Receiving end power system scheduling method and device in typhoon scene

Abstract

The invention relates to the technical field of electric power system operation, in particular to a receiving end electric power system dispatching method and device in a typhoon scene, and the method comprises the steps: building an offshore wind plant output model considering typhoon wind field condition dominance, and generating cluster wind power simulation output according to typhoon meteorological characteristic parameters in combination with a typical sample machine output model; establishing a wind power supply and load mismatch space-time model, and determining a load supply level under the N-1 line fault; establishing a thermal power rescheduling model, and correcting a thermal power generating unit scheduling plan; establishing an electric vehicle excitation model, and determining the discharge state and excitation cost of the electric vehicle; a two-stage distribution robust optimization method is established, and a strong duality theory is used for conversion into a single-layer optimization problem. Therefore, the problems that in the prior art, typhoon wind field meteorological characteristics cannot be fused, the line breaking risk of a special power transmission line is difficult to comprehensively consider, and the electric power supporting effect of an electric vehicle serving as a distributed energy storage resource is not considered are solved.

Description

Technical Field

[0001] The present application relates to the technical field of power system operation, and in particular to a method and device for dispatching a receiving-end power system in a typhoon scenario. Background Art

[0002] The power generation capacity of regional power systems of wind power is extremely dependent on weather conditions. Under the influence of special wind farm conditions, the extreme response characteristics of wind power clusters, especially offshore wind farms, may disrupt the energy supply of the entire power system. Therefore, resilience assessment in typhoon weather has an important impact on system stability.

[0003] Existing technologies have some limitations in the power system's response to typhoon weather. Specifically, in terms of power supply, the research on the output characteristics of wind power generation in typhoon weather has failed to integrate the meteorological characteristics of typhoon wind fields, resulting in inadequate prediction of typhoon extreme output; in terms of power transmission, the risk of disconnection of special transmission lines has not been fully considered, especially the changes in system flow caused by disconnection of wind power cluster transmission lines; in the power restoration stage, the existing thermal power, photovoltaic, and energy storage power stations are often considered to make up for the load gap, which increases the cost of power generation and creates the risk of overvoltage of power generation equipment, without considering the power support role of electric vehicles as distributed energy storage resources.

[0004] In typhoon scenarios, the random uncertainty of wind power generation may cause scheduling errors and operational risks. Traditional uncertainty scheduling methods have significant limitations when dealing with typhoon scenarios. The requirement of stochastic optimization methods to build accurate scenario trees based on a large amount of historical data is difficult to meet the low frequency and high volatility characteristics of typhoon events. The uncertainty of the robust optimization model uses a deterministic set and is not affected by any possible situation 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, existing technologies are unable to integrate the meteorological characteristics of typhoon wind fields, find it difficult to fully consider the risk of disconnection of special transmission lines, and fail to consider the role of electric vehicles as distributed energy storage resources in supporting electricity, which needs to be urgently addressed. Summary of the invention

[0006] The present application provides a receiving-end power system dispatching method and device in a typhoon scenario to solve the problems that the existing technology cannot integrate the meteorological characteristics of the typhoon wind field, it is difficult to fully consider the risk of disconnection of special transmission lines, and fails to consider the power support role of electric vehicles as distributed energy storage resources.

[0007] The first aspect of the present application provides a method for dispatching a receiving-end power system in a typhoon scenario, comprising 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 a cluster wind power simulation output based on a pre-built sample machine output model and the wind speed distribution parameter model; based on the cluster wind power simulation output and the preset bus load shedding priority, constructing a load shedding model under an N-1 line fault, and obtaining the load shedding result of the regional power system through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding result, and correcting the preset thermal power unit dispatching plan through the thermal power re-dispatching model to obtain a corrected dispatching plan for the thermal power unit; determining the target according to the load shedding result Incentive indicators for electric vehicles are obtained, and an electric vehicle incentive model is constructed based on the incentive indicators 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 revised dispatch plan of the thermal power unit and the dispatch incentive cost, a first-stage collaborative dispatch model is constructed, and the error adjustment cost corresponding to the cluster wind power is calculated according to the first-stage collaborative dispatch model, and the corresponding adjustment cost objective function is established 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 the preset strong dual strategy and affine constraints to obtain the target dispatch scheme of the cluster wind power and the target electric vehicle under the typhoon scenario.

[0008] Optionally, in one embodiment of the present application, the typhoon meteorological characteristic parameters corresponding to the preset typhoon scenario are obtained, and a wind speed distribution parameter model is established through the typhoon meteorological characteristic parameters to generate a cluster wind power simulation output based on a pre-constructed model machine output model and the wind speed distribution parameter model, including: obtaining the model machine rated power, wind farm real-time wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed corresponding to the preset model machine, and obtaining the wind farm real-time wind speed according to the wind speed distribution parameter model; constructing the model machine output model based on the model machine rated power, the wind farm real-time wind speed, the cut-in wind speed, the rated wind speed, and the cut-out wind speed to calculate the simulated output of each wind turbine group in the cluster wind power through the model machine output model; calculating the wind farm simulated output at each moment according to the simulated output of each wind turbine group, and performing cumulative summation operations on the wind farm simulated output at each moment to obtain the cluster wind power simulation output.

