Power grid dispatching optimization method, system, equipment, medium and product considering dynamic current-carrying capacity of line

Abstract

The invention relates to the technical field of power systems, and discloses a power grid dispatching optimization method, system, device, medium and product considering dynamic current-carrying capacity of a line. According to the method, operation time sequence data and environment time sequence data of an area along the line of a target power transmission line are input into a pre-trained current-carrying capacity prediction model; outputting current-carrying capacity prediction data, determining a dynamic current-carrying capacity operation limit value according to the current-carrying capacity prediction data, determining constraint conditions of an optimization target according to the dynamic current-carrying capacity operation limit value and a power grid balance steady state by taking minimization of the total power generation cost as the optimization target, and constructing a power grid dispatching optimization model; a power grid dispatching optimization scheme is optimized and determined through the power grid dispatching optimization model, so that by considering the dynamic current-carrying capacity characteristics of the line, the power grid is ensured to safely and stably operate within the dynamic current-carrying capacity operation limit value while the requirement of minimizing the total power generation cost is met, and the power grid dispatching precision and robustness are improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a power grid dispatch optimization method, system, equipment, medium and product that considers the dynamic current carrying capacity of lines. Background Technology

[0002] With the large-scale integration of renewable energy into the power system, transmission system operators face an urgent need to improve the utilization efficiency of existing transmission lines. Static Line Rating (SLR) is a traditional method for determining the maximum permissible current carrying capacity of overhead transmission lines. This method is based on a set of pre-defined, fixed environmental condition parameters. These parameters are typically conservative values ​​chosen to consider long-term safe operation, such as higher ambient temperatures, lower wind speeds, and specific solar radiation intensities (usually referencing the most severe summer or year-round extreme weather conditions). The current carrying capacity calculated by SLR is a constant value that generally remains unchanged during seasonal changes or routine operation. It provides a clear and unified benchmark for the long-term planning, design, and safe operation of lines, facilitating stable and reliable dispatching by power system operators.

[0003] The real-time current carrying capacity of overhead transmission lines is significantly affected by environmental conditions (such as ambient temperature, wind speed, wind direction, and solar radiation). Its dynamic characteristics make it difficult for traditional static line rating methods to meet real-time operation requirements. The SLR sets a fixed current carrying capacity limit based on the worst weather conditions, resulting in the line capacity being underutilized for a long time, which limits the grid connection and transmission efficiency of renewable energy.

[0004] Currently, in the process of power grid dispatching, only the static current carrying capacity information of the lines is generally considered. In the context of large-scale integration of renewable energy, this dispatching method results in poor power grid dispatching accuracy and low robustness, which can hardly meet the needs of efficient and flexible power grid operation. Summary of the Invention

[0005] In view of this, the present invention provides a power grid dispatch optimization method, system, equipment, medium and product that takes into account the dynamic current carrying capacity of the lines, which solves the technical problem that the power grid dispatch accuracy is poor and the robustness is low, making it difficult to meet the needs of efficient and flexible operation of the power grid.

[0006] The first aspect of this invention provides a power grid dispatch optimization method considering the dynamic carrying capacity of transmission lines, comprising:

[0007] Acquire runtime timing data and environmental timing data of the area along the target transmission line;

[0008] The runtime timing data and the environmental timing data are input into the pre-trained load capacity prediction model, so that the load capacity prediction model outputs load capacity prediction data.

[0009] The dynamic capacity carrying capacity operating limit is determined based on the predicted capacity carrying capacity data.

[0010] With minimizing the total power generation cost as the optimization objective, the constraints of the optimization objective are determined based on the dynamic current carrying capacity operating limit and the grid balance steady state, and a grid dispatch optimization model is constructed.

[0011] The power grid dispatch optimization model is optimized and solved. Based on the optimal solution, a power grid dispatch optimization scheme is determined and the power grid is made to execute the power grid dispatch optimization scheme.

[0012] Preferably, the training process of the pre-trained traffic flow prediction model includes:

[0013] Acquire historical operational sequence data and historical environmental time-series data of the area along the target transmission line;

[0014] Based on the steady-state thermal equilibrium relationship of the conductor, the historical current-carrying capacity data of the target transmission line is determined according to the historical operating sequence data and the historical environmental time sequence data.

[0015] Using the historical runtime time series data and the historical environmental time series data as input samples, and the historical load data as the mapping output sample of the input samples, the initial quantile regression forest model is trained to obtain the pre-trained load prediction model.

