Power grid optimization scheduling method and device of offshore wind power grid connection system, medium

Abstract

The application discloses a power grid optimization scheduling method and device of an offshore wind power grid-connected system, a medium, and the method comprises the following steps: determining a plurality of regulation state conversion time periods of the system based on typhoon time-varying characteristic information; determining target system operation information and performing corresponding system operation when being in any one regulation state conversion time period; constructing a system frequency response model and a frequency safety constraint formula; constructing a generator set resource cost control model and a first constraint formula; constructing a system load shedding response model and a second constraint formula; optimizing a target model constructed based on the generator set resource cost control model and the system frequency response model by using a target constraint formula constructed by using the first constraint formula and the second constraint formula, and solving the optimized target model by using a Benders decoupling method. The application predicts the regulation state conversion time of the system based on typhoon time-varying characteristics, adjusts system operation, and performs frequency, load shedding amount and other constraints on the offshore wind power grid-connected system, so that the safety of system operation is ensured.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of offshore wind power grid-connected system dispatching technology, and in particular to a grid optimization dispatching method, device, and medium for offshore wind power grid-connected systems. Background Technology

[0002] Offshore wind power plays a crucial role in the low-carbon energy transition. However, the operation of offshore wind farms is heavily influenced by typhoons. Before landfall, excessively high wind speeds not only put immense stress on the structure of offshore wind turbines but also cause significant fluctuations in the output power of the offshore wind power grid-connected system. After landfall, transmission lines may be damaged. Typhoon disasters affect the power balance of the offshore wind power grid-connected system, thereby threatening the system's frequency security. Existing methods utilize active turbine combination schemes to achieve flexible scheduling and proactive management of generation-side, battery storage systems, and demand-side resources. However, these methods do not address the uncertainties surrounding turbines and the duration of typhoon events, thus failing to guarantee the operational safety of individual devices within the offshore wind power grid-connected system and the system's frequency security. Summary of the Invention

[0003] This application provides a grid optimization scheduling method, device, and medium for an offshore wind power grid-connected system, which can ensure the operational safety of each module in the offshore wind power grid-connected system and the system frequency safety during typhoons.

[0004] In a first aspect, embodiments of this application provide a grid optimization scheduling method for an offshore wind power grid-connected system. This method is applied to the offshore wind power grid-connected system and includes:

[0005] The typhoon time-varying characteristic information is determined. The typhoon time-varying characteristic information includes the typhoon translational speed, typhoon forward direction information, typhoon intensity information, reference distance, gradient wind speed, and wind power corresponding to the current timestamp. The reference distance is used to indicate the distance between the offshore wind power grid-connected system and the center point of the typhoon. The wind power is the maximum output power of the wind turbine of the offshore wind power grid-connected system under different wind speed conditions.

[0006] Based on the typhoon translation speed, typhoon direction information, typhoon intensity information, reference distance, gradient wind speed, and wind power corresponding to the current timestamp, multiple control state transition time periods of the offshore wind power grid-connected system are determined, and each state transition time period uniquely corresponds to a system state.

[0007] When the current timestamp falls within any of the multiple control state transition time periods, the corresponding target system state is determined, and the target system operation information is determined from the preset relational mapping table based on the target system state. The system operation corresponding to the target system operation information is then executed. The relational mapping table is used to indicate the mapping relationship between the system state and the system operation information, and the system operation information represents the controllable resources and scheduling operation information of the offshore wind power grid-connected system.

[0008] Based on the system's overall inertia, frequency regulation increment of energy storage output, system frequency deviation, and system damping parameters of the offshore wind power grid-connected system, a system frequency response model is constructed. Based on the system frequency response model and a preset system safety frequency threshold, a target frequency safety constraint, a frequency safety linearization constraint, and a frequency safety constraint are constructed.

[0009] Based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system, a generator resource cost control model is constructed, and a first constraint formula is constructed based on the preset minimum start-up time, preset shutdown duration, and generator status constraints.

[0010] Based on the generator fuel cost, energy storage charging and discharging cost, energy storage charging and discharging power, load shedding cost, and load shedding amount of the offshore wind power grid-connected system, a system load shedding response model is constructed, and a second constraint formula is constructed based on preset energy storage capacity threshold, load shedding increase threshold, and load shedding decrease threshold.

[0011] Based on the target frequency security constraint, frequency security linearization constraint, and frequency security constraint optimization, the system frequency response model is optimized.

[0012] A target constraint is constructed based on the first constraint and the second constraint. A target model is constructed based on the generator set resource cost control model and the system load shedding response model. The target model is then optimized based on the target constraint.

[0013] The optimized target model is solved based on the distributed decoupling algorithm of the multiple Benders cuts.

[0014] In some embodiments, the control state transition time period includes a warning state time period, an emergency state time period, a repair state time period, and a normal state time period. The offshore wind power grid-connected system includes generator sets, transmission lines, and a load control module. Each generator set, transmission line, and load control module has corresponding reference operating parameters. Each corresponding reference operating parameter is used to indicate the operating parameters of the generator set, transmission line, and load control module when the offshore wind power grid-connected system is in a normal state. When the current timestamp is in any one of the multiple control state transition time periods, the corresponding target system state is determined. Based on the target system state, target system operation information is determined from a preset relational mapping table, and the system operation corresponding to the target system operation information is executed, including:

[0015] If the current timestamp falls within the warning state time period, determine the target system state corresponding to the warning state time period as the warning state, obtain the target system operation information corresponding to the warning state from the relationship mapping table, and pre-schedule the generator set according to the corresponding target system operation information;

[0016] When the current timestamp falls within the emergency state time period, the target system state corresponding to the emergency state time period is determined to be an emergency state. The target system operation information corresponding to the emergency state is obtained from the relationship mapping table. Based on the corresponding target system operation information, the transmission line is controlled to output a preset emergency transmission capacity. At the same time, the load control module is switched off. The emergency transmission capacity is greater than the normal transmission capacity of the transmission line.

[0017] If the current timestamp falls within the repair status time period, the target system status corresponding to the repair status time period is determined to be in the repair status, and the target system operation information corresponding to the repair status is obtained from the relationship mapping table. The load control module is then switched off according to the corresponding target system operation information.

