A power grid operation monitoring method based on multi-energy coupling

Abstract

The present application belongs to the technical field of multi-energy coupling power grid monitoring, and discloses a power grid operation monitoring method based on multi-energy coupling. The present application predicts wind power by constructing a wind speed-wind power output mapping relationship, and analyzes the regional aggregation fluctuation rate in combination with the spatial distribution of the wind power generation equipment, and finally forms a dynamic credible domain of wind power output. The wind power output prediction is more in line with the actual operation scene, and provides more accurate basic data for risk identification. The present application outputs a feasible domain of thermal power output adjustment by comprehensively considering the historical operation records and technical output safety range of the thermal power generation equipment. Both the technical limit of the equipment and the constraint of the actual adjustment speed are considered, so that the judgment of the thermal power adjustment capacity is more comprehensive and more in line with the actual operation conditions. The present application outputs hierarchical scheduling instructions for the risk scenarios of lack or surplus, improves the pertinence and economy of scheduling, and improves the stability and efficiency of power grid operation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of multi-energy coupling power grid monitoring, and relates to a power grid operation monitoring method based on multi-energy coupling. BACKGROUND

[0002] With the continuous advancement of energy structure transformation, multi-energy forms such as wind power generation and thermal power generation are increasingly coupled to the power grid. In the process of power grid operation, wind power is strongly volatile and uncertain due to natural conditions, while thermal power is relatively stable but has technical constraints in regulation capacity, and the collaborative operation of the two directly affects the safety and economy of the power grid.

[0003] In a multi-energy coupled power grid system, accurately monitoring and predicting the output of various types of energy, timely identifying potential output risks and effectively optimizing power dispatching are key to ensuring stable and efficient operation of the power grid. Therefore, a power grid operation monitoring method that considers the characteristics of multiple energies is needed to cope with complex energy coupling scenarios.

[0004] The prior art only relies on a single wind speed-output mapping relationship for prediction, without considering the influence of the spatial distribution of wind power generation equipment on output fluctuations, which reduces the accuracy of wind power prediction and leads to a large deviation between the predicted results and the actual output, making it difficult to support subsequent risk judgment.

[0005] The prior art only focuses on the technical output range of thermal power equipment, ignoring the variable load rate constraint in historical operation records, which leads to a one-sided analysis of thermal power regulation capacity and inaccurate judgment of the thermal power regulation feasible region, affecting the effectiveness of the dispatching instructions.

[0006] The prior art does not match and analyze the wind power dynamic output range and the thermal power regulation feasible region, making it difficult to comprehensively identify risk scenarios such as output shortages or surpluses, which may lead to dispatching lag or misjudgment. The dispatching strategy for output risks lacks hierarchical and detailed operation instructions, and cannot take optimal measures according to the risk level, reducing the efficiency of power grid operation. SUMMARY

[0007] In view of this, to solve the problems raised in the background art, a power grid operation monitoring method based on multi-energy coupling is proposed.

[0008] The purpose of the application can be achieved by the following technical solution: a power grid operation monitoring method based on multi-energy coupling, comprising: wind power short-term prediction correction: obtaining the predicted wind speed at the location of the wind power plant in the next monitoring period, outputting the predicted wind power based on the pre-constructed wind speed-wind power output mapping relationship, analyzing the regional aggregation fluctuation rate based on the spatial distribution of wind power generation equipment, and comprehensively outputting the wind power output dynamic credible region.

[0009] Thermal power regulation capability analysis: based on the historical operation record of thermal power equipment, the variable load rate constraint value is analyzed, and the thermal power output regulation feasible region is output in combination with the allowed technical output safety range of the thermal power equipment.

[0010] Output risk situation identification: the wind power output dynamic credible region and the thermal power output regulation feasible region are matched, and whether there is an output risk is judged in combination with the predicted actual load demand of the target power consumption unit, the specific output risk scenario is identified, including output shortage and output surplus.

[0011] Power dispatching optimization analysis: output power dispatching optimization instructions for specific output risk scenarios, and perform power dispatching optimization operations based on the instructions.

