A method for planning a deep-sea wind power sea-land integrated flexible direct current transmission system based on Benders decomposition

Abstract

The present application relates to power transmission planning, in particular to a kind of deep sea wind power sea-land integrated flexible transmission system planning method based on Benders decomposition. Including: step 1) establishing deep sea wind power sea-land integrated flexible transmission system planning total model;Step 2) using Benders decomposition algorithm, the planning total model is decomposed into investment main problem and operation total sub-problem;Step 3) by alternating direction multiplier method, the operation total sub-problem is solved in parallel coordination;The operation total sub-problem is decomposed into offshore wind power cluster side sub-model, flexible side sub-model and land grid side sub-model;Step 4) according to the solution of each sub-model, if each sub-model is feasible, generate Benders optimal cut;If any sub-model is not feasible, generate Benders feasibility cut;Step 5) the generated Benders cut plane is fed back to the investment main problem as additional constraint. It realizes the global optimization of planning scheme, economic and efficient and engineering practical, for deep sea wind power large-scale grid connection provides technical support.

Description

Technical Field

[0001] This invention relates to power transmission planning, and more particularly to a planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition. Background Technology

[0002] Currently, offshore wind power is developing rapidly, and the industry is gradually expanding from nearshore to deep-sea and large-capacity applications. Flexible DC transmission technology integrating land and sea is a key component of this development. Flexible DC transmission (VSC-HVDC) has advantages such as long-distance power transmission, independent active and reactive power regulation, and strong fault ride-through capability, making it the mainstream technology for grid connection of deep-sea wind power.

[0003] Current methods for planning grid connection of deep-sea wind power mostly employ single-model solutions, failing to fully consider the coupling characteristics of offshore wind farms, flexible DC transmission systems, and onshore power grids. These methods suffer from high model dimensionality, high coupling between multiple systems, and significant solution challenges, making it difficult to achieve optimal lifecycle costs while ensuring safe and stable system operation. Furthermore, existing methods do not systematically integrate investment costs, operating costs, operating loss costs, and wind curtailment penalty costs, making it difficult to coordinate and optimize economic efficiency and safety.

[0004] While the Benders decomposition algorithm has been applied to decoupling and iterative solutions for complex large-scale systems, it has not yet been adapted to the coupling characteristics and planning requirements of the three sides: the offshore wind farm side, the flexible DC transmission side, and the onshore power grid side. Furthermore, offshore wind power output is intermittent and fluctuating, flexible DC transmission faces constraints such as converter station capacity configuration and voltage stability, and the onshore power grid faces planning challenges such as access node selection and line expansion. Moreover, existing planning methods cannot simultaneously address the uncertainty of wind power output while achieving the dual objectives of optimal system lifecycle cost and safe operation of all three sides. They also lack parallel and coordinated solution strategies that balance solution efficiency and optimization accuracy, thus hindering the economic viability and practicality of planning integrated offshore-onshore flexible DC transmission systems for offshore wind power. Therefore, it is necessary to find a three-side collaborative planning technology adapted to the offshore scenario to solve key problems such as multi-side coupling, high solution difficulty, handling of wind power uncertainties, and low engineering adaptability of planning schemes. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition, comprising the following steps:

[0007] Step 1) Establish a comprehensive planning model for a deep-sea wind power integrated onshore-sea flexible DC transmission system that minimizes the overall cost within the planning period;

[0008] Step 2) Use the Benders decomposition algorithm to decompose the overall planning model into the main investment problem and the overall operation subproblem;

[0009] Among them, the overall operation sub-problem corresponds to the operation optimization problem of the deep-sea wind power integrated onshore-sea flexible DC transmission system;

[0010] Step 3) Solve the overall subproblem in parallel using the alternating direction multiplier method;

[0011] The overall operational subproblem is decomposed into an offshore wind power cluster side model, a flexible DC side model, and an onshore power grid side model;

[0012] Data interaction between the sub-models is accomplished using cross-side communication power as the coupling variable.