[0016] Preferably, the historical operating time series data includes conductor temperature; the historical environmental time series data includes ambient temperature, wind speed, and wind direction angle; and the historical current carrying capacity data includes historical steady-state current carrying capacity and historical transient current carrying capacity.

[0017] The conductor-based steady-state thermal balance relationship, based on the historical operating time series data and the historical environmental time series data, determines the historical current-carrying capacity data of the target transmission line, including:

[0018] The convective heat dissipation power is determined based on the conductor temperature, the ambient temperature, the wind speed, and the wind direction angle.

[0019] The radiative heat dissipation power is determined based on the conductor temperature and the ambient temperature.

[0020] Based on the steady-state thermal balance of the conductor, the historical steady-state current carrying capacity is determined according to the convective heat dissipation power, the radiative heat dissipation power, and the pre-acquired solar radiation heat.

[0021] Based on the steady-state thermal equilibrium relationship of the conductor, the historical transient current carrying capacity is determined according to the conductor's maximum allowable temperature and the conductor's temperature at the current moment, as well as the convective heat dissipation power, the radiative heat dissipation power, and the pre-acquired solar radiation heat.

[0022] Preferably, the traffic capacity prediction data includes a traffic capacity probability distribution; determining the dynamic traffic capacity operating limit based on the traffic capacity prediction data includes:

[0023] Based on the aforementioned load capacity probability distribution and the preset planned load capacity, the overload risk probability is determined;

[0024] If the overload risk probability reaches a preset maximum risk threshold, the latest carrying capacity is determined as the dynamic carrying capacity operating limit.

[0025] Preferably, the objective function corresponding to the optimization objective is:

[0026]

[0027] In the formula, The objective function value, Let g be the power generation cost function of generator g. Let g be the active power output of generator g at time t. This is a collection of generators.

[0028] Preferably, the constraints include node power balance constraints, node voltage safety constraints, generator output limit constraints, non-DLR line power limit constraints, and DLR line time-varying power limit constraints; wherein, the power limit in the DLR line time-varying power limit constraints is determined based on the dynamic current carrying capacity operating limit and rated voltage.

[0029] Secondly, the present invention also provides a power grid dispatch optimization system that considers the dynamic current carrying capacity of lines, comprising:

[0030] The data acquisition module is used to acquire runtime timing data and environmental timing data of the area along the target transmission line;

[0031] The load capacity prediction module is used to input the runtime timing data and the environmental timing data into a pre-trained load capacity prediction model, so that the load capacity prediction model outputs load capacity prediction data.

[0032] The flow rate limit determination module is used to determine the dynamic flow rate operating limit based on the flow rate prediction data;

[0033] The optimization model construction module is used to construct a power grid dispatch optimization model with the goal of minimizing the total power generation cost, based on the dynamic current carrying capacity operating limit and the power grid balance steady state, to determine the constraints of the optimization goal.

[0034] The scheduling optimization module is used to find the optimal solution for the power grid scheduling optimization model, determine the power grid scheduling optimization scheme based on the optimal solution, and enable the power grid to execute the power grid scheduling optimization scheme.

[0035] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the power grid dispatch optimization method considering line dynamic current carrying capacity as described in the first aspect.

[0036] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the power grid dispatch optimization method considering the dynamic current carrying capacity of lines as described in the first aspect.

[0037] Fifthly, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the power grid dispatch optimization method considering line dynamic current carrying capacity as described in the first aspect.

[0038] As can be seen from the above technical solutions, this invention inputs the runtime sequence data and environmental time sequence data of the target transmission line's area into a pre-trained current carrying capacity prediction model, outputs current carrying capacity prediction data, and determines dynamic current carrying capacity operating limits based on the current carrying capacity prediction data. Furthermore, with minimizing total power generation cost as the optimization objective, it constructs a power grid dispatch optimization model based on the dynamic current carrying capacity operating limits and the grid's steady-state equilibrium, and uses this model to find and determine the optimal power grid dispatch optimization scheme. By considering the dynamic current carrying capacity characteristics of the line, it not only minimizes total power generation cost but also ensures the safe and stable operation of the power grid within the dynamic current carrying capacity operating limits, improving the accuracy and robustness of power grid dispatch and effectively enhancing the economy and reliability of power grid operation. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is an application environment diagram of a power grid dispatch optimization method considering the dynamic carrying capacity of lines, provided by an embodiment of the present invention.

