdown status of a thermal power unit at time t on day d, and is a 0-1 variable, where 0 represents the shutdown status and 1 represents the start-up status. th,d,t-1 This indicates the start-up and shutdown status of the thermal power unit at time t-1 on day d;

[0201] The upper and lower limits of the output of the thermal power unit are constrained as follows:

[0202] u th,d,t P th,min ≤P th,d,t ≤u th,d,t P th,max ;

[0203] In the formula, Pth,min P represents the minimum output of a thermal power unit. th,max This indicates the maximum output of the thermal power unit;

[0204] The ramping constraint for the thermal power unit is:

[0205]

[0206] In the formula, R th P represents the gradeability of a thermal power unit. th,d,t-1 This represents the output of the thermal power unit at time t-1 on day d;

[0207] The energy storage charging and discharging power constraint is:

[0208]

[0209] In the formula, P ε,ch,max P represents the maximum charging power of energy storage. ε,dis,max This indicates the maximum discharge power of the stored energy;

[0210] The energy storage SOC constraint is:

[0211]

[0212] In the formula, SOC ε,d,t This represents the State of Charge (SOC) of energy storage at time t on day d. ε,d,t-1 This represents the State of Charge (SOC) of the energy storage at time t-1 on day d. ε,min SOC represents the minimum value of SOC. ε,max η represents the maximum value of SOC. ε,ch Indicates charging efficiency, η ε,dis P represents the discharge efficiency. ε,ch,d,t-1 P represents the charging power of the ε-th type of energy storage at time t-1 on day d. ε,dis,d,t-1 SOC represents the discharge power of the ε-th type of energy storage at time t-1 on day d. ε,d,1 This represents the initial SOC (State of Charge) of energy storage after one day. ε,d,96 This represents the final state of SOC (State of Charge) for one day of energy storage, where the second equality constraint indicates that the initial and final states of SOC for one day of energy storage are equal.

[0213] S33. Establish a third objective function for minimizing ultra-short-term costs and a third constraint condition corresponding to the third objective function;

[0214] The third objective function, calculated using a 96×15×60s time-series production and operation simulation, yields the minimum daily operating cost of power-type energy storage:

[0215]

[0216] C ε,d,t,s =(c ε,ch,d,t,s P ε,ch,d,t,s +c ε,dis,d,t,s P ε,dis,d,t,s )Δt;

[0217] I ε,d,t,s =b ε,d,t,s P ε,p,d,t,s ;

[0218]

[0219] In the formula, C vs,d C represents ultra-short-term costs. ε,d,t,s I represents the operating cost of power-type energy storage. ε,d,t,s This indicates the revenue generated by power-type energy storage participating in primary frequency regulation, c ε,ch,d,t,s c represents the charging cost coefficient for the ε-th power type energy storage at time s during period t on day d. ε,dis,d,t,s P represents the discharge cost coefficient at time s on day d of the ε-type power storage system during period t. ε,ch,d,t,s P represents the charging power at time s on day d of the ε-type power storage system during period t. ε,dis,d,t,s b represents the discharge power at time s on day d of the ε-type power storage system during period t. ε,d,t,s P represents the benefit factor of the ε-type power storage participating in primary frequency regulation at time s during period t on day d. ε,p,d,t,s P indicates the depth of power-type energy storage's participation in primary frequency regulation. ε,p,d,t,s A value of 0 indicates that the system frequency is currently in the primary frequency regulation dead zone, and energy storage does not participate in primary frequency regulation. P0 represents the allowable change in wind power value corresponding to the frequency dead zone, and f d,t,s This represents the system frequency at time s during period t on day d.

[0220] The third constraint includes active power balance constraint, wind curtailment constraint, load shedding constraint, upper and lower limit constraints of thermal power unit output, thermal power unit ramping constraint, energy storage charging and discharging power constraint, energy storage SOC constraint and system maximum RoCof constraint.

[0221] The active power balance constraint, wind curtailment constraint, load shedding constraint, upper and lower limit constraints of thermal power unit output, and thermal power unit ramping constraint in the third constraint condition are the same as the corresponding constraints in the second constraint condition, and will not be repeated here.

[0222] The energy storage charging and discharging power constraint in the third constraint condition is:

[0223]

[0224] In the formula, P ε,ch,d,t,s P represents the charging power at time s on day d of the ε-type energy storage system during period t.ε,dis,d,t,s This represents the discharge power at time s during period t on day d of the ε-type energy storage.

