New energy power fluctuation early warning method and system

Abstract

The invention provides a new energy power fluctuation early warning method and system, and belongs to the technical field of power systems. Comprising the following steps: acquiring a plurality of predefined fluctuation early warning levels; determining a corresponding power grid frequency deviation range for each grade; converting each power grid frequency deviation range into a corresponding new energy power deviation threshold range; obtaining predicted power data of the new energy and calculating a predicted power deviation value; and comparing the predicted power deviation value with a plurality of new energy power deviation threshold ranges to determine a fluctuation early warning level to which the predicted power deviation value belongs. According to the method, the power grid frequency deviation is introduced as the reference, and the quantitative conversion relation between the power grid frequency deviation and the power fluctuation is established, so that the early warning threshold is scientifically determined, the problems of subjective threshold setting and lack of basis in the prior art are solved, the accuracy and reliability of early warning are improved, and the initiative and foresight of power grid dispatching are enhanced.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method and system for early warning of power fluctuations in new energy sources. Background Technology

[0002] As the proportion of renewable energy generation such as wind and solar power in the power grid continues to increase, their inherent intermittency and volatility pose significant challenges to the safe and stable operation of the grid. When the output of renewable energy sources changes drastically, it can cause fluctuations in the grid frequency, which may threaten grid security in severe cases. Therefore, accurate early warning of power fluctuations from renewable energy sources is crucial.

[0003] Existing technologies for early warning of power fluctuations in renewable energy sources suffer from the following shortcomings. One approach is based on machine learning or deep learning, using historical data to train models to predict future power fluctuations. However, the power fluctuation thresholds corresponding to the warning levels of such methods typically rely on empirical settings or simple statistical quantiles, lacking objective physical basis linked to the actual capacity of the power grid. This leads to unreasonable threshold divisions and insufficient accuracy and reliability of the warnings. Another approach is based on power grid condition monitoring. For example, some schemes directly monitor the grid frequency and divide frequency deviations into different zones, such as normal zones, warning zones, and emergency adjustment zones, triggering corresponding alarms when the real-time frequency enters a specific zone. However, this method is passive and lagging; it only reacts after the grid frequency has already shifted, failing to provide early warning. More importantly, it only provides a frequency-based condition assessment and does not address how to provide a forward-looking, grid-safe, power-based permissible fluctuation range for power dispatching of renewable energy plants, thus failing to guide plants in proactive power change management.

[0004] In summary, existing technologies generally lack a method that can quantitatively correlate new energy power fluctuations with core grid stability indicators, thereby scientifically and objectively determining multi-level early warning thresholds. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for early warning of new energy power fluctuations, so as to solve the technical problem that the setting of early warning thresholds for new energy power fluctuations in the prior art lacks objective physical basis, resulting in inaccurate early warnings.

[0006] To achieve the above objectives, this application provides a method for early warning of power fluctuations in new energy sources, comprising: Get multiple predefined fluctuation warning levels; For each of the multiple fluctuation warning levels, a corresponding power grid frequency deviation range is determined based on a preset power grid stability criterion; Based on a preset quantitative relationship model between grid frequency deviation and new energy power deviation, each grid frequency deviation range is converted into a corresponding new energy power deviation threshold range. Obtain the predicted power data of new energy sources, and calculate the predicted power deviation value based on the predicted power data; The predicted power deviation value is compared with the range of power deviation thresholds for multiple new energy sources to determine the fluctuation warning level to which the predicted power deviation value belongs.

[0007] Furthermore, the preset power grid stability criteria include national standards, industry standards, or internal operation control standards for specific power grids.

[0008] Furthermore, the preset quantitative relationship model is the formula: ; Where ΔP is the power deviation of new energy sources, ΔF is the grid frequency deviation, and F is the nominal frequency of the grid. σ represents the total installed capacity of new energy sources in the region, and σ is the droop coefficient.