[0018] If the current timestamp falls within the normal state time period, the target system state corresponding to the normal state time period is determined to be in a normal state. The target system operation information corresponding to the normal state is obtained from the relational mapping table. Based on the corresponding target system operation information, the generator set, the transmission line, and the load control module are controlled respectively according to the corresponding reference operation parameters.

[0019] In some embodiments, the gradient wind speed is calculated according to the following formula:

[0020]

[0021] in, The gradient wind speed, r is the scaling factor ω The reference distance is δ, which is an adjustment factor used to describe minute changes in wind speed. t represents the distance between the wind farm and the typhoon center, ω represents the current timestamp, and ω represents the current scene information.

[0022] The wind power output is calculated using the following formula:

[0023]

[0024] in, The wind power is used to indicate the wind speed at time t. Below, the maximum power point tracking output power of the wind turbine is V. 启 V is the minimum wind speed at which the wind turbine of the wind turbine unit begins generating electricity. 额 Rated wind speed, used to indicate the wind speed at which the wind turbine of the wind turbine unit achieves maximum power output, V 停 P is the shutdown wind speed, used to indicate the wind turbine of the wind turbine unit at which it stops operating due to protective equipment. 额 τ represents the rated power output of the wind turbine of the wind turbine unit, and τ represents the rotor radius of the wind turbine unit.

[0025] In some embodiments, the frequency security constraint formula is constructed based on the system frequency response model and the preset system security frequency threshold, and is obtained according to the following formula:

[0026]

[0027] Where, Δf max The preset system security frequency threshold is given, and f0 is the current system frequency. These are weighting coefficients related to load changes, used to measure the impact of power changes caused by generator inertia on system frequency. Let be the power change of the l-th load at time t and under scenario ω. R is a weighting factor related to the system's reserve power, used to quantify the contribution of frequency regulation reserve to system frequency stability. sys,t Let be the total reserve power of the system at time t. These are weighting coefficients related to system inertia, used to reflect the suppressive effect of system inertia on frequency changes. Let be the total inertia of the system at time t and under scenario ω. D is a weighting coefficient related to system damping, used to represent the ability to dampen frequency oscillations. sys,tLet be the total damping coefficient of the system at time t. These are constants related to the system.

[0028] The frequency safety constraint is obtained by transforming the following formula using a piecewise linearization method based on convex optimization:

[0029]

[0030] in, Let D be the frequency deviation at the lowest point of the system frequency in scenario ω at time t. sys R is the system damping parameter. sys For the system's frequency regulation reserve, T g F is the time constant of the governor of the thermal power unit. sys The percentage of system power generation, Let ξ be the system's total inertia, ξ be the damping ratio, representing the degree of system damping, and ω be the damping ratio. n Let t be the natural frequency of the system. nadir The time required to reach the lowest frequency point;

[0031]

[0032]

[0033] Where, ω d U is the damped oscillation angular frequency. g,t Let g be the operating state of generator set g at time t. K represents the maximum output power of generator set g. g R is the mechanical power gain factor of the thermal power unit. g This is the active power droop control coefficient for thermal power units. For generator set collection, F g R represents the proportion of electricity generated by high-pressure boilers. f Let be the droop control coefficient for offshore wind farms, and w be the value of the offshore wind farm. For offshore wind farm clusters, P f R is the rated power of the offshore wind farm f. s P is the droop control coefficient for energy storage devices. s Let S be the output power of energy storage device s, where s is a specific energy storage device, S is a battery energy storage system set, and D is the output power of the energy storage device s. g denoted as g, which is the damping coefficient of a conventional generator set.

[0034] In some embodiments, a generator resource cost control model is constructed based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system, and is obtained according to the following formula:

[0035]

[0036] Where f(x) is the objective function corresponding to the generator set resource cost control model, c 开,g c is the start-up cost of the generator set. 关,g For the shutdown cost of the generator set, α g,t β is the identifier for the generator set startup operation. g,t c is the generator set shutdown operation identifier. T Let x be the cost coefficient vector, x be the decision variable vector, and Δt be the time change. For time sets, Let t be the set of generator sets, and t be the current timestamp.

[0037] In some embodiments, the first constraint includes a first sub-constraint, a second sub-constraint, a third sub-constraint, and a fourth sub-constraint. The first constraint is constructed based on a preset minimum start-up cost, a preset downtime, and generator state constraints, and is obtained according to the following formula:

[0038] The expression for the first sub-constraint is:

[0039]

[0040] The expression for the second sub-constraint is:

[0041]

[0042] The expression for the third sub-constraint is:

[0043]

[0044] The expression for the fourth sub-constraint is:

[0045]

[0046] in, For the generator set at time t, α represents the shutdown state. g,b β represents the starting state of the generator set at time b. g,b For the generator set at time b, u is the shutdown state. g,0 Let UT be the operating state of generator set g at the initial time. g DT is the minimum start-up time of generator set g. g UT is the minimum shutdown time for generator set g. r DT represents the minimum start-up time of generator set g under initial conditions. r This represents the minimum shutdown time for generator set g in the initial state.

[0047] In some embodiments, a system load shedding response model is constructed based on the generator fuel cost, energy storage charging and discharging cost, energy storage charging and discharging power, load shedding cost, and load shedding amount of the offshore wind power grid-connected system, and is obtained according to the following formula:

[0048]

[0049] Where q(y) ω ) is the objective function corresponding to the system load shedding response model, α g The fuel cost for generating electricity from the unit. Let c be the power output of generator set g at time t under scenario ω. LCOE The cost of charging and discharging the energy storage, The energy storage charging and discharging power, For the load shedding cost, c VOLL For the load shedding cost, The load shedding amount is t, and the current timestamp is t.

