Multi-energy collaborative optimization method of large-scale offshore wind power plant cluster access system and related device

Abstract

The invention discloses a multi-energy collaborative optimization method and related device for a large-scale offshore wind farm cluster access system, and the method comprises the steps: building a collaborative coupling model of a multi-energy system and the offshore wind farm cluster access system based on the offshore scene features of the offshore wind farm cluster access system; establishing a collaborative optimization mechanism of the multi-energy system and the offshore wind power plant cluster access system based on the collaborative coupling model; on the collaborative optimization mechanism, an optimization objective function of the offshore wind farm cluster access system coupled with the multi-energy system is established; under the condition that the supplementary constraint of the multi-energy system is met, the target function is minimized and optimized as a target; and carrying out optimization solution on the topological structure of the offshore wind plant cluster access system coupled with the multi-energy system by adopting an optimization algorithm, and outputting a multi-energy collaborative optimization scheme. Through deep coupling and cooperative operation of the multi-energy system and the offshore wind power plant cluster access system, the economy and flexibility of the system are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power technology, and in particular to a multi-energy collaborative optimization method and related apparatus for a large-scale offshore wind farm cluster access system. Background Technology

[0002] With the large-scale development of offshore wind power in my country, the proportion of offshore wind power in coastal provinces continues to increase. However, the contradiction between the high proportion of uncertain offshore wind power grid connection and the insufficient flexibility of the power grid is becoming increasingly prominent. The volatility and randomness of large-scale offshore wind farms pose significant challenges to the stability, security, and economic operation of the power grid. To address this challenge, it is necessary to improve the grid system's ability to accommodate high-proportion uncertain offshore wind power by enhancing the flexibility and economy of offshore wind farm cluster access systems. Current optimization technologies mainly focus on optimizing the transmission system topology or planning and designing from the perspective of power supply flexibility. However, these methods are difficult to adapt to the application scenarios of large-scale offshore wind power clusters, resulting in unsatisfactory economic efficiency and flexibility in their access. Summary of the Invention

[0003] This invention provides a multi-energy collaborative optimization method and related apparatus for a large-scale offshore wind farm cluster access system, which addresses the technical problem of poor economic efficiency and flexibility in existing large-scale offshore wind farm cluster access systems.

[0004] This invention provides a multi-energy collaborative optimization method for a large-scale offshore wind farm cluster access system, the method comprising:

[0005] Based on the marine scenario characteristics of the offshore wind farm cluster access system, a collaborative coupling model between the multi-energy system and the offshore wind farm cluster access system is established.

[0006] Based on the aforementioned collaborative coupling model, a collaborative optimization mechanism is established between the multi-energy system and the offshore wind farm cluster access system.

[0007] Based on the aforementioned collaborative optimization mechanism, an optimization objective function is established for the offshore wind farm cluster access system that couples the multi-energy system.

[0008] Under the condition of satisfying the supplementary constraints of the multi-energy system, with the goal of minimizing the optimization objective function, the optimization algorithm is used to optimize the topology of the offshore wind farm cluster access system coupled with the multi-energy system, and output a multi-energy collaborative optimization scheme.

[0009] Furthermore, the collaborative coupling model includes an energy storage system model, an offshore hydrogen production system model, and a wind power controllable load collaborative model;

[0010] The steps for establishing a collaborative coupling model between multiple energy systems and the offshore wind farm cluster access system based on the offshore scene characteristics of the offshore wind farm cluster access system include:

[0011] Based on the characteristics of the offshore wind farm cluster access system, the multiple energy types of the multi-energy system that operates in coordination with the offshore wind farm cluster access system are determined; the multiple energy types include energy storage type, offshore hydrogen production type and controllable load type.

[0012] Under the condition of coupling with the offshore wind farm cluster access system, an energy storage system model for smoothing short-term fluctuations is constructed according to the energy storage type; an offshore hydrogen production system model for absorbing long-term surplus wind power is constructed according to the offshore hydrogen production type; and a wind power controllable load coordination model for adjusting the controllable load capacity of wind power is constructed according to the controllable load type.

[0013] Furthermore, the energy storage system model includes a battery energy storage system model and a flywheel energy storage system model; the battery energy storage system of the battery energy storage system model is configured near the offshore booster station of the offshore wind farm cluster access system, and the flywheel energy storage system of the flywheel energy storage system model is configured on the wind farm collection side of the offshore wind farm cluster access system.

[0014] The battery charging and discharging constraints of the battery energy storage system model include:

[0015]

[0016] In the formula: This represents the charging power of the battery energy storage system at time t; This represents the smoothed output power of the wind power at time t; This represents the real-time transmission power of submarine cable line l in the offshore wind farm cluster access system at time t. This represents the discharge power of the battery energy storage system at time t; This represents the rated transmission power of the submarine cable at time t;

[0017] The battery capacity constraints of the battery energy storage system model are as follows:

[0018]

[0019] In the formula: This indicates the maximum battery capacity of the battery energy storage system. t1-t2 represents the peak wind power period of the offshore wind farm cluster access system, and t3-t4 represents the off-peak wind power period of the offshore wind farm cluster access system.

[0020] The hydrogen production power constraints of the marine hydrogen production system model include:

[0021]

[0022]

[0023] In the formula: This represents the power of the hydrogen electrolyzer in the offshore hydrogen production system at time t. This indicates the rated power of the hydrogen electrolyzer in the offshore hydrogen production system; Capacity of offshore booster stations for offshore wind farm cluster access systems;

[0024] The load regulation constraints of the wind power controllable load coordination model are as follows:

[0025]

[0026] In the formula: This represents the controllable wind power load at time t; This indicates the controllable rated load power of wind power.

[0027] Furthermore, the collaborative optimization mechanism between the multi-energy system and the offshore wind farm cluster access system includes a power collaborative allocation mechanism, a capacity collaborative optimization mechanism, and an economic collaborative mechanism.

[0028] Under the aforementioned power coordination and allocation mechanism, smoothed wind power output is preferentially transmitted to the onshore power grid via the submarine cable. When a short-term power surplus occurs, the battery energy storage system is preferentially activated to charge and smooth the increase in controllable wind power load. When a short-term power deficit occurs, the battery energy storage system is preferentially activated to discharge and the flywheel energy storage system is preferentially activated to replenish energy. When there is still a long-term power surplus after the battery energy storage is fully charged, all offshore hydrogen production systems are activated to produce hydrogen electrolyzers to absorb the long-term surplus power. Finally, if there is still a power surplus, the wind curtailment control strategy is activated.

[0029] Under the capacity synergy optimization mechanism, the maximum discharge power of the battery energy storage system is matched with the transmission capacity of the submarine cable; the rated capacity of the flywheel energy storage system is matched with the maximum capacity of the collection lines of the offshore wind farm cluster access system; the rated power of the hydrogen production equipment of the offshore hydrogen production system is matched with the capacity of the offshore booster station of the offshore wind farm cluster access system; the total planned capacity of the offshore booster station of the offshore wind farm cluster access system includes the sum of the capacities of the wind farms and multiple energy systems connected to it.

