Direct current channel power transmission curve drawing method, device and equipment and storage medium

Abstract

The invention discloses a direct current channel power transmission curve drawing-up method, device and equipment and a storage medium, and is applied to the technical field of power system planning, and the method comprises the steps: obtaining the transmitting end historical data and the receiving end historical data of a target power grid; performing density estimation processing on the sending end historical data and the receiving end historical data to obtain a net load uncertainty set of the target power grid; determining a probability capacity reservation constraint based on the net load uncertainty set; constructing a collaborative adaptive optimization model by taking maximized new energy consumption as an objective function and taking probability capacity reservation constraint as a constraint condition; solving the collaborative adaptive optimization model to obtain a day-ahead DC power transmission plan curve; and performing rolling optimization processing on the acquired intra-day real-time interval data and the day-ahead direct current transmission plan curve to obtain a direct current channel transmission curve. The method provided by the invention can solve the technical problem of inaccurate curve drawing in the prior art so as to ensure the real-time power balance and stable operation of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, in particular to a DC channel power transmission curve formulation method, device, equipment and storage medium. BACKGROUND

[0002] The formulation of the DC channel power transmission curve is a core link for the safe, economic and efficient operation of the cross-regional power grid. The power transmission curve not only concerns the reliable execution of the medium and long-term contract power of the cross-provincial region, but also directly affects the real-time power balance and stable operation of the power grid.

[0003] In order to ensure the accuracy of the formulation of the power transmission curve, it is necessary to reasonably configure flexible resources to cope with the uncertainty of new energy and load. However, the existing curve formulation technology mostly adopts a formulation method based on deterministic planning. This method uses a single new energy and load prediction scenario to construct a deterministic optimization model, takes the minimum total operation cost or the maximum new energy consumption as the objective function, generates a power transmission curve, but this method ignores the uncertainty of prediction. When the actual new energy output deviates from the predicted value, the generated power transmission curve becomes infeasible quickly, resulting in the need for dispatchers to frequently and non-optimally intervene in emergency, which not only increases the safety risk, but also often leads to curtailment of wind or light or insufficient spinning reserve. SUMMARY

[0004] The present application provides a DC channel power transmission curve formulation method, device, equipment and storage medium to solve the technical problem of inaccurate existing curve formulation and to ensure the real-time power balance and stable operation of the power grid.

[0005] In order to solve the above technical problems, the present application provides a DC channel power transmission curve formulation method, comprising: obtaining the sending end historical data and the receiving end historical data of the target power grid; performing density estimation processing on the sending end historical data and the receiving end historical data to obtain a set of net load uncertainty of the target power grid; determining a probability capacity reservation constraint based on the set of net load uncertainty; constructing a cooperative adaptive optimization model with the maximum new energy consumption as the objective function and at least the probability capacity reservation constraint as the constraint condition; solving the cooperative adaptive optimization model to obtain a day-ahead DC power transmission planning curve, wherein the solving process is designed as a piecewise linear fitting method based on the least square method, the cooperative adaptive optimization model is linearized to obtain a mixed integer linear programming model, and the mixed integer linear programming model is analyzed to obtain the day-ahead DC power transmission planning curve; The obtained intraday real-time interval data and the day-ahead DC transmission planning curve are subjected to rolling optimization processing to obtain a DC channel transmission curve of the target power grid.

[0006] As one of the preferred solutions, the density estimation processing on the sending-end historical data and the receiving-end historical data to obtain a net load uncertainty set of the target power grid comprises: Based on the sending-end historical data and the receiving-end historical data, historical net load data is determined; The historical net load data is input into a time series prediction model constructed by a neural network to obtain predicted net load; The predicted net load is processed by a non-parametric kernel density estimation to obtain the net load uncertainty set.

[0007] As one of the preferred solutions, the probability capacity reservation constraint is determined based on the net load uncertainty set, comprising: The net load uncertainty set is subjected to boundary extraction processing by a boundary statistical method to obtain a confidence interval of net load prediction error; The confidence interval of the net load prediction error is subjected to robust optimization processing to obtain the probability capacity reservation constraint.

