Output scheduling method and device of new energy power plant, computer equipment, computer readable storage medium and computer program product

Abstract

The invention relates to an output scheduling method and device of a new energy power plant, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of power grids. The method comprises the following steps: based on a new energy output prediction scene set of each new energy station in a new energy power plant, determining a target new energy output reference parameter and a target new energy output prediction error parameter of each new energy station, determining a new energy output uncertainty set of each new energy station based on the target new energy output reference parameter and the target new energy output prediction error parameter of each new energy station; and by taking the new energy output uncertainty set of each new energy station as a constraint condition, solving an output scheduling objective function pre-constructed for the new energy power plant to obtain a target net output value of each new energy station so as to perform output scheduling on each new energy station. By adopting the method, the function of the energy storage device configured in the new energy power plant can be fully played.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for output scheduling of a new energy power plant. Background Art

[0002] The output of new energy is highly unstable. New energy power plants often store excess new energy electricity in energy storage devices to effectively smooth out the instability of new energy output and enhance the output reliability of new energy power plants.

[0003] However, some new energy power plants lack a dispatching mechanism to autonomously call upon energy storage devices, resulting in the energy storage devices configured at new energy sites not being fully utilized. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product that can fully exert the role of the energy storage device configured in the above-mentioned new energy power plant to address the technical problem that the role of the energy storage device configured in the new energy power plant cannot be fully exerted.

[0005] In a first aspect, the present application provides a method for dispatching output of a new energy power plant, comprising:

[0006] Obtain a set of new energy output prediction scenarios for each new energy station in a new energy power plant;

[0007] Based on the set of new energy output prediction scenarios of each new energy station, determine the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, and based on the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, determine the new energy output uncertainty set of each new energy station;

[0008] Taking the uncertainty set of the new energy output of each new energy station as a constraint condition, solving the output dispatch objective function pre-constructed for the new energy power plant, and obtaining the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value;

[0009] According to the target net output value of each new energy station, output dispatch is performed on each new energy station.

[0010] In one embodiment, the determining of the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station based on the new energy output prediction scenario set of each new energy station includes:

[0011] For each new energy station, determine a target new energy output prediction scenario whose corresponding new energy output value is greater than other new energy output prediction scenarios from the set of new energy output prediction scenarios of the new energy station;

[0012] Initializing a new energy output benchmark parameter and a new energy output prediction error parameter based on the new energy predicted output value of the target new energy output prediction scenario;

[0013] Based on the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter are iteratively updated to obtain target new energy output benchmark parameter and target new energy output prediction error parameter that meet iteration conditions.

[0014] In one of the embodiments, the iterative updating of the new energy output benchmark parameter and the new energy output prediction error parameter based on the new energy output prediction scenario set to obtain a target new energy output benchmark parameter and a target new energy output prediction error parameter that meet the iteration conditions includes:

[0015] Solving the first parameter and the second parameter in the uncertainty region constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter;

[0016] When any one of the first parameter and the second parameter is greater than or equal to the condition parameter corresponding to the iteration condition, updating the new energy output benchmark parameter and the new energy output prediction error parameter to obtain an updated new energy output benchmark parameter and an updated new energy output prediction error parameter;

[0017] The updated new energy output benchmark parameter is used as the new new energy output benchmark parameter, and the updated new energy output prediction error parameter is used as the new new energy output prediction error parameter, and the step of solving the first parameter and the second parameter in the uncertainty area constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter is returned until the first parameter and the second parameter obtained by solving are both smaller than the conditional parameter, and the corresponding new energy output benchmark parameter is determined as the target new energy output benchmark parameter, and the corresponding new energy output prediction error parameter is determined as the target new energy output prediction error parameter.

[0018] In one embodiment, the updating of the new energy output benchmark parameter and the new energy output prediction error parameter to obtain the updated new energy output benchmark parameter and the updated new energy output prediction error parameter includes:

[0019] Update the new energy output benchmark parameter according to a preset first update step length to obtain an updated new energy output benchmark parameter;

[0020] The new energy output prediction error parameter is updated according to a preset second update step size to obtain an updated new energy output prediction error parameter.

[0021] In one of the embodiments, the new energy output prediction value based on the target new energy output prediction scenario, initializing the new energy output benchmark parameter and the new energy output prediction error parameter, includes:

[0022] The new energy output benchmark parameter and the new energy output prediction error parameter are both initialized to half of the new energy prediction output value corresponding to the target new energy output prediction scenario.

[0023] In one embodiment, the pre-constructed output scheduling objective function is constructed in the following manner:

[0024] For each dispatching period of each dispatching period of the new energy power plant, determine the unit penalty price in the dispatching period, determine the time-of-use electricity price of the new energy power plant in the dispatching period, and determine the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station in the dispatching period;

[0025] Determine the charging efficiency and discharging efficiency of the energy storage device of each new energy station, and determine the preset prediction error threshold corresponding to each new energy station;

[0026] Based on the unit penalty price in each scheduling period, the time-of-use electricity price of the new energy power plant in each scheduling period, the day-ahead new energy predicted output value and the day-ahead new energy output prediction error value of each new energy station in each scheduling period, the charging efficiency and the discharging efficiency of the energy storage device of each new energy station, and the preset prediction error threshold corresponding to each new energy station, the output scheduling objective function of the new energy power plant is constructed with maximizing the output profit of the new energy power plant as the optimization goal.

[0027] In a second aspect, the present application also provides an output dispatching device for a new energy power plant, comprising:

[0028] A scenario set acquisition module is used to acquire a new energy output prediction scenario set for each new energy station in a new energy power plant;

[0029] An output information determination module, used to determine a target new energy output benchmark parameter and a target new energy output prediction error parameter of each new energy station based on a set of new energy output prediction scenarios of each new energy station, and to determine a new energy output uncertainty set of each new energy station based on the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station;

[0030] An objective function solving module is used to solve the output dispatch objective function pre-constructed for the new energy power plant by taking the new energy output uncertainty set of each new energy station as a constraint condition, and obtain the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value;

[0031] The station output dispatching module is used to dispatch the output of each new energy station according to the target net output value of each new energy station.

