Offshore wind power and onshore power grid collaborative planning method based on clean energy consumption

Abstract

The application discloses a method for offshore wind power and onshore power grid collaborative planning based on clean energy consumption. The method: obtains basic data of power grid collaborative planning, and divides multiple planning stages; an optimization model with the minimum system total cost as the target, operation constraints and clean energy consumption constraints is established, the target function is replaced by an exogenous fixed clean cost parameter with clean energy consumption constraints, the clean energy consumption constraints allow the transfer of the consumption quota between adjacent planning stages with depreciation; the optimization model is constructed as a mixed integer linear programming model for solving, the optimal investment scheme is obtained, the binary variables are fixed, the linear programming problem is solved, and the dual variables of the clean energy consumption constraints are extracted as the endogenous consumption cost; and the planning scheme of each stage is output. The application eliminates the sensitivity of the clean cost parameter, smoothens the consumption cost fluctuation through the inter-period adjustment mechanism, and provides decision support for the collaborative planning of offshore wind power transmission and onshore power grid.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, and specifically relates to a collaborative planning method for offshore wind power and onshore power grid based on clean energy consumption. Background Technology

[0002] With the introduction of the "dual carbon" target, the power system is accelerating its low-carbon energy transition. Large-scale development of offshore wind power resources in coastal areas is a crucial path to increasing the proportion of clean energy. The grid integration and consumption of large-scale offshore wind power has created an urgent need for the expansion of onshore transmission networks. The coordinated planning of offshore wind power transmission projects and onshore grid expansion has become a key technical challenge in the field of power system planning.

[0003] Regarding the aforementioned collaborative planning issues, existing research has achieved relatively mature results in areas such as investment timing optimization, uncertainty handling, and generation-transmission coordination decisions for transmission expansion planning. In the area of ​​offshore wind power transmission, research on multi-wind farm transmission topology optimization, reliable collection system design, and joint planning of offshore power grid and power generation investment has also received increasing attention. Furthermore, a growing body of literature emphasizes the need to explicitly consider environmental externalities such as clean energy consumption in planning models. However, existing planning research has the following shortcomings in handling clean energy consumption constraints: First, most studies use exogenous fixed clean energy conversion cost parameters to internalize emission costs into the objective function. This method essentially treats emission costs as continuously monetized soft costs, making the planning results highly sensitive to clean energy conversion cost parameters. The estimates of clean energy conversion costs differ significantly across institutions and scenarios; differences in clean energy cost assumptions alone can lead to drastically different planning schemes. Second, the fixed clean energy conversion cost parameter method is incompatible with China's current renewable energy consumption guarantee mechanism. Since the implementation of the renewable energy power consumption guarantee mechanism, China has adopted a consumption responsibility weight system framework, with the binding regulatory tool being the consumption responsibility weight rather than fixed cost parameters. Therefore, the truly effective signal in the planning model should be the binding force of the absorption quota, rather than exogenous social cost parameters. Third, existing emission constraint planning studies are limited to single-stage static settings and lack a flexible mechanism for adjusting absorption quotas across time periods. This fails to reflect the role of inter-period adjustments in smoothing marginal absorption costs and stabilizing long-term investment signals.

[0004] Based on the above-mentioned shortcomings, there is an urgent need for a collaborative planning method for offshore wind power and onshore power grid that can eliminate the sensitivity of clean energy cost parameters, be compatible with the current consumption quota system, and have the ability to flexibly adjust consumption indicators across periods. Summary of the Invention

[0005] To address the aforementioned technical issues, this invention proposes a collaborative planning method for offshore wind power and onshore power grids based on clean energy consumption. This method resolves the technical problems of existing planning methods based on exogenous fixed clean energy cost parameters, such as high parameter sensitivity, incompatibility with consumption quota systems, and lack of flexible adjustment mechanisms for inter-period consumption indicators. It achieves efficient and robust multi-stage collaborative expansion planning of offshore wind power transmission and onshore power grids under explicit clean energy consumption constraints, and smooths inter-period consumption cost fluctuations through an inter-period adjustment mechanism for consumption indicators, providing more stable price signals for long-term investment decisions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, this invention proposes a collaborative planning method for offshore wind power and onshore power grids based on clean energy consumption, comprising the following steps: Acquire basic data for power grid collaborative planning and divide the planning horizon into multiple sequential planning phases; Based on the aforementioned fundamental data, an optimization model is established with the goal of minimizing the total system cost, subject to operational constraints and clean energy consumption constraints. The objective function replaces the exogenous fixed clean energy cost parameters with clean energy consumption constraints, including discounted annual operating costs, annualized power generation investment costs, annualized transmission investment costs, annualized energy storage investment costs, and penalties for non-compliance with consumption targets. Operational constraints include node power balance constraints, energy storage operation constraints, and DC power flow network constraints. Clean energy consumption constraints impose consumption index constraints at each planning stage, and allow consumption quotas to be transferred across periods with depreciation between adjacent planning stages. The optimization model is constructed as a mixed-integer linear programming model and solved to obtain the optimal investment scheme. Then, all binary investment decision variables are fixed to their optimal values, and the model is transformed into a linear programming problem to be solved. The dual variable of the clean energy consumption constraint is extracted as the endogenous consumption cost. Based on the solution results, the planning schemes for each stage are output.

[0007] Furthermore, the basic data includes: Information on onshore power grid nodes, their generator sets, loads, and transmission corridors; Information on offshore wind power access nodes, their planned installed capacity, and commissioning schedule; Candidate submarine cable corridors and their voltage levels and transmission capacity parameters; The technical parameters of existing generator sets include unit type, rated capacity, minimum technical output, gradeability and emission coefficient; Investment cost parameters for newly built power generation technologies and energy storage systems; Load forecast data and renewable energy output curves; Emissions conversion factor, as well as discount rate and economic life of equipment.

[0008] Furthermore, the objective function is expressed as: ; in, Indicates the index of the current planning stage. Indicates the first Annual operating costs after phase discounting; Indicates the first Annualized investment cost for power generation after phase discounting; Indicates the first Annualized investment cost of power transmission after phase discounting; Indicates the first The annualized investment cost of energy storage after phase discounting; Penalties will be imposed for failure to meet clean energy consumption standards; This indicates that the slack variables have not been adequately absorbed.

