Unified optimization method for offshore wind farm cluster collection system with diverse dc topologies

Abstract

The present application relates to a kind of offshore wind farm group collection system unified optimization method containing diverse direct current topology, method includes: the basic data of offshore wind farm group is collected, for the node-branch and voltage-current common constraint of the direct current collection topology of each wind farm is abstracted, unified model framework is constructed, and for the commutation / voltage configuration of each type of direct current collection topology, coupling relationship and reconfigurable ability are constructed Differentiated constraint, establish the model of different direct current collection topology under unified model framework;Based on the model of different direct current collection topology under unified model framework, with the minimum life cycle cost as target, construct the double-layer optimization model of key engineering constraint is applied;Double-layer optimization model includes outer model and inner model;Different solving methods are used to solve outer model and inner model, and obtain wind power station topology optimization scheme.Compared with prior art, the present application realizes integrated planning and global coordination across wind farm.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power grid connection engineering and power system planning technology, and in particular to a unified optimization method for an offshore wind farm cluster system with diverse DC topologies. Background Technology

[0002] With the continuous increase in offshore wind power installed capacity and the accelerated trend of offshore development, multiple wind farms are often developed in parallel in a single sea area, with capacity ranging from hundreds of megawatts to gigawatts, and the distance from shore extending from tens of kilometers near the coast to hundreds of kilometers or even further. The traditional method of planning the power collection system independently for each farm is no longer sufficient to simultaneously address the complex objectives of electrical constraints, construction accessibility, and life-cycle economics brought about by large-scale, long-distance power transmission. Especially given the scarcity of offshore resources and access channels, the lack of coordination between farms will lead to overlapping submarine cable paths, excessive channel occupancy, and further compression of maintenance windows, significantly increasing the difficulty of construction and operation and maintenance.

[0003] Existing offshore DC power collection systems primarily include parallel topologies, series-parallel topologies (composed of series stacks connected in parallel), and matrix topologies that incorporate reconfigurable switch networks. Parallel topologies offer a clear structure and electrical decoupling between units, facilitating local fault isolation and rapid recovery. However, they require large-capacity offshore converter / boost platforms and often rely on multiple medium-voltage submarine cables for long-distance transmission, making their overall investment and marine footprint potentially less advantageous. Series-parallel topologies, through a combination of series-connected units progressively increasing voltage and multiple stacks in parallel, enable long-distance, centralized power transmission with higher voltage levels and larger cross-section submarine cables, offering potential economic viability in offshore scenarios. However, voltage / power coupling exists between units, and local shutdowns or faults can induce overvoltage, power limitations, and wind curtailment. Matrix topologies further enhance topology reconfiguration and fault tolerance by configuring parallel interconnected switches between adjacent stacks, but this comes at the cost of increased equipment quantity and system redundancy, leading to significant initial investment and maintenance complexity.

[0004] From an engineering practice perspective, offshore converter stations (including platform civil engineering, converter valves / transformers, and auxiliary control equipment) play a crucial role in cluster planning. Their number, capacity, and site location not only determine the on-site collection voltage level, submarine cable cross-section, and number of loops, but also directly affect the relative economics and feasibility of parallel or series-parallel connection for each wind farm. Wind farms closer to shore or near converter stations tend to adopt parallel solutions to take advantage of the cost and reliability benefits of medium-voltage short-distance laying. Wind farms farther from shore and with larger relative distances from adjacent farms are more likely to use series-parallel connection to boost voltage and connect to high-voltage transmission channels, thereby reducing the number of parallel medium-voltage loops, lowering overall submarine cable investment, and conserving marine channel resources.

[0005] On the other hand, current planning studies often focus on one-time equipment investment, failing to fully and systematically incorporate wind curtailment losses and power outage losses into a unified life-cycle cost assessment. The expected energy loss, determined by the average annual failure rate and average repair time of submarine cables, varies significantly with topology, cable specifications, and path length; power-voltage coupling in series-parallel structures may further amplify this loss under extreme operating or fault conditions. If these operational losses are not quantified along with the initial investment, the resulting solution may be "most economical" on paper, but not economical in terms of total life-cycle cost.

[0006] Furthermore, joint optimization at the cluster scale must also satisfy a series of electrical and geometric constraints, including long-term current carrying capacity and short-circuit thermal stability verification of submarine cables, insulation voltage level matching, topology voltage / current feasibility, path non-intersection, and construction accessibility. Because different topologies exhibit differentiated constraint structures and cost compositions, the traditional "site-by-site optimization, manual comparison" process is insufficient to effectively search for a globally near-optimal solution within the vast combinatorial space. This necessitates a planning framework that can uniformly abstract the commonalities of multiple topologies at the cluster level, characterize their differences, and collaboratively solve converter station site selection and capacity determination with on-site topology / path optimization.