[0012] Compared with the prior art, the beneficial effects of the present application are as follows: (1) The present application outputs the predicted wind power by constructing the wind speed-wind power mapping relationship, and analyzes the regional aggregation fluctuation rate in combination with the spatial distribution of the wind power equipment, and finally forms the wind power output dynamic credible region. The wind power output prediction is more in line with the actual operation scenario, and provides more accurate basic data for risk identification.

[0013] (2) The present application outputs the thermal power output regulation feasible region by comprehensively analyzing the historical operation record and the technical output safety range of the thermal power equipment. Both the technical limit of the equipment and the constraint of the actual regulation speed are considered, so that the judgment of the thermal regulation capability is more comprehensive and more in line with the actual operation conditions.

[0014] (3) The present application can accurately judge whether there is an output risk and the specific risk scenario by matching the wind power output dynamic credible region and the thermal regulation feasible region, and in combination with the predicted load demand of the target power consumption unit. Compared with the prior art, it covers multiple risk scenarios under the coupling scenario of wind and thermal energy, and avoids the risk omission caused by single energy dimension analysis.

[0015] (4) The present application outputs hierarchical dispatching instructions for shortage or surplus risk scenarios, improves the pertinence and economy of dispatching, and improves the stability and efficiency of power grid operation. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 The method steps of the present application are illustrated in the schematic diagram.

[0018] Figure 2 This is a schematic diagram illustrating the implementation steps of the wind power prediction analysis according to one embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram illustrating the implementation steps of a regional aggregate volatility analysis according to one embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram illustrating the implementation steps of the variable load rate constraint value analysis in one embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0022] Please see Figure 1 As shown, the present invention provides a power grid operation monitoring method based on multi-energy coupling, including: short-term wind power prediction correction: obtaining the predicted wind speed at the location of the wind power plant in the next preset monitoring period, outputting the predicted wind power based on the pre-constructed wind speed-wind power output mapping relationship, analyzing the regional aggregate volatility based on the spatial distribution location of the wind power equipment, and comprehensively outputting the dynamic credibility domain of wind power output.

[0023] It should be explained that the next monitoring period is the target time range for short-term wind power forecasting correction, which means that the predicted wind speed at the location of the wind farm needs to be obtained during this period. It is also the target period for thermal power regulation capacity analysis, output risk identification, and power dispatch optimization analysis. All analyses revolve around wind power output, thermal power regulation capacity, and load demand during this period, and the final output power dispatch optimization instructions are also used for grid operation regulation during this period.

[0024] The specific duration of the next monitoring period can be set according to the actual power grid monitoring needs.

[0025] It should be noted that the predicted wind speed is wind speed forecast data obtained through meteorological forecasting systems, professional wind speed prediction models, or related meteorological data services for the next preset monitoring period, corresponding to the specific location of the wind power plant. In a preferred embodiment, the analysis not only considers wind speed but also the effectiveness analysis of the corresponding wind direction.

[0026] For a preferred embodiment of the present invention, please refer to Figure 2As shown, the specific analysis method for predicting wind power is as follows: A11, collect the measured wind speed data and the corresponding actual wind power output of the wind power plant within the preset reference time span.

[0027] It should be noted that the preset reference time span is a manually set continuous time interval used to collect historical operational data, such as the past 30 days. Its length can be adjusted according to actual needs, but it must include enough historical data to support statistical analysis. If the time span is too short, the statistical results may be significantly biased due to insufficient data, failing to reflect long-term operational patterns. If the time span is too long, it may include outdated data, such as performance differences before and after equipment aging, causing the analysis results to be inconsistent with the current equipment status, while also increasing data processing costs.

[0028] A12. Sort the wind speed values ​​in ascending order and divide them into several continuous wind speed intervals. Match each measured wind speed with each wind speed interval to obtain the data set corresponding to each wind speed interval.

[0029] Preferably, all measured wind speeds are sorted from smallest to largest and divided into several continuous wind speed intervals, including , , ...among them, wind speeds of 3.5 m / s are included. The interval is a data set that includes all wind speeds and their corresponding power values ​​that fall within this range.

[0030] A13. The average value of the actual wind power output corresponding to each measured wind speed in the data set corresponding to each wind speed range is used to calculate the wind power output corresponding to each wind speed range.