[0050] In some embodiments, the second constraint is obtained according to the following formula:

[0051]

[0052] Among them, u g,t This refers to the status information of the generator set. p is the minimum power limit for generator set g. g,t Let g be the actual power output of generator set g at time t. For the maximum power limit of generator set g, R g,t Let be the standby capacity of generator set g at time t, used to cope with load changes or faults. Let g be the actual power output of generator set g at time t under scenario ω. u represents the maximum power boost capability provided by generator set g within the time interval Δt. g,t-1 Let SU be the operating state of generator set g at time t-1. g Let α be the slope of the starting power of generator set g. g,t Let g be the starting state of generator set g at time t. SD represents the maximum power reduction capability provided by generator set g within time interval Δt. g Let β be the slope of the shutdown power of generator set g. g,t Let ω represent the shutdown state of generator set g at time t, and let ω represent different scenarios. For any timestamp, Let be the switching state indicator variable of line ij at time t and under scenario ω. Let B be the active power flow of line ij at time t and scenario ω. ij Let be the value of the line admittance matrix ij. Let i be the phase angle of node i at time t and scene ω. Let be the phase angle of node j at time t and in scenario ω, where i and j are nodes in the line, and ij is the transmission line connecting nodes i and j. Let be the maximum active power of line ij. Let be the state indicator variable for the disturbance at time t and in scenario ω. Let be the maximum power change of line ij after considering disturbances. Let be the maximum active power of line ij under preset condition k, and e be the energy state of the energy storage device. Let be the charging power of the energy storage device s at time t and under scenario ω. The maximum charging power of the energy storage device s. Let be the discharge power of the energy storage device s at time t and under scenario ω. Let be the maximum discharge power of the energy storage device s, ch be the charging process, and dc be the discharging process.

[0053] Secondly, embodiments of this application provide a control device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the grid optimization scheduling method for offshore wind power grid-connected systems as described in the first aspect.

[0054] Thirdly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for executing the grid optimization scheduling method for an offshore wind power grid-connected system as described in the first aspect.

[0055] This application provides a grid optimization scheduling method, apparatus, and medium for an offshore wind power grid-connected system. The method includes: determining typhoon time-varying characteristic information, wherein the typhoon time-varying characteristic information includes the typhoon's translational speed, typhoon's forward direction information, typhoon intensity information, reference distance, gradient wind speed, and wind power at the current timestamp; wherein the reference distance is used to indicate the distance between the offshore wind power grid-connected system and the typhoon's center point, and the wind power is the maximum output power of the wind turbine generators of the offshore wind power grid-connected system under different wind speed conditions; based on the typhoon's translational speed, typhoon's forward direction information, typhoon intensity information, reference distance, gradient wind speed, and wind power at the current timestamp... The wind speed and wind power are used to determine multiple control state transition time periods for the offshore wind power grid-connected system, each of which uniquely corresponds to a system state. When the current timestamp falls within any of these control state transition time periods, the corresponding target system state is determined. Based on the target system state, target system operation information is determined from a preset relational mapping table, and the system operation corresponding to the target system operation information is executed. The relational mapping table indicates the mapping relationship between system states and system operation information, and the system operation information represents the controllable resources and scheduling operation information of the offshore wind power grid-connected system. Based on the offshore wind power... A system frequency response model is constructed based on the system's overall inertia, frequency regulation increment of energy storage output, system frequency deviation, and system damping parameters. Based on this model and a preset system safety frequency threshold, target frequency safety constraints, frequency safety linearization constraints, and frequency safety constraints are then established. A generator resource cost control model is constructed based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system. A first constraint is constructed based on preset minimum start-up time, preset shutdown duration, and generator status constraints. Finally, a generator power generation fuel cost model is established based on the offshore wind power grid-connected system. The system load shedding response model is constructed based on the energy storage charging and discharging costs, energy storage charging and discharging power, load shedding costs, and load shedding amounts. A second constraint is constructed based on preset energy storage capacity thresholds, load shedding increase thresholds, and load shedding decrease thresholds. The system frequency response model is optimized based on the target frequency safety constraint, frequency safety linearization constraint, and frequency safety constraint. A target constraint is constructed based on the first and second constraints. A target model is constructed based on the generator set resource cost control model and the system load shedding response model. The target model is optimized based on the target constraint. The optimized target model is solved using the distributed decoupling algorithm with multiple Benders cuts.According to the solution provided in the embodiments of this application, the control state transition of the offshore wind power grid-connected system is predicted and the system operation is adjusted based on the time-varying characteristic information of typhoons. Frequency constraints, load shedding constraints and operating cost constraints are imposed on the offshore wind power grid-connected system after the state switch, thereby reducing the adverse effects of typhoons on the offshore wind power grid-connected system before and after the arrival of typhoons, and thus effectively ensuring the operational safety of each module in the system and the frequency safety of the system. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the steps of a grid optimization scheduling method for an offshore wind power grid-connected system according to an embodiment of this application;

[0057] Figure 2 This is a flowchart illustrating the steps of an offshore wind power grid-connected system performing different system operations in different system states, according to another embodiment of this application.

[0058] Figure 3 This is a structural diagram of a control device provided in another embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] It is understandable that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0061] Offshore wind power plays a crucial role in the low-carbon energy transition. However, the operation of offshore wind farms is heavily influenced by typhoons. Before landfall, excessively high wind speeds not only put immense stress on the structure of offshore wind turbines but also cause significant fluctuations in the output power of the offshore wind power grid-connected system. After landfall, transmission lines may be damaged. Typhoon disasters affect the power balance of the offshore wind power grid-connected system, thereby threatening the system's frequency security. Existing methods utilize active turbine combination schemes to achieve flexible scheduling and proactive management of generation-side, battery storage systems, and demand-side resources. However, these methods do not address the uncertainties surrounding turbines and the duration of typhoon events, thus failing to guarantee the operational safety of individual devices within the offshore wind power grid-connected system and the system's frequency security.

