Receiving-end power system dispatching method and device under typhoon scenario

Abstract

This application relates to the technical field of power system operation, and particularly to a dispatching method and device for a receiving-end power system under typhoon scenarios. The method includes: establishing an output model of an offshore wind farm dominated by typhoon wind field conditions, generating cluster wind power simulated output according to typhoon meteorological characteristic parameters in combination with the output model of a typical sample machine; establishing a spatio-temporal model of the mismatch between wind power supply and load, and determining the load supply level under N-1 line faults; establishing a thermal power re-dispatching model to correct the dispatching plan of thermal power units; establishing an electric vehicle incentive model to determine the discharge state and incentive cost of electric vehicles; and establishing a two-stage distributionally robust optimization method, which is transformed into a single-layer optimization problem using strong duality theory. Thus, it solves the problems that the prior art cannot integrate typhoon wind field meteorological characteristics, is difficult to comprehensively consider the disconnection risk of special transmission lines, and fails to consider the power support role of electric vehicles as distributed energy storage resources, etc.

Description

Technical Field

[0001] This application relates to the technical field of power system operation, and particularly relates to a receiving-end power system scheduling method and device under typhoon scenarios. Background Art

[0002] The power generation capacity of regional power systems with wind power highly depends on weather conditions. Affected by its special wind field conditions, especially for offshore wind farms, the extreme response characteristics of concentrated wind power clusters may disrupt the energy supply of the entire power system. Therefore, resilience assessment under typhoon weather has an important impact on system stability.

[0003] There are some limitations in the existing technologies for power systems to cope with typhoon weather. Specifically, in terms of power supply, the research on the output characteristics of wind power under typhoon weather fails to integrate the meteorological characteristics of typhoon wind fields, resulting in inaccurate prediction of extreme typhoon output; in terms of power transmission, the disconnection risk of special transmission lines is not comprehensively considered, especially the system power flow changes caused by the disconnection of the transmission lines for wind power clusters; in the power restoration stage, it often considers dispatching existing thermal power, photovoltaic, and energy storage power stations to make up the load gap, which not only increases the power generation cost but also causes the risk of overvoltage of power generation equipment, without considering the power support role of electric vehicles as distributed energy storage resources.

[0004] Under typhoon scenarios, the random uncertainty of wind power generation may cause dispatching errors and operation risks, and traditional uncertainty dispatching methods have significant limitations in dealing with typhoon scenarios. The requirements of stochastic optimization methods to construct accurate scenario trees relying on a large amount of historical data are difficult to meet the low-frequency and high-fluctuation characteristics of typhoon events. The uncertainty of the robust optimization model uses a deterministic set and is not affected by any possible situations of the uncertainty set, but its "worst-case scenario"-oriented decision-making mechanism often leads to economic losses due to excessive conservatism.

[0005] In summary, the existing technologies cannot integrate the meteorological characteristics of typhoon wind fields, are difficult to comprehensively consider the disconnection risk of special transmission lines, and fail to consider the power support role of electric vehicles as distributed energy storage resources, which urgently need to be solved. Summary of the Invention

[0006] This application provides a receiving-end power system scheduling method and device under typhoon scenarios to solve problems such as the inability of existing technologies to integrate the meteorological characteristics of typhoon wind fields, the difficulty in comprehensively considering the disconnection risk of special transmission lines, and the failure to consider the power support role of electric vehicles as distributed energy storage resources.

[0007] The first aspect of the embodiments of this application provides a method for dispatching a receiving-end power system under typhoon scenarios, including the following steps: obtaining typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and establishing a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate the simulated output of cluster wind power based on a pre-constructed sample machine output model and the wind speed distribution parameter model; constructing a load shedding model under N-1 line faults based on the simulated output of cluster wind power and a preset bus load shedding priority, and obtaining the load shedding result of the regional power system through the load shedding model, so as to establish a thermal power re-dispatch model according to the load shedding result, and modifying a preset thermal power unit dispatch plan through the thermal power re-dispatch model to obtain a modified thermal power unit dispatch plan; determining an incentive index for a target electric vehicle according to the load shedding result, and constructing an electric vehicle incentive model according to the incentive index, so as to obtain the dispatch incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; constructing a first-stage collaborative dispatch model based on the simulated output of cluster wind power, the load shedding model, the modified thermal power unit dispatch plan, and the dispatch incentive cost, calculating the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative dispatch model, and establishing a corresponding differential cost objective function by using the error adjustment cost, so as to construct a second-stage optimization model through the differential cost objective function, and solving the second-stage optimization model according to a preset strong duality strategy and affine constraints to obtain a target dispatch plan for the cluster wind power and the target electric vehicle under the typhoon scenario.

[0008] Optionally, in an embodiment of this application, the obtaining typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and establishing a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate the simulated output of cluster wind power based on a pre-constructed sample machine output model and the wind speed distribution parameter model, includes: obtaining the rated power of a preset sample machine, the real-time wind speed of a wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, and obtaining the real-time wind speed of the wind farm according to the wind speed distribution parameter model; constructing the sample machine output model based on the rated power of the sample machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, so as to calculate the simulated output of each wind turbine in the cluster wind power through the sample machine output model; calculating the simulated output of the wind farm at each moment according to the simulated output of each wind turbine, and performing an accumulation summation operation on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power.

[0009] Optionally, in an embodiment of the present application, the method of constructing a load shedding model under an N-1 line fault based on the simulated output of the cluster wind power and the preset bus load shedding priority, and obtaining the load shedding result of the regional power system through the load shedding model, so as to establish a thermal power rescheduling model according to the load shedding result, and correcting the preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain a corrected thermal power unit scheduling plan includes: determining the line disconnection scenario corresponding to the cluster wind power, and constructing a power flow state constraint according to the line disconnection indication factor corresponding to the line disconnection scenario; determining the load shedding cost, load shedding state, different-level indication factors corresponding to each bus, load shedding cost coefficients for each level, and the load shedding amount of each bus at each moment corresponding to the cluster wind power, and obtaining the load shedding result of the regional power system based on the power flow state constraint, the load shedding cost, the load shedding state, the indication factor, the load shedding cost coefficient, and the load shedding amount; determining the total load shedding amount according to the load shedding result, and judging whether the total load shedding amount is greater than a preset warning value, wherein when the total load shedding amount is greater than the warning value, correcting the thermal power unit scheduling plan to obtain the corrected thermal power unit scheduling plan.

[0010] Optionally, in an embodiment of the present application, the method of determining the incentive index of the target electric vehicle according to the load shedding result and constructing an electric vehicle incentive model according to the incentive index includes: determining at least one target bus with a non-zero load shedding amount, and calculating the ratio between the total load shedding amount of each target bus in the at least one target bus and the preset total load demand, and using the ratio as the load shedding ratio; determining the incentive index of the target electric vehicle according to the load shedding ratio, so as to construct the electric vehicle incentive model through the incentive index.

