Optimal configuration method and system for hybrid energy storage capacity of offshore wind plant

Abstract

The invention relates to the field of offshore wind power energy storage optimal configuration, and discloses an offshore wind power plant hybrid energy storage capacity optimal configuration method and system.The method comprises the steps that a hybrid energy storage mathematical model of an offshore wind power plant hybrid energy storage system is established based on an offshore wind power output model, and the hybrid energy storage mathematical model aims at the minimum investment; and solving the hybrid energy storage mathematical model by using a particle swarm algorithm to obtain an optimal energy storage distribution strategy, constructing a day-ahead power generation plan model with the lowest day-ahead operation cost of the system as a target under the condition of meeting grid-connected power fluctuation constraints, and making an optimal day-ahead power generation plan. According to the method, on the premise that technical constraints are met, the ownership cost in the whole project life cycle is minimized, excessive investment or insufficient configuration is avoided, the economic feasibility of the project is remarkably improved, and economic optimization operation of an offshore wind plant is facilitated.

Description

Technical Field

[0001] This invention relates to the field of optimized configuration of offshore wind power energy storage, and in particular to a method and system for optimized configuration of hybrid energy storage capacity in offshore wind farms. Background Technology

[0002] Offshore wind power technology boasts advantages such as high and stable wind speeds, low wind shear, low noise pollution, and ease of grid integration. However, the intermittent, random, and fluctuating nature of wind power generation leads to safety issues and severely restricts its development. Existing energy storage devices can be categorized into two types based on their characteristics: energy-type and power-type. Energy-type energy storage devices, represented by lithium-ion batteries and flow batteries, have high energy density but low power density, making them suitable for handling low-frequency fluctuating power. Power-type energy storage devices, represented by supercapacitors and superconducting magnetic energy storage, have high power density, can meet high-frequency charging and discharging requirements, and are more suitable for handling high-frequency fluctuating power.

[0003] In the context of large-scale wind power grid integration, energy storage technology can mitigate the intermittency, randomness, and volatility of wind power, contributing to peak shaving and valley filling of the grid's net load after large-scale wind power integration. Combining multiple types of energy storage devices to form a hybrid energy storage system (HESS) not only compensates for the shortcomings of single energy storage technologies but also effectively mitigates wind power fluctuations. Therefore, energy storage technology is considered one of the key technologies for building new power systems and an important solution to the uncertainty of renewable energy output.

[0004] Therefore, methods for rationally configuring the power and capacity of HESS while meeting wind power fluctuation constraints, as well as cost control of energy storage equipment, are urgent issues that need to be addressed. Summary of the Invention

[0005] This invention provides a method and system for optimizing the configuration of hybrid energy storage capacity in offshore wind farms to solve the problems in the prior art.

[0006] Firstly, this application provides a method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms, including: Establish an offshore wind power output model to obtain the actual output of the wind farm; The actual power output of the wind farm is decomposed to obtain a first high-frequency component and a first low-frequency component, and the first high-frequency component is further divided into a second high-frequency component and a second low-frequency component. The supercapacitor is set according to the supercapacitor constraint conditions, and the lithium battery is set according to the lithium battery constraint conditions. The second high-frequency component is allocated to the supercapacitor, the second low-frequency component is allocated to the lithium battery, and a hybrid energy storage mathematical model of the hybrid energy storage system of the offshore wind farm is established. The optimal energy storage allocation strategy is obtained by solving the hybrid energy storage mathematical model using the particle swarm optimization algorithm. Under the condition of satisfying the grid-connected power fluctuation constraints, the first low-frequency component is incorporated into the grid to construct a day-ahead power generation planning model with the goal of minimizing the system's day-ahead operating cost. The day-ahead power generation plan model was solved using the CPLEX solver to obtain the optimal day-ahead power generation plan; The optimal energy storage allocation strategy and the optimal day-ahead power generation plan are used for optimization.

[0007] Secondly, this application provides a hybrid energy storage capacity optimization configuration system for offshore wind farms, 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 the steps of the method described in the first aspect above.