[0031] A14. Based on the predicted wind speed at the location of the wind power plant within a preset monitoring period, construct the predicted wind speed change curve corresponding to the wind power plant, and uniformly distribute several monitoring points on the curve to construct a predicted wind speed dataset.

[0032] A15. Match each predicted wind speed value in the predicted wind speed dataset with each wind speed interval to obtain the predicted wind power output corresponding to each predicted wind speed value.

[0033] A16. The predicted wind power is obtained by averaging the predicted wind power output corresponding to each predicted wind speed value.

[0034] It should be noted that by averaging the values ​​from multiple monitoring points, potential biases in a single predicted wind speed value are offset, such as power deviations caused by overestimating or underestimating the wind speed at a particular point in time, resulting in more stable results. The final output predicted wind power is the average predicted value for the entire next monitoring period, rather than an instantaneous value at a specific moment. This better aligns with the analytical needs of power grid operation monitoring for the overall power output level over a given period, providing a foundational average for constructing the subsequent dynamic reliability domain of wind power output.

[0035] For a preferred embodiment of the present invention, please refer to Figure 3 As shown, the specific analysis method of the regional aggregate volatility is as follows: A21, collect the actual wind power output power of each wind power generation device in the wind power plant within the preset reference time span.

[0036] A22. The average wind power output power corresponding to each historical wind power data record is obtained by averaging the actual wind power output power of each wind power generation device.

[0037] A23. Calculate the relative deviation between the actual wind power output power and the average wind power output power of each wind power generation device to obtain the wind power output power fluctuation of each wind power generation device, and then classify them according to the sign of the sign.

[0038] A24. The average value of the wind power output power fluctuation of each wind power generation device corresponding to each historical wind power data record is calculated according to the sign to obtain the positive region aggregate volatility and the negative region aggregate volatility.

[0039] It should be noted that the analysis of regional aggregate volatility essentially quantifies the extent to which wind power output within a region may be higher or lower than the predicted mean by distinguishing between positive and negative fluctuations. This compensates for the deficiency that predicted power alone cannot reflect the range of fluctuations. This parameter is a key basis for subsequently constructing the dynamic credibility domain of wind power output, expanding the prediction of wind power output from a single mean to a credibility interval that includes the range of fluctuations, thus better reflecting the uncertainty characteristics of wind power output.

[0040] In a preferred embodiment of the present invention, the specific analysis method of the dynamic credibility domain of wind power output is as follows: the predicted wind power is multiplied by the positive region aggregate volatility and the negative region aggregate volatility to obtain the positive predicted wind power adjustment value and the negative predicted wind power adjustment value.

[0041] The upper limit and lower limit of wind power output are calculated by summing the predicted wind power output with the positive and negative predicted wind power output adjustment values, respectively.

[0042] A dynamic credibility domain for wind power output is constructed, wherein the dynamic credibility domain for wind power output is greater than the lower limit of wind power output and less than the upper limit of wind power output.

[0043] It should be noted that the analysis of the dynamic reliability domain of wind power output is necessary because wind power output is highly volatile and uncertain due to the influence of natural conditions and the spatial distribution of equipment. A single predicted power output cannot reflect the range of fluctuations. This reliability domain, by determining the upper and lower limits of output, clarifies the possible fluctuation range of wind power output in the next monitoring period, providing a reliable basis for identifying output risks. This ensures accurate assessment of the matching between wind power output and the feasible regulation domain of thermal power and load demand, thereby supporting the scientific nature of power dispatch optimization, avoiding misjudgments of risks or dispatch failures due to deviations in a single predicted value, and ensuring the stable and economical operation of the power grid.

[0044] It should be noted that this invention predicts wind power output by constructing a wind speed-wind power output mapping relationship, and combines this with regional aggregate volatility analysis of the spatial distribution of wind power generation equipment to ultimately form a dynamic credibility domain for wind power output. This makes wind power output prediction more closely aligned with actual operating scenarios and provides more accurate basic data for risk identification.

[0045] Analysis of thermal power regulation capacity: Based on the analysis of the historical operation records of thermal power generation equipment, the load rate constraint value is analyzed, and the allowable technical output safety range of thermal power generation equipment is combined to output the feasible domain of thermal power output regulation.

