Multi-energy collaborative optimization method for fusion of offshore wind power plant cluster collection system

Abstract

The invention discloses a multi-energy collaborative optimization method for an offshore wind plant cluster collection system, and the method comprises the steps: building a corresponding energy storage equipment model, a hydrogen load matching strategy, a hydrogen energy income model, and a wind power-controllable load collaborative model through the fusion of multi-energy equipment, and taking a multi-energy coupling model as a new optimization variable. Constructing a collaborative optimization mechanism for the collection system, wherein the collaborative optimization mechanism comprises a power collaborative distribution mechanism and an equipment capacity and collection system matching mechanism; taking the comprehensive benefit of the power transmission line as an optimization target of the collection system, establishing a collection system constraint and a multi-energy equipment constraint, and forming an optimization target system; and finally, based on the optimization target system, solving the optimal collaborative topology of the collection system by adopting an improved genetic algorithm based on a dynamic variable weight minimum spanning tree. An offshore wind power cluster collection system is combined with multi-energy equipment, the optimal collaborative topology of the system is solved, and the wind curtailment cost is further reduced and the comprehensive benefits of the system are improved by stabilizing residual fluctuation through energy storage and absorbing the curtailment through hydrogen production.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power technology, and in particular to a method for the synergistic optimization of multiple energy sources in an offshore wind farm cluster aggregation system. Background Technology

[0002] Existing research on the optimization of offshore wind farm cluster aggregation systems focuses more on the economics and reliability of the topology, with less consideration given to the power output characteristics and smoothing effects of offshore wind farm clusters. In reality, offshore wind farms have long utilization periods and exhibit strong correlation and spatial smoothing effects. The high correlation results in significant fluctuations in cluster power output; the spatial smoothing effect reduces the impact of these fluctuations, making the cluster power output more stable. In practical engineering design, neglecting to consider the impact of offshore wind farm cluster power output characteristics on the capacity of the aggregation and transmission systems can lead to over-planned capacity and poor economic efficiency.

[0003] Researchers have noted the power output characteristics of offshore wind farm clusters and proposed a topology optimization method for offshore wind farm cluster aggregation systems that considers correlation and smoothing effects. However, these optimization methods do not consider integrating the offshore wind farm cluster aggregation system with multiple energy sources for collaborative optimization, resulting in high wind curtailment costs and low overall system efficiency. There is an urgent need for a method that integrates multiple energy sources for collaborative optimization of offshore wind farm cluster aggregation systems to improve overall efficiency, address the problem of limited single-energy absorption capacity of offshore wind power, and provide a more economical and sustainable planning scheme for the large-scale development of deep-sea offshore wind power. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a multi-energy collaborative optimization method for offshore wind farm cluster aggregation systems, aiming to improve the overall benefits of the project.

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

[0006] A method for multi-energy synergistic optimization of an offshore wind farm cluster system includes the following steps:

[0007] Step 1: Based on the power output characteristics of offshore wind farm clusters, determine the types of multi-energy equipment to be integrated and establish a corresponding multi-energy coupling model. The multi-energy coupling model includes a multi-energy equipment model, a hydrogen load matching strategy, a hydrogen energy revenue model, and a wind power-controllable load coordination model.

[0008] Step 2: Using the multi-energy coupling model as a new optimization variable, construct a collaborative optimization mechanism for the aggregation system, including a power collaborative allocation mechanism and a matching mechanism between equipment capacity and the aggregation system.

[0009] Step 3: Take the comprehensive benefits of transmission lines as the optimization target of the collection system, and establish collection system constraints and multi-energy equipment constraints to form an optimization target system;

[0010] Step 4: Based on the optimization target system, the optimal cooperative topology of the aggregation system is solved by using an improved genetic algorithm based on dynamic variable weight minimum spanning tree.

