New energy power station output and power grid bearing capacity matching method, device, equipment and medium

Abstract

The invention belongs to the technical field of new energy power plant output, and particularly relates to a new energy power plant output and power grid bearing capacity matching method and device, equipment and a medium, and the method comprises the following specific steps: obtaining real-time operation data and meteorological prediction information of a new energy power plant to carry out power plant output prediction, and obtaining a new energy output prediction result; power grid operation parameters are obtained, load flow calculation and stability analysis are carried out, and power grid bearing capacity sequences in different time periods are determined; inputting the new energy output prediction result and a power grid bearing capacity sequence into a pre-established matching optimization model, and outputting a matching optimization result; and generating a power scheduling instruction of the new energy power station based on the matching optimization result. And by fusing output prediction and bearing capacity evaluation, taking a new energy output prediction result and a power grid bearing capacity sequence as input, performing multi-objective optimization calculation, generating a power scheduling instruction and performing real-time adjustment, thereby realizing dynamic matching of the new energy output and the power grid bearing capacity.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy power plant output, and particularly relates to a new energy power station output and power grid carrying capacity matching method, device, equipment and medium. BACKGROUND

[0002] With the rapid development of wind power, photovoltaic and other new energy power stations, their grid-connected scale is continuously expanding, but due to the intermittent, fluctuating and random characteristics of new energy power generation, there is often a mismatch between power station output and power grid carrying capacity. On the one hand, new energy output is significantly affected by weather conditions and is difficult to stabilize and control; on the other hand, the carrying capacity of the power grid is subject to tidal flow distribution, equipment operating limits and system stability, and has obvious time-varying characteristics. If the relationship between the two cannot be effectively coordinated, it is easy to cause problems such as power grid voltage out-of-limit, frequency fluctuation and large-scale wind and light abandonment, affecting the level of new energy consumption and the safe operation of the power grid. SUMMARY

[0003] The purpose of the present application is to provide a new energy power station output and power grid carrying capacity matching method, device, equipment and medium, which solves the problem of matching between power grid carrying capacity and new energy output in the background art.

[0004] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: In a first aspect of the present application, a new energy power station output and power grid carrying capacity matching method is provided, comprising the following specific steps: Obtaining real-time operation data and weather forecast information of the new energy power station to predict the power station output and obtain a new energy output prediction result; Obtaining power grid operation parameters to perform tidal flow calculation and stability analysis and determine a power grid carrying capacity sequence in different time periods; Inputting the new energy output prediction result and the power grid carrying capacity sequence into a pre-established matching optimization model to output a matching optimization result; The matching optimization model is a multi-objective optimization function with system safety, economy and new energy consumption rate as optimization objectives, and the multi-objective optimization function includes constraint conditions; Based on the matching optimization result, a power dispatching instruction of the new energy power station is generated and issued to each execution end to adaptively adjust the power station output.

[0005] Preferably, it also includes real-time monitoring of power grid operation state and new energy output to capture sudden fluctuation points; After reliability verification of the sudden fluctuation points, the power dispatching instruction is real-time corrected according to the fluctuation range to dynamically match the new energy output and the power grid carrying capacity.

[0006] Preferably, in the step of obtaining new energy power station real-time operation data and weather forecast information to predict power station output, the new energy output prediction result is obtained by: Based on the equipment operation parameters and environmental monitoring data in the real-time operation data, and the atmospheric state parameters in the weather forecast information, after time sequence feature extraction, the power prediction initial sequence is generated by inputting the prediction model for fusion calculation; The power prediction initial sequence is subjected to uncertainty quantification analysis, and the new energy output prediction result containing a confidence interval is output by probability prediction.

[0007] Preferably, in the step of obtaining grid operation parameters, performing power flow calculation and stability analysis, and determining the grid carrying capacity sequence in different time periods, the step includes: The network topology structure and equipment operation limit value in the grid operation parameters are extracted, combined with real-time load distribution data, dynamic power flow calculation is performed, and the injection power safety boundary of each power transmission channel is obtained; Based on the injection power safety boundary, transient stability analysis and voltage stability analysis are performed, and the grid carrying capacity sequence in different time periods is generated, the grid carrying capacity sequence contains the maximum allowed new energy access capacity in each period.