[0030] Under the aforementioned economic synergy mechanism, the total lifecycle cost of the offshore wind farm cluster access system includes component depreciation costs, maintenance costs, wind curtailment losses, initial investment costs, and equipment recovery costs. The initial investment costs include the costs of offshore booster stations, submarine cables, reactive power compensation equipment, converter stations, battery energy storage, flywheel energy storage, and hydrogen electrolyzers. The total revenue of the offshore wind farm cluster access system includes electricity sales revenue, hydrogen sales revenue, and revenue from purchased electricity saved by controllable loads.

[0031] Furthermore, the optimization objective function is expressed as:

[0032]

[0033] In the formula: f1 represents the full life-cycle engineering benefits of the offshore wind farm cluster access system coupled with the multi-energy system; S is the set of each operating scenario; Let s be the probability of scenario s occurring. The total lifecycle cost of the offshore wind farm cluster access system under scenario s; For electricity sales revenue in scenario s; and f1 represents the revenue from hydrogen sales and the revenue from purchased electricity saved by controllable loads under scenario s; f2 represents the flexibility target of the offshore wind farm cluster access system coupled with the multi-energy system. For flexibility indicators after integrating multiple energy sources;

[0034] in,

[0035]

[0036] In the formula: The optimal scheduling cycle number is determined by the number of offshore wind farm cluster access systems that couple the multi-energy systems. To optimize the time set of the scheduling cycle, This refers to the set of all lines in the offshore wind farm cluster access system that couples the multi-energy system. The adjusted flexibility weighting coefficient. The load factor of line l after multi-energy regulation at time t;

[0037] in,

[0038]

[0039] In the formula: The mean line load rate of the offshore wind farm cluster access system coupled with the multi-energy system during the time period T is given. The load rate fluctuation variance of line l in the offshore wind farm cluster access system that couples the multi-energy system.

[0040] Furthermore, the supplementary constraints of the multi-energy system include energy storage charging and discharging constraints, flywheel energy storage discharging constraints, hydrogen production equipment start-up and shutdown constraints, hydrogen production equipment capacity constraints, and controllable load constraints.

[0041] The energy storage charging and discharging constraints are expressed as follows:

[0042]

[0043] In the formula: This represents the start-up and shutdown state of the battery energy storage system at time t.

[0044] The flywheel energy storage discharge constraint is expressed as follows:

[0045]

[0046] In the formula: Let be the discharge power of the flywheel energy storage system at time t. This represents the maximum energy storage capacity of the flywheel energy storage system.

[0047] The start-stop constraints of the hydrogen production equipment are expressed as follows:

[0048]

[0049] In the formula: and These refer to the start-up and shutdown times of the hydrogen electrolyzers in the offshore hydrogen production system.

[0050] The capacity constraint of the hydrogen production equipment is expressed as follows:

[0051]

[0052] In the formula: For the efficiency of hydrogen production by electrolysis. Let be the hydrogen production load power at time t. The maximum capacity of the marine hydrogen production system at time t;

[0053] The controllable load constraint is expressed as follows:

[0054] .

[0055] Furthermore, the step of optimizing the topology of the offshore wind farm cluster access system coupled with the multi-energy system, with the objective function being minimized under the condition of satisfying the supplementary constraints of the multi-energy system, and outputting a multi-energy collaborative optimization scheme, includes:

[0056] An extended chain coding method is used to encode and characterize the topology of the offshore wind farm cluster access system coupled with the multi-energy system, resulting in a system matrix structure. The system matrix structure includes node connection information, the number of wind turbines loaded on each node, the cable length between nodes, and the access type of multi-energy equipment.

[0057] An initial population is generated based on the system matrix structure, and the fitness of the initial population is evaluated and calculated according to a preset fitness function to obtain the initial fitness result.

[0058] Based on the initial fitness results, the individuals in the initial population are iteratively optimized to drive the population to iteratively optimize in the direction of minimizing the optimization objective function under the condition of satisfying the multi-energy system supplementation constraint.

[0059] During the iterative optimization of each generation of the population, connectivity is verified for each individual in the population. If they are not connected, the topology is automatically repaired using graph theory topology repair, transforming them into a connected network. If they are connected, the capacity of the multi-energy equipment of the individuals that pass the connectivity verification is matched with the capacity of the offshore wind farm cluster access system. If the capacity does not match, the equipment capacity of the individual in the population is automatically adjusted to within the preset constraints, and the verified and repaired individuals are output. If the capacity matches, the matched individuals are directly output. Fitness evaluation is performed on the output individuals, and the fitness results are updated.

[0060] Based on the updated fitness evaluation results, the NSGA-II algorithm is used to perform crossover iterations on the individuals in the population to generate a new generation of the population until the preset iteration conditions are met, and a multi-energy collaborative optimization scheme is output.

[0061] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the multi-energy collaborative optimization method as described above.

[0062] The present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of the multi-energy collaborative optimization method as described above.

[0063] The present invention also provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed by a processor, they implement the steps of the multi-energy collaborative optimization method as described above.

[0064] As can be seen from the above technical solutions, the present invention has the following advantages:

[0065] This invention provides a multi-energy collaborative optimization method and related apparatus for a large-scale offshore wind farm cluster access system. The method includes: establishing a collaborative coupling model between the multi-energy system and the offshore wind farm cluster access system based on the offshore scene characteristics of the offshore wind farm cluster access system; establishing a collaborative optimization mechanism between the multi-energy system and the offshore wind farm cluster access system based on the collaborative coupling model; establishing an optimization objective function for the offshore wind farm cluster access system coupled with the multi-energy system based on the collaborative optimization mechanism; minimizing the optimization objective function under the condition of satisfying the supplementary constraints of the multi-energy system; and using an optimization algorithm to optimize and solve the topology of the offshore wind farm cluster access system coupled with the multi-energy system, outputting a multi-energy collaborative optimization scheme.

[0066] In this invention, multiple energy systems, including energy storage, hydrogen production, and controllable loads, are deeply coupled and operated collaboratively with the offshore wind power cluster access system. The collaborative coupling model effectively addresses the wind power volatility of the offshore wind power cluster access system, and the multi-energy system flexibly manages wind power consumption. The collaborative optimization mechanism optimizes the operational allocation capabilities of the multi-energy system and the offshore wind power cluster access system. An optimization objective function is established, and under the condition of satisfying the supplementary constraints of the multi-energy system, the goal is to minimize the optimization objective function. At the same time, the optimization solution is combined with the topology structure to pursue the economy and flexibility of the offshore wind power cluster access system. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 A flowchart illustrating the steps of a multi-energy collaborative optimization method for a large-scale offshore wind farm cluster access system provided in this embodiment of the invention;

[0069] Figure 2 A flowchart illustrating the connectivity verification steps provided in an embodiment of the present invention. Detailed Implementation

[0070] This invention provides a multi-energy collaborative optimization method and related apparatus for a large-scale offshore wind farm cluster access system, which addresses the technical problem of poor economic efficiency and flexibility in existing large-scale offshore wind farm cluster access systems.