[0011] Optionally, in an embodiment of the present application, calculating the error adjustment cost corresponding to the clustered wind power according to the first-stage collaborative scheduling model, and establishing a corresponding adjustment cost objective function by using the error adjustment cost, so as to construct a second-stage optimization model through the adjustment cost objective function, and solving the second-stage optimization model according to a preset strong duality strategy and affine constraints to obtain the target scheduling scheme of the clustered wind power and the target electric vehicle under the typhoon scenario, including: calculating the output error corresponding to the clustered wind power according to the simulated output of the clustered wind power, and obtaining the sample data corresponding to the output error, where the output error follows a true distribution and the sample data follows an empirical distribution; calculating the corresponding Wasserstein distance based on the empirical distribution and the true distribution, and constructing a fuzzy uncertainty set of the wind power output under the typhoon state according to the Wasserstein distance; calculating the corresponding error adjustment cost according to the output error and a preset error adjustment cost coefficient, and establishing the adjustment cost objective function through the output error, the error adjustment cost and the fuzzy uncertainty set, so as to construct the second-stage optimization model based on the adjustment cost objective function; performing standardization processing on the sample data to obtain corresponding standard sample data, and converting the second-stage optimization model into a target optimization scheduling model based on the standard sample data and the strong duality strategy, and solving the target optimization scheduling model through a preset affine strategy to obtain the target scheduling scheme of the clustered wind power and the target electric vehicle under the typhoon scenario.

[0012] Optionally, in an embodiment of the present application, the mathematical expression of the target optimization scheduling model is:

[0013]

[0014] Wherein, 、 and are respectively the operating cost coefficients of the thermal power units in the clustered wind power; is the error adjustment coefficient corresponding to the thermal power unit; is the dual variable; is the rd standard sample data; is the auxiliary variable; is the Wasserstein radius; is the number of samples of the sample data; represents the maximum value in the standard sample data; represents the minimum value in the standard sample data.

[0015] The second aspect of the embodiments of the present application provides a receiving-end power system scheduling device under typhoon scenarios, including: a generating module, configured to obtain typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and establish a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate the simulated output of cluster wind power based on a pre-constructed template machine output model and the wind speed distribution parameter model; a correcting module, configured to construct a load shedding model under N-1 line faults based on the simulated output of cluster wind power and a preset bus load shedding priority, and obtain the load shedding result of the regional power system through the load shedding model, so as to establish a thermal power rescheduling model according to the load shedding result, and correct a preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain a corrected thermal power unit scheduling plan; a modeling module, configured to determine the incentive index of a target electric vehicle according to the load shedding result, and construct an electric vehicle incentive model according to the incentive index, so as to obtain the scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; a solving module, configured to construct a first-stage collaborative scheduling model based on the simulated output of cluster wind power, the load shedding model, the corrected thermal power unit scheduling plan, and the scheduling incentive cost, calculate the error regulation cost corresponding to the cluster wind power according to the first-stage collaborative scheduling model, and establish a corresponding differential cost objective function by using the error regulation cost, so as to construct a second-stage optimization model through the differential cost objective function, and solve the second-stage optimization model according to a preset strong duality strategy and affine constraints to obtain the target scheduling plan of the cluster wind power and the target electric vehicle under the typhoon scenario.

[0016] Optionally, in an embodiment of the present application, the generating module includes: a first obtaining unit, configured to obtain the rated power of a preset template machine, the real-time wind speed of a wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, and obtain the real-time wind speed of the wind farm according to the wind speed distribution parameter model; a first calculating unit, configured to construct the template machine output model based on the rated power of the template machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, so as to calculate the simulated output of each wind turbine in the cluster wind power through the template machine output model; a summing unit, configured to calculate the simulated output of the wind farm at each moment according to the simulated output of each wind turbine, and perform an accumulation summation operation on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power.

[0017] Optionally, in an embodiment of the present application, the correction module includes: a first determination unit, configured to determine a line disconnection scenario corresponding to the clustered wind power, and construct a power flow state constraint according to a line disconnection indication factor corresponding to the line disconnection scenario; a second determination unit, configured to determine a load shedding cost, a load shedding state, indication factors of different levels corresponding to each bus, a load shedding cost coefficient of each level, and a load shedding amount of each bus at each moment corresponding to the clustered wind power, and obtain a load shedding result of the regional power system based on the power flow state constraint, the load shedding cost, the load shedding state, the indication factors, the load shedding cost coefficient, and the load shedding amount; a judgment unit, configured to determine a total load shedding amount according to the load shedding result, and judge whether the total load shedding amount is greater than a preset warning value, wherein when the total load shedding amount is greater than the warning value, the thermal power unit scheduling plan is corrected to obtain the corrected scheduling plan of the thermal power unit.

[0018] Optionally, in an embodiment of the present application, the modeling module includes: a third determination unit, configured to determine at least one target bus with a non-zero load shedding amount, calculate a ratio between the total load shedding amount of each target bus in the at least one target bus and a preset total load demand amount, and use the ratio as a load shedding ratio; a construction unit, configured to determine an incentive index of the target electric vehicle according to the load shedding ratio, and construct the electric vehicle incentive model through the incentive index.

[0019] Optionally, in an embodiment of the present application, the solution module includes: a second acquisition unit, configured to calculate an output error corresponding to the clustered wind power according to the simulated output of the clustered wind power, and acquire sample data corresponding to the output error, wherein the output error follows a true distribution, and the sample data follows an empirical distribution; a second calculation unit, configured to calculate a corresponding Wasserstein distance based on the empirical distribution and the true distribution, and construct a fuzzy uncertainty set of the wind power output under the typhoon state according to the Wasserstein distance; an establishment unit, configured to calculate a corresponding error adjustment cost according to the output error and a preset error adjustment cost coefficient, and establish the adjustment cost objective function through the output error, the error adjustment cost, and the fuzzy uncertainty set, and construct the second-stage optimization model based on the adjustment cost objective function; a standardization unit, configured to perform standardization processing on the sample data to obtain corresponding standard sample data, and convert the second-stage optimization model into a target optimization scheduling model based on the standard sample data and the strong duality strategy, and solve the target optimization scheduling model through a preset affine strategy to obtain a target scheduling plan of the clustered wind power and the target electric vehicle under the typhoon scenario.

[0020] Optionally, in an embodiment of the present application, the mathematical expression of the target optimization scheduling model is as follows:

[0021]

[0022] Wherein, 、 and are respectively the operating cost coefficients of the thermal power units in the cluster wind power; is the corresponding error adjustment coefficient of the thermal power unit; is the dual variable; is the th standard sample data; is the auxiliary variable; is the Wasserstein radius; is the number of samples of the sample data; represents the maximum value in the standard sample data; represents the minimum value in the standard sample data.

[0023] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the receiving-end power system scheduling method under the typhoon scenario as described in the above embodiment.

