Offshore wind farm partition-based transmission system interconnection planning method and device

Abstract

The application discloses a kind of based on offshore wind power base partition sending-out system interconnection planning method and device, it is related to offshore wind power planning technical field, main purpose is to solve the problem that existing sending-out system planning method cannot meet the demand of wind power access to power grid transmission capacity.It mainly includes wind turbine clustering division in base interior according to the installed capacity information of each wind turbine in offshore wind power base and power consumption area information;Interconnection line between different wind turbine partitions is constructed, and the reliability parameters corresponding to different interconnection line combinations are determined;According to the reliability parameters, a double-layer model of sending-out system interconnection planning is constructed;The double-layer model of sending-out system interconnection planning is solved by using piecewise genetic algorithm combined with improved clustering algorithm, and the equipment planning information of medium-voltage booster station and offshore converter station respectively and the line planning information of wind turbine partition interconnection line are obtained.It is mainly used for planning the interconnection scheme of sending-out system based on offshore wind power base partition.

Description

TECHNICAL FIELD

[0001] The present application relates to the offshore wind power planning technical field, in particular to a kind of based on offshore wind power base zoning sending-out system interconnection planning method and device. BACKGROUND

[0002] In recent years, with the rapid development of new energy industry, offshore wind power base emerges as the times require, maintains the momentum of upward growth in the global range. Offshore wind power zoning planning presents multi-dimensional positive trend, offshore wind power zoning planning scheme develops continuously, has gradually formed from near sea to deep sea expansion, from single project to large-scale base construction transformation pattern. In addition, with the continuous maturity of offshore wind power technology, cost gradually reduces, economic advantage gradually highlights, and offshore wind turbine faces climate, environment and other problems can stable power supply, so that reliability increasingly highlights, in order to improve the economy and reliability of offshore wind power sending-out system, more fully utilize offshore wind power resources, industry and academia have proposed a variety of planning models about offshore wind power sending-out system.

[0003] In the field of offshore wind power development, with the continuous expansion of wind farm scale, the existing planning model cannot consider the planning of offshore wind power access to onshore power grid, and it is difficult to efficiently coordinate the spatial distribution of wind turbine, demand matching of power consumption area and economic and reliability of multi-zoning interconnection line, so as to meet the demand of wind power access to power transmission capacity. SUMMARY

[0004] Therefore, the present application provides a kind of based on offshore wind power base zoning sending-out system interconnection planning method and device, the main purpose is to solve the problem that existing sending-out system planning method cannot meet the demand of wind power access to power transmission capacity.

[0005] According to one aspect of the present application, a sending-out system interconnection planning method based on offshore wind power base zoning is provided, comprising:

[0006] According to the installed capacity information of each wind turbine in the offshore wind power base and the power consumption area information, the wind turbines in the base are clustered and divided to obtain a plurality of wind turbine zoning;

[0007] Interconnection lines between different wind turbine zoning are constructed to obtain a plurality of interconnection line combinations, and the reliability parameters corresponding to different interconnection line combinations are determined;

[0008] According to the wind turbine zoning, the plurality of interconnection line combinations and the reliability parameters corresponding to different interconnection line combinations, a double-layer model of sending-out system interconnection planning is constructed;

[0009] The segmented genetic algorithm is combined with the improved clustering algorithm to solve the double-layer model of the interconnection planning of the sending-out system, to obtain the device planning information of the medium-voltage booster station and the offshore converter station, and the line planning information of the wind turbine partition interconnection line.

[0010] Further, the installed capacity information includes coordinate information and device capacity, and the power consumption area information includes the power grid level to which the power consumption area belongs and the wind power consumption amount, the wind turbine partition is obtained by clustering and dividing the wind turbines in the offshore wind power base according to the installed capacity information of each wind turbine and the power consumption area information, and includes:

[0011] The distance between the wind turbines is determined according to the coordinate information of each wind turbine, and the density peak clustering algorithm is used to cluster in the distance dimension to obtain a plurality of initial wind turbine partitions;

[0012] The total capacity of each initial wind turbine partition is calculated according to the device capacity of the wind turbines in the partition;

[0013] The initial wind turbine partitions are adjusted by taking the total capacity of each initial wind turbine partition as the optimization target and taking the power grid level to which the different power consumption areas belong and the wind power consumption amount as the constraint condition, to obtain a plurality of wind turbine partitions.

[0014] Further, the reliability parameters include the failure opportunity cost, the annual outage hours and the system availability, and for each interconnection line combination, the reliability parameters corresponding to the interconnection line combination are determined, including:

[0015] The wind curtailment occurrence probability of the interconnection line combination is calculated according to the maximum transmission capacity of the cable and the global wind power base output value;

[0016] The topological equivalent outage probability is calculated according to the wind curtailment occurrence probability, the cable failure probability, the line failure probability and the offshore wind farm failure probability, the annual power generation loss expectation value is calculated according to the topological equivalent outage probability and the offshore wind farm active power output value, and the annual failure opportunity cost is calculated according to the annual power generation loss expectation value and the on-grid electricity price;

[0017] The annual outage hours of the sending-out system expected to be planned according to the interconnection line combination are calculated according to the annual power generation loss expectation value and the total capacity of the global wind power base;

[0018] The system availability of the sending-out system is calculated according to the annual outage hours.

[0019] Further, the double-layer model of the interconnection planning of the sending-out system is constructed according to the wind turbine partition, the plurality of interconnection line combinations and the reliability parameters corresponding to the different interconnection line combinations, including:

[0020] constructing a medium-voltage booster station and offshore converter station planning model according to the wind turbine partition, taking the total cost of equipment as an objective function, and taking equipment capacity constraints as constraint conditions;

[0021] constructing a wind turbine partition interconnection line planning model according to a plurality of said interconnection line combinations and the reliability parameters corresponding to different interconnection line combinations, taking the comprehensive cost as an objective function, and taking the power transmission constraints as constraint conditions;

[0022] constructing a sending-out system interconnection planning double-layer model with the medium-voltage booster station and offshore converter station planning model as an upper model and the wind turbine partition interconnection line planning model as a lower model.

[0023] Further, the equipment capacity constraints include at least one of medium-voltage booster station capacity constraints, converter station capacity constraints, distribution decision variable constraints, and booster station and converter station number constraints;

[0024] The power transmission constraints include at least one of DC submarine cable capacity constraints, submarine cable load flow constraints, onshore converter station capacity constraints, submarine cable crossing constraints, and voltage level constraints.

[0025] Further, the sending-out system interconnection planning double-layer model is solved by using a segmented genetic algorithm combined with an improved clustering algorithm to obtain device planning information of the medium-voltage booster station and offshore converter station respectively, and line planning information of the wind turbine partition interconnection line, including:

[0026] For each wind turbine partition, initial information of the corresponding medium-voltage booster station and offshore converter station is determined according to the clustering results of each wind turbine point in the wind turbine partition, wherein the initial information includes initial position, initial number, and capacity;

[0027] The initial information is substituted into the upper model of the sending-out system interconnection planning double-layer model, and the segmented genetic algorithm is used to optimize the upper model to obtain updated device planning information of the medium-voltage booster station and offshore converter station respectively;

[0028] Based on the updated device planning information, the lower model of the upper model of the sending-out system interconnection planning double-layer model is optimized by using a CPLEX solver, and the objective function of the lower model is fed back to the fitness function of the upper model;

[0029] The optimization processes of the upper model and the lower model are iteratively performed until the optimization objectives of the upper model and the lower model are met, device planning information is extracted from the upper model, and line planning information is extracted from the lower model, wherein the device planning information comprises site selection, device quantity and device capacity of the medium-voltage booster station and the offshore converter station respectively, and the line planning information comprises cable selection, cable data and line path distribution.