[0032] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Obtain a set of new energy output prediction scenarios for each new energy station in a new energy power plant;

[0034] Based on the set of new energy output prediction scenarios of each new energy station, determine the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, and based on the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, determine the new energy output uncertainty set of each new energy station;

[0035] Taking the uncertainty set of the new energy output of each new energy station as a constraint condition, solving the output dispatch objective function pre-constructed for the new energy power plant, and obtaining the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value;

[0036] According to the target net output value of each new energy station, output dispatch is performed on each new energy station.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] Obtain a set of new energy output prediction scenarios for each new energy station in a new energy power plant;

[0039] Based on the set of new energy output prediction scenarios of each new energy station, determine the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, and based on the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, determine the new energy output uncertainty set of each new energy station;

[0040] Taking the uncertainty set of the new energy output of each new energy station as a constraint condition, solving the output dispatch objective function pre-constructed for the new energy power plant, and obtaining the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value;

[0041] According to the target net output value of each new energy station, output dispatch is performed on each new energy station.

[0042] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0043] Obtain a set of new energy output prediction scenarios for each new energy station in a new energy power plant;

[0044] Based on the set of new energy output prediction scenarios of each new energy station, determine the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, and based on the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, determine the new energy output uncertainty set of each new energy station;

[0045] Taking the uncertainty set of the new energy output of each new energy station as a constraint condition, solving the output dispatch objective function pre-constructed for the new energy power plant, and obtaining the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value;

[0046] According to the target net output value of each new energy station, output dispatch is performed on each new energy station.

[0047] The output dispatching method, device, computer equipment, computer-readable storage medium and computer program product of the above-mentioned new energy power plant first obtain a set of new energy output prediction scenarios for each new energy station in the new energy power plant; then, based on the set of new energy output prediction scenarios for each new energy station, determine the target new energy output benchmark parameters and target new energy output prediction error parameters for each new energy station, and based on the target new energy output benchmark parameters and target new energy output prediction error parameters for each new energy station, determine the new energy output uncertainty set for each new energy station; then, using the new energy output uncertainty set of each new energy station as a constraint condition, solve the output dispatching objective function pre-constructed for the new energy power plant, and obtain the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value; finally, according to the target net output value of each new energy station, perform output dispatch for each new energy station. In this way, through the set of new energy output prediction scenarios of each new energy station, the target new energy output benchmark parameters and target new energy output prediction error parameters used to construct the new energy output uncertainty set can be determined, and then the new energy output uncertainty set of the new energy station can be constructed; based on the new energy output uncertainty set of each new energy station, the output scheduling objective function pre-constructed for the new energy power plant can be solved, and the target new energy predicted output value and target energy storage output value of each new energy station can be obtained, and then the output scheduling of each new energy station can be carried out. The output scheduling method of the new energy power plant based on the above process can determine the target new energy predicted output value and target energy storage output value for each new energy station, so that the role of the energy storage device configured in the new energy station can be fully exerted. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0049] FIG1 is a schematic flow chart of an output dispatching method for a new energy power plant in one embodiment;

[0050] 2 is a flowchart of steps for determining a target new energy output benchmark parameter and a target new energy output prediction error parameter for each new energy station based on a set of new energy output prediction scenarios for each new energy station in one embodiment;

[0051] 3 is a flowchart of the steps of iteratively updating the new energy output benchmark parameters and the new energy output prediction error parameters based on the new energy output prediction scenario set in one embodiment to obtain the target new energy output benchmark parameters and the target new energy output prediction error parameters that meet the iteration conditions;

[0052] FIG4 is a flow chart of steps for constructing an output scheduling objective function in one embodiment;

[0053] FIG5 is a schematic flow chart of an output dispatching method for a new energy power plant in another embodiment;

[0054] FIG6 is a structural block diagram of an output dispatching device of a new energy power plant in one embodiment;

[0055] FIG. 7 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0058] In one embodiment, as shown in FIG1 , a method for dispatching output of a new energy power plant is provided. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a server and a terminal, and is implemented through the interaction between the server and the terminal; wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services; the terminal can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers, etc. In this embodiment, the method includes the following steps:

[0059] Step S102, obtaining a set of new energy output prediction scenarios for each new energy station in the new energy power plant.

[0060] Among them, the new energy power plant includes new energy generators and energy storage devices. The new energy power field includes at least one new energy station; each new energy station is composed of at least one new energy generator; each new energy station is equipped with at least one energy storage device to store the excess new energy power in the new energy generator of the new energy station.

[0061] Specifically, the server first obtains a set of new energy output prediction scenarios for each new energy station of the new energy power plant.

[0062] Step S104, based on the new energy output prediction scenario set of each new energy station, determine the target new energy output benchmark parameters and target new energy output prediction error parameters of each new energy station, and based on the target new energy output benchmark parameters and target new energy output prediction error parameters of each new energy station, determine the new energy output uncertainty set of each new energy station.

[0063] Among them, the target new energy output benchmark parameter is used to characterize the benchmark situation of the new energy output of the new energy station; in actual application, the target new energy output benchmark parameter is the new energy output benchmark value.

[0064] Among them, the target new energy output prediction error parameter is used to characterize the reference situation of the new energy output prediction error of the new energy station; the new energy output prediction error is used to characterize the error between the target new energy prediction output value and the actual new energy output value; in practical applications, the new energy output prediction error can be the error value between the target new energy prediction output value and the actual new energy output value, or it can be the fluctuation value between the two, where the fluctuation value is between.