[0024] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the receiving-end power system scheduling method under the typhoon scenario as above.

[0025] An embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, and the computer program is executed to be used to implement the receiving-end power system scheduling method under the typhoon scenario as described above.

[0026] Therefore, the embodiments of the present application have the following beneficial effects:

[0027] Embodiments of the present application can obtain typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, establish a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, generate a simulated output of cluster wind power based on a pre-constructed template machine output model and the wind speed distribution parameter model; construct a load shedding model under N-1 line faults based on the simulated output of cluster wind power and a preset bus load shedding priority, and obtain the load shedding result of the regional power system through the load shedding model, establish a thermal power re-scheduling model based on the load shedding result, and correct the preset thermal power unit scheduling plan through the thermal power re-scheduling model to obtain a corrected scheduling plan for thermal power units; determine the incentive index of the target electric vehicle according to the load shedding result, and construct an electric vehicle incentive model according to the incentive index to obtain the scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; construct a first-stage collaborative scheduling model based on the simulated output of cluster wind power, the load shedding model, the corrected scheduling plan for thermal power units, and the scheduling incentive cost, calculate the error regulation cost corresponding to cluster wind power according to the first-stage collaborative scheduling model, establish a corresponding differential cost objective function using the error regulation cost, construct a second-stage optimization model through the differential cost objective function, and solve the second-stage optimization model according to a preset strong duality strategy and affine constraints to obtain a target scheduling plan for cluster wind power and the target electric vehicle under the typhoon scenario. The present application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system under typhoon weather through a two-stage distributionally robust optimization strategy of thermal power unit re-scheduling and electric vehicle incentives under typhoon scenarios. Thus, the problems in the prior art, such as the inability to integrate the meteorological characteristics of typhoon wind fields, the difficulty in comprehensively considering the disconnection risk of special transmission lines, and the failure to consider the power support role of electric vehicles as distributed energy storage resources, are solved.

[0028] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, where:

[0030] Figure 1 is a flowchart of a method for dispatching a receiving-end power system under a typhoon scenario according to an embodiment of the present application;

[0031] Figure 2 is an execution logic schematic diagram of a method for dispatching a receiving-end power system under a typhoon scenario provided by an embodiment of the present application;

[0032] Figure 3 is an example diagram of a device for dispatching a receiving-end power system under a typhoon scenario according to an embodiment of the present application;

[0033] Figure 4 This is a schematic structural diagram of the electronic device provided by the embodiment of the present application.

[0034] Among them, 10 - receiving-end power system scheduling device under typhoon scenario; 100 - generating module, 200 - correcting module, 300 - modeling module, 400 - solving module; 401 - memory, 402 - processor, 403 - communication interface. Specific embodiments

[0035] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, but should not be construed as a limitation to the present application.

[0036] The method and device for scheduling the receiving-end power system under typhoon scenario according to the embodiment of the present application will be described below with reference to the accompanying drawings. In view of the problems mentioned in the above background technology, the present application provides a method for scheduling the receiving-end power system under typhoon scenario. In this method, by obtaining the typhoon meteorological characteristic parameters corresponding to the preset typhoon scenario, and establishing a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, to generate the simulated output of the cluster wind power based on the pre-constructed template machine output model and the wind speed distribution parameter model; based on the simulated output of the cluster wind power and the preset bus load shedding priority, constructing a load shedding model under N-1 line fault, and obtaining the load shedding result of the regional power system through the load shedding model, to establish a thermal power rescheduling model according to the load shedding result, and correcting the preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain the corrected scheduling plan of the thermal power unit; determining the incentive index of the target electric vehicle according to the load shedding result, and constructing an electric vehicle incentive model according to the incentive index, to obtain the scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; based on the simulated output of the cluster wind power, the load shedding model, the corrected scheduling plan of the thermal power unit and the scheduling incentive cost, constructing a first-stage collaborative scheduling model, and calculating the error regulation cost corresponding to the cluster wind power according to the first-stage collaborative scheduling model, and establishing a corresponding differential cost objective function by using the error regulation cost, to construct a second-stage optimization model through the differential cost objective function, and solving the second-stage optimization model according to the preset strong duality strategy and affine constraints to obtain the target scheduling scheme of the cluster wind power and the target electric vehicle under the typhoon scenario. The present application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system under typhoon weather through a two-stage distributionally robust optimization strategy of thermal power unit rescheduling and electric vehicle incentive under typhoon scenario. Thus, the problems in the prior art that it is impossible to integrate the meteorological characteristics of the typhoon wind field, it is difficult to comprehensively consider the disconnection risk of special transmission lines, and the power support role of electric vehicles as distributed energy storage resources is not considered are solved.

[0037] Specifically, Figure 1 is a flowchart of a receiving-end power system dispatching method under a typhoon scenario provided by an embodiment of the present application.

[0038] As Figure 1 shown, the receiving-end power system dispatching method under the typhoon scenario includes the following steps:

[0039] In step S101, typhoon meteorological characteristic parameters are obtained, and based on a pre-constructed template machine output model and the typhoon meteorological characteristic parameters, the simulated output of the cluster wind power is generated.

[0040] In the embodiment of the present application, typhoon meteorological characteristic parameters such as central pressure, maximum wind speed, and maximum wind speed radius can be obtained first, and combined with the template machine output model to generate the simulated output of the cluster wind power, as Figure 2 shown, so as to establish a cluster wind power output model considering the typhoon wind field conditions and provide reliable technical support for the construction of subsequent load shedding models, etc.

[0041] Optionally, in an embodiment of the present application, obtaining typhoon meteorological characteristic parameters and generating the simulated output of the cluster wind power based on a pre-constructed template machine output model and the typhoon meteorological characteristic parameters includes: obtaining the rated power of the template machine corresponding to the preset template machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, and obtaining the real-time wind speed of the wind farm according to the wind speed distribution parameter model; constructing a template machine output model based on the rated power of the template machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, so as to calculate the simulated output of each wind turbine in the cluster wind power through the template machine output model; calculating the simulated output of the wind farm at each moment according to the simulated output of each wind turbine, and performing an accumulation summation operation on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power.

[0042] It should be noted that the embodiment of the present application can construct a typhoon meteorological characteristic parameter model according to the typhoon meteorological characteristic parameters, and the mathematical expression of the typhoon meteorological characteristic parameter model is:

[0043] (1)

[0044] (2)

[0045] Wherein, is the air pressure at a distance from the typhoon center; is the lowest air pressure at the typhoon center; is the ambient air pressure; is the maximum wind speed radius; is the air density; describes the radius Gradient wind speed at is the Coriolis parameter; is the maximum wind speed; is the base of the natural logarithm.

[0046] Combined with the wind speed distribution of the wind farm, the mathematical expression of the output model of the prototype machine is:

[0047] (3)

[0048] (4)

[0049] Among them, is the simulated output of the wind turbine (i.e., the simulated output of each wind turbine in the cluster wind power); is the rated power of the prototype machine; is the real-time wind speed of the wind farm; is the cut-in wind speed; is the rated wind speed; is the cut-out wind speed; are the fan power output coefficients respectively.