[0009] Furthermore, the annualized investment cost for power generation Represented as: ; in, Indicates an index of investment stages; Represents the index of the power grid node; This represents the discount-survival factor of power generation technology; Indicates the first Annualized unit investment cost of staged gas-fired carbon capture units; Represents a node In the The newly added capacity of gas-fired carbon capture units in this phase; Indicates the first Annualized unit investment cost of staged gas turbines; Represents a node In the The newly added gas turbine capacity in this phase; Indicates the first Annualized unit investment cost for onshore / offshore wind power at different stages; Represents a node In the The newly added wind power capacity in this phase; Indicates the first Annualized unit investment cost of photovoltaic power generation at different stages; Represents a node In the The newly added photovoltaic installed capacity in this phase; Annualized investment cost of power transmission Represented as: ; in, This represents the discount-survival factor for transmission lines. This represents the set of candidate land routes; Indicates land route The annualized unit investment cost; Indicates land route In the Phase upgrade binary decision variables; This represents a set of candidate submarine cable corridors at sea; This represents a set of voltage levels for submarine cables; Indicates sea route Medium voltage level Annualized unit investment cost of submarine cables; Indicates sea route Medium voltage level The submarine cable in the first Binary decision variables for phased construction; Indicates land route Medium voltage level The corresponding investment cost of the converter station; Indicates the variable for selecting the voltage level of the submarine cable; Energy storage investment costs Represented as: ; in, This represents the discount-survival factor of energy storage. Indicates the first Annualized investment cost per unit energy capacity for staged energy storage; Represents a node In the The rated energy capacity of the newly added energy storage in each phase; Indicates the first Annualized investment cost per unit power capacity of staged energy storage; Represents a node In the The rated power capacity of newly added energy storage in each phase.

[0010] Furthermore, the operational constraints include: The node power balance constraint requires that, for each planning stage, each grid node, each representative day, and each hour, the sum of the node's power generation output, available renewable energy output, energy storage discharge power, and line injection power is equal to the sum of the node's load demand, energy storage charging power, and line outflow power, and allows load shedding and wind / solar curtailment as relaxation variables. Energy storage operation constraints include upper and lower limits of energy storage state of charge, upper and lower limits of charge and discharge power, and time-series coupling constraints of state of charge between adjacent time periods, which are used to characterize the energy storage characteristics and operating boundaries of energy storage. DC power flow network constraints are established based on the DC power flow model to establish a linear relationship between line transmission power and node phase angle, and upper and lower capacity limits are imposed on the transmission power of each line to characterize the physical transmission characteristics of the power network.

[0011] Furthermore, the constraints on clean energy consumption are expressed as follows:

[0012] in, Represents the day The weights; Indicates generator set The emission conversion factor; Indicates generator set In the Phase, Representative Day , No. Hours of effort; Indicates the first The remaining balance of the consumption indicators at the end of the phase; Indicates the depreciation rate across periods; Indicates the first The remaining balance of the consumption target at the end of the phase; Indicates the first The upper limit of the absorption target for each stage; Indicates slack variables indicating failure to meet absorption targets; This represents the set of planning stages.

[0013] Furthermore, the upper limit constraint on the balance of the consumption quota adjustment is:

[0014] in, This is to adjust the upper limit ratio of the balance.

[0015] Furthermore, when the first Phase and the When the balance of the absorption index adjustment in each stage does not reach the upper and lower limits, the endogenous absorption cost obtained by the model solution satisfies: ; in, Indicates the first The endogenous absorption cost of the stage; Indicates the first The endogenous absorption cost of the stage.

[0016] Furthermore, the optimization model is constructed as a mixed-integer linear programming model for solution to obtain the optimal investment plan, specifically as follows: The binary and continuous variables in the power generation investment decision variables, power transmission investment decision variables, and energy storage investment decision variables in the optimization model are combined to construct a mixed integer linear programming model. The model is solved using a mixed-integer linear programming solver to obtain the construction decisions for each candidate line at each stage, the integer solutions for the new unit capacity at each node, and the corresponding continuous variable solutions for operation scheduling, which serve as the optimal investment scheme.

[0017] Furthermore, the planning schemes for each stage include at least the endogenous absorption costs for each stage, which are used to guide the intertemporal trading pricing of clean energy absorption indicators.

[0018] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects: I. Eliminate sensitivity to clean energy cost parameters and avoid over-investment. Existing technologies internalize emission costs into the objective function by using exogenous fixed clean-up cost parameters, making the planning results highly sensitive to these parameters. Estimations of clean-up costs vary significantly across different institutions and scenarios; even variations in clean-up cost assumptions can lead to drastically different planning schemes. When clean-up cost parameters are set too low, the planned emission reduction effect is insufficient; when set too high, it leads to over-investment and a substantial increase in the total system cost.

[0019] This invention replaces the exogenous fixed clean energy conversion cost parameter mechanism with a clean energy consumption constraint, directly incorporating the consumption index as a hard constraint into the optimization model. The planning results directly meet the preset clean energy consumption target, eliminating the differences in planning schemes caused by different assumptions about clean energy conversion cost parameters. The numerical examples show that under the fixed clean energy conversion cost parameter method, the total system cost increases significantly when the clean energy conversion cost parameter is increased from a low value to a high value; while the consumption constraint model proposed in this invention, while meeting the emission reduction target, has a total system cost far lower than the high-cost scenario of the fixed clean energy conversion cost parameter method, effectively avoiding over-investment.

[0020] II. Smoothing out fluctuations in intertemporal absorption costs and providing stable investment signals Existing emission control planning studies are limited to single-stage static settings and lack a flexible allocation mechanism for cross-period emission reduction quotas. This fails to reflect the role of inter-period adjustments in smoothing marginal emission reduction costs and stabilizing long-term investment signals. The dramatic fluctuations in emission reduction costs at each stage introduce uncertainty into long-term investment decisions.