[0008] Preferably, the multi-objective optimization function is: ; Wherein, is a multi-objective optimization function; is a system safety objective function; is a system economy objective function; is a new energy consumption rate objective function; , , is a target weight coefficient, determined according to system operation strategy and dispatching demand, and satisfies 1; The system safety objective function is: ; Wherein, represents the active power flow of line at time , is the rated power limit of line at time , is the voltage of node at time , is the reference voltage of node , T is the total number of time periods, a total number of transmission lines in the power grid, a total number of nodes in the power grid, i.e., a total number of buses; the system economy objective function is: wherein, represents a generation cost of a conventional power source at time represents a storage charging and discharging cost at time represents a network power loss cost at time the new energy consumption rate objective function is: wherein, is curtailed wind and light power at time is predicted new energy available power at time

[0009] Preferably, the constraint conditions include power balance constraints, output upper and lower limit constraints, ramp rate constraints, storage capacity constraints, and power grid safety constraints.

[0010] Preferably, the step of generating a power dispatch instruction of the new energy power station based on the matching optimization result and issuing the power dispatch instruction to each execution end for adaptive adjustment of the power station output comprises: determining an active power regulation curve and a reactive power regulation curve of the new energy power station according to the matching optimization result; converting to generate a power dispatch instruction based on the active power regulation curve and the reactive power regulation curve.

[0011] In a second aspect of the present application, a new energy power station output and power grid carrying capacity matching method and device are provided, characterized by comprising: an output module configured to obtain real-time operation data and weather forecast information of a new energy power station to perform power station output prediction and obtain a new energy output prediction result; a carrying module configured to obtain power grid operation parameters, perform power flow calculation and stability analysis, and determine a power grid carrying capacity sequence at different time periods; a matching module configured to input the new energy output prediction result and the power grid carrying capacity sequence into a pre-established matching optimization model to output a matching optimization result; the matching optimization model is a multi-objective optimization function with system safety, economy, and new energy consumption rate as optimization objectives, and the multi-objective optimization function includes constraint conditions; a dispatch module configured to generate a power dispatch instruction of the new energy power station based on the matching optimization result and issue the power dispatch instruction to each execution end for adaptive adjustment of the power station output.​​​​​​​

[0012] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method according to any one of claims 1-7. In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores at least one instruction, and the at least one instruction, when executed by a processor, implements the method according to any one of claims 1-7.

[0013] Compared with the prior art, the present application has the following beneficial effects: The present application fuses power output prediction and carrying capacity evaluation, takes the new energy power output prediction result and the power grid carrying capacity sequence as input, performs multi-objective optimization calculation, generates power dispatch instructions and adjusts in real time, realizes the dynamic matching of new energy power output and power grid carrying capacity, solves the technical problems that the new energy power station output fluctuation is strong and the power grid carrying capacity is obviously time-varying in the prior art, and the two lack effective matching mechanism, leading to insufficient new energy consumption and increased power grid operation risk, realizes the dynamic coordination of new energy power output and power grid carrying capacity through adaptive matching, and improves the technical effect of new energy consumption rate and power grid operation stability. BRIEF DESCRIPTION OF DRAWINGS

[0014] The drawings accompanying the specification of the present application form a part thereof, serve to provide further understanding of the present application, and together with the specification explain the exemplary embodiments of the present application and its description, and do not constitute improper limitations on the present application. In the drawings: Figure 1 A flowchart of the new energy power station output and power grid carrying capacity matching method of the present application embodiment 1; Figure 2 A structural block diagram of the new energy power station output and power grid carrying capacity matching device of the present application embodiment 2; Figure 3 A structural block diagram of the electronic device of the present application embodiment 3. DETAILED DESCRIPTION

[0015] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0016] The following detailed description is exemplary description, which is intended to provide further detailed description of the present application. Unless otherwise specified, all technical terms used in the present application have the same meaning as understood by those skilled in the art. The terms used in the present application are only used to describe the specific embodiments, and are not intended to limit the exemplary embodiments according to the present application.

[0017] Embodiment 1 As Figure 1 shown, the application provides a new energy power station output and power grid carrying capacity adaptive matching method, which comprises: S1: obtaining real-time operation data and weather forecast information of the new energy power station to predict the power station output, and obtaining a new energy output prediction result.