[0050] After that, the embodiments of the present application can calculate the simulated output of the wind farm at each moment, and perform an accumulation summation operation on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power. The expressions of the simulated output of the wind farm and the cluster wind power are:

[0051] (5)

[0052] (6)

[0053] Among them, is the simulated output of the wind farm at moment (i.e., the simulated output of the wind farm at each moment); is the set of wind turbines belonging to the wind farm ; is the simulated output of the cluster wind power at moment (i.e., the simulated output of the cluster wind power), is the set of wind farms belonging to the cluster .

[0054] In step S102, based on the simulated output of cluster wind power and the preset bus load shedding priority, a load shedding model under N-1 line fault is constructed, and the load shedding results of the regional power system are obtained through the load shedding model. Then, a thermal power rescheduling model is established according to the load shedding results, and the preset thermal power unit scheduling plan is corrected through the thermal power rescheduling model to obtain the corrected thermal power unit scheduling plan.

[0055] Furthermore, in the embodiment of the present application, on the basis of the simulated output of cluster wind power, the load shedding model under N-1 line fault is determined according to the bus load shedding priority to obtain load shedding results such as the load shedding status and load shedding ratio. Then, in the embodiment of the present application, a thermal power rescheduling model is established according to the load shedding results to correct the thermal power unit scheduling plan, so as to obtain the corrected thermal power unit scheduling plan.

[0056] Optionally, in an embodiment of the present application, based on the simulated output of cluster wind power and the preset bus load shedding priority, a load shedding model under N-1 line fault is constructed, and the load shedding results of the regional power system are obtained through the load shedding model. Then, a thermal power rescheduling model is established according to the load shedding results, and the preset thermal power unit scheduling plan is corrected through the thermal power rescheduling model to obtain the corrected thermal power unit scheduling plan, including: determining the line disconnection scenarios corresponding to the cluster wind power, and constructing the power flow state constraints according to the line disconnection indication factors corresponding to the line disconnection scenarios; determining the corresponding load shedding costs, load curtailment status, different-level indication factors corresponding to each bus, load shedding cost coefficients for each level, and load shedding amounts of each bus at each moment of the cluster wind power, and obtaining the load shedding results of the regional power system based on the power flow state constraints, load shedding costs, load curtailment status, indication factors, load shedding cost coefficients, and load shedding amounts; determining the total load shedding amount according to the load shedding results, and judging whether the total load shedding amount is greater than the preset alarm value. When the total load shedding amount is greater than the alarm value, the thermal power unit scheduling plan is corrected to obtain the corrected thermal power unit scheduling plan.

[0057] Specifically, in the embodiment of the present application, the line disconnection scenarios can be set first, and the power flow state is constrained according to the line disconnection indication factors. The expression is as follows:

[0058] (7)

[0059] Wherein, is the DC power flow of line at moment; is the power flow upper limit; is the line fault state.

[0060] Secondly, the embodiments of the present application can evaluate the power supply priority of the loads carried according to the standard bus voltage level (the higher the power supply priority, the lower the corresponding bus load shedding priority) to obtain the load shedding cost, and its expression is as follows:

[0061] (8)

[0062] (9)

[0063] Among them, is the indication factor corresponding to the node (i.e., the bus) corresponding level ; is the load shedding cost; is the load shedding cost coefficient corresponding to the corresponding level s ; is the load shedding amount of the bus at time ; is the load reduction state; represents the indication factor of the load shedding of the bus n at t moment.

[0064] After that, the embodiments of the present application use the total system load shedding amount as the judgment basis. When the total load shedding amount exceeds the set warning value (i.e., the load shedding threshold), new units are rescheduled to start up, so as to achieve emergency power supply, and the expression of the start-up logic setting is as follows:

[0065] (10)

[0066] Among them, is the load shedding amount of the bus at time ; is the set of buses; is the load shedding threshold that causes the unit rescheduling to start up; represents the decision variable for rescheduling start-up.

[0067] In step S103, the incentive index of the target electric vehicle is determined according to the load shedding result, and an electric vehicle incentive model is constructed according to the incentive index to obtain the scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model.

[0068] Furthermore, the embodiments of the present application also need to obtain the load shedding ratio according to the load shedding result, and use the load shedding ratio as the incentive index to establish an electric vehicle incentive model, so as to determine the electric vehicle discharge state and the scheduling incentive cost.

[0069] Optionally, in an embodiment of the present application, an incentive index of a target electric vehicle is determined according to the load shedding result, and an electric vehicle incentive model is constructed according to the incentive index, including: determining at least one target bus with a non-zero load shedding amount, and calculating a ratio between the total load shedding amount of each target bus in the at least one target bus and a preset total load demand, and using the ratio as the load shedding ratio; determining the incentive index of the target electric vehicle according to the load shedding ratio, so as to construct an electric vehicle incentive model through the incentive index.

[0070] Specifically, the embodiment of the present application can retrieve the obtained load shedding result, and calculate the ratio of the total load shedding amount on the bus with a non-zero load shedding amount to the total load demand as the load shedding ratio according to the load shedding result, and use this load shedding ratio as an indication factor (i.e., incentive index) for taking incentive measures for electric vehicles. The load shedding ratio is positively correlated with the scale of the electric vehicle fleet participating in power supply and the incentive cost, so as to construct an electric vehicle incentive model. The mathematical expression of this electric vehicle incentive model is as follows:

[0071] (11)

[0072] (12)

[0073] (13)

[0074] (14)

[0075] (15)

[0076] Among them, is the incentive factor for vehicle discharging at time 、 are respectively the load shedding amount and load demand of bus ; is the load shedding decision variable; and are respectively the participation ratio and ratio upper limit of the electric vehicle fleet; is the scale of the electric vehicle fleet; is the electric vehicle fleet discharging power; is the discharging power of electric vehicle ; is the electric vehicle discharging decision variable; is the electric vehicle cluster belonging to fleet ; is the electric vehicle incentive cost (i.e., dispatching incentive cost); It is the incentive cost coefficient for electric vehicles.

[0077] In step S104, based on the simulated output of cluster wind power, the load shedding model, the corrected scheduling plan of thermal power units, and the scheduling incentive cost, a first-stage coordinated scheduling model is constructed, and the error regulation cost corresponding to the cluster wind power is calculated according to the first-stage coordinated scheduling model. Moreover, a differential cost objective function is established using the error regulation cost, so as to construct a second-stage optimization model through the differential cost objective function, and the second-stage optimization model is solved according to the preset strong duality strategy and affine constraints to obtain the target scheduling plan of the cluster wind power and the target electric vehicle under the typhoon scenario.