[0011] In some embodiments, the multi-energy device model includes at least a battery energy storage sub-model and a gas-solid two-phase hydrogen storage sub-model; wherein, during the construction of the battery energy storage sub-model, the constraint condition is set that the battery energy storage device is charged during the peak output of the offshore wind farm cluster and discharged during the off-peak output; the construction process of the gas-solid two-phase hydrogen storage sub-model includes at least a hydrogen production stage and a power generation stage, and the solid hydrogen storage capacity is set as a constraint condition.

[0012] In some implementations, during the construction of the hydrogen production load matching strategy, the power adjustable range of the hydrogen production equipment is set to 0.3~1.0, and the hydrogen production equipment is restricted to preferentially consuming the surplus power after wind power smoothing; the hydrogen energy revenue model is incorporated into the hydrogen production conversion rate.

[0013] In some implementations, during the construction of the wind power-controllable load collaborative model, a constraint condition is set that the controllable load can be adjusted within ±20% of the rated power range.

[0014] In some implementations, the power coordination and allocation mechanism of the multi-energy aggregation system includes: setting allocation priorities for offshore wind farm clusters, battery energy storage systems, gas-solid two-phase hydrogen storage systems, and controllable loads.

[0015] In some implementations, the equipment capacity and aggregation system matching mechanism includes: matching the maximum discharge power of the battery energy storage equipment with the submarine cable capacity; matching the hydrogen production capacity of the hydrogen production equipment with the seasonal maximum surplus power of the offshore wind farm cluster; and matching the total capacity of the offshore booster station aggregation cable with the access capacity of the multi-energy equipment.

[0016] In some implementations, the aggregation system constraints include submarine cable transmission capacity constraints, submarine cable current carrying capacity constraints, submarine cable connection to wind turbine number constraints, submarine cable crossover avoidance constraints, and aggregation substation capacity constraints.

[0017] In some implementations, the constraints on the multi-energy devices include constraints on the charging and discharging power of energy storage, constraints on the start-up and shutdown of hydrogen production equipment, and constraints on the balance between hydrogen supply and demand.

[0018] The beneficial effects of this invention are as follows: By integrating the types of multi-energy devices and establishing corresponding multi-energy device models, hydrogen load matching strategies, hydrogen energy revenue models, and wind power-controllable load coordination models, a power coordination allocation mechanism for the aggregation system and a matching mechanism between equipment capacity and the aggregation system are constructed using the multi-energy coupling model as a new optimization variable. The comprehensive benefits of transmission lines are used as the optimization objective of the aggregation system, and constraints on the aggregation system and multi-energy devices are established to form an optimization objective system. Finally, based on the optimization objective system, an improved genetic algorithm based on dynamic variable weight minimum spanning tree is used to solve the optimal coordination topology of the aggregation system. By combining the offshore wind power cluster aggregation system with multi-energy devices, the optimal coordination topology of the system is solved. Through energy storage to smooth residual fluctuations and hydrogen production to absorb wind curtailment, the cost of wind curtailment is further reduced, and the comprehensive benefits of the system are improved. Attached Figure Description

[0019] Figure 1 This is the annual power output curve of a certain offshore wind farm;

[0020] Figure 2 This is a schematic diagram of the process of using an improved genetic algorithm to solve for the optimal cooperative topology of the pooling system in step 4 of an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the content of this invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to this invention are shown in the accompanying drawings, not all of them.

[0022] This embodiment proposes a multi-energy collaborative optimization method for offshore wind farm cluster aggregation systems, including the following steps:

[0023] Step 1: Based on the power output characteristics of offshore wind farm clusters, determine the types of multi-energy equipment to be integrated and establish corresponding multi-energy coupling models. The multi-energy coupling models include multi-energy equipment models, hydrogen load matching strategies, hydrogen energy revenue models, and wind power-controllable load coordination models.