[0009] Furthermore, the droop coefficient σ is a segmented droop coefficient; different droop coefficient values ​​are used for different ranges of power grid frequency deviation.

[0010] Furthermore, the multiple fluctuation warning levels include: red warning, orange warning, yellow warning, and blue warning.

[0011] Furthermore, the power grid frequency deviation range corresponding to the blue warning is 0.2Hz to 0.3Hz, the power grid frequency deviation range corresponding to the yellow warning is 0.3Hz to 0.4Hz, the power grid frequency deviation range corresponding to the orange warning is 0.4Hz to 0.5Hz, and the power grid frequency deviation range corresponding to the red warning is 0.5Hz to 1.0Hz.

[0012] Furthermore, the step of calculating the predicted power deviation value specifically includes: Calculate the predicted power difference between adjacent future time points, and use it as the predicted power deviation value.

[0013] Furthermore, the method further includes the step of: The determined fluctuation warning level information will be reported to the power grid dispatch center and new energy power plants.

[0014] Furthermore, the method further includes the step of: The total installed capacity of the new energy sources will be updated periodically based on changes in information about the new energy power plants. And recalculate the new energy power deviation threshold range.

[0015] To achieve the above objectives, this application also provides a new energy power fluctuation early warning system, applied to any of the above-described new energy power fluctuation early warning methods, comprising: The level acquisition module is used to acquire multiple predefined fluctuation warning levels; The threshold calculation module is used to determine a corresponding power grid frequency deviation range for each of the multiple fluctuation warning levels based on a preset power grid stability criterion; and to convert each power grid frequency deviation range into a corresponding new energy power deviation threshold range based on a preset quantification relationship model between power grid frequency deviation and new energy power deviation. The early warning determination module is used to acquire the predicted power data of new energy sources, calculate the predicted power deviation value based on the predicted power data, and compare the predicted power deviation value with the power deviation threshold range of multiple new energy sources to determine the fluctuation early warning level to which the predicted power deviation value belongs.

[0016] Compared with the prior art, this application has the following beneficial effects: 1. Scientific quantification of the warning threshold: This application introduces the objective physical quantity of power grid frequency deviation as a benchmark and establishes a quantitative conversion relationship between it and power fluctuation, so that the warning threshold has a clear physical meaning and can truly reflect the potential impact of power fluctuation on power grid safety, fundamentally solving the problem of subjective and unfounded setting of warning thresholds in the existing technology.

[0017] 2. Improved accuracy and reliability of early warning: Since the threshold is scientifically calculated based on power grid safety criteria, it can effectively distinguish between power fluctuations that the power grid can withstand and dangerous, severe fluctuations, significantly reducing false alarms and missed alarms.

[0018] 3. Enhanced initiative and foresight in power grid dispatch: By providing early warnings of predicted power, the power grid dispatch center can learn in advance about potential large power fluctuations and their risk levels, gaining valuable response time windows. This allows for the early deployment of peak-shaving and frequency regulation resources, transforming passive response into proactive management, thereby effectively ensuring the safe and stable operation of the power grid.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for early warning of power fluctuations in new energy sources, according to an exemplary embodiment. Figure 2 A flowchart illustrating a method for early warning of power fluctuations in new energy sources provided in this application embodiment; Figure 3 A functional module diagram of a new energy power fluctuation early warning system provided in this application embodiment; Figure 4 This is a schematic diagram of the signaling interaction timing of a new energy power fluctuation early warning method provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached diagram: S10 - Predefined warning level step; S20 - Determine frequency deviation range step; S30 - Calculate power deviation threshold step; S40 - Obtain predicted power deviation step; S50 - Compare and judge step; S60 - Output warning result step; 100 - Warning system; 110 - Data acquisition module; 120 - Threshold calculation module; 121 - Stability criterion library; 122 - PF quantification model; 130 - Warning judgment module; 140 - Warning release module. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be described in detail below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In one embodiment, see Figure 1 , Figure 1 This is a flowchart illustrating a method for early warning of power fluctuations in new energy sources, according to an exemplary embodiment. The method includes: S1. Obtain multiple predefined fluctuation warning levels; S2. For each of the multiple fluctuation warning levels, a corresponding power grid frequency deviation range is determined based on a preset power grid stability criterion; S3. Based on a preset quantitative relationship model between grid frequency deviation and new energy power deviation, each grid frequency deviation range is converted into a corresponding new energy power deviation threshold range; S4. Obtain the predicted power data of the new energy source, and calculate the predicted power deviation value based on the predicted power data; S5. Compare the predicted power deviation value with the range of power deviation thresholds of the plurality of new energy sources to determine the fluctuation warning level to which the predicted power deviation value belongs.