[0078] After that, the embodiments of the present application also need to determine a two-stage distributionally robust optimization strategy based on the simulated output of cluster wind power, the load shedding model, the corrected scheduling plan of thermal power units, and the scheduling incentive cost, so as to construct a scheduling model for handling the uncertainty of the simulated error of wind power output.

[0079] It should be noted that for the two-stage distributionally robust optimization strategy of the embodiments of the present application, the first stage is the conventional scheduling optimization stage, and its objective function includes the operating cost of thermal power units, the start-up cost, the load shedding cost, and the incentive cost for electric vehicles. And according to all operation constraints, a coordinated scheduling model considering wind power, thermal power, and electric vehicles (i.e., the first-stage coordinated scheduling model) is constructed; the second-stage optimization model mainly focuses on the scheduling process in the scenario where there is a simulated error in wind power output, and the objective function of this stage is to minimize the regulation cost for compensating the simulated error of wind power.

[0080] Thus, the embodiments of the present application fully consider the distribution characteristics of typhoon weather, and thereby improve the efficiency of the power system under typhoon weather through the two-stage distributionally robust optimization strategy of rescheduling thermal power units and incentivizing electric vehicles under the typhoon scenario.

[0081] Optionally, in an embodiment of the present application, the error regulation cost corresponding to the cluster wind power is calculated according to the first-stage collaborative scheduling model, and the corresponding regulation cost objective function is established by using the error regulation cost, so as to construct the second-stage optimization model through the regulation cost objective function, and solve the second-stage optimization model according to the preset strong duality strategy and affine constraints, so as to obtain the target scheduling scheme of the cluster wind power and the target electric vehicle under the typhoon scenario, including: calculating the output error corresponding to the cluster wind power according to the simulated output of the cluster wind power, and obtaining the sample data corresponding to the output error, wherein the output error follows the real distribution, and the sample data follows the empirical distribution; calculating the corresponding Wasserstein distance based on the empirical distribution and the real distribution, and constructing a fuzzy uncertainty set of the wind power output under the typhoon state according to the Wasserstein distance; calculating the corresponding error regulation cost according to the output error and the preset error regulation cost coefficient, and establishing a regulation cost objective function through the output error, the error regulation cost and the fuzzy uncertainty set, so as to construct a second-stage optimization model based on the regulation cost objective function; performing standardization processing on the sample data to obtain the corresponding standard sample data, and converting the second-stage optimization model into a target optimization scheduling model based on the standard sample data and the strong duality strategy, and solving the target optimization scheduling model through the preset affine strategy to obtain the target scheduling scheme of the cluster wind power and the target electric vehicle under the typhoon scenario.

[0082] Specifically, the process of constructing and solving the second-stage optimization model in the embodiment of the present application is described as follows:

[0083] 1. Simulation error uncertainty modeling:

[0084] Those skilled in the art should understand that due to the certain uncertainty of the simulation error of the wind power output, the first-stage collaborative scheduling model cannot reflect the real system operation cost. Therefore, the embodiment of the present application needs to consider the fluctuation of the simulation error of the wind power output and construct a fuzzy uncertainty set of the simulation error of the wind power output under the typhoon state according to the Wasserstein distance.

[0085] Among them, the Wasserstein distance represents the distance between the empirical distribution and the real distribution, and its expression is as follows:

[0086] (16)

[0087] Among them, is the sample data of the simulation error (i.e., the output error), which follows the empirical distribution ; is the simulation error that follows the real distribution ; is and 's joint distribution; is the number of samples of the sample data.

[0088] Secondly, the embodiment of the present application can construct a fuzzy uncertainty set of wind power output under typhoon conditions (i.e., the fuzzy set of wind power output simulation error) according to the Wasserstein distance, and its expression is as follows:

[0089] (17)

[0090] Wherein, is the fuzzy set based on the true distribution ; represents the corresponding support set; is a subset of the sample space; is the Wasserstein radius; is the empirical distribution.

[0091] Thirdly, the embodiment of the present application can standardize the sample set (i.e., the sample data), and its expression is as follows:

[0092] (18)

[0093] Wherein, represents the variance of the sample data; represents the mean of the sample data.

[0094] 2. Construction of the objective function in the second stage:

[0095] The embodiment of the present application can calculate the error regulation cost according to the output error and the error regulation cost coefficient, so as to establish an adjustment cost objective function through the output error, the error regulation cost and the fuzzy uncertainty set, as shown in the following formula:

[0096] (19)

[0097] (20)

[0098] Wherein, is the error regulation cost; is the error regulation cost coefficient.

[0099] Optionally, in an embodiment of the present application, the mathematical expression of the target optimization scheduling model is:

[0100]

[0101] Wherein, , and are the operating cost coefficients of the thermal power units in the cluster wind power respectively; is the corresponding error adjustment coefficient for the thermal power unit; is the dual variable; is the th standard sample data; is the auxiliary variable; is the Wasserstein radius; is the number of samples of the sample data; represents the maximum value in the standard sample data; represents the minimum value in the standard sample data.

[0102] After that, the embodiments of the present application can construct a second-stage optimization model (i.e., a distributionally robust optimization model) based on the regulation cost objective function. This distributionally robust optimization model is a multi-layer optimization problem and cannot be directly solved using a solver. Therefore, the embodiments of the present application can transform the distributed robust optimization model using strong duality theory and adopt an affine strategy as the adjustment strategy for the thermal power unit to respond to the wind power simulation error, and the expression for the solution is derived as follows:

[0103] (21)

[0104]

[0105] where is the dual variable; is the th standard sample data; ; is the auxiliary variable.

[0106] (23)

[0107] where , and are the operating cost coefficients of the thermal power unit respectively; is the coefficient of the thermal power unit participating in error regulation.

[0108] (24)

[0109] Finally, the distributionally robust optimization model can be transformed into an MILP (Mixed Integer Linear Programming) optimal scheduling model (i.e., the target optimal scheduling model) to solve the scheduling problem after the occurrence of a line disconnection fault including wind power clusters, electric vehicles, and the uncertainty of renewable energy.

[0110] Accordingly, in the embodiments of the present application, aiming at the problem of the uncertainty of wind power output, the strong duality theory is adopted to transform the worst expectation problem of the distributionally robust optimization model into a single-layer optimization problem, thereby reducing the complexity of the optimization algorithm.