[0009] Optionally, in one embodiment of the present application, based on the simulated output of the 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 result of the regional power system is obtained through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding result, and the preset thermal power unit dispatching plan is corrected through the thermal power re-dispatching model to obtain a corrected dispatching plan for the thermal power unit, including: determining the line disconnection scenario corresponding to the cluster wind power, and constructing a flow state constraint according to the line disconnection indication factor corresponding to the line disconnection scenario; determining the corresponding load shedding cost of the cluster wind power. , load reduction status, indicator factors of different levels corresponding to each bus, load shedding cost coefficient of each level and load shedding amount of each bus at each moment, and based on the flow state constraint, the load shedding cost, the load reduction status, the indicator factor, the load shedding cost coefficient and the load shedding amount, obtain the load shedding result of the regional power system; 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 alarm value, wherein when the total load shedding amount is greater than the alarm value, correct the scheduling plan of the thermal power unit to obtain the corrected scheduling plan of the thermal power unit.

[0010] Optionally, in one embodiment of the present application, determining the incentive index of the target electric vehicle according to the load shedding result, and constructing the electric vehicle incentive model according to the incentive index, includes: determining at least one target bus whose load shedding amount is not 0, and calculating the 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 the electric vehicle incentive model through the incentive index.

[0011] Optionally, in an embodiment of the present application, calculating the error regulation cost corresponding to the cluster wind power according to the first-stage collaborative scheduling model, and establishing a corresponding regulation cost objective function by using the error regulation cost, so as to construct a second-stage optimization model through the regulation 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 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 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 regulation cost according to the output error and a preset error regulation cost coefficient, and establishing the regulation cost objective function through the output error, the error regulation cost and the fuzzy uncertainty set, so as to construct the second-stage optimization model based on the regulation 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 optimal scheduling model based on the standard sample data and the strong duality strategy, and solving the target optimal scheduling model through a preset affine strategy to obtain the target scheduling scheme of the cluster 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 optimal scheduling model is:

[0013] Wherein, 、 and are respectively the operating cost coefficients of the thermal power units in the cluster wind power; is the corresponding error regulation 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.

[0014] The second aspect of the present application provides a receiving-end power system dispatching device under a typhoon scenario, including: a generation module, used 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 cluster wind power simulation output based on a pre-built sample machine output model and the wind speed distribution parameter model; a correction module, used to construct a load shedding model under an N-1 line fault based on the cluster wind power simulation output 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 re-dispatching model according to the load shedding result, and correct the preset thermal power unit dispatching plan through the thermal power re-dispatching model to obtain a corrected dispatching plan of the thermal power unit; a modeling module, used to generate a cluster wind power simulation output based on the ... load shedding result The invention relates to a method for determining an incentive index of a target electric vehicle based on the incentive index, and constructing an electric vehicle incentive model according to the incentive index, so as to obtain a dispatch incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; a solving module is used to construct a first-stage collaborative dispatch model based on the simulated output of the cluster wind power, the load shedding model, the revised dispatch plan of the thermal power unit and the dispatch incentive cost, and calculate the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative dispatch 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 dual strategy and affine constraints, so as to obtain a target dispatch scheme for the cluster wind power and the target electric vehicle under the typhoon scenario.

[0015] Optionally, in one embodiment of the present application, the generation module includes: a first acquisition unit, used to acquire the model machine rated power, 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 a preset model machine, and acquire the real-time wind speed of the wind farm according to the wind speed distribution parameter model; a first calculation unit, used to construct the model machine output model based on the model machine rated power, 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 group in the cluster wind power through the model machine output model; a summation unit, used to calculate the simulated output of the wind farm at each moment according to the simulated output of each wind turbine group, and perform cumulative summation operations on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power.

[0016] Optionally, in one embodiment of the present application, the correction module includes: a first determination unit, used to determine the line disconnection scenario corresponding to the cluster wind power, and construct a flow state constraint according to the line disconnection indicator factor corresponding to the line disconnection scenario; a second determination unit, used to determine the corresponding load shedding cost, load reduction status, the indicator factors of different levels corresponding to each bus, the load shedding cost coefficient of each level and the load shedding amount of each bus at each moment, and based on the flow state constraint, the load shedding cost, the load reduction status, the indicator factor, the load shedding cost coefficient and the load shedding amount, obtain the load shedding result of the regional power system; a judgment unit, used 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 alarm value, wherein when the total load shedding amount is greater than the alarm value, the scheduling plan of the thermal power unit is corrected to obtain the corrected scheduling plan of the thermal power unit.

[0017] Optionally, in one embodiment of the present application, the modeling module includes: a third determination unit, used to determine at least one target bus whose load shedding amount is not 0, and 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; a construction unit, used to determine 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.

[0018] Optionally, in one embodiment of the present application, the solution module includes: a second acquisition unit, used to calculate the output error corresponding to the cluster wind power according to the simulated output of the cluster wind power, and obtain sample data corresponding to the output error, wherein the output error obeys the true distribution and the sample data obeys the empirical distribution; a second calculation unit, used to calculate the corresponding Wasserstein distance based on the empirical distribution and the true distribution, and construct a fuzzy uncertain set of wind power output under typhoon state according to the Wasserstein distance; an establishment unit, used to 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 uncertain set, so as to construct the second-stage optimization model based on the adjustment cost objective function; a standardization unit, used to standardize the sample data to obtain the 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 dual strategy, and solve the target optimization scheduling model through a preset affine strategy to obtain the target scheduling scheme of the cluster wind power and the target electric vehicle under the typhoon scenario.