[0024] In step 1, when integrating multi-energy equipment, priority should be given to energy types that are compatible with the offshore wind farm cluster scenario and can complement the volatility of wind power. In this solution, the equipment combination of "battery energy storage (short-term) + gas-solid two-phase hydrogen storage (long-term)" is preferred, so as to adapt to the characteristics of offshore wind power "frequent short-term fluctuations and large seasonal output differences", and to smooth out short-term fluctuations and peak shaving.

[0025] The aforementioned multi-energy device model includes a battery energy storage sub-model and a gas-solid two-phase hydrogen storage sub-model. In the construction of the battery energy storage sub-model, the constraint condition is set that the battery energy storage device is charged during the peak output of the offshore wind farm cluster and discharged during the off-peak output. The construction process of the gas-solid two-phase hydrogen storage sub-model includes at least a hydrogen production stage and a power generation stage, and the solid hydrogen storage capacity is set as a constraint condition.

[0026] Step 101, specifically, based on the capacity constraints of the offshore converter station and submarine cable, this scheme configures a battery energy storage system (BESS) near the offshore booster station. Its charging and discharging power needs to be coordinated with the power of the collection system. The modeling process of the battery energy storage sub-model is as follows:

[0027] (1)

[0028] In the formula, t represents the battery's charging and discharging power at time t, with positive values ​​for discharging and negative values ​​for charging. This refers to the charge / discharge efficiency, which can be taken as 0.9~0.95. This refers to the battery's rated capacity. Batteries need to be charged during peak wind power output and discharged during off-peak hours to smooth out short-term fluctuations and reduce wind curtailment.

[0029] Step 102: This scheme selects to configure electrolyzers, solid-state hydrogen storage tanks, and fuel cells near the wind farm cluster to produce hydrogen using surplus wind power and release hydrogen to generate electricity when there is a power shortage. The model needs to be associated with the power of the collection system. The modeling of the gas-solid two-phase hydrogen storage sub-model is as follows:

[0030] ① Hydrogen production stage:

[0031] (2)

[0032] In the formula, It refers to the power of the electrolytic cell; This is the rated power of the electrolytic cell; It is the output power of wind power after smoothing; This is the maximum capacity of the submarine cable line.

[0033] ② Power generation stage:

[0034] (3)

[0035] In the formula, It refers to the power of the fuel cell; This is the rated power of the fuel cell.

[0036] Solid-state hydrogen storage capacity must meet the following requirements:

[0037] (4)

[0038] In the formula, It refers to the solid-state hydrogen storage capacity; It is the electrolysis efficiency; It refers to fuel cell efficiency.

[0039] To address the seasonal surplus output of offshore wind power in deep-sea areas, hydrogen production equipment should be configured and connected to offshore hydrogen energy loads (such as hydrogen refueling for ocean-going vessels and heating for offshore platforms). The model needs to connect the collection system with the hydrogen energy industry chain.

[0040] Step 103, the hydrogen production load matching strategy is as follows: hydrogen production equipment power The adjustable range is 0.3~1.0. Priority should be given to absorbing the surplus power remaining after wind power smoothing. This is to avoid long-term wind curtailment due to line capacity limitations.

[0041] Step 104, the hydrogen energy benefit model is as follows:

[0042] (5)

[0043] In the formula, For hydrogen production conversion rate; This refers to the price of hydrogen.

[0044] In this plan, a wind power-controllable load collaborative model needs to be established by combining the controllable load of offshore platforms (such as oil and gas extraction platforms and seawater desalination plants).

[0045] Step 105, the construction process of the wind power-controllable load collaborative model is as follows:

[0046] In the process of constructing the wind power-controllable load collaborative model, controllable loads are set. Under the constraint of adjusting within ±20% of rated power, the corresponding formula for wind power output change is:

[0047] (6)

[0048] The coordination strategy is as follows: during peak wind power output, increase controllable load power (such as increasing seawater desalination) to absorb surplus wind power; during off-peak wind power output, reduce controllable load power to reduce the need for external power purchases.