[0225] The energy storage SOC constraint in the third constraint condition is:

[0226]

[0227] In the formula, SOC ε,d,t,s This represents the State of Charge (SOC) of energy storage at time s in period t on day d. ε,d,t,s-1 P represents the State of Charge (SOC) of energy storage at time s-1 in the t period on day d. ε,ch,d,t,s-1 P represents the charging power at time s-1 of period t on day d of the ε-th energy storage type. ε,dis,d,t,s-1 SOC represents the discharge power at time s-1 of period t on day d of the ε-th energy storage type. ε,1,1,1 This represents the initial State of Charge (SOC) on the first day of energy storage (i.e., the first moment of the first time period on day 1). ε,1,96,60 This indicates the final state of SOC on the first day of energy storage (i.e., the 60th moment of the 96th time period on day 1);

[0228] The maximum RoCof constraint of the system is:

[0229] |RoCof max |≤0.1(Hz / s);

[0230] In the formula, RoCof max This represents the maximum RoCof.

[0231] S34. Construct a multi-timescale production operation simulation model based on the second objective function, the second constraint, the third objective function, and the third constraint, such as... Figure 5 As shown, it is:

[0232] min C op,ty,d =min[(min C ml,ty,d )+C vs,ty,d ];

[0233] In the formula, C ml,ty,d This represents the medium- to long-term cost on the ty-th typical day, with a daily operating cycle and 15 minutes as the basic data unit. C vs,ty,d This represents the ultra-short-term cost of the ty-th typical day, with a period of 15 minutes and a basic unit of seconds.

[0234] S4. Generate a two-layer configuration model based on the multi-scenario energy storage capacity and energy configuration model and the multi-timescale production and operation simulation model. Solve the two-layer configuration model based on the energy storage configuration capacity requirements under the multi-scenario conditions and the optimal energy storage type to obtain the energy storage configuration results, such as... Figure 6 As shown.

[0235] The upper-level model, which considers energy storage capacity and energy configuration across multiple scenarios, is a linear programming problem. The lower-level model, which is a multi-timescale production and operation simulation model, is a mixed-integer quadratic programming problem. The upper-level model comprehensively considers the energy storage needs of four scenarios to optimize capacity configuration and then passes the corresponding configuration scheme to the lower-level model. The lower-level model is divided into two production simulations based on the time scale of different scenarios: a daily cycle and a 15-hour / minute cycle. The former targets the typical mid-day wind power prediction error and wind power ramp-up event demand scenario, with the objective function being the minimum typical daily operating cost. The latter targets the inertia and primary frequency regulation demand scenario with a smaller typical mid-day time scale, handling the power balance problem through ultra-short-term simulation. After obtaining the operating results under typical days, the corresponding operating cost is fed back to the upper-level model. The upper and lower-level models iterate repeatedly until the objective function of the upper-level model reaches its optimum or meets the termination condition (i.e., the current iteration number reaches the preset maximum iteration number), obtaining the optimal capacity configuration result that meets the energy storage needs of different scenarios and improving the economic efficiency of energy storage configuration.

[0236] Please refer to Figure 2 Embodiment two of the present invention is as follows:

[0237] An offshore wind farm energy storage configuration terminal for multi-scenario needs includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps in the offshore wind farm energy storage configuration method for multi-scenario needs in Embodiment 1.

[0238] In summary, this invention provides a method and terminal for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios. It calculates the energy storage capacity requirements of the power system under various scenarios, selects the optimal energy storage type based on these requirements and the characteristics of different energy storage types, and constructs an energy storage capacity and energy configuration model for multiple scenarios with the minimum annual comprehensive cost as the first objective function under a first constraint. It also constructs a multi-timescale production and operation simulation model, generates a two-layer configuration model based on the two models, and solves it to obtain the energy storage configuration results. This comprehensively considers the energy storage requirements under extreme scenarios such as inertia, primary frequency regulation, wind power prediction errors, and ramp-up events. The constructed model ensures that the system operates economically and stably while achieving effective energy storage configuration. Furthermore, based on the energy storage capacity requirements under various scenarios and the characteristics of different energy storage types, a mapping matching method is used to select the optimal energy storage type. By accurately selecting the optimal energy storage type using the mapping matching method, effective energy storage configuration results can be obtained subsequently.

[0239] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention's specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios, characterized in that, Including the following steps: The energy storage configuration capacity requirements of the power system are calculated under multiple scenarios, including inertia support scenario, primary frequency regulation scenario, wind power prediction error scenario, and ramping event extreme scenario. The optimal energy storage type is selected based on the energy storage configuration capacity requirements under the aforementioned multiple scenarios and the characteristics of different types of energy storage. Under the first constraint, a multi-scenario energy storage capacity and energy configuration model is constructed with the minimum annual comprehensive cost as the first objective function, and a multi-timescale production and operation simulation model is also constructed. A two-layer configuration model is generated based on the multi-scenario energy storage capacity and energy configuration model and the multi-timescale production and operation simulation model. The two-layer configuration model is then solved based on the energy storage configuration capacity requirements under the multi-scenario model and the optimal energy storage type to obtain the energy storage configuration results. The first constraint includes power-type energy storage constraints, energy-type energy storage constraints, and the combined constraints of power-type energy storage and energy-type energy storage; The first objective function is: ； In the formula, C1 represents the annual comprehensive cost. r Y Indicates the discount rate. Y Indicates the investment period, It represents a collection of energy storage devices. c ε,P Indicates the first ε The unit power cost of this type of energy storage equipment P ε,inv Indicates the first ε The power capacity configured for this type of energy storage device, c ε,E Indicates the first ε The unit energy cost of this type of energy storage equipment E ε,inv Indicates the first ε The energy capacity configured in this type of energy storage device T y This represents the number of typical daily scenarios. D s This indicates the number of days corresponding to a typical daily scenario. Indicates the first ty Operating costs per typical day; The construction of the multi-timescale production operation simulation model includes: Establish a second objective function for minimizing medium- and long-term costs and a second constraint condition corresponding to the second objective function; Establish a third objective function for minimizing ultra-short-term costs and a third constraint condition corresponding to the third objective function; A multi-timescale production operation simulation model is constructed based on the second objective function, the second constraint, the third objective function, and the third constraint.