[0030] Further, the initial information of the corresponding medium-voltage booster station and offshore converter station is determined according to the clustering results of each wind turbine point in the wind turbine subarea, including:

[0031] The local density and relative distance of each wind turbine point in the wind turbine subarea are calculated by using a clustering algorithm based on density peaks, the wind turbine points satisfying the local density condition and the relative distance condition are selected as initial clustering centers, the remaining wind turbine points are distributed to the nearest high-density point cluster in descending order of local density, the medium-voltage booster station capacity constraint is dynamically checked, until all wind turbine point distribution is completed, and the initial information of the medium-voltage booster station is determined;

[0032] Based on the initial information of the medium-voltage booster station, the offshore converter station planning is optimized by using an improved K-means clustering algorithm, the minimum cost is taken as the target, and the capacity is dynamically limited, after traversing the number of clusters, a set of planning parameters with the lowest total cost is selected as the initial information of the offshore converter station.

[0033] According to another aspect of the present application, a device for interconnection planning of a sending-out system based on subarea division of an offshore wind power base is provided, comprising:

[0034] The subarea division module is configured to perform clustering and division of wind turbine generators in the base according to the installed capacity information of each wind turbine generator in the offshore wind power base and the power consumption area information, to obtain a plurality of wind turbine generator subareas.

[0035] The line construction module is configured to construct interconnection lines between different wind turbine generator subareas, to obtain a plurality of interconnection line combinations, and to determine reliability parameters corresponding to different interconnection line combinations.

[0036] The model construction module is configured to construct a double-layer model for interconnection planning of the sending-out system according to the wind turbine generator subareas, the plurality of interconnection line combinations and the reliability parameters corresponding to different interconnection line combinations.

[0037] The model solution module is configured to solve the double-layer model for interconnection planning of the sending-out system by using a piecewise genetic algorithm and an improved clustering algorithm, to obtain device planning information of the medium-voltage booster station and the offshore converter station respectively, and line planning information of the interconnection lines of the wind turbine generator subareas.

[0038] According to another aspect of the present application, a storage medium is provided, in which at least one executable instruction is stored, which makes a processor execute operations corresponding to the offshore wind farm partition-based transmission system interconnection planning method described above.

[0039] According to still another aspect of the present application, a terminal is provided, which comprises a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface being in communication with each other through the communication bus.

[0040] The memory is configured to store at least one executable instruction, which makes the processor execute operations corresponding to the offshore wind farm partition-based transmission system interconnection planning method described above.

[0041] By means of the technical solutions described above, the technical solutions provided by the embodiments of the present application have at least the following advantages:

[0042] The present application provides an offshore wind farm partition-based transmission system interconnection planning method and device, and embodiments of the present application obtain multiple wind turbine partitioning by clustering and dividing wind turbines within a wind farm according to the installed capacity information of each wind turbine in the offshore wind farm and the power consumption area information; construct interconnection lines between different wind turbine partitions to obtain multiple interconnection line combinations, and determine the reliability parameters corresponding to different interconnection line combinations; construct a transmission system interconnection planning double-layer model according to the wind turbine partition, the multiple interconnection line combinations and the reliability parameters corresponding to different interconnection line combinations; solve the transmission system interconnection planning double-layer model by using a piecewise genetic algorithm combined with an improved clustering algorithm to obtain device planning information of the medium-voltage booster station and the offshore converter station, and line planning information of the wind turbine partition interconnection line. By constructing a double-layer optimization model and integrating intelligent algorithms, the present application realizes accurate planning of the offshore wind farm transmission system, significantly improves the grid interconnection reliability and new energy consumption capacity, reduces the life cycle investment cost and network loss, improves the economic efficiency and reliability of the planning scheme, and thus meets the demand of wind power integration for power transmission capacity.

[0043] The above description is only a summary of the technical solutions of the present application, in order to enable one skilled in the art to better understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments with reference made to the accompanying drawings. The drawings are for purposes of illustration only and are not intended to limit the present application thereto. The same reference numerals in different drawings denote the same or similar components. In the drawings:

[0045] Figure 1 A flow chart of a method for offshore wind power base partition-based transmission system interconnection planning is shown.

[0046] Figure 2 A combination diagram of interconnection lines between offshore wind power bases is shown.

[0047] Figure 3 A schematic diagram of a sea cable cross situation is shown.

[0048] Figure 4 A schematic diagram of power grid level division is shown.

[0049] Figure 5 A block diagram of a device for offshore wind power base partition-based transmission system interconnection planning is shown.

[0050] Figure 6 A schematic diagram of a terminal structure is shown. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms without being limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be thoroughly and completely understood, and so that the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0052] The existing planning model cannot comprehensively consider the planning of offshore wind power access to the onshore power grid, and it is difficult to efficiently coordinate the spatial distribution of wind turbine generators, the demand matching of power consumption areas, and the economic efficiency and reliability of multi-partition interconnection lines, so as to meet the problem that the transmission capacity demand of the power grid cannot meet the wind power access. The embodiments of the present application provide a method for offshore wind power base partition-based transmission system interconnection planning, as shown in Figure 1 The method comprises the following steps.

[0053] 101. According to the installed capacity information of each wind turbine generator in the offshore wind power base and the power consumption area information, the wind turbine generators in the base are clustered and divided to obtain a plurality of wind turbine generator partitions.

[0054] In the embodiment of the present application, the offshore wind power transmission system includes multiple offshore wind power bases, each of which is distributed with multiple wind turbines, and one offshore wind power system can supply power to multiple power consumption areas on land. To achieve more efficient power transmission and resource allocation, the wind turbines in each offshore wind power base are clustered and divided, and the installed information of each wind turbine in the offshore wind power base includes the rated power, single machine capacity, blade size, tower height and other key parameters of the wind turbine, which can fully reflect the power generation capacity and characteristics of the wind turbine. At the same time, combined with the power consumption area information, including the geographic location of the power consumption area, the power consumption load size, the power consumption time period distribution and the power consumption stability requirement, etc. Based on the above two types of information, a specific clustering algorithm or analysis model is used to cluster and divide the wind turbines within the base. Through this process, the wind turbines with similar geographic location, similar power generation characteristics and high power supply and demand matching degree with a specific power consumption area are classified into the same partition, so as to obtain multiple wind turbine partitions. And the corresponding relationship between different wind turbine partitions and their corresponding power consumption areas is clearly established, so that the power generated by each wind turbine partition can be more accurately supplied to the matching power consumption area, optimizing the power transmission path, reducing the transmission loss, and improving the overall operation efficiency and power supply reliability of the offshore wind power base.

[0055] 102. Interconnecting lines between different wind turbine partitions are constructed to obtain multiple interconnecting line combinations, and the reliability parameters corresponding to different interconnecting line combinations are determined.

[0056] In the embodiment of the present application, based on the different wind turbine partitions divided in the early stage, the geographic location, power generation capacity, power output characteristics of each partition and the overall layout planning of the offshore wind power base are comprehensively considered to construct the interconnecting lines between different wind turbine partitions. The design of these interconnecting lines needs to fully consider the offshore environmental factors, such as marine weather conditions, seawater corrosiveness, seabed geological conditions, etc., to ensure the feasibility and durability of the lines. Through the construction process, multiple different interconnecting line combination schemes can be obtained, each of which represents a specific power connection mode between the wind turbine partitions.

[0057] Further, for the obtained multiple interconnection line combinations, a reliability evaluation method is used to determine the reliability parameters corresponding to different interconnection line combinations. The determination of the reliability parameters involves multiple aspects, such as the availability rate of the line, i.e., the probability that the line can operate normally within a certain period of time, which is affected by factors such as line equipment failure rate, maintenance cycle, repair capacity, etc.; failure rate, which reflects the frequency of line failure within a certain period of time, and is closely related to the design quality, construction quality, and operating environment of the line; and reliability indicators of power transmission, such as continuity of power transmission, voltage stability, frequency stability, etc., which can be calculated by simulating the power transmission under different operating scenarios (such as different power generation output, different changes in power consumption load, etc.). Through the determination of these reliability parameters, the reliability level of each interconnection line combination in actual operation can be comprehensively evaluated, providing data support for subsequent selection of the optimal interconnection line combination and ensuring the stability of power supply of the offshore wind power base.