[0007] Chinese patent CN112270446B discloses a planning method for a DC offshore wind farm series-parallel collection system. Addressing the issue of wind curtailment caused by inconsistent submarine cable insulation requirements at different locations and long-term uneven wind resource distribution in offshore wind farm DC series-parallel collection systems, the method constructs a model of the curtailed wind power under the series-parallel connection lines. It incorporates submarine cable voltage and insulation requirements into the optimization model, achieving the planning of the offshore wind farm DC series-parallel collection system through topology optimization and minimizing the entire lifecycle cost. However, this invention only plans for a single DC offshore wind farm, performing topology optimization and cost calculation only for the series-parallel collection system within a single farm. It does not involve the collaborative planning of multiple wind farm clusters, failing to solve the inherent problems of independent planning for multiple wind farms and lacking cluster-level resource sharing design. Furthermore, the cost calculation ignores the cost of the offshore converter station, resulting in incomplete cost accounting. It only considers wind curtailment losses, neglecting fault-related losses, and its operational loss coverage is incomplete.

[0008] Based on the aforementioned pain points and needs, the industry urgently requires a unified planning method for offshore wind farm clusters. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a unified optimization method for offshore wind farm cluster aggregation system with diverse DC topologies. This method can accurately characterize the coupling relationship between different wind farms in terms of geographical location, offshore distance, capacity scale and transmission path, and provide an optimal configuration strategy of centralized aggregation + differentiated topologies.

[0010] The objective of this invention can be achieved through the following technical solutions: A unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies, the method comprising: Collect basic data of offshore wind farm clusters, abstract the common constraints of node-branch and voltage-current for DC collection topologies of each wind farm, construct a unified model framework, and construct differentiated constraints for converter / boost configuration, coupling relationship and reconfigurability of various DC collection topologies, and establish models of different DC collection topologies under the unified model framework. Based on the models of different DC aggregation topologies under the unified model framework, a two-layer optimization model with key engineering constraints is constructed with the goal of minimizing the total life cycle cost. The two-layer optimization model includes an outer model and an inner model. The outer model is used to optimize the number, location and capacity of offshore converter stations, while the inner model is used to optimize the aggregation topology, access method and submarine cable path and specifications of each wind farm. Different solution methods are used to solve the outer and inner models to obtain the wind power plant topology optimization scheme.

[0011] Furthermore, the basic data of the offshore wind farm cluster includes wind farm cluster parameters, equipment parameters, economic and operational parameters, and constraint boundary parameters; The wind farm cluster parameters include the installed capacity, number of wind turbines, offshore distance, spatial coordinates within the sea area, and turbine distribution of each wind farm. The equipment parameters include the unit length cost of submarine cables, current carrying capacity, short-circuit thermal stability coefficient, insulation voltage level range, annual average failure rate, average repair time, unit capacity cost of converter valves / platforms / supporting devices of offshore converter stations, and rated capacity range. The economic and operational parameters include the on-grid tariff for offshore wind power, the total life cycle, the discount rate, and the rated output voltage and power of the wind turbine. The constraint boundary parameters include sea area channel planning, construction accessibility restrictions, and short-circuit time standards.

[0012] Furthermore, the process of abstracting the common constraints of node-branch and voltage-current for the DC collection topology of each wind farm includes: Based on the basic data of the offshore wind farm cluster, the connection rules, basic voltage or current constraints, and power balance equations of all wind farm nodes and branches are uniformly defined. The nodes include wind turbines, converter units, and landing points, and the branches include submarine cables.

[0013] Furthermore, the DC collection topology includes parallel topology, series-parallel topology and matrix topology, and the series-parallel topology includes a series or parallel hybrid topology.

[0014] Furthermore, the process of constructing the differentiated constraints includes: For parallel topologies: Add electrical decoupling constraints for the units and clarify the configuration associations of large-capacity converters or booster platforms; For series-parallel topologies: add voltage or power coupling constraints and series stack and parallel segment structure constraints; For matrix topologies: Add reconfigurable switch action logic constraints and redundant device configuration constraints.

[0015] Furthermore, the total life cycle cost includes equipment investment costs and operational losses; The equipment investment cost includes the cost of medium-voltage and high-voltage cables and the cost of electrical equipment for the offshore converter station. The cost of medium-voltage and high-voltage cables is calculated based on the total number of submarine cables, the length of submarine cables, and the unit length cost of submarine cables. The cost of electrical equipment for the offshore converter station is obtained by calculating the total cost of the converter valve equipment, the cost of the supporting electrical equipment and control and protection devices for the offshore converter station, and the cost of the offshore platform for the offshore converter station. The operational losses include the cost of wind curtailment in different collection topologies and the power outage losses caused by submarine cable faults. The calculation process of the operational losses includes: calculating the power loss during submarine cable fault repairs throughout the entire life cycle based on the average repair time after a submarine cable fault and the annual average fault rate of the submarine cable; obtaining the total power loss based on the power loss during submarine cable fault repairs throughout the entire life cycle and the power loss from wind curtailment in different collection topologies, and obtaining the operational losses based on the offshore wind power grid connection price.

[0016] Furthermore, the objective function of the outer model is: in, For the cost of medium and high voltage cables, Cost of electrical equipment for offshore converter stations Losses incurred during the operational period; The objective function of the inner model is: .