[0062] To address the aforementioned problems, this application provides a grid optimization scheduling method, apparatus, and medium for an offshore wind power grid-connected system. The method includes: determining typhoon time-varying characteristic information, wherein the typhoon time-varying characteristic information includes the typhoon's translational speed, typhoon's forward direction information, typhoon intensity information, reference distance, gradient wind speed, and wind power corresponding to the current timestamp; wherein the reference distance indicates the distance between the offshore wind power grid-connected system and the typhoon's center point, and the wind power is the maximum output power of the wind turbine generators of the offshore wind power grid-connected system under different wind speed conditions; based on the typhoon's translational speed, typhoon's forward direction information, typhoon intensity information, and gradient wind speed corresponding to the current timestamp... The reference distance, the gradient wind speed, and the wind power determine multiple control state transition time periods for the offshore wind power grid-connected system, each of which uniquely corresponds to a system state. When the current timestamp falls within any of these control state transition time periods, the corresponding target system state is determined. Based on the target system state, target system operation information is determined from a preset relational mapping table, and the system operation corresponding to the target system operation information is executed. The relational mapping table indicates the mapping relationship between system states and system operation information, and the system operation information represents the controllable resources and scheduling operation information of the offshore wind power grid-connected system. A system frequency response model is constructed based on the system's overall inertia, frequency regulation increment of energy storage output, system frequency deviation, and system damping parameters of the offshore wind power grid-connected system. Based on this model and a preset system safety frequency threshold, target frequency safety constraints, frequency safety linearization constraints, and frequency safety constraints are then established. A generator resource cost control model is constructed based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system. A first constraint is then established based on preset minimum start-up time, preset shutdown duration, and generator status constraints. Finally, a fuel consumption control model is established based on the generator power generation fuel consumption of the offshore wind power grid-connected system. The system load shedding response model is constructed based on material costs, energy storage charging and discharging costs, energy storage charging and discharging power, load shedding costs, and load shedding amounts. A second constraint is constructed based on preset energy storage capacity thresholds, load shedding increase thresholds, and load shedding decrease thresholds. The system frequency response model is optimized based on the target frequency safety constraint, frequency safety linearization constraint, and frequency safety constraint. A target constraint is constructed based on the first and second constraints. A target model is constructed based on the generator set resource cost control model and the system load shedding response model. The target model is optimized based on the target constraint. The optimized target model is solved using the distributed decoupling algorithm with multiple Benders cuts.According to the solution provided in this application, the control state transition of the offshore wind power grid-connected system is predicted and the system operation is adjusted based on the time-varying characteristics of typhoons. Frequency constraints, load shedding constraints, and operating cost constraints are applied to the offshore wind power grid-connected system after the state switch, reducing the adverse effects of typhoons on the offshore wind power grid-connected system before and after their arrival, thereby effectively ensuring the operational safety of each module in the system and the system frequency security. The embodiments of this application will be further described below with reference to the accompanying drawings.

[0063] refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a grid optimization scheduling method for an offshore wind power grid-connected system according to an embodiment of this application. This application provides a grid optimization scheduling method for an offshore wind power grid-connected system, which is applied to the offshore wind power grid-connected system and includes, but is not limited to, the following steps:

[0064] Step S10: Determine the typhoon time-varying characteristic information. The typhoon time-varying characteristic information includes the typhoon translation speed corresponding to the current timestamp, the typhoon's direction of movement information, the typhoon's intensity information, the reference distance, the gradient wind speed, and the wind power. The reference distance is used to indicate the distance between the offshore wind power grid-connected system and the typhoon's center point.

[0065] Step S20: Based on the typhoon translation speed, typhoon direction of travel information, typhoon intensity information, reference distance and gradient wind speed corresponding to the current timestamp, determine multiple control state transition time periods of the offshore wind power grid-connected system. Each control state transition time period uniquely corresponds to a system state.

[0066] It is understandable that this embodiment can effectively analyze the impact of typhoons on the power grid composed of offshore wind farms, transmission lines, transmission towers, generator units, etc. in the offshore wind power grid-connected system before and after landfall by calculating the typhoon's translational speed, typhoon's forward direction information (i.e., typhoon trajectory characteristics), typhoon intensity information, reference distance, and gradient wind speed (i.e. typhoon wind field characteristics) corresponding to the current timestamp. This provides an effective data foundation for the specific timestamp of the subsequent prediction system's control and transition state.

[0067] It should be noted that the gradient wind speed is calculated using the following formula:

[0068]

[0069] in, For gradient wind speed, r is the scaling factor ω The reference distance is δ, which is an adjustment factor used to describe minute changes in wind speed. ω represents the distance between the wind farm and the typhoon center, t represents the current timestamp, and ω represents the current scene information.

[0070] It should be noted that the typhoon's translational speed The typhoon's forward direction information θ is calculated using the following formula:

[0071]

[0072] Where a1, a2, and a3 are the correlation coefficients between the typhoon's translational velocity at time t and other times, obtained from historical data. The random error is represented by b1, b2, and b3, which are the correlation coefficients between the typhoon's forward direction at time t and other times, obtained from historical data. For the site dimension at time t-1, Let be the relative intensity at time t-1.

[0073] Step S30: When the current timestamp is in any of the multiple control state transition time periods, determine the corresponding target system state, determine the target system operation information from the preset relation mapping table based on the target system state, and execute the system operation corresponding to the target system operation information. The relation mapping table is used to indicate the mapping relationship between the system state and the system operation information. The system operation information represents the controllable resources and scheduling operation information of the offshore wind power grid connection system.

[0074] Specifically, the control state transition time periods predicted based on typhoon time-varying characteristic information include warning state time periods, emergency state time periods, repair state time periods, and normal state time periods. The offshore wind power grid-connected system includes generator sets, transmission lines, and load control modules. Each generator set, transmission line, and load control module has corresponding reference operating parameters. These reference operating parameters indicate the operating parameters of the generator set, transmission line, and load control module when the offshore wind power grid-connected system is in a normal state. Figure 2 , Figure 1 Step S30 includes, but is not limited to, the following steps:

[0075] Step S31: If the current timestamp is within the warning state time period, determine the target system state corresponding to the warning state time period as the warning state, obtain the target system operation information corresponding to the warning state from the relationship mapping table, and pre-schedule the generator set according to the corresponding target system operation information.

[0076] Step S32: When the current timestamp is within an emergency state time period, determine the target system state corresponding to the emergency state time period as an emergency state, obtain the target system operation information corresponding to the emergency state from the relationship mapping table, and control the transmission line to output the preset emergency transmission capacity according to the corresponding target system operation information, while cutting the load on the load control module. The emergency transmission capacity is greater than the normal transmission capacity of the transmission line.

[0077] Step S33: If the current timestamp is in the repair state time period, determine the target system state corresponding to the repair state time period as the repair state, obtain the target system operation information corresponding to the repair state from the relation mapping table, and perform load shedding on the load control module according to the corresponding target system operation information.

[0078] Step S34: If the current timestamp is within a normal state time period, determine the target system state corresponding to the normal state time period as normal state, obtain the target system operation information corresponding to the normal state from the relation mapping table, and control the generator set, transmission line and load control module respectively based on the corresponding target system operation information and the corresponding reference operation parameters.