[0058] 103. According to the partitioning of the wind turbine, the multiple interconnection line combinations, and the reliability parameters corresponding to different interconnection line combinations, a double-layer model for interconnection planning of the sending-out system is constructed.

[0059] In the embodiment of the application, the double-layer model for interconnection planning of the sending-out system includes an upper layer model and a lower layer model. The upper layer model takes the total cost of the sending-out planning of the offshore wind power base partitioning as the objective function, which can include the cost of the medium-voltage booster station and the offshore converter station of each partition of the offshore wind power base, as well as the construction cost of the flexible direct-current transmission topology and the onshore converter station. The upper layer constraint condition is established from the medium-voltage booster station capacity constraint, the converter station capacity constraint, the distribution decision variable constraint, and the booster station and converter station quantity constraint. The lower layer model takes the annual comprehensive cost of the sending-out system planning as the objective function, which can include the annual initial investment cost, the annual operation and maintenance cost, the annual repair cost, the landing converter cost, the construction cost of the equipment required by the offshore sending-out system, and the annual failure opportunity cost in the reliability indicators. The lower layer constraint condition is established from the direct-current submarine cable capacity constraint, the submarine cable load flow constraint, the onshore converter station capacity constraint, the submarine cable crossing constraint, and the voltage level constraint.

[0060] By solving the upper layer model, a number of candidate schemes that perform better in the overall benefit of the system can be selected from the multiple interconnection line combinations. The lower layer model can accurately analyze the power supply reliability of each candidate scheme in the actual operation of each power consumption area based on the reliability parameters corresponding to different interconnection line combinations, and feed back the related results to the upper layer model. Through the construction of the double-layer model, the overall benefit of the system and the reliability of power supply can be considered in the process of system planning, so as to seek the optimal balance between the two, while meeting the needs of reliability and economics.

[0061] 104. Using a segmented genetic algorithm combined with an improved clustering algorithm, the two-layer model of the power transmission system interconnection planning is solved to obtain the equipment planning information of the medium-voltage booster station and the offshore converter station, as well as the line planning information of the wind turbine group regional interconnection lines.

[0062] In this embodiment of the invention, an improved clustering algorithm is used to optimize the wind turbine group zoning for a two-layer model of power transmission system interconnection planning. This improved clustering algorithm introduces spatial and electrical constraints on top of traditional clustering algorithms (such as K-means and hierarchical clustering). Spatial constraints consider factors such as the geographical distribution of wind turbine group zoning and marine usage restrictions to ensure the rationality of the spatial layout of the clustering results. Electrical constraints cover the power generation characteristics of each zone, voltage level matching requirements, and power transmission compatibility, making the clustered zones more electrically conducive to subsequent interconnection planning. Through the improved clustering algorithm, a more optimized wind turbine group zoning scheme can be obtained, laying the foundation for solving the two-layer model. Then, a segmented genetic algorithm is used to solve the two-layer model. During the algorithm iteration process, the segmented genetic algorithm and the improved clustering algorithm cooperate. The clustering algorithm provides the genetic algorithm with an initial population or local optimization direction, while the global search capability of the genetic algorithm helps to escape the local optima that the clustering algorithm may get stuck in. The two interact, continuously optimizing the model parameters. By combining the global optimization capability of the segmented genetic algorithm with the local refinement of the improved clustering algorithm, the complex optimization problem of the two-layer model of the outgoing system interconnection planning is effectively solved, thereby ensuring the scientificity and effectiveness of the planning.

[0063] In one embodiment of the present invention, for further explanation and limitation, the steps involve clustering and dividing the wind turbines within the offshore wind power base based on the installed capacity information of each wind turbine and the power consumption area information, resulting in multiple wind turbine group zones, including:

[0064] The distance between wind turbines is determined based on the coordinate information of each wind turbine, and the density peak clustering algorithm is used to cluster them from the distance dimension to obtain multiple initial wind turbine group partitions;

[0065] For each of the initial wind turbine group zones, the total capacity of each of the initial wind turbine group zones is calculated based on the equipment capacity of the wind turbines in the zone.

[0066] Using the total capacity of each initial wind turbine group as the optimization target, and the grid level and wind power absorption of different power consumption areas as constraints, the initial wind turbine group is divided and adjusted to obtain multiple wind turbine group zones.

[0067] The installed information includes coordinate information and equipment capacity of the wind turbine. The power consumption area information includes the power grid level to which the power consumption area belongs and the wind power consumption amount. In the embodiment of the present application, the density peak clustering algorithm is used for the partitioning of the wind turbines. The density clustering algorithm is different from the traditional distance-based clustering, and it does not depend on the pre-set cluster number. On the contrary, the algorithm finds those points with high density in the surrounding area and relatively far away from other high-density points as the clustering center. The density peak clustering algorithm itself is a clustering method based on density and distance, without an internal constraint mechanism. Therefore, in order to solve the partition capacity constraint problem, a constraint adjustment mechanism can be added in the clustering allocation process. The clustering process of the partitioning specifically includes:

[0068] 1) Set N w total number of wind turbines in the offshore wind power base, and the wind turbine set can be represented as W. The local density of the wind turbine point w i is calculated according to the following formula:

[0069]

[0070] where d ij represents the Euclidean distance between two wind turbines; d c represents the cut-off distance; when d ij -d c ≥ 0, χ(d ij -d c ) = 0, otherwise χ(d ij -d c ) = 1.

[0071] 2) For the wind turbine point with the maximum local density, its relative distance refers to the distance between the wind turbine points farthest from it; for other wind turbine points, the relative distance refers to the minimum value of the distances from the current wind turbine point to all wind turbine points with higher density. The relative distance of the wind turbine point w i is represented as:

[0072] where δ i represents the relative distance of the wind turbine point w FC .

[0073] 3) Select a preset number of wind turbine points with large local density and relative distance as the clustering center, and sequentially assign the remaining wind turbine points to the class of the nearest neighbor wind turbine point with higher local density according to the descending order of the local density, thereby obtaining the initial partition.

[0074] 4) For each initial partition, calculate the total capacity of the corresponding internal wind turbine and judge whether the partition capacity constraint is satisfied. If the total capacity of any initial partition exceeds the preset capacity constraint, adjust the clustering allocation scheme.

[0075] 5) For the initial partition of the excess capacity, the wind turbines in the partition are sorted according to the distance to other cluster centers, and the wind turbines close to other cluster centers are preferentially reassigned to adjacent partitions to reduce the excess capacity. The capacity check and partition adjustment are repeated until all the initial partitions meet the capacity constraint, and multiple wind turbine partitions are obtained.

[0076] In an embodiment of the present application, for further illustration and definition, for each interconnection line combination, the reliability parameter corresponding to the interconnection line combination is determined, including:

[0077] The wind curtailment occurrence probability of the interconnection line combination is calculated according to the maximum transmission capacity of the cable and the global wind power plant output value;

[0078] The topological equivalent outage probability is calculated according to the wind curtailment occurrence probability, the cable fault probability, the line fault probability and the offshore wind farm fault probability, the annual generation loss expectation value is calculated according to the topological equivalent outage probability and the offshore wind farm active power output value, and the annual failure opportunity cost is calculated according to the annual generation loss expectation value and the on-grid electricity price;

[0079] The annual outage hours of the transmission system expected to be planned according to the interconnection line combination are calculated according to the annual generation loss expectation value and the total capacity of the global wind power plant;

[0080] The system availability of the transmission system is calculated according to the annual outage hours.