[0041] Figure 2 A flowchart of a power grid dispatch optimization method considering dynamic line carrying capacity provided in an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a power grid dispatch optimization system considering the dynamic carrying capacity of lines, provided in an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] The power grid dispatch optimization method considering dynamic line carrying capacity provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102 or placed on a cloud or other network server. Terminal 101 or server 102 acquires runtime timing data and environmental timing data of the area along the target transmission line; inputs the runtime timing data and environmental timing data into a pre-trained current carrying capacity prediction model, causing the current carrying capacity prediction model to output current carrying capacity prediction data; determines dynamic current carrying capacity operating limits based on the current carrying capacity prediction data; constructs a power grid dispatch optimization model with the goal of minimizing total power generation cost, based on the dynamic current carrying capacity operating limits and the steady-state equilibrium of the power grid; seeks the optimal solution for the power grid dispatch optimization model, determines the power grid dispatch optimization scheme based on the optimal solution, and enables the power grid to execute the power grid dispatch optimization scheme.

[0046] Terminal 101 can be, but is not limited to, various personal computers, laptops, smartphones, and tablets.

[0047] Server 102 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0048] like Figure 2 As shown, this application provides a power grid dispatch optimization method that considers the dynamic current carrying capacity of lines, and applies this method to... Figure 1 Taking terminal 101 or server 102 as an example, the explanation includes the following steps S1 to S5. Wherein:

[0049] Step S1: Obtain the runtime timing data and environmental timing data of the area along the target transmission line.

[0050] Among these methods, environmental time-series data, including ambient temperature, is acquired through distributed weather stations and conductor temperature sensors. Wind speed ,wind direction Solar radiation heat And so on. It also obtains the current and planned power flow of the line, the operating status of adjacent lines, the network topology, and the conductor temperature T. C .

[0051] Specifically, Kalman filtering is used to assimilate runtime and environmental time-series data to correct forecast biases and construct feature vectors for machine learning. In addition to basic meteorological parameters, historical thermal inertia indices (such as the conductor temperature rise rate over the past hour) are introduced. , For the temperature rise, (Time interval of transient processes) and spatial gradient characteristics (such as wind speed gradient along the line direction) ).

[0052] Step S2: Input the runtime timing data and environmental timing data into the pre-trained load capacity prediction model, so that the load capacity prediction model outputs load capacity prediction data.

[0053] The training process of the pre-trained load capacity prediction model includes:

[0054] Step S21: Obtain historical operation sequence data and historical environmental time sequence data of the area along the target transmission line;

[0055] Step S22: Based on the steady-state thermal balance relationship of the conductor, determine the historical current carrying capacity data of the target transmission line according to historical operating sequence data and historical environmental time sequence data.

[0056] In some embodiments, historical operating time-series data includes conductor temperature; historical environmental time-series data includes ambient temperature, wind speed, and wind direction angle; historical current-carrying data includes historical steady-state current-carrying capacity and historical transient current-carrying capacity; in this case, based on the conductor's steady-state thermal balance relationship, the historical current-carrying capacity data of the target transmission line is determined according to the historical operating time-series data and the historical environmental time-series data, including:

[0057] Step S221: Determine the convective heat dissipation power based on the conductor temperature, ambient temperature, wind speed, and wind direction angle.

[0058] The calculation process for convective heat dissipation power is as follows:

[0059]

[0060] In the formula, k represents the convective heat dissipation power. f Let A be the thermal conductivity of air, and B and n be constants. Let be the wind direction angle, when 0 < When <24°, A=0.42, B=0.68, n=1.08; when 24°≤ ≤90°, A=0.42, B=0.58, n=0.9; V s Where is the wind speed, C is the convection correction factor related to the Reynolds number, and D is the outer diameter of the conductor. The dynamic viscosity of air;

[0061] in,

[0062]

[0063] Step S222: Determine the radiative heat dissipation power based on the conductor temperature and ambient temperature.

[0064] The calculation process for radiative heat dissipation power is as follows:

[0065]

[0066] In the formula, Indicates radiative heat dissipation power. The surface emissivity of the conductor is 0.23~0.43 for bright new wires and 0.90~0.95 for old wires or wires coated with black anti-corrosion agent. For Stefan-Boltzmann constant, =5.67×10 -8 .

[0067] Step S223: Based on the steady-state thermal balance relationship of the conductor, determine the historical steady-state current carrying capacity according to the convective heat dissipation power, the radiative heat dissipation power and the pre-acquired solar radiation heat.