[0021] This invention introduces an inter-period emission reduction quota adjustment mechanism, allowing for limited, depreciated transfers of remaining emission reduction quotas between adjacent planning stages. When actual emissions in a certain stage fall below the emission reduction target, the remaining quotas can be stored for use in subsequent stages; when emission reduction pressure is high in subsequent stages, the accumulated adjustment balance can be utilized. This mechanism redistributes emission reduction quotas over time, tending to balance the discounted marginal emission reduction costs of each stage. Under no-arbitrage conditions, the ratio of emission reduction costs between adjacent stages is approximately equal to the reciprocal of the inter-period depreciation factor, thus effectively compressing the fluctuation range of inter-period emission reduction costs. Calculation results show that without the inter-period adjustment mechanism, the inter-period fluctuation range of endogenous emission reduction costs is large; after enabling the inter-period adjustment mechanism, the fluctuation range is significantly compressed, while the total system cost remains almost unchanged, providing a more stable emission reduction cost signal for long-term investment decisions.

[0022] Third, to achieve the operational extraction of endogenous absorption costs and reveal the true emission reduction costs. Existing technologies employ exogenous fixed clean-up cost parameters, the values ​​of which depend on the analyst's prior assumptions and lack objective basis. Different studies use vastly different clean-up cost parameters, making it difficult to compare and verify planning results.

[0023] This invention achieves the operational extraction of endogenous absorption costs through a two-step method: first solving the MILP (Minimum Intake Limitation Problem), then fixing integer variables to solve the LP (Limited Intake Limitation Problem). After obtaining the optimal investment plan, all binary investment decision variables are fixed, transforming the original model into a pure operation and scheduling LP problem. The dual variables of the clean energy absorption constraint are extracted and converted into endogenous absorption costs measured per unit of emissions. This endogenous absorption cost has a clear economic meaning: under the optimal investment plan, the system cost saving corresponding to a one-unit relaxation of the absorption index is equivalent to the system's marginal emission reduction cost or marginal absorption cost. The numerical examples show that the endogenous absorption cost extracted by this invention is far lower than the high value assumption of the exogenous fixed clean energy cost parameters, revealing the problem that traditional methods may overestimate clean energy costs.

[0024] IV. Achieving multi-stage coordinated optimization between offshore wind power transmission and onshore power grid Existing studies often separate offshore wind power transmission planning from onshore power grid expansion planning, or only consider single-stage static optimization, making it difficult to capture the dynamic coupling relationship between power grid expansion and power generation investment, and also failing to reflect the impact of different construction sequences on the overall economic efficiency of the system.

[0025] This invention integrates offshore wind power transmission planning and onshore power grid expansion into a multi-stage collaborative optimization framework. It considers joint decisions regarding submarine cable corridor selection, voltage level selection, converter station investment, and onshore line upgrades, and coordinates these with generator unit investment (including carbon capture units, gas turbines, wind power, and photovoltaics) and energy storage investment. The planning horizon is divided into multiple sequentially connected planning stages, with investment decisions in each stage affecting available resources and expansion options in subsequent stages. This multi-stage collaborative optimization framework can generate optimal transmission network topology schemes coupled with generation mix and absorption constraints, achieving joint optimized configuration of generation, transmission, and storage.

[0026] V. Highly compatible with the existing consumption guarantee mechanism The existing fixed cost-to-cleanliness approach is incompatible with the current renewable energy consumption guarantee mechanism. The current mechanism uses a consumption responsibility weighting system, where the binding regulatory tool is the consumption responsibility weighting rather than the fixed cost parameter.

[0027] This invention uses clean energy consumption constraints as the core constraint of the planning model. The consumption indicators directly correspond to the consumption responsibility weights or emission caps in the policy framework, enabling the planning model to accurately reflect the constraint characteristics of the consumption quota system. The inter-period adjustment mechanism of consumption indicators is consistent with the institutional design in policy practice that allows for the inter-period carryover of quotas, improving the policy adaptability and feasibility of the planning scheme. The endogenous consumption costs at each stage of the planning output can provide a reference for the inter-period trading pricing of consumption indicators.

[0028] VI. Ability to handle complex operational constraints This invention considers the operating characteristics of the power system in detail in the planning model, including node power balance constraints, energy storage operation constraints (upper and lower limits of state of charge, charging and discharging power limits, and timing coupling constraints), and DC power flow network constraints (linear relationship between line power and node phase angle, and transmission capacity limits). The inclusion of these operational constraints ensures that the planning scheme is not only economically optimal but also technically feasible, avoiding the problem of the planning scheme failing to be implemented in actual operation due to violations of physical constraints. Attached Figure Description

[0029] Figure 1 This is a flowchart of the collaborative planning method for offshore wind power and onshore power grid based on clean energy consumption proposed in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the network topology of a test system in a certain province proposed in Embodiment 2 of the present invention (including 16 onshore nodes and 8 offshore wind power access nodes). Figure 3 This is a comparison chart of the system cost breakdown and total cost at each stage under the four scenarios proposed in Embodiment 2 of the present invention; Figure 4This is the final planned power transmission network topology diagram for scenario S1 proposed in Embodiment 2 of the present invention; Figure 5 This is the final planned power transmission network topology diagram for scenario S2 proposed in Embodiment 2 of the present invention; Figure 6 This is the final planned power transmission network topology diagram for scenario S3 proposed in Embodiment 2 of the present invention; Figure 7 This is the final planned power transmission network topology diagram for scenario S4 proposed in Embodiment 2 of the present invention; Figure 8 This is a schematic diagram of the equipment for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption, as proposed in Embodiment 3 of the present invention. Detailed Implementation

[0030] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0031] Example 1 Embodiment 1 of this invention proposes a collaborative planning method for offshore wind power and onshore power grids based on clean energy consumption. In a multi-stage extended planning model for power generation, transmission, and energy storage, clean energy consumption constraints replace the traditional exogenous fixed clean energy cost parameter mechanism, and an inter-period consumption index adjustment mechanism is introduced to allow limited transfer of remaining consumption quotas between adjacent planning stages. After solving the mixed-integer linear programming problem to obtain the optimal investment decision, binary investment variables are fixed and the corresponding linear programming problem is solved, extracting the dual variable of the clean energy consumption constraint as the endogenous consumption cost.