[0081] In an embodiment of the present application, in order to realize maximum wind power consumption under normal conditions and reliable power supply by using replacement channels under fault conditions, the interconnection line combination between each partition and between each offshore wind power plant is proposed, and the reliability of the interconnection structure composed of the interconnection line combination is analyzed. The reliability parameters include the failure opportunity cost, the annual outage hours and the system availability.

[0082] The annual failure opportunity cost refers to the annual economic loss caused by the reduction of offshore wind power plant output power due to the failure of electrical elements such as converters and cables, resulting in the outage of the transmission system. In addition, when multiple offshore wind power plants transmit power through the same submarine DC cable, the total power is greater than the capacity of the submarine DC cable, causing wind curtailment and economic loss. The probability of such a situation is the wind curtailment occurrence probability. The annual failure opportunity cost C FC The calculation of the annual failure opportunity cost C ELPF The topological equivalent outage probability is the overall probability of the outage state caused by the change of the topological structure (such as the change of the system topology caused by equipment failure, line disconnection, etc.), that is, the sum of the probabilities of the system in different outage states.FC The calculation formula is represented as:

[0083]

[0084] Wherein, C FC is the annual failure opportunity cost, PR1 is the on-grid electricity price, E ELPF is the annual power generation loss expectation value; P AW is the probability of wind curtailment caused by capacity size (wind curtailment probability), if the system does not exist wind curtailment phenomenon, it can be considered that P AW is 1; P j L is the maximum transmission capacity of the jth cable, when the xth submarine cable fails, j≠x; P i FJ is the output of the ith offshore wind power base; p i-stop is the probability that the system is in the outage state i; p li is the probability that the ith submarine DC cable fails (cable failure probability); p HLn is the probability that the nth interconnection line fails (line failure probability); p m-FJ is the probability that the mth group of offshore wind farms fails (offshore wind farm failure probability); P ti is the active power output of the ith wind farm at time t.

[0085] The calculation formula of the outage hours T stop is represented as: Wherein, Q S is the total capacity of the system. The calculation formula of the system availability P use is represented as:

[0086] It should be noted that the offshore wind power base sending-out system shows different failure rates for each device, the failure probability is low, but once it occurs, it needs a long time to repair, which has a significant impact on the stable operation of the entire sending-out system. Providing more power transmission paths through interconnection lines is conducive to reliable transmission of wind power by the system.

[0087] In one application example, a combination of interconnection lines between offshore wind power bases is as shown in Figure 2If one of the submarine DC cables connected to the offshore converter fails and cannot transmit power, offshore wind farms 2, 4 and 6 can be connected together through interconnection lines Line 1, 2 and 3 and offshore wind farms 1, 3 and 4, and transmit power through the remaining normal submarine DC cables; Line 6 can make the wind power flowing into the 220KV and 500KV onshore power grid flow in the opposite direction to meet the demand and supply balance requirement, and in the case of failure, the power flowing into the 220KV or 500KV onshore power grid can be supplied to two levels of onshore power grid at the same time to ensure the power demand of important loads; Line 7 can ensure the power demand of the 500KV onshore power grid in the case of failure, and has the same effect as Line 1, 2 and 3. Figure 2 In the orange AC interconnection line, Line 4 has the same effect as Line 6, and Line 5 has the same effect as Line 7.

[0088] In an embodiment of the present application, in order to further illustrate and limit, the steps are to build a sending-out system interconnection planning double-layer model according to wind turbine partition, a plurality of interconnection line combinations and reliability parameters corresponding to different interconnection line combinations, including:

[0089] A medium-voltage booster station and offshore converter station planning model is constructed according to wind turbine partition, with total equipment cost as the objective function and equipment capacity constraints as the constraint condition;

[0090] A wind turbine partition interconnection line planning model is constructed according to a plurality of interconnection line combinations and reliability parameters corresponding to different interconnection line combinations, with comprehensive cost as the objective function and power transmission constraints as the constraint condition;

[0091] A sending-out system interconnection planning double-layer model is constructed with the medium-voltage booster station and offshore converter station planning model as the upper model and the wind turbine partition interconnection line planning model as the lower model.

[0092] In an embodiment of the present application, the upper model of the sending-out system interconnection planning double-layer model is the medium-voltage booster station and offshore converter station planning model, which takes the total equipment cost as the objective function, and the total equipment cost includes but is not limited to the cost of medium-voltage booster stations and offshore converter stations in each partition of the offshore wind farm, as well as the construction cost of the flexible DC transmission topology and the onshore converter station. The upper optimization objective function F1 can be represented as:

[0093]

[0094] Wherein, C1 is the total cost of the sending-out planning of the offshore wind farm partition; Na is the partition number; a is the partition serial number; is the investment cost of the equipment required for collecting offshore wind power in the a-th partition; This represents the investment cost of the equipment required for the path connecting the offshore converter station to the onshore converter station in the a-th partition.

[0095] Among them, the investment costs of the equipment required for offshore wind power are included. The calculation formula is expressed as:

[0096]

[0097] in, These represent the investment costs for the medium-voltage booster station, the offshore converter station, and the AC submarine cable required to connect the two within the a-th zone, respectively. For the nth ms The m-type medium-voltage booster station and the nth hs For each offshore converter station of model h, a 0-1 decision variable is used: if the value is 1, construction is initiated; otherwise, construction is not initiated. m and M represent the type index and number of types for medium-voltage booster stations, respectively; h and H represent the type index and number of types for offshore converter stations, respectively; Λ ms Λ hs These are candidate sets of existing models for medium-voltage step-up substations and offshore converter stations, respectively, to ensure standardized equipment selection, with each model corresponding to different voltage levels and capacities; N ms N hs These represent the planned number of medium-voltage booster stations and converter stations, respectively; c m c h The unit construction costs are respectively for the medium-voltage step-up substation of model m and the converter station of model h; Assigning decision variables, if the value is 1, it indicates that the nth... ms The intermediate-pressure booster station and the nth hs There are AC submarine cable connections of type mh between each converter station; if the value is 0, it means that there are no such connections. For the nth ms The nth intermediate-voltage booster station is connected to it. hs The required length of AC submarine cable between converter stations; c mh Price per unit length for AC submarine cable with model number mh.

[0098] The lower-level model of the two-layer model for the transmission system interconnection planning is the wind turbine group regional interconnection line planning model. This model uses comprehensive cost as the objective function. Comprehensive cost includes, but is not limited to, annual initial investment cost, annual operation and maintenance cost, annual repair cost, post-landing converter cost, construction cost of equipment required for the offshore transmission system, and annual opportunity cost of failure in the reliability index. The lower-level optimization objective function F2 can be expressed as:

[0099]

[0100] Among them, C ACC To deliver the overall annual cost of the system plan; CINV is the annual initial investment cost, which refers to the purchase and installation cost of the interconnection line, offshore converter station and submarine DC cable of the outfeed system, which is converted to each year according to the life cycle; C OM is the annual operation and maintenance cost, which refers to the cost required for inspection and maintenance of the submarine cable and converter station each year; C FC is the annual failure opportunity cost; C LOSS is the annual line loss cost, which refers to the economic loss caused by the conductor power loss and dielectric loss of the submarine cable during the operation each year; is the construction cost of the DC submarine cable required for the offshore wind power outfeed of the a-th subarea; is the construction cost of the land converter station required for the offshore wind power outfeed of the a-th subarea.