[0008] As one of the preferred solutions, the piecewise linear fitting method based on the least square method is used to linearize the collaborative adaptive optimization model to obtain a mixed integer linear programming model, comprising: The collaborative adaptive optimization model is subjected to identification of non-linear cost functions and constraints to obtain a non-linear function set; The non-linear function set is linearized by the piecewise linear fitting method based on the least square method to obtain the mixed integer linear programming model.

[0009] As one of the preferred solutions, the day-ahead DC transmission planning curve is obtained by analyzing the mixed integer linear programming model, comprising: The mixed integer linear programming model is subjected to model solving processing by a solver to obtain an optimal solution set; The optimal solution set is subjected to decision variable extraction and curve reconstruction processing to obtain the day-ahead DC transmission planning curve.

[0010] As one of the preferred solutions, the DC channel transmission curve of the target power grid is obtained by rolling optimization processing on the obtained intraday real-time interval data and the day-ahead DC transmission planning curve, comprising: A rolling window is constructed based on a fixed time window rolling method; determine an optimization model corresponding to the rolling window based on the intra-day interval data and the day-ahead DC power transmission plan curve; perform rolling optimization on the optimization model by using an online mixed integer linear programming solver to obtain the DC channel power transmission curve.

[0011] As one of the preferred solutions, after obtaining the DC channel power transmission curve, the DC channel power transmission curve formulation method further comprises: input the DC channel power transmission curve into the constructed full power flow calculation model for processing to obtain voltage data and line load rate of the target power grid; based on the voltage data and the line load rate, adaptively adjust the DC channel power transmission curve.

[0012] The application further provides a DC channel power transmission curve formulation device, comprising: an acquisition module configured to acquire historical data of a sending end and historical data of a receiving end of a target power grid; an estimation module configured to perform density estimation processing on the historical data of the sending end and the historical data of the receiving end to obtain a set of net load uncertainty of the target power grid; a determination module configured to determine a probabilistic capacity reservation constraint based on the set of net load uncertainty; a construction module configured to construct a cooperative adaptive optimization model with a target function of maximizing new energy consumption and at least a constraint condition of the probabilistic capacity reservation constraint; a solution module configured to solve the cooperative adaptive optimization model to obtain a day-ahead DC power transmission plan curve, wherein the solution process is designed as a piecewise linear fitting method based on least squares to linearize the cooperative adaptive optimization model to obtain a mixed integer linear programming model, and analyze the mixed integer linear programming model to obtain the day-ahead DC power transmission plan curve; an optimization module configured to perform rolling optimization on the intra-day interval data and the day-ahead DC power transmission plan curve to obtain a DC channel power transmission curve of the target power grid.

[0013] The application further provides a DC channel power transmission curve formulation device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the DC channel power transmission curve formulation method as described above when executing the computer program.

[0014] The application further provides a computer readable storage medium storing a computer program, wherein when a device where the computer readable storage medium is located executes the computer program, the direct current channel power transmission curve planning method is realized.

[0015] Compared with the prior art, the application has at least one of the following advantages: The application obtains the sending end historical data and the receiving end historical data of a target power grid, performs density estimation processing on the sending end historical data and the receiving end historical data to obtain a net load uncertainty set of the target power grid, determines a probability capacity reservation constraint based on the net load uncertainty set, takes maximizing new energy consumption as an objective function, at least takes the probability capacity reservation constraint as a constraint condition, and constructs a collaborative adaptive optimization model, solves the collaborative adaptive optimization model to obtain a day-ahead direct current transmission plan curve, wherein the solving process is designed as a piecewise linear fitting method based on a least square method, the collaborative adaptive optimization model is linearized to obtain a mixed integer linear programming model, the mixed integer linear programming model is analyzed to obtain the day-ahead direct current transmission plan curve, and the obtained intraday real-time interval data and the day-ahead direct current transmission plan curve are subjected to rolling optimization processing to obtain a direct current channel power transmission curve of the target power grid.