[0032] Figure 1 This is a flowchart of the collaborative planning method for offshore wind power and onshore power grid based on clean energy consumption proposed in Embodiment 1 of the present invention. In step S1, basic data for power grid collaborative planning is obtained, and the planning level year is divided into multiple sequential planning stages; In this invention, the basic data includes: Information on onshore power grid nodes, their generator sets, loads, and transmission corridors; Information on offshore wind power access nodes, their planned installed capacity, and commissioning schedule; Candidate submarine cable corridors and their voltage levels and transmission capacity parameters; The technical parameters of existing generator sets include unit type, rated capacity, minimum technical output, gradeability and emission coefficient; Investment cost parameters for newly constructed power generation technologies and energy storage systems. Newly constructed power generation technologies include gas-fired carbon capture system (CCS) units, gas turbine CT, onshore / offshore wind power, photovoltaics, etc. Load forecast data and renewable energy output curves; Emissions conversion factor, as well as discount rate and economic life of equipment.

[0033] The planning horizon is divided into multiple planning epochs, each lasting 5 years, with investment decisions made at the beginning of each epoch. Within each epoch, system operation is characterized by a set of representative days and stress operating conditions.

[0034] The scope of protection of this invention is not limited to the 5 years listed in Example 1, and those skilled in the art can make reasonable choices based on the actual situation.

[0035] In step S2, an objective function is constructed. This invention establishes an optimization model with the goal of minimizing the total system cost. The objective function includes the discounted operating cost, the annualized investment cost of power generation, the annualized investment cost of power transmission, the annualized investment cost of energy storage, and a penalty term for failure to meet the consumption standards. It does not include exogenous fixed clean energy cost parameters.

[0036] The objective function is expressed as: ; in, Indicates the index of the current planning stage. Indicates the first Annual operating costs after phase discounting; Indicates the first Annualized investment cost for power generation after phase discounting; Indicates the first Annualized investment cost of power transmission after phase discounting; Indicates the first The annualized investment cost of energy storage after phase discounting; Penalties will be imposed for failure to meet clean energy consumption standards; This indicates that the slack variables have not been adequately absorbed.

[0037] The three investment cost components are presented in the form of annualized capital recovery factor (CRF): The capital recovery factor for various types of equipment is defined as follows:

[0038] in, Indicates the capital recovery factor; This represents the device type index. Do not refer to power generation equipment Output lines Energy storage equipment ; The discount rate is... This refers to the economic lifespan of the corresponding equipment.

[0039] Annualized investment cost for power generation Represented as: ; in, Indicates an index of investment stages; Represents the index of the power grid node; This represents the discount-survival factor of power generation technology; Indicates the first Annualized unit investment cost of staged gas-fired carbon capture units; Represents a node In the The newly added capacity of gas-fired carbon capture units in this phase; Indicates the first Annualized unit investment cost of staged gas turbines; Represents a node In the The newly added gas turbine capacity in this phase; Indicates the first Annualized unit investment cost for onshore / offshore wind power at different stages; Represents a node In the The newly added wind power capacity in this phase; Indicates the first Annualized unit investment cost of photovoltaic power generation at different stages; Represents a node In the The newly added photovoltaic installed capacity in this phase; Annualized investment cost of power transmission Represented as: ; in, This represents the discount-survival factor for transmission lines. This represents the set of candidate land routes; Indicates land route The annualized unit investment cost; Indicates land route In the Phase upgrade binary decision variables; This represents a set of candidate submarine cable corridors at sea; This represents a set of voltage levels for submarine cables; Indicates sea route Medium voltage level Annualized unit investment cost of submarine cables; Indicates sea route Medium voltage level The submarine cable in the first Binary decision variables for phased construction; Indicates land route Medium voltage level The corresponding investment cost of the converter station; Indicates the variable for selecting the voltage level of the submarine cable; Energy storage investment costs Represented as: ; in, This represents the discount-survival factor of energy storage. Indicates the first Annualized investment cost per unit energy capacity for staged energy storage; Represents a node In the The rated energy capacity of the newly added energy storage in each phase; Indicates the first Annualized investment cost per unit power capacity of staged energy storage; Represents a node In the The rated power capacity of newly added energy storage in each phase.

[0040] In step S3, operational constraints are set, including node power balance constraints, energy storage operation constraints, and network transmission constraints based on the DC power flow model, to ensure that the operation is feasible on representative days of each planning stage.

[0041] The node power balance constraint requires that at each planning stage, at each grid node, at each representative day, and at each hour, the sum of the node's power generation output, available renewable energy output, energy storage discharge power, and line injection power must equal the sum of the node's load demand, energy storage charging power, and line outflow power, and allows load shedding and wind / solar curtailment as slack variables. The node power balance constraint is expressed as: ; in, Represents a node In the Phase, Representative Day , No. Hourly output of the gas-fired carbon capture unit; Represents a node In the Phase, Representative Day , No. Gas turbine output per hour; Represents a node A collection of generator sets at the location; Indicates the unit In the Phase, Representative Day , No. Hours of effort; Represents a node In the Phase, Representative Day , No. The wind power output available before the wind curtailment occurs within hours; Represents a node In the Phase, Representative Day , No. Wind power curtailment per hour; Represents a node In the The newly added wind power capacity in this phase; Represents a node In the Phase, Representative Day , No. Normalized output curve of newly added wind power per hour; Represents a node In the The newly added photovoltaic installed capacity in this phase; Represents a node In the Phase, Representative Day , No. The normalized output curve of newly added photovoltaic power generation per hour; Represents a node In the Phase, Representative Day , No. Hourly load demand; Represents a node In the Phase, Representative Day , No. Hourly demand elasticity adjustment; Represents a node In the Phase, Representative Day , No. Hourly load shedding capacity; Represents a node In the Phase, Representative Day , No. Hourly net node output power based on DC power flow equations; Represents a node In the Phase, Representative Day , No. Hourly energy storage charging power; Represents a node In the Phase, Representative Day , No. Hourly energy storage and discharge power.

[0042] Energy storage operation constraints include upper and lower limits of energy storage state of charge, upper and lower limits of charging and discharging power, and time-series coupling constraints of state of charge between adjacent time periods, which are used to characterize the energy storage characteristics and operating boundaries of energy storage. DC power flow network constraints establish a linear relationship between line transmission power and node phase angle based on the DC power flow model, and impose upper and lower capacity limits on the transmission power of each line to characterize the physical transmission characteristics of the power network.

[0043] In step S4, clean energy consumption constraints are set.