[0079] It is understood that, referring to the description of the above embodiments, when the typhoon is far away from the offshore wind power grid connection system, the offshore wind power grid connection system is in a normal state. The system meets the N-1 safety standard; if a typhoon is approaching and the current timestamp falls within the warning period, the system status will be adjusted to the warning state to account for the adverse effects of the approaching typhoon. At this point, pre-scheduling actions are required, disregarding the N-1 operating condition of the system, i.e., pre-scheduling the generator units to disconnect; as the typhoon gets closer to the offshore wind power grid-connected system, if a fault is detected, the system status will be adjusted to emergency mode. In this state, the transmission lines are controlled to output a preset emergency transmission capacity, while the load control module is switched off. The emergency transmission capacity is greater than the normal transmission capacity of the transmission lines. When it is determined that the typhoon center is far from the offshore wind power grid system, the system state is adjusted to a repair state, the load control module is switched off, and the transmission lines are reconfigured to minimize system costs. After system repair is completed, the system returns to normal. Due to the uncertainty of the typhoon's path and impact, the system state during the dispatch period is represented as follows: In other words, this embodiment predicts the timestamp of the change in system state of the offshore wind power grid connection system by acquiring the time-varying characteristics of typhoons in real time, and performs different system operations based on the current system state of the offshore wind power grid connection system, which can ensure the safe operation of the offshore wind power grid connection system before and after the arrival of typhoons.

[0080] Step S40: Construct a system frequency response model based on the system comprehensive inertia, frequency regulation increment of energy storage output, system frequency deviation and system damping parameters of the offshore wind power grid-connected system, and construct a frequency safety constraint formula based on the system frequency response model and the preset system safety frequency threshold.

[0081] Specifically, based on the constructed system frequency response model, frequency dynamic analysis and calculation are performed to obtain the timestamp t of the system frequency minimum point. nadir The result is obtained from the following formula:

[0082]

[0083]

[0084] Where, ω d ω is the damping frequency. n T is the natural frequency of the system, used to indicate the natural frequency of the system's oscillation in the undamped state. g D is the time constant of the governor of the thermal power unit. sys R is the system damping parameter. sys This is the system active power droop control coefficient. For the overall inertia of the system, F sys Let ξ be the system's power generation ratio, and ξ be the damping ratio, representing the degree of system damping.

[0085] The system parameters involved in the above formula are obtained from the following formula:

[0086]

[0087] Where, ω d U is the damped oscillation angular frequency. g,t Let g be the operating state of generator set g at time t. K represents the maximum output power of generator set g. g R is the mechanical power gain factor of the thermal power unit. g This is the active power droop control coefficient for thermal power units. For generator set collection, F g R represents the proportion of electricity generated by high-pressure boilers. f Let be the droop control coefficient for offshore wind farms, and w be the value of the offshore wind farm. For offshore wind farm clusters, P f R is the rated power of the offshore wind farm f. s P is the droop control coefficient for energy storage devices. s Let S be the output power of energy storage device s, where s is a specific energy storage device, S is a battery energy storage system set, and D is the output power of the energy storage device s. g denoted as g, which is the damping coefficient of a conventional generator set.

[0088] Then, frequency safety constraints required for the offshore wind power grid-connected system are established using frequency analytical solutions, namely, the frequency deviation at the lowest point of the system frequency should be less than the preset system safety frequency threshold:

[0089]

[0090] Where, Δf max To preset the system's safe frequency threshold, This represents the frequency deviation at the lowest point of the system frequency.

[0091] Next, the equivalent aggregation model for the incremental processing capacity of the thermal power unit is calculated:

[0092]

[0093] in, Let K be the rate of change of power of generator set g at time t under scenario ω. g F is the mechanical power gain factor of the thermal power unit. g R represents the proportion of electricity generated by high-pressure boilers. g R is the active power droop control coefficient for thermal power units. s T is the active power droop control coefficient for energy storage. s Let Δf be the time constant of the energy storage speed regulator. t ω Let be the change in system frequency at time t under scenario ω. Let Δp be the rate of change of power of the energy storage device s at time t under scenario ω. s,t Let be the power change of the energy storage device at time t.

[0094] Based on the timestamp expression for the occurrence of the system's lowest frequency point and the equivalent aggregation model for the thermal power unit's incremental processing, the system's lowest frequency point constraint is approximated using a piecewise linearization method. The resulting frequency safety constraint is derived from the following formula:

[0095]

[0096] Where, Δf max The preset system safety frequency threshold is f0, where f0 is the current system frequency. These are weighting coefficients related to load changes, used to measure the impact of power changes caused by generator inertia on system frequency. Let be the power change of the l-th load at time t and under scenario ω. R is a weighting factor related to the system's reserve power, used to quantify the contribution of frequency regulation reserve to system frequency stability. sys,t Let be the total reserve power of the system at time t. These are weighting coefficients related to system inertia, used to reflect the suppressive effect of system inertia on frequency changes. Let be the total inertia of the system at time t and under scenario ω. D is a weighting coefficient related to system damping, used to represent the ability to dampen frequency oscillations. sys,t Let be the total damping coefficient of the system at time t. These are constants related to the system.

[0097] It can be understood that the frequency safety constraint formula mentioned above can effectively constrain the minimum value of the system frequency under abnormal conditions, thereby ensuring the safe operation of the offshore wind power grid connection system.

[0098] The frequency security constraint above is obtained by transforming the following formula using a piecewise linearization method based on convex optimization:

[0099]

[0100] in, D represents the frequency deviation between time t and the lowest point of the system frequency in scenario ω. sys R is the system damping parameter. sys For the system's frequency regulation reserve, T g F is the time constant of the governor of the thermal power unit. sys The percentage of system power generation,

[0101] Let ξ be the system's total inertia, ξ be the damping ratio, representing the degree of system damping, and ω be the damping ratio. n t is the natural frequency of the system, used to indicate the natural frequency of the system's oscillation in the undamped state. nadir The timestamp of the point where the system frequency is at its lowest;

[0102]

[0103]

[0104] Where, ω d U is the damped oscillation angular frequency. g,t Let g be the operating state of generator set g at time t. K represents the maximum output power of generator set g. g R is the mechanical power gain factor of the thermal power unit. g denoted as , where g is the active power droop control coefficient for the thermal power unit, and g is the generator unit. For generator set collection, F g R represents the proportion of electricity generated by high-pressure boilers. f Let be the droop control coefficient for offshore wind farms, and w be the value of the offshore wind farm. For offshore wind farm clusters, P f R is the rated power of the offshore wind farm f. sP is the droop control coefficient for energy storage devices. s Let S be the output power of energy storage device s, where s is a specific energy storage device, S is a battery energy storage system set, and D is the output power of the energy storage device s. g denoted as g, which is the damping coefficient of a conventional generator set.