[0019] Optionally, in one embodiment of the present application, the mathematical expression of the target optimization scheduling model is:

[0020] in, , and are respectively the operating cost coefficients of thermal power units in the wind power cluster; is the error adjustment coefficient corresponding to the thermal power unit; is the dual variable; For the Standard sample data; is an auxiliary variable; is the Wasserstein radius; is the sample number of the sample data; represents the maximum value in the standard sample data; Indicates the minimum value in the standard sample data.

[0021] The third aspect of the present application provides an electronic device, comprising: 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 dispatching method in a typhoon scenario as described in the above embodiment.

[0022] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, it implements the above-mentioned receiving-end power system scheduling method in a typhoon scenario.

[0023] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned receiving-end power system scheduling method in the typhoon scenario.

[0024] Therefore, the embodiments of the present application have the following beneficial effects: The embodiments of the present application can 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 cluster wind power simulation output based on a pre-built sample machine output model and a wind speed distribution parameter model; based on the cluster wind power simulation output and the preset bus load shedding priority, a load shedding model under an N-1 line fault is constructed, and the load shedding results of the regional power system are obtained through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding results, and the preset thermal power unit dispatching plan is corrected through the thermal power re-dispatching model to obtain a corrected dispatching plan for the thermal power unit; the incentive for the target electric vehicle is determined according to the load shedding results. Indicators, and construct an electric vehicle incentive model based on the incentive indicators, so as to obtain the dispatch incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; based on the cluster wind power simulation output, load shedding model, thermal power unit revised dispatch plan and dispatch incentive cost, the first stage collaborative dispatch model is constructed, and the error adjustment cost corresponding to the cluster wind power is calculated according to the first stage collaborative dispatch model, and the corresponding adjustment cost objective function is established using the error adjustment cost, so as to construct the second stage optimization model through the adjustment cost objective function, and solve the second stage optimization model according to the preset strong dual strategy and affine constraint, so as to obtain the target dispatch plan for cluster wind power and target electric vehicles in the typhoon scenario. This application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system in typhoon weather through the two-stage distributed blue-robust optimization strategy of thermal power unit re-dispatching and electric vehicle incentive in typhoon scenarios. As a result, the problems that the existing technology cannot integrate the meteorological characteristics of typhoon wind fields, it is difficult to fully consider the risk of disconnection of special transmission lines, and fails to consider the power support role of electric vehicles as distributed energy storage resources are solved.

[0025] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a receiving-end power system dispatching method in a typhoon scenario provided according to an embodiment of the present application; Figure 2 A schematic diagram of the execution logic of a receiving-end power system dispatching method in a typhoon scenario provided by an embodiment of the present application; Figure 3 This is an example diagram of a receiving-end power system dispatching device in a typhoon scenario according to an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0027] Among them, 10 is a receiving-end power system dispatching device under a typhoon scenario; 100 is a generation module, 200 is a correction module, 300 is a modeling module, 400 is a solution module; 401 is a memory, 402 is a processor, and 403 is a communication interface. DETAILED DESCRIPTION

[0028] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the receiving-end power system dispatching method and device in a typhoon scenario of an embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a receiving-end power system dispatching method in a typhoon scenario, in which the method obtains typhoon meteorological characteristic parameters corresponding to a preset typhoon scenario, and establishes a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate a cluster wind power simulation output based on a pre-built sample machine output model and a wind speed distribution parameter model; based on the cluster wind power simulation output and the preset bus load shedding priority, a load shedding model under an N-1 line fault is constructed, and the load shedding results of the regional power system are obtained through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding results, and the preset thermal power unit dispatching plan is corrected through the thermal power re-dispatching model to obtain a corrected dispatching plan for the thermal power unit. ; 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 dispatch incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model; Based on the simulated output of cluster wind power, load shedding model, revised dispatch plan and dispatch incentive cost of thermal power units, build the first-stage collaborative dispatch model, and calculate the error adjustment cost corresponding to cluster wind power according to the first-stage collaborative dispatch model, and use the error adjustment cost to establish the corresponding differential cost objective function, so as to build the second-stage optimization model through the differential cost objective function, and solve the second-stage optimization model according to the preset strong dual strategy and affine constraint, so as to obtain the target dispatch scheme of cluster wind power and target electric vehicle under typhoon scenario. This application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system under typhoon weather through the two-stage distributed blue-robust optimization strategy of thermal power unit re-dispatching and electric vehicle incentive under typhoon scenario. Therefore, it solves the problems that the existing technology cannot integrate the meteorological characteristics of typhoon wind field, it is difficult to fully consider the risk of disconnection of special transmission lines, and fails to consider the power support role of electric vehicles as distributed energy storage resources.

[0030] Specifically, Figure 1A flowchart of a receiving-end power system dispatching method in a typhoon scenario provided in an embodiment of the present application.

[0031] like Figure 1 As shown, the receiving-end power system dispatching method in the typhoon scenario includes the following steps: In step S101, typhoon meteorological characteristic parameters are obtained, and cluster wind power simulation output is generated based on a pre-built sample machine output model and the typhoon meteorological characteristic parameters.

[0032] The embodiment of the present application can first obtain typhoon meteorological characteristic parameters, such as central air pressure, maximum wind speed and maximum wind speed radius, and combine them with the sample machine output model to generate cluster wind power simulation output, such as Figure 2 As shown, a cluster wind power output model considering typhoon wind field conditions is established, providing reliable technical support for the construction of subsequent load shedding models.