[0044] At each planning stage, clean energy consumption targets are imposed, with a mechanism for adjusting these targets across different periods allowed. The clean energy consumption targets are expressed as follows:

[0045] in, Represents the day The weights; Indicates generator set The emission conversion factor; Indicates generator set In the Phase, Representative Day , No. Hours of effort; Indicates the first The remaining balance of the consumption target at the end of the phase; Indicates the depreciation rate across periods; Indicates the first The remaining balance of the consumption target at the end of the phase; Indicates the first The upper limit of the absorption target for each stage; Indicates slack variables indicating failure to meet absorption targets; This represents the set of planning stages.

[0046] When the constraint is tight, its dual variable is the endogenous absorption cost of this stage, which represents the reduction in the total system cost brought about by relaxing the absorption index by one unit at the margin, and is equivalent to the marginal absorption cost of the system.

[0047] When the Phase and the When the balance of the absorption index adjustment in each stage does not reach the upper and lower limits, the endogenous absorption cost obtained by the model solution satisfies: ; in, Indicates the first The endogenous absorption cost of the stage; Indicates the first The endogenous absorption cost of the stage.

[0048] When actual emissions in the current phase are lower than the consumption target, the remaining quota can be stored for use in subsequent phases. When the adjustment mechanism is active, the following conditions apply to inter-period arbitrage:

[0049] This condition indicates that the ratio of absorption costs in adjacent stages is approximately equal to the reciprocal of the inter-period depreciation factor of the absorption index, reflecting the smoothing effect of the inter-period adjustment mechanism of the absorption index on the fluctuation of inter-period absorption costs.

[0050] The balance of the transfer shall not exceed a certain percentage of the current period's consumption quota to prevent excessive inter-period transfer. The upper limit constraint for the balance of the consumption quota transfer is as follows:

[0051] in, This is the upper limit ratio coefficient for adjusting the balance. When the inter-period adjustment mechanism for consumption indicators is disabled, it is fixed at zero, and the clean energy consumption constraint degenerates into independent consumption indicator constraints for each stage.

[0052] It should be noted that the "clean energy consumption constraint" described in this invention is not a constraint that directly limits the amount or proportion of clean energy power generation in the traditional sense, but rather an indirect form of total carbon emission control. Its technical principle is as follows: In the context of large-scale offshore wind power grid connection, the main source of system carbon emissions is fossil fuel generating units; by setting a decreasing carbon emission ceiling, the output space of fossil fuel generating units is gradually compressed, thereby forcing clean energy to make way for consumption. Therefore, this constraint is mathematically represented by a carbon emission inequality, but its technical purpose and policy effect are to ensure the full consumption of clean energy. Those skilled in the art should understand that in a power system where the proportion of clean energy continues to increase, carbon emission constraints and clean energy consumption constraints are equivalent under certain conditions, which is why this invention adopts this expression.

[0053] In step S5, the optimization model is constructed as a mixed integer linear programming model and solved to obtain the optimal investment scheme; then, all binary investment decision variables are fixed to their optimal values, the model is transformed into a linear programming problem to be solved, and the dual variable of the clean energy consumption constraint is extracted as the endogenous consumption cost. The specific solution process includes: combining the binary and continuous variables in the power generation investment decision variables, power transmission investment decision variables, and energy storage investment decision variables in the optimization model to construct a mixed integer linear programming model (MILP). The model is solved using a mixed-integer linear programming solver (such as Gurobi) to obtain the construction decisions for each candidate line at each stage, the integer solutions for the new unit capacity at each node, and the corresponding continuous variable solutions for operation scheduling, which serve as the optimal investment scheme.

[0054] Since the MILP problem does not directly provide constrained dual variables, the following LP relaxation method is used to extract the endogenous absorption cost: (1) Solve the MILP to obtain the optimal solution; (2) Fix all binary investment decision variables to their optimal values ​​and transform the model into a linear programming (LP) problem; (3) Solve the LP problem and extract the dual variables of the clean energy consumption constraint; (4) Convert the original dual variables according to the objective function coefficient and emission unit to obtain the endogenous consumption cost in units of $ / tCO2.

[0055] This method ensures that the endogenous absorption cost retains its economic meaning under the optimal MILP investment decision: it represents the system cost saving corresponding to one unit of marginal relaxation of the absorption index under a given investment plan and scheduling strategy.

[0056] In step S6, the planning schemes for each stage are output based on the solution results.

[0057] The planning schemes for each stage include: the optimal power generation (CCS unit, gas turbine, wind power, photovoltaic) investment schemes for each stage, investment schemes and network topologies for submarine cables and onshore transmission lines, investment schemes for energy storage systems, the status of grid connection compliance and the remaining balance of grid connection indicators for each stage, and the endogenous grid connection costs for each stage.

[0058] The offshore wind power and onshore power grid collaborative planning method based on clean energy consumption proposed in Embodiment 1 of this invention replaces the exogenous fixed clean energy cost parameters with clean energy consumption constraints and introduces an intertemporal adjustment mechanism. This achieves multi-stage collaborative optimization planning of offshore wind power transmission and onshore power grid while eliminating parameter sensitivity and smoothing intertemporal consumption cost fluctuations.

[0059] Example 2 This embodiment 2 provides an example of applying the method described in embodiment 1 to the testing system in Shandong Province to verify the effectiveness of the proposed method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption.

[0060] I. Basic Data Acquisition Implementation of step S1: Using power grid data from 16 prefecture-level cities in Shandong Province, Figure 2 This is a schematic diagram of the network topology of a test system in a certain province proposed in Embodiment 2 of the present invention (including 16 onshore nodes and 8 offshore wind power access nodes). The installed capacity, output characteristics, and emission conversion factors of existing thermal power (coal-fired), gas-fired, and nuclear power units at each node are obtained.

[0061] According to Shandong Province's offshore wind power development plan, offshore wind power resource blocks in the Bohai and Yellow Sea regions are clustered into 8 offshore wind power access nodes, with a total planned installed capacity of 35GW. Among them, 3 nodes (LZW, WFJ, BH) with an installed capacity of 13.9GW will be put into operation in the first phase (E1), with the landing point in Dongying; 2 nodes (BDN_N, BDN_F) with an installed capacity of 12.6GW will be put into operation in the second phase (E2), with the landing points in the second and third phases and Weihai; and 3 nodes (BDS_EN, BDS_ES, BDS_W) with an installed capacity of 8.5GW will be put into operation in the third phase (E3), with the landing point in Weihai.