[0111] According to the receiving-end power system scheduling method under the typhoon scenario proposed in the embodiments of the present application, by obtaining the typhoon meteorological characteristic parameters corresponding to the preset typhoon scenario, and establishing a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, to generate the simulated output of cluster wind power based on the pre-constructed template machine output model and the wind speed distribution parameter model; based on the simulated output of cluster wind power and the preset bus load shedding priority, construct a load shedding model under the N-1 line fault, and obtain the load shedding result of the regional power system through the load shedding model, to establish a thermal power rescheduling model according to the load shedding result, and correct the preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain the corrected scheduling plan of the thermal power unit; determine the incentive index of the target electric vehicle according to the load shedding result, and construct an electric vehicle incentive model according to the incentive index, to obtain the scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; based on the simulated output of cluster wind power, the load shedding model, the corrected scheduling plan of the thermal power unit and the scheduling incentive cost, construct a first-stage collaborative scheduling model, and calculate the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative scheduling model, and establish a corresponding adjustment cost objective function by using the error adjustment cost, to construct a second-stage optimization model through the adjustment cost objective function, and solve the second-stage optimization model according to the preset strong duality strategy and affine constraints to obtain the target scheduling plan of the cluster wind power and the target electric vehicle under the typhoon scenario. The present application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system under typhoon weather through a two-stage distributionally robust optimization strategy of thermal power unit rescheduling and electric vehicle incentive under the typhoon scenario.

[0112] Secondly, a receiving-end power system scheduling device under the typhoon scenario proposed in the embodiments of the present application is described with reference to the accompanying drawings.

[0113] Figure 3 It is a block diagram of the receiving-end power system scheduling device under the typhoon scenario of the embodiments of the present application.

[0114] As Figure 3 shown, the receiving-end power system scheduling device 10 under the typhoon scenario includes: a generation module 100, a correction module 200, a modeling module 300, and a solving module 400.

[0115] Among them, the generation module 100 is used to obtain the typhoon meteorological characteristic parameters corresponding to the preset typhoon scenario, and establish a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate the simulated output of cluster wind power based on the pre-constructed template machine output model and the wind speed distribution parameter model.

[0116] A correction module 200, configured to build a load shedding model under an N-1 line fault based on the simulated output of the cluster wind power and the preset bus load shedding priority, and obtain the load shedding result of the regional power system through the load shedding model, so as to establish a thermal power rescheduling model according to the load shedding result, and correct the preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain a corrected scheduling plan for the thermal power unit.

[0117] A modeling module 300, configured to determine the incentive index of the target electric vehicle according to the load shedding result, and build an electric vehicle incentive model according to the incentive index, so as to obtain the scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model.

[0118] A solving module 400, configured to build a first-stage coordinated scheduling model based on the simulated output of the cluster wind power, the load shedding model, the corrected scheduling plan of the thermal power unit, and the scheduling incentive cost, calculate the error regulation cost corresponding to the cluster wind power according to the first-stage coordinated scheduling model, and use the error regulation cost to establish a corresponding differential cost objective function, so as to build a second-stage optimization model through the differential cost objective function, and solve the second-stage optimization model according to the preset strong duality strategy and affine constraint to obtain the target scheduling plan of the cluster wind power and the target electric vehicle under the typhoon scenario.

[0119] Optionally, in an embodiment of the present application, the generation module 100 includes: a first acquisition unit, a first calculation unit, and a summation unit.

[0120] Wherein, the first acquisition unit is configured to acquire the rated power of the preset prototype machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, and acquire the real-time wind speed of the wind farm according to the wind speed distribution parameter model.

[0121] The first calculation unit is configured to build a prototype machine output model based on the rated power of the prototype machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed, and the cut-out wind speed, so as to calculate the simulated output of each wind turbine in the cluster wind power through the prototype machine output model.

[0122] The summation unit is configured to calculate the simulated output of the wind farm at each moment according to the simulated output of each wind turbine, and perform an accumulation summation operation on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power.

[0123] Optionally, in an embodiment of the present application, the correction module 200 includes: a first determination unit, a second determination unit, and a judgment unit.

[0124] Wherein, the first determination unit is configured to determine the line disconnection scenario corresponding to the cluster wind power, and build a power flow state constraint according to the line disconnection indication factor corresponding to the line disconnection scenario.

[0125] A second determination unit, configured to determine the corresponding load shedding cost, load shedding status, indication factors of different levels corresponding to each bus, load shedding cost coefficients of each level, and load shedding amounts of each bus at each moment of the cluster wind power, and obtain the load shedding result of the regional power system based on the power flow state constraint, load shedding cost, load shedding status, indication factors, load shedding cost coefficients, and load shedding amounts.

[0126] A judgment unit, configured to determine the total load shedding amount according to the load shedding result, and judge whether the total load shedding amount is greater than a preset warning value. Wherein, when the total load shedding amount is greater than the warning value, the thermal power unit dispatch plan is corrected to obtain a corrected thermal power unit dispatch plan.

[0127] Optionally, in an embodiment of the present application, the modeling module 300 includes: a third determination unit and a construction unit.

[0128] Wherein, the third determination unit is configured to determine at least one target bus with a non-zero load shedding amount, calculate the ratio between the total load shedding amount of each target bus in the at least one target bus and the preset total load demand amount, and use the ratio as the load shedding ratio.

[0129] The construction unit is configured to determine the incentive index of the target electric vehicle according to the load shedding ratio, so as to construct an electric vehicle incentive model through the incentive index.

[0130] Optionally, in an embodiment of the present application, the solving module 400 includes: a second obtaining unit, a second calculating unit, a establishing unit, and a standardizing unit.

[0131] Wherein, the second obtaining unit is configured to calculate the output error corresponding to the cluster wind power according to the simulated output of the cluster wind power, and obtain the sample data corresponding to the output error, wherein the output error follows a true distribution, and the sample data follows an empirical distribution.

[0132] The second calculating unit is configured to calculate the corresponding Wasserstein distance based on the empirical distribution and the true distribution, and construct a fuzzy uncertainty set of the wind power output under typhoon conditions according to the Wasserstein distance.

[0133] The establishing unit is configured to calculate the corresponding error adjustment cost according to the output error and the preset error adjustment cost coefficient, and establish a differential adjustment cost objective function through the output error, the error adjustment cost, and the fuzzy uncertainty set, so as to construct a second-stage optimization model based on the differential adjustment cost objective function.

[0134] A normalization unit is used to normalize sample data to obtain corresponding standardized sample data, convert the second-stage optimization model into a target optimal scheduling model based on the standardized sample data and the strong duality strategy, and solve the target optimal scheduling model through a preset affine strategy to obtain a target scheduling plan for the clustered wind power and target electric vehicles under typhoon scenarios.

[0135] Optionally, in an embodiment of the present application, the mathematical expression of the target optimal scheduling model is:

[0136]

[0137] Wherein, 、 and are respectively the operating cost coefficients of thermal power units in the clustered wind power; is the corresponding error regulation coefficient of the thermal power unit; is the dual variable; is the rd standardized sample data; is the auxiliary variable; is the Wasserstein radius; is the number of samples of the sample data; represents the maximum value in the standardized sample data; represents the minimum value in the standardized sample data.

[0138] It should be noted that the foregoing explanation of the embodiment of the receiving-end power system scheduling method under typhoon scenarios also applies to the receiving-end power system scheduling device under typhoon scenarios of this embodiment, and will not be elaborated here.