[0008] The present invention has the following beneficial effects: This application presents a method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms. Based on an offshore wind power output model, a hybrid energy storage mathematical model is established for the offshore wind farm's hybrid energy storage system. This model aims to minimize investment. The optimal energy storage allocation strategy is obtained by solving the hybrid energy storage mathematical model using a particle swarm optimization algorithm. While satisfying grid-connected power fluctuation constraints, a day-ahead power generation plan model is constructed with the goal of minimizing the system's day-ahead operating cost, thus formulating the optimal day-ahead power generation plan. This method minimizes the total cost of ownership over the entire project lifecycle while meeting technical constraints, avoiding over-investment or under-configuration, significantly improving the project's economic feasibility, and contributing to the economically optimized operation of offshore wind farms.

[0009] In addition to the objectives, features and advantages described above, the present invention has other objectives, features and advantages.

[0010] The present invention will now be described in further detail with reference to the figures. Attached Figure Description

[0011] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is one of the flowcharts of a preferred embodiment of the present invention for a method of optimizing the configuration of hybrid energy storage capacity in an offshore wind farm; Figure 2 This is a second schematic diagram of a preferred embodiment of the present invention, illustrating a method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms. Figure 3 This is a schematic diagram of wavelet packet decomposition according to a preferred embodiment of the present invention; Figure 4 This is a schematic diagram of the actual power output of a wind farm according to a preferred embodiment of the present invention; Figure 5 This is a simulation diagram of the actual power and grid-connected power of an offshore wind farm according to a preferred embodiment of the present invention; Figure 6 This is a schematic diagram of the fluctuation rate of grid-connected power in a preferred embodiment of the present invention at 1-minute and 10-minute time scales; Figure 7 This is a schematic diagram of the high-frequency fluctuation component separated from the original power of the wind farm after being smoothed by the energy storage system according to a preferred embodiment of the present invention. Figure 8 This is a schematic diagram of the daytime power generation plan obtained after optimized calculation according to a preferred embodiment of the present invention; Detailed Implementation The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "an" or "a," and similar terms, do not indicate a quantity limitation, but rather indicate the presence of at least one.

[0013] Please see Figure 1 This application provides a method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms, including: Establish an offshore wind power output model to obtain the actual output of the wind farm; The actual power output of the wind farm is decomposed to obtain a first high-frequency component and a first low-frequency component, and the first high-frequency component is further divided into a second high-frequency component and a second low-frequency component. The supercapacitor is set according to the supercapacitor constraint conditions, and the lithium battery is set according to the lithium battery constraint conditions. The second high-frequency component is allocated to the supercapacitor, the second low-frequency component is allocated to the lithium battery, and a hybrid energy storage mathematical model of the hybrid energy storage system of the offshore wind farm is established. The optimal energy storage allocation strategy is obtained by solving the hybrid energy storage mathematical model using the particle swarm optimization algorithm. Under the condition of satisfying the grid-connected power fluctuation constraints, the first low-frequency component is incorporated into the grid to construct a day-ahead power generation planning model with the goal of minimizing the system's day-ahead operating cost. The day-ahead power generation plan model was solved using the CPLEX solver to obtain the optimal day-ahead power generation plan; The optimal energy storage allocation strategy and the optimal day-ahead power generation plan are used for optimization.

[0014] The aforementioned method for optimizing the capacity allocation of hybrid energy storage in offshore wind farms establishes a hybrid energy storage mathematical model based on the offshore wind power output model. This model aims to minimize investment, and the optimal energy storage allocation strategy is obtained by solving the model using a particle swarm optimization algorithm. Under the constraint of grid-connected power fluctuations, a day-ahead power generation plan model is constructed with the goal of minimizing the system's day-ahead operating cost, thus formulating the optimal day-ahead power generation plan. This method minimizes the total cost of ownership over the entire project lifecycle while meeting technical constraints, avoiding over-investment or under-configuration, significantly improving the economic feasibility of the project, and contributing to the economically optimized operation of offshore wind farms.