[0065] Among them, the new energy output uncertainty set represents the uncertainty of new energy stations in the output process through uncertainty variables.

[0066] Specifically, for each new energy station, the server determines, based on the new energy output prediction field set of the new energy station, a target new energy output benchmark parameter for characterizing a benchmark condition of the new energy output of the new energy station, and a target new energy output prediction error parameter for characterizing a reference condition of the new energy output prediction error of the new energy station; and then, based on the target new energy output benchmark parameter and the target new energy output prediction error parameter, a new energy output uncertainty set is constructed for the new energy station, which characterizes the uncertainty of the new energy station in the output process through uncertainty variables.

[0067] In specific applications, the uncertainty set of renewable energy output is a box-type uncertainty set.

[0068] In practical applications, the uncertainty set of renewable energy output is shown in Formula 1:

[0069] (Formula 1)

[0070] in, It is a collection of new energy stations under new energy power plants; It is the logo of the new energy station; For the The uncertainty set of new energy output of each new energy station; For the The target new energy predicted output value of each new energy station; For the The target new energy output prediction error parameter of each new energy station; is the average value of the target new energy output prediction error parameters of each new energy station; For the Target new energy output benchmark parameters for each new energy station; For the The new energy uncertainty variables of new energy stations are: ; Represents the norm.

[0071] Furthermore, according to the duality theory, based on the above formula 1, we can obtain the equality constraint shown in formula 2:

[0072] (Formula 2)

[0073] in, is a preset new energy output prediction error threshold, which may be an error value threshold or an error rate threshold; Represents the norm, usually 1-norm, 2-norm or ∞-norm.

[0074] Step S106, using the uncertainty set of the new energy output of each new energy station as a constraint condition, solving the output dispatch objective function pre-constructed for the new energy power plant, and obtaining the target net output value of each new energy station.

[0075] Among them, the output dispatch objective function takes maximizing the profit of the new energy power plant in the output process as the optimization goal; the profit of the new energy power plant in the output process mainly considers the following two factors: the net output (including new energy output and energy storage output) profit of the new energy power plant, and the penalty fee caused by the error between the target new energy predicted output value and the actual new energy output value.

[0076] Among them, the target net output value includes the target new energy predicted output value and the target energy storage output value.

[0077] Among them, the target new energy predicted output value refers to the new energy predicted output value of the new energy station, and the target energy storage output value refers to the energy storage output value of the energy storage device corresponding to the new energy station.

[0078] Specifically, the server uses the uncertainty set of new energy output of each new energy station as the constraint condition of the output scheduling objective function pre-built for the new energy power plant, and combines other constraints of the output scheduling objective function to solve the output scheduling objective function to obtain the target new energy predicted output value and target energy storage output value of each new energy station.

[0079] Step S108: dispatching the output of each new energy station according to the target net output value of each new energy station.

[0080] Specifically, for each new energy station, the server dispatches the output of the new energy station according to the target new energy predicted output value and target energy storage output value of the new energy station, so that the new energy station can output new energy and energy storage according to the corresponding target new energy predicted output value and target energy storage output value.

[0081] In the output dispatching method of the above-mentioned new energy power plant, first, the server obtains the new energy output prediction scenario set of each new energy station in the new energy power plant; then, based on the new energy output prediction scenario set of each new energy station, the server determines the target new energy output benchmark parameters and the target new energy output prediction error parameters of each new energy station, and determines the new energy output uncertainty set of each new energy station based on the target new energy output benchmark parameters and the target new energy output prediction error parameters of each new energy station; then, the server uses the new energy output uncertainty set of each new energy station as a constraint condition to solve the output dispatching objective function pre-constructed for the new energy power plant, and obtains the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value; finally, the server performs output dispatch for each new energy station according to the target net output value of each new energy station. In this way, through the set of new energy output prediction scenarios of each new energy station, the server can determine the target new energy output benchmark parameters and target new energy output prediction error parameters used to construct the new energy output uncertainty set, and then construct the new energy output uncertainty set of the new energy station; based on the new energy output uncertainty set of each new energy station, the server can solve the output scheduling objective function pre-constructed for the new energy power plant, and obtain the target new energy predicted output value and target energy storage output value of each new energy station, and then can perform output scheduling for each new energy station. Based on the output scheduling method of the new energy power plant in the above process, the server can determine the target new energy predicted output value and target energy storage output value for each new energy station, so that the role of the energy storage device configured in the new energy station can be fully exerted.

[0082] In an exemplary embodiment, as shown in FIG. 2 , in the above step S104, based on the new energy output prediction scenario set of each new energy station, the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station are determined, which specifically includes the following steps:

[0083] Step S202 , for each new energy station, determine a target new energy output prediction scenario whose corresponding new energy output value is greater than other new energy output prediction scenarios from the new energy output prediction scenario set of the new energy station.

[0084] Step S204 , initializing a new energy output benchmark parameter and a new energy output prediction error parameter based on the new energy output prediction value of the target new energy output prediction scenario.

[0085] Step S206, based on the new energy output prediction scenario set, iteratively update the new energy output benchmark parameters and the new energy output prediction error parameters to obtain target new energy output benchmark parameters and target new energy output prediction error parameters that meet the iteration conditions.

[0086] Among them, each new energy output prediction scenario has a corresponding new energy prediction output value.

[0087] The iteration condition may be the number of iterations or a corresponding condition parameter.

[0088] Specifically, for each new energy station, the specific process of the server determining the target new energy output benchmark parameter and the target new energy output prediction error parameter of the new energy station is as follows:

[0089] First, the server determines a new energy output prediction scene with the largest corresponding new energy output value from the new energy output prediction scene set of the new energy station, and determines the new energy output prediction scene as the target new energy output prediction scene.

[0090] Then, the server initializes the new energy output benchmark parameter and the new energy output prediction error parameter according to the new energy predicted output value of the target new energy output prediction scenario.