[0046] For a preferred embodiment of the present invention, please refer to Figure 4 As shown, the specific analysis process of the variable load rate constraint value is as follows: B1. Obtain the actual variable load span value and variable load operation duration corresponding to each variable load operation from the historical operation records of thermal power generation equipment.

[0047] B2. Divide the actual load change span value corresponding to each load change operation by the load change operation duration to obtain the actual load change rate corresponding to each load change operation.

[0048] B3. Arrange the actual load rate corresponding to each load change operation in ascending order, remove load change operations that are greater than the rated load rate, and calculate the average of the actual load rates corresponding to the remaining load change operations to obtain the load rate constraint value.

[0049] It should be noted that the analysis of the load factor constraint value is because the regulation capacity of thermal power equipment is limited by historical operating conditions, and rated parameters alone cannot reflect the true regulation speed. By extracting the span and duration of historical load factor operations, calculating the actual load factor, and averaging the values ​​after removing those exceeding the rated values, the upper limit of the stable regulation speed of the equipment can be determined. This value is the core parameter for defining the feasible domain of thermal power output regulation. It can accurately reflect the actual adjustable output range of thermal power within a specific time period, combined with the technical output range. This provides a basis for subsequent matching of the wind power reliability domain, identification of output risks, and formulation of dispatch instructions, ensuring that the thermal power regulation capacity analysis is consistent with reality and guaranteeing the effectiveness of grid dispatch.

[0050] In a preferred embodiment of the present invention, the specific analysis method of the feasible domain of thermal power output adjustment is as follows: obtain the interval between the start time of the preset monitoring period and the current time, and calculate the maximum adjustment amount of thermal power output by multiplying the variable load rate constraint value.

[0051] The maximum adjustment range of thermal power output is constructed based on the total output power of current thermal power generation equipment and the maximum adjustment amount of thermal power output.

[0052] In one embodiment, the maximum adjustment range of thermal power output is constructed as follows: taking the total output power of the current thermal power generation equipment as a benchmark, the upper limit of the maximum adjustment range of thermal power output is the sum of the total output power of the current thermal power generation equipment and the maximum adjustment amount of thermal power output, and the lower limit of the maximum adjustment range of thermal power output is the difference between the total output power of the current thermal power generation equipment and the maximum adjustment amount of thermal power output.

[0053] By comparing the maximum adjustable range of thermal power output with the safe range of allowable technical output of thermal power generation equipment, the feasible range for adjusting thermal power output is output.

[0054] In one embodiment, the feasible region for adjusting thermal power output is constructed as follows: the maximum adjustable range of thermal power output is compared with the allowable technical output safety range of the thermal power generating equipment, and the intersection of the two is taken as the final feasible region for adjusting thermal power output. Specifically: if the upper limit of the maximum adjustable range exceeds the upper limit of the technical safety range, the upper limit of the feasible region is the upper limit of the technical safety range; if the lower limit of the maximum adjustable range is lower than the lower limit of the technical safety range, the lower limit of the feasible region is the lower limit of the technical safety range; if the maximum adjustable range is completely within the technical safety range, the feasible region is that range.

[0055] It should be noted that comparing the maximum adjustable range of thermal power output with the permissible safe range of technical output is to ensure that the analysis of thermal power regulation capacity is both feasible and safe. The maximum adjustable range is derived based on load factor constraints and interval duration, reflecting the output range under the limitation of regulation speed; the safe range of technical output is the limit boundary of the physical operation of the equipment. The feasible region obtained by taking the intersection of the comparisons conforms to the actual regulation speed and does not exceed the safety limits of the equipment. It can truly reflect the adjustable capacity of thermal power in a specific period, providing an accurate basis for subsequent matching of the wind power reliability region, identification of output risks, and formulation of dispatch instructions. This avoids dispatch instructions exceeding the equipment capacity due to ignoring technical limits, and ensures the safe and stable operation of the power grid.

[0056] It should be noted that this invention outputs a feasible range for regulating thermal power output by comprehensively considering the historical operating records and technical output safety range of the thermal power generation equipment. This approach takes into account both the technical limits of the equipment and the constraints of its actual regulation speed, ensuring a more comprehensive assessment of the thermal power regulation capability that better reflects actual operating conditions.