[0015] Optionally, establishing the offshore wind power output model includes: establishing an actual offshore wind farm output model based on the superposition of predicted power and random fluctuation components, satisfying the following relationship:

[0016] in, The actual power output of the offshore wind farm at time t; Let t be the predicted wind power value. Fluctuations in wind power output; Among them, the fluctuation of wind power output follows a normal distribution, and its mean is... Standard deviation The following relationship must be satisfied:

[0017] in, For the installed capacity of offshore wind farms; At time t, the actual power output of the offshore wind farm satisfies the following constraint relationship: .

[0018] Optionally, the step of decomposing the actual power output of the wind farm to obtain a first high-frequency component and a first low-frequency component includes: like Figure 3 As shown, wavelet packet decomposition technology is used to decompose the actual power output of the wind farm, and the original power signal S is processed. Layer decomposition yields the corresponding first low-frequency component. and the first high-frequency part , The bandwidth of each signal band The following relationship must be satisfied: ; in, is the signal sampling frequency; n is the wavelet packet decomposition level.

[0019] Optionally, the lithium battery constraint condition satisfies the following relationship:

[0020] in, , These are the upper and lower limits of the state of charge of a lithium battery. This represents the maximum power of a single component in a battery. This refers to the state of charge of the lithium battery. , This represents the charging and discharging operating states of the lithium battery at time t. Let be the charging power of the lithium battery at time t. The number of lithium batteries used. Let t be the discharge power of the lithium battery at time t; The constraints of supercapacitors satisfy the following relationship:

[0021] in, Let SOC be the state of charge of the supercapacitor at time t. , The charging and discharging states of the supercapacitor at time t; This represents the maximum charge and discharge power of the supercapacitor. , These are the upper and lower limits of the state of charge of a supercapacitor, respectively. and These represent the charging power and discharging power of the supercapacitor at time t, respectively. This refers to the number of supercapacitors deployed.

[0022] Optionally, the establishment of the hybrid energy storage mathematical model for the offshore wind farm hybrid energy storage system includes: The cumulative charging capacity of the lithium battery and supercapacitor from the initial time to time t (the final hybrid energy storage mathematical model) satisfies the following relationship:

[0023] Where T is the running time; and This represents the charging and discharging operating state of the lithium battery at time t. and These represent the charging and discharging states of the supercapacitor at time t; Let t be the charging power of the lithium battery at time t; Let t be the discharge power of the lithium battery at time t; and These are the unit device capacities of lithium batteries and supercapacitors, respectively. and These are the charge and discharge efficiencies of lithium batteries and supercapacitors after passing through the converter, respectively. The cumulative charge of the lithium battery from the start time to time t; The total amount of charge accumulated by the supercapacitor from the start time to time t; The state of charge (SOC) of the lithium battery and supercapacitor at time t is calculated, and the following relationships are satisfied respectively:

[0024]

[0025] in, The initial state of charge (SOC) of the lithium battery; It is the initial state of charge (SOC) of the supercapacitor; Let S be the state of charge (SOC) of the lithium battery at time t. Let SOC be the state of charge of the supercapacitor at time t. a b represents the number of lithium batteries deployed; b represents the number of supercapacitors deployed. Establish the objective function It satisfies the following relationship:

[0026] in, The daily cost of lithium batteries includes daily investment cost and daily operation and maintenance cost; The daily input cost of supercapacitors; The volatility penalty cost refers to the penalty cost incurred by the inability of storage to fully replenish or absorb the high-frequency fluctuations in wind power. in: ; in, Cost per unit of lithium battery components; The discount rate; This refers to the lifespan of a lithium battery. The maintenance and operation costs of lithium battery charging and discharging;

[0027] in, Cost per unit of supercapacitor; The discount rate; This refers to the service life of a supercapacitor. Reduce the maintenance costs of charging and discharging supercapacitors;

[0028] in, The wind curtailment penalty coefficient is used to determine whether the storage can fully absorb the high-frequency fluctuation power of wind power. Storage cannot fully compensate for the power shortage penalty coefficient corresponding to the high-frequency fluctuation power of wind power; , These are the power reduction values ​​for lithium batteries and supercapacitors, respectively.