[0091] Next, the server iteratively updates the new energy output benchmark parameters and the new energy output prediction error parameters according to the occurrence probability of each new energy output prediction scenario in the new energy output prediction scenario set and the new energy output prediction value, until the updated new energy output benchmark parameters and the new energy output prediction error parameters meet the iteration conditions, and determines the updated new energy output benchmark parameters that meet the iteration conditions as the target new energy output benchmark parameters, and determines the updated new energy output prediction error parameters that meet the iteration conditions as the target new energy output prediction error parameters.

[0092] In this embodiment, the server can initialize the new energy output benchmark parameters and new energy output prediction error parameters of the new energy station based on the set of new energy output prediction scenarios of the new energy station, and implement iterative update of the new energy output benchmark parameters and the new energy output prediction error parameters, thereby obtaining the target new energy output benchmark parameters and target new energy output prediction error parameters of the new energy station; the above process fully considers the actual conditions of different new energy output prediction scenarios, thereby improving the robustness of the target new energy output benchmark parameters and the target new energy output prediction error parameters, thereby improving the reliability of the target net output value obtained by subsequent solution.

[0093] In an exemplary embodiment, as shown in FIG3 , the above step S206, based on the new energy output prediction scenario set, iteratively updates the new energy output benchmark parameter and the new energy output prediction error parameter to obtain the target new energy output benchmark parameter and the target new energy output prediction error parameter that meet the iteration conditions, specifically includes the following steps:

[0094] Step S302, solving the first parameter and the second parameter in the uncertainty area constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter.

[0095] Step S304, when any one of the first parameter and the second parameter is greater than or equal to the condition parameter corresponding to the iteration condition, the new energy output benchmark parameter and the new energy output prediction error parameter are updated to obtain the updated new energy output benchmark parameter and the updated new energy output prediction error parameter.

[0096] Step S306, using the updated new energy output benchmark parameter as the new new energy output benchmark parameter, using the updated new energy output prediction error parameter as the new new energy output prediction error parameter, returning to the step of solving the first parameter and the second parameter in the uncertainty area constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter, until the solved first parameter and the second parameter are both less than the conditional parameter, determining the corresponding new energy output benchmark parameter as the target new energy output benchmark parameter, and determining the corresponding new energy output prediction error parameter as the target new energy output prediction error parameter.

[0097] Among them, the new energy output benchmark parameter corresponding to the target new energy output benchmark parameter refers to the new energy output benchmark parameter whose first parameter and second parameter are calculated to satisfy the conditional parameters; the new energy output prediction error parameter corresponding to the target new energy output prediction error parameter refers to the new energy output prediction error parameter whose first parameter and second parameter are calculated to satisfy the conditional parameters.

[0098] Specifically, the specific process of the server iteratively updating the new energy output benchmark parameter and the new energy output prediction error parameter is as follows:

[0099] First, for each new energy station, the server substitutes the occurrence probability and new energy predicted output value of each new energy output prediction scenario corresponding to the new energy station, as well as the new energy output benchmark parameter and new energy output prediction error parameter of the new energy station into the uncertainty region constraint function of the new energy station, and solves the first parameter and the second parameter in the uncertainty region constraint function; wherein the uncertainty region constraint function is shown in Formula 3:

[0100] (Formula 3)

[0101] in, For the Uncertainty regional constraint function of each new energy station; For the A collection of new energy output prediction scenarios for new energy stations. Identification of predicted scenarios for new energy output; For the The first new energy station The probability of occurrence of each new energy output prediction scenario; For the The first new energy station The predicted new energy output value of each new energy output prediction scenario; is the first parameter; is the second parameter.

[0102] Then, when any one of the calculated first parameter and the second parameter is greater than or equal to the condition parameter corresponding to the iteration condition, the server updates the new energy output benchmark parameter and the new energy output prediction error parameter to obtain the updated new energy output benchmark parameter and the updated new energy output prediction error parameter.

[0103] Next, the server uses the updated new energy output benchmark parameter as the new new energy output benchmark parameter, and uses the updated new energy output prediction error parameter as the new new energy output prediction error parameter, and returns to step S302 to continue calculating the new first parameter and the new second parameter until the calculated first parameter and the second parameter are both smaller than the conditional parameter corresponding to the iteration condition; then the new energy output benchmark parameter of the first parameter and the second parameter that both meet the conditional parameters are determined as the target new energy output benchmark parameter, and the new energy output prediction error parameter of the first parameter and the second parameter that both meet the conditional parameters are determined as the target new energy output prediction error parameter.

[0104] In this embodiment, the server can solve the target new energy output benchmark parameters and target new energy output prediction error parameters of the new energy station based on the new energy output prediction scenario set of the new energy station and the uncertainty area constraint function of the new energy station; the above process fully considers the actual conditions and uncertainty factors of different new energy output prediction scenarios, thereby improving the robustness of the target new energy output benchmark parameters and the target new energy output prediction error parameters, and thus improving the reliability of the target net output value obtained by subsequent solution.

[0105] In an exemplary embodiment, in the above step S304, the new energy output benchmark parameters and the new energy output prediction error parameters are updated to obtain updated new energy output benchmark parameters and updated new energy output prediction error parameters, which specifically includes the following contents: updating the new energy output benchmark parameters according to a preset first update step to obtain updated new energy output benchmark parameters; updating the new energy output prediction error parameters according to a preset second update step to obtain updated new energy output prediction error parameters.

[0106] Specifically, the present application presets a first update step for the new energy output benchmark parameter, and presets a second update step for the new energy output prediction error parameter; when any one of the first parameter and the second parameter calculated by the server is greater than or equal to the conditional parameter, as shown in Formula 4, the new energy output benchmark parameter is updated according to the first update step to obtain the updated new energy output benchmark parameter, and the new energy output prediction error parameter is updated according to the preset second update step to obtain the updated new energy output prediction error parameter:

[0107] (Formula 4)

[0108] in, is the first update step length, is the second update step length; To update the new energy output benchmark parameters, is the updated prediction error parameter of new energy output.