[0057] Output risk identification: Match the dynamic credibility domain of wind power output with the feasible domain of thermal power output adjustment, and combine the predicted actual load demand of the target power user to determine whether there is output risk, and identify specific output risk scenarios, including output deficit and output surplus.

[0058] In a preferred embodiment of the present invention, the specific method for determining whether there is a risk of power output is as follows: the predicted actual load demand of the target power-consuming unit in the next monitoring period is summed with the preset allowable power fluctuation adjustment threshold to obtain the predicted actual load demand correction amount.

[0059] It should be explained that predicted actual load demand refers to a preliminary estimate of the total amount of electricity actually consumed by a target electricity-consuming unit within a specific monitoring period. It is calculated based on multiple dimensions of factors such as historical electricity consumption data, current economic activities, meteorological conditions, and user electricity consumption habits, using tools such as statistical models and machine learning algorithms, and reflects the basic electricity demand scale on the consumption side in the future period.

[0060] It's important to explain that the permissible power fluctuation regulation threshold is a pre-set, acceptable upper limit of the deviation between the actual and predicted power load values ​​during grid operation. It serves as a flexible buffer to address load forecasting errors and sudden fluctuations on the demand side. The size of this threshold needs to be determined by considering factors such as grid regulation capacity and demand-side stability: grids with strong regulation capacity can set a larger threshold to improve fault tolerance; scenarios with extremely high stability requirements need a smaller threshold. The ultimate goal is to avoid misjudging minor load fluctuations as output risks, while ensuring the grid has sufficient capacity to handle fluctuations within a reasonable range, thus guaranteeing power supply reliability.

[0061] The maximum wind power output, minimum wind power output, maximum thermal power output, and minimum thermal power output are obtained based on the dynamic credibility domain of wind power output and the feasible domain of thermal power output adjustment.

[0062] If the sum of the maximum wind power output and the maximum thermal power output is less than the predicted actual load demand correction, or if the sum of the minimum wind power output and the minimum thermal power output is greater than the predicted actual load demand correction, then there is a risk of power output failure; otherwise, there is no risk of power output failure.

[0063] It needs further explanation that if the sum of the maximum wind power output and the maximum thermal power output is less than the correction amount for the predicted actual load demand, it means that even if both wind power and thermal power operate at maximum capacity, they still cannot meet the load demand, and an output risk is identified. If the sum of the minimum wind power output and the minimum thermal power output is greater than the correction amount for the predicted actual load demand, it means that even if both wind power and thermal power operate at minimum capacity, they still exceed the load demand, and an output risk is identified.

[0064] In a preferred embodiment of the present invention, the specific method for identifying specific power output risk scenarios is as follows: if the sum of the minimum wind power output and the minimum thermal power output is greater than the predicted actual load demand correction amount, then the specific power output risk scenario is identified as a power output surplus.

[0065] If the sum of the maximum wind power output and the maximum thermal power output is less than the predicted actual load demand correction, then the specific power output risk scenario is identified as a power output deficit.

[0066] It should be noted that identifying specific power output risk scenarios is necessary because different risk types require differentiated response strategies. Simply determining the existence of a risk is insufficient to guide subsequent dispatching. Once the scenario is clearly defined, energy storage and backup power sources can be dispatched to fill the gap in power output, while power rationing and energy storage charging can be implemented to absorb surplus power output. This improves the accuracy of grid response, avoids blind response strategies, ensures that effective solutions are quickly matched when risks occur, and guarantees the balance of power supply and demand and the stable operation of the grid.

[0067] It should be noted that this invention, by matching the dynamic reliability domain of wind power output with the feasible domain of thermal power regulation, and combining this with the predicted load demand of the target electricity user, can accurately determine whether there is output risk and the specific risk scenario. Compared with existing technologies, it covers multiple risk scenarios under wind and thermal energy coupling scenarios, avoiding risk omissions caused by analysis from a single energy dimension.

[0068] Power dispatch optimization analysis: Output power dispatch optimization instructions for specific power output risk scenarios, and perform power dispatch optimization operations based on the instructions.