[0029] Optionally, the grid-connected power fluctuation constraint is as follows:

[0030] In the formula: , These represent the maximum limits on the active power fluctuation values ​​that a wind farm is allowed to output to the grid on 1-minute and 10-minute time scales, respectively.

[0031] Optionally, the lowest day-ahead operating cost satisfies the following relationship:

[0032]

[0033] in, The cost of purchasing electricity from conventional generating units; Let t be the power generation capacity of conventional unit i during time period t; Let be the electricity price at time t; T be the number of day-ahead dispatch periods. This refers to the number of conventional generating units.

[0034] The system power balance constraint satisfies the following relationship:

[0035] in, For wind power connected to the grid during time period t; Let be the load power during time period t; The system's energy storage charging and discharging power during time period t is greater than zero when discharging and less than zero when charging.

[0036] It is worth explaining that the system power balance constraint is the power balance equation of the grid after the wind power is connected to the grid. After obtaining the grid-connected wind power in the above steps, the minimum day-ahead power generation is obtained after connecting it to the grid.

[0037] like Figure 2As shown below, the steps of the above-mentioned method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms are described with a specific example: Step 1: Input the parameters required for the system and optimization algorithm, set the maximum number of iterations, population size, etc., and initialize the position and velocity of the particles. This application takes an offshore wind farm as the research object and analyzes the energy storage capacity optimization configuration problem of the wind-storage system. The installed capacity of the wind farm is 10MW. The inertia weight ω=1 and the learning factor c1=c2=2 of the particle swarm optimization algorithm used in this application are used. The maximum number of iterations of the algorithm is... Set to 1500 times.

[0038] Step 2: A stochastic simulation of wind power output fluctuations is performed using an offshore wind power output model. A Monte Carlo simulation sampling method is then used to sample these fluctuations, and the actual daily offshore wind power output is obtained by adding a predetermined offshore wind power forecast value. The actual wind farm output is as follows: Figure 4 As shown.

[0039] Step 3: Use the particle swarm optimization algorithm to obtain the power smoothing of high-frequency fluctuations and the grid connection component of low-frequency fluctuations with the goal of minimizing the total economic investment of the energy storage system.

[0040] Step 4: The wind power connected to the grid is input into the day-ahead scheduling model and the minimum day-ahead operating cost is solved using a solver.

[0041] This embodiment takes an offshore wind farm as the research object and analyzes the problem of optimizing the energy storage capacity configuration of the wind-storage system. The wind farm has an installed capacity of 10MW, and various different scenarios are established using MATLAB for research.

[0042] For the above-described system embodiments, we will analyze three scenarios: scenario 1, which uses only lithium batteries to mitigate wind power output; scenario 2, which uses only supercapacitors to mitigate wind power output; and scenario 3, which is the main focus of this study, which uses hybrid energy storage to mitigate wind power output using both methods. Table 1 shows the energy storage configuration schemes and investment costs for the three scenarios.

[0043] Table 1 Simulation results under different scenarios

[0044] As shown in Table 1 above, when offshore wind farms use lithium batteries as energy storage devices, the energy storage investment cost is 14,856 × 10⁴ yuan. When using hybrid energy storage devices for wind power leveling tasks, the energy storage investment cost is 11,950 × 10⁴ yuan, a cost reduction of 19.56%. When using supercapacitors as energy storage devices, the investment cost is 21,152 × 10⁴ yuan, indicating that the investment cost is reduced by 43.5% when using a hybrid energy storage system. This demonstrates that using a hybrid energy storage system can effectively reduce the investment cost of wind power storage systems compared to single energy storage systems.

[0045] Based on the optimal configuration model of the hybrid energy storage system, the actual power and grid-connected power of the offshore wind farm are obtained through simulation, such as... Figure 5 As shown in the figure. Analysis indicates that the system effectively mitigates wind power fluctuations, and the fluctuation rates of its grid-connected power on both the 1-minute and 10-minute timescales meet the relevant national technical standards, such as... Figure 6 As shown. At this time, the high-frequency fluctuation component separated from the original power of the wind farm after being smoothed by the energy storage system is as follows: Figure 7 As shown.