[0049] Step 2: Using the multi-energy coupling model as a new optimization variable, construct a collaborative optimization mechanism for the aggregation system, including a power collaborative allocation mechanism and a matching mechanism between equipment capacity and the aggregation system.

[0050] The power allocation mechanism for the multi-energy aggregation system includes: setting allocation priorities for offshore wind farm clusters, battery energy storage systems, gas-solid two-phase hydrogen storage systems, and controllable loads to ensure power balance and optimal economic efficiency of the aggregation system.

[0051] Step 201, the specific priority settings include:

[0052] Priority 1: Meet the transmission needs of the onshore power grid. Prioritize smoothing the output of wind power. The power is transmitted to the onshore power grid via a collection system, with a maximum transmission capacity equal to the rated capacity of the submarine cable. ;

[0053] Priority 2: Battery energy storage regulation. When At times, battery energy storage and charging absorb short-term surplus wind power; when At this time, the battery stores energy and discharges to make up for the power gap;

[0054] Priority 3: Hydrogen production and consumption. If there is still surplus power after the battery storage is fully charged (i.e., ), start hydrogen production equipment to consume long-term surplus;

[0055] Priority 4: Controllable load adjustment. If the above measures still cannot achieve power balance, adjust the controllable load to ultimately ensure the curtailment of wind power. minimize.

[0056] The equipment capacity and aggregation system matching mechanism includes: matching the maximum discharge power of battery energy storage equipment with the submarine cable capacity; matching the hydrogen production capacity of hydrogen production equipment with the seasonal maximum surplus power of offshore wind farm clusters; and matching the total capacity of offshore booster station aggregation cables with the access capacity of multi-energy equipment.

[0057] Step 202, the specific matching logic includes:

[0058] ① Energy storage capacity matching submarine cable: maximum discharge power of battery energy storage It should not exceed the difference between the submarine cable capacity and the wind power off-peak output (i.e.) This ensures that the discharge power can be transmitted through the collection system;

[0059] ② Matching hydrogen production capacity with wind farm clusters: Rated power of hydrogen production equipment It should not exceed the seasonal maximum surplus power of the wind farm cluster (calculated based on the smoothed annual continuous power output curve, usually 15%-25% of the cluster's installed capacity).

[0060] ③ Substation capacity expansion: The capacity of offshore substations must be no less than the total capacity of the collection cables and the capacity of multi-energy equipment access (such as the access power of battery storage and fuel cells), i.e.

[0061] (7)

[0062] In the formula, N represents the cable capacity; N is the number of cables that converge at the substation. For battery energy storage access capacity; For fuel cell access capacity; The capacity of the offshore booster station.

[0063] Step 3: Take the comprehensive benefits of transmission lines as the optimization target of the collection system, and establish collection system constraints and multi-energy equipment constraints to form an optimization target system.

[0064] In one example, the overall benefits of the above transmission line are:

[0065] (8)

[0066] In the formula, Annual income; Revenue from hydrogen sales; Annual cost; denoted as , where is the investment and operation cost of multi-energy equipment; r is the interest rate; and N is the operating life.

[0067] Ignoring price differences in electricity transmission at different times, the annual revenue of a wind farm Revenue from wind power and the cost of power curtailment compensation constitute, It mainly depends on the annual power generation of the wind farm. It mainly depends on the amount of power wasted due to transmission capacity limitations, and the calculation formula is as follows:

[0068] (9)

[0069] (10)

[0070] In the formula, For wind power revenue; To compensate for the cost of abandoned electricity; , Price coefficient; This represents the annual power generation of the wind farm. This refers to the annual amount of abandoned electricity generated by wind farms.

[0071] Analyze the annual power output data of a certain offshore wind farm (or based on historical data) and plot its annual power output curve for reference. Figure 1 The annual power output curve of a certain offshore wind farm shown is... .