[0016] Compared with the prior art, the application quantifies the uncertainty of new energy and load by using historical data, constructs a net load probability set, forms a probability capacity reservation constraint based on the net load probability set, ensures that the day-ahead plan reserves sufficient flexibility resources for potential fluctuations, converts the random optimization model containing the constraint into a mathematical model that can be efficiently solved to generate a day-ahead benchmark curve embedded with robustness, and finally introduces rolling optimization in the intraday stage to continuously correct the day-ahead plan by using real-time data, so that the power transmission curve can dynamically track the actual state change of the system. This process effectively reduces the risk caused by prediction deviation, enables the system to automatically respond to uncertainty, ensures real-time power balance and stable operation of the power grid under the premise of minimizing artificial intervention, and maximizes new energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of a direct current channel power transmission curve planning method in one of the embodiments of the application; Figure 2 is a structural schematic diagram of a direct current channel power transmission curve planning device in one of the embodiments of the application; Figure 3 is a structural schematic diagram of a direct current channel power transmission curve planning device in one of the embodiments of the application; Reference signs: Among them, 11. Acquisition module; 12. Estimation module; 13. Determination module; 14. Construction module; 15. Solving module; 16. Optimization module; 21. Processor; 22. Memory. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0020] One embodiment of the present invention provides a method for determining the transmission curve of a DC channel. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shown is a flowchart illustrating a method for determining a DC transmission curve in one embodiment of the present invention. The method includes: S1: Obtain historical data of the sending end and receiving end of the target power grid; S2: Perform density estimation processing on the historical data of the sending end and the historical data of the receiving end to obtain the net load uncertainty set of the target power grid; S3: Based on the aforementioned set of net load uncertainties, determine the probabilistic capacity reservation constraints; S4: Construct a collaborative adaptive optimization model with the objective function of maximizing the consumption of new energy sources and at least the aforementioned probabilistic capacity reservation constraint as the constraint condition; S5: Solve the cooperative adaptive optimization model to obtain the day-ahead DC transmission plan curve. The solution process is designed as a piecewise linear fitting method based on the least squares method. Linearize the cooperative adaptive optimization model to obtain a mixed integer linear programming model. Analyze the mixed integer linear programming model to obtain the day-ahead DC transmission plan curve. S6: Perform rolling optimization processing on the acquired intraday real-time interval data and the day-ahead DC transmission plan curve to obtain the DC channel transmission curve of the target power grid.

[0021] Specifically, the sending end of the target power grid is usually the power generation side where new energy (wind power, photovoltaic, etc.) is centrally connected to the grid. The data needs to reflect the volatility and correlation of new energy output and the operating status of the supporting power grid. Specifically, it includes new energy output data, data on factors affecting new energy output (historical meteorological data that are strongly correlated with new energy output, including wind speed, solar irradiance, temperature, precipitation, etc.) and data such as bus voltage and line power flow on the sending end side.

[0022] The receiving end of the target power grid is usually the electricity consumption side with concentrated loads (such as cities and industrial parks). The data needs to reflect the load change pattern, the support capacity of local power sources, and the grid's acceptance capacity. Specifically, it includes receiving end load data, receiving end local power source data, and receiving end grid operation data.

[0023] In step S2, density estimation processing is performed on the historical data of the sending end and the historical data of the receiving end to obtain the net load uncertainty set of the target power grid. The specific processing includes: determining historical net load data based on the historical data of the sending end and the historical data of the receiving end; inputting the historical net load data into a time series prediction model constructed by a neural network to obtain the predicted net load; and processing the predicted net load using nonparametric kernel density estimation to obtain the net load uncertainty set.

[0024] First, the data from the sending end (generation side) and the receiving end (load side) are integrated to calculate the historical net load value that reflects the actual transmission demand of the transmission channel.

[0025] Specifically, historical actual power output data of new energy sources is extracted from historical data of the sending end, and historical actual load data of the region is extracted from historical data of the receiving end. The historical net load value is obtained based on the formula (historical net load data = historical load of receiving end - historical power output of new energy sources at the sending end).