[0109] In this embodiment, the server can iteratively update the new energy output benchmark parameter and the new energy output prediction error parameter based on a preset update step size.

[0110] In an exemplary embodiment, the above step S204 initializes the new energy output benchmark parameter and the new energy output prediction error parameter based on the new energy output value of the target new energy output prediction scenario, and specifically includes the following contents: initializing the new energy output benchmark parameter and the new energy output prediction error parameter to half of the new energy output value corresponding to the target new energy output prediction scenario.

[0111] Specifically, when initializing the new energy output benchmark parameter and the new energy output prediction error parameter, the server first initializes the new energy output benchmark parameter to half of the new energy predicted output value corresponding to the target new energy output prediction scenario, and then initializes the new energy output prediction error parameter to the difference between the new energy predicted output value corresponding to the target new energy output prediction scenario and the initialized new energy output benchmark parameter; since the initialized new energy output benchmark parameter is half of the new energy predicted output value corresponding to the target new energy output prediction scenario, the difference between the new energy predicted output value corresponding to the target new energy output prediction scenario and the initialized new energy output benchmark parameter is actually also half of the new energy predicted output value corresponding to the target new energy output prediction scenario, that is, the new energy output prediction error parameter is also initialized to half of the new energy predicted output value corresponding to the target new energy output prediction scenario.

[0112] In this embodiment, the server can initialize the new energy output benchmark parameter and the new energy output prediction error parameter through the new energy predicted output value of the target new energy output prediction scenario.

[0113] In an exemplary embodiment, the conditional parameter corresponding to the iterative condition in the iterative update of the new energy output benchmark parameter and the new energy output prediction error parameter in the present application is half of the new energy prediction output value of the target new energy output prediction scenario.

[0114] In an exemplary embodiment, as shown in FIG4 , the pre-constructed output scheduling objective function in any embodiment of the present application is constructed by the following steps:

[0115] Step S402, for each scheduling period of each scheduling period of the new energy power plant, determine the unit penalty price in the scheduling period, determine the time-of-use electricity price of the new energy power plant in the scheduling period, and determine the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station in the scheduling period.

[0116] Step S404, determining the charging efficiency and discharging efficiency of the energy storage device of each new energy station, and determining a preset prediction error threshold corresponding to each new energy station.

[0117] Step S406, based on the unit penalty price in each dispatching period, the time-of-use electricity price of the new energy power plant in each dispatching period, the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station in each dispatching period, the charging efficiency and discharging efficiency of the energy storage device of each new energy station, and the preset forecast error threshold value corresponding to each new energy station, the output dispatch objective function of the new energy power plant is constructed with maximizing the output profit of the new energy power plant as the optimization goal.

[0118] Among them, the unit penalty price refers to the unit penalty fee for the error between the target new energy predicted output value and the actual new energy output value and the preset prediction error threshold; among them, the preset prediction error threshold is used to constrain the error range between the target new energy predicted output value and the actual new energy output value.

[0119] Among them, the time-of-use electricity price is the price at which new energy power plants sell electricity to the outside during the corresponding dispatching period.

[0120] Among them, the day-ahead predicted new energy output value refers to the target predicted new energy output value in the corresponding scheduling period on the previous day.

[0121] Among them, the day-ahead new energy output forecast error value refers to the new energy output forecast error value in the corresponding scheduling period on the previous day.

[0122] Specifically, for each scheduling period of each scheduling period of the new energy power plant, first determine the unit penalty electricity price in the scheduling period, determine the time-of-use electricity price of the new energy power plant in the scheduling period, and determine the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station of the new energy power plant in the scheduling period.

[0123] Next, for each new energy station of the new energy power plant, the server determines the charging efficiency and discharging efficiency of the energy storage device configured for the new energy station, as well as the corresponding preset prediction error threshold preset for the new energy station.

[0124] Finally, the server combines the unit penalty price in each scheduling period, the time-of-use electricity price of the new energy power plant in each scheduling period, the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station in each scheduling period, the charging efficiency and discharge efficiency of the energy storage device of each new energy station, and the preset prediction error threshold corresponding to each new energy station, and takes the maximization of the output benefit of the new energy power plant between the target net output (including the target new energy predicted output and energy storage output) and the penalty fee caused by the error between the target new energy predicted output value and the actual new energy output value as the optimization goal, to construct the output scheduling objective function of the new energy power plant.

[0125] In practical applications, the output dispatch objective function of a new energy power plant is shown in Formula 5:

[0126] (Formula 5)

[0127] in, is the dispatching period collection of new energy power plants, It is the dispatching period mark of the new energy power plant; For new energy power plants in the Time-of-use electricity price under each dispatch period; For the New energy sites are located in The target net output value under each dispatch period (including the target predicted output value of new energy and the target energy storage output value, the target net output value is the variable to be solved for the output dispatch objective function); For the New energy sites are located in In a dispatch period, when the actual output value of the new energy is higher than the predicted output value of the target new energy, the penalty fee caused by the error between the actual output value of the new energy and the predicted output value of the target new energy exceeds the preset prediction error threshold is charged. For the New energy sites are located in In a scheduling period, when the actual output value of the new energy is lower than the target new energy predicted output value, the penalty fee is caused by the error between the actual output value of the new energy and the target new energy predicted output value exceeding the preset prediction error threshold.