[0105] Step S50: Based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system, a generator resource cost control model is constructed, and a first constraint formula is constructed based on the preset minimum start-up time, preset shutdown duration, and generator status constraints.

[0106] Step S60: Based on the unit power generation fuel cost, energy storage charging and discharging cost, energy storage charging and discharging power, load shedding cost, and load shedding amount of the offshore wind power grid-connected system, construct a system load shedding response model, and construct a second constraint formula based on the preset energy storage capacity threshold, load shedding increase threshold, and load shedding decrease threshold.

[0107] Step S70: Optimize the system frequency response model based on frequency security constraints;

[0108] Step S80: Construct a target constraint based on the first and second constraints, construct a target model based on the generator set resource cost control model and the system load shedding response model, and optimize the target model based on the target constraint.

[0109] Step S90: Solve the optimized target model based on the distributed decoupling algorithm of multiple Benders cuts.

[0110] It should be noted that the generator resource cost control model is constructed based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system. This model is derived from the following formula:

[0111]

[0112] Where f(x) is the objective function corresponding to the generator set resource cost control model, that is, the objective function corresponding to the first stage optimization, c 开,g For generator set start-up costs, c 关,g For generator shutdown costs, α g,t For generator set start-up operation identification, β g,t For generator set shutdown operation indication, c T Let x be the cost coefficient vector, x be the decision variable vector, and Δt be the time change. For the time set, that is, all time periods considered. Let t be the set of generator sets, and t be the current timestamp.

[0113] It should be noted that the first constraint includes a first sub-constraint, a second sub-constraint, a third sub-constraint, and a fourth sub-constraint. The first constraint is constructed based on the preset minimum start-up cost, the preset downtime, and generator state constraints, and is obtained according to the following formula:

[0114] The expression for the first sub-constraint is:

[0115]

[0116] The expression for the second sub-constraint is:

[0117]

[0118] The expression for the third sub-constraint is:

[0119]

[0120] The expression for the fourth sub-constraint is:

[0121]

[0122] in, For generator set g in the shutdown state at time t, α g,b For the starting state of generator set g at time b, β g,b For generator set g to be in the shutdown state at time b, u g,0 Let UT be the operating state of generator set g at the initial time. g DT is the minimum start-up time of generator set g. g UT is the minimum shutdown time for generator set g. r DT represents the minimum start-up time of generator set g under initial conditions. r This represents the minimum shutdown time for generator set g in the initial state.

[0123] It is understandable that the first constraint mentioned above can constrain various parameters in the generator set resource cost control model, such as limiting the generator's operating state transition, as well as the minimum start-up cost, downtime, and initial state constraints of the generator. This ensures that the offshore wind power grid-connected system can control the safety of generator set and demand-side resource operation and minimize operating costs under abnormal conditions.

[0124] It should be noted that the system load shedding response model is constructed based on the generator fuel cost, energy storage charging and discharging cost, energy storage charging and discharging power, load shedding cost, and load shedding amount of the offshore wind power grid-connected system, and is obtained according to the following formula:

[0125]

[0126] This formula represents the objective function for the second-stage optimization, where q(y) ω ) represents the objective function corresponding to the system load shedding response model, α g For the fuel cost of generating electricity, Let c be the power output of generator set g at time t under scenario ω. LCOE To reduce the cost of energy storage charging and discharging, For energy storage charging and discharging power, For load shedding costs, c VOLL To reduce load shedding costs, The load shedding amount is t, and t is the current timestamp.

[0127] It should be noted that the second constraint is obtained from the following formula:

[0128]

[0129] Among them, u g,t This is the generator set status information. p is the minimum power limit for generator set g. g,t Let g be the actual power output of generator set g at time t. For the maximum power limit of generator set g, R g,t Let be the standby capacity of generator set g at time t, used to cope with load changes or faults. Let g be the actual power output of generator set g at time t under scenario ω. u represents the maximum power boost capability provided by generator set g within the time interval Δt. g,t-1 Let SU be the operating state of generator set g at time t-1. g Let α be the slope of the starting power of generator set g. g,t Let g be the starting state of generator set g at time t. SD represents the maximum power reduction capability provided by generator set g within time interval Δt. g Let β be the slope of the shutdown power of generator set g. g,t Let ω represent the shutdown state of generator set g at time t, and let ω represent different scenarios. For any timestamp, Let be the switching state indicator variable of line ij at time t and under scenario ω. Let B be the active power flow of line ij at time t and scenario ω. ij Let be the value of the line admittance matrix ij. Let i be the phase angle of node i at time t and scene ω. Let be the phase angle of node j at time t and in scenario ω, where i and j are nodes in the line, and ij is the transmission line connecting nodes i and j. Let be the maximum active power of line ij. Let be the state indicator variable for the disturbance at time t and in scenario ω. Let be the maximum power change of line ij after considering disturbances. Let be the maximum active power of line ij under preset condition k, and e be the energy state of the energy storage device. Let be the charging power of the energy storage device s at time t and under scenario ω. The maximum charging power of the energy storage device s. Let be the discharge power of the energy storage device s at time t and under scenario ω. Let be the maximum discharge power of the energy storage device s, where s is the energy storage device, ch is the charging process, and dc is the discharging process.

[0130] It is understandable that the second constraint mentioned above can constrain various parameters in the system load shedding response model, such as limiting the capacity of energy storage and limiting the rise and fall of the total load shedding, thereby reducing the load shedding value of the system in emergency and repair states.