[0026] Of course, before the calculation, it is necessary to ensure that the timestamps of the data sent and received are completely aligned, and to remove outliers caused by equipment failure or communication errors.

[0027] Next, neural networks (such as Long Short-Term Memory Network LSTM, Gated Recurrent Unit GRU, or Transformer) are used to construct time series prediction models.

[0028] The historical net load data calculated in the first step is used as training samples to train the neural network model. During the training process, the model will learn various patterns in the net load data, such as the trend of data change.

[0029] After the model training is completed and its accuracy is verified, the model is used to predict the net load for the next 24 hours or longer, and outputs a time-by-time deterministic predicted net load curve.

[0030] However, the predicted net load is only predicted data, which usually has prediction errors compared with the actual data. Therefore, a historical prediction error dataset is constructed based on the predicted net load and the actual net load. The construction process is as follows: the model is trained using data of a certain period in the past, the value at a future time point is predicted, and then compared with the actual value at the time point to calculate an error. By repeating this process, a large number of historical prediction error samples can be obtained.

[0031] The historical prediction error samples obtained in the above step are input into the kernel density estimation algorithm. The kernel density estimation algorithm places a Gaussian function on each data point, and then adds all the Gaussian functions to smoothly estimate the probability density function of the entire error distribution. Based on the estimated probability density function of the prediction error, an uncertainty set of the net load can be constructed. This set is not a single value, but a set describing the range of possible values of the future net load and the corresponding probabilities.

[0032] The generated net load uncertainty set is the direct basis for constructing the probabilistic capacity reservation constraint. For example, the system can determine how much reserve capacity needs to be reserved according to the tail characteristics of the uncertainty set to ensure safe and stable operation of the system in high-probability scenarios.

[0033] Specifically, in step S3, the probabilistic capacity reservation constraint is determined based on the net load uncertainty set, including: performing boundary extraction processing on the net load uncertainty set using a boundary statistical method to obtain a confidence interval of the net load prediction error; and performing robust optimization processing on the confidence interval of the net load prediction error to obtain the probabilistic capacity reservation constraint.

[0034] This step takes the net load prediction error as the core processing object and extracts quantifiable fluctuation boundaries from the discrete net load uncertainty set using a non-parametric statistical method. Specifically, the prediction error samples of each period are first selected from the uncertainty set, and the upper and lower bounds of the error of each period are determined by sorting the quantile or sampling expansion according to the pre-set confidence level (adjusted according to the safety requirements of the power grid), and finally a "period-error confidence interval" corresponding table is formed.

[0035] The core role is to convert the abstract probability scenario set into a specific numerical range, and to clearly define the fluctuation risk boundary that the power grid needs to cope with, providing a quantitative basis for subsequent constraint construction. At the same time, the confidence level adaptation balances the safety requirements and the risk of resource waste.

[0036] The non-parametric statistical method is preferably a commonly used quantile method or Bootstrap method.

[0037] To convert the confidence interval into a mathematical constraint executable by the optimization model, first associate the power balance logic to determine the "positive / negative adjustment requirements" corresponding to the upper and lower bounds of the confidence interval (positive for coping with over-expected load and negative for coping with lower-than-expected load); then introduce a robust coefficient λ (0<λ≤1) to balance robustness and economy and modify the adjustment requirements; finally, convert the adjustment requirements into linear constraints and superimpose the self-limitations of flexible resources to obtain the probabilistic capacity reservation constraint.

[0038] In step S4, a collaborative adaptive optimization model is constructed with the objective function of maximizing new energy consumption and the constraint condition of at least the probabilistic capacity reservation constraint.

[0039] In this process, the objective function adopts a "main target + secondary target" weighting structure: the main target focuses on maximizing new energy consumption by calculating the total sum of the difference between the total available power of the new energy at the sending end and the power abandoned, thereby directly reducing the abandoned power; the secondary target controls the cost of flexible resources, including the charging and discharging of energy storage, demand response subsidies and conventional standby costs, while setting a high weight to prioritize consumption and balance economy and consumption demand.