[0017] Furthermore, the key engineering constraints include the current carrying capacity constraints of the outer model and the current carrying capacity constraints, short-circuit thermal stability constraints, insulation class constraints, and path non-intersection constraints of the inner model. The expression for the current carrying capacity constraint of the outer model is: in, Let be the planned capacity of the i-th offshore converter station. This indicates the grid connection method chosen by wind farm j. This indicates direct login. This indicates connection to an offshore converter station. This represents the installed capacity of wind farm j connected to the i-th converter station; The expression for the current carrying capacity constraint of the inner model is: in, This is the maximum continuous load current of the submarine cable. This is the correction factor for the current carrying capacity of submarine cables. For long-term operating current carrying capacity of submarine cables The expression for the short-circuit thermal stability constraint is: in, The minimum cross-section required for submarine cable short-circuit current verification. This represents the short-circuit current of the submarine cable under steady-state conditions. For short circuit time, The thermal stability coefficient of the submarine cable; The expression for the insulation class constraint is: in, The minimum insulation voltage requirement for the submarine cable γ in wind farm j. This represents the position of submarine cable γ in the DC series stack. Let V be the output voltage of the DC wind turbine in wind farm j. This indicates the collection method chosen by wind farm j. Indicates series and parallel connection methods. Indicates parallel connection method, The output voltage of the wind turbine in the wind farm; The expression for the path non-intersection constraint is: in, and This indicates a collection of different wind turbine units, each connected to one end of a submarine cable.

[0018] Furthermore, the process of solving the outer and inner models using different solution methods includes: For the outer model, objective function-driven fuzzy clustering is used to cluster the wind farm cluster. Based on the clustering results and key engineering constraints, the location and capacity of converter stations are changed to optimize the number, location and capacity of offshore converter stations. For the inner layer model, if the topology is a parallel topology, the connection within the field is solved by minimum spanning tree and genetic algorithm. If the topology is a series-parallel topology or a matrix topology, the improved PGA iterative search for series-parallel structure and submarine cable cross-section is used to solve the model.

[0019] Furthermore, after obtaining the wind power plant topology optimization scheme, based on simulation examples and sensitivity analysis, the topology optimization rules that vary with offshore distance and relative position are extracted for rapid screening of preliminary schemes, investment calculation and comparison decision-making in subsequent optimization.

[0020] Compared with the prior art, the beneficial effects of the present invention include: 1. This invention introduces multiple DC aggregation topologies within the same framework and performs differentiated modeling. Simultaneously, it collaboratively optimizes the number, location, and capacity of offshore converter stations, the access methods of each wind farm, and the in-farm collection topology, achieving integrated planning and global coordination across wind farms. In this invention, the outer model determines the site and capacity of the offshore converter station, while the inner model selects the optimal aggregation topology and grid connection method for each wind farm. It also optimizes the in-farm submarine cable path and specifications to meet engineering constraints such as current carrying capacity, short-circuit thermal stability, insulation level, and non-intersecting paths. This accurately depicts the coupling relationships between different wind farms in terms of geographical location, offshore distance, capacity scale, and transmission path, providing an optimal configuration strategy for centralized aggregation + differentiated topology.

[0021] 2. This invention achieves a comparable comparison of parallel, series-parallel, and matrix topologies through common abstraction and differentiated constraint modeling, solving the problem of inconsistent evaluation standards and inability to directly compare different topologies in traditional methods.

[0022] 3. This invention aims to minimize the total life cycle cost (LCC). The LCC includes not only the investment cost of equipment such as submarine cables and offshore converter stations, but also wind curtailment losses and power outage losses caused by submarine cable failures. This unifies economic indicators and operational reliability indicators under the same evaluation criteria, avoiding the one-sidedness of traditional planning that only considers the initial investment and ignores long-term losses.

[0023] 4. The model solution of this invention adopts a two-layer optimization framework of outer fuzzy clustering and inner topology adaptation algorithm. The outer layer quickly locks the optimal configuration of the converter station, while the inner layer uses a special algorithm for different topologies, which greatly improves the search efficiency and avoids blind iteration in a huge combination space. Compared with manual selection and field-by-field optimization, this method can effectively find the global near-optimal solution and avoid the overall economic loss of the cluster caused by local optima.

[0024] 5. This invention not only outputs specific engineering solutions, but also forms engineering criteria that can be used for early solution screening. Through this systematic and unified planning method, the overall investment cost of the cluster can be significantly reduced, power outage losses caused by submarine cable failures can be suppressed, wind curtailment can be reduced, and the overall power transmission efficiency and operational reliability of the wind farm cluster can be improved. This provides directly implementable technical support for the engineering design and decision-making of large-scale offshore wind power clusters. Attached Figure Description

[0025] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a map showing the location distribution of offshore wind farms in an embodiment of the present invention; Figure 3 This is a diagram showing the distribution of turbine units in each wind farm in an embodiment of the present invention; Figure 4 This is a diagram showing the optimization results of the offshore wind farm cluster power collection system in an embodiment of the present invention; Figure 5 This is a flowchart illustrating the specific solution process for the unified optimization method of the offshore wind farm cluster aggregation system with diverse DC topologies in this embodiment of the invention. Figure 6 This is a cost comparison chart of different aggregation and grid connection methods in embodiments of the present invention; Figure 7 This is a schematic diagram illustrating the change in the offshore distance of wind farm 3 in an embodiment of the present invention; Figure 8 This is a diagram showing the total system cost and the wind farm 3 collection method as a function of offshore distance in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] Example 1 This embodiment discloses a unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies. This method is as follows: Figure 1 As shown, the process specifically includes steps S1-S6, each described in detail below: Step S1: Collect basic data on offshore wind farm clusters.