[0131] It should be noted that, based on the first and second constraints, a target constraint is constructed. This target constraint reflects the coupling variables between the first stage (i.e., the optimization of the generator resource cost control model corresponding to the first constraint) and the second stage (i.e., the optimization of the system load shedding response model corresponding to the second constraint). This transforms the nonlinear pursuit problem into a linear pursuit problem. Then, a distributed decoupling algorithm based on multiple Benders cuts is used to solve the transformed linear pursuit problem. In other words, a target model is constructed based on the generator resource cost control model and the system load shedding response model, and the target model is optimized based on the target constraint. The optimized target model is then solved using a distributed decoupling algorithm based on multiple Benders cuts. This ensures the operational safety of each module in the offshore wind power grid-connected system and the system frequency safety during typhoons.

[0132] like Figure 3 As shown, Figure 3 This is a structural diagram of a control device provided in one embodiment of this application. The present invention also provides a control device 300, comprising:

[0133] The processor 310 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0134] The memory 320 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 320 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310 to execute the grid optimization scheduling method for the offshore wind power grid-connected system of the embodiments of this application.

[0135] Input / output interface 330 is used to realize information input and output;

[0136] The communication interface 340 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0137] Bus 350 transmits information between various components of the device (e.g., processor 310, memory 320, input / output interface 330, and communication interface 340);

[0138] The processor 310, memory 320, input / output interface 330 and communication interface 340 are connected to each other within the device via bus 350.

[0139] In addition, this application also provides an electronic device, including the control device 300 described in the above embodiments.

[0140] In addition, this application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described grid optimization scheduling method for offshore wind power grid-connected systems.

[0141] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0143] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A grid optimization scheduling method for an offshore wind power grid-connected system, characterized in that, The method, applied to offshore wind power grid-connected systems, includes: The typhoon time-varying characteristic information is determined. The typhoon time-varying characteristic information includes the typhoon translational speed, typhoon forward direction information, typhoon intensity information, reference distance, gradient wind speed, and wind power corresponding to the current timestamp. The reference distance is used to indicate the distance between the offshore wind power grid-connected system and the center point of the typhoon. The wind power is the maximum output power of the wind turbine of the offshore wind power grid-connected system under different wind speed conditions. Based on the typhoon's translational speed, typhoon's forward direction information, typhoon's intensity information, reference distance, gradient wind speed, and wind power corresponding to the current timestamp, multiple control state transition time periods of the offshore wind power grid-connected system are determined, and each state transition time period uniquely corresponds to a system state; When the current timestamp falls within any of the multiple control state transition time periods, the corresponding target system state is determined, and the target system operation information is determined from the preset relational mapping table based on the target system state. The system operation corresponding to the target system operation information is then executed. The relational mapping table is used to indicate the mapping relationship between the system state and the system operation information, and the system operation information represents the controllable resources and scheduling operation information of the offshore wind power grid-connected system. A system frequency response model is constructed based on the system comprehensive inertia, frequency regulation increment of energy storage output, system frequency deviation, and system damping parameters of the offshore wind power grid-connected system. A frequency safety constraint formula is then constructed based on the system frequency response model and a preset system safety frequency threshold. Based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system, a generator resource cost control model is constructed, and a first constraint formula is constructed based on the preset minimum start-up time, preset shutdown duration, and generator status constraints. Based on the generator fuel cost, energy storage charging and discharging cost, energy storage charging and discharging power, load shedding cost, and load shedding amount of the offshore wind power grid-connected system, a system load shedding response model is constructed, and a second constraint formula is constructed based on preset energy storage capacity threshold, load shedding increase threshold, and load shedding decrease threshold. The system frequency response model is optimized based on the aforementioned frequency security constraints. A target constraint is constructed based on the first constraint and the second constraint. A target model is constructed based on the generator set resource cost control model and the system load shedding response model. The target model is then optimized based on the target constraint. A distributed decoupling algorithm based on multiple Benders cuts is used to solve the optimized target model.

2. The grid optimization scheduling method for offshore wind power grid-connected systems according to claim 1, characterized in that, The control state transition time period includes a warning state time period, an emergency state time period, a repair state time period, and a normal state time period. The offshore wind power grid-connected system includes generator sets, transmission lines, and a load control module. Each generator set, transmission line, and load control module has corresponding reference operating parameters. Each corresponding reference operating parameter is used to indicate the operating parameters of the generator set, transmission line, and load control module when the offshore wind power grid-connected system is in a normal state. When the current timestamp is in any of the multiple control state transition time periods, the corresponding target system state is determined. Based on the target system state, target system operation information is determined from a preset relational mapping table, and the system operation corresponding to the target system operation information is executed, including: If the current timestamp falls within the warning state time period, determine the target system state corresponding to the warning state time period as the warning state, obtain the target system operation information corresponding to the warning state from the relationship mapping table, and pre-schedule the generator set according to the corresponding target system operation information; When the current timestamp falls within the emergency state time period, the target system state corresponding to the emergency state time period is determined to be an emergency state. The target system operation information corresponding to the emergency state is obtained from the relationship mapping table. Based on the corresponding target system operation information, the transmission line is controlled to output a preset emergency transmission capacity. At the same time, the load control module is switched off. The emergency transmission capacity is greater than the normal transmission capacity of the transmission line. If the current timestamp falls within the repair status time period, the target system status corresponding to the repair status time period is determined to be in the repair status, and the target system operation information corresponding to the repair status is obtained from the relationship mapping table. The load control module is then switched off according to the corresponding target system operation information. If the current timestamp falls within the normal state time period, the target system state corresponding to the normal state time period is determined to be in a normal state. The target system operation information corresponding to the normal state is obtained from the relational mapping table. Based on the corresponding target system operation information, the generator set, the transmission line, and the load control module are controlled respectively according to the corresponding reference operation parameters.

3. The grid optimization scheduling method for offshore wind power grid-connected systems according to claim 1, characterized in that, The gradient wind speed is calculated using the following formula: ； in, The gradient wind speed, As a proportional factor, The reference distance is... This is an adjustment factor used to describe minute changes in wind speed. This represents the distance between the wind farm and the center of the typhoon. t The current timestamp, Information about the current scene; The wind power output is calculated using the following formula: ； in, The wind power is represented by the value at time t. t Wind speed Below, the maximum power point tracking output power of the wind turbine generator. The minimum wind speed at which a wind turbine begins generating electricity, in order to activate the turbine. Rated wind speed, used to indicate the wind speed at which the wind turbine achieves its maximum power output. This refers to the shutdown wind speed, used to indicate the wind speed at which the wind turbine unit stops operating as a protective measure. This refers to the rated power output of the wind turbine. τ The radius of the wind turbine rotor is given.