[0068] Based on the steady-state thermal equilibrium relationship of the conductor, when the rate of change of the conductor temperature with time... Substituting this into the above equation, we obtain the steady-state thermal equilibrium equation:

[0069]

[0070] In the formula, Current temperature Let I be the AC resistance of the conductor and I be the current. Therefore, the steady-state current carrying capacity is obtained from the steady-state heat balance equation:

[0071]

[0072] In the formula, This represents the steady-state current carrying capacity.

[0073] Step S224: Based on the steady-state thermal balance relationship of the conductor, determine the historical transient current carrying capacity according to the conductor's maximum allowable temperature and the current temperature of the conductor, as well as the convective heat dissipation power, radiative heat dissipation power and the pre-acquired solar radiation heat.

[0074] Transient current carrying capacity is the current that allows a conductor to carry current for a finite amount of time. From the current temperature Transition to the maximum allowable temperature This method is applicable to short-term overload or scheduling response scenarios and is obtained by solving the following equation:

[0075]

[0076] In the formula, For transient load capacity, The heat capacity per unit length of conductor. Time interval The average conductor temperature inside, Average conductor temperature The AC resistance of the conductor.

[0077] Step S23: Using historical runtime time series data and historical environmental time series data as input samples, and using historical load data as the mapping output sample of the input samples, train the initial quantile regression forest model to obtain the pre-trained load prediction model.

[0078] Quantile Regression Forests (QRF) is an ensemble learning method based on decision trees. By constructing multiple decision trees and combining their predictions, it can effectively capture nonlinear relationships and heteroscedasticity in the data, thereby more accurately predicting the value of carrying capacity at different quantiles.

[0079] Quantile regression forests estimate the conditional distribution of a target variable by constructing multiple decision trees and aggregating observations within all leaf nodes. For a given predictor variable X, its predicted quantile q is estimated using the following formula:

[0080]

[0081] In the formula, For observation The weights are determined by the input. The value is determined by all observations within the leaf node to which the leaf node falls; This is the indicator function. Using this cumulative distribution function, the predicted carrying capacity at any quantile can be obtained. Dedicated QRF models are trained for different prediction time scales (e.g., 0-4 hour ultra-short-term, 4-24 hour short-term), with the corresponding feature vectors as input. The model directly outputs the future carrying capacity at different times (including...). and The conditional probability distribution of , i.e., multiple quantiles Predicted values .

[0082] Step S3: Determine the dynamic capacity carrying capacity operating limit based on the capacity carrying capacity prediction data.

[0083] The determination of dynamic current carrying capacity operating limits requires comprehensive consideration of current carrying capacity forecast data, line safety operation requirements, and the actual operating conditions of the power grid. Specifically, current carrying capacity forecast data includes the probability distribution of current carrying capacity; in this case, the dynamic current carrying capacity operating limits are determined based on the current carrying capacity forecast data, including:

[0084] Step S301: Determine the overload risk probability based on the probability distribution of the carrying capacity and the preset planned carrying capacity.

[0085] Among them, by defining the risk probability This refers to the probability that the predicted load capacity will be lower than the actual planned power flow (the preset planned load capacity), which is the load capacity limit on which the scheduling decision is based, and the probability that it will actually be exceeded.

[0086]

[0087] In the formula, This application provides a predicted load capacity for a future time t+h. It is not a fixed number, but a probability distribution (a range of possible values ​​and their corresponding probabilities output by the QRF model). The planned load capacity for the same future time t+h. It is the predicted carrying capacity. Less Than Planned Trend The probability of overload risk at future time t+h. .

[0088] Step S302: When the overload risk probability reaches the preset maximum risk threshold, determine the latest carrying capacity as the dynamic carrying capacity operating limit.

[0089] Among them, setting a maximum risk threshold If the overload risk probability is less than or equal to the maximum risk threshold (e.g., 5%), then the dynamic carrying capacity operating limit is the maximum carrying capacity value corresponding to the predicted probability in the carrying capacity distribution, provided that the overload risk probability at future time t+h is less than or equal to this maximum risk threshold. Specifically, when the overload risk probability reaches the preset maximum risk threshold, it means that if the carrying capacity is further increased, the overload risk will exceed the acceptable range, i.e.:

[0090]

[0091] In the formula, These are the dynamic load capacity operating limits.