[0033] Optionally, in one embodiment of the present application, typhoon meteorological characteristic parameters are obtained, and based on a pre-constructed model output model and typhoon meteorological characteristic parameters, a cluster wind power simulation output is generated, including: obtaining the model machine rated power, wind farm real-time wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed corresponding to the preset model machine, and obtaining the wind farm real-time wind speed according to the wind speed distribution parameter model; constructing a model machine output model based on the model machine rated power, wind farm real-time wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed, so as to calculate the simulated output of each wind turbine group in the cluster wind power through the model machine output model; calculating the wind farm simulated output at each moment according to the simulated output of each wind turbine group, and performing cumulative summation operations on the wind farm simulated output at each moment to obtain the cluster wind power simulation output.

[0034] It should be noted that the embodiments of the present application can construct a typhoon meteorological characteristic parameter model according to the typhoon meteorological characteristic parameters. The mathematical expression of the typhoon meteorological characteristic parameter model is: (1) (2) in, Distance from the center of the typhoon The air pressure at The lowest air pressure at the center of the typhoon; is the ambient air pressure; is the maximum wind speed radius; is the air density; Description Radius The gradient wind speed at is the Coriolis parameter; is the maximum wind speed; is the base of natural logarithms.

[0035] Combined with the wind speed distribution of the wind farm, the mathematical expression of the sample machine output model is: (3) (4) in, For wind turbines The simulated output of each wind turbine in the wind power cluster; is the rated power of the prototype machine; Real-time wind speed for wind farms; is the cut-in wind speed; is the rated wind speed; To cut out the wind speed; are the fan power output coefficients respectively.

[0036] Afterwards, the embodiment of the present application can calculate the simulated output of the wind farm at each moment, and perform cumulative 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: (5) (6) in, For wind farms exist Simulated output at each moment (i.e. simulated output of the wind farm at each moment); For wind farms A collection of wind turbines, Cluster wind power exist The simulated output at the moment (i.e. the simulated output of cluster wind power), Belong to the cluster Collection of wind farms.

[0037] 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 result of the regional power system is obtained through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding result, and the preset thermal power unit dispatching plan is corrected through the thermal power re-dispatching model to obtain a corrected dispatching plan of the thermal power unit.

[0038] Furthermore, the embodiments of the present application also need to determine the load shedding model under the N-1 line fault according to the bus load shedding priority on the basis of the simulated output of the cluster wind power, so as to obtain the load shedding results such as the load shedding status and the load shedding ratio; thereafter, the embodiments of the present application also need to establish a thermal power re-dispatching model according to the load shedding results to correct the dispatching plan of the thermal power units, thereby obtaining a corrected dispatching plan of the thermal power units.

[0039] Optionally, in one 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 result of the regional power system is obtained through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding result, and the preset thermal power unit dispatching plan is corrected through the thermal power re-dispatching model to obtain a corrected dispatching plan of the thermal power unit, including: determining the line disconnection scenario corresponding to the cluster wind power, and constructing a flow state constraint according to the line disconnection indicator factor corresponding to the line disconnection scenario; determining the corresponding load shedding cost, load reduction status, indicator factors of different levels corresponding to each bus, load shedding cost coefficient of each level and load shedding amount of each bus at each time of the cluster wind power, and obtaining the load shedding result of the regional power system based on the flow state constraint, load shedding cost, load reduction status, indicator factor, load shedding cost coefficient and 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 the preset alarm value, wherein when the total load shedding amount is greater than the alarm value, the dispatching plan of the thermal power unit is corrected to obtain a corrected dispatching plan of the thermal power unit.

[0040] Specifically, the embodiment of the present application may first set a line disconnection scenario, and constrain the power flow state according to the line disconnection indication factor, which is expressed as follows: (7) in, For line exist The DC current of the moment; The upper limit of the trend; It is the line fault state.

[0041] Secondly, the embodiment of the present application can evaluate the power supply priority of the load according to the bus standard voltage level (the higher the power supply priority, the lower the corresponding bus load shedding priority) to obtain the load shedding cost, which is expressed as follows: (8) (9) in, is the node (i.e. busbar) Corresponding level Indicator factor; is the load shedding cost; For the corresponding level s The load shedding cost coefficient; For bus In time The load shedding capacity; It is in load shedding state; Indicates busbarn exist t Indicative factor of load shedding at the moment.

[0042] Afterwards, the embodiment of the present application uses the total load shedding amount of the system as the judgment basis. When the total load shedding amount exceeds the set alarm value (i.e., the load shedding threshold), the new unit is re-scheduled and started, thereby realizing emergency power supply. The expression of the startup logic setting is as follows: (10) in, For bus In time The load shedding capacity; is the busbar set; The load shedding threshold that causes the unit to be re-dispatched; Represents the decision variable for rescheduling startup.

[0043] In step S103, an 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 a dispatch incentive cost corresponding to the target electric vehicle through the electric vehicle incentive model.

[0044] 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 an incentive indicator to establish an electric vehicle incentive model, thereby determining the electric vehicle discharge state and the scheduling incentive cost.

[0045] Optionally, in one embodiment of the present application, an incentive index for a target electric vehicle is determined based on the load shedding result, and an electric vehicle incentive model is constructed based on the incentive index, including: determining at least one target bus whose load shedding amount is not 0, and calculating the ratio between the total load shedding amount of each target bus in at least one target bus and a preset total load demand, and using the ratio as the load shedding ratio; determining the incentive index for the target electric vehicle based on the load shedding ratio, so as to construct an electric vehicle incentive model through the incentive index.