[0101] The calculation formula of the corresponding index is:

[0102]

[0103] wherein Nc is the number of submarine cable sections; P i is the unit length price of the submarine cable; (x i1 , x i2 ) and (y i1 , y i2 ) are the coordinates of the two ends of the i-th submarine cable; P j sea-con is the purchase and installation price of the j-th offshore converter station; is the operation and maintenance cost per unit length of the i-th submarine cable; C CS-OAM is the operation and maintenance cost of the converter station; K SC is the submarine cable loss coefficient; I i is the working current of the submarine cable; Ri is the resistance per unit length of the i-th submarine cable; L i is the length of the i-th submarine cable; t is the annual power generation time of the wind turbine (converted to the rated capacity); Λ cable is the candidate set of DC submarine cable models corresponding to each rated voltage grade and transmission capacity; Λ dcl is the candidate set of land converter station models; Λ o is the set of alternative landing points; N o is the total number of alternative landing points; N ls is the total number of alternative land converter stations; G, g are respectively the number of types of DC submarine cable and the type index; is the 0-1 decision variable of the DC submarine cable of type g between the offshore converter station n hs and the land landing point o; is the number of DC submarine cables; is the 0-1 decision variable of the n ls -th land converter station of type d; is the offshore converter station nhs Distance to the landing point o, i.e. the length of the DC submarine cable; c g Cost per unit distance of the DC submarine cable of type g; c d Cost of the onshore converter station of type d.

[0104] In one embodiment of the present application, for further illustration and limitation, the equipment capacity constraints include at least one of the medium voltage booster station capacity constraints, the converter station capacity constraints, the allocation decision variable constraints, the booster station and converter station number constraints;

[0105] The power transmission constraints include at least one of the DC submarine cable capacity constraints, the submarine cable current carrying capacity constraints, the onshore converter station capacity constraints, the transmission submarine cable crossing constraints, the voltage level constraints.

[0106] In one embodiment of the present application, the medium voltage booster station capacity constraints are used to constrain the capacity of each medium voltage booster station must be able to receive the total power of all wind turbines connected thereto, i.e. the capacity of the medium voltage booster station should be no less than the sum of the capacities of the wind turbines connected thereto, expressed as: Wherein, is the capacity of the nth medium voltage booster station of type m; ms is the capacity of the wth wind turbine in the sub-zone; Λ wm is the set of offshore wind turbines connected to the nth medium voltage booster station. ms

[0107] The converter station capacity constraints are used to constrain the capacity of the converter station must be sufficient to receive the output power of all the medium voltage booster stations connected thereto, i.e. the capacity of the converter station should be no less than the sum of the capacities of the medium voltage booster stations connected thereto, expressed as: Wherein, is the capacity of the nth converter station of type h; hs is the capacity of the nth medium voltage booster station; Λ mh mh is the set of medium voltage booster stations connected to the nth converter station. hs

[0108] The allocation decision variable constraints are used to constrain each medium voltage booster station can only be connected to one converter station. The booster station and converter station number constraints are used to constrain the number of medium voltage booster stations and converter stations should be between the upper and lower limits. The upper and lower limits of the number depend on the minimum and maximum capacity of the equipment type, respectively. If all equipment selects the minimum capacity type, the number of built reaches the upper limit; the lower limit can be obtained in the same way. Therefore, the allocation decision variable constraints are expressed as:

[0109] Wherein, is the upper and lower limits of the number of medium voltage booster stations, respectively;​​​​​ These represent the upper and lower limits for the number of offshore converter stations.

[0110] The DC submarine cable capacity constraint is used to ensure that the transmission capacity of the DC submarine cable is not less than the capacity of the connected offshore converter station, and is expressed as:

[0111] in, For the nth hs The capacity of a marine converter station of model h; The capacity of the selected DC submarine cable model; Λ mhs For the nth hs A collection of DC submarine cables connecting several offshore converter stations.

[0112] Submarine cable current carrying capacity constraints are used to limit the current carrying capacity of submarine cables, ensuring that the maximum current carrying capacity of a submarine cable segment is not less than [amount missing]. The total capacity of the j-th wind turbine connected to the i-th submarine cable. The current-carrying capacity constraint of the submarine cable can be expressed as:

[0113] Among them, I sc N represents the current carrying capacity of the submarine cable. i P represents the number of wind turbines connected to the i-th submarine cable; wt The rated output of a single wind turbine; U c δ is the rated voltage of the submarine cable; cosδ is the power factor.

[0114] The capacity constraint for onshore converter stations is used to ensure that the capacity of an onshore converter station is not less than the total capacity of the offshore converter stations connected to it, and is expressed as:

[0115] in, For the nth lh The capacity of a land-based converter station of model d; The capacity of the selected offshore converter station model; Λ lh For the nth lh A collection of onshore converter stations connected to offshore converter stations.

[0116] To address the installation and practical engineering requirements of submarine power transmission cables, crossings between them are prohibited. Cable crossings can be categorized as follows: Figure 3 The three scenarios are shown. The node coordinates are defined as points A(a1,a2), B(b1,b2), C(c1,c2), and D(d1,d2). These node coordinates are used to determine whether submarine cables cross. The crossing constraint of the power transmission submarine cables is represented as follows:

[0117]

[0118] The voltage level constraint is used to constrain the voltage levels of the offshore converter station, the DC sea cable and the land converter station to be consistent when selecting the type. The voltage level here is based on the voltage level of the land power grid, which divides the land power grid into n levels. As shown in Figure 4 the figure is a schematic diagram of the division of a power grid level. In the design of the two-level model of the interconnection planning of the sending-out system based on the zoning of the offshore wind power base, the 220kV network frame in the coastal area is set as the first level, and the 500kV network frame in the whole province is set as the second level.

[0119] In an embodiment of the present application, in order to further illustrate and limit, the step uses a segmented genetic algorithm combined with an improved clustering algorithm to solve the two-level model of the interconnection planning of the sending-out system, to obtain the device planning information of the medium-voltage booster station and the offshore converter station respectively, and the line planning information of the interconnection line of the wind turbine unit zoning, including:

[0120] For each wind turbine unit zoning, according to the clustering results of each wind turbine point in the wind turbine unit zoning, the initial information of the corresponding medium-voltage booster station and offshore converter station is determined;

[0121] The initial information is substituted into the upper model of the two-level model of the interconnection planning of the sending-out system, and the segmented genetic algorithm is used to optimize the upper model to obtain the updated device planning information of the medium-voltage booster station and the offshore converter station respectively;

[0122] Based on the updated device planning information, the lower model of the upper model of the two-level model of the interconnection planning of the sending-out system is optimized by using the CPLEX solver, and the objective function of the lower model is fed back to the fitness function of the upper model;

[0123] The optimization process of the upper model and the lower model is iteratively executed until the optimization objectives of the upper model and the lower model are met, the device planning information is extracted from the upper model, and the line planning information is extracted from the lower model.

[0124] In the embodiment of the present application, the initial information includes initial positions, initial quantities and capacities, that is, the initial positions, initial quantities and initial capacities of the medium-voltage booster stations and the initial positions, initial quantities and initial capacities of the offshore converter stations are determined according to the clustering results of the wind turbine points in each wind turbine group partition. In the process of optimizing the upper model by using the segmented genetic algorithm, the initial population is constructed based on the initial information. In the segmented genetic algorithm, the chromosomes are logically divided into several subsegments according to the partition number, and each subsegment includes the gene coding of the number and selection of the medium-voltage booster stations and the offshore converter stations in the partition. When performing crossover and mutation, the point crossover and point mutation are performed on the two parent chromosome subsegments to optimize the position, quantity and capacity of the medium-voltage booster stations and the offshore converter stations. The lower model is a mixed integer nonlinear programming model. Based on the optimization result of the upper model, the CPLEX solver is used to optimize the cable selection and quantity in the lower model. After completing the optimization of the lower model once, the objective function of the lower model is fed back to the fitness function of the upper model, and the number and capacity of the medium-voltage booster stations and the offshore converter stations are optimized by using the genetic algorithm, and finally the planning scheme of the interconnection optimization model of the sending-out system based on the partition of the offshore wind power base is obtained through iteration. The fitness function is the optimization function in the segmented genetic algorithm. Through the information interaction and parameter optimization of the upper model and the lower model, the collaborative optimization of the equipment and line of the sending-out system is realized through multiple iterations, so as to balance the investment cost and operation efficiency, and ensure the reliability and economy of the sending-out system. The equipment planning information includes the site selection, equipment quantity and equipment capacity of the medium-voltage booster stations and the offshore converter stations, and the line planning information includes the cable selection, cable data and line path distribution