[0025] This application aims to address the problem of early warning for sudden changes in the predicted power of new energy wind and solar power. First, it classifies the early warning levels according to the power industry. Then, it determines the range of grid frequency deviation by using the grid frequency deviation fluctuation analysis of the national standard. Next, it calculates the early warning level threshold for sudden changes in new energy wind and solar power based on the relationship between the power grid frequency and the power grid frequency. Finally, it uses the threshold method to identify the early warning level and early warning information of the predicted power of new energy wind and solar power, thereby improving the grid's ability to cope with sudden changes in new energy wind and solar power.

[0026] This embodiment provides a method for early warning of renewable energy power fluctuations in wind farm cluster application scenarios. The core of this method lies in linking abstract risk levels with measurable physical quantities of the power grid (i.e., frequency deviation), and using a quantitative physical model to convert this physical quantity into a power fluctuation threshold that has direct guiding significance for renewable energy power plants, thereby achieving scientific and forward-looking early warning.

[0027] Figure 2 This illustration shows an overall flowchart of a new energy power fluctuation early warning method provided in an embodiment of this application. The method can be executed by a computer system or device, and its specific steps will be described in detail in this embodiment using an example of a regional wind farm cluster.

[0028] Reference Figure 2 This method first executes step S10 to obtain a set of predefined, hierarchical fluctuation warning levels. In one embodiment of this application, the warning level information can be stored in the system's configuration file or database for convenient management and retrieval. As an optional implementation, the warning levels are divided into four levels: red warning, orange warning, yellow warning, and blue warning. These four levels, from high to low, represent different levels of risk, ranging from significant to minor, that the power fluctuations of new energy sources may cause to the power grid.

[0029] In practical implementation, based on the power industry's common use of red, orange, yellow, and blue colors to represent risk levels, the early warning levels for fluctuations in renewable energy wind and solar power output are also divided into four levels: red, orange, yellow, and blue. This risk level classification is an effective early warning management method for the supply and demand process between power plants and the grid. By classifying and issuing early warnings about the safety risks of sudden fluctuations in renewable energy wind and solar power output, managers can take corresponding preventative and control measures to reduce power grid generation targets and the occurrence of accidents at power plants.

[0030] The following table shows the early warning levels for fluctuations in the output of new energy wind and solar power.

[0031] Table 1

[0032] Specifically, the risk warning description for fluctuations in the output of new energy wind and solar power is as follows: (1) Red Alert A red alert represents a Level 1 impact, indicating a very significant and unacceptable impact. A continuous red alert is required for wind and solar power plants, necessitating immediate reporting to the grid, modification of power generation assignments, and even shutdown of wind and solar power equipment to prevent short circuits and equipment damage. For this level of risk, wind and solar power equipment should only be restarted once the impact level has decreased.

[0033] (2) Orange alert Orange risk is a Level 2 impact, which is a high impact (relatively significant). It requires continuous orange alerts to wind and solar power plants, but for a shorter period than red alerts. The purpose is to draw the attention of wind and solar power plants. The duration needs to be observed. If the duration is too long, it is necessary to report to the power grid and modify the power distribution task.