[0046] Specifically, the embodiment of the present application can retrieve the load shedding results obtained above, and calculate the ratio of the total load shedding amount to the total load demand on the bus whose load shedding amount is not 0 according to the load shedding results as the load shedding ratio, and use the load shedding ratio as an indicator 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, thereby constructing an electric vehicle incentive model. The mathematical expression of the electric vehicle incentive model is as follows: (11) (12) (13) (14) (15) in, for The incentive factor for vehicle discharge at all times; , Busbar Load shedding and load demand; is the load shedding decision variable; and They are the participation ratio and upper limit of the electric vehicle fleet; for the size of the electric vehicle fleet; For electric vehicle fleets The discharge power; For electric vehicles The discharge power; For electric vehicles Decision variables for discharge; For the fleet electric vehicle clusters; is the incentive cost for electric vehicles (i.e., dispatch incentive cost); is the electric vehicle incentive cost coefficient.

[0047] In step S104, based on the simulated output of cluster wind power, the load shedding model, the revised dispatch plan of thermal power units and the dispatch incentive cost, a first-stage collaborative dispatch model is constructed, and the error adjustment cost corresponding to the cluster wind power is calculated according to the first-stage collaborative dispatch model, and the corresponding adjustment cost objective function is established 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 the preset strong dual strategy and affine constraints to obtain the target dispatch plan for cluster wind power and target electric vehicles under the typhoon scenario.

[0048] Afterwards, the embodiments of the present application also need to determine a two-stage distributed robust optimization strategy based on the cluster wind power simulated output, load shedding model, revised scheduling plan of thermal power units and scheduling incentive cost, so as to construct a scheduling model that handles the uncertainty of wind power output simulation errors.

[0049] It should be noted that the two-stage distributed robust optimization strategy of the embodiment of the present application, the first stage is the conventional scheduling optimization stage, and its objective function includes the operating cost of the thermal power unit, the startup cost, the load shedding cost, and the electric vehicle incentive cost, and constructs a collaborative scheduling model considering wind power, thermal power, and electric vehicles based on all operating constraints (that is, the first stage collaborative scheduling model); the second stage optimization model mainly focuses on the scheduling process in the scenario where wind power output simulation errors exist, and the objective function of this stage is to minimize the adjustment cost of compensating for the wind power simulation errors.

[0050] Therefore, the embodiments of the present application fully consider the distribution characteristics of typhoon weather, thereby improving the efficiency of the power system under typhoon weather through a two-stage distributed robust optimization strategy of thermal power unit re-dispatching and electric vehicle incentives under typhoon scenarios.

[0051] Optionally, in one embodiment of the present application, the error adjustment cost corresponding to the cluster wind power is calculated according to the first-stage collaborative scheduling model, and the corresponding adjustment cost objective function is established using the error adjustment cost, so as to construct the 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 constraint to obtain the target scheduling plan 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 sample data corresponding to the output error, wherein the output error obeys the true distribution and the sample data obeys the empirical distribution; based on the empirical distribution and the true distribution, calculating the corresponding Wasserst ein distance, and construct the fuzzy uncertainty set of wind power output under typhoon state according to Wasserstein distance; calculate the corresponding error adjustment cost according to the output error and the preset error adjustment cost coefficient, and establish the adjustment cost objective function through the output error, error adjustment cost and fuzzy uncertainty set, so as to construct the second-stage optimization model based on the adjustment cost objective function; standardize the sample data to obtain the corresponding standard sample data, and convert the second-stage optimization model into the target optimization scheduling model based on the standard sample data and strong dual strategy, and solve the target optimization scheduling model through the preset affine strategy to obtain the target scheduling scheme of cluster wind power and target electric vehicles under typhoon scenario.

[0052] Specifically, the process of constructing and solving the second-stage optimization model in the embodiment of the present application is as follows: 1. Modeling of simulation error uncertainty: Those skilled in the art should understand that, due to certain uncertainties in the simulation error of wind power output, the first-stage collaborative scheduling model cannot reflect the actual system operating costs. Therefore, the embodiment of the present application needs to take the fluctuations of the wind power output simulation error into account and construct a fuzzy uncertainty set of the wind power output simulation error under typhoon conditions based on the Wasserstein distance.

[0053] Among them, Wasserstein distance represents the distance between the empirical distribution and the true distribution, and its expression is as follows: (16) in, is the sample data of simulation error (i.e. output error), which follows the empirical distribution ; To simulate the error to follow the true distribution ; for and The joint distribution of is the number of samples of the sample data.

[0054] Secondly, the embodiment of the present application can construct a fuzzy uncertainty set of wind power output under typhoon conditions (i.e., a fuzzy set of wind power output simulation errors) according to the Wasserstein distance, and its expression is as follows: (17) in, Based on the real distribution fuzzy sets; represents the corresponding support set; is a subset of the sample space; is the Wasserstein radius; The empirical distribution.

[0055] Again, the embodiment of the present application can perform standardization processing on the sample set (ie, sample data), and the expression is as follows: (18) in, Represents the variance of sample data; Represents the mean of the sample data.

[0056] 2. Construction of the second stage objective function: The embodiment of the present application can calculate the error adjustment cost according to the output error and the error adjustment cost coefficient, so as to establish the error adjustment cost objective function through the output error, the error adjustment cost and the fuzzy uncertainty set, as shown in the following formula: (19) (20) in, is the error adjustment cost; is the error adjustment cost factor.