[0062] The candidate submarine cable corridor connects offshore wind farm access nodes to onshore busbars, offering high-voltage AC (HVAC) submarine cable options at different voltage levels. Discount rate According to the standard, the cost of power generation technology is set with reference to the NREL 2022 Annual Technology Baseline Report. Each planning phase lasts for 5 years, with the planning horizon from 2026 to 2040, for a total of 3 planning phases.

[0063] Representative days, including typical days and stress operating conditions, are extracted from the annual operating conditions using a representative day clustering method.

[0064] Implementation of step S2: An optimization model with the goal of minimizing the total system cost is established according to the method in Example 1. The objective function includes operating cost, power generation investment cost, power transmission investment cost, energy storage investment cost, and a penalty term for failure to meet the consumption target, but does not include exogenous fixed clean energy cost parameters.

[0065] Implementation of step S3: Establish node power balance constraints, covering all planning phases, all nodes, all representative days, and all hours.

[0066] Establish constraints for energy storage operation, including upper and lower limits of state of charge and limits on charging and discharging power.

[0067] Establish constraints on the DC power flow network, including the linear relationship between line transmission power and node phase angle and line capacity limitations.

[0068] Establish renewable energy quota standards (RPS) constraints, unit combination constraints, and operational reserve constraints.

[0069] Implementation of step S4 The emission reduction targets for each stage are set. In Embodiment 2 of this invention, the emission reduction target trajectory is set as follows: E1 stage 400 MtCO2 / yr, E2 stage 350 MtCO2 / yr, and E3 stage 300 MtCO2 / yr. This indicates a gradual tightening of emission requirements.

[0070] Set the parameters for inter-period adjustment of the consumption index. The adjustment depreciation rate is set at 5%, and the upper limit of the adjustment balance is set as stipulated. Initial adjustment balance.

[0071] For scenarios where inter-period adjustment of consumption indicators is enabled, establish complete consumption constraints (including inter-period adjustment items) and upper limit constraints on adjustment balances; for scenarios where inter-period adjustment is disabled, fix the adjustment balance to zero.

[0072] Implementation of step S5 The objective function and all constraints described above are combined into a MILP model, modeled using Julia / JuMP, and solved using the Gurobi solver to obtain the optimal investment scheme. All binary investment decision variables (including transmission line construction decisions) are fixed. , Find its optimal value. Transform the model into an LP problem and solve it. Extract the dual variables corresponding to the cancellation constraint, and convert them into endogenous consumption costs according to the objective function coefficients and emission units.

[0073] Using the Shandong Province testing system described above, four scenarios were designed for verification.

[0074] Table 1: Scene Design

[0075] All non-absorption parameters were kept consistent to ensure that observed differences could be attributed to different ways of handling absorption constraints.

[0076] Table 2: Main Quantitative Results

[0077] Figure 3 This is a comparison chart of the system cost breakdown and total cost at each stage under the four scenarios proposed in Embodiment 2 of the present invention; from Figure 3 It can be seen that: In the S1 (FP50) scenario, the total system cost is US$55.42 billion, with an average CO2 emission of 300.1 MtCO2 / yr, achieving moderate emission reduction. In the S2 (FP200) scenario, after the clean energy cost parameter is increased to US$200 / tCO2, the total system cost increases sharply to US$85.71 billion, an increase of 55%, and the newly installed gas-fired power capacity surges from 21.84 GW to 87.61 GW, indicating significant overinvestment. This demonstrates that the fixed clean energy cost parameter method has extremely high parameter sensitivity, and the planning results are highly dependent on the clean energy cost parameters set by the analyst beforehand.

[0078] In S3 (CAP+BK) and S4 (CAP) scenarios, under the constraint of decreasing emission targets of 400 / 350 / 300 MtCO2 / yr, the total system costs are US$50.03 billion and US$49.51 billion, respectively, which are significantly lower than the US$85.71 billion in the S2 scenario, while effectively meeting the absorption targets at each stage. This indicates that the clean energy absorption constraint model proposed in this invention can directly execute the absorption target without relying on preset clean energy cost parameters, eliminating parameter sensitivity and effectively avoiding over-investment.

[0079] Comparing S3 and S4 scenarios, their cumulative emissions and system costs are similar, but their emission timelines differ. Scenario S3 utilizes a phased adjustment mechanism for emission quotas. In the first two stages, emissions were below the emission quotas by 79 Mt and 49 Mt respectively, accumulating adjustment balances which were then depreciated and transferred to the third stage. Scenario S4 does not utilize this mechanism; each stage independently meets the emission quotas. This demonstrates that the phased adjustment mechanism provides the system with phased flexibility, allowing for more optimal allocation of emission quotas over time.

[0080] Figures 4 to 7 The diagram illustrates the final planned transmission network topology under different scenarios, including: Figure 4 This is the planned final transmission network topology for scenario S1 (with a fixed clean energy cost parameter of $50 / tCO2) proposed in Embodiment 2 of the present invention; as follows: Figure 4 As shown, scenario S1 utilizes 10 submarine cable corridors and upgrades 4 terrestrial lines. The transmission network exhibits a relatively dispersed layout, with submarine cable capacity distribution being relatively even. This is because when the clean energy cost parameter is low, the system tends to invest moderately rather than over-construct, resulting in a more moderate coupling relationship between the transmission network and the power generation combination.