[0013] The three sub-models are solved in parallel using the parallel alternating direction multiplier method.

[0014] Step 4) Based on the solution results of each sub-model, if each sub-model is feasible, generate the Benders optimal cut; if any sub-model is not feasible, generate the Benders feasible cut.

[0015] Step 5) Feed the generated Benders cutting plane as an additional constraint to the principal investment problem, update the constraints of the principal investment problem, and return to solve the principal investment problem again until the convergence allowable error between the lower bound of the principal investment problem objective and the upper bound of the overall planning model objective is satisfied, and output the optimal planning scheme.

[0016] Furthermore, the comprehensive cost within the planning period in the overall planning model refers to the comprehensive life-cycle cost of the deep-sea wind power system within the planning period, including the investment and construction costs, operation and maintenance costs, and operating loss costs of the offshore wind power cluster, offshore substation, offshore converter station, onshore substation, onshore converter station, and submarine cables used to connect the offshore, flexible DC, and onshore equipment, as well as the system's wind curtailment penalty cost; the comprehensive cost within the planning period is expressed as:

[0017]

[0018] in, The comprehensive life-cycle cost of deep-sea wind power systems during the planning period. For system equipment investment costs, For system operation and maintenance costs, For system operating losses and costs, The cost of system-wide wind curtailment penalties.

[0019] Furthermore, the investment master problem described in step 2) aims to minimize the sum of the lower bound estimate of the objective function value of the system equipment investment cost and the total operating subproblem. The mathematical model of the investment master problem is expressed as follows:

[0020]

[0021] in, To construct the variable matrix Determined system equipment investment costs; To provide a lower bound estimate for the objective function value of the overall subproblem; Variable matrix related to the construction of the integrated land-sea flexible DC transmission system; The constraint set is the main problem of investment, which includes power balance constraints, line power flow constraints, line capacity constraints, generator output constraints, and slack node phase angle constraints.

[0022] Furthermore, the offshore wind power cluster side-side model described in step 3) takes minimizing the comprehensive cost of offshore wind power cluster construction and operation as its local optimization objective. The constraints of the offshore wind power cluster side-side model include constraints on the selectable site and output range of wind farms, constraints on the sea area where the wind farm can be deployed, and constraints on the transmission capacity of the power collection lines. The output collection balance constraint of the offshore wind power cluster side-side model is:

[0023]

[0024] in, For the actual power output of the i-th offshore wind farm, N represents the total actual power output of offshore wind farms, and N represents the number of offshore wind farms.

[0025] Furthermore, the flexible DC side sub-model described in step 3) aims to minimize the investment and construction costs, operation and maintenance costs, and operation loss costs of the flexible DC transmission system as a local optimization objective. The constraints of the flexible DC side sub-model include upper and lower limits for converter station output power, DC line transmission power constraints, and DC line voltage safety constraints. The DC line voltage safety constraints are as follows:

[0026]

[0027] in, This is the per-unit value of the DC line voltage.

[0028] Furthermore, the onshore power grid sub-model described in step 3) takes minimizing the comprehensive cost of the transformation and operation of the onshore power grid and onshore converter stations as the local optimization objective; the constraints of the onshore power grid sub-model include onshore power grid power balance and flow constraints, bus voltage safety constraints, line transmission capacity constraints, upper and lower limits of onshore converter station output power constraints, and maximum power absorption constraints of the onshore power grid.

[0029] Furthermore, in step 3), the data interaction between the sub-models using cross-side interconnection power as the coupling variable refers to iterative data interaction using the cross-side transmitted active power between the offshore wind power cluster sub-model and the flexible DC sub-model, and between the flexible DC sub-model and the onshore power grid sub-model, as the coupling variable. After each iteration, the cross-side transmitted active power coupling variables used for the (k+1)th iteration are as follows:

[0030]

[0031]

[0032] In the formula, The average active power transmitted between the offshore wind power cluster side and the flexible DC side at the (k+1)th iteration; k is the iteration number; The average active power transmitted between the flexible DC side and the onshore power grid side at the (k+1)th iteration; , , These represent the output power of the offshore wind power cluster, the transmission power of the flexible DC transmission side, and the absorption power of the onshore power grid side after the kth iteration, respectively.