[0092] By selecting the current carrying capacity value in the predicted current carrying capacity distribution that exactly makes the overload risk probability equal to the maximum risk threshold, the dynamic current carrying capacity operating limit is determined. This approach fully utilizes the transmission capacity of the line while keeping the overload risk within an acceptable range, ensuring the safe and stable operation of the line and the power grid.

[0093] Step S4: With minimizing the total power generation cost as the optimization objective, determine the constraints of the optimization objective based on the dynamic current carrying capacity operating limit and the grid balance steady state, and construct a grid dispatch optimization model.

[0094] The objective function corresponding to the optimization objective is:

[0095]

[0096] In the formula, The objective function value, Let g be the power generation cost function of generator g. Let g be the active power output of generator g at time t. This is a collection of generators.

[0097] The constraints include node power balance constraints, node voltage safety constraints, generator output limit constraints, non-DLR line power limit constraints, and DLR line time-varying power limit constraints. Among them, the power limit in the DLR line time-varying power limit constraint is determined based on the dynamic current carrying capacity operating limit and the rated voltage.

[0098] Specifically, the node power balance constraint is:

[0099]

[0100] In the formula, Let be the active power of line l flowing out of node i. Let be the active load of node i at time t. Let i be the set of generators connected to node i. Let i be the set of lines connected to node i. It is a set of nodes.

[0101] The node voltage safety constraint is:

[0102]

[0103] In the formula, Let be the voltage at node i at time t. , These are the lower and upper voltage limits for node i, respectively.

[0104] The generator output limit constraint is as follows:

[0105]

[0106] In the formula, Let g be the power of the generator. , These represent the lower and upper limits of the power output of generator g, respectively.

[0107] The power limit constraints for non-DLR (Dynamic Line Rating) lines are as follows:

[0108]

[0109] In the formula, For maximum apparent power capacity, This is a collection of dynamic current carrying capacity technology lines. For the set of all lines, For the set of all lines In addition to the application of the dynamic current carrying capacity technology of this invention, the circuit set is also included. All lines except those mentioned above.

[0110] The time-varying power limit constraint for DLR lines is:

[0111]

[0112] In the formula, The transmission power of a DLR line with dynamic capacity at time t. The maximum permissible transmission power of a DLR line with dynamic carrying capacity at time t; where:

[0113]

[0114] In the formula, Rated voltage, These are the dynamic load capacity operating limits.

[0115] Step S5: Find the optimal solution for the power grid dispatch optimization model, determine the power grid dispatch optimization scheme based on the optimal solution, and make the power grid execute the power grid dispatch optimization scheme.

[0116] The power grid dispatch optimization scheme includes the generating capacity of each generator. By distributing the optimization scheme to each generator in the power grid and executing it, the operating state of the power grid is changed, thereby minimizing the power generation cost while ensuring the stable operation of the power grid. Various optimization algorithms can be used in the optimization process, such as genetic algorithms, particle swarm optimization algorithms, linear programming, or nonlinear programming methods.

[0117] It should be noted that this application inputs the runtime sequence data and environmental time sequence data of the area along the target transmission line into a pre-trained current carrying capacity prediction model, outputs current carrying capacity prediction data, and determines the dynamic current carrying capacity operating limit based on the current carrying capacity prediction data. Furthermore, with minimizing the total generation cost as the optimization objective, the application constructs a power grid dispatch optimization model based on the dynamic current carrying capacity operating limit and the grid balance steady state to determine the constraints of the optimization objective. The power grid dispatch optimization model is then used to find the optimal power grid dispatch optimization scheme. By considering the dynamic current carrying capacity characteristics of the line, while minimizing the total generation cost, it also ensures the safe and stable operation of the power grid within the dynamic current carrying capacity operating limit, improving the accuracy and robustness of power grid dispatch and effectively enhancing the economy and reliability of power grid operation.

[0118] Based on the same inventive concept, this application also provides a power grid dispatch optimization system that considers the dynamic carrying capacity of lines for implementing the power grid dispatch optimization method that considers the dynamic carrying capacity of lines mentioned above.

[0119] The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more power grid dispatch optimization system embodiments considering line dynamic current carrying capacity provided below can be found in the limitations of the power grid dispatch optimization method considering line dynamic current carrying capacity described above, and will not be repeated here.