[0034] (3) Yellow alert Orange risk is a level 3 impact, which is a moderate (significant) impact. It requires issuing an orange alert for wind and solar power plants, but the time is much shorter than that of a red alert. The purpose is to inform wind and solar power plants that their power output may be moderately affected and that they need to wait to see if the impact level rises. If it does, they need to report to the power grid and modify the power distribution task.

[0035] (4) Blue Alert Blue risk is a level 4 impact, which is a slight (not very significant) impact. It requires issuing an orange alert for wind and solar power plants to indicate that the wind and solar power plants are beginning to be affected. It is necessary to wait and see if the impact level continues to rise. If the impact level rises, it is necessary to report to the power grid and modify the power distribution task; otherwise, the prediction model parameters should be adjusted to avoid the impact of the blue alert.

[0036] After determining the warning level, the method proceeds to step S20, where a corresponding power grid frequency deviation range is defined for each warning level. This step aims to quantitatively correlate the abstract levels defined in step S10 with frequency deviation, a key physical indicator characterizing the power grid's safety status. It is understood that this correlation is based on a preset power grid stability criterion. In this embodiment, this criterion can adopt national standards, such as those specified in "Power Quality - Power System Frequency Deviation" (GB / T15945-2008). This standard states that for a power system with a nominal frequency of 50Hz, the permissible frequency deviation range during normal operation is ±0.2Hz; deviations exceeding this range indicate varying degrees of disturbance to power grid stability. Based on this criterion, this embodiment further subdivides the frequency deviation exceeding the normal range into intervals corresponding to the four warning levels. Specifically, one possible classification is as follows: the absolute value range of the grid frequency deviation corresponding to a blue warning is (0.2Hz, 0.3Hz); the absolute value range of the grid frequency deviation corresponding to a yellow warning is (0.3Hz, 0.4Hz); the absolute value range of the grid frequency deviation corresponding to an orange warning is (0.4Hz, 0.5Hz); and the absolute value range of the grid frequency deviation corresponding to a red warning is (0.5Hz, 1.0Hz). It should be noted that the 1.0Hz here is a relatively lenient upper limit set considering the extreme cases of grid relay protection operation. Through this step, each warning level is given a clear physical meaning. That is to say, the power fluctuation that triggers a blue warning has the potential impact equivalent to causing a grid frequency shift of 0.2Hz to 0.3Hz.

[0037] Subsequently, in step S30, the threshold range for renewable energy power deviation is calculated. This step is based on a preset quantitative relationship model describing the relationship between grid frequency deviation and renewable energy power deviation, thereby converting the grid-side safety indicators into controllable indicators on the renewable energy side. In this embodiment, the model is characterized by a simplified linear relationship derived from the basic principle of primary frequency regulation in power systems, specifically: (1) in, The threshold representing the new energy power deviation to be calculated is the actual power deviation of new energy wind and solar power every 15 minutes. It is the droop coefficient, usually 0.04 or 0.05. Here it is 0.05 to improve the warning width of sudden changes in the power of new energy wind and solar power (reduce the lower limit of the actual power deviation) and prevent some sudden changes from being ignored. It reflects the comprehensive response capability of all generator sets in the system participating in primary frequency regulation. It is a dimensionless coefficient. This refers to the power grid frequency deviation; fluctuations within 0.2 Hz are considered normal, while fluctuations between 0.2 and 1.0 Hz are considered warning fluctuations. This is the nominal frequency of China's power system, usually 50Hz; This refers to the installed capacity.