[0033] Furthermore, in step 3), when solving the three sub-models in parallel, the alternating direction multiplier method is used to construct the following augmented Lagrangian function to constrain the consistency of cross-side transmitted active power:

[0034]

[0035] Where x, y, and z are the optimization variables of the offshore wind power cluster side-side model, the flexible DC side-side model, and the onshore power grid side-side model, respectively; f(x), f(y), and f(z) are the cost functions of the offshore wind power cluster side-side model, the flexible DC side-side model, and the onshore power grid side-side model, respectively. For penalty parameters, These are the dual variables corresponding to the power consistency constraints between the offshore wind power cluster side-side model and the flexible direct current side-side model. These are the dual variables corresponding to the power consistency constraints between the flexible DC side sub-model and the onshore power grid side sub-model; , , These represent the active power output from the offshore wind power cluster side sub-model to the flexible DC side sub-model, the cross-side active power transmitted by the flexible DC side sub-model, and the active power absorbed by the onshore power grid side sub-model, respectively.

[0036] Further, in step 4), based on the solution results of each sub-model in the overall subproblem, if each sub-model is feasible, then the optimal solution of each sub-model and the corresponding dual variable are used to generate a Benders optimal cut; if any sub-model is infeasible, then the infeasibility information of the infeasible sub-model is used to generate a Benders feasible cut; the Benders optimal cut or Benders feasible cut is fed back as an additional constraint to the main investment problem to update the constraints of the main investment problem.

[0037] Further, the determination of the convergence allowable error in step 5) includes inner-layer convergence determination and outer-layer convergence determination; the inner-layer convergence determination is based on the original residual and dual residual in the iterative process of the alternating direction multiplier method. When both the original residual and the dual residual are less than the corresponding preset convergence accuracy, the running subproblem is determined to satisfy the inner-layer convergence condition; the outer-layer convergence determination is based on the difference between the lower bound of the main problem objective and the upper bound of the overall objective. When the difference is less than the preset convergence allowable error, the outer-layer Benders convergence condition is satisfied, and the optimal planning scheme is output.

[0038] Compared with the prior art, the present invention has the following advantages.

[0039] This invention divides the system model into a three-sided collaborative model through Benders decomposition and improves the solution efficiency by combining parallel decomposition and coordination methods. It achieves the global optimality, cost-effectiveness and engineering practicality of the planning scheme, and provides technical support for the large-scale grid connection of deep-sea wind power. Attached Figure Description

[0040] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The scope of protection of the present invention is not limited to the following description.

[0041] Figure 1 This is a general flowchart of the planning technology for a deep-sea wind power integrated land-sea flexible DC transmission system based on Benders decomposition.

[0042] Figure 2 This is a topology diagram of a deep-sea wind power transmission scheme;

[0043] Figure 3 This is a schematic diagram illustrating the selection principles for deep-sea wind power transmission modes;

[0044] Figure 4 This refers to the distribution of the receiving end of a wind farm. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] The purpose of this invention is to provide a planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] like Figure 1-4 Example 1: Planning Method for Deep-Sea Wind Power Integrated Onshore-Sea Flexible DC Transmission System Based on Benders Decomposition. The invention will be further explained below with reference to this example. This example addresses the planning scenario of large-scale deep-sea wind power clusters connecting to the onshore power grid via flexible DC transmission. It treats offshore wind power aggregation, flexible DC transmission, onshore transmission, and receiving-end consumption as an integrated planning object. It decouples construction decisions from operational decisions through Benders decomposition and coordinates the overall operational subproblem using the parallel alternating direction multiplier method. The planning method described in this example includes the following steps:

[0049] 1. Establish a comprehensive planning model for a deep-sea wind power integrated onshore-sea flexible DC transmission system that minimizes overall costs within the planning period;

[0050] 2. The Benders decomposition algorithm is used to decompose the overall planning model into the main investment problem and the overall operational subproblem;

[0051] 3. The overall operational subproblem is further decomposed into offshore wind power cluster side sub-model, flexible direct current side sub-model, and onshore power grid side sub-model, and then solved in a coordinated manner using the parallel alternating direction multiplier method;

[0052] 4. Generate Benders optimal cut or Benders feasible cut based on the solution results of the three sub-models;

[0053] 5. Feed the generated cutting plane back to the investment master problem and solve iteratively until the inner and outer convergence conditions are met, and output the optimal planning scheme.

[0054] I. Establishing a Comprehensive Planning Model for an Integrated Offshore-Land Wind Power System Based on the development needs of deep-sea wind power, this model coordinates the entire process of offshore wind power aggregation, flexible DC transmission, onshore transmission, and receiving-end consumption. It incorporates system topology, equipment parameters, economic costs, operational safety, and power balance into a unified planning framework, constructing an integrated offshore-land planning model. The comprehensive cost within the planning period includes the investment and construction costs, operation and maintenance costs, and operational loss costs of offshore wind power clusters, offshore substations, offshore converter stations, onshore substations, onshore converter stations, and submarine cables connecting the offshore, flexible DC, and onshore equipment, as well as the system's wind curtailment penalty cost. The comprehensive cost is expressed as:

[0055]

[0056] In the formula, The comprehensive life-cycle cost of deep-sea wind power systems during the planning period; For system equipment investment costs, For system operation and maintenance costs, For system operating losses and costs, The cost of system-wide wind curtailment penalties.

[0057] The constraints of the overall planning model include at least the following: construction logic constraints, wind farm site selection and output range constraints, offshore deployment range constraints, transmission capacity constraints of collection lines, upper and lower limits of converter station output power constraints, DC line transmission power constraints, DC line voltage safety constraints, power balance and flow constraints of onshore power grid, bus voltage safety constraints, onshore line transmission capacity constraints, generator output constraints, phase angle constraints of balancing nodes, and maximum power absorption constraints of onshore power grid.

[0058] II. Using Benders decomposition to form the principal investment problem and the subproblem of operation.

[0059] The Benders decomposition algorithm is used to decompose the original overall planning model into a principal investment problem and a general operation subproblem. The principal investment problem is used to determine the construction scheme of the integrated land-sea flexible DC transmission system, while the general operation subproblem is used to solve the system operation optimization problem under a given construction scheme.

[0060] The main investment problem aims to minimize the sum of the lower bound estimates of the objective function values ​​of the system equipment investment cost and the total operating cost subproblem. Its mathematical model is expressed as follows:

[0061]

[0062] in, To construct the variable matrix Determined system equipment investment costs; To provide a lower bound estimate for the objective function value of the overall subproblem; Variable matrix related to the construction of the integrated land-sea flexible DC transmission system; The constraint set is the main problem of investment, which includes power balance constraints, line power flow constraints, line capacity constraints, generator output constraints, and slack node phase angle constraints.

[0063] After solving the principal investment problem, the current candidate construction scheme is obtained. This candidate construction scheme is then fixed, and a corresponding operational sub-problem is established to determine whether the scheme can meet the requirements for offshore wind power aggregation, flexible DC transmission, and onshore power grid absorption.

[0064] III. Decomposition of the Overall Subproblem

[0065] The overall operational subproblem is decomposed into three sub-models: an offshore wind power cluster sub-model, a flexible DC sub-model, and an onshore power grid sub-model. Each sub-model retains its own local optimization variables, objective function, and operational constraints, and uses cross-side active power transmission as a coupling variable for information exchange.