[0046] The wind power grid-connected power after hybrid energy storage smoothing in Scenario 3 is input into the day-ahead generation plan model. The day-ahead generation plan obtained after optimization calculation is as follows: Figure 8 As shown. The plan demonstrates excellent cost control, with a total cost of only 2.0205 × 10³ yuan. Figure 8 As can be seen, the system fully considers electricity prices and wind power characteristics. The system selects to generate electricity when electricity prices are lower and wind power is lower, such as 1:00-7:00 and 20:00-21:00, in order to effectively reduce power generation costs and optimize resource utilization.

[0047] This flexible dispatch strategy based on electricity prices and wind power characteristics fully demonstrates the significant advantages of the day-ahead generation planning model in improving the economic efficiency and operational efficiency of the power system.

[0048] In summary, this application establishes a hybrid energy storage mathematical model for offshore wind farm hybrid energy storage systems based on the offshore wind power output model. This hybrid energy storage mathematical model aims to minimize input costs. The optimal energy storage allocation strategy is obtained by solving the hybrid energy storage mathematical model using a particle swarm optimization algorithm. Under the constraint of grid-connected power fluctuations, a day-ahead power generation plan model is constructed with the goal of minimizing the system's day-ahead operating cost, thus formulating the optimal day-ahead power generation plan. This method minimizes the total cost of ownership over the entire project lifecycle while meeting technical constraints, avoiding over-investment or under-configuration, significantly improving the economic feasibility of the project, and contributing to the economic optimization of offshore wind farm operation.

[0049] This application also provides a hybrid energy storage capacity optimization configuration system for offshore wind farms, 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 the steps of the above-described method. This hybrid energy storage capacity optimization configuration system for offshore wind farms can implement various embodiments of the above-described hybrid energy storage capacity optimization configuration method for offshore wind farms and achieve the same beneficial effects; further details are omitted here.

[0050] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms, characterized in that, include: Establish an offshore wind power output model to obtain the actual output of the wind farm; The actual power output of the wind farm is decomposed to obtain a first high-frequency component and a first low-frequency component, and the first high-frequency component is further divided into a second high-frequency component and a second low-frequency component. The supercapacitor is set according to the supercapacitor constraint conditions, and the lithium battery is set according to the lithium battery constraint conditions. The second high-frequency component is allocated to the supercapacitor, the second low-frequency component is allocated to the lithium battery, and a hybrid energy storage mathematical model of the hybrid energy storage system of the offshore wind farm is established. The optimal energy storage allocation strategy is obtained by solving the hybrid energy storage mathematical model using the particle swarm optimization algorithm. Under the condition of satisfying the grid-connected power fluctuation constraints, the first low-frequency component is incorporated into the grid to construct a day-ahead power generation planning model with the goal of minimizing the system's day-ahead operating cost. The day-ahead power generation plan model was solved using the CPLEX solver to obtain the optimal day-ahead power generation plan; The optimal energy storage allocation strategy and the optimal day-ahead power generation plan are used for optimization.

2. The method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms according to claim 1, characterized in that, The establishment of the offshore wind power output model includes: establishing an actual offshore wind farm output model based on the superposition of predicted power and random fluctuation components, satisfying the following relationship: in, The actual power output of the offshore wind farm at time t; Let t be the predicted wind power value. Fluctuations in wind power output; Among them, the fluctuation of wind power output follows a normal distribution, and its mean is... Standard deviation The following relationship must be satisfied: in, For the installed capacity of offshore wind farms; At time t, the actual power output of the offshore wind farm satisfies the following constraint relationship: 。 3. The method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms according to claim 1, characterized in that, The process of decomposing the actual power output of the wind farm to obtain a first high-frequency component and a first low-frequency component includes: The actual power output of the wind farm is decomposed using wavelet packet decomposition technology, and the original power signal S is processed. Layer decomposition yields the corresponding first low-frequency component. and the first high-frequency part , The bandwidth of each signal band The following relationship must be satisfied: ； in, is the signal sampling frequency; n is the wavelet packet decomposition level.