[0018] Further, the embodiment of the application step S1 further comprises: S11: based on the equipment operation parameters and environmental monitoring data in the real-time operation data, and the atmospheric state parameters in the weather forecast information, after time sequence feature extraction, input the prediction model for fusion calculation to generate the power prediction initial sequence; S12: performing uncertainty quantification analysis on the power prediction initial sequence, and outputting a new energy output prediction result containing a confidence interval through probability prediction.

[0019] By comprehensively considering the real-time operation data and weather forecast information of the new energy power station, the accurate prediction of the new energy power station output is realized.

[0020] The real-time operation data of the new energy power station is collected, which includes the equipment operation parameters inside the power station, such as the speed of the wind turbine generator, the blade pitch angle, the direct current voltage, the current and the power factor of the photovoltaic inverter, and the environmental monitoring data from the surrounding of the power station, such as wind speed, wind direction, light intensity, temperature and humidity, etc. At the same time, the weather forecast information of the future period is obtained from the weather system, especially the atmospheric state parameters, such as solar radiation intensity, wind speed and direction prediction, temperature change trend, etc. These factors are the key external disturbance variables affecting the new energy power generation output.

[0021] Then, for the above multi-dimensional input data, time sequence feature extraction operation is performed. This operation aims to mine the time correlation and dynamic change law between different data sources, such as identifying the periodic fluctuations in the wind speed sequence, the daily variation characteristics of light intensity, the nonlinear influence of temperature on photovoltaic component efficiency, etc. Through the construction of time sequence feature vector, the multi-source data is uniformly input to the prediction model for fusion calculation. The prediction model can be a deep learning time sequence model, such as long short-term memory network, gated recurrent unit, or time sequence convolution network, and can embed physical mechanism constraints as necessary, such as wind turbine power curve or photovoltaic component I-V characteristic model, to improve the explainability and robustness of the prediction. Through the above fusion calculation, the power prediction initial sequence of the new energy power station in the future period is obtained, which is indexed by time and clearly gives the predicted power value at each time point.

[0022] Next, considering the inherent volatility of new energy output and the uncertainty of weather prediction, the initial prediction sequence is further analyzed for uncertainty quantification. The specific method includes using a probabilistic prediction model, such as a Bayesian neural network, quantile regression, or Monte Carlo sampling prediction framework, to sample and statistically fit the prediction results multiple times, thereby obtaining the value range of power prediction at different confidence levels. Through this analysis, the final output of the new energy output prediction result not only contains a single point prediction value, but also contains the corresponding confidence interval, such as the upper and lower power values at a 95% confidence level, to characterize the fluctuation range and risk boundary of the prediction result. The confidence interval information can provide a basis for power grid safety checking and reserve capacity configuration, thereby ensuring high proportion of new energy consumption while considering the safety and reliability of power grid operation.

[0023] S2: Obtain power grid operation parameters, perform power flow calculation and stability analysis, and determine the power grid carrying capacity sequence in different time periods.

[0024] Further, the step S2 of the embodiments of the present application further comprises: S21: Extract the network topology structure and device operating limit value in the power grid operation parameters, combine the real-time load distribution data, perform dynamic power flow calculation, and obtain the injection power safety boundary of each power transmission channel; S22: Perform transient stability analysis and voltage stability analysis based on the injection power safety boundary, and generate the power grid carrying capacity sequence in different time periods, which contains the maximum allowable new energy access capacity of each period. By obtaining the power grid operation parameters and performing power flow calculation and stability analysis, the power grid carrying capacity sequence in different time periods is determined.

[0025] Specifically, the network topology structure and device operating limit value in the power grid operation parameters are extracted. The network topology structure refers to the connection mode of each electrical device (such as generator, transformer, transmission line, etc.) in the power grid, which determines the transmission path of power in the power grid. The device operating limit value refers to the maximum current, voltage, power, etc. that each electrical device can withstand under safe operating conditions. Combined with real-time load distribution data, i.e. the load condition of each node in the current power grid, dynamic power flow calculation is performed. Dynamic power flow calculation is a power flow calculation method that considers the change of power grid operating state over time, which can more accurately reflect the power distribution of the power grid under different operating conditions. Through dynamic power flow calculation, the node voltage, branch power flow direction and injection power distribution are solved, and the injection power safety boundary of each power transmission channel under the condition of meeting the operating constraints is further derived, i.e. the maximum power injection value that each power transmission channel can withstand under the premise of ensuring safe operation of the power grid.