[0139] The receiving-end power system scheduling device under typhoon scenarios proposed according to the embodiments of the present application includes a generating module 100, configured to obtain typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and establish a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate a clustered wind power simulated output based on a pre-constructed template machine output model and the wind speed distribution parameter model; a correcting module 200, configured to construct a load shedding model under an N-1 line fault based on the clustered wind power simulated output and a preset bus load shedding priority, and obtain a load shedding result of the regional power system through the load shedding model, so as to establish a thermal power re-scheduling model according to the load shedding result, and correct a preset thermal power unit scheduling plan through the thermal power re-scheduling model to obtain a corrected thermal power unit scheduling plan; a modeling module 300, configured to determine an incentive index of a target electric vehicle according to the load shedding result, and construct an electric vehicle incentive model according to the incentive index, so as to obtain a scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; a solving module 400, configured to construct a first-stage collaborative scheduling model based on the clustered wind power simulated output, the load shedding model, the corrected thermal power unit scheduling plan, and the scheduling incentive cost, calculate an error regulation cost corresponding to the clustered wind power according to the first-stage collaborative scheduling model, and establish a corresponding differential cost objective function by using the error regulation cost, so as to construct a second-stage optimization model through the differential cost objective function, and solve the second-stage optimization model according to a preset strong duality strategy and affine constraints to obtain a target scheduling plan for the clustered wind power and the target electric vehicle under typhoon scenarios. The present application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system under typhoon weather through a two-stage distributionally robust optimization strategy of thermal power unit re-scheduling and electric vehicle incentive under typhoon scenarios.

[0140] Figure 4 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include:

[0141] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0142] When the processor 402 executes the program, it implements the receiving-end power system scheduling method under typhoon scenarios provided in the above embodiments.

[0143] Further, the electronic device further includes:

[0144] A communication interface 403, configured for communication between the memory 401 and the processor 402.

[0145] The memory 401 is used to store a computer program executable on the processor 402.

[0146] The memory 401 may include high-speed RAM memory and may also include non-volatile memory, such as at least one magnetic disk memory.

[0147] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0148] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a single chip, the memory 401, the processor 402, and the communication interface 403 can communicate with each other through an internal interface.

[0149] The processor 402 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0150] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-described receiving-end power system scheduling method under typhoon scenarios is implemented.

[0151] The embodiments of the present application also provide a computer program product, including a computer program, and when the computer program is executed, it is used to implement the above-described receiving-end power system scheduling method under typhoon scenarios.

[0152] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0153] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0154] Any process or method description, whether in a flowchart or described otherwise herein, can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a manner that is not in the order shown or discussed, including in a substantially simultaneous manner according to the functions involved or in the reverse order, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0155] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence list of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0156] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), and the like.

[0157] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0158] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0159] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A dispatching method for a receiving-end power system under typhoon scenarios, characterized in that Including the following steps: Obtain the typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and establish a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate the simulated output of the cluster wind power based on a pre-constructed template machine output model and the wind speed distribution parameter model; Based on the simulated output of the cluster wind power and the preset bus load shedding priority, construct a load shedding model under the N-1 line fault, and obtain the load shedding result of the regional power system through the load shedding model, so as to establish a thermal power re-dispatch model according to the load shedding result, and correct the preset thermal power unit dispatch plan through the thermal power re-dispatch model to obtain the corrected dispatch plan of the thermal power unit; Determine the incentive index of the target electric vehicle according to the load shedding result, and construct an electric vehicle incentive model according to the incentive index, so as to obtain the dispatch incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; Based on the simulated output of the cluster wind power, the load shedding model, the corrected dispatch plan of the thermal power unit and the dispatch incentive cost, construct a corresponding objective function, and combine the preset operation constraints to construct a first-stage collaborative dispatch model considering wind power, thermal power and electric vehicles, and calculate the error regulation cost corresponding to the cluster wind power according to the first-stage collaborative dispatch model, and establish a corresponding differential cost objective function by using the error regulation cost, so as to construct a second-stage optimization model through the differential cost objective function, and solve the second-stage optimization model according to the preset strong duality strategy and affine constraints to obtain the target dispatch plan of the cluster wind power and the target electric vehicle under the typhoon scenario; Among them, the mathematical expression of the wind speed distribution parameter model is: Among them, is the air pressure at a distance from the typhoon center; is the lowest air pressure at the typhoon center; is the ambient air pressure; is the radius of the maximum wind speed; is the air density; is the radius at which the gradient wind speed is located; is the Coriolis parameter; is the maximum wind speed; is the base of the natural logarithm; The mathematical expression of the template machine output model is: Among them, is the simulated output of each wind turbine in the cluster wind power; is the rated power of the prototype; is the real-time wind speed of the wind farm; is the cut-in wind speed; is the rated wind speed; is the cut-out wind speed; are the fan power output coefficients respectively; The mathematical expression of the simulated output of the cluster wind power is: Among them, is the simulated output of the wind farm at each moment; is the set of wind turbines belonging to the wind farm ; is the simulated output of the cluster wind power, is the set of wind farms belonging to the cluster ; Evaluate the power supply priority of the load carried by the bus according to the standard voltage level of the bus. The higher the power supply priority of the load carried, the lower the bus load shedding priority corresponding to the power supply priority of the load carried, so as to construct a load shedding model under the N-1 line fault. The mathematical expression of the load shedding model is: Among them, is the busbar corresponding grade of the indication factor; is the cost of load shedding; corresponding grade s of the load shedding cost coefficient; is the busbar at time of the load shedding amount; is the load shedding state; indicates the busbar n at t the indication factor of the load shedding at the moment; The mathematical expression of the thermal power re-dispatch model is: Among them, is the busbar at time is the load shedding amount; is the set of busbars; is the load shedding threshold that causes the unit to be rescheduled for startup; represents the decision variable for rescheduling startup; The mathematical expression of the second-stage optimization model is: Among them, is the error adjustment cost; is the error adjustment cost coefficient.

2. The dispatching method of the receiving-end power system under typhoon scenarios according to claim 1, characterized in that The obtaining of the typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and the establishment of a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate the simulated output of the cluster wind power based on a pre-constructed template machine output model and the wind speed distribution parameter model, includes: Obtain the rated power of the template machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed and the cut-out wind speed corresponding to the preset template machine, and obtain the real-time wind speed of the wind farm according to the wind speed distribution parameter model; Based on the rated power of the template machine, the real-time wind speed of the wind farm, the cut-in wind speed, the rated wind speed and the cut-out wind speed, construct the template machine output model, so as to calculate the simulated output of each wind turbine in the cluster wind power through the template machine output model; Calculate the simulated output of the wind farm at each moment according to the simulated output of each wind turbine, and perform an accumulation summation operation on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power.