[0120] like Figure 3 As shown in the figure, this application embodiment also provides a power grid dispatch optimization system that considers the dynamic carrying capacity of lines, including:

[0121] The data acquisition module 100 is used to acquire runtime timing data and environmental timing data of the area along the target transmission line;

[0122] The load capacity prediction module 200 is used to input runtime timing data and environmental timing data into a pre-trained load capacity prediction model, so that the load capacity prediction model outputs load capacity prediction data.

[0123] The flow rate limit determination module 300 is used to determine the dynamic flow rate operating limit based on the flow rate prediction data.

[0124] The optimization model construction module 400 is used to construct a power grid dispatch optimization model with the goal of minimizing the total power generation cost, based on the constraints of the dynamic current carrying capacity operation limit and the steady state of the power grid balance.

[0125] The scheduling optimization module 500 is used to find the optimal solution for the power grid scheduling optimization model, determine the power grid scheduling optimization scheme based on the optimal solution, and enable the power grid to execute the power grid scheduling optimization scheme.

[0126] In some embodiments, the training module of the pre-trained load capacity prediction model includes:

[0127] The historical data acquisition module is used to acquire historical operating sequence data and historical environmental time sequence data of the area along the target transmission line;

[0128] The current carrying capacity data determination module is used to determine the historical current carrying capacity data of the target transmission line based on the steady-state thermal balance relationship of the conductor and according to historical operating sequence data and historical environmental time sequence data.

[0129] The model training module is used to train the initial quantile regression forest model with historical runtime data and historical environmental time series data as input samples and historical load data as the mapping output sample, so as to obtain a pre-trained load prediction model.

[0130] In some embodiments, historical runtime time series data includes conductor temperature; historical environmental time series data includes ambient temperature, wind speed, and wind direction angle; historical current carrying capacity data includes historical steady-state current carrying capacity and historical transient current carrying capacity.

[0131] The load capacity data determination module is used for:

[0132] The convective heat dissipation power is determined based on the conductor temperature, ambient temperature, wind speed, and wind direction angle.

[0133] The radiative heat dissipation power is determined based on the conductor temperature and ambient temperature.

[0134] Based on the conductor's steady-state thermal equilibrium relationship, the historical steady-state current carrying capacity is determined according to the convective heat dissipation power, the radiative heat dissipation power, and the pre-acquired solar radiation heat.

[0135] Based on the steady-state thermal equilibrium relationship of the conductor, the historical transient current carrying capacity is determined according to the conductor's maximum allowable temperature, the conductor's temperature at the current moment, as well as the convective heat dissipation power, the radiative heat dissipation power, and the pre-acquired solar radiation heat.

[0136] In some embodiments, the traffic flow prediction data includes a traffic flow probability distribution; the traffic flow limit determination module 300 is configured to:

[0137] Based on the probability distribution of carrying capacity and the preset planned carrying capacity, the probability of overload risk is determined;

[0138] When the probability of overload risk reaches the preset maximum risk threshold, the latest carrying capacity is determined as the dynamic carrying capacity operating limit.

[0139] In some embodiments, the objective function corresponding to the optimization objective is:

[0140]

[0141] In the formula, The objective function value, Let g be the power generation cost function of generator g. Let g be the active power output of generator g at time t. This is a collection of generators.

[0142] In some embodiments, the constraints include node power balance constraints, node voltage safety constraints, generator output limit constraints, non-DLR line power limit constraints, and DLR line time-varying power limit constraints; wherein, the power limit in the DLR line time-varying power limit constraint is determined based on the dynamic current carrying capacity operating limit and the rated voltage.

[0143] like Figure 4As shown, this application provides an electronic device 10, which includes a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 performs the steps of the power grid dispatch optimization method considering the dynamic carrying capacity of the line as described in the above embodiment.

[0144] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed, implements the steps of the power grid dispatch optimization method considering the dynamic current carrying capacity of lines as described in the above embodiments.

[0145] This application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the power grid dispatch optimization method considering the dynamic carrying capacity of lines as described in the above embodiments.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, computer storage media, and computer program products described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0148] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0149] In the several embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0153] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid dispatch optimization method considering the dynamic current carrying capacity of transmission lines, characterized in that, include: Acquire runtime timing data and environmental timing data of the area along the target transmission line; The runtime timing data and the environmental timing data are input into the pre-trained load capacity prediction model, so that the load capacity prediction model outputs load capacity prediction data. The dynamic capacity carrying capacity operating limit is determined based on the predicted capacity carrying capacity data. With minimizing the total power generation cost as the optimization objective, the constraints of the optimization objective are determined based on the dynamic current carrying capacity operating limit and the grid balance steady state, and a grid dispatch optimization model is constructed. The power grid dispatch optimization model is optimized and solved. Based on the optimal solution, a power grid dispatch optimization scheme is determined and the power grid is made to execute the power grid dispatch optimization scheme.