[0026] Based on the injected power safety boundary, further grid stability analysis is performed, including transient stability analysis and voltage stability analysis. Transient stability analysis studies whether state variables, such as generator rotor angle and power angular velocity, can recover to a stable state within a finite time after the grid is subjected to large disturbances, such as short-circuit faults or equipment tripping. Voltage stability analysis studies whether the grid's node voltage can remain within allowable ranges under load changes or fault conditions. Through these two stability analyses, a grid carrying capacity sequence for different time periods is generated. This sequence includes the maximum allowable renewable energy access capacity for each time period, i.e., the maximum renewable energy output that the grid can accept in each time period while ensuring safe and stable grid operation.

[0027] S3: Input the predicted output of the new energy source and the power grid carrying capacity sequence into the pre-established matching optimization model, and output the matching optimization result.

[0028] Furthermore, in establishing a matching optimization model, step S3 of this embodiment also includes: S31: Use the new energy output prediction results and the power grid carrying capacity sequence as input variables; S32: Construct a multi-objective optimization function with system safety, economy, and renewable energy absorption rate as optimization objectives: ;in, To comprehensively optimize the objective function; This is the objective function for system security, used to characterize the stability of power grid operation and the degree to which constraints are satisfied; This is the objective function for system economics, used to characterize the economic cost of operating the power system; The objective function for the renewable energy absorption rate is used to characterize the maximization of renewable energy utilization. , , The target weight coefficient is determined based on the system operation strategy and scheduling requirements, and satisfies... 1.

[0029] The system security objective function is: ;in, Indicates time Time Line Active power flow, This is the rated power limit of the line. For nodes Voltage, Here, T is the reference voltage, and T is the total number of time periods. This represents the total number of transmission lines in the power grid. The total number of nodes in the power grid, i.e. the total number of busbars, is used to minimize line load rate and voltage deviation, and to ensure the safe operation of the power grid.

[0030] The system's economic objective function is: ;in, This represents the cost of generating electricity from conventional power sources. This indicates the cost of energy storage charging and discharging. This represents the cost of network power loss, and the objective is used to achieve economical system operation.

[0031] The objective function for the new energy consumption rate is: ;in, For a moment The power of wind and solar power curtailment This target is used to maximize the utilization rate of renewable energy, based on the predicted renewable energy generation capacity.

[0032] S33: Set power balance constraints, output upper and lower limit constraints, ramp rate constraints, energy storage capacity constraints, and grid safety constraints, and combine them with the multi-objective optimization function to form a matching optimization model with multi-objective constraints.

[0033] Optionally, a matching optimization model can be constructed. This model takes the power output forecast results of new energy sources and the grid carrying capacity sequence as inputs, and generates matching optimization results through multi-objective optimization calculations to achieve accurate matching between the power output of new energy power plants and the grid carrying capacity. The power output forecast results of new energy sources provide the power level available for grid access in future periods, while the grid carrying capacity sequence limits the maximum acceptable grid access capacity in different time periods. Both, as input variables, can form the basic data for source-grid interaction.

[0034] A multi-objective optimization function is constructed, with system safety, economy, and renewable energy absorption rate as the optimization objectives, as shown in the formula above. System safety ensures the grid maintains stable operation after incorporating renewable energy output, avoiding grid failures caused by fluctuations in renewable energy output. Economy optimizes grid operating costs, including generation costs, energy storage costs, and transmission losses, to achieve economical operation. Renewable energy absorption rate aims to maximize the utilization rate of renewable energy, reduce wind and solar curtailment, and improve the economic and social benefits of renewable energy. As shown in the formula above, the multi-objective optimization function can be established using weighted summation or hierarchical optimization. By quantifying these objectives, a comprehensive optimization objective system is formed to guide subsequent optimization calculations.