[0040] The constraint condition takes the probabilistic capacity reservation constraint as the core: the positive constraint requires the total ability of energy storage discharge, positive standby and demand response reduction to cover the upper bound of the error after robust modification; the negative constraint requires the total ability of energy storage charging, negative standby and new energy abandoned power to cover the lower bound of the error after modification, and the robust coefficient λ dynamically balances safety and cost.

[0041] Meanwhile, basic constraints such as power balance, energy storage SOC and DC ramping are supplemented to ensure the feasible operation of the power grid.

[0042] After the collaborative adaptive optimization model is established, the collaborative adaptive optimization model is solved to obtain a day-ahead HVDC transmission planning curve, wherein the solving process is designed as a piecewise linear fitting method based on the least square method to linearize the collaborative adaptive optimization model to obtain a mixed integer linear programming model; the mixed integer linear programming model is analyzed to obtain the day-ahead HVDC transmission planning curve. The piecewise linear fitting method based on the least square method linearizes the collaborative adaptive optimization model to obtain a mixed integer linear programming model, specifically including: identifying the non-linear cost function and constraint processing of the collaborative adaptive optimization model to obtain a set of non-linear functions; linearizing the set of non-linear functions by the piecewise linear fitting method based on the least square method to obtain the mixed integer linear programming model.

[0043] Specifically, the collaborative adaptive optimization model is first decomposed into two modules of objective function and constraint condition, and then the mathematical expressions in each module are analyzed one by one to determine whether there is a nonlinear feature. Because the model objective function contains flexibility resource cost items, some of which have a nonlinear relationship with decision variables, these cost items that cannot be expressed as "cost = coefficient * variable" belong to the nonlinear cost function to be identified; in the model constraints, some constraints caused by physical laws are also nonlinear.

[0044] The identified nonlinear cost functions and nonlinear constraints are classified according to variable association types, and repeated or redundant items are removed, such as the same nonlinear expression repeatedly appearing in different constraints, to finally form a structured nonlinear function set.

[0045] For each nonlinear function that needs to be linearized, a series of breakpoints are selected within the effective variation range of its independent variable, and in each small interval divided, an optimal straight line is fitted using the least squares method, and the error square sum of the multiple straight line segments and the original function curve on the whole is minimized. This is more reflective of the overall trend of the function than simple linear interpolation, is not sensitive to noise and fluctuations, and has better robustness, thereby obtaining the mixed integer linear programming model.

[0046] The mixed integer linear programming model is analyzed to obtain the day-ahead HVDC power transmission plan curve, including: using a solver to perform model solving processing on the mixed integer linear programming model to obtain an optimal solution set; and performing decision variable extraction and curve reconstruction processing on the optimal solution set to obtain the day-ahead HVDC power transmission plan curve.

[0047] The mixed integer linear programming model contains continuous variables such as HVDC power, energy storage charging and discharging power, and integer variables such as binary variables marking piecewise linear fitting intervals, and a professional solver supporting MILP solving and having computing power suitable for power grid dispatching scenarios needs to be selected, and the main choices are Gurobi, CPLEX, or the domestic Pangu solver.

[0048] Such solvers have built-in core algorithms such as branch and bound method, cutting plane method, and heuristic algorithm, and can quickly process models containing hundreds of variables and thousands of constraints to meet the time requirements of intra-day planning.

[0049] Taking the mainstream branch and bound method as an example, the solving process can be divided into three steps: 1. The possible values (0 / 1) of the integer variables are split into different branches to form a search tree; 2. For each branch, temporarily relax the integer constraint of the integer variable, solve the linear programming subproblem to obtain the lower bound of the objective function of the branch; by comparing the lower bounds of each branch, the branches with lower bounds worse than the current optimal solution are pruned; 3. Repeat the above process until an integer feasible solution that meets the accuracy requirement is found, and the objective function value of the solution is better than the lower bounds of all other branches, at which point the solution is the global optimal solution.

[0050] After the solution is completed, the solver outputs an optimal solution set, which contains not only the DC power but also the optimal values of all decision variables in the model.