2. The method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios, as described in claim 1, is characterized in that... The calculation of energy storage configuration capacity requirements for the power system under multiple scenarios includes: Calculate the first energy storage configuration capacity requirement of the power system under inertia support scenario; Calculate the second energy storage configuration capacity requirement of the power system under the primary frequency regulation scenario; Calculate the capacity requirement of the third energy storage configuration for the power system under the wind power prediction error scenario; Calculate the capacity requirement of the fourth energy storage configuration for the power system under the extreme scenario of a ramp-up event.

3. The method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios, as described in claim 2, is characterized in that... The first energy storage configuration capacity requirement of the calculated power system under the inertia support scenario includes: The equivalent inertial constant is calculated based on the power system's unit start-up and shutdown status, unit rated capacity, and the proportion of new energy sources; Determine whether the equivalent inertia constant is greater than or equal to a preset value. If yes, it is determined that no power compensation for inertia is required through energy storage. If no, it is determined that power compensation for inertia is required through energy storage, and the minimum inertia to be compensated is calculated based on the equivalent inertia constant. The power required for energy storage compensation is calculated based on the minimum inertia to be compensated, and the power required for energy storage compensation is taken as the first energy storage configuration capacity requirement in the inertia support scenario.

4. The method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios, as described in claim 2, is characterized in that... The calculation of the second energy storage configuration capacity requirement of the power system under the primary frequency regulation scenario includes: The primary frequency regulation power capacity and primary frequency regulation energy capacity are determined based on the frequency of the power system, and the primary frequency regulation power capacity and primary frequency regulation energy capacity are used as the second energy storage configuration capacity requirement under the primary frequency regulation scenario.

5. The method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios, as described in claim 2, is characterized in that... The calculation of the third energy storage configuration capacity requirement of the power system under the wind power prediction error scenario includes: The average error power of the sampling period is calculated based on the actual power and predicted power of the wind farm within the sampling period. The capacity status of the energy storage system is calculated based on the average error power of the sampling period. The power requirement of the energy storage configuration is determined based on the average error power of the sampling period, and the energy requirement of the energy storage configuration is determined based on the capacity status of the energy storage system. The power requirement and energy requirement of the energy storage configuration are used as the third energy storage configuration capacity requirement under the wind power prediction error scenario.

6. A method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios, as described in claim 2, is characterized in that, The calculation of the fourth energy storage configuration capacity requirement of the power system under the extreme scenario of a ramping event includes: Based on the multi-parameter segmentation algorithm, typical ramping scenarios at different time scales are obtained, and energy storage configuration targets corresponding to the typical ramping scenarios at different time scales are determined. Calculate the amount of wind power variation that needs to be smoothed out; Calculate the dynamic uphill capability of the power system when a downhill climb event occurs and the dynamic downhill capability when an uphill climb event occurs; The required power capacity for energy storage compensation is calculated based on the amount of wind power fluctuation that needs to be smoothed, the dynamic uphill climbing capability, and the dynamic downhill climbing capability. Calculate the energy capacity requirement for energy storage compensation based on the power capacity requirement that needs to be compensated. The power capacity requirement and the energy capacity requirement that need energy storage compensation are taken as the fourth energy storage configuration capacity requirement under the extreme scenario of ramping event.

7. The method for configuring energy storage in offshore wind farms to meet the needs of multiple scenarios as described in claim 1, characterized in that, The selection of the optimal energy storage type based on the energy storage configuration capacity requirements under the aforementioned multiple scenarios and the characteristics of different types of energy storage includes: Based on the energy storage configuration capacity requirements in the aforementioned multiple scenarios and the characteristics of different types of energy storage, a mapping matching method is used to select the optimal energy storage type.

8. An offshore wind farm energy storage configuration terminal for multi-scenario needs, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the offshore wind farm energy storage configuration method according to any one of claims 1 to 7, which is oriented towards multi-scenario needs.

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