[0071] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0072] Please see Figure 1 This invention provides a multi-energy collaborative optimization method for a large-scale offshore wind farm cluster access system, the method comprising:

[0073] Step 101: Based on the marine scenario characteristics of the offshore wind farm cluster access system, establish a collaborative coupling model between the multi-energy system and the offshore wind farm cluster access system.

[0074] In this embodiment, the offshore wind farm cluster access system includes an offshore wind farm cluster and a grid access system. The offshore wind farm cluster includes several wind turbines, and the grid access system includes collection lines, submarine cables, an offshore substation, a converter station, and an onshore power grid. The wind turbines convert wind energy into electrical energy and collect the generated electrical energy into collection lines, which then transmit it to the offshore substation. The offshore substation uses transformers to step up and convert the electrical energy, and transmits the stepped-up electrical energy to a circulating station via submarine cables. In the converter station, the AC power transmitted via the submarine cables is converted into DC power by the offshore converter station, and then the DC power is converted into usable AC power by the onshore converter station and transmitted to the onshore power grid.

[0075] It should be noted that the offshore scenario characteristics of offshore wind farm cluster access systems include offshore environmental attributes (such as the volatility and randomness of wind power output and deep-sea location), economic investment attributes (such as high investment), and grid connection flexibility. This embodiment determines the multi-energy system matching the offshore wind farm cluster access system based on the offshore scenario characteristics, and establishes a corresponding collaborative coupling model based on the multi-dimensional coupling characteristics (such as power, capacity, and cost) between the matched multi-energy system and the offshore wind farm cluster access system.

[0076] In one specific implementation method, step 101 includes the following steps:

[0077] Step S11: Based on the marine scene characteristics of the offshore wind farm cluster access system, determine the multiple energy types of the multi-energy system that operates in coordination with the offshore wind farm cluster access system.

[0078] This specific embodiment prioritizes selecting multiple energy types that are adapted to the characteristics of offshore scenarios, and establishes a collaborative coupling model between these multiple energy types of multi-energy systems and offshore wind farm cluster access systems.

[0079] It should be noted that the multi-energy types selected in this specific embodiment include energy storage type, offshore hydrogen production type and controllable load type; the multi-energy systems corresponding to energy storage type, offshore hydrogen production type and controllable load type include energy storage system, offshore hydrogen production system and offshore controllable load system.

[0080] Understandably, energy storage systems, offshore hydrogen production systems, and offshore controllable load systems can be adapted to equipment such as submarine cables and offshore substations in offshore wind farm cluster access systems, increasing grid connection flexibility. Specifically, for wind power volatility, energy storage systems can smooth out short-term fluctuations (e.g., battery storage for rapid energy response and regulation); for wind power uncertainty and deep-sea locations, offshore controllable load systems utilize offshore platform loads to provide controllable load regulation capabilities (e.g., desalination plants increasing power during peak wind power periods); and for economic investment purposes, offshore hydrogen production systems can provide hydrogen production and sales while simultaneously absorbing long-term surplus wind power to reduce wind curtailment. This embodiment introduces multiple energy forms, including energy storage, hydrogen production, and controllable loads, providing diverse regulation tools for offshore wind farm cluster access systems.

[0081] Step S12: Under the condition of satisfying the coupling with the offshore wind farm cluster access system, construct an energy storage system model for smoothing short-term fluctuations according to the energy storage type; construct an offshore hydrogen production system model for absorbing long-term surplus wind power according to the offshore hydrogen production type; and construct a wind power controllable load coordination model for adjusting the controllable load capacity of wind power according to the controllable load type.

[0082] In this embodiment, the collaborative coupling model includes an energy storage system model, an offshore hydrogen production system model, and a wind power controllable load collaborative model. The energy storage system model constructed in this embodiment is used to smooth out short-term fluctuations and improve flexibility; the offshore hydrogen production system is constructed to absorb long-term surplus wind power to reduce wind curtailment; and the wind power controllable load collaborative model is established in conjunction with the offshore controllable load to enhance the flexibility of the access system and improve the ability of the offshore wind farm cluster access system to cope with fluctuations. It should be noted that different types of multi-energy models need to meet the coupling conditions with the offshore wind farm cluster access system when modeling; for example, for battery energy storage, it needs to meet the coupling conditions with the submarine cable power and capacity of the offshore wind farm cluster access system, while flywheel energy storage needs to meet the coupling conditions with the collector line capacity of the offshore wind farm cluster access system.

[0083] (1) Energy storage system model

[0084] The energy storage system model constructed in this embodiment is used to smooth out short-term fluctuations and improve system flexibility.

[0085] Specifically, the energy storage system model includes a battery energy storage system model and a flywheel energy storage system model. In order to match the characteristics of large short-term power output fluctuations and the need for rapid response of offshore wind power in the offshore wind farm cluster access system, the battery energy storage system of the battery energy storage system model provided in this embodiment is configured near the offshore booster station of the offshore wind farm cluster access system, and the flywheel energy storage system of the flywheel energy storage system model is configured on the wind farm collection side of the offshore wind farm cluster access system.

[0086] For battery energy storage system models, the charging and discharging power of the battery energy storage system model needs to be coordinated with the transmission power of the submarine cable to avoid exceeding the line capacity limit. Among them, the transmission capacity of the submarine cable line is a key factor affecting the flexibility of the offshore wind farm cluster access system, and the line load rate directly affects the system's transmission capacity and scheduling space.

[0087] The formula for line load rate is as follows:

[0088] (1)

[0089] in, Let L be the load rate of line l at time t; This represents the current transmission power of the offshore wind farm cluster access system. Capacity configured for submarine cables; This is the topology set for the offshore wind farm cluster access system.

[0090] Based on the submarine cable line load factor formula (1), the battery charging and discharging constraints for the battery energy storage system model include:

[0091] Charging constraints:

[0092] (2)

[0093] In the formula, It is the charging power of the battery energy storage system at time t; It is the output power of wind power after smoothing; Let L be the real-time transmission power of submarine cable line l in the offshore wind farm cluster access system at time t. The line load rate after charging must meet the following requirements. This is to avoid wasting flexibility margins due to light line loads.

[0094] Discharge constraint:

[0095] (3)

[0096] In the formula: It is the discharge power of the battery energy storage system at time t; This represents the rated transmission power of the submarine cable at time t; the load factor after discharge must meet the following requirements. This is to meet the flexibility requirements for load rate fluctuations.

[0097] At the same time, the battery capacity of the battery energy storage system model needs to match the flexibility requirements of the offshore wind farm cluster access system, that is:

[0098] (4)

[0099] In the formula, This indicates the maximum battery capacity of the battery energy storage system. t1-t2 represents the peak wind power period of the offshore wind farm cluster access system, and t3-t4 represents the off-peak wind power period of the offshore wind farm cluster access system.

[0100] For the flywheel energy storage system model, the flywheel energy storage system is mainly configured on the collector side of the wind farm. Its response speed is fast (millisecond level) and can smooth out instantaneous fluctuations in wind power, such as ±10% fluctuations in rated power caused by gusts.