[0028] The basic data for offshore wind farm clusters include wind farm cluster parameters, equipment parameters, economic and operational parameters, and constraint boundary parameters; Wind farm cluster parameters include the installed capacity of each wind farm, the number of wind turbines, the distance from the shore, the spatial coordinates within the sea area, and the distribution of the units; Equipment parameters include the unit length cost of submarine cables, current carrying capacity, short-circuit thermal stability coefficient, insulation voltage level range, annual average failure rate, average repair time, unit capacity cost of converter valves / platforms / supporting devices of offshore converter stations, and rated capacity range. Economic and operational parameters include the on-grid tariff for offshore wind power, the total life cycle, the discount rate, and the rated output voltage and power of the wind turbine. The constraint boundary parameters include sea area channel planning, construction accessibility restrictions, and short-circuit time standards.

[0029] Step S2 involves abstracting the common constraints of node-branch and voltage-current for the DC aggregation topology of each wind farm, constructing a unified model framework, and constructing differentiated constraints for the converter / boost configuration, coupling relationship and reconfigurability of various DC aggregation topologies, thus establishing models of different DC aggregation topologies under the unified model framework.

[0030] DC collection topologies include parallel topologies, series-parallel topologies, and matrix topologies. Series-parallel topologies include mixed series or parallel topologies.

[0031] Analyzing the planning requirements and grid connection combinations of DC power collection systems for offshore wind farm clusters can help to form a unified planning object and decision-making space.

[0032] The process of abstracting the common constraints of node-branch and voltage-current characteristics for the DC collection topology of each wind farm includes: Based on the basic data of offshore wind farm clusters, the connection rules, basic voltage or current constraints, and power balance equations of all wind farm nodes and branches are uniformly defined. Nodes include wind turbines, converter units, and landing points, and branches include submarine cables.

[0033] The process of constructing differentiated constraints includes: For parallel topologies: Add electrical decoupling constraints for the units and clarify the configuration associations of large-capacity converters or booster platforms; For series-parallel topologies: add voltage or power coupling constraints and series stack and parallel segment structure constraints; For matrix topologies: Add reconfigurable switch action logic constraints and redundant device configuration constraints.

[0034] Step S3: Based on models of different DC aggregation topologies under a unified model framework, establish a full life cycle cost target and explicitly include operational losses.

[0035] Total life cycle cost includes equipment investment cost and operating period losses; The equipment investment cost includes the cost of medium-voltage and high-voltage cables and the cost of electrical equipment for the offshore converter station. The cost of medium-voltage and high-voltage cables is calculated based on the total number of submarine cables, the length of submarine cables, and the unit length cost of submarine cables. The cost of electrical equipment for the offshore converter station is obtained by calculating the total cost of the converter valve equipment, the cost of the supporting electrical equipment and control and protection devices for the offshore converter station, and the cost of the offshore platform for the offshore converter station. The operating period losses include the curtailment costs of different collection topologies and the power outage losses caused by submarine cable failures; the calculation process of the operating period losses includes: calculating the loss of power during the repair period of submarine cable failures within the whole life cycle based on the average repair time after submarine cable failures and the annual average failure rate of submarine cables; obtaining the total loss of power based on the loss of power during the repair period of submarine cable failures within the whole life cycle and the curtailment loss of power of different collection topologies, and obtaining the losses during the operation period according to the onshore electricity price of offshore wind power.

[0036] Specifically, the calculation expressions for the costs of medium-voltage and high-voltage cables are as follows: Among them, is the cable cost, represents the total number of planned submarine cables (including medium-voltage and high-voltage), represents the submarine cable length, represents the cable length margin coefficient (taking 105%), is the unit length cost of the submarine cable.

[0037] The calculation expression for the costs of offshore converter station electrical equipment (offshore platforms, converter transformers and other auxiliary electrical equipment) is as follows: Among them, represents the cost of the converter valve equipment of the i-th offshore converter station, represents the costs of the supporting electrical equipment and control and protection devices, etc. of the i-th offshore converter station, represents the cost of the offshore platform of the i-th offshore converter station, and respectively represent the unit capacity costs of the converter valve equipment and the supporting devices, represents the planned capacity of the i-th offshore converter station.

[0038] The calculation expression for the operating period losses is as follows: Among them, represents the loss of power during the repair period of the submarine cable failure, is the curtailment loss of power of the series-parallel topology; is the original output power of the fan when the submarine cable does not fail; is the average repair time after the submarine cable failure; is the annual average failure rate of the submarine cable; For submarine cable Number of generating units shut down due to malfunctions; For the entire life cycle time, This refers to the on-grid electricity price for offshore wind power.