[0125] In one embodiment of the present application, in order to further illustrate and limit, the step determines the initial information of the corresponding medium-voltage booster station and offshore converter station according to the clustering results of the wind turbine points in each wind turbine group partition, including:

[0126] The local density and relative distance of each wind turbine point in the wind turbine group partition are calculated by using the density peak-based clustering algorithm, the wind turbine points satisfying the local density condition and the relative distance condition are selected as the initial clustering centers, the remaining wind turbine points are distributed to the nearest high-density point cluster in descending order of local density, the medium-voltage booster station capacity constraint is dynamically checked, and the initial information of the medium-voltage booster station is determined until the distribution of all wind turbine points is completed;

[0127] Based on the initial information of the medium-voltage booster station, the improved K-means clustering algorithm is used to optimize the offshore converter station planning, the minimum cost is taken as the target and the capacity is dynamically limited, and after traversing the clustering number, the set of planning parameters with the lowest total cost is selected as the initial information of the offshore converter station.

[0128] In the embodiment of the application, the fan in each partition is taken as a clustering sample, the local density and relative distance of each fan point in the partition are calculated by using the density peak clustering algorithm, and the fan points are sorted in descending order of local density The change value of adjacent local density is calculated as Δρ = ρ i -ρ i+1 , and the average value of the change value is A decision graph is constructed with the local density as the horizontal axis and the relative distance as the vertical axis. If the density change value of adjacent two points is , then the decision graph ρ M The data points on the left of the point are considered to be noise points, and the data points on the right are considered to be noise-free points. In the presence of noise points, in order to avoid selecting noise points as clustering centers, the algorithm selects the fan points that satisfy the conditions of local density being greater than the average local density and the relative distance being greater than the average relative distance as the clustering centers. In the absence of noise points, the fan points with the relative distance greater than the average relative distance are selected as the clustering centers for clustering. In the clustering process, the fan points are sequentially assigned to the class in which the nearest high-density point is located according to the descending order of local density, forming a clustering cluster. In order to ensure that the capacity of the medium-voltage booster station does not exceed the upper limit, the capacity of the current clustering cluster is dynamically checked during the assignment process. If the assignment leads to the capacity exceeding the limit, the next shortest-distance clustering center is selected. After the assignment is completed, the center position of each clustering cluster is updated, and the above steps are repeated until all fan points are assigned. Finally, the clustering center points are the initial positions of the medium-voltage booster stations, and the number and total capacity of the clustering clusters are the initial number and initial capacity of the medium-voltage booster stations, respectively.

[0129] After obtaining the initial information such as the initial number, initial capacity and initial position of the medium-voltage booster stations, an improved K-means algorithm is used to optimize the initial position, initial number and initial capacity of the converter stations. Specifically, the reasonable range of the number of clusters is determined according to the upper and lower limits of the capacity of the offshore converter stations Randomly select medium-voltage booster stations as initial clustering centers, and calculate the connection cost C(x i , c j ) = d(x i , c j )c i for each medium-voltage booster station x j and each clustering center c ij . Check the capacity constraint of the offshore converter station, and if the connection cost C(x iThe cluster center with the minimum connection cable cost and the capacity not exceeding the limit is allocated. Then, the position of each cluster center is recalculated according to the current allocation result. The above steps are repeated until the position of the cluster center converges. All possible values in the range of the number of clusters are traversed, and the corresponding planning cost is calculated for each number of clusters, including the converter station construction cost and the AC sea cable connection cost. The number of clusters with the minimum cost is selected as the initial value of the number of offshore converter stations, and the corresponding cluster center is the initial value of the offshore converter station position, and the capacity allocation in the cluster range is the initial capacity of the offshore converter station.

[0130] It should be noted that, in the case of unknown number of offshore converter stations, the optimal number of clusters is determined by calculating the planning cost. In the clustering process, the capacities of the various medium-voltage booster stations are different, and the AC sea cable connection cost between the medium-voltage booster stations and the offshore converter stations is also different. Moreover, the capacity of the offshore converter station has an upper limit, that is, the capacity of the cluster center needs to be limited. Based on the above conditions, the improved K-means algorithm is used to change the clustering target from the minimum distance of each point to the cluster center to the minimum cost of each point connecting the cluster center, and dynamically check the capacity constraint of the offshore converter station during the clustering process to ensure that the capacity upper limit is not exceeded, so that the capacity allocation and cost optimization are more reasonable, and the number, capacity and position of the offshore converter station are more in line with the actual engineering requirements.

[0131] The application provides a sending-out system interconnection planning method based on offshore wind power base partitioning. The application further provides a sending-out system interconnection planning device based on offshore wind power base partitioning. The device comprises a clustering module, a line planning module and a device planning module. The clustering module is configured to perform clustering division on wind power generators in the offshore wind power base according to the installation information of the wind power generators and the power consumption region information to obtain a plurality of wind power generator partitions. The line planning module is configured to construct interconnection lines between different wind power generator partitions to obtain a plurality of interconnection line combinations and determine reliability parameters corresponding to the different interconnection line combinations. The device planning module is configured to construct a sending-out system interconnection planning double-layer model according to the wind power generator partitions, the plurality of interconnection line combinations and the reliability parameters corresponding to the different interconnection line combinations. The device planning module is further configured to solve the sending-out system interconnection planning double-layer model by using a segmented genetic algorithm combined with an improved clustering algorithm to obtain device planning information of medium-voltage booster stations and offshore converter stations and line planning information of the interconnection lines of the wind power generator partitions. The application can realize accurate planning of the sending-out system of the offshore wind power base by constructing a double-layer optimization model and fusing an intelligent algorithm, significantly improve the grid interconnection reliability and new energy consumption capacity, reduce the life cycle investment cost and network loss, improve the economy and reliability of the planning scheme, and meet the demand of wind power access for the transmission capacity of the power grid.

[0132] Further, as an implementation of the method shown in the above Figure 1 The application further provides a sending-out system interconnection planning device based on offshore wind power base partitioning. Figure 5 The device comprises a clustering module, a line planning module and a device planning module.

[0133] The partition division module 31 is configured to perform wind turbine cluster division in the offshore wind power base according to the installed capacity information of each wind turbine in the offshore wind power base and the power consumption area information, to obtain a plurality of wind turbine partitions;

[0134] The line construction module 32 is configured to construct interconnected lines between different wind turbine partitions, to obtain a plurality of interconnected line combinations, and to determine reliability parameters corresponding to different interconnected line combinations;

[0135] The model construction module 33 is configured to construct a sending system interconnection planning bi-level model according to the wind turbine partitions, the plurality of interconnected line combinations, and the reliability parameters corresponding to different interconnected line combinations.

[0136] The model solution module 34 is configured to solve the sending system interconnection planning bi-level model by using a piecewise genetic algorithm and an improved clustering algorithm, to obtain device planning information of the medium-voltage booster station and the offshore converter station respectively, and line planning information of the wind turbine partition interconnected lines.

[0137] Further, the partition division module 31 comprises:

[0138] The clustering unit is configured to determine the distance between wind turbines according to the coordinate information of each wind turbine, and to perform clustering from the distance dimension by using a density peak clustering algorithm, to obtain a plurality of initial wind turbine partitions.