[0072] When the theoretical power output of the wind farm exceeds the active power limit of the output line... At that time, any excess power will be curtailed. Annual curtailment of wind farms. and annual power generation The calculation formula is:

[0073] (11)

[0074] (12)

[0075] In the formula, For output greater than Time; This represents the wind farm's maximum annual output. The theoretical annual power generation of the wind farm includes:

[0076] (13)

[0077] Investment and maintenance costs of multi-energy equipment Including battery energy storage costs Hydrogen production and storage costs and controllable load modification costs ,Right now:

[0078] (14)

[0079] The formulas for calculating each cost are as follows:

[0080] Battery energy storage cost:

[0081] (15)

[0082] In the formula, Cost per unit power of battery energy storage; Cost per unit capacity of battery energy storage; Annual operation and maintenance costs for battery energy storage.

[0083] Hydrogen production and storage costs:

[0084] (16)

[0085] In the formula, Cost per unit power of the electrolytic cell; This represents the maximum mass of solid hydrogen storage. Cost per unit mass of hydrogen storage; The annual operation and maintenance cost of hydrogen production and storage.

[0086] Controllable load retrofit cost:

[0087] (17)

[0088] In the formula, The cost of modification per unit of controllable load.

[0089] Step 301: The constraints of the aggregation system include submarine cable transmission capacity constraints, submarine cable current carrying capacity constraints, submarine cable connection to wind turbine number constraints, submarine cable crossover avoidance constraints, and aggregation substation capacity constraints.

[0090] In one example, the pooling system constraints include:

[0091] ① Submarine cable transmission capacity constraint: The electrical energy transmitted by a submarine cable must be lower than its maximum transmission capacity, as expressed in the following expression:

[0092] (18)

[0093] In the formula, For the electrical energy of wind turbine j connected to submarine cable i; M i The number of wind turbines connected to submarine cable i; is the maximum transmission capacity of submarine cable i; N is the number of submarine cables.

[0094] ② Submarine cable current carrying capacity constraint: This includes two parts: wind turbine submarine cables and collection submarine cables. The expression is:

[0095] (19)

[0096] In the formula, The current carrying capacity of submarine cable i connected to wind turbine j; The rated power of fan j; This refers to the rated voltage of the submarine cable ij. Let be the power factor of fan j; For connecting M i The current carrying capacity of the submarine cable i of the typhoon generator; The rated voltage of the submarine cable i is used for collection.

[0097] ③ Constraint on the number of wind turbines connected by submarine cables: M is the maximum number of wind turbines that each submarine cable can connect. i It is finite, and its calculation formula is:

[0098] (20)

[0099] In the formula, To collect the maximum current carrying capacity of submarine cable i; To collect the power factor of submarine cable i; The average rated power of the wind turbines that collect submarine cable i.

[0100] ④ Submarine cable crossing avoidance constraints: Submarine cables are prohibited from crossing each other. Based on the latitude and longitude of the wind turbine location, list the wind turbine coordinates, and then perform submarine cable crossing judgment. The expression is:

[0101] (twenty one)

[0102] In the formula, X1, X2 and Y1, Y2 are the coordinates of the four wind turbines; and These represent the calculation of dot product and cross product, respectively.

[0103] ⑤ Capacity constraints of the collection substation: see equation (7).

[0104] Step 302, the constraints of multi-energy devices include energy storage charging and discharging power constraints, hydrogen production equipment start-up and shutdown constraints, and hydrogen supply and demand balance constraints.

[0105] In one example, multi-energy device constraints include:

[0106] ① Energy storage charging and discharging power constraints: The charging and discharging power of the battery needs to be coordinated with the power of the collection system to avoid exceeding the transmission capacity of the submarine cable. The expression is:

[0107] (twenty two)

[0108] ② Start-up and shutdown constraints for hydrogen production equipment: The number of start-ups and shutdowns of the electrolyzer should be ≤ 1 time / day to avoid lifespan loss caused by frequent start-ups and shutdowns, i.e.:

[0109] (twenty three)

[0110] In the formula, This indicates the start / stop status of the electrolytic cell; 1 means running and 0 means stopped.