[0101] When modeling, the flywheel power of the flywheel energy storage system model needs to match the collector line capacity of the offshore wind farm cluster access system, that is: ,in The maximum capacity of the collection line; the enhanced effect of flywheel energy storage on the smoothing effect of offshore wind farm clusters, that is, after the flywheel smooths out the cluster output fluctuation coefficient by 15-20%, indirectly reduces the fluctuation of submarine cable load rate, thereby improving the system flexibility index.

[0102] (2) Model of offshore hydrogen production system

[0103] Offshore wind power output exhibits seasonal surpluses, such as low output in summer due to low wind speeds and high output in winter due to high wind speeds. To address this, this embodiment constructs an offshore hydrogen production system model to absorb long-term surplus wind power and reduce system curtailment. The offshore hydrogen production system is located near an offshore substation and includes a hydrogen electrolyzer and solid hydrogen storage tanks to absorb surplus wind power. During modeling, the offshore hydrogen production system model needs to be matched with the transmission capacity and economic objectives of the offshore wind farm cluster access system.

[0104] In the offshore hydrogen production system model, the hydrogen production power is prioritized to absorb the excess power that still exceeds the submarine cable transmission capacity after wind power smoothing.

[0105] (5)

[0106] In the formula: This represents the power of the hydrogen electrolyzer in the offshore hydrogen production system at time t. This indicates the rated power of the hydrogen electrolyzer in the offshore hydrogen production system;

[0107] Meanwhile, the hydrogen production capacity of the offshore hydrogen production system model must match the capacity of the offshore booster station to ensure redundancy in the connected system capacity, i.e.:

[0108] (6)

[0109] In the formula: The capacity of offshore booster stations for offshore wind farm cluster access systems.

[0110] The economic coupling objective of the offshore hydrogen production system model includes hydrogen sales revenue. Investment cost of hydrogen production electrolyzer and the operation and maintenance costs of hydrogen electrolyzers ;

[0111] Revenue from hydrogen sales Represented as:

[0112] (7)

[0113] In the formula, For hydrogen production conversion rate; This refers to the price of hydrogen.

[0114] Investment cost of hydrogen electrolyzer Represented as:

[0115] (8)

[0116] In the formula: Cost per unit power of hydrogen electrolyzer.

[0117] Operation and maintenance costs of hydrogen electrolyzers The expression for (generally 2% of annual maintenance costs) is:

[0118]

[0119] (3) Wind power controllable load coordination model

[0120] This embodiment combines offshore controllable load systems (such as offshore oil and gas extraction platforms and seawater desalination plants located near offshore booster stations) to establish a wind power controllable load collaborative model, thereby enhancing the system's flexibility by utilizing the wind power controllable load capability.

[0121] When modeling, the controllable load of the wind power controllable load coordination model needs to respond to changes in wind power output of the offshore wind farm cluster access system;

[0122] Specifically, the load regulation constraints of the wind power controllable load coordination model are as follows:

[0123] Controllable load It has power regulation capability, typically ±25% of rated power, to respond to changes in wind power output, expressed as:

[0124] (10)

[0125] In the formula, Indicates the controllable load power of wind power; This indicates the controllable rated load power of wind power.

[0126] Specifically, during peak wind power periods, the load power can be increased to This allows for the absorption of excess wind power; during off-peak hours, the load power can be reduced to [amount missing]. This is to reduce the amount of electricity purchased from external sources.

[0127] Meanwhile, the controllable load adjustment of the wind power controllable load coordination model can be incorporated into the flexibility index calculation, thereby improving the flexibility index by reducing the fluctuation of submarine cable load rate. Specifically, during peak wind power periods, the load absorbs excess power to prevent excessive load on submarine cables, thus reducing the load factor. During off-peak wind power periods, reduce load power to prevent excessively low load rates on submarine cables, thus maintaining a high load factor. This reduces the variance of load rate fluctuations, thereby improving the flexibility weighting coefficient and overall flexibility index of subsequent access systems.

[0128] Step 102: Based on the collaborative coupling model, establish a collaborative optimization mechanism for the multi-energy system and the offshore wind farm cluster access system.

[0129] It should be noted that this embodiment adds a multi-energy system as an optimization variable, coupling the topology of the offshore wind farm cluster access system with the submarine cable capacity and the booster station capacity to form three major mechanisms: power allocation, capacity matching, and economic synergy.

[0130] The collaborative optimization mechanism provided in this embodiment includes a power collaborative allocation mechanism, a capacity collaborative optimization mechanism, and an economic collaborative mechanism. Through these collaborative optimization mechanisms, the various adjustment methods mentioned above can work together in an orderly and efficient manner.

[0131] In a specific implementation method, the collaborative optimization process of the collaborative optimization mechanism in this step includes the following steps:

[0132] Step S21: Under the power collaborative allocation mechanism, the smoothed wind power output is preferentially transmitted to the onshore power grid via submarine cables; when there is a short-term power surplus, the battery energy storage system is preferentially activated to charge and smooth the increase of controllable wind power load; when there is a short-term power deficit, the battery energy storage system is preferentially used to discharge and the flywheel energy storage system is preferentially used to replenish energy; when there is still a long-term power surplus after the battery energy storage is fully charged, all offshore hydrogen production systems are activated to produce hydrogen electrolyzers to absorb the long-term surplus power; finally, if there is still a power surplus, the wind curtailment control strategy is activated.

[0133] Specifically, the power collaborative allocation mechanism establishes a power allocation priority of "wind power-energy storage-hydrogen production-controllable load" to balance the power of the grid connection system and improve its flexibility. The priority strategies of the power collaborative allocation mechanism include wind power output strategy, short-term adjustment strategy, long-term adjustment strategy, and wind curtailment control strategy. This embodiment sets clear power allocation priorities (priority power transmission → energy storage charging → load boosting → hydrogen production → wind curtailment) so that the offshore wind farm cluster can find the optimal power flow under any output condition, maximizing the absorption of wind power and maintaining grid stability.

[0134] (1) Wind power output strategy: The smoothed wind power output is Priority should be given to transmitting it to the onshore power grid via submarine cables, and the transmission capacity should not exceed the rated capacity of the submarine cables. The power constraints are as follows:

[0135] (11)

[0136] In the formula, The power between lines m and n. It represents its maximum transmission capacity; I is the long-term current carrying capacity of the submarine cable. This is the overall correction factor for the long-term allowable current carrying capacity of submarine cables; This is the rated voltage of the high-voltage submarine cable.

[0137] (2) Short-term adjustment strategy: when When needed, the battery storage system will be prioritized for charging and load power will be reduced (up to a maximum of 100 kW). ), absorbing short-term excess wind power; when When necessary, the battery energy storage system is activated to discharge and the flywheel energy storage system is activated to replenish the power deficit.

[0138] (3) Long-term adjustment strategy: If there is still surplus power after the battery is fully charged, i.e. ,in The instantaneous power after the controllable load increase is used to start the hydrogen production electrolyzer of the offshore hydrogen production system to absorb the long-term surplus power and prevent wind curtailment due to the capacity limitation of the submarine cable.