[0139] The first calculation unit is configured to calculate the total capacity of each initial wind turbine partition according to the device capacity of the wind turbines in the partition for each initial wind turbine partition.

[0140] The partition unit is configured to adjust the initial wind turbine partitions according to the total capacity of each initial wind turbine partition as the optimization target, and the power grid level and wind power consumption of different power consumption areas as the constraint conditions, to obtain a plurality of wind turbine partitions.

[0141] Further, the line construction module 32 comprises:

[0142] The second calculation unit is configured to calculate the wind curtailment occurrence probability of the interconnected line combination according to the maximum transmission capacity of the cable and the global wind power base output value.

[0143] The third calculation unit is configured to calculate the topological equivalent outage probability according to the wind curtailment occurrence probability, the cable fault probability, the line fault probability, and the offshore wind farm fault probability, to calculate the annual power generation loss expectation value according to the topological equivalent outage probability and the offshore wind farm active power output value, and to calculate the annual fault opportunity cost according to the annual power generation loss expectation value and the on-grid electricity price.

[0144] a fourth calculation unit configured to calculate, according to the annual loss of generation capacity expectation value and the total capacity of the global wind power base, an annual outage hour number of the sending system expected to be planned according to the combination of the interconnection lines;

[0145] a fifth calculation unit configured to calculate, according to the annual outage hour number, a system availability of the sending system.

[0146] Further, the model construction module 33 comprises:

[0147] a first construction unit configured to construct, according to the wind turbine subarea, a medium-voltage booster station and offshore converter station planning model with a total equipment cost as a target function and equipment capacity constraints as constraint conditions;

[0148] a second construction unit configured to construct, according to a plurality of the combination of the interconnection lines and the reliability parameters corresponding to different combinations of the interconnection lines, a wind turbine subarea interconnection line planning model with a comprehensive cost as a target function and power transmission constraints as constraint conditions;

[0149] a third construction unit configured to construct a sending system interconnection planning two-layer model with the medium-voltage booster station and offshore converter station planning model as an upper-layer model and the wind turbine subarea interconnection line planning model as a lower-layer model.

[0150] Further, in a specific application scenario, the equipment capacity constraints in the first construction unit comprise at least one of a medium-voltage booster station capacity constraint, a converter station capacity constraint, a distribution decision variable constraint, and a booster station and converter station number constraint;

[0151] The power transmission constraints in the second construction unit comprise at least one of a DC submarine cable capacity constraint, a submarine cable current carrying capacity constraint, an onshore converter station capacity constraint, a submarine cable crossing constraint, and a voltage level constraint.

[0152] Further, the model solution module 34 comprises:

[0153] a determination unit configured to determine, for each wind turbine subarea, initial information of a corresponding medium-voltage booster station and offshore converter station according to the clustering results of each wind turbine point in the wind turbine subarea, wherein the initial information comprises an initial position, an initial number, and a capacity;

[0154] a first optimization unit configured to substitute the initial information into an upper-layer model of the sending system interconnection planning two-layer model and optimize the upper-layer model by using a piecewise genetic algorithm to obtain updated equipment planning information of the medium-voltage booster station and the offshore converter station respectively;

[0155] a second optimization unit configured to optimize, based on the updated device planning information, a lower layer model of an upper layer model of the transmission system interconnection planning bi-level model using a CPLEX solver, and feed a target function of the lower layer model to a fitness function of the upper layer model;

[0156] an iterative optimization unit configured to iteratively perform the optimization process of the upper layer model and the lower layer model until the optimization objectives of the upper layer model and the lower layer model are both satisfied, extract device planning information from the upper layer model, and extract line planning information from the lower layer model, wherein the device planning information comprises site selection, device quantity and device capacity of the medium-voltage booster station and the offshore converter station respectively, and the line planning information comprises cable selection, cable data and line path distribution.

[0157] Further, in a specific application scenario, the determination unit is specifically configured to calculate local density and relative distance of each wind turbine point in the wind turbine group partition using a density peak-based clustering algorithm, select wind turbine points satisfying local density conditions and relative distance conditions as initial clustering centers, distribute the remaining wind turbine points to the nearest high-density point in the clustering cluster in descending order of local density, dynamically check the medium-voltage booster station capacity constraint, until all wind turbine point distribution is completed, and determine the initial information of the medium-voltage booster station;

[0158] Based on the initial information of the medium-voltage booster station, the offshore converter station planning is optimized using an improved K-means clustering algorithm, with the minimum cost as the target and dynamic capacity limitation, and after traversing the number of clusters, a set of planning parameters with the lowest total cost is selected as the initial information of the offshore converter station.

[0159] The application provides a transmission system interconnection planning device based on offshore wind power base partitioning. The device performs wind turbine group clustering and partitioning in a wind power base according to the installed capacity information of each wind turbine group in the offshore wind power base and the power consumption area information, obtains a plurality of wind turbine group partitions, constructs interconnection lines between different wind turbine group partitions, obtains a plurality of interconnection line combinations, and determines the reliability parameters corresponding to different interconnection line combinations. A transmission system interconnection planning bi-level model is constructed according to the wind turbine group partitions, the plurality of interconnection line combinations, and the reliability parameters corresponding to different interconnection line combinations. A segmented genetic algorithm combined with an improved clustering algorithm is used to solve the transmission system interconnection planning bi-level model, and device planning information of the medium-voltage booster station and the offshore converter station and line planning information of the interconnection lines of the wind turbine group partitions are obtained. The device realizes accurate planning of the transmission system of the offshore wind power base by constructing a bi-level optimization model and integrating intelligent algorithms, significantly improves the reliability of power grid interconnection and the new energy consumption capacity, reduces the life cycle investment cost and network loss, improves the economy and reliability of the planning scheme, and meets the demand of wind power integration for power transmission capacity.

[0160] According to an embodiment of the present application, a storage medium is provided, which stores at least one executable instruction, and the computer executable instruction is used to execute the offshore wind farm partition-based transmission system interconnection planning method in any of the above method embodiments.

[0161] Figure 6 A structure diagram of a terminal according to an embodiment of the present application is shown, and the specific embodiments of the present application do not limit the specific implementation of the terminal.

[0162] As shown in Figure 6 , the terminal can include a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0163] The processor 402, the communications interface 404, and the memory 406 can communicate with each other through the communications bus 408.

[0164] The communications interface 404 is configured to perform network communication with other devices such as a client or other servers.

[0165] The processor 402 is configured to execute the program 410, and specifically can execute the related steps in the above offshore wind farm partition-based transmission system interconnection planning method embodiments.

[0166] Specifically, the program 410 can include program code, and the program code includes computer operation instructions.

[0167] The processor 402 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits planned to implement the embodiments of the present application. The one or more processors included in the terminal can be the same type of processor, such as one or more CPUs; or can be different types of processors, such as one or more CPUs and one or more ASICs.

[0168] The memory 406 is configured to store the program 410. The memory 406 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0169] The program 410 can be specifically used to cause the processor 402 to perform the following operations:

[0170] According to the installed information of each wind turbine generator in the offshore wind power base and the power consumption area information, wind turbine generators in the base are clustered and divided into multiple wind turbine generator partitions;

[0171] Interconnection lines between different wind turbine generator partitions are constructed to obtain multiple interconnection line combinations, and reliability parameters corresponding to different interconnection line combinations are determined;

[0172] According to the wind turbine generator partitions, the multiple interconnection line combinations and the reliability parameters corresponding to different interconnection line combinations, a double-layer model of the sending-out system interconnection planning is constructed;

[0173] The double-layer model of the sending-out system interconnection planning is solved by using a piecewise genetic algorithm combined with an improved clustering algorithm to obtain device planning information of the medium-voltage booster station and the offshore converter station respectively and line planning information of the wind turbine generator partition interconnection lines.