[0128] in, and It can be further expressed as formula 6:

[0129] (Formula 6)

[0130] Among them, the forecast error values ​​of renewable energy output include and , For the New energy sites are located in The positive value of the forecast error of the new energy output in the day-ahead dispatch period (the error value when the actual output of new energy is higher than the target new energy forecast output), For the New energy sites are located in The negative value of the forecast error of the new energy output in the day-ahead dispatch period (the error value when the actual output of new energy is lower than the target new energy forecast output); it is easy to understand that and One of the items is zero.

[0131] in, For the The preset prediction error threshold of each new energy station; For the New energy sites are located in The predicted output value of renewable energy in the day-ahead dispatching period; For the The charging efficiency of the energy storage device of each new energy station, For the The discharge efficiency of the energy storage device of each new energy station; For the The unit penalty price under each scheduling period.

[0132] in, For the The energy storage device of the new energy station is The charging power used to reduce the error between the predicted output value of the target new energy and the actual output value of the new energy in each scheduling period, For the The energy storage device of the new energy station is The discharge power used to reduce the error between the predicted output value of the target renewable energy and the actual output value of renewable energy in a scheduling period; it is easy to understand that and is a variable.

[0133] In this embodiment, based on the unit penalty price of the new energy power plant, time-of-use electricity price, the predicted output value of new energy on the previous day, the predicted error value of new energy output on the previous day, the charging efficiency, the discharging efficiency, the preset prediction error threshold and other information of the new energy power plant, the server can construct an output dispatch objective function of the new energy power plant with the optimization goal of maximizing the profit between the target net output (including the target new energy predicted output and the energy storage output) and the penalty fee caused by the error between the target new energy predicted output value and the actual new energy output value; based on the output dispatch objective function constructed as above, the output profit of the new energy power plant can be maximized between the target net output profit and the penalty fee.

[0134] It should be noted that in the related art, for the output scheduling objective function shown in the above formula 5 and formula 6, there are usually the following constraints:

[0135] First, there is a predicted output constraint as shown in Formula 7:

[0136] (Formula 7)

[0137] Secondly, there is a net output constraint as shown in Equation 8:

[0138] (Formula 8)

[0139] Thirdly, there are other physical property constraints, including the connection constraints of energy storage SOC (State of Charge), energy storage cycle number and other constraints, which are unified as shown in formula 9:

[0140] (Formula 9)

[0141] According to the predicted output constraint shown in Formula 7, the relevant technology assumes that the new energy station already has a relatively accurate new energy predicted output value to carry out output dispatch, but in actual operation, the prediction accuracy of the new energy predicted output value is often low, which leads to the above output dispatch objective function. The predicted output constraint uses 0 to the new energy predicted output value as the output boundary of the new energy power plant, which may deviate far from the optimal solution under actual conditions, and may also cause the optimization result to be too conservative.

[0142] In the output dispatching method of the new energy power plant provided in the present application, the predicted output constraint shown in Formula 7 is replaced by the new energy output uncertainty set shown in Formula 1, and the predicted output constraint of the new energy power plant is reconstructed by extracting the uncertainty data related to the new energy, thereby reducing the conservatism of the output dispatching objective function and improving the economy, thereby ensuring the engineering implementation value of the output dispatching objective function.

[0143] In an exemplary embodiment, as shown in FIG5 , another output dispatching method of a new energy power plant is provided, which is described by taking the application of the method to a server as an example, and includes the following steps:

[0144] Step S502, obtaining a set of new energy output prediction scenarios for each new energy station in the new energy power plant.

[0145] Step S504, for each new energy station, determine a target new energy output prediction scenario whose corresponding new energy output value is greater than other new energy output prediction scenarios from the new energy output prediction scenario set of the new energy station.

[0146] Step S506 , initializing a new energy output benchmark parameter and a new energy output prediction error parameter based on the new energy output prediction value of the target new energy output prediction scenario.

[0147] Step S508, based on the new energy output prediction scenario set, iteratively update the new energy output benchmark parameters and the new energy output prediction error parameters to obtain target new energy output benchmark parameters and target new energy output prediction error parameters that meet the iteration conditions.

[0148] Step S510, determining a new energy output uncertainty set of each new energy station based on a target new energy output benchmark parameter and a target new energy output prediction error parameter of each new energy station.

[0149] Step S512, using the uncertainty set of the new energy output of each new energy station as a constraint condition, solving the output dispatch objective function pre-constructed for the new energy power plant, and obtaining the target net output value of each new energy station.

[0150] Step S514: dispatching the output of each new energy station according to the target net output value of each new energy station.

[0151] In this embodiment, through the set of new energy output prediction scenarios of each new energy station, the server can determine the target new energy output benchmark parameters and target new energy output prediction error parameters used to construct the new energy output uncertainty set, and then construct the new energy output uncertainty set of the new energy station; based on the new energy output uncertainty set of each new energy station, the server can solve the output scheduling objective function pre-constructed for the new energy power plant, obtain the target new energy predicted output value and target energy storage output value of each new energy station, and then be able to perform output scheduling for each new energy station. Based on the output scheduling method of the new energy power plant in the above process, the server can determine the target new energy predicted output value and target energy storage output value for each new energy station, so that the role of the energy storage device configured in the new energy power plant can be fully exerted.

[0152] In order to more clearly illustrate the output dispatching method of the new energy power plant provided in the embodiment of the present application, the output dispatching method of the new energy power plant is specifically described below with a specific embodiment, but it should be understood that the embodiment of the present application is not limited to this. In one of the exemplary embodiments, the present application also provides a model improvement method for new energy and energy storage to participate in the electric energy market, which specifically includes the following steps:

[0153] The existing model of new energy combined with energy storage participating in the electric energy market generally includes the output dispatch objective function shown in Formula 5 and Formula 6, and the constraints shown in Formula 7 to Formula 9. In other words, the relevant technology assumes that the new energy station has a relatively accurate new energy predicted output value to carry out output dispatch, but in actual operation, the prediction accuracy of the new energy predicted output value is often low, which leads to the above output dispatch objective function. The predicted output constraint uses 0 to the new energy predicted output value as the output boundary of the new energy power plant, which may be far from the optimal solution in the actual situation, and may also cause the optimization result to be too conservative.