[0035] The power balance constraint, the output upper and lower limit constraint, the ramp rate constraint, the energy storage capacity constraint and the power grid safety constraint condition are set, and a multi-objective constraint matching optimization model is formed in combination with a multi-objective optimization function. The power balance constraint is used to ensure real-time balance of active power of the system; the output upper and lower limit constraint can ensure that the output of the new energy power station is within the safe operation range; the ramp rate constraint can constrain the rapid change of the output of the new energy power station, so as to avoid the impact on the power grid; the energy storage capacity constraint considers the capacity limitation of the energy storage system, so as to ensure that the energy storage system can effectively participate in the power grid regulation; and the power grid safety constraint can further ensure the stable operation of the power grid after receiving the new energy output.

[0036] For example, the power balance constraint is: ; wherein, is the active power of the conventional unit, is the charge and discharge power of the energy storage system, is the actual output power of the new energy power station, is the system load power, i.e. the user electricity demand, is the system network loss power.

[0037] The output upper and lower limit constraint is: ; wherein, is the minimum allowable output of the new energy power station at time , is the maximum allowable output of the new energy power station at time , is the actual output power of the new energy power station at time .

[0038] The ramp rate constraint is: ; wherein, , is the output power of the new energy power station at adjacent two times, is the maximum ramp rate allowed by the new energy output.

[0039] The energy storage capacity constraint is: ; wherein, is the residual energy capacity of the energy storage system at time , is the minimum energy capacity allowed by the energy storage system, is the maximum energy capacity of the energy storage system.

[0040] The power grid safety constraint is: ; wherein, is the voltage amplitude of node at time , , is the voltage amplitude of node a minimum and a maximum value of the voltage allowed, for a power transmission line at a time instant of active power flow, for a power transmission line a rated power transmission limit. On the basis of the above constraints, a multi-objective optimization function is combined with the constraint set to form a complete multi-objective constraint optimization model. Through the calculation of the model, an optimal new energy power station output scheduling scheme can be generated under the premise of meeting all the constraints, thereby realizing the adaptive matching of the new energy power station output and the power grid carrying capacity, and improving the stability of the power grid and the utilization rate of new energy.

[0041] Further, the new energy output prediction result and the power grid carrying capacity sequence are input to perform multi-objective optimization calculation to generate a matching optimization result. The step S30 of the embodiment of the application further includes: S34: Spatiotemporal alignment of the confidence interval of the new energy output prediction result and the power grid carrying capacity sequence is performed to construct a multi-period optimization scenario; and S35: Based on the multi-period optimization scenario, iterative calculation is performed on the multi-objective optimization function and the constraint condition of the matching optimization model to output a matching optimization result containing an optimal power distribution scheme.

[0042] As a preferred example of the above embodiment, the process of multi-objective optimization calculation can be further refined to ensure that the generated matching optimization result is more scientific and practical.

[0043] Before multi-objective optimization calculation, the confidence interval of the new energy output prediction result is first spatiotemporally aligned with the power grid carrying capacity sequence to construct a multi-period optimization scenario. Spatiotemporal alignment means that in the time dimension, the output range under different confidence levels in the prediction result is one-to-one corresponding to the carrying capacity of the power grid at the corresponding time instant; and in the space dimension, the output prediction interval of different power stations or different nodes is matched with the carrying capacity of the corresponding regional power grid. Through this alignment process, a multi-period optimization scenario covering multiple future periods and considering the upper and lower bounds of prediction uncertainty can be generated. Specifically, the confidence interval of the new energy output prediction is matched with the power grid carrying capacity sequence according to the time stamp to construct optimization scenarios for multiple periods. Each optimization scenario contains the possible output range of the new energy power station and the maximum output value that can be accepted by the power grid in a specific period, thereby providing an explicit spatiotemporal framework for subsequent optimization calculation.

[0044] Then, based on the constructed multi-period optimization scenario, an iterative calculation is performed by matching the multi-objective optimization function of the optimization model with the constraint conditions. In this process, the multi-objective optimization function comprehensively considers multiple objectives such as system safety, economy, and new energy consumption rate, while the constraint conditions include output upper and lower limits, climbing rate restrictions, energy storage capacity constraints, and grid safety constraints. The iterative process can use heuristic intelligent algorithms (such as genetic algorithms, particle swarm optimization algorithms), mixed integer programming methods, or dynamic optimization methods based on reinforcement learning to converge to an approximate global optimal solution within an acceptable calculation time. Specifically, starting from the initial power allocation scheme, the output allocation of the new energy power station is gradually adjusted so that the multi-objective optimization function reaches the optimal value. Each iteration updates the power allocation scheme based on the feedback of the optimization function and checks whether all constraint conditions are met. When the preset number of iterations or the optimization function value converges, the iterative calculation ends. Finally, the matching optimization result containing the optimal power allocation scheme is output. This result not only gives the optimal output value of the new energy power station in each time period, but also considers the uncertainty of new energy output (reflected through the confidence interval), thereby providing more accurate and reliable decision-making basis for grid dispatching, achieving adaptive matching of new energy power station output and grid carrying capacity, and improving the stability of grid operation and the consumption efficiency of new energy.