[0038] Before calculation, the system needs to obtain the total installed capacity of new energy sources in the region. In this embodiment, it is assumed that the total installed capacity of the wind farm cluster in the region is 3189.23MW. Please refer to Table 2. This data can be periodically collected from the monitoring information systems or management systems of each wind farm through a data acquisition system. Simultaneously, the adjustment coefficient... In this embodiment, based on the typical characteristics of the regional power grid, an empirical value of 0.05 is used. Based on the above model and parameters, substituting each frequency deviation range determined in step S20 into the formula, the corresponding new energy power deviation threshold range can be calculated. The calculation process is as follows: For a blue alert, the frequency deviation range is (0.2Hz, 0.3Hz], and the lower limit of the corresponding power deviation range is (1 / 0.05). (0.2 / 50) 3189.23 = 255.14 MW, with an upper limit of (1 / 0.05). (0.3 / 50) 3189.23 = 382.71 MW. Therefore, the power deviation threshold range corresponding to the blue alert is (255.14 MW, 382.71 MW). Accordingly, the threshold ranges for other alert levels can be calculated: the power deviation corresponding to the yellow alert... The range is (382.71MW, 510.28MW); the power deviation corresponding to the orange alert. The range is (510.28MW, 637.85MW); the power deviation corresponding to the red alert. The range is (637.85MW, 1275.69MW). These calculated threshold ranges apply to both power increases (climbing) and power decreases (drops), i.e., positive and negative symmetry. For example, the complete power deviation range for a red alert is: negative deviation threshold range (MW) [-1275.69, -637.85); positive deviation threshold range (MW) (637.85, 1275.69]MW. The system stores these calculated threshold ranges as the basis for subsequent alert determinations.

[0039] Table 2

[0040] The method continues with step S40 to obtain the predicted power deviation value. The system obtains predicted power data for one or more future time points from an independent new energy power prediction system via an interface. This prediction system can be generated based on numerical weather prediction and machine learning algorithms (such as Long Short-Term Memory networks). In this embodiment, assuming a prediction time resolution of 15 minutes, the system obtains the predicted power values ​​for the t-th and t-1-th future 15-minute time points and calculates the difference between them; this difference is the predicted power deviation value. For example, if the prediction system predicts that the total wind power output in the region will increase from 2000MW to 2700MW in the next 15-minute cycle, the calculated predicted power deviation value is 2700-2000=+700MW.

[0041] In step S50, the predicted power deviation value (+700MW) calculated in step S40 is compared with the multiple new energy power deviation threshold ranges calculated in step S30, and it is determined that +700MW falls within the power deviation threshold range of the red warning (637.85MW, 1275.69MW).

[0042] Finally, in step S60, based on the judgment result of step S50, the system determines that the predicted power fluctuation event belongs to the red warning level, and generates and outputs a warning result containing information such as the warning level, predicted fluctuation amount, and occurrence time.

[0043] Figure 4 The signaling interaction timing of this method in practical application is illustrated. As shown in the figure, after the early warning system completes its internal calculations and judgments, it reports the generated early warning information (e.g., a "red warning") to the power grid dispatch center and relevant renewable energy power plants via the communication network. Upon receiving a high-level early warning, the power grid dispatch center can activate its emergency plan in advance, such as scheduling backup pumped storage power plants or gas turbine units to prepare for grid connection, or adjusting the transmission plans of tie lines connected to the region. This provides a valuable time window to cope with the upcoming massive power surge, thereby effectively ensuring the stability of the power grid frequency.

[0044] To ensure the long-term effectiveness of the early warning threshold, this application embodiment also includes a dynamic update mechanism. This is because the installed capacity of new energy power stations within the region... The total installed capacity may change due to the commissioning of new power plants, the decommissioning of old power plants, or equipment upgrades. The system will update the total installed capacity periodically (e.g., monthly or quarterly) based on changes in the information of new energy power plants. The system calculates and updates the new energy power deviation threshold range for all warning levels, thereby ensuring that the warning system can always accurately reflect the actual situation of the current power grid.