[0154] Therefore, this embodiment provides a model improvement method, which retains the overall structure of the model in the related art and reconstructs the predicted output constraints of the model by extracting uncertainty data related to new energy. The specific process is as follows:

[0155] The server is reconstructed in the form of robust optimization, and the new energy output set is represented by a box-shaped uncertainty set, and the new energy output uncertainty set is obtained as shown in Formula 1 and Formula 2, and the new energy output uncertainty set is used to replace the predicted output constraints in related technologies.

[0156] So far, the original model has been initially improved into a robust optimization general model form; when the uncertainty set of renewable energy output and When a specific value is taken, the improved model is equivalent to the original model, and decision makers can flexibly select and The value of can be adjusted to achieve the relaxation and tightening of the feasible domain of the improved model, thereby achieving a balance between economy and conservatism of the improved model.

[0157] In order to further improve the operability and practicality of the improved model, the model improvement method provided in this embodiment also designs a corresponding calculation process to solve and .

[0158] Specifically, the server iteratively solves the uncertainty region constraint function shown in Formula 3 and the new energy output prediction scenario set of the new energy station and .

[0159] In this embodiment, first, based on the norm theory, a robust optimization model suitable for new energy storage participating in the electric energy market is proposed, which can flexibly construct a robust optimization model for the uncertainty factors in different application scenarios, that is, it takes into account the uncertainty of new energy output and retains the overall structure of the original model, and has a strong engineering value. Second, through the set of new energy output prediction scenarios, an automatic adjustment scheme for the model boundary is designed. The conservatism of the model after the boundary adjustment can be further reduced, which improves the economic value of the model and makes the optimization results closer to the actual operation needs. In summary, the model improvement method for the new energy combined with energy storage participating in the electric energy market provided in this embodiment comprehensively considers the inaccuracy of new energy prediction, rewrites the existing model of new energy combined with energy storage participating in the electric energy market, and the improved model retains the overall structure of the original model, has strong interpretability, and can be flexibly expanded to other similar application scenarios in the future.

[0160] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0161] Based on the same inventive concept, the embodiment of the present application also provides a new energy power plant output dispatching device for implementing the above-mentioned new energy power plant output dispatching method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the output dispatching device of one or more new energy power plants provided below can refer to the limitations of the output dispatching method of new energy power plants above, and will not be repeated here.

[0162] In an exemplary embodiment, as shown in FIG6 , a power output dispatching device for a new energy power plant is provided, comprising: a scene set acquisition module 602, an output information determination module 604, an objective function solving module 606 and a station output dispatching module 608, wherein:

[0163] The scenario set acquisition module 602 is used to acquire a new energy output prediction scenario set for each new energy station in the new energy power plant.

[0164] The output information determination module 604 is used to determine the target new energy output benchmark parameters and target new energy output prediction error parameters of each new energy station based on the new energy output prediction scenario set of each new energy station, and to determine the new energy output uncertainty set of each new energy station based on the target new energy output benchmark parameters and target new energy output prediction error parameters of each new energy station.

[0165] The objective function solving module 606 is used to solve the output scheduling objective function pre-constructed for the new energy power plant with the new energy output uncertainty set of each new energy station as a constraint condition, and obtain the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value.

[0166] The station output dispatching module 608 is used to dispatch the output of each new energy station according to the target net output value of each new energy station.

[0167] In an exemplary embodiment, the output information determination module 604 is also used to determine, for each new energy station, a target new energy output prediction scenario whose corresponding new energy predicted output value is greater than the remaining new energy output prediction scenarios from the new energy output prediction scenario set of the new energy station; initialize new energy output benchmark parameters and new energy output prediction error parameters based on the new energy predicted output value of the target new energy output prediction scenario; iteratively update the new energy output benchmark parameters and new energy output prediction error parameters based on the new energy output prediction scenario set to obtain the target new energy output benchmark parameters and target new energy output prediction error parameters that meet the iteration conditions.

[0168] In an exemplary embodiment, the output information determination module 604 is also used to solve the first parameter and the second parameter in the uncertainty area constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter; when any one of the first parameter and the second parameter is greater than or equal to the condition parameter corresponding to the iteration condition, the new energy output benchmark parameter and the new energy output prediction error parameter are updated to obtain the updated new energy output benchmark parameter and the updated new energy output prediction error parameter; the updated new energy output benchmark parameter is used as the new new energy output benchmark parameter, and the updated new energy output prediction error parameter is used as the new new energy output prediction error parameter, and the step of solving the first parameter and the second parameter in the uncertainty area constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter is returned, until the first parameter and the second parameter obtained by the solution are both less than the condition parameter, the corresponding new energy output benchmark parameter is determined as the target new energy output benchmark parameter, and the corresponding new energy output prediction error parameter is determined as the target new energy output prediction error parameter.

[0169] In an exemplary embodiment, the output information determination module 604 is also used to update the new energy output benchmark parameters according to a preset first update step to obtain updated new energy output benchmark parameters; and to update the new energy output prediction error parameters according to a preset second update step to obtain updated new energy output prediction error parameters.

[0170] In an exemplary embodiment, the output information determination module 604 is further used to initialize the new energy output reference parameter and the new energy output prediction error parameter to half of the new energy prediction output value corresponding to the target new energy output prediction scenario.