2. The power grid dispatch optimization method considering dynamic line current carrying capacity according to claim 1, characterized in that, The training process of the pre-trained load capacity prediction model includes: Acquire historical operational sequence data and historical environmental time-series data of the area along the target transmission line; Based on the steady-state thermal equilibrium relationship of the conductor, the historical current-carrying capacity data of the target transmission line is determined according to the historical operating sequence data and the historical environmental time sequence data. Using the historical runtime time series data and the historical environmental time series data as input samples, and the historical load data as the mapping output sample of the input samples, the initial quantile regression forest model is trained to obtain the pre-trained load prediction model.

3. The power grid dispatch optimization method considering dynamic line current carrying capacity according to claim 2, characterized in that, The historical operating time series data includes conductor temperature; the historical environmental time series data includes ambient temperature, wind speed, and wind direction angle; the historical current carrying capacity data includes historical steady-state current carrying capacity and historical transient current carrying capacity. The conductor-based steady-state thermal balance relationship, based on the historical operating time series data and the historical environmental time series data, determines the historical current-carrying capacity data of the target transmission line, including: The convective heat dissipation power is determined based on the conductor temperature, the ambient temperature, the wind speed, and the wind direction angle. The radiative heat dissipation power is determined based on the conductor temperature and the ambient temperature. Based on the steady-state thermal balance of the conductor, the historical steady-state current carrying capacity is determined according to the convective heat dissipation power, the radiative heat dissipation power, and the pre-acquired solar radiation heat. Based on the steady-state thermal equilibrium relationship of the conductor, the historical transient current carrying capacity is determined according to the conductor's maximum allowable temperature and the conductor's temperature at the current moment, as well as the convective heat dissipation power, the radiative heat dissipation power, and the pre-acquired solar radiation heat.

4. The power grid dispatch optimization method considering dynamic line current carrying capacity according to claim 2, characterized in that, The traffic capacity prediction data includes a traffic capacity probability distribution; determining the dynamic traffic capacity operating limit based on the traffic capacity prediction data includes: Based on the aforementioned load capacity probability distribution and the preset planned load capacity, the overload risk probability is determined; If the overload risk probability reaches a preset maximum risk threshold, the latest carrying capacity is determined as the dynamic carrying capacity operating limit.

5. The power grid dispatch optimization method considering dynamic line current carrying capacity according to claim 1, characterized in that, The objective function corresponding to the optimization objective is: In the formula, The objective function value, Let g be the power generation cost function of generator g. Let g be the active power output of generator g at time t. This is a collection of generators.

6. The power grid dispatch optimization method considering dynamic line current carrying capacity according to claim 1 or 5, characterized in that, The constraints include node power balance constraints, node voltage safety constraints, generator output limit constraints, non-DLR line power limit constraints, and DLR line time-varying power limit constraints; wherein, the power limit in the DLR line time-varying power limit constraint is determined based on the dynamic current carrying capacity operating limit and rated voltage.

7. A power grid dispatch optimization system considering the dynamic current carrying capacity of transmission lines, characterized in that, include: The data acquisition module is used to acquire runtime timing data and environmental timing data of the area along the target transmission line; The load capacity prediction module is used to input the runtime timing data and the environmental timing data into a pre-trained load capacity prediction model, so that the load capacity prediction model outputs load capacity prediction data. The flow rate limit determination module is used to determine the dynamic flow rate operating limit based on the flow rate prediction data; The optimization model construction module is used to construct a power grid dispatch optimization model with the goal of minimizing the total power generation cost, based on the dynamic current carrying capacity operating limit and the power grid balance steady state, to determine the constraints of the optimization goal. The scheduling optimization module is used to find the optimal solution for the power grid scheduling optimization model, determine the power grid scheduling optimization scheme based on the optimal solution, and enable the power grid to execute the power grid scheduling optimization scheme.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the power grid dispatch optimization method considering the dynamic carrying capacity of lines as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the steps of the power grid dispatch optimization method considering the dynamic carrying capacity of lines as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the power grid dispatch optimization method considering line dynamic current carrying capacity as described in any one of claims 1-6.

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