[0057] Optionally, in one embodiment of the present application, the mathematical expression of the target optimization scheduling model is:

[0058] in, , and They are the operating cost coefficients of thermal power units in cluster wind power; is the error adjustment coefficient corresponding to the thermal power unit; is the dual variable; For the Standard sample data; is an auxiliary variable; is the Wasserstein radius; is the sample number of sample data; Indicates the maximum value in the standard sample data; Indicates the minimum value in the standard sample data.

[0059] Afterwards, the embodiment of the present application can construct a second-stage optimization model (i.e., a distributed robust optimization model) based on the adjustment cost objective function. The distributed robust optimization model is a multi-layer optimization problem and cannot be solved directly using a solver; therefore, the embodiment of the present application can use the strong duality theory to transform the distributed robust optimization model, and use the affine strategy as the adjustment strategy for the thermal power unit to respond to the wind power simulation error. The derived expression is as follows: (twenty one)

[0060] in, is the dual variable; For the Standard sample data; ; is an auxiliary variable.

[0061] (twenty three) in, , and are the operating cost coefficients of thermal power units respectively; It is the coefficient of thermal power unit participating in error adjustment.

[0062] (twenty four) Finally, the distributed robust optimization model can be transformed into a MILP (Mixed Integer Linear Programming) optimization scheduling model (i.e., a target optimization scheduling model) to solve scheduling problems after line disconnection failures involving wind power clusters, electric vehicles, and renewable energy uncertainties.

[0063] Therefore, in order to solve the problem of uncertainty in wind power output, the embodiment of the present application adopts the strong duality theory to transform the worst expectation problem of the distributed robust optimization model into a single-layer optimization problem, thereby reducing the complexity of the optimization algorithm.

[0064] According to the receiving-end power system dispatching method under typhoon scenario proposed in the embodiment of the present application, by 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, a cluster wind power simulation output is generated based on a pre-constructed sample machine output model and a wind speed distribution parameter model; based on the cluster wind power simulation output and the preset bus load shedding priority, a load shedding model under N-1 line fault is constructed, and the load shedding result of the regional power system is obtained through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding result, and to correct the preset thermal power unit dispatching plan through the thermal power re-dispatching model to obtain a corrected dispatching plan for the thermal power unit; and the corrected dispatching plan for the thermal power unit is determined according to the load shedding result. Determine the incentive index of the target electric vehicle, and build an electric vehicle incentive model based on the incentive index, so as to obtain the corresponding dispatch incentive cost of the target electric vehicle through the electric vehicle incentive model; construct the first-stage collaborative dispatch model based on the simulated output of cluster wind power, load shedding model, revised dispatch plan of thermal power units and dispatch incentive cost, and calculate the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative dispatch model, and use the error adjustment cost to establish the corresponding adjustment cost objective function, so as to build the second-stage optimization model through the adjustment cost objective function, and solve the second-stage optimization model according to the preset strong dual strategy and affine constraints, so as to obtain the target dispatch plan of cluster wind power and target electric vehicles in the typhoon scenario. This application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system in typhoon weather through the two-stage distributed robust optimization strategy of thermal power unit re-dispatching and electric vehicle incentives in typhoon scenarios.

[0065] Secondly, the receiving-end power system dispatching device in a typhoon scenario proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0066] Figure 3 It is a block diagram of a receiving-end power system dispatching device in a typhoon scenario according to an embodiment of the present application.

[0067] like Figure 3As shown, the receiving-end power system dispatching device 10 in the typhoon scenario includes: a generating module 100 , a correcting module 200 , a modeling module 300 and a solving module 400 .

[0068] Among them, the generation module 100 is used to obtain typhoon meteorological characteristic parameters corresponding to a preset typhoon scene, and establish a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate cluster wind power simulation output based on the pre-built sample machine output model and wind speed distribution parameter model.

[0069] The correction module 200 is used to construct a load shedding model under N-1 line fault based on the simulated output of cluster wind power and the preset bus load shedding priority, and obtain the load shedding results of the regional power system through the load shedding model, so as to establish a thermal power re-dispatching model according to the load shedding results, and to correct the preset thermal power unit dispatching plan through the thermal power re-dispatching model to obtain a corrected dispatching plan for the thermal power unit.

[0070] The modeling module 300 is used to determine the incentive index of the target electric vehicle according to the load shedding result, and to 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.

[0071] The solution module 400 is used to construct a first-stage collaborative scheduling model based on the simulated output of cluster wind power, the load shedding model, the revised scheduling plan of thermal power units and the scheduling incentive cost, and calculate the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative scheduling model, and use the error adjustment cost to establish the corresponding adjustment cost objective function, so as 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 for cluster wind power and target electric vehicles under the typhoon scenario.

[0072] Optionally, in one embodiment of the present application, the generation module 100 includes: a first acquisition unit, a first calculation unit and a summing unit.

[0073] Among them, the first acquisition unit is used to obtain the model machine rated power, wind farm real-time wind speed, cut-in wind speed, rated wind speed, cut-out wind speed corresponding to the preset model machine, and obtain the wind farm real-time wind speed according to the wind speed distribution parameter model.

[0074] The first calculation unit is used to construct a model output model based on the rated power of the model 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 model output model.