[0066] (I) Side Model of Offshore Wind Power Cluster

[0067] The offshore wind power cluster sub-model aims to minimize the overall construction and operation cost of offshore wind power clusters, determining the operating output of offshore wind farms and the power transmitted to the flexible vertical transmission line. The constraints of this sub-model include constraints on the selectable site selection and output range of wind farms, the feasible deployment range in the sea area, and the transmission capacity of the power collection lines.

[0068] The power output pooling balance constraint on the offshore wind power cluster side is expressed as:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] In the formula: As an auxiliary variable for the subproblem, , , , These include the annual investment and construction costs, maintenance costs, loss costs, and wind curtailment penalty costs for the planned offshore wind power clusters. For the first The actual output of a deep-sea offshore wind farm For the first Wind curtailment from deep-sea offshore wind farms For the first Total output of offshore wind farms This refers to the actual power output of the offshore wind farm, i.e., the total load of the system. The power delivered from offshore wind farms to offshore converter stations.

[0075] (ii) Flexible straight lateral model

[0076] The flexible DC transmission sub-model aims to minimize the investment and construction costs, operation and maintenance costs, and operating loss costs of the flexible DC transmission system, and is used to determine the operating status of the flexible DC converter station and DC lines. The constraints of this sub-model include upper and lower limits for converter station output power, DC line transmission power constraints, and DC line voltage safety constraints.

[0077] DC line voltage safety constraints are expressed as follows:

[0078]

[0079] in, This is the per-unit value of the DC line voltage.

[0080] (III) Onshore power grid side sub-model

[0081] The onshore power grid sub-model aims to minimize the comprehensive cost of upgrading and operating the onshore power grid and onshore converter stations, and is used to determine the power flow distribution, line operating status, and wind power absorption capacity of the onshore power grid. The constraints of this sub-model include onshore power grid power balance and flow constraints, bus voltage safety constraints, line transmission capacity constraints, upper and lower limits of onshore converter station output power constraints, and maximum power absorption capacity constraints of the onshore power grid.

[0082] IV. Parallel Coordination via Cross-Side Interconnection Power: The offshore wind farm side-side model and the flexible DC side-side model, as well as the flexible DC side-side model and the onshore power grid side-side model, are coordinated in parallel. Cross-side transmitted active power is used as the coupling variable for iterative data interaction. Let the output power of the offshore wind farm side after the k-th iteration be... The power of the flexible direct side transmission is The power absorption capacity of the onshore power grid is Then the cross-side average transmitted active power used in the (k+1)th iteration is expressed as follows:

[0083]

[0084]

[0085] Where, in the formula, The average active power transmitted between the offshore wind power cluster side and the flexible DC side at the (k+1)th iteration; It represents the average active power transmitted between the flexible DC side and the onshore power grid side during the (k+1)th iteration.

[0086] An augmented Lagrangian function is constructed by constraining the power consistency of the two cross-side interfaces using the alternating direction multiplier method:

[0087]

[0088] Where x, y, and z are the optimization variables of the offshore wind power cluster side-side model, the flexible DC side-side model, and the onshore power grid side-side model, respectively; f(x), f(y), and f(z) are the cost functions of the offshore wind power cluster side-side model, the flexible DC side-side model, and the onshore power grid side-side model, respectively. For penalty parameters, These are the dual variables corresponding to the power consistency constraints between the offshore wind power cluster side-side model and the flexible direct current side-side model. These are the dual variables corresponding to the power consistency constraints between the flexible DC side sub-model and the onshore power grid side sub-model; , , These represent the active power output from the offshore wind power cluster side sub-model to the flexible DC side sub-model, the cross-side active power transmitted by the flexible DC side sub-model, and the active power absorbed by the onshore power grid side sub-model, respectively.

[0089] In each round of inner-layer iteration, the three sub-models solve their respective local optimization problems in parallel; after completing the solution of this round, they exchange the cross-side transmission active power and update the coupling variables until the inner-layer convergence condition is reached.

[0090] V. Generate the Benders cutting plane

[0091] Construct the Benders cutting plane based on the solution results of the three sub-models in the overall subproblem.