3. The receiving-end power system dispatching method under the typhoon scenario according to claim 2, characterized in that Based on the simulated output of the cluster wind power and the preset bus load shedding priority, construct a load shedding model under N-1 line faults, and obtain the load shedding results of the regional power system through the load shedding model, so as to establish a thermal power rescheduling model according to the load shedding results, and correct the preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain the corrected thermal power unit scheduling plan, including: Determine the line disconnection scenarios corresponding to the cluster wind power, and construct a power flow state constraint according to the line disconnection indication factors corresponding to the line disconnection scenarios; Determine the load shedding cost, load shedding status, different-level indication factors corresponding to each bus, load shedding cost coefficients for each level, and the load shedding amount of each bus at each moment corresponding to the cluster wind power, and obtain the load shedding results of the regional power system based on the power flow state constraint, the load shedding cost, the load shedding status, the indication factors, the load shedding cost coefficients, and the load shedding amount; Determine the total load shedding amount according to the load shedding results, and judge whether the total load shedding amount is greater than a preset alarm value. Among them, when the total load shedding amount is greater than the alarm value, correct the thermal power unit scheduling plan to obtain the corrected thermal power unit scheduling plan.

4. The dispatching method for the receiving-end power system under typhoon scenarios according to claim 3, characterized in that, Determine the incentive index of the target electric vehicle according to the load shedding results, and construct an electric vehicle incentive model according to the incentive index, including: Determine at least one target bus with a non-zero load shedding amount, calculate the ratio between the total load shedding amount of each target bus in the at least one target bus and the preset total load demand, and use the ratio as the load shedding ratio; Determine the incentive index of the target electric vehicle according to the load shedding ratio, and construct the electric vehicle incentive model through the incentive index.

5. The receiving-end power system dispatching method under typhoon scenarios according to claim 4, characterized in that, Calculate the error regulation cost corresponding to the cluster wind power according to the first-stage coordinated scheduling model, and use the error regulation cost to establish a corresponding differential cost objective function, so as to construct a second-stage optimization model through the differential cost objective function, and solve the second-stage optimization model according to the preset strong duality strategy and affine constraints to obtain the target scheduling plan of the cluster wind power and the target electric vehicle under the typhoon scenario, including: Calculate the output error corresponding to the cluster wind power according to the simulated output of the cluster wind power, and obtain the sample data corresponding to the output error, where the output error follows a true distribution and the sample data follows an empirical distribution; Based on the empirical distribution and the true distribution, calculate the corresponding Wasserstein distance, and construct a fuzzy uncertainty set of wind power output under the typhoon state according to the Wasserstein distance; Calculate the corresponding error adjustment cost according to the output error and a preset error adjustment cost coefficient, and establish the adjustment cost objective function through the output error, the error adjustment cost, and the fuzzy uncertainty set, so as to construct the second-stage optimization model based on the adjustment cost objective function; Perform standardization processing on the sample data to obtain corresponding standard sample data, convert the second-stage optimization model into a target optimal scheduling model based on the standard sample data and the strong duality strategy, and solve the target optimal scheduling model through a preset affine strategy to obtain the target scheduling plan of the cluster wind power and the target electric vehicle under the typhoon scenario.

6. The receiving-end power system dispatching method under typhoon scenarios according to claim 5, characterized in that The mathematical expression of the target optimal scheduling model is: Among them, , and are respectively the operating cost coefficients of the thermal power units in the cluster wind power; is the corresponding error adjustment coefficient of the thermal power unit; is the dual variable; is the th standard sample data; is the auxiliary variable; is the Wasserstein radius; is the number of samples of the sample data; represents the maximum value in the standard sample data; represents the minimum value in the standard sample data.

7. A cluster wind power and electric vehicle scheduling device considering line faults under typhoon scenarios, characterized in that, including: A generation module, configured to obtain typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and establish a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate the simulated output of the cluster wind power based on a pre-constructed prototype machine output model and the wind speed distribution parameter model; A correction module, configured to construct a load shedding model under an N-1 line fault based on the simulated output of the cluster wind power and a preset bus load shedding priority, obtain the load shedding result of the regional power system through the load shedding model, establish a thermal power rescheduling model according to the load shedding result, and correct the preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain a corrected thermal power unit scheduling plan; A modeling module, configured to determine the incentive index of the target electric vehicle according to the load shedding result, construct an electric vehicle incentive model according to the incentive index, and obtain the scheduling incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; A solving module, configured to construct a corresponding objective function based on the simulated output of the cluster wind power, the load shedding model, the corrected thermal power unit scheduling plan, and the scheduling incentive cost, and construct a first-stage collaborative scheduling model considering wind power, thermal power, and electric vehicles in combination with preset operation constraints, calculate the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative scheduling model, and establish a corresponding adjustment cost objective function using the error adjustment cost, so as to construct a second-stage optimization model through the adjustment cost objective function, and solve the second-stage optimization model according to a preset strong duality strategy and affine constraints to obtain the target scheduling plan of the cluster wind power and the target electric vehicle under the typhoon scenario; wherein, the mathematical expression of the wind speed distribution parameter model is: Among them, is the air pressure at a distance from the typhoon center; is the lowest air pressure at the typhoon center; is the environmental air pressure; is the radius of the maximum wind speed; is the air density; is the radius at which the gradient wind speed is located; is the Coriolis parameter; is the maximum wind speed; is the base of the natural logarithm; The mathematical expression of the prototype machine output model is: Among them, is the simulated output of each wind turbine in the cluster wind power; is the rated power of the prototype machine; is the real-time wind speed of the wind farm; is the cut-in wind speed; is the rated wind speed; is the cut-out wind speed; are the fan power output coefficients respectively; The mathematical expression of the simulated output of the cluster wind power is: Among them, is the simulated output of the wind farm at each moment; is the set of wind turbines belonging to the wind farm ; is the simulated output of the cluster wind power, is the set of wind farms belonging to the cluster ; Evaluate the power supply priority of the load carried by the bus according to the standard voltage level of the bus. The higher the power supply priority of the load carried, the lower the bus load shedding priority corresponding to the power supply priority of the load carried, so as to construct a load shedding model under an N-1 line fault. The mathematical expression of the load shedding model is: Among them, is the busbar corresponding level of the indication factor; is the cost of load shedding; corresponding level s of the load shedding cost coefficient; is the busbar at time of the load shedding amount; is the load shedding state; indicates the busbar n at t the indication factor of the load shedding at the moment; The mathematical expression of the thermal power rescheduling model is: （10） Among them, is the busbar at time of the load shedding amount; is the set of busbars; is the load shedding threshold that causes the unit to be rescheduled for startup; represents the decision variable for rescheduling startup; The mathematical expression of the second-stage optimization model is: Among them, is the error adjustment cost; is the error adjustment cost coefficient.

8. An electronic device, characterized in that, including: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the receiving-end power system scheduling method under a typhoon scenario according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the receiving-end power system scheduling method under a typhoon scenario according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed to implement the receiving-end power system scheduling method under a typhoon scenario according to any one of claims 1-6.

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