[0111] ③ Hydrogen supply and demand balance constraints: The daily hydrogen filling and discharging capacity of solid hydrogen storage tanks must match the demand for hydrogen production and consumption to avoid overfilling and discharging of hydrogen tanks, i.e.:

[0112] (twenty four)

[0113] Step 4: Based on the optimization objective system, an improved genetic algorithm based on dynamic variable weight minimum spanning tree is used to solve the optimal cooperative topology of the aggregation system.

[0114] Step 4 of this scheme mainly adopts an improved genetic algorithm based on Dynamic Variable Weight Minimum Spanning Tree (DVW_MST). At the same time, in view of the complexity of multi-energy coordination, the algorithm's encoding, fitness function and iterative logic are optimized to solve the optimal topology of offshore wind farms.

[0115] Step 401: First, expand the encoding method. This scheme designs a completely new linked list encoding method. Matrix A m×nA spanning tree containing m rows of elements and n nodes is represented as follows:

[0116] (25)

[0117] It should be noted that the optimization model of the present invention (i.e., matrix A) m×n In the spanning tree matrix, m=4. The first row represents the node connection status, the second row represents the number of wind turbines loaded on the node, the third row represents the cable length between two nodes, and the fourth row represents the access type of multi-energy devices. In the spanning tree matrix, each "individual" (i.e., each wind turbine point) has corresponding information. For example: the i-th element in the first row of the matrix is ​​j, indicating that the i-th submarine cable node is connected to the j-th submarine cable node, and the power flow is unidirectional; that is, the power passing through node i must pass through node j to reach the offshore booster station. The i-th element in the second row is k, indicating that there are k wind turbines between the nodes. The i-th element in the third row is l, indicating that the cable length between the nodes is l. When the i-th element in the fourth row is 0, it indicates that no multi-energy devices are connected; when it is 1, it indicates that the i-th node is connected to a battery energy storage device, and the energy storage capacity of that node is recorded simultaneously; when it is 2, it indicates that the i-th node is connected to hydrogen production energy storage, and the energy storage capacity of that node is recorded simultaneously.

[0118] Step 402: Determine the fitness function.

[0119] After integrating multiple energy devices, the fitness function of an offshore wind farm cluster with the objective of "maximizing net present value" is expressed as:

[0120] (26)

[0121] In the formula, x i For each individual in the population (including topological and multi-energy configuration information), the higher the fitness, the better the individual, ensuring that the algorithm iterates in the direction of "maximum net benefit".

[0122] Step 403: Add collaborative judgment of multiple energy devices during iteration.

[0123] In the algorithm crossover and mutation stages, a "collective system synergy judgment" is added, as follows:

[0124] ① In cross-operation, if the access nodes of the multi-energy devices of the two parent individuals are different (e.g., parent 1 accesses battery energy storage at node 3, and parent 2 accesses hydrogen production energy storage at node 5), the offspring needs to verify whether the power of the new access node matches the cable capacity through the DVW_MST algorithm. If it exceeds the limit, the access position is adjusted.

[0125] ② In the mutation operation, when randomly mutating the capacity of multi-energy devices, the "capacity-topology" matching constraint must be satisfied (e.g., the mutated battery capacity must not exceed the remaining transmission capacity of the corresponding cable) to avoid generating infeasible solutions.

[0126] Step 404, solve the problem.

[0127] Based on the economic cost model of multi-energy equipment integration in offshore wind farms, the above-mentioned DVW_MST-based algorithm is used to solve the planning problem for multi-energy equipment integration in offshore wind power cluster systems. Operations include initial population generation, encoding, fitness calculation, selection, crossover, and mutation. The specific solution process is as follows: Figure 2 As shown: First, the coordinates of the offshore substation and wind farm, as well as the access information of multiple energy devices, are input to generate an initial population. Second, the net revenue of each wind farm is calculated, and the fitness function is used to determine if the termination criterion is met. If it is met, the population is output as the optimal individual, included in the optimal solution, and a next-generation population with better performance is generated. If it is not met, two individuals are selected for crossover and mutation, and the DVW_MST algorithm is used to generate a subtree to obtain offspring, and the iteration is repeated.