[0069] In a preferred embodiment of the present invention, the output power dispatch optimization instruction includes an output surplus dispatch optimization instruction, the specific analysis method of which is as follows: when a specific output risk scenario is identified as an output surplus, the output power dispatch optimization instruction 1 is output: reduce the thermal power output to the minimum thermal power output.

[0070] It's important to explain that thermal power, as a controllable power source, has a wide output adjustment range. Reducing its output to the minimum can minimize the total amount of electricity generated, directly reducing the surplus. Compared to renewable energy sources like wind power, thermal power output adjustment is more stable and controllable. Prioritizing output reduction from thermal power during periods of surplus avoids resource waste caused by forced curtailment of renewable energy, while also ensuring the stability of grid parameters such as frequency. The minimum thermal power output is the lower limit for safe operation of the equipment. Operating within this range ensures that thermal power equipment does not shut down or become damaged due to excessive output reduction, maintaining its standby status so that output can be quickly restored according to load changes.

[0071] The actual power surplus is calculated by subtracting the sum of the minimum wind power output and the minimum thermal power output from the predicted actual load demand.

[0072] When the actual output surplus is less than the preset actual output surplus threshold, output power dispatch optimization instruction 2: use energy storage equipment for energy storage operation.

[0073] When the actual output surplus is greater than or equal to the preset actual output surplus threshold, the minimum output of each thermal power generation unit is arranged in ascending order, and the thermal power generation unit with the continuously smaller output is selected to output the corresponding number. The selection of the thermal power generation unit with the continuously smaller output must meet the following conditions:

[0074] Condition 1: The sum of the minimum output of each thermal power generation unit is greater than the actual output surplus.

[0075] Condition 2: The number of thermal power generation equipment that meets Condition 1 is the minimum.

[0076] It should be noted that Condition 1 means that the total output reduction after the selected equipment is shut down must be sufficient to cover the current surplus, ensuring that the surplus can be completely eliminated by shutting down these devices, and avoiding residual surplus problems due to insufficient reduction. Condition 2, under the premise of meeting Condition 1, prioritizes the combination of equipment with the fewest number of units. This condition can reduce the impact of shutdown operations on the overall stability of the thermal power system, reduce equipment start-up and shutdown losses, simplify the scheduling execution process, and improve the efficiency of surplus handling.

[0077] Output power dispatch optimization instruction 3: Shut down the thermal power generation equipment corresponding to the output number.

[0078] In a preferred embodiment of the present invention, the output power dispatch optimization instruction further includes an output deficit dispatch optimization instruction, the specific analysis method of which is as follows: when a specific output risk scenario is identified as an output deficit, the power dispatch optimization instruction 4 is output: increase the thermal power output to the maximum thermal power output.

[0079] The actual power deficit is obtained by subtracting the sum of the minimum wind power output and the minimum thermal power output from the predicted actual load demand correction.

[0080] The maximum adjustment amount of thermal power output is calculated by multiplying the variable load rate constraint value and the interval between the start time of the preset monitoring period and the current time.

[0081] The number of standby thermal power units that need to be activated is obtained by calculating the ratio of the actual power output deficit to the maximum adjustment of thermal power output and rounding it up.

[0082] Obtain the historical start-up and shutdown counts of each standby thermal power unit, arrange them in ascending order, and select the standby thermal power units with consecutive smaller numbers based on the number of standby thermal power units that need to be started, and output their corresponding numbers:

[0083] It should be noted that the historical start-up and shutdown counts of each standby thermal power unit are first counted and sorted from lowest to highest. Based on the number of standby units that need to be activated, the units with the highest consecutive counts are selected from the sorted list, and their numbers are output. This selection is based on the fact that units with fewer start-up and shutdown counts have lower mechanical wear and higher reliability. Prioritizing their activation reduces the risk of equipment failure, extends unit lifespan, improves the stability of standby startup, and ensures the efficiency of emergency power supply to the power grid.

[0084] Output power dispatch optimization instruction 5: Activate the standby power supply corresponding to the output number.

[0085] It should be noted that this invention improves the targeting and economy of scheduling by outputting hierarchical scheduling instructions for scenarios with deficit or surplus risks, thereby enhancing the stability and efficiency of power grid operation.