4. The method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms according to claim 1, characterized in that, The constraints on the lithium battery satisfy the following relationship: in, , These are the upper and lower limits of the state of charge of a lithium battery. This represents the maximum power of a single component in a battery. This refers to the state of charge of the lithium battery. , This represents the charging and discharging operating states of the lithium battery at time t. Let be the charging power of the lithium battery at time t. The number of lithium batteries used. Let t be the discharge power of the lithium battery at time t; The constraints of supercapacitors satisfy the following relationship: in, Let SOC be the state of charge of the supercapacitor at time t. , The charging and discharging states of the supercapacitor at time t; This represents the maximum charge and discharge power of the supercapacitor. , These are the upper and lower limits of the state of charge of a supercapacitor, respectively. and These represent the charging power and discharging power of the supercapacitor at time t, respectively. This refers to the number of supercapacitors deployed.

5. The method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms according to claim 1, characterized in that, The establishment of the hybrid energy storage mathematical model for the hybrid energy storage system of the offshore wind farm includes: The cumulative charging amount of the lithium battery and supercapacitor from the initial moment to time t satisfies the following relationship: Where T is the running time; and This represents the charging and discharging operating state of the lithium battery at time t. and These represent the charging and discharging states of the supercapacitor at time t; Let t be the charging power of the lithium battery at time t; Let be the discharge power of the lithium battery at time t. and These represent the charging power and discharging power of the supercapacitor at time t, respectively. and These are the unit device capacities of lithium batteries and supercapacitors, respectively. and These are the charge and discharge efficiencies of lithium batteries and supercapacitors after passing through the converter, respectively. The cumulative charge of the lithium battery from the start time to time t; The total amount of charge accumulated by the supercapacitor from the start time to time t; The state of charge (SOC) of the lithium battery and supercapacitor at time t is calculated, and the following relationships are satisfied respectively: in, The initial state of charge (SOC) of the lithium battery; It is the initial state of charge (SOC) of the supercapacitor; Let S be the state of charge (SOC) of the lithium battery at time t. Let SOC be the state of charge of the supercapacitor at time t. a b represents the number of lithium batteries deployed; b represents the number of supercapacitors deployed. Establish the objective function It satisfies the following relationship: in, The daily cost of lithium batteries includes daily investment cost and daily operation and maintenance cost; The daily input cost of supercapacitors; The volatility penalty cost refers to the penalty cost incurred by the inability of storage to fully replenish or absorb the high-frequency fluctuations in wind power. in: ； in, Cost per unit of lithium battery components; The discount rate; This refers to the lifespan of a lithium battery. The maintenance and operation costs of lithium battery charging and discharging; in, Cost per unit of supercapacitor; The discount rate; This refers to the service life of a supercapacitor. Reduce the maintenance costs of charging and discharging supercapacitors; in, The wind curtailment penalty coefficient is used to determine whether the storage can fully absorb the high-frequency fluctuation power of wind power. Storage cannot fully compensate for the power shortage penalty coefficient corresponding to the high-frequency fluctuation power of wind power; , These are the power reduction values ​​for lithium batteries and supercapacitors, respectively.

6. The method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms according to claim 1, characterized in that, The The grid-connected power fluctuation constraints are as follows: In the formula: , These represent the maximum limits on the active power fluctuation values ​​that a wind farm is allowed to output to the grid on 1-minute and 10-minute time scales, respectively.

7. The method for optimizing the configuration of hybrid energy storage capacity in offshore wind farms according to claim 1, characterized in that, The lowest day-to-day operating cost satisfies the following relationship: in, The cost of purchasing electricity from conventional generating units; Let t be the power generation capacity of conventional unit i during time period t; Let be the electricity price at time t; T be the number of day-ahead dispatch periods. This is the number of conventional generating units; The system power balance constraint satisfies the following relationship: in, For wind power connected to the grid during time period t; Let be the load power during time period t; The system's energy storage charging and discharging power during time period t is greater than zero when discharging and less than zero when charging.

8. A hybrid energy storage capacity optimization configuration system for offshore wind farms, 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 the steps of any of the methods described in claims 1-7.

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