[0139] (4) Wind curtailment control strategy: If there is still surplus power after the above-mentioned consumption strategy has been used, the final wind curtailment control strategy will be activated, where the final wind curtailment power expression is:

[0140] (12)

[0141] This ensures the lowest possible wind curtailment rate while keeping the submarine cable load rate within a reasonable range (0.2~0.9) to guarantee flexibility.

[0142] Step S22: Under the capacity synergy optimization mechanism, the maximum discharge power of the battery energy storage system is matched with the transmission capacity of the submarine cable; the rated capacity of the flywheel energy storage system is matched with the maximum capacity of the collection line of the offshore wind farm cluster access system; the rated power of the hydrogen production equipment of the offshore hydrogen production system is matched with the capacity of the offshore booster station of the offshore wind farm cluster access system; the total planned capacity of the offshore booster station of the offshore wind farm cluster access system includes the sum of the capacities of the wind farms and multiple energy systems connected to it.

[0143] It is understandable that the capacity of multi-energy systems should be coordinated and optimized with the key parameters of offshore wind farm cluster access systems to prevent equipment redundancy or access bottlenecks.

[0144] The capacity collaborative optimization mechanism provided in this embodiment includes matching strategies for energy storage and submarine cables, matching strategies for hydrogen production equipment and booster stations, and booster station capacity expansion strategies, as detailed below:

[0145] (1) Matching strategy between energy storage and submarine cables

[0146] The maximum discharge power of battery energy storage must meet the following requirements. ,in This is the minimum output after wind power smoothing, ensuring that the discharge power can be transmitted through the submarine cable without exceeding the cable's capacity; the flywheel energy storage capacity must meet the requirements. It can ensure that fluctuations of 5% of rated power are suppressed for 1 hour.

[0147] (2) Matching strategy between hydrogen production equipment and booster station

[0148] The rated power of the hydrogen production equipment must meet the requirements. This is to prevent excessive hydrogen production capacity from overloading the offshore station. Among other things, Let i be the capacity of the j-th wind farm connected to the i-th substation.

[0149] (3) Strategy for expanding the capacity of booster stations

[0150] The capacity of the booster station must include the equipment access capacity of multiple energy systems. The expression for the capacity constraint of the booster station is:

[0151] (13)

[0152] In the formula, Let be the capacity of the i-th offshore booster station; , , These refer to the access capacity of batteries, flywheels, and hydrogen production equipment, respectively.

[0153] Step S23: Under the economic synergy mechanism, the total life cycle cost of the offshore wind farm cluster access system includes component depreciation cost, maintenance cost, wind curtailment loss fee, initial investment cost, and equipment recovery cost. Among them, the initial investment cost includes the cost of offshore booster station, submarine cable, reactive power compensation equipment, converter station, battery energy storage, flywheel energy storage, and hydrogen electrolyzer. The total revenue of the offshore wind farm cluster access system includes electricity sales revenue, hydrogen energy sales revenue, and revenue from purchased electricity saved by controllable loads.

[0154] Under the economic synergy mechanism, the costs and benefits of multiple energy sources are integrated, which can achieve synergistic optimization of the benefits of multiple energy sources and the cost of offshore wind farm cluster access system.

[0155] Specifically, the total lifecycle cost of offshore wind farm cluster access systems. The expression is:

[0156]

[0157] In the formula, Cost of component wear and tear; To maintain costs; Cost of wind curtailment losses; Calculate the coefficients for present value; This refers to the initial investment cost; For equipment recycling costs; This is the discount factor.

[0158] Under an economic cooperation mechanism, the initial investment cost Including the cost of offshore booster stations Submarine cable cost Reactive power compensation equipment cost Converter station cost Battery energy storage cost Flywheel energy storage cost Cost of hydrogen electrolyzer The initial investment cost The calculation formula is expressed as:

[0159] (15)

[0160] In the formula, , , , These are the total number of booster stations, the number of submarine cable sections, the number of wind farms using reactive power compensation, and the total number of converter stations; For transformer capacity; This refers to the number of transformers. Construction and installation costs for offshore platforms; This refers to the unit cost of submarine cables. For submarine cable transmission distance; Cost of reactive power compensation equipment per unit capacity; Let be the reactive power compensation capacity of the i-th wind farm; Cost of converter station.

[0161] Among them, operation and maintenance costs and loss cost The calculation formulas are as follows:

[0162] (16)

[0163]

[0164] In the formula, Maintenance fee rate; Cost of loss at offshore booster stations; Cost of submarine cable loss; Costs associated with converter station losses; Transmission loss rate; For the lifespan of components or equipment; This is an empirical coefficient; For grid connection electricity price; The apparent power transmitted via submarine cable; This is the rated voltage of the high-voltage submarine cable; The resistance per unit length of submarine cable; This refers to the actual transmission distance of the submarine cable; This represents the number of split cables / parallel loops in the submarine cable. The maintenance rate involved in this embodiment... and transmission loss rate As shown in Table 1.

[0165] Table 1 Maintenance rates for offshore wind farm components, energy storage, and hydrogen production equipment, and transmission loss rates for related equipment.

[0166]

[0167] In addition, the total revenue from offshore wind farm cluster access systems, excluding electricity sales revenue, is not included. It also increased revenue from hydrogen energy sales. and the revenue from purchased electricity saved by controllable load The calculation formula is as follows:

[0168]

[0169] In the formula, This refers to the amount of electricity transmitted under capacity limitations. This refers to the load power during off-peak hours. This refers to the price of electricity purchased from external sources.

[0170] in, The calculation formula is as follows:

[0171] (19)

[0172] In the formula, Given the transmission capacity of the line; The power output of the wind farm cluster is higher than The duration of continuous output; This is the annual continuous output curve of wind power.

[0173] The cost of wind curtailment for offshore wind farm clusters connected to the system The calculation formula is as follows:

[0174] (20)

[0175] In the formula, This refers to the loss of electricity due to wind curtailment. This is compensation for the wind curtailment loss per unit of electricity. By employing a multi-energy consumption strategy, the amount of wind curtailment loss is reduced, thereby decreasing the cost of wind curtailment and increasing net benefits over the entire life cycle.

[0176] Step 103: Based on the collaborative optimization mechanism, establish the optimization objective function for the offshore wind farm cluster access system that couples multiple energy systems.

[0177] This embodiment incorporates the impact of multiple energy sources on economics and flexibility, establishing an optimization objective function for an offshore wind farm cluster access system coupled with multiple energy systems. The optimization objective function includes objective function f1 and objective function f2; objective function f1 represents the total lifecycle engineering benefits of the cluster access system, and f2 represents the system flexibility objective, expressed as follows:

[0178] (twenty one)

[0179] In the formula, S represents the set of each running scenario; Let s be the probability of scenario s occurring. The total lifecycle cost of the access system under scenario s; For electricity sales revenue in scenario s; and These represent the revenue from hydrogen sales and the revenue from purchased electricity saved under controllable load, respectively, in scenario s. To ensure flexibility after incorporating multiple energy sources, the impact of energy storage, hydrogen production, and controllable loads on load factor fluctuations was considered. These operational scenarios include the operation of multiple energy devices, such as battery energy storage systems deployed near offshore booster stations; flywheel energy storage systems deployed on the collector side of wind farms; and offshore hydrogen production systems and controllable loads both deployed offshore.