[0045] S4: Based on the matching optimization result, generate power dispatch instructions for the new energy power station and issue them to each execution end for adaptive adjustment of the power station output.

[0046] Further, the step S4 of the embodiments of the present application further includes: S41: Determine the active power regulation curve and the reactive power regulation curve of the new energy power station according to the matching optimization result; S42: Convert the active power regulation curve and the reactive power regulation curve to generate power dispatch instructions.

[0047] Optionally, the optimization calculation result is converted into executable control actions, thereby realizing adaptive adjustment of the new energy power station output to the grid carrying capacity.

[0048] First, the active power regulation curve and the reactive power regulation curve of the new energy power station are determined according to the matching optimization result. The matching optimization result contains the optimal output value of the new energy power station in each time period, which includes both active power and reactive power. The optimal output values are arranged in time sequence to form the active power regulation curve and the reactive power regulation curve. The active power regulation curve is used to describe the power output reference value and its dynamic change range of the power station at each time, to ensure that the output level meets the carrying capacity requirements of the power grid in the corresponding period; the reactive power regulation curve is used to constrain the reactive output of the inverter or generator of the power station, so as to meet the stability requirements of the power grid operation in voltage support and power factor regulation. The above curves not only contain static set values, but also include dynamic characteristics such as power change rate and ramp limit, to prevent the impact on the power grid caused by too fast fluctuation of the output.

[0049] Then, the power scheduling instruction is converted based on the active power regulation curve and the reactive power regulation curve. Specifically, the data points in the active power regulation curve and the reactive power regulation curve are converted into specific scheduling instructions. These instructions include the active power and reactive power values that the new energy power station needs to adjust in each time period. The format of the instructions should meet the communication protocol and control interface requirements of each execution end (such as inverters, converters, etc.), so that they can be accurately received and executed. The generated power scheduling instruction is issued to each execution end of the new energy power station through the communication network, and each execution end automatically adjusts its output power according to the received instruction, realizing the adaptive adjustment of the output of the new energy power station.

[0050] Further, the step S4 of the embodiment of the present application further includes: S43: Real-time monitoring of power grid operation state and new energy output, capturing sudden fluctuation points; S44: After reliability verification of the sudden fluctuation points, real-time correction of the power scheduling instruction according to the fluctuation range, dynamic matching of new energy output and power grid carrying capacity.

[0051] Specifically, the generation and execution process of the power scheduling instruction can be further expanded, and a dynamic response mechanism for sudden fluctuations is established by real-time monitoring of the power grid operation state and the new energy output, to ensure that the dynamic matching of the new energy power station output and the power grid carrying capacity is more flexible and reliable.

[0052] In the process of executing the power scheduling instruction, the grid operation state and the new energy output are monitored in real time. Through sensors and monitoring devices deployed in the grid and new energy power stations, real-time data such as voltage, current, frequency of the grid, and actual power generation, equipment operation state of the new energy power station are collected. Through real-time processing and analysis of these data, sudden fluctuation points in the grid operation state and the new energy output are captured in time, such as sudden increase in short-time wind speed, sharp decay of light, line fault or load surge, etc. These fluctuation points may be caused by weather changes, equipment failure, load mutation, etc.