[0075] A summation unit is configured to calculate the simulated output of a wind farm at each moment based on the simulated output of each wind turbine generator set, and perform an accumulation and summation operation on the simulated output of the wind farm at each moment to obtain the simulated output of the cluster wind power.

[0076] 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.

[0077] Among them, the first determination unit is configured to determine the line disconnection scenario corresponding to the cluster wind power, and construct a power flow state constraint according to the line disconnection indication factor corresponding to the line disconnection scenario.

[0078] The second determination unit is configured to determine the load shedding cost, load shedding state, 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 corresponding to 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 state, indication factors, load shedding cost coefficients, and load shedding amounts.

[0079] The judgment unit is 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 dispatching plan of the thermal power unit is corrected to obtain the corrected dispatching plan of the thermal power unit.

[0080] Optionally, in an embodiment of the present application, the modeling module 300 includes: a third determination unit and a construction unit.

[0081] Among them, 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.

[0082] The construction unit is configured to determine the incentive index of the target electric vehicle according to the load shedding ratio, and construct an electric vehicle incentive model through the incentive index.

[0083] Optionally, in an embodiment of the present application, the solution module 400 includes: a second acquisition unit, a second calculation unit, an establishment unit, and a normalization unit.

[0084] Among them, the second acquisition 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.

[0085] The second calculation unit is used to calculate the corresponding Wasserstein distance based on the empirical distribution and the real distribution, and to construct a fuzzy uncertainty set of wind power output under typhoon conditions according to the Wasserstein distance.

[0086] A unit is established to calculate the corresponding error adjustment cost according to the output error and the preset error adjustment cost coefficient, and to establish an error 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 error adjustment cost objective function.

[0087] The standardization unit is used to standardize the sample data to obtain the 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 dual strategy, and solve the target optimization scheduling model through a preset affine strategy to obtain the target scheduling plan for cluster wind power and target electric vehicles under the typhoon scenario.

[0088] Optionally, in one embodiment of the present application, the mathematical expression of the target optimization scheduling model is:

[0089] in, , and They are the operating cost coefficients of thermal power units in cluster wind power; is the error adjustment coefficient corresponding to the thermal power unit; is the dual variable; For the Standard sample data; is an auxiliary variable; is the Wasserstein radius; is the sample number of sample data; Indicates the maximum value in the standard sample data; Indicates the minimum value in the standard sample data.

[0090] It should be noted that the aforementioned explanation of the embodiment of the receiving-end power system dispatching method in a typhoon scenario is also applicable to the receiving-end power system dispatching device in a typhoon scenario of this embodiment, and will not be repeated here.

[0091] According to the receiving-end power system dispatching device in a typhoon scenario proposed in an embodiment of the present application, it includes a generating module 100, which is used 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 cluster wind power simulation output based on a pre-built sample machine output model and a wind speed distribution parameter model; a correction module 200, which is used to construct a load shedding model under an N-1 line fault based on the cluster wind power simulation output 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 re-dispatching model according to the load shedding result, and correct the preset thermal power unit dispatching plan through the thermal power re-dispatching model to obtain a corrected dispatching plan of the thermal power unit; a modeling module 300, It is used to determine the incentive index of the target electric vehicle according to the load shedding result, and to build 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; the solution module 400 is used to build a first-stage collaborative dispatch model based on the simulated output of cluster wind power, the load shedding model, the revised dispatch plan of the thermal power unit and the dispatch incentive cost, and calculate the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative dispatch model, and use the error adjustment cost to establish the corresponding adjustment cost objective function, so as to build a second-stage optimization model through the adjustment cost objective function, and solve the second-stage optimization model according to the preset strong dual strategy and affine constraints to obtain the target dispatch plan of cluster wind power and target electric vehicle under the typhoon scenario. This application fully considers the distribution characteristics of typhoon weather, and improves the efficiency of the power system under typhoon weather through a two-stage distributed robust optimization strategy of thermal power unit re-dispatching and electric vehicle incentive under typhoon scenarios.

[0092] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0093] When the processor 402 executes the program, the receiving-end power system scheduling method in the typhoon scenario provided in the above embodiment is implemented.

[0094] Furthermore, the electronic device also includes: The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0095] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0096] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0097] 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 connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0098] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0099] 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.

[0100] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned receiving-end power system dispatching method in a typhoon scenario.

[0101] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned receiving-end power system scheduling method in a typhoon scenario.

[0102] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present 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 may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0103] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0104] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0106] 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 embodiment, 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 by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0107] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0108] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, 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. If 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.

[0109] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, 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 cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for dispatching a receiving-end power system in a typhoon scenario, characterized in that: The following steps are involved: Acquire 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 cluster wind power simulation output based on a pre-built sample 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, a load shedding model under the N-1 line fault is constructed, and the load shedding result of the regional power system is obtained through the load shedding model, so as to establish a thermal power rescheduling model according to the load shedding result, and the preset thermal power unit dispatching plan is corrected through the thermal power rescheduling model to obtain a corrected dispatching plan of the thermal power unit; Determining an incentive index of 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 a 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 revised dispatch plan of the thermal power units and the dispatch incentive cost, a first-stage collaborative dispatch model is constructed, and the error adjustment cost corresponding to the cluster wind power is calculated according to the first-stage collaborative dispatch model, and the corresponding adjustment cost objective function is established 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 the preset strong dual strategy and affine constraints, so as to obtain the target dispatch scheme of the cluster wind power and the target electric vehicle under the typhoon scenario.