[0180] Flexibility indicators after incorporating multiple energy sources The calculation formula is as follows:

[0181] (twenty two)

[0182] In the formula: To optimize the number of scheduling cycles for offshore wind farm clusters that couple multiple energy systems to access the system. To optimize the time set of the scheduling cycle, For the set of all lines in the access system of an offshore wind farm cluster that couples multiple energy systems; The adjusted flexibility weighting coefficient. The load factor of line l after multi-energy regulation at time t;

[0183] In this embodiment, The calculation method is as follows: First, calculate the average line load rate within the time period T. Then calculate the load rate fluctuation variance of each line. Then, the relative volatility weighting method is used to calculate the flexibility weighting coefficient of each line in the access system. Specifically, and The calculation formulas involved are as follows:

[0184] (twenty three)

[0185] The larger the cluster size, the greater the flexibility of the system.

[0186] In this embodiment, the economic efficiency and system flexibility throughout the entire life cycle are simultaneously optimized, and the costs and benefits of multiple energy sources are integrated to achieve a balance between the economic efficiency and flexibility of the offshore wind farm cluster access system.

[0187] Step 104: Under the condition of satisfying the supplementary constraints of the multi-energy system, with the goal of minimizing the optimization objective function, the optimization algorithm is used to optimize the topology of the offshore wind farm cluster access system coupled with the multi-energy system, and output the multi-energy collaborative optimization scheme.

[0188] In this embodiment, under the condition of satisfying the supplementary constraints of the multi-energy system, the objective is to minimize the optimization objective function. At the same time, the optimization algorithm is used to optimize the topology of the offshore wind farm cluster access system coupled with the multi-energy system. Under the complex conditions of multiple energy sources, multiple objectives, and multiple constraints, the globally optimal collaborative solution is searched and output.

[0189] Specifically, the constraints for multi-energy system supplementation include energy storage charging and discharging constraints, flywheel energy storage discharging constraints, hydrogen production equipment start-up and shutdown constraints, hydrogen production equipment capacity constraints, and controllable load constraints.

[0190] To prevent lifespan degradation, the energy storage battery is limited to no more than two charge-discharge cycles per day. The energy storage charge-discharge constraint is expressed as follows:

[0191] (twenty four)

[0192] In the formula: This represents the start-up and shutdown state of the battery energy storage system at time t.

[0193] The continuous discharge time of flywheel energy storage shall not exceed 15 minutes (in hours), and the discharge constraint of flywheel energy storage is expressed as follows:

[0194] (25)

[0195] In the formula: Let be the discharge power of the flywheel energy storage system at time t. This represents the maximum energy storage capacity of the flywheel energy storage system.

[0196] The start-up and shutdown interval of the hydrogen electrolyzer is required to be no less than 8 hours to prevent frequent start-ups and shutdowns from causing a decrease in hydrogen production efficiency. The start-up and shutdown constraints of the hydrogen production equipment are expressed as follows:

[0197] (26)

[0198] In the formula: and These refer to the start-up and shutdown times of the hydrogen electrolyzer in the offshore hydrogen production system.

[0199] The capacity of the solid hydrogen storage tank must meet the daily supply and demand balance. The capacity constraint of the hydrogen production equipment is expressed as follows:

[0200] (27)

[0201] In the formula: Let be the hydrogen production capacity of the marine hydrogen production system at time t. For the efficiency of hydrogen production by electrolysis. Let be the hydrogen production load power at time t. Let t be the maximum capacity of the marine hydrogen production system at time t.

[0202] The load regulation rate is required to be no more than 5% of rated power per minute to prevent impact on the power grid. The controllable load constraint is expressed as follows:

[0203] (28)

[0204] This embodiment solves the problem by minimizing the objective function while satisfying the supplementary constraints of the multi-energy system. To optimize the solution quality and speed, this embodiment specifically addresses the complexity of multi-energy collaboration by employing an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and a graph-based topology repair framework (i.e., using the NSGA-II algorithm to optimize power grid planning and utilizing a graph-based topology repair strategy to reduce the generation of invalid offspring). The solution for the multi-energy collaborative optimization scheme is achieved through optimized algorithm encoding, fitness functions, and iterative logic.

[0205] In one specific implementation method, step 104 includes the following steps:

[0206] Step S31: The topology of the offshore wind farm cluster access system with coupled multi-energy systems is encoded and characterized using the extended chain coding method to obtain the system matrix structure; wherein, the system matrix structure includes the node connection status, the number of wind turbines in the node load, the cable length between nodes, and the access type of multi-energy equipment;

[0207] In this step, a linked list encoding method is used to digitally represent the topology of the offshore wind farm cluster access system that integrates multiple energy systems. This encoding method constructs a 4-row, n-column system matrix structure, where each column corresponds to a node in the offshore wind farm cluster access system. Nodes correspond to access points in the offshore wind farm cluster access system that can perform energy collection, transmission, or conversion, such as the collection point of the wind farm cluster, the offshore booster station, the booster station, and the access points of multi-energy equipment.

[0208] The row vectors define: node connection relationships, the number of wind turbines connected to or loaded by the node, the cable length between nodes, and the type of multi-energy equipment connected to the node (where 0, 1, 2, and 3 represent no equipment, battery storage, hydrogen production equipment, and controllable load, respectively). The system matrix structure is represented as follows:

[0209] (29)

[0210] For example, assuming the element in the 4th row of the i-th column of the system matrix structure is "2", it means that the i-th node is connected to the hydrogen production equipment and the capacity of the equipment is recorded synchronously.

[0211] Understandably, this coding method serves as the foundational data architecture for subsequent power allocation, capacity matching, and economic optimization, thereby improving the iterative efficiency of searching for the optimal power grid cabling scheme and the best combination and layout of energy equipment.

[0212] Step S32: Generate an initial population based on the system matrix structure, and perform fitness evaluation calculation on the initial population according to the preset fitness function to obtain the initial fitness result;

[0213] Specifically, the initial population can first be randomly generated using a combination of random generation and heuristic rules. Each individual in the population represents a feasible solution with a specific topology and multiple energy configurations, represented by a 4xn matrix. Then, the initial population is evaluated and its fitness is calculated according to a preset fitness function to obtain the initial fitness results.

[0214] The preset fitness function is expressed as:

[0215]

[0216] In the formula, For individuals in a population that includes multiple energy configurations; Represents individuals in a population The net present value (NPV) is a core indicator used in life-cycle economic analysis to assess project profitability. Its calculation logic is "the sum of the present values ​​of net cash flows in future years minus the initial investment." In this embodiment, The fitness is negatively correlated with the full lifecycle engineering benefits of the cluster access system as shown in formula (21). Therefore, the higher the fitness, the better the individual population, thus ensuring that the iterative solution evolves towards the optimal direction of "economy-flexibility-multi-energy benefits". In addition, while evaluating the fitness of individual populations, it is also necessary to evaluate the flexibility of individual populations using formula (21).