[0081] Figure 5 This is the planned final transmission network topology for scenario S2 (with a fixed clean energy cost parameter of $200 / tCO2) proposed in Embodiment 2 of the present invention; as follows: Figure 5As shown, scenario S2 utilizes nine submarine cable corridors, while the number of onshore lines has increased to seven. Compared to S1, the number of onshore line upgrades has significantly increased. This is due to the substantial increase in gas turbine investment driven by high clean energy cost parameters (from 21.84GW to 87.61GW), necessitating corresponding upgrades to the onshore grid to transmit power from these new units. This topology reflects the transmission network expansion pattern under conditions of over-investment. Figure 6 This is the final planned transmission network topology diagram for scenario S3 (absorption constraint + inter-period adjustment of absorption index) proposed in Embodiment 2 of the present invention; as follows: Figure 6 As shown, scenario S3 utilizes eight submarine cable corridors, with five onshore lines upgraded. Compared to S1 and S2, the offshore network is more compact, with transmission concentrated in high-capacity corridors. This is because the absorption constraint model needs to minimize the total system cost while meeting emission targets, thus favoring the more economical high-capacity transmission corridors rather than evenly distributing capacity. The inter-period adjustment mechanism smooths the absorption cost across different stages, further optimizing the timing decisions for transmission investment. Figure 7 This is the final planned transmission network topology diagram for scenario S4 (withintake constraints, excluding inter-period adjustment of intake indicators) proposed in Embodiment 2 of the present invention; as follows: Figure 7 As shown, scenario S4 also utilizes eight submarine cable corridors, with four onshore lines upgraded. Compared to S3, the submarine cable capacity distribution is more even, while the number of onshore line upgrades is smaller. This is because, in the absence of an inter-period adjustment mechanism, each stage must independently meet the absorption targets, and the system cannot smooth investment through inter-period quota transfers. Therefore, the transmission network topology exhibits different distribution characteristics. Table 3: Comparison of Disposal Costs

[0082] In Table 3, values ​​marked with * are exogenous fixed values, and S3 / S4 are endogenously determined through the dual variables of LP. In S4, the endogenous absorption cost increased from $14.96 / tCO2 as the absorption quota tightened, reflecting the increasing marginal absorption cost. After enabling inter-period adjustment of the absorption quota (S3), the absorption cost path was significantly smoothed, with the fluctuation range compressed from $11.5 to $2.4 / tCO2. Moreover, the absorption cost sequence of S3 approximately met the no-arbitrage condition, verifying the effectiveness of the inter-period adjustment mechanism in smoothing the fluctuation of absorption costs.

[0083] Comparison of power transmission network topologies S1 and S2 utilize 10 and 9 submarine cable corridors, respectively, with 4 and 7 upgraded onshore. S3 and S4 employ a more compact offshore network, both with 8 submarine cable corridors, and 5 and 4 upgraded onshore, respectively. S3 concentrates transmission in high-capacity corridors, while S4 distributes cable capacity more evenly. Topology selection is closely coupled with generation mix and absorption constraint strategies.

[0084] This embodiment 2, through verification in four comparative scenarios, draws the following conclusions: The fixed clean energy cost parameter method is highly sensitive to parameters, and the planning results are heavily dependent on the artificially set clean energy cost parameters, which can easily lead to over-investment. The consumption constraint model proposed in this invention directly executes the consumption target, eliminating parameter sensitivity. Second, the intertemporal adjustment mechanism of the consumption index introduced in this invention can effectively smooth the fluctuation of intertemporal consumption costs, compressing the fluctuation range from $11.5 / tCO2 to $2.4 / tCO2, while almost not increasing the total system cost. Third, the endogenous consumption cost extracted through LP duality has a clear economic meaning, revealing the problem that the fixed clean energy cost parameter method may overestimate clean energy costs. Fourth, the transmission network topology is closely coupled with the generation combination and consumption constraint strategy, and the method proposed in this invention can generate an optimal transmission network topology scheme adapted to the consumption constraint.

[0085] The proposed method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption can be applied to the field of power system planning, and is particularly suitable for coordinated planning scenarios of large-scale offshore wind power cluster grid connection and onshore power grid expansion.

[0086] Example 3 This invention also proposes a collaborative planning device for offshore wind power and onshore power grids based on clean energy consumption. Figure 8 This is a schematic diagram of the equipment for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption, as proposed in Embodiment 3 of the present invention.

[0087] At the hardware level, the electronic device 800 includes a processor 810, and optionally, an internal bus 820, a network interface 830, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may also include non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0088] The processor 810, network interface 830, and memory can be interconnected via an internal bus 820. This internal bus 820 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in this diagram, but this does not imply that there is only one bus or one type of bus. The memory is used to store programs. Specifically, the program can include program code, which includes computer operation instructions. The memory can include main memory 840 and non-volatile memory 850, and provides instructions and data to the processor 810.

[0089] The processor 810 reads the corresponding computer program from the non-volatile memory 850 into the main memory 840 and then runs it, forming a device for locating the target user at the logical level. The processor 810 executes the program stored in the memory and specifically performs the following: In step S1, basic data for power grid collaborative planning is obtained, and the planning level year is divided into multiple sequential planning stages; In step S2, an objective function is constructed. This invention establishes an optimization model with the goal of minimizing the total system cost. The objective function includes the discounted operating cost, the annualized investment cost of power generation, the annualized investment cost of power transmission, the annualized investment cost of energy storage, and a penalty term for failure to meet the consumption standards. It does not include exogenous fixed clean energy cost parameters.

[0090] In step S3, operational constraints are set, including node power balance constraints, energy storage operation constraints, and network transmission constraints based on the DC power flow model, to ensure that the operation is feasible on representative days of each planning stage.

[0091] In step S4, clean energy consumption constraints are set.

[0092] At each planning stage, clean energy consumption targets are imposed as constraints, and a mechanism for adjusting consumption targets across periods is allowed.

[0093] In step S5, the optimization model is constructed as a mixed integer linear programming model and solved to obtain the optimal investment scheme; then, all binary investment decision variables are fixed to their optimal values, the model is transformed into a linear programming problem to be solved, and the dual variable of the clean energy consumption constraint is extracted as the endogenous consumption cost. In step S6, the planning schemes for each stage are output based on the solution results.

[0094] Figure 1 It can be applied to processor 810, or implemented by processor 810. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit in the processor or by instructions in the form of software. The processor mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0095] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0096] The description of the relevant part of the equipment for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption provided in Embodiment 3 of this application can be found in the detailed description of the corresponding part of the method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption provided in Embodiment 1 of this application, and will not be repeated here.