2. The receiving-end power system dispatching method in a typhoon scenario according to claim 1 is characterized in that: The step of obtaining typhoon meteorological characteristic parameters corresponding to a preset typhoon scene, and establishing a wind speed distribution parameter model through the typhoon meteorological characteristic parameters, so as to generate cluster wind power simulation output based on a pre-built sample machine output model and the wind speed distribution parameter model, includes: Obtain the model machine rated power, wind farm real-time wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed corresponding to the preset model machine, and obtain the wind farm real-time wind speed according to the wind speed distribution parameter model; Based on the rated power of the model 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, the model machine output model is constructed to calculate the simulated output of each wind turbine in the cluster wind power through the model machine output model; The simulated output of the wind farm at each moment is calculated according to the simulated output of each wind turbine generator set, and the simulated output of the wind farm at each moment is accumulated and summed to obtain the simulated output of the cluster wind power.

3. The receiving-end power system dispatching method in a typhoon scenario according to claim 2 is characterized in that: The method comprises: constructing a load shedding model under N-1 line fault based on the cluster wind power simulation output and the preset bus load shedding priority, obtaining the load shedding result of the regional power system through the load shedding model, establishing a thermal power rescheduling model according to the load shedding result, and correcting the preset thermal power unit dispatching plan through the thermal power rescheduling model to obtain a corrected dispatching plan of the thermal power unit, including: Determine a line disconnection scenario corresponding to the cluster wind power, and construct a power flow state constraint according to a line disconnection indication factor corresponding to the line disconnection scenario; Determine the corresponding load shedding cost of the cluster wind power, the load reduction state, the indicator factors of different levels corresponding to each bus, the load shedding cost coefficient of each level and the load shedding amount of each bus at each moment, and obtain the load shedding result of the regional power system based on the flow state constraint, the load shedding cost, the load reduction state, the indicator factor, the load shedding cost coefficient and the load shedding amount; The total amount of load shedding is determined according to the load shedding result, and it is determined whether the total amount of load shedding is greater than a preset alarm value, wherein when the total amount of load shedding is greater than the alarm value, the scheduling plan of the thermal power unit is corrected to obtain a corrected scheduling plan of the thermal power unit.

4. The receiving-end power system dispatching method in a typhoon scenario according to claim 3 is characterized in that: Determining the incentive index of the target electric vehicle according to the load shedding result, and constructing the electric vehicle incentive model according to the incentive index, includes: Determine at least one target bus whose load shedding amount is not 0, and 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; An incentive index of the target electric vehicle is determined according to the load shedding ratio, so as to construct the electric vehicle incentive model through the incentive index.

5. The receiving-end power system dispatching method in a typhoon scenario according to claim 4 is characterized in that: The error adjustment cost corresponding to the cluster wind power is calculated according to the first-stage collaborative dispatch model, and the corresponding error adjustment cost objective function is established by using the error adjustment cost, so as to construct a second-stage optimization model through the error adjustment cost objective function, and solve the second-stage optimization model according to the preset strong dual strategy and affine constraint to obtain the target dispatching scheme of the cluster wind power and the target electric vehicle under the typhoon scenario, including: Calculating an output error corresponding to the cluster wind power according to the cluster wind power simulation output, and obtaining sample data corresponding to the output error, wherein the output error obeys a real distribution and the sample data obeys an empirical distribution; Based on the empirical distribution and the true distribution, the corresponding Wasserstein distance is calculated, and a fuzzy uncertainty set of wind power output under typhoon conditions is constructed 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 error 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 error adjustment cost objective function; The sample data is standardized to obtain corresponding standard sample data, and the second-stage optimization model is converted into a target optimization scheduling model based on the standard sample data and the strong dual strategy, and the target optimization scheduling model is solved by a preset affine strategy to obtain a target scheduling scheme for the cluster wind power and the target electric vehicle under the typhoon scenario.

6. The receiving-end power system dispatching method in a typhoon scenario according to claim 5 is characterized in that: The mathematical expression of the target optimization scheduling model is: in, , and are respectively the operating cost coefficients of thermal power units in the wind power cluster; is the error adjustment coefficient corresponding to the thermal power unit; is the dual variable; For the Standard sample data; is an auxiliary variable; is the Wasserstein radius; is the sample number of the sample data; Indicates the maximum value in the standard sample data; Indicates the minimum value in the standard sample data.

7. A cluster wind power and electric vehicle dispatching device considering line failure in typhoon scenarios, characterized in that: include: A generation module, used 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 cluster wind power simulation output based on a pre-built sample machine output model and the wind speed distribution parameter model; A correction module is used to construct a load shedding model under 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 to correct the preset thermal power unit scheduling plan through the thermal power rescheduling model to obtain a corrected scheduling plan of the thermal power unit; A modeling module, used to determine the incentive index of the target electric vehicle according to the load shedding result, and to 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; A solution module is used to construct a first-stage collaborative scheduling model based on the simulated output of the cluster wind power, the load shedding model, the revised scheduling plan of the thermal power units and the scheduling incentive cost, and calculate the error adjustment cost corresponding to the cluster wind power according to the first-stage collaborative scheduling model, and use the error adjustment cost to establish a corresponding adjustment cost objective function, 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 dual strategy and affine constraints to obtain a target scheduling plan for the cluster wind power and the target electric vehicle under the typhoon scenario.

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

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

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