[0217] Step S33: Based on the initial fitness results, iteratively optimize the individuals in the initial population to drive the population to iteratively optimize in the direction of minimizing the optimization objective function under the condition of satisfying the multi-energy system supplementation constraint.

[0218] Understandably, based on the fitness and flexibility evaluation results of the individuals in the population, mutation and crossover operations are performed on the initial population individuals, so that the population evolves in the direction of minimizing the optimization objective function under the condition of satisfying the multi-energy system supplementation constraint.

[0219] Step S34: During the iterative optimization of each generation of the population, the connectivity of individual population members is checked. If they are not connected, the topology is automatically repaired using graph theory topology repair method, transforming them into a connected network. If they are connected, the capacity of multi-energy equipment and the capacity of the offshore wind farm cluster access system are matched for the population members that pass the connectivity check. If the capacity does not match, the equipment capacity of the population member is automatically adjusted to within the preset constraint range, and the checked and repaired population member is output. If the capacity matches, the matched population member is directly output. The fitness evaluation of the output population member is calculated, and the fitness results are updated.

[0220] It should be noted that directly incorporating the connectivity of the topological graph as a criterion into the optimization algorithm will generate a large number of invalid offspring during the iteration process, reducing the computational efficiency and convergence rate of the iterative solution. Therefore, this implementation uses graph theory-based topology repair to repair invalid offspring.

[0221] Please see Figure 2 First, the nodes in the topology are traversed for connectivity. If the topology of a node is not connected, it indicates that the population corresponding to that node is not connected to the offshore wind farm cluster access system. In this case, the topology can be automatically repaired using graph theory topology repair. During the automatic repair process, it is checked whether there is an associated minimum spanning tree (i.e., the combination of the shortest connecting cable routes) in the graph. If not, the intersection of the node sets is calculated. If there is no intersection, any node in the intersection is added to the subgraph, thereby adding a connection so that the network has the condition to form a minimum spanning tree. If the topology is connected, the population individuals that pass the connectivity check need to be matched with the capacity of the multi-energy equipment and the capacity of the offshore wind farm cluster access system. If they match, it indicates that the population individuals meet the constraints. If not, the equipment capacity of the population individuals is adjusted to the constraint range according to the supplementary constraints of the multi-energy system, generating an effective topology without isolated nodes, ensuring that all wind farms and energy equipment are electrically connected, and outputting effective individuals that meet the constraints. Then, the fitness evaluation of the output population individuals is calculated, and the fitness results are updated.

[0222] Understandably, by verifying the capacity matching between multi-energy devices and the access system, if the capacity of multi-energy devices in offspring exceeds the capacity of the booster station / submarine cable line, the system can automatically adjust the device capacity to within the constraints, thereby reducing invalid offspring. If the hydrogen production power planned for an individual in the population is too large, exceeding the redundant capacity of the booster station, the system will determine it as "mismatched" and automatically reduce the capacity of the hydrogen production equipment to within the constraints. This automatic adjustment ensures the technical feasibility of the planning scheme and avoids equipment redundancy or access bottlenecks.

[0223] Step S35: Based on the updated fitness evaluation results, the NSGA-II algorithm is used to perform cross-iteration on the individuals in the population to generate a new generation of population until the preset iteration conditions are met, and a multi-energy collaborative optimization scheme is output.

[0224] It should be noted that the NSGA-II algorithm, based on updated fitness evaluation results, drives the population to evolve towards a more economical and flexible direction through selection, crossover, and mutation operations. The final output is a multi-energy collaborative optimization scheme that improves the economy and flexibility of offshore wind farm cluster access systems. The preset iteration condition is set to a preset iteration count threshold.

[0225] In this embodiment, the multi-energy synergistic optimization scheme can include system topology, multi-energy device configuration, synergistic operation strategy, and performance indicators. This enables synergistic optimization between the offshore wind farm cluster access system and multiple energy sources. Simultaneously, the multi-energy system, through short-term energy storage to mitigate fluctuations, long-term hydrogen production to utilize surplus capacity, and flexible load balancing, can enhance the access system's ability to cope with wind power uncertainties and expand revenue sources, achieving a deep synergy between economic efficiency and flexibility. The performance indicators are used to quantify the economic effects and flexibility level of the offshore wind farm cluster access system.

[0226] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above-mentioned multi-energy collaborative optimization methods.

[0227] The present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of any of the above-mentioned multi-energy collaborative optimization methods.

[0228] The present invention also provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed by a processor, they implement the steps of any of the above-mentioned multi-energy collaborative optimization methods.

[0229] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0230] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0231] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0232] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0233] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0234] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-energy collaborative optimization method for a large-scale offshore wind farm cluster access system, characterized in that, The method includes: Based on the marine scenario characteristics of the offshore wind farm cluster access system, a collaborative coupling model between the multi-energy system and the offshore wind farm cluster access system is established. Based on the aforementioned collaborative coupling model, a collaborative optimization mechanism is established between the multi-energy system and the offshore wind farm cluster access system. Based on the aforementioned collaborative optimization mechanism, an optimization objective function is established for the offshore wind farm cluster access system that couples the multi-energy system. Under the condition of satisfying the supplementary constraints of the multi-energy system, with the goal of minimizing the optimization objective function, the optimization algorithm is used to optimize the topology of the offshore wind farm cluster access system coupled with the multi-energy system, and output a multi-energy collaborative optimization scheme.

2. The multi-energy synergistic optimization method according to claim 1, characterized in that, The collaborative coupling model includes an energy storage system model, an offshore hydrogen production system model, and a wind power controllable load collaborative model. The steps for establishing a collaborative coupling model between multiple energy systems and the offshore wind farm cluster access system based on the offshore scene characteristics of the offshore wind farm cluster access system include: Based on the characteristics of the offshore wind farm cluster access system, the multiple energy types of the multi-energy system that operates in coordination with the offshore wind farm cluster access system are determined; the multiple energy types include energy storage type, offshore hydrogen production type and controllable load type. Under the condition of coupling with the offshore wind farm cluster access system, an energy storage system model for smoothing short-term fluctuations is constructed according to the energy storage type; an offshore hydrogen production system model for absorbing long-term surplus wind power is constructed according to the offshore hydrogen production type; and a wind power controllable load coordination model for adjusting the controllable load capacity of wind power is constructed according to the controllable load type.