[0097] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0098] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A collaborative planning method for offshore wind power and onshore power grid based on clean energy consumption, characterized in that, Includes the following steps: Acquire basic data for power grid collaborative planning and divide the planning horizon into multiple sequential planning phases; Based on the aforementioned fundamental data, an optimization model is established with the goal of minimizing the total system cost, subject to operational constraints and clean energy consumption constraints. The objective function replaces the exogenous fixed clean energy cost parameters with clean energy consumption constraints, including discounted annual operating costs, annualized power generation investment costs, annualized transmission investment costs, annualized energy storage investment costs, and penalties for non-compliance with consumption targets. Operational constraints include node power balance constraints, energy storage operation constraints, and DC power flow network constraints. Clean energy consumption constraints impose consumption index constraints at each planning stage, and allow consumption quotas to be transferred across periods with depreciation between adjacent planning stages. The optimization model is constructed as a mixed-integer linear programming model and solved to obtain the optimal investment plan. Then, fix all binary investment decision variables to their optimal values, transform the model into a linear programming problem to be solved, extract the dual variable of clean energy consumption constraints as the endogenous consumption cost, and output the planning scheme for each stage based on the solution results.

2. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 1, characterized in that, The basic data includes: Information on onshore power grid nodes, their generator sets, loads, and transmission corridors; Information on offshore wind power access nodes, their planned installed capacity, and commissioning schedule; Candidate submarine cable corridors and their voltage levels and transmission capacity parameters; The technical parameters of existing generator sets include unit type, rated capacity, minimum technical output, gradeability and emission coefficient; Investment cost parameters for newly built power generation technologies and energy storage systems; Load forecast data and renewable energy output curves; Emissions conversion factor, as well as discount rate and economic life of equipment.

3. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 1, characterized in that, The objective function is expressed as: ； in, Indicates the index of the current planning stage. Indicates the first Annual operating costs after phase discounting; Indicates the first Annualized investment cost for power generation after phase discounting; Indicates the first Annualized investment cost of power transmission after phase discounting; Indicates the first The annualized investment cost of energy storage after phase discounting; Penalties will be imposed for failure to meet clean energy consumption standards; This indicates that the slack variables have not been adequately absorbed.

4. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 3, characterized in that, The annualized investment cost of power generation Represented as: ； in, Indicates an index of investment stages; Represents the index of the power grid node; This represents the discount-survival factor of power generation technology; Indicates the first Annualized unit investment cost of staged gas-fired carbon capture units; Represents a node In the The newly added capacity of gas-fired carbon capture units in this phase; Indicates the first Annualized unit investment cost of staged gas turbines; Represents a node In the The newly added gas turbine capacity in this phase; Indicates the first Annualized unit investment cost for onshore / offshore wind power at different stages; Represents a node In the The newly added wind power capacity in this phase; Indicates the first Annualized unit investment cost of photovoltaic power generation at different stages; Represents a node In the The newly added photovoltaic installed capacity in this phase; Annualized investment cost of power transmission Represented as: ； in, This represents the discount-survival factor for transmission lines. This represents the set of candidate land routes; Indicates land route The annualized unit investment cost; Indicates land route In the Phase upgrade binary decision variables; This represents a set of candidate submarine cable corridors at sea; This represents a set of voltage levels for submarine cables; Indicates sea route Medium voltage level Annualized unit investment cost of submarine cables; Indicates sea route Medium voltage level The submarine cable in the first Binary decision variables for phased construction; Indicates land route Medium voltage level The corresponding investment cost of the converter station; Indicates the variable for selecting the voltage level of the submarine cable; Energy storage investment costs Represented as: ； in, This represents the discount-survival factor of energy storage. Indicates the first Annualized investment cost per unit energy capacity for staged energy storage; Represents a node In the The rated energy capacity of the newly added energy storage in the phase; Indicates the first Annualized investment cost per unit power capacity of staged energy storage; Represents a node In the The rated power capacity of newly added energy storage in each phase.

5. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 1, characterized in that, The operational constraints include: The node power balance constraint requires that, for each planning stage, each grid node, each representative day, and each hour, the sum of the node's power generation output, available renewable energy output, energy storage discharge power, and line injection power is equal to the sum of the node's load demand, energy storage charging power, and line outflow power, and allows load shedding and wind / solar curtailment as relaxation variables. Energy storage operation constraints include upper and lower limits of energy storage state of charge, upper and lower limits of charge and discharge power, and time-series coupling constraints of state of charge between adjacent time periods, which are used to characterize the energy storage characteristics and operating boundaries of energy storage. DC power flow network constraints are established based on the DC power flow model to establish a linear relationship between line transmission power and node phase angle, and upper and lower capacity limits are imposed on the transmission power of each line to characterize the physical transmission characteristics of the power network.

6. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 1, characterized in that, The constraints on clean energy consumption are expressed as follows: in, Representing the day The weights; Indicates generator set The emission conversion factor; Indicates generator set In the Phase, Representative Day , No. Hours of effort; Indicates the first The remaining balance of the consumption target at the end of the phase; Indicates the depreciation rate across periods; Indicates the first The remaining balance of the consumption target at the end of the phase; Indicates the first The upper limit of the absorption target for each stage; Indicates slack variables that fail to meet the absorption targets; This represents the set of planning stages.

7. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 6, characterized in that, The upper limit constraint for the balance of the consumption quota adjustment is: in, This is to adjust the upper limit ratio of the balance.

8. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 7, characterized in that, When the Phase and the When the balance of the absorption index adjustment in each stage does not reach the upper and lower limits, the endogenous absorption cost obtained by the model solution satisfies: ； in, Indicates the first The endogenous absorption cost of the stage; Indicates the first The endogenous absorption cost of the stage.

9. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 1, characterized in that, The optimization model is constructed as a mixed-integer linear programming model and solved to obtain the optimal investment plan, specifically: The binary and continuous variables in the power generation investment decision variables, power transmission investment decision variables, and energy storage investment decision variables in the optimization model are combined to construct a mixed integer linear programming model. The model is solved using a mixed-integer linear programming solver to obtain the construction decisions for each candidate line at each stage, the integer solutions for the new unit capacity at each node, and the corresponding continuous variable solutions for operation scheduling, which serve as the optimal investment scheme.

10. The method for coordinated planning of offshore wind power and onshore power grid based on clean energy consumption according to claim 1, characterized in that, The planning schemes for each stage include at least the endogenous absorption costs for each stage, which are used to guide the intertemporal trading pricing of clean energy absorption indicators.

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