[0053] For the captured sudden fluctuation points, reliability verification is performed, that is, by comparing the multi-source monitoring data with the historical characteristic curve, it is confirmed that the fluctuation belongs to the real working condition rather than measurement error or communication noise. The verification method can include comparison with historical data, consistency check with data of other monitoring points, etc. After confirming the effectiveness of the fluctuation point, according to its fluctuation range and duration, combined with the current carrying capacity dynamic threshold of the grid, the existing power scheduling instruction is modified in real time. The purpose of the modification is to adjust the output of the new energy power station to adapt to the change of the carrying capacity of the grid, and to ensure the stable operation of the grid. Specific modification measures can include: in the case of small fluctuation amplitude and fast adjustment by energy storage or reactive power compensation equipment, directly adjusting the active / reactive power output reference value; in the case of large fluctuation amplitude that may threaten the safety of the grid, triggering the reduced capacity operation or rapid reduction instruction of the overall output of the power station to prevent system instability. The modified power scheduling instruction will be immediately issued to each execution end, and each execution end will adjust its operation state according to the new instruction, so as to realize the dynamic matching of the new energy output and the carrying capacity of the grid.

[0054] Through the above dynamic modification mechanism, the new energy output can be ensured to realize continuous dynamic matching with the carrying capacity of the grid in actual operation, and the stability of the grid operation and the consumption efficiency of the new energy are improved.

[0055] Embodiment 2 As Figure 2 shown, based on the same inventive concept as the above embodiment, the present application also provides a new energy power station output and grid carrying capacity matching device, comprising: An output module is configured to obtain real-time operation data and weather forecast information of the new energy power station to predict the power station output, and obtain a new energy output prediction result. A carrying module is configured to obtain grid operation parameters, perform power flow calculation and stability analysis, and determine a grid carrying capacity sequence in different time periods. The matching module is used to input the new energy output prediction results and the power grid carrying capacity sequence into a pre-established matching optimization model, and output the matching optimization results; the matching optimization model is a multi-objective optimization function with system safety, economy and new energy absorption rate as optimization objectives, and the multi-objective optimization function includes constraints. The scheduling module is used to generate power scheduling instructions for new energy power plants based on the matching optimization results, and send them to each execution terminal to adaptively adjust the power output of the power plants.

[0056] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for realizing a method of matching the output of a new energy power plant with the carrying capacity of the power grid; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0057] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for matching the output of new energy power plants with the grid carrying capacity in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0058] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0059] The at least one processor 102 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 components, discrete hardware components, or the like. The processor 102 can be a microprocessor or can also be any conventional processor. The processor 102 is a control center of the electronic device 100, and is connected to various parts of the electronic device 100 through various interfaces and lines.

[0060] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a new energy power plant output and power grid carrying capacity matching method. The processor 102 can execute the plurality of instructions to implement the following: Obtain real-time operation data and weather prediction information of the new energy power plant to predict the power plant output, and obtain a new energy output prediction result; Obtain power grid operation parameters, perform power flow calculation and stability analysis, and determine a power grid carrying capacity sequence in different time periods; Input the new energy output prediction result and the power grid carrying capacity sequence into a pre-established matching optimization model, and output a matching optimization result. The matching optimization model is a multi-objective optimization function with system safety, economy and new energy consumption rate as optimization objectives, and the multi-objective optimization function includes constraint conditions; Based on the matching optimization result, generate a power dispatch instruction of the new energy power plant, and issue the power dispatch instruction to each execution end to adaptively adjust the power plant output.

[0061] Embodiment 4 The modules / units integrated in the electronic device 100, if implemented in the form of software function units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment described above 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. The computer-readable medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, and read-only memory (ROM).

[0062] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0064] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0065] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0066] In the description of the present specification, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered in the protection scope of the claims of the present application.

Claims

1. A method for matching the output of a new energy power plant with the carrying capacity of the power grid, characterized in that, The specific steps include the following: Real-time operation data and meteorological forecast information of new energy power plants are obtained to predict the power plant output and obtain the new energy output prediction results. Obtain power grid operating parameters, perform power flow calculations and stability analysis, and determine the power grid carrying capacity sequence for different time periods; The predicted output of new energy sources and the power grid carrying capacity sequence are input into a pre-established matching optimization model, and the matching optimization results are output. The matching optimization model is a multi-objective optimization function with the optimization objectives of system safety, economy and new energy consumption rate. The multi-objective optimization function includes constraints. Based on the matching optimization results, power scheduling instructions for new energy power plants are generated and sent to each execution terminal to adaptively adjust the power plant output.