[0039] Step S4: With the goal of minimizing the total life cycle cost, construct a two-layer optimization model that imposes key engineering constraints.

[0040] The two-layer optimization model consists of an outer layer model and an inner layer model. The outer layer model is used to optimize the number, location and capacity of offshore converter stations, while the inner layer model jointly optimizes the aggregation topology (parallel / series-parallel / matrix) and access methods (direct landing / access to converter stations) and the submarine cable path and specifications within the wind farm at each wind farm scale.

[0041] The objective function of the outer model is: in, For the cost of medium and high voltage cables, Cost of electrical equipment for offshore converter stations Losses incurred during the operational period; The objective function of the inner model is: .

[0042] Key engineering constraints include current carrying capacity constraints of the outer model and current carrying capacity constraints, short-circuit thermal stability constraints, insulation class constraints, and path non-crossing constraints of the inner model. The expression for the current carrying capacity constraint of the outer model is: in, Let be the planned capacity of the i-th offshore converter station. This indicates the grid connection method chosen by wind farm j. This indicates direct login. This indicates connection to an offshore converter station. This represents the installed capacity of wind farm j connected to the i-th converter station; The expression for the current carrying capacity constraint of the inner model is: in, This is the maximum continuous load current of the submarine cable. This is the correction factor for the current carrying capacity of submarine cables. For long-term operating current carrying capacity of submarine cables The expression for the short-circuit thermal stability constraint is: in, The minimum cross-section required for submarine cable short-circuit current verification. This represents the short-circuit current of the submarine cable under steady-state conditions. For short circuit time, The thermal stability coefficient of the submarine cable; The expression for the insulation class constraint is: in, The minimum insulation voltage requirement for the submarine cable γ in wind farm j. This represents the position of submarine cable γ in the DC series stack. Let V be the output voltage of the DC wind turbine in wind farm j. This indicates the collection method chosen by wind farm j. Indicates series and parallel connection methods. Indicates parallel connection method, The output voltage of the wind turbine in the wind farm; The expression for the path non-intersection constraint is: in, and This indicates a collection of different wind turbine units, each connected to one end of a submarine cable.

[0043] Step S5: Solve the outer and inner models using different solution methods to obtain the wind power plant topology optimization scheme.

[0044] The process of solving the outer and inner models using different solution methods includes: For the outer model, objective function-driven fuzzy clustering is used to cluster the wind farm cluster. Based on the clustering results and key engineering constraints, the location and capacity of the converter stations are changed to optimize the number, location and capacity of offshore converter stations. For the inner layer model, if the topology is a parallel topology, the connection within the field is solved by the minimum spanning tree and genetic algorithm. If the topology is a series-parallel topology or a matrix topology, the improved PGA iterative search for series-parallel structures and submarine cable cross-sections is used to solve the model.

[0045] Step S6: Based on simulation examples and sensitivity analysis, extract the topology optimization rules that vary with offshore distance and relative position, which are used for rapid screening of preliminary schemes, investment calculation and comparison decision-making in subsequent optimization.

[0046] This planning methodology not only outputs specific engineering solutions (including converter station layout, collector topology, submarine cable route and specifications), but also forms engineering criteria that can be used for early-stage solution selection: For example, in a typical 1970 MW six-wind farm scenario, optimization results show that the scheme of using two offshore converter stations for centralized convergence and configuring parallel or series-parallel topologies for different wind farms is superior to the schemes of "all parallel," "all series-parallel," or "direct landing without converter stations" in terms of total life-cycle cost. Meanwhile, the distance of approximately 120–130 km offshore can be considered a sensitive switching range between parallel and series-parallel topologies; stations at greater distances are more suitable for series-parallel connections and integration into the high-voltage transmission circuit. This systematic and unified planning approach can significantly reduce the overall investment cost of the wind farm cluster, suppress power outage losses caused by submarine cable faults, reduce wind curtailment, and improve the overall transmission efficiency and operational reliability of the wind farm cluster, providing directly implementable technical support for the engineering design and decision-making of large-scale offshore wind power clusters.

[0047] This invention aims to construct a unified planning method and system for precise and efficient DC collection systems in offshore wind farm clusters. It introduces multiple DC collection topologies (including parallel, series-parallel, and reconfigurable matrix types) within the same framework and performs differentiated modeling. Simultaneously, it collaboratively optimizes the number, location, and capacity of offshore converter stations, the access methods of each wind farm, and the collection topology within the farm, achieving integrated planning and global coordination across wind farms. The method aims to minimize life-cycle cost (LCC), which includes not only the investment costs of submarine cables and offshore converter stations but also wind curtailment losses and power outage losses due to submarine cable failures, thus unifying economic indicators and operational reliability indicators under the same evaluation caliber.