[0128] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for multi-energy synergistic optimization of an offshore wind farm cluster aggregation system, characterized in that, Includes the following steps: Based on the power output characteristics of offshore wind farm clusters, the types of multi-energy equipment to be integrated are determined, and a corresponding multi-energy coupling model is established. The multi-energy coupling model includes a multi-energy equipment model, a hydrogen load matching strategy, a hydrogen energy revenue model, and a wind power-controllable load coordination model. Using the aforementioned multi-energy coupling model as a new optimization variable, a collaborative optimization mechanism for the aggregation system is constructed, including a power collaborative allocation mechanism and a matching mechanism between equipment capacity and the aggregation system. The comprehensive benefits of transmission lines are used as the optimization target of the collection system, and collection system constraints and multi-energy equipment constraints are established to form an optimization target system; Based on the aforementioned optimization objective system, an improved genetic algorithm based on dynamic variable weight minimum spanning tree is used to solve for the optimal cooperative topology of the aggregation system.

2. The method for multi-energy collaborative optimization of offshore wind farm cluster aggregation system as described in claim 1, characterized in that, The multi-energy device model includes at least a battery energy storage sub-model and a gas-solid two-phase hydrogen storage sub-model. In the construction process of the battery energy storage sub-model, the battery energy storage device is set to charge during the peak output of the offshore wind farm cluster and discharge during the off-peak output. The construction process of the gas-solid two-phase hydrogen storage sub-model includes at least a hydrogen production stage and a power generation stage, and the solid hydrogen storage capacity is set as a constraint.

3. The method for multi-energy collaborative optimization of offshore wind farm cluster aggregation system as described in claim 1, characterized in that, In the process of constructing the hydrogen production load matching strategy, the power adjustment range of the hydrogen production equipment is set to 0.3~1.0, and the hydrogen production equipment is restricted to preferentially consuming the surplus power after wind power smoothing; the hydrogen production conversion rate is incorporated into the construction process of the hydrogen energy revenue model.

4. The method for multi-energy collaborative optimization of offshore wind farm cluster aggregation system as described in claim 1, characterized in that, In the process of constructing the wind power-controllable load collaborative model, a constraint condition is set that the controllable load can be adjusted within ±20% of the rated power.

5. The method for multi-energy collaborative optimization of offshore wind farm cluster aggregation system as described in claim 1, characterized in that, The power coordination and allocation mechanism of the multi-energy collection system includes: priority allocation for offshore wind farm clusters, battery energy storage systems, gas-solid two-phase hydrogen storage systems, and controllable loads.

6. The method for multi-energy collaborative optimization of offshore wind farm cluster aggregation system as described in claim 1, characterized in that, The equipment capacity and aggregation system matching mechanism includes: matching the maximum discharge power of battery energy storage equipment with the submarine cable capacity; matching the hydrogen production capacity of hydrogen production equipment with the seasonal maximum surplus power of offshore wind farm clusters; and matching the total capacity of offshore booster station aggregation cables with the access capacity of multi-energy equipment.

7. The method for multi-energy collaborative optimization of offshore wind farm cluster aggregation system as described in claim 1, characterized in that, The constraints of the aggregation system include submarine cable transmission capacity constraints, submarine cable current carrying capacity constraints, submarine cable connection to wind turbine number constraints, submarine cable crossover avoidance constraints, and aggregation substation capacity constraints.

8. The method for multi-energy collaborative optimization of offshore wind farm cluster aggregation system as described in claim 1, characterized in that, The constraints on multi-energy devices include energy storage charging and discharging power constraints, hydrogen production equipment start-up and shutdown constraints, and hydrogen supply and demand balance constraints.

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