2. The method for matching the output of a new energy power plant with the grid carrying capacity as described in claim 1, characterized in that, It also includes real-time monitoring of power grid operation status and new energy output, and capturing sudden fluctuations; After verifying the reliability of the sudden fluctuation point, the power dispatch command is modified in real time according to the fluctuation range to dynamically match the output of new energy sources with the grid carrying capacity.

3. The method for matching the output of a new energy power plant with the grid carrying capacity as described in claim 1, characterized in that, The step of obtaining real-time operating data and meteorological forecast information of the new energy power plant to predict the power plant output and obtain the new energy output prediction result includes: Based on the equipment operating parameters and environmental monitoring data in the real-time operating data, as well as the atmospheric state parameters in the meteorological forecast information, after extracting time-series features, the data are input into the prediction model for fusion calculation to generate the initial power prediction sequence. Uncertainty quantification analysis is performed on the initial power prediction sequence, and the new energy output prediction results containing confidence intervals are output through probability prediction.

4. The method for matching the output of a new energy power plant with the grid carrying capacity as described in claim 1, characterized in that, The steps of obtaining power grid operating parameters, performing power flow calculations and stability analysis, and determining the power grid carrying capacity sequence for different time periods include: The network topology and equipment operating limits are extracted from the power grid operating parameters, and combined with real-time load distribution data, dynamic power flow calculation is performed to obtain the injection power safety boundary for each transmission channel. Based on the injected power safety boundary, transient stability analysis and voltage stability analysis are performed to generate a grid carrying capacity sequence for different time periods. The grid carrying capacity sequence includes the maximum allowable new energy access capacity for each time period.

5. The method for matching the output of a new energy power plant with the grid carrying capacity as described in claim 1, characterized in that, The multi-objective optimization function is: ； in, It is a multi-objective optimization function; The objective function for system security; The objective function is the system's economic performance. The objective function is the renewable energy consumption rate. , , The target weight coefficient is determined based on the system operation strategy and scheduling requirements, and satisfies... 1; The system security objective function is: ； in, Indicates time Time Line Active power flow, For a moment Time Line The rated power limit, For a moment Time node Voltage, For nodes Reference voltage, T This represents the total number of time periods. This represents the total number of transmission lines in the power grid. This represents the total number of nodes in the power grid, i.e., the total number of busbars. The system's economic objective function is: ； in, Indicates time The cost of generating electricity from conventional power sources, Indicates time The cost of energy storage charging and discharging. Indicates time Network power loss cost; The objective function for the new energy consumption rate is: ; in, For a moment The power of wind and solar power curtailment For a moment The predicted renewable energy generation capacity.

6. The method for matching the output of a new energy power plant with the grid carrying capacity as described in claim 1, characterized in that, The constraints include power balance constraints, upper and lower limits of output constraints, ramp rate constraints, energy storage capacity constraints, and grid security constraints.

7. The method for matching the output of a new energy power plant with the grid carrying capacity as described in claim 1, characterized in that, The step of generating power dispatch instructions for new energy power plants based on the matching optimization results and issuing them to each execution terminal to adaptively adjust the power plant output includes: The active power regulation curve and reactive power regulation curve of the new energy power station are determined based on the matching optimization results. Based on the active power adjustment curve and the reactive power adjustment curve, a power dispatch command is generated.

8. A method and apparatus for matching the output of a new energy power plant with the carrying capacity of the power grid, characterized in that, include: The power output module is used to acquire real-time operating data and meteorological forecast information of the new energy power plant to predict the power plant's output and obtain the new energy power output prediction results. The load-bearing module is used to acquire power grid operating parameters, perform power flow calculations and stability analysis, and determine the power grid carrying capacity sequence for different time periods. The matching module is used to input the new energy output prediction results and the power grid carrying capacity sequence into a pre-established matching optimization model, and output the matching optimization results. The matching optimization model is a multi-objective optimization function with system safety, economy and new energy consumption rate as optimization objectives. The multi-objective optimization function includes constraints. The scheduling module is used to generate power scheduling instructions for new energy power plants based on the matching optimization results, and send them to each execution terminal to adaptively adjust the power output of the power plants.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the method for matching the output of a new energy power plant with the grid carrying capacity 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 at least one instruction, which, when executed by a processor, implements the method for matching the output of a new energy power plant with the carrying capacity of the power grid as described in any one of claims 1 to 7.

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