[0051] The optimal solution set is subjected to decision variable extraction and curve reconstruction processing to obtain a day-ahead DC power transmission plan curve, wherein the extracted decision variable is the DC power of each time period.

[0052] The obtained intra-day real-time interval data and the day-ahead DC power transmission plan curve are subjected to rolling optimization processing to obtain a DC channel power transmission curve of the target power grid, including: based on a fixed time window rolling method, a rolling window is constructed; based on the intra-day real-time interval data and the day-ahead DC power transmission plan curve, an optimization model corresponding to the rolling window is determined; the optimization model is subjected to rolling optimization processing by using an online mixed integer linear programming (MILP) solver to obtain the DC channel power transmission curve.

[0053] The fixed time window is defined by two key parameters: 1. Control time domain: the total length of the window, i.e., the length of the future time period to be considered in each optimization, which is usually set to 3-6 hours with a time granularity of 15 minutes or 30 minutes. The basis for selecting this length is that it needs to cover the short-term fluctuation characteristics of new energy and load, and also needs to avoid excessive time domain length leading to a dramatic increase in calculation amount and a decrease in prediction accuracy.

[0054] 2. Execution cycle: the step size of the window sliding, i.e., the time interval between two optimizations, which is usually set to 15 minutes or 30 minutes. The execution cycle determines the update frequency of the dispatch, and the higher the frequency, the faster the response to real-time fluctuations, but the heavier the calculation burden.

[0055] In each rolling window, the latest intra-day real-time interval data and the day-ahead plan curve are used to dynamically update the input parameters and constraint conditions of the optimization model, ensuring that the model is highly matched with the current power grid state.

[0056] The input data at least includes intra-day real-time interval data, ultra-short-term prediction data, and a day-ahead DC power transmission plan curve.

[0057] The dynamically adjusted constraint conditions at least include probability capacity reservation constraints, device state constraints, and day-ahead plan tracking constraints.

[0058] In each rolling window, an online MILP solver is called to quickly solve the updated optimization model, generate optimal dispatch instructions within the window, and form a complete intra-day DC channel power transmission curve through multi-window result splicing.

[0059] After obtaining the DC transmission curve, the DC transmission curve formulation method further includes: inputting the DC transmission curve into the constructed full power flow calculation model for processing to obtain the voltage data and line load rate of the target power grid; and adaptively adjusting the DC transmission curve based on the voltage data and the line load rate.

[0060] By simulating the entire power grid operation state during the execution of the DC transmission curve through full power flow calculation, local constraint conflicts not covered by the simplified optimization model can be accurately captured, such as node voltage over-limit and branch overload.

[0061] Voltage data and line load rate are core indicators for the safe operation of the power grid. Excessive voltage can cause user equipment to malfunction, while excessive voltage can damage insulation. Excessive line load rate can exacerbate line losses and even cause thermal instability. These data provide clear optimization targets for subsequent adjustments.

[0062] To address voltage overruns and line overload issues discovered in full power flow calculations, the time-period distribution or amplitude of DC transmission power is adjusted to ensure the curve meets the overall power grid safety constraints.

[0063] Another embodiment of the present invention provides a DC transmission curve estimation device; for details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The diagram shown illustrates the structure of a DC transmission curve estimation device according to one embodiment of the present invention, comprising: The acquisition module 11 is used to acquire historical data of the sending end and the receiving end of the target power grid; Estimation module 12 is used to perform density estimation processing on the historical data of the sending end and the historical data of the receiving end to obtain the net load uncertainty set of the target power grid; Module 13 is used to determine probabilistic capacity reservation constraints based on the set of net load uncertainties. Module 14 is used to construct a collaborative adaptive optimization model with the objective function of maximizing the absorption of new energy sources and at least with the probabilistic capacity reservation constraint as the constraint condition. Solution module 15 is used to solve the cooperative adaptive optimization model to obtain the day-ahead DC transmission plan curve. The solution process is designed as a piecewise linear fitting method based on the least squares method to linearize the cooperative adaptive optimization model to obtain a mixed integer linear programming model. The mixed integer linear programming model is then analyzed to obtain the day-ahead DC transmission plan curve. The optimization module 16 is used to perform rolling optimization processing on the acquired intraday real-time interval data and the day-ahead DC transmission plan curve to obtain the DC channel transmission curve of the target power grid.