[0048] Example 2 This embodiment, based on Embodiment 1 above, discloses a specific application example of a unified optimization method for an offshore wind farm cluster system with diverse DC topologies, as shown below: This embodiment selects six offshore wind farms with a total installed capacity of 1970MW for optimized planning of their power collection systems. The offshore wind farms are located approximately 90-150km offshore, as shown in the following figures. Figure 2 As shown in Table 1, the wind turbine output voltage is 50kV, and the high-voltage submarine cable transmission level of the converter station is ±250kV. The number of turbines and installed capacity of each wind farm are shown in Table 1 below, and the layout of the turbines within the farm is as follows. Figure 2 and Figure 3 As shown.

[0049] Table 1 Installed capacity of various offshore wind farms In this scenario, the unified optimization method for offshore wind farm cluster aggregation system with diverse DC topologies proposed in Example 1 is used to perform multi-topology + two-layer optimization planning and solution: The outer layer optimizes the number, location, and capacity configuration of offshore converter stations. The inner layer optimizes the collection topology (parallel, series, series-parallel, or matrix) and grid connection method (direct landing or connection to the offshore converter station) for each wind farm, while simultaneously optimizing the on-site collection cable path, medium-voltage / high-voltage cable cross-section, and transmission method. In the solution process, the objective function is to minimize the total life-cycle cost, including the equipment investment cost of submarine cables and offshore converter stations, as well as the operational loss cost. The operational loss consists of two parts: first, wind curtailment losses due to voltage-power coupling in series-parallel topologies; and second, power outage losses determined by the submarine cable failure rate and mean time to repair (i.e., the discounted value of lost power generation). All these factors are uniformly included in the same economic evaluation caliber for comparison to ensure comparability between different wind farms, topologies, and grid connection methods.

[0050] Specifically, it will be implemented through the following steps: (1) Cluster topology modeling and economic target construction Taking six offshore wind farms with a total installed capacity of approximately 1970 MW as examples, this study first establishes a cluster-scale electrical / connectivity description based on the spatial distribution, offshore distance (approximately 90-150 km), and turbine arrangement of each wind farm. This clarifies the possible grid connection methods for each wind farm (direct landing or transmission via an offshore converter station) and the possible on-site DC collection topology types (parallel or series-parallel, with matrix topology as an optional redundancy). This modeling treats each wind farm as a candidate grid connection unit and integrates them with candidate offshore converter station locations and landing points to form a unified network. Figure 4 The results of the optimization of the power collection system for offshore wind farm clusters.

[0051] Based on this, a lifecycle cost (LCC) objective function is constructed to quantitatively evaluate each candidate cluster scheme. Lifecycle cost comprises two main parts: 1) Equipment investment costs mainly include the length, cross-section and unit length cost of medium / high voltage submarine cables in and between the sites, as well as the cost of converter valve equipment, supporting electrical equipment and platform body of the offshore converter station; 2) Operating losses include wind curtailment losses that may occur under the series-parallel structure, as well as power generation losses caused by submarine cable failures and outages during the mean time to repair.

[0052] The above costs are all summed after discounting over the entire life cycle, making different topologies and different grid connection paths comparable under the same economic caliber, thereby providing a unified evaluation index for subsequent optimization.

[0053] (2) Design of the planning solver and configuration of constraints Based on the established cost model and network structure description, a two-layer optimization solution framework is constructed. The outer-layer optimization variables are the number of offshore converter stations, their locations, and the capacity allocation for each station; the inner-layer optimization variables are the aggregation topology, grid connection method, and the route and cross-sectional specifications of the submarine cables within and between wind farms. This two-layer model can be expressed as the formula in Example 1: the outer-layer objective is to minimize the cluster-level lifecycle cost, and the inner-layer objective is to minimize the cost of wind farm access and collection network under a given site configuration, while simultaneously satisfying engineering constraints. The main constraints include: 1) Electrical safety constraints: The long-term current carrying capacity of the submarine cable must be greater than the maximum continuous load current, and the cross-section of the submarine cable must meet the short-circuit thermal stability constraints and insulation class requirements; 2) Network feasibility constraints: The voltage distribution of units in series-parallel connections must meet the superposition relationship, and the total voltage of series units must be consistent with the voltage level of the output circuit; 3) Geometric and construction constraints: The submarine cable route shall not intersect with each other in the sea area, and the turning radius and route margin coefficient shall meet the requirements for laying and maintenance. 4) Grid connection / transmission constraints: The capacity of the converter station, the current collected, and the transmission level of the high-voltage transmission circuit must be matched with the installed capacity of the wind farm.

[0054] The solution process is as follows: The outer layer searches for the sites and service areas of different numbers of offshore converter stations by using clustering / fuzzy clustering with life-cycle cost as the similarity metric; The field connections of the parallel topology are obtained through the minimum spanning tree (MST) to obtain the initial connection structure, and then the genetic algorithm is used to refine the path selection and submarine cable cross-section. The series-parallel topology searches for the series stack size, parallel segment combination, and outgoing submarine cable cross-section using an improved PGA algorithm; The inner solution is iterated through different site selection schemes, and the total lifecycle cost is compared until the globally optimal site-topology combination scheme is obtained (see the overall process). Figure 5 ).