[0092] When all three sub-models are feasible, the optimal solutions of the three sub-models and their corresponding dual variables are used to generate the Benders optimal cut; when the solver determines that any sub-model is infeasible, the Benders feasible cut is generated based on the infeasibility information returned by the infeasible sub-model.

[0093] The generated Benders optimal cut or Benders feasible cut is added as an additional constraint to the principal investment problem, the constraint set of the principal investment problem is updated, and the principal investment problem is solved again.

[0094] VI. Convergence Criteria for Inner and Outer Layers

[0095] (a) Inner layer convergence criterion

[0096] The inner-layer convergence criterion is based on the original and dual residuals during the iterative process of the alternating direction multiplier method. When both the original and dual residuals are less than the corresponding preset convergence accuracy, the overall subproblem is deemed to satisfy the inner-layer convergence condition, which can be expressed as:

[0097]

[0098]

[0099] And in the formula, r k Let d be the original residual of the k-th iteration. k Let ε be the dual residual of the k-th iteration. r and ε d These are the preset convergence accuracies corresponding to the original residual and the dual residual, respectively.

[0100] (ii) Outer layer convergence determination

[0101] The outer convergence criterion is based on the difference between the lower bound of the investment principal problem objective and the upper bound of the overall planning model objective. When the difference is not greater than the preset outer convergence allowable error, the Benders outer convergence condition is satisfied, which can be expressed as:

[0102]

[0103] In the formula, UB is the upper bound of the overall planning model objective obtained based on the current feasible planning scheme; LB is the lower bound of the objective obtained from the main investment problem; This is the preset allowable error for outer layer convergence.

[0104] VII. Planning Results Output

[0105] When both the inner and outer convergence conditions are met simultaneously, the iteration stops, and the optimal planning scheme is output. The planning scheme includes the construction and capacity configuration scheme of offshore wind power clusters, the configuration scheme of offshore and onshore converter stations, the submarine cable configuration scheme, the onshore power grid transformation scheme, and the corresponding comprehensive system cost.

[0106] Figure 1 This is the overall flowchart, which is a schematic diagram of the overall process of the planning method described in this invention. First, the parameters are initialized, the main investment problem is solved, and the Benders lower bound is updated. Then, three operational sub-problems—the offshore wind power cluster side, the flexible DC side, and the onshore power grid side—are solved in parallel, and their feasibility is judged. If they are not feasible, a Benders feasibility cut set is generated and fed back to the main problem. If they are feasible, the sub-problems are solved again and the optimal cut set is generated, and the upper bound of the Benders algorithm is updated. By judging the convergence condition, if the convergence condition is met, the calculation ends; otherwise, the iteration continues and the main problem is solved again.

[0107] Figure 2This is a topology diagram of a deep-sea wind power transmission scheme, illustrating an integrated onshore and offshore flexible direct transmission scheme for deep-sea wind power provided in this embodiment of the invention. The overall process is as follows: the electricity from the offshore wind farm is transmitted to an offshore booster station via a collecting submarine cable, then converted to DC power by an offshore converter station, and transmitted to an onshore terminal station at the landing point via a DC submarine cable. From there, it is directly transmitted to the onshore converter station via an overhead DC line or a DC land cable, and then inverted and connected to the main grid. This scheme, through a combination of submarine cable and overhead / land cable, balances low-loss, long-distance transmission of large-capacity deep-sea electricity with optimized allocation of onshore corridor resources, providing efficient and economical transmission path support for offshore wind power projects with long distances and large installed capacities.