[0064] Referring to Figure 3 which is a structural schematic diagram of the direct current channel power transmission curve fitting device provided by the embodiment of the present application. The direct current channel power transmission curve fitting device provided by the embodiment of the present application comprises a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, the steps in the above direct current channel power transmission curve fitting method embodiments are implemented, such as steps S1-S6 described in the above embodiment. Figure 1 Alternatively, when the processor 21 executes the computer program, the functions of the modules in the above device embodiments are implemented, such as the function of the obtaining module 11.

[0065] For example, the computer program can be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the direct current channel power transmission curve fitting device. For example, the computer program can be divided into an obtaining module 11, an estimating module 12, a determining module 13, etc., and the specific functions of the modules are as follows: The obtaining module 11 is configured to obtain the sending-end historical data and the receiving-end historical data of a target power grid. The estimating module 12 is configured to perform density estimation processing on the sending-end historical data and the receiving-end historical data to obtain a set of net load uncertainty of the target power grid. The determining module 13 is configured to determine a probability capacity reservation constraint based on the set of net load uncertainty. The constructing module 14 is configured to construct a cooperative adaptive optimization model with the maximum new energy consumption as an objective function and at least the probability capacity reservation constraint as a constraint condition. The solving module 15 is configured to solve the cooperative adaptive optimization model to obtain a day-ahead direct current transmission plan curve. The solving process is designed as a piecewise linear fitting method based on the least square method, the cooperative adaptive optimization model is linearized to obtain a mixed integer linear programming model, and the mixed integer linear programming model is analyzed to obtain the day-ahead direct current transmission plan curve. The optimization module 16 is configured to perform rolling optimization processing on the obtained intraday real-time interval data and the day-ahead direct current transmission plan curve to obtain a direct current channel power transmission curve of the target power grid.

[0066] The direct current channel power transmission curve planning device can include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the direct current channel power transmission curve planning device, and does not constitute a limitation on the direct current channel power transmission curve planning device, and can include more or fewer components than the schematic diagram, or combine certain components, or different components, for example, the direct current channel power transmission curve planning device can also include an input / output device, a network access device, a bus, etc.

[0067] The processor 21 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor 21 is the control center of the direct current channel power transmission curve planning device, and connects various parts of the direct current channel power transmission curve planning device through various interfaces and lines.

[0068] The memory 22 can be used to store computer programs and / or modules, and the processor 21 realizes various functions of the direct current channel power transmission curve planning device by running or executing computer programs and / or modules stored in the memory 22, and calling data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0069] If the modules integrated by the direct current channel power transmission curve planning device are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0070] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned various method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0071] Correspondingly, the embodiment of the present application provides a computer readable storage medium, which includes a stored computer program. When the computer program is running, it controls the device where the computer readable storage medium is located to perform the steps in the direct current channel power transmission curve planning method of the above-mentioned embodiment, such as the steps S1-S6 in the above-mentioned embodiment. Figure 1

[0072] The above-mentioned embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it cannot be understood as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the present application patent should be subject to the appended claims.​

Claims

1. A method for determining the transmission curve of a DC channel, characterized in that, include: Acquire historical data of the sending and receiving ends of the target power grid; Density estimation processing is performed on the historical data of the sending end and the historical data of the receiving end to obtain the net load uncertainty set of the target power grid; Based on the aforementioned set of net load uncertainties, determine the probabilistic capacity reservation constraints; With maximizing the absorption of new energy sources as the objective function and at least the aforementioned probabilistic capacity reservation constraint as the constraint condition, a collaborative adaptive optimization model is constructed. The cooperative adaptive optimization model is solved to obtain the day-ahead DC transmission plan curve. The solution process is designed as a piecewise linear fitting method based on the least squares method. The cooperative adaptive optimization model is linearized to obtain a mixed integer linear programming model. The mixed integer linear programming model is analyzed to obtain the day-ahead DC transmission plan curve. The acquired intraday real-time interval data and the day-ahead DC transmission plan curve are subjected to rolling optimization processing to obtain the DC channel transmission curve of the target power grid.