[0174] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be realized by a general computing device, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described can be executed in an order different from that shown here, or they can be manufactured into individual integrated circuit modules, or multiple modules or steps thereof can be manufactured into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.

[0175] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for planning the interconnection of power transmission systems based on the zoning of offshore wind power bases, characterized in that, include: Based on the installed capacity information of each wind turbine in the offshore wind power base and the power consumption area information, the wind turbines within the base are clustered and divided into multiple wind turbine groups. Construct interconnection lines between different wind turbine groups to obtain multiple interconnection line combinations, and determine the reliability parameters corresponding to different interconnection line combinations; Based on the wind turbine group zoning, multiple interconnection line combinations, and the reliability parameters corresponding to different interconnection line combinations, a two-layer model for power transmission system interconnection planning is constructed. This includes: constructing a planning model for medium-voltage booster stations and offshore converter stations based on wind turbine group zoning, with total equipment cost as the objective function and equipment capacity constraints as the constraint condition; constructing a wind turbine group zoning interconnection line planning model based on multiple interconnection line combinations and the reliability parameters corresponding to different interconnection line combinations, with comprehensive cost as the objective function and power transmission constraints as the constraint condition; and constructing a two-layer model for power transmission system interconnection planning, using the medium-voltage booster station and offshore converter station planning model as the upper-layer model and the wind turbine group zoning interconnection line planning model as the lower-layer model. The two-layer model of the power transmission system interconnection planning is solved by using a segmented genetic algorithm combined with an improved clustering algorithm to obtain the equipment planning information of the medium-voltage booster station and the offshore converter station, as well as the route planning information of the wind turbine group regional interconnection lines.

2. The method according to claim 1, characterized in that, The installed capacity information includes coordinate information and equipment capacity. The power consumption area information includes the power grid level and wind power absorption capacity of the power consumption area. Based on the installed capacity information of each wind turbine in the offshore wind power base and the power consumption area information, the wind turbines within the base are clustered and divided into multiple wind turbine group zones, including: The distance between wind turbines is determined based on the coordinate information of each wind turbine, and the density peak clustering algorithm is used to cluster them from the distance dimension to obtain multiple initial wind turbine group partitions; For each of the initial wind turbine group zones, the total capacity of each of the initial wind turbine group zones is calculated based on the equipment capacity of the wind turbines in the zone. Using the total capacity of each initial wind turbine group as the optimization target, and the grid level and wind power absorption of different power consumption areas as constraints, the initial wind turbine group is divided and adjusted to obtain multiple wind turbine group zones.

3. The method according to claim 1, characterized in that, The reliability parameters include opportunity cost of failure, annual downtime hours, and system availability. For each interconnection combination, the corresponding reliability parameters are determined, including: The probability of wind curtailment for the interconnected line combination is calculated based on the maximum transmission capacity of the cable and the total output value of the wind power base. The topological equivalent outage probability is calculated based on the wind curtailment probability, cable fault probability, line fault probability and offshore wind farm fault probability. The annual power generation loss expectation value is calculated based on the topological equivalent outage probability and the active power output value of the offshore wind farm. The annual fault opportunity cost is calculated based on the annual power generation loss expectation value and the grid-connected electricity price. The annual outage hours of the transmission system are calculated based on the expected value of annual power generation loss and the total capacity of the global wind power base, according to the expected planning of the interconnection line combination. The system availability rate of the outgoing system is calculated based on the annual downtime hours.

4. The method according to claim 1, characterized in that, The equipment capacity constraints include at least one of the following: medium-voltage booster station capacity constraints, converter station capacity constraints, allocation decision variable constraints, and constraints on the number of booster stations and converter stations; The power transmission constraints include at least one of the following: DC submarine cable capacity constraints, submarine cable current carrying capacity constraints, onshore converter station capacity constraints, power transmission submarine cable crossing constraints, and voltage level constraints.

5. The method according to claim 1, characterized in that, The method utilizes a segmented genetic algorithm combined with an improved clustering algorithm to solve the two-layer model of the power transmission system interconnection planning, obtaining equipment planning information for the medium-voltage booster station and the offshore converter station, as well as route planning information for the wind turbine group regional interconnection lines, including: For each wind turbine group zone, based on the clustering results of each wind turbine point in the wind turbine group zone, the initial information of the corresponding medium-pressure booster station and offshore converter station is determined, wherein the initial information includes the initial location, initial quantity and capacity; The initial information is substituted into the upper layer of the two-layer model of the interconnection planning of the transmission system, and the upper layer model is optimized by a segmented genetic algorithm to obtain the updated equipment planning information of the medium-voltage booster station and the offshore converter station respectively. Based on the updated equipment planning information, the upper and lower models of the two-layer model of the outgoing system interconnection planning are optimized using the CPLEX solver, and the objective function of the lower model is fed back to the fitness function of the upper model. The optimization process of the upper-level model and the lower-level model is iteratively executed until both the upper-level model and the lower-level model meet the optimization objective. Equipment planning information is extracted from the upper-level model and line planning information is extracted from the lower-level model. The equipment planning information includes the site selection, number of equipment and equipment capacity of the medium-voltage booster station and the offshore converter station, respectively. The line planning information includes cable selection, cable data and line path distribution.

6. The method according to claim 5, characterized in that, For each wind turbine group zone, based on the clustering results of each wind turbine point in the zone, the initial information of the corresponding medium-pressure booster station and offshore converter station is determined, including: The local density and relative distance of each wind turbine point in the wind turbine group are calculated using a clustering algorithm based on density peaks. Wind turbine points that meet the local density and relative distance conditions are selected as initial cluster centers. The remaining wind turbine points are assigned to the clusters of the nearest high-density points in descending order of local density. The capacity constraints of the medium-pressure booster station are dynamically checked until all wind turbine points are assigned, and the initial information of the medium-pressure booster station is determined. Based on the initial information of the medium-pressure booster station, the planning of the offshore converter station is optimized using an improved K-means clustering algorithm. With the goal of minimizing cost and dynamic capacity limitation, the planning parameters with the lowest total cost are selected as the initial information of the offshore converter station after traversing the number of clusters.

7. A power transmission system interconnection planning device based on offshore wind power base zoning, characterized in that, include: The partitioning module is used to cluster and divide the wind turbines within the offshore wind power base based on the installed capacity information of each wind turbine and the power consumption area information, resulting in multiple wind turbine group partitions. The line construction module is used to construct interconnection lines between different wind turbine groups, obtain multiple interconnection line combinations, and determine the reliability parameters corresponding to different interconnection line combinations. The model building module is used to construct a two-layer model for power transmission system interconnection planning based on wind turbine group zoning, multiple interconnection line combinations, and reliability parameters corresponding to different interconnection line combinations. This includes: constructing a planning model for medium-voltage booster stations and offshore converter stations based on wind turbine group zoning, with total equipment cost as the objective function and equipment capacity constraints as the constraint condition; constructing a wind turbine group zoning interconnection line planning model based on multiple interconnection line combinations and reliability parameters corresponding to different interconnection line combinations, with comprehensive cost as the objective function and power transmission constraints as the constraint condition; and constructing a two-layer model for power transmission system interconnection planning using the medium-voltage booster station and offshore converter station planning model as the upper-layer model and the wind turbine group zoning interconnection line planning model as the lower-layer model. The model solving module is used to solve the two-layer model of the power transmission system interconnection planning using a segmented genetic algorithm and an improved clustering algorithm, so as to obtain the equipment planning information of the medium-voltage booster station and the offshore converter station, and the route planning information of the wind turbine group regional interconnection lines.

8. A storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the power transmission system interconnection planning method based on offshore wind power base zoning as described in any one of claims 1-6.

9. A terminal, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the power transmission system interconnection planning method based on offshore wind power base zoning as described in any one of claims 1-6.

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