4. The grid optimization scheduling method for offshore wind power grid-connected systems according to claim 1, characterized in that, Based on the system frequency response model and the preset system safety frequency threshold, the frequency safety constraint formula is constructed and obtained according to the following formula: ； in, The preset system security frequency threshold, The current system frequency. These are weighting coefficients related to load changes, used to measure the impact of power changes caused by generator inertia on system frequency. In order to be in t Time and Context Next, the l The power change of each load, These are weighting coefficients related to the system's reserve power, used to quantify the contribution of frequency regulation reserves to system frequency stability. For the system in t Total reserve power at any given time These are weighting coefficients related to system inertia, used to reflect the suppressive effect of system inertia on frequency changes. For the system in t Time and Context Total inertia, These are weighting coefficients related to system damping, used to represent the ability to dampen frequency oscillations. For the system in t The total damping coefficient at time t is These are constants related to the system. The frequency safety constraint is obtained by transforming the following formula using a piecewise linearization method with convex optimization: ； ； in, for t time, Frequency deviation at the lowest point of the system frequency in the scenario. For system damping parameters, For the system's frequency regulation reserve, The time constant of the governor of the thermal power unit. The percentage of system power generation, For the overall system inertia, The damping ratio represents the degree of damping in the system. ω n The natural frequency of the system is used to indicate the inherent frequency of the system's oscillations in the undamped state. The timestamp of the point where the system frequency is at its lowest; ； ； ； ； ； ； ； in, The frequency of the damped oscillation. For generator sets g exist t The running status at any given moment, For generator sets g Maximum output power The mechanical power gain factor of the thermal power unit. This is the active power droop control coefficient for thermal power units. For generator set collection, F g The percentage of electricity generated by high-pressure boilers. This represents the droop control coefficient for offshore wind farms. w For offshore wind farms, For offshore wind farms, For offshore wind farms f Rated power, This is the sag control coefficient for energy storage devices. For energy storage devices s 'output power' s For a certain energy storage device, S For battery energy storage system integration, For conventional generator sets g The damping coefficient.

5. The grid optimization scheduling method for offshore wind power grid-connected systems according to claim 4, characterized in that, Based on the generator start-up cost, generator shutdown cost, generator start-up operation identifier, generator shutdown operation identifier, and generator status information of the offshore wind power grid-connected system, a generator resource cost control model is constructed, which is obtained according to the following formula: ； in, Let be the objective function corresponding to the generator set resource cost control model. The startup cost of the generator set. The shutdown cost of the generator set. This serves as the start-up operation identifier for the generator set. This is the generator set shutdown operation indicator. c T This is a vector of cost coefficients. x Let Δ be the vector of decision variables. t For the time change, For time sets, For generator set collection, t This is the current timestamp.

6. The grid optimization scheduling method for offshore wind power grid-connected systems according to claim 5, characterized in that, The first constraint includes a first sub-constraint, a second sub-constraint, a third sub-constraint, and a fourth sub-constraint. The first constraint is constructed based on a preset minimum start-up cost, a preset downtime, and generator state constraints, and is obtained according to the following formula: The expression for the first sub-constraint is: ； The expression for the second sub-constraint is: ； The expression for the third sub-constraint is: ； The expression for the fourth sub-constraint is: ； in, For generator sets t Always in a powered-off state. For generator sets t The startup status at any given moment. For generator sets b The state of being constantly shut down. For generator sets g The initial running state, UT g For generator sets g Minimum boot time, DT g For generator sets g Minimum shutdown time, UT r Generator set in initial state g Minimum boot time, DT r Generator set in initial state g Minimum shutdown time.

7. The grid optimization scheduling method for offshore wind power grid-connected systems according to claim 1, characterized in that, Based on the generator fuel cost, energy storage charging and discharging cost, energy storage charging and discharging power, load shedding cost, and load shedding amount of the offshore wind power grid-connected system, a system load shedding response model is constructed, which is obtained according to the following formula: ; in, Let be the objective function corresponding to the system load shedding response model. The fuel cost for generating electricity from the unit. For the scene Below, generator set g In time t Power generation capacity, The cost of charging and discharging the energy storage, The energy storage charging and discharging power, For the load shedding cost, For the load shedding cost, For the shear load, t This is the current timestamp.

8. The grid optimization scheduling method for offshore wind power grid-connected systems according to claim 1, characterized in that, The second constraint is obtained from the following formula: ； ； ； ； ； ； ； ； ； ； ； ； in, For generator sets g exist t The running status at any given moment, For generator sets g Minimum power limit, For generator sets g exist t Actual power generation at any given time For generator sets g Maximum power limit For generator sets g exist t The reserve capacity at any time is used to cope with load changes or failures. In the scene Below, generator set g exist t Actual power generation at any given time For generator sets g In time interval The maximum power boost capability provided internally For generator sets g At any moment t -1 running status, For generator sets g The starting power slope, For generator set g at time... t The startup state, For generator sets g In time interval Maximum power reduction capability provided internally. For generator sets g The shutdown power slope, For generator sets g At any moment t The shutdown status To represent different scenarios, For any timestamp For the line ij exist t Time and Scene The switch status indicator variable below, For the line ij exist t Time and Scene The active power flow below, For the line ij The value of the admittance matrix, For nodes i exist t Time and Scene The lower phase angle, For nodes j exist t Time and Scene The lower phase angle, i, j Each of these is a node in the line. ij For connecting nodes i , j The power transmission lines For the line ij Maximum active power, for t Time and State indicator variables for disturbances in the scenario. For the line ij Taking into account the maximum power change after the disturbance For the line ij Under preset conditions k Maximum active power at the bottom e The energy state of the energy storage device. for t Time and Scene Underground energy storage devices s The charging power, For energy storage devices s Maximum charging power, for t Time and Scene Underground energy storage devices s The discharge power, For energy storage devices s Maximum discharge power, ch For the charging process, DC This is the discharge process.

9. A control device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform the grid optimization scheduling method for an offshore wind power grid-connected system as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to execute the grid optimization scheduling method for an offshore wind power grid-connected system as described in any one of claims 1 to 8.

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