[0055] (3) Scheme comparison, result verification and engineering criterion extraction Set the comparison strategy as follows: To verify the effectiveness of the proposed method, a multi-scheme comparison was conducted on the wind farm, a cluster with a total installed capacity of approximately 1970 MW. Typical comparison schemes included: by Figure 4The result is the baseline scheme (the optimal scheme determined by this invention, hereinafter referred to as Case A): Two offshore converter stations are set up. Wind farm 1 and wind farm 2 are connected to converter station 1 in a parallel topology; wind farm 3 adopts a series-parallel topology to raise the voltage and then connects to the high-voltage side of converter station 1 for centralized transmission; wind farms 4, 5 and 6 are connected to converter station 2 in a parallel topology, and then the voltage is raised by converter station 2 and transmitted through high-voltage submarine cables.

[0056] Case B: Keep the location and capacity of the converter station unchanged, but force all wind farms to adopt a parallel topology.

[0057] Case C: Keep the location and capacity of the converter station unchanged, but force all wind farms to adopt a series-parallel topology and connect them to the corresponding converter station.

[0058] Case D: No offshore converter station is set up, and all wind farms are directly connected to the landing point using a series-parallel topology.

[0059] Power collection system planning is performed for different scenarios, and the cost comparison of planning schemes under each scenario is obtained, for example. Figure 6 As shown.

[0060] The comparison results show that: 1) In terms of total life-cycle cost (including equipment investment, wind curtailment loss, and power outage loss), Case A has the lowest total cost. For wind farms located far away, boosting their power to a high voltage level through series and parallel connections and concentrating it into the high-voltage transmission circuit can significantly reduce the number and length of long-distance medium-voltage parallel circuits, reducing submarine cable investment and marine land occupation. For wind farms located close to the converter station, maintaining the parallel topology has the advantages of small line cross-section, high reliability, and low wind curtailment loss within the medium-voltage range.

[0061] 2) Both converting all to parallel (Case B) and all to series-parallel (Case C) will bring structural disadvantages: All parallel connection requires multiple long-distance parallel laying of medium-voltage submarine cables for offshore wind farms, resulting in high costs and significant channel occupancy; all series-parallel connection introduces unnecessary high-voltage current collection and series coupling into nearshore wind farms, increasing cable cross-section and wind curtailment losses after operational failures. While eliminating converter station direct landing (Case D) eliminates platform investment, multiple wind farms directly landing via high-voltage channels lead to a sharp increase in the number and length of high-voltage submarine cables, and significantly increase power outage losses during faults, thus increasing the overall LCC (Lower Capacity).

[0062] 3) Further sensitivity analysis of offshore distance was conducted by shifting the coordinates of one offshore wind farm (Wind Farm 3) along the nearshore direction, gradually reducing its offshore distance from approximately 150 km to approximately 110 km, and resolving the problem at each location. The results show that when the offshore distance is greater than approximately 130 km, the series-parallel topology remains the optimal access method; when the offshore distance is less than approximately 110 km, the parallel topology becomes a more cost-effective choice; and in the range of approximately 120-130 km, the life-cycle costs of the two topologies are very close, which can be considered a sensitive zone for topology switching. This invention can provide rapid criteria for different engineering designs: wind farms outside the sensitive zone should adopt a series-parallel topology and transmit power centrally through the high-voltage side; wind farms within the sensitive zone should adopt a parallel topology to obtain higher reliability and lower on-site power collection costs. Specific results are as follows: Figure 7 , Figure 8 As shown.

[0063] In summary, this embodiment verifies the core features of the method of the present invention: on the one hand, it can simultaneously provide the optimal solution for converter station configuration, grid connection strategy and on-site power collection topology at the cluster scale; on the other hand, it can also refine the optimization results into rapid engineering criteria, providing direct technical basis for the early planning, investment calculation and scheme comparison of large-scale offshore wind farm clusters.

[0064] Example 3 Based on Embodiment 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the aforementioned unified optimization method for an offshore wind farm cluster system with diverse DC topologies.

[0065] At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the aforementioned unified optimization method for offshore wind farm cluster aggregation systems with diverse DC topologies. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0066] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0067] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0068] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies, characterized in that, The method includes: Collect basic data of offshore wind farm clusters, abstract the common constraints of node-branch and voltage-current for DC collection topologies of each wind farm, construct a unified model framework, and construct differentiated constraints for converter / boost configuration, coupling relationship and reconfigurability of various DC collection topologies, and establish models of different DC collection topologies under the unified model framework. Based on the models of different DC aggregation topologies under the unified model framework, a two-layer optimization model with key engineering constraints is constructed with the goal of minimizing the total life cycle cost. The two-layer optimization model includes an outer model and an inner model. The outer model is used to optimize the number, location and capacity of offshore converter stations, while the inner model is used to optimize the aggregation topology, access method and submarine cable path and specifications of each wind farm. Different solution methods are used to solve the outer and inner models to obtain the wind power plant topology optimization scheme.

2. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, The basic data of the offshore wind farm cluster includes wind farm cluster parameters, equipment parameters, economic and operational parameters, and constraint boundary parameters; The wind farm cluster parameters include the installed capacity, number of wind turbines, offshore distance, spatial coordinates within the sea area, and turbine distribution of each wind farm. The equipment parameters include the unit length cost of submarine cables, current carrying capacity, short-circuit thermal stability coefficient, insulation voltage level range, annual average failure rate, average repair time, unit capacity cost of converter valves / platforms / supporting devices of offshore converter stations, and rated capacity range. The economic and operational parameters include the on-grid tariff for offshore wind power, the total life cycle, the discount rate, and the rated output voltage and power of the wind turbine. The constraint boundary parameters include sea area channel planning, construction accessibility restrictions, and short-circuit time standards.

3. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, The process of abstracting the common constraints of node-branch and voltage-current for the DC collection topology of each wind farm includes: Based on the basic data of the offshore wind farm cluster, the connection rules, basic voltage or current constraints, and power balance equations of all wind farm nodes and branches are uniformly defined. The nodes include wind turbines, converter units, and landing points, and the branches include submarine cables.

4. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, The DC collection topology includes parallel topology, series-parallel topology and matrix topology, and the series-parallel topology includes series or parallel hybrid topology.

5. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 4, characterized in that, The process of constructing the differentiated constraints includes: For parallel topologies: Add electrical decoupling constraints for the units and clarify the configuration associations of large-capacity converters or booster platforms; For series-parallel topologies: add voltage or power coupling constraints and series stack and parallel segment structure constraints; For matrix topologies: Add reconfigurable switch action logic constraints and redundant device configuration constraints.

6. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, The total life cycle cost includes equipment investment costs and operational losses. The equipment investment cost includes the cost of medium-voltage and high-voltage cables and the cost of electrical equipment for the offshore converter station. The cost of medium-voltage and high-voltage cables is calculated based on the total number of submarine cables, the length of submarine cables, and the unit length cost of submarine cables. The cost of electrical equipment for the offshore converter station is obtained by calculating the total cost of the converter valve equipment, the cost of the supporting electrical equipment and control and protection devices for the offshore converter station, and the cost of the offshore platform for the offshore converter station. The operational losses include the cost of wind curtailment in different collection topologies and the power outage losses caused by submarine cable faults. The calculation process of the operational losses includes: calculating the power loss during submarine cable fault repairs throughout the entire life cycle based on the average repair time after a submarine cable fault and the annual average fault rate of the submarine cable; obtaining the total power loss based on the power loss during submarine cable fault repairs throughout the entire life cycle and the power loss from wind curtailment in different collection topologies, and obtaining the operational losses based on the offshore wind power grid connection price.

7. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, The objective function of the outer model is: in, For the cost of medium and high voltage cables, Cost of electrical equipment for offshore converter stations Losses incurred during the operational period; The objective function of the inner model is: 。 8. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, The key engineering constraints include the current carrying capacity constraints of the outer model and the current carrying capacity constraints, short-circuit thermal stability constraints, insulation class constraints, and path non-intersection constraints of the inner model. The expression for the current carrying capacity constraint of the outer model is: in, Let be the planned capacity of the i-th offshore converter station. This indicates the grid connection method chosen by wind farm j. This indicates direct login. This indicates connection to an offshore converter station. This represents the installed capacity of wind farm j connected to the i-th converter station; The expression for the current carrying capacity constraint of the inner model is: in, This is the maximum continuous load current of the submarine cable. This is the correction factor for the current carrying capacity of submarine cables. For long-term operating current carrying capacity of submarine cables The expression for the short-circuit thermal stability constraint is: in, The minimum cross-section required for submarine cable short-circuit current verification. This represents the short-circuit current of the submarine cable under steady-state conditions. For short circuit time, The thermal stability coefficient of the submarine cable; The expression for the insulation class constraint is: in, The minimum insulation voltage requirement for the submarine cable γ in wind farm j. This represents the position of submarine cable γ in the DC series stack. Let V be the output voltage of the DC wind turbine in wind farm j. This indicates the collection method chosen by wind farm j. Indicates series and parallel connection methods. Indicates parallel connection. The output voltage of the wind turbine in the wind farm; The expression for the path non-intersection constraint is: in, and This indicates a collection of different wind turbine units, each connected to one end of a submarine cable.

9. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, The process of solving the outer and inner models using different solution methods includes: For the outer model, objective function-driven fuzzy clustering is used to cluster the wind farm cluster. Based on the clustering results and key engineering constraints, the location and capacity of the converter stations are changed to optimize the number, location and capacity of offshore converter stations. For the inner layer model, if the topology is a parallel topology, the connection within the field is solved by the minimum spanning tree and genetic algorithm. If the topology is a series-parallel topology or a matrix topology, the improved PGA iterative search for series-parallel structures and submarine cable cross-sections is used to solve the model.

10. The unified optimization method for an offshore wind farm cluster aggregation system with diverse DC topologies according to claim 1, characterized in that, After obtaining the wind power plant topology optimization scheme, the topology optimization rules that vary with offshore distance and relative position are extracted based on simulation examples and sensitivity analysis. This is used for rapid screening of early schemes, investment calculation and comparison decision-making in subsequent optimization.

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