[0108] Figure 3 This diagram illustrates the selection principles for deep-sea wind power transmission modes, and is a schematic diagram of an integrated onshore and offshore flexible direct current transmission mode architecture provided by an embodiment of the present invention. This architecture divides the integrated onshore and offshore flexible direct current transmission mode into three core technical modules from a system-wide perspective: offshore aggregation mode, DC transmission mode, and onshore transmission mode. The offshore aggregation mode can be further divided into AC aggregation and DC aggregation, corresponding to different aggregation paths for offshore wind farm power. The DC transmission mode is the core transmission link, encompassing three key technical dimensions: voltage level, operating mode, and submodule type. Specific options include voltage sequences such as ±320kV, ±500kV, and ±525kV, operating modes such as true bipolar and pseudo bipolar, and submodule types such as full-bridge, half-bridge, and hybrid full-half-bridge. The onshore transmission mode, depending on the receiving-end scenario requirements, can employ either overhead DC lines or DC cables to reliably transmit DC power to the onshore receiving-end power grid. This architecture clearly defines the technical boundaries and optional implementation paths of the integrated land-sea flexible direct transmission system of the present invention, and provides multi-dimensional system configuration solutions for deep-sea wind power projects with different offshore distances and installed capacities.

[0109] A computer-storable medium storing a computer program, which, when executed by a processor, implements the aforementioned planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition.

[0110] Embodiments of the present invention may be provided as methods, systems, or computer program products. The invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the invention may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition.

[0115] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0116] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition, characterized in that, Includes the following steps: Step 1) Establish a comprehensive planning model for a deep-sea wind power integrated onshore-sea flexible DC transmission system that minimizes the overall cost within the planning period; Step 2) Use the Benders decomposition algorithm to decompose the overall planning model into the main investment problem and the overall operation subproblem; Step 3) Solve the overall subproblem in parallel using the alternating direction multiplier method; The overall operational subproblem is decomposed into an offshore wind power cluster side model, a flexible DC side model, and an onshore power grid side model; Data interaction between the sub-models is accomplished using cross-side communication power as the coupling variable. The three sub-models are solved in parallel using the parallel alternating direction multiplier method. Step 4) Based on the solution results of each sub-model, if each sub-model is feasible, generate the Benders optimal cut; if any sub-model is not feasible, generate the Benders feasible cut. Step 5) Feed the generated Benders cutting plane as an additional constraint to the principal investment problem, update the constraints of the principal investment problem, and return to solve the principal investment problem again until the convergence allowable error between the lower bound of the principal investment problem objective and the upper bound of the overall planning model objective is satisfied, and output the optimal planning scheme.

2. The planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 1, characterized in that: The comprehensive cost within the planning period in the overall planning model refers to the comprehensive life-cycle cost of the deep-sea wind power system within the planning period. This includes the investment and construction costs, operation and maintenance costs, and operating loss costs of the offshore wind farm, offshore substations, offshore converter stations, onshore substations, onshore converter stations, and submarine cables connecting the offshore, flexible, and onshore equipment, as well as the system's wind curtailment penalty costs. The comprehensive cost within the planning period is expressed as follows: in, The comprehensive life-cycle cost of deep-sea wind power systems during the planning period. For system equipment investment costs, For system operation and maintenance costs, For system operating losses and costs, The cost of system-wide wind curtailment penalties.

3. The planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 1, characterized in that: The investment master problem described in step 2) aims to minimize the sum of the lower bound estimates of the objective function values ​​of the system equipment investment cost and the total operating subproblem. The mathematical model of the investment master problem is expressed as follows: in, To construct the variable matrix Determined system equipment investment costs; To provide a lower bound estimate for the objective function value of the overall subproblem; Variable matrix related to the construction of the integrated land-sea flexible DC transmission system; The constraint set is the main problem of investment, which includes power balance constraints, line power flow constraints, line capacity constraints, generator output constraints, and slack node phase angle constraints.

4. The planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 1, characterized in that: The offshore wind power cluster side-side model described in step 3) aims to minimize the comprehensive cost of offshore wind power cluster construction and operation in a local optimization context. The constraints of this model include constraints on selectable wind farm sites and output range, available sea area for deployment, and transmission capacity of power collection lines. The output pooling balance constraints of the offshore wind power cluster side-side model are as follows: in, For the actual power output of the i-th offshore wind farm, N represents the total actual power output of offshore wind farms, and N represents the number of offshore wind farms.