3. The multi-energy synergistic optimization method according to claim 2, characterized in that, The energy storage system model includes a battery energy storage system model and a flywheel energy storage system model; the battery energy storage system of the battery energy storage system model is configured near the offshore booster station of the offshore wind farm cluster access system, and the flywheel energy storage system of the flywheel energy storage system model is configured on the wind farm collection side of the offshore wind farm cluster access system. The battery charging and discharging constraints of the battery energy storage system model include: In the formula: This represents the charging power of the battery energy storage system at time t; This represents the smoothed output power of the wind power at time t; This represents the real-time transmission power of submarine cable line l in the offshore wind farm cluster access system at time t. This represents the discharge power of the battery energy storage system at time t; This represents the rated transmission power of the submarine cable at time t; The battery capacity constraints of the battery energy storage system model are as follows: In the formula: This indicates the maximum battery capacity of the battery energy storage system. t1-t2 represents the peak wind power period of the offshore wind farm cluster access system, and t3-t4 represents the off-peak wind power period of the offshore wind farm cluster access system. The hydrogen production power constraints of the marine hydrogen production system model include: In the formula: This represents the power of the hydrogen electrolyzer in the offshore hydrogen production system at time t. This indicates the rated power of the hydrogen electrolyzer in the offshore hydrogen production system; Capacity of offshore booster stations for offshore wind farm cluster access systems; The load regulation constraints of the wind power controllable load coordination model are as follows: In the formula: This represents the controllable wind power load at time t; This indicates the controllable rated load power of wind power.

4. The multi-energy synergistic optimization method according to claim 3, characterized in that, The collaborative optimization mechanism between the multi-energy system and the offshore wind farm cluster access system includes a power collaborative allocation mechanism, a capacity collaborative optimization mechanism, and an economic collaborative mechanism. Under the power coordination and allocation mechanism, the smoothed wind power output is preferentially transmitted to the onshore power grid via the submarine cable; when there is a short-term power surplus, the battery energy storage system is preferentially activated to charge and smooth the increase of controllable wind power load; when there is a short-term power deficit, the battery energy storage system is preferentially used to discharge and the flywheel energy storage system is preferentially used to replenish energy. When there is still a long-term power surplus after the battery storage is fully charged, all offshore hydrogen production systems are activated to produce hydrogen electrolyzers to absorb the long-term power surplus; finally, if there is still a power surplus, the wind curtailment control strategy is activated. Under the capacity synergy optimization mechanism, the maximum discharge power of the battery energy storage system is matched with the transmission capacity of the submarine cable; the rated capacity of the flywheel energy storage system is matched with the maximum capacity of the collection line of the offshore wind farm cluster access system; and the rated power of the hydrogen production equipment of the offshore hydrogen production system is matched with the capacity of the offshore booster station of the offshore wind farm cluster access system. The total planned capacity of the offshore substation of the offshore wind farm cluster access system includes the sum of the capacities of the wind farms and multiple energy systems it connects to. Under the aforementioned economic synergy mechanism, the total lifecycle cost of the offshore wind farm cluster access system includes component depreciation costs, maintenance costs, wind curtailment losses, initial investment costs, and equipment recovery costs. The initial investment costs include the costs of offshore booster stations, submarine cables, reactive power compensation equipment, converter stations, battery energy storage, flywheel energy storage, and hydrogen electrolyzers. The total revenue of the offshore wind farm cluster access system includes electricity sales revenue, hydrogen sales revenue, and revenue from purchased electricity saved by controllable loads.

5. The multi-energy synergistic optimization method according to claim 1, characterized in that, The optimization objective function is expressed as: In the formula: f1 represents the full life-cycle engineering benefits of the offshore wind farm cluster access system coupled with the multi-energy system; S is the set of each operating scenario; Let s be the probability of scenario s occurring. The total lifecycle cost of the offshore wind farm cluster access system under scenario s; For electricity sales revenue in scenario s; and f1 represents the revenue from hydrogen sales and the revenue from purchased electricity saved by controllable loads under scenario s; f2 represents the flexibility target of the offshore wind farm cluster access system coupled with the multi-energy system. For flexibility indicators after integrating multiple energy sources; in, In the formula: The optimal scheduling cycle number is determined by the number of offshore wind farm cluster access systems that couple the multi-energy systems. To optimize the time set of the scheduling cycle, This refers to the set of all lines in the offshore wind farm cluster access system that couples the multi-energy system. The adjusted flexibility weighting coefficient. The load factor of line l after multi-energy regulation at time t; in, In the formula: The mean line load rate of the offshore wind farm cluster access system coupled with the multi-energy system during the time period T is given. The load rate fluctuation variance of line l in the offshore wind farm cluster access system that couples the multi-energy system.

6. The multi-energy synergistic optimization method according to claim 5, characterized in that, The multi-energy system supplementary constraints include energy storage charging and discharging constraints, flywheel energy storage discharging constraints, hydrogen production equipment start-up and shutdown constraints, hydrogen production equipment capacity constraints, and controllable load constraints. The energy storage charging and discharging constraints are expressed as follows: In the formula: This represents the start-up and shutdown state of the battery energy storage system at time t. The flywheel energy storage discharge constraint is expressed as follows: In the formula: Let be the discharge power of the flywheel energy storage system at time t. This represents the maximum energy storage capacity of the flywheel energy storage system. The start-stop constraints of the hydrogen production equipment are expressed as follows: In the formula: and These refer to the start-up and shutdown times of the hydrogen electrolyzers in the offshore hydrogen production system. The capacity constraint of the hydrogen production equipment is expressed as follows: In the formula: For the efficiency of hydrogen production by electrolysis. Let be the hydrogen production load power at time t. The maximum capacity of the marine hydrogen production system at time t; The controllable load constraint is expressed as follows: 。 7. The multi-energy synergistic optimization method according to claim 1, characterized in that, The objective is to minimize the optimization objective function while satisfying the multi-energy system supplementary constraints. The steps of optimizing the topology of the offshore wind farm cluster access system coupled with the multi-energy system using an optimization algorithm and outputting a multi-energy collaborative optimization scheme include: An extended chain coding method is used to encode and characterize the topology of the offshore wind farm cluster access system coupled with the multi-energy system, resulting in a system matrix structure. The system matrix structure includes node connection information, the number of wind turbines loaded on each node, the cable length between nodes, and the access type of multi-energy equipment. An initial population is generated based on the system matrix structure, and the fitness of the initial population is evaluated and calculated according to a preset fitness function to obtain the initial fitness result. Based on the initial fitness results, the individuals in the initial population are iteratively optimized to drive the population to iteratively optimize in the direction of minimizing the optimization objective function under the condition of satisfying the multi-energy system supplementation constraint. During the iterative optimization of each generation of the population, connectivity is verified for each individual in the population. If they are not connected, the topology is automatically repaired using graph theory topology repair, transforming them into a connected network. If they are connected, the capacity of the multi-energy equipment of the individuals that pass the connectivity verification is matched with the capacity of the offshore wind farm cluster access system. If the capacity does not match, the equipment capacity of the individual in the population is automatically adjusted to within the preset constraints, and the verified and repaired individuals are output. If the capacity matches, the matched individuals are directly output. Fitness evaluation is performed on the output individuals, and the fitness results are updated. Based on the updated fitness evaluation results, the NSGA-II algorithm is used to perform crossover iterations on the individuals in the population to generate a new generation of the population until the preset iteration conditions are met, and a multi-energy collaborative optimization scheme is output.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the multi-energy collaborative optimization method as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the multi-energy collaborative optimization method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the multi-energy collaborative optimization method as described in any one of claims 1-7.

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