[0086] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A power grid operation monitoring method based on multi-energy coupling, characterized in that, The method comprises the following steps: Wind power short-term prediction correction: obtain the predicted wind speed of the location where the wind power plant is located in the next preset monitoring period, output the predicted wind power based on the pre-constructed wind speed-wind power output mapping relationship, analyze the regional aggregated fluctuation rate based on the spatial distribution position of the wind power generation equipment, and comprehensively output the dynamic credible domain of wind power output; The specific analysis method of the regional aggregated fluctuation rate is as follows: A21, collect the actual wind power output of each wind power generation equipment of the wind power plant in a preset reference time span; A22, perform mean value calculation on the actual wind power output of each wind power generation equipment to obtain the average wind power output corresponding to each historical wind power data record; A23, perform relative deviation degree calculation on the actual wind power output of each wind power generation equipment and the average wind power output to obtain the wind power output fluctuation degree of each wind power generation equipment, and then classify according to the sign positive and negative; A24, perform mean value calculation on the wind power output fluctuation degree of each wind power generation equipment corresponding to each historical wind power data record according to the sign to obtain the positive regional aggregated fluctuation rate and the negative regional aggregated fluctuation rate; Thermal power regulation capability analysis: analyze the variable load rate constraint value based on the historical operation record of the thermal power generation equipment, and output the thermal power output regulation feasible domain in combination with the allowed technical output safety range of the thermal power generation equipment; Output risk situation identification: match the dynamic credible domain of wind power output and the thermal power output regulation feasible domain, judge whether there is an output risk in combination with the predicted actual load demand of the target power utilization unit, identify the specific output risk scenario, including output shortage and output surplus; Power dispatching optimization analysis: output the power dispatching optimization instruction for the specific output risk scenario, and perform power dispatching optimization operation based on the instruction.

2. The method for power grid operation monitoring based on multi-energy coupling according to claim 1, characterized in that: The specific analysis method of the predicted wind power is as follows: A11, collect the measured wind speed data and the corresponding actual wind power output of the wind power plant in a preset reference time span; A12, sort the wind speed values in ascending order and divide them into several continuous wind speed intervals, and match each measured wind speed with each wind speed interval to obtain the data set corresponding to each wind speed interval; A13, perform mean value calculation on the actual wind power output corresponding to each measured wind speed in the data set corresponding to each wind speed interval to obtain the wind power output corresponding to each wind speed interval; A14, construct a predicted wind speed change curve of the wind power plant based on the predicted wind speed of the location where the wind power plant is located in the preset monitoring period, uniformly arrange a plurality of monitoring points on the curve, and construct a predicted wind speed data set; A15, match each predicted wind speed value in the predicted wind speed data set with each wind speed interval to obtain the predicted wind power output corresponding to each predicted wind speed value; A16, perform mean value calculation on the predicted wind power output corresponding to each predicted wind speed value to obtain the predicted wind power.

3. The method for power grid operation monitoring based on multi-energy coupling according to claim 2, characterized in that: The specific analysis method of the dynamic credible domain of wind power output is as follows: Perform product calculation on the predicted wind power and the positive regional aggregated fluctuation rate and the negative regional aggregated fluctuation rate to obtain the positive predicted wind power adjustment value and the negative predicted wind power adjustment value; The predicted wind power is summed with the positive predicted wind power adjustment value and the negative predicted wind power adjustment value respectively to obtain the upper limit value of wind power output and the lower limit value of wind power output; A wind power output dynamic credible domain is constructed, which is greater than the lower limit value of wind power output and less than the upper limit value of wind power output.

4. The method for power grid operation monitoring based on multi-energy coupling according to claim 1, characterized in that: The specific analysis process of the variable load rate constraint value is as follows: B1. Obtain the actual variable load span value corresponding to each variable load operation and the variable load operation time length from the historical operation records of the thermal power generation equipment; B2. Divide the actual variable load span value corresponding to each variable load operation by the variable load operation time length to obtain the actual variable load rate corresponding to each variable load operation; B3. Arrange the actual variable load rates corresponding to each variable load operation in ascending order, eliminate the variable load operations greater than the rated variable load rate, and perform mean value calculation on the actual variable load rates of the remaining variable load operations to obtain the variable load rate constraint value.