5. The planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 1, characterized in that: The flexible DC side sub-model described in step 3) aims to minimize the investment and construction costs, operation and maintenance costs, and operation loss costs of the flexible DC transmission system. The constraints of the flexible DC side sub-model include upper and lower limits for converter station output power, DC line transmission power constraints, and DC line voltage safety constraints. The DC line voltage safety constraints are as follows: in, This is the per-unit value of the DC line voltage.

6. The planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 1, characterized in that: The onshore power grid sub-model described in step 3) takes minimizing the comprehensive cost of the transformation and operation of the onshore power grid and onshore converter stations as the local optimization objective. The constraints of the onshore power grid sub-model include onshore power grid power balance and flow constraints, bus voltage safety constraints, line transmission capacity constraints, upper and lower limits of onshore converter station output power constraints, and maximum power absorption constraints of the onshore power grid.

7. The planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 1, characterized in that: Step 3) involves data interaction between sub-models using cross-side interconnection power as the coupling variable. This means iterative data interaction using the cross-side active power transmitted between the offshore wind power cluster sub-model and the flexible DC sub-model, and between the flexible DC sub-model and the onshore power grid sub-model, as the coupling variable. After each iteration, the cross-side active power coupling variables used for the (k+1)th iteration are as follows: In the formula, The average active power transmitted between the offshore wind power cluster side and the flexible DC side at the (k+1)th iteration; k is the iteration number; The average active power transmitted between the flexible DC side and the onshore power grid side at the (k+1)th iteration; , , These represent the output power of the offshore wind power cluster, the transmission power of the flexible DC transmission side, and the absorption power of the onshore power grid side after the kth iteration, respectively.

8. The planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 1, characterized in that: In step 3), when solving the three sub-models in parallel, the alternating direction multiplier method is used to construct the following augmented Lagrangian function to constrain the consistency of cross-side transmitted active power: Where x, y, and z are the optimization variables of the offshore wind power cluster side-side model, the flexible DC side-side model, and the onshore power grid side-side model, respectively; f(x), f(y), and f(z) are the cost functions of the offshore wind power cluster side-side model, the flexible DC side-side model, and the onshore power grid side-side model, respectively. For penalty parameters, These are the dual variables corresponding to the power consistency constraints between the offshore wind power cluster side-side model and the flexible direct current side-side model. These are the dual variables corresponding to the power consistency constraints between the flexible DC side sub-model and the onshore power grid side sub-model; , , These represent the active power output from the offshore wind power cluster side sub-model to the flexible DC side sub-model, the active power transmitted across sides by the flexible DC side sub-model, and the active power absorbed by the onshore power grid side sub-model, respectively.

9. A planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 8, characterized in that: In step 4), based on the solution results of each sub-model in the overall subproblem, if each sub-model is feasible, then the optimal solution of each sub-model and the corresponding dual variable are used to generate the Benders optimal cut; if any sub-model is infeasible, then the Benders feasible cut is generated based on the infeasibility information of the infeasible sub-model. The Benders optimal cut or Benders feasible cut is fed back as an additional constraint to the investment master problem, thus updating the constraints of the investment master problem.

10. A planning method for a deep-sea wind power integrated onshore-sea flexible DC transmission system based on Benders decomposition according to claim 9, characterized in that: The determination of the convergence allowable error in step 5) includes inner-layer convergence determination and outer-layer convergence determination. The inner-layer convergence determination is based on the original residual and dual residual in the iterative process of the alternating direction multiplier method. When both the original residual and the dual residual are less than the corresponding preset convergence accuracy, the running subproblem is determined to meet the inner-layer convergence condition. The outer-layer convergence determination is based on the difference between the lower bound of the main problem objective and the upper bound of the overall objective. When the difference is less than the preset convergence allowable error, the outer-layer Benders convergence condition is met, and the optimal planning scheme is output.

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