[0171] In an exemplary embodiment, the output dispatching device of the new energy power plant also includes an objective function construction module, for each dispatching period of each dispatching period of the new energy power plant, a unit penalty price in the dispatching period is determined, the time-of-use electricity price of the new energy power plant in the dispatching period is determined, and the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station in the dispatching period are determined; the charging efficiency and the discharging efficiency of the energy storage device of each new energy station are determined, and the preset prediction error threshold corresponding to each new energy station is determined; based on the unit penalty price in each dispatching period, the time-of-use electricity price of the new energy power plant in each dispatching period, the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station in each dispatching period, the charging efficiency and the discharging efficiency of the energy storage device of each new energy station, and the preset prediction error threshold corresponding to each new energy station, the output dispatching objective function of the new energy power plant is constructed with maximizing the output benefit of the new energy power plant as the optimization goal.

[0172] Each module in the output dispatching device of the above-mentioned new energy power plant can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0173] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG7. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the day-ahead output data of the new energy power plant. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for output scheduling of a new energy power plant is implemented.

[0174] Those skilled in the art will understand that the structure shown in FIG. 7 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0175] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0176] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0177] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0178] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0179] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0180] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for dispatching output of a new energy power plant, characterized in that: The method comprises: Obtain a set of new energy output prediction scenarios for each new energy station in a new energy power plant; Based on the set of new energy output prediction scenarios of each new energy station, determine the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, and based on the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station, determine the new energy output uncertainty set of each new energy station; Taking the uncertainty set of the new energy output of each new energy station as a constraint condition, solving the output dispatch objective function pre-constructed for the new energy power plant, and obtaining the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value; According to the target net output value of each new energy station, output dispatch is performed on each new energy station.

2. The method according to claim 1, characterized in that The step of determining a target new energy output benchmark parameter and a target new energy output prediction error parameter of each new energy station based on the new energy output prediction scenario set of each new energy station includes: For each new energy station, determine a target new energy output prediction scenario whose corresponding new energy output value is greater than other new energy output prediction scenarios from the set of new energy output prediction scenarios of the new energy station; Initializing a new energy output benchmark parameter and a new energy output prediction error parameter based on the new energy predicted output value of the target new energy output prediction scenario; Based on the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter are iteratively updated to obtain target new energy output benchmark parameter and target new energy output prediction error parameter that meet iteration conditions.

3. The method according to claim 2, characterized in that The iterative updating of the new energy output benchmark parameter and the new energy output prediction error parameter based on the new energy output prediction scenario set to obtain a target new energy output benchmark parameter and a target new energy output prediction error parameter that meet the iteration conditions includes: Solving the first parameter and the second parameter in the uncertainty region constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter; When any one of the first parameter and the second parameter is greater than or equal to the condition parameter corresponding to the iteration condition, updating the new energy output benchmark parameter and the new energy output prediction error parameter to obtain an updated new energy output benchmark parameter and an updated new energy output prediction error parameter; The updated new energy output benchmark parameter is used as the new new energy output benchmark parameter, and the updated new energy output prediction error parameter is used as the new new energy output prediction error parameter, and the step of solving the first parameter and the second parameter in the uncertainty area constraint function of the new energy station according to the new energy output prediction scenario set, the new energy output benchmark parameter and the new energy output prediction error parameter is returned until the first parameter and the second parameter obtained by solving are both smaller than the conditional parameter, and the corresponding new energy output benchmark parameter is determined as the target new energy output benchmark parameter, and the corresponding new energy output prediction error parameter is determined as the target new energy output prediction error parameter.

4. The method according to claim 3, characterized in that The updating of the new energy output benchmark parameter and the new energy output prediction error parameter to obtain the updated new energy output benchmark parameter and the updated new energy output prediction error parameter includes: Update the new energy output benchmark parameter according to a preset first update step length to obtain an updated new energy output benchmark parameter; The new energy output prediction error parameter is updated according to a preset second update step size to obtain an updated new energy output prediction error parameter.

5. The method according to claim 2, characterized in that: The new energy output prediction value based on the target new energy output prediction scenario, initializing the new energy output benchmark parameter and the new energy output prediction error parameter, includes: The new energy output benchmark parameter and the new energy output prediction error parameter are both initialized to half of the new energy prediction output value corresponding to the target new energy output prediction scenario.

6. The method according to any one of claims 1 to 5, characterized in that: The pre-constructed output scheduling objective function is constructed in the following way: For each dispatching period of each dispatching period of the new energy power plant, determine the unit penalty price in the dispatching period, determine the time-of-use electricity price of the new energy power plant in the dispatching period, and determine the day-ahead new energy output forecast value and the day-ahead new energy output forecast error value of each new energy station in the dispatching period; Determine the charging efficiency and discharging efficiency of the energy storage device of each new energy station, and determine the preset prediction error threshold corresponding to each new energy station; Based on the unit penalty price in each scheduling period, the time-of-use electricity price of the new energy power plant in each scheduling period, the day-ahead new energy predicted output value and the day-ahead new energy output prediction error value of each new energy station in each scheduling period, the charging efficiency and the discharging efficiency of the energy storage device of each new energy station, and the preset prediction error threshold corresponding to each new energy station, the output scheduling objective function of the new energy power plant is constructed with maximizing the output profit of the new energy power plant as the optimization goal.

7. An output dispatching device for a new energy power plant, characterized in that: The device comprises: A scenario set acquisition module is used to acquire a new energy output prediction scenario set for each new energy station in a new energy power plant; An output information determination module, used to determine a target new energy output benchmark parameter and a target new energy output prediction error parameter of each new energy station based on a set of new energy output prediction scenarios of each new energy station, and to determine a new energy output uncertainty set of each new energy station based on the target new energy output benchmark parameter and the target new energy output prediction error parameter of each new energy station; An objective function solving module is used to solve the output dispatch objective function pre-constructed for the new energy power plant by taking the new energy output uncertainty set of each new energy station as a constraint condition, and obtain the target net output value of each new energy station; the target net output value includes the target new energy predicted output value and the target energy storage output value; The station output dispatching module is used to dispatch the output of each new energy station according to the target net output value of each new energy station.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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