2. The method for determining DC transmission curves as described in claim 1, characterized in that, The density estimation processing of the historical data at the sending end and the historical data at the receiving end yields the net load uncertainty set of the target power grid, including: Based on the historical data of the sending end and the historical data of the receiving end, determine the historical net load data; The historical net load data is input into a time series prediction model constructed by a neural network to obtain the predicted net load; The predicted net load is processed using nonparametric kernel density estimation to obtain the set of uncertainties in the net load.

3. The method for determining the DC transmission curve as described in claim 1, characterized in that, The determination of probabilistic capacity reservation constraints based on the net load uncertainty set includes: The boundary extraction process is performed on the net load uncertainty set using boundary statistics methods to obtain the confidence interval of the net load prediction error; Robust optimization processing is performed on the confidence interval of the net load prediction error to obtain the probabilistic capacity reservation constraint.

4. The method for determining the DC transmission curve as described in claim 1, characterized in that, The piecewise linear fitting method based on least squares linearizes the collaborative adaptive optimization model to obtain a mixed-integer linear programming model, including: The collaborative adaptive optimization model is subjected to identification of nonlinear cost functions and constraint processing to obtain a set of nonlinear functions; The set of nonlinear functions is linearized using the piecewise linear fitting method of least squares to obtain the mixed integer linear programming model.

5. The method for determining DC transmission curves as described in claim 1, characterized in that, The analysis of the mixed-integer linear programming model yields the day-ahead DC transmission plan curve, including: The mixed-integer linear programming model is solved using a solver to obtain the optimal solution set. The optimal solution set is subjected to decision variable extraction and curve reconstruction to obtain the day-ahead DC transmission plan curve.

6. The method for determining DC transmission curves as described in claim 1, characterized in that, The step of performing rolling optimization processing on the acquired intraday real-time interval data and the day-ahead DC transmission plan curve to obtain the DC channel transmission curve of the target power grid includes: Construct a scrolling window based on a fixed-time-window scrolling method; Based on the intraday real-time interval data and the day-ahead DC transmission plan curve, an optimization model corresponding to the rolling window is determined; The optimization model is subjected to rolling optimization using an online mixed-integer linear programming solver to obtain the DC channel transmission curve.

7. The method for determining DC transmission curves as described in claim 1, characterized in that, After obtaining the DC channel transmission curve, the method for determining the DC channel transmission curve further includes: The DC transmission curve is input into the constructed full power flow calculation model for processing to obtain the voltage data and line load rate of the target power grid; Based on the voltage data and the line load rate, the DC transmission curve is adaptively adjusted.

8. A device for determining DC transmission curves, characterized in that, include: The acquisition module is used to acquire historical data of the sending end and the receiving end of the target power grid; The estimation module is used to perform density estimation processing on the historical data of the sending end and the historical data of the receiving end to obtain the net load uncertainty set of the target power grid; The determination module is used to determine the probabilistic capacity reservation constraint based on the set of net load uncertainties; A construction module is used to build a collaborative adaptive optimization model with the objective function of maximizing the absorption of new energy sources and at least the probabilistic capacity reservation constraint as the constraint condition. The solution module is used to solve the cooperative adaptive optimization model to obtain the day-ahead DC transmission plan curve. The solution process is designed as a piecewise linear fitting method based on the least squares method to linearize the cooperative adaptive optimization model to obtain a mixed integer linear programming model. The mixed integer linear programming model is then analyzed to obtain the day-ahead DC transmission plan curve. The optimization module is used to perform rolling optimization processing on the acquired intraday real-time interval data and the day-ahead DC transmission plan curve to obtain the DC channel transmission curve of the target power grid.

9. A device for determining DC transmission curves, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the DC channel transmission curve formulation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the DC channel transmission curve formulation method as described in any one of claims 1 to 7.

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