5. The method for power grid operation monitoring based on multi-energy coupling according to claim 4, characterized in that: The specific analysis method of the thermal power output adjustment feasible domain is as follows: Obtain the interval time length between the preset monitoring time period start time and the current time, multiply the variable load rate constraint value to obtain the maximum thermal power output adjustment amount; Based on the total output power of the current thermal power generation equipment and the maximum thermal power output adjustment amount, a maximum thermal power output adjustment interval is constructed; Compare the maximum thermal power output adjustment interval with the allowed technical output power safety range of the thermal power generation equipment, and output the thermal power output adjustment feasible domain.

6. The method for power grid operation monitoring based on multi-energy coupling according to claim 1, characterized in that: The specific method of judging whether there is an output risk is as follows: Sum the predicted actual load demand of the target power consumption unit in the next monitoring period with the preset allowed power fluctuation adjustment threshold to obtain the predicted actual load demand correction amount; Based on the wind power output dynamic credible domain and the thermal power output adjustment feasible domain, obtain the maximum wind power output, the minimum wind power output, the maximum thermal power output, and the minimum thermal power output; If the sum of the maximum wind power output and the maximum thermal power output is less than the predicted actual load demand correction amount, or the sum of the minimum wind power output and the minimum thermal power output is greater than the predicted actual load demand correction amount, it is determined that there is an output risk, otherwise, it is determined that there is no output risk.

7. The method for power grid operation monitoring based on multi-energy coupling according to claim 6, characterized in that: The specific method of identifying the specific output risk scenario is as follows: If the sum of the minimum wind power output and the minimum thermal power output is greater than the predicted actual load demand correction amount, the specific output risk scenario is identified as output surplus; If the sum of the maximum wind power output and the maximum thermal power output is less than the predicted actual load demand correction amount, the specific output risk scenario is identified as output shortage.

8. The method for power grid operation monitoring based on multi-energy coupling according to claim 1, characterized in that: The output power dispatching optimization instruction includes an output surplus dispatching optimization instruction, and the specific analysis method is as follows: When the specific output risk scenario is identified as output surplus, output power dispatching optimization instruction 1: reduce the thermal power output to the minimum thermal power output; Difference the sum of the minimum wind power output and the minimum thermal power output from the predicted actual load demand correction amount to obtain the actual output surplus amount; When the actual output surplus amount is less than the preset actual output surplus threshold, output power dispatching optimization instruction 2: use the energy storage equipment to perform energy storage operation; When the actual output surplus amount is greater than or equal to the preset actual output surplus amount threshold, the minimum outputs of the thermal power generation devices are arranged in ascending order, and the output corresponding number of the continuous smaller thermal power generation device is selected, wherein the selection of the continuous smaller thermal power generation device requires to meet the following conditions: Condition 1: The sum of the minimum outputs of the thermal power generation devices is greater than the actual output surplus amount; Condition 2: The number of the thermal power generation devices meeting the condition 1 is the least; The output power dispatch optimization instruction 3: the thermal power generation device corresponding to the output number is closed.

9. The method for power grid operation monitoring based on multi-energy coupling according to claim 8, characterized in that: The output power dispatch optimization instruction further comprises an output shortage dispatch optimization instruction, and the specific analysis manner is as follows: When the specific output risk scenario is identified as an output shortage, the output power dispatch optimization instruction 4: the thermal power output is increased to the maximum thermal power output; The predicted actual load demand correction amount is subtracted by the sum of the minimum wind power output and the minimum thermal power output to obtain an actual output shortage amount; The maximum adjustment amount of the thermal power output is calculated based on the product of the variable load rate constraint value and the interval time length between the preset monitoring time period start time and the current time; The number of the standby thermal power units to be started is obtained by the ratio calculation of the actual output shortage amount and the maximum adjustment amount of the thermal power output and then rounding up; The historical start-stop times of each standby thermal power unit are obtained, arranged in ascending order, and the output corresponding number of the continuous smaller standby thermal power unit is selected based on the number of the standby thermal power units to be started: The output power dispatch optimization instruction 5: the standby thermal power unit corresponding to the output number is started.

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