[0045] Figure 3 A functional module diagram of a new energy power fluctuation early warning system 100 to implement the above method is shown below. This system 100 can be deployed on a server in a power grid dispatch center or a regional new energy centralized control center. In this embodiment, the system 100 includes: a data acquisition module 110, a threshold calculation module 120, an early warning judgment module 130, and an early warning release module 140. The data acquisition module 110 is used to obtain the required data from external data sources (such as power plant monitoring information systems and power prediction systems), including the total installed capacity of new energy. The threshold calculation module 120, as the core of the system, contains a stability criterion library 121 and a PF quantization model 122. The stability criterion library 121 stores the power grid stability criteria, i.e., the correspondence between each warning level and the frequency deviation range; the PF quantization model 122 solidifies the aforementioned power-frequency conversion formula. Based on the data provided by the data acquisition module 110, and by calling the library 121 and model 122, the threshold calculation module 120 calculates the power deviation threshold range for each level. The early warning determination module 130 acquires predicted power data from the data acquisition module 110, calculates the predicted power deviation value, and then compares it with the threshold range provided by the threshold calculation module 120 to finally determine the early warning level. The early warning release module 140 is used to package the determination result of the early warning determination module 130 into a standard format early warning information and send it to target users such as the dispatch center and new energy power plants through the communication interface.

Claims

1. A method for early warning of power fluctuations in new energy sources, characterized in that, include: Get multiple predefined fluctuation warning levels; For each of the multiple fluctuation warning levels, a corresponding power grid frequency deviation range is determined based on a preset power grid stability criterion; Based on a preset quantitative relationship model between grid frequency deviation and new energy power deviation, each grid frequency deviation range is converted into a corresponding new energy power deviation threshold range. Obtain the predicted power data of new energy sources, and calculate the predicted power deviation value based on the predicted power data; The predicted power deviation value is compared with the range of power deviation thresholds for multiple new energy sources to determine the fluctuation warning level to which the predicted power deviation value belongs.

2. The method according to claim 1, characterized in that, The preset power grid stability criteria include national standards, industry standards, or internal operation control standards for specific power grids.

3. The method according to claim 1, characterized in that, The preset quantitative relationship model is a formula: ; Where ΔP is the power deviation of new energy sources, ΔF is the grid frequency deviation, and F is the nominal frequency of the grid. σ represents the total installed capacity of new energy sources in the region, and σ is the droop coefficient.

4. The method according to claim 3, characterized in that, The droop coefficient σ is a segmented droop coefficient; different droop coefficient values ​​are used for different ranges of power grid frequency deviation.

5. The method according to claim 1, characterized in that, The multiple fluctuation warning levels include: red warning, orange warning, yellow warning and blue warning.

6. The method according to claim 5, characterized in that, The power grid frequency deviation range corresponding to the blue warning is 0.2Hz to 0.3Hz, the power grid frequency deviation range corresponding to the yellow warning is 0.3Hz to 0.4Hz, the power grid frequency deviation range corresponding to the orange warning is 0.4Hz to 0.5Hz, and the power grid frequency deviation range corresponding to the red warning is 0.5Hz to 1.0Hz.

7. The method according to claim 1, characterized in that, The step of calculating the predicted power deviation value specifically includes: Calculate the predicted power difference between adjacent future time points, and use it as the predicted power deviation value.

8. The method according to claim 1, characterized in that, The method further includes the following steps: The determined fluctuation warning level information will be reported to the power grid dispatch center and new energy power plants.

9. The method according to claim 3, characterized in that, The method further includes the following steps: The total installed capacity of the new energy sources will be updated periodically based on changes in information about the new energy power plants. And recalculate the new energy power deviation threshold range.

10. A new energy power fluctuation early warning system, applied to the new energy power fluctuation early warning method according to any one of claims 1-9, characterized in that, include: The level acquisition module is used to acquire multiple predefined fluctuation warning levels; The threshold calculation module is used to determine a corresponding power grid frequency deviation range for each of the multiple fluctuation warning levels based on a preset power grid stability criterion; and to convert each power grid frequency deviation range into a corresponding new energy power deviation threshold range based on a preset quantification relationship model between power grid frequency deviation and new energy power deviation. The early warning determination module is used to acquire the predicted power data of new energy sources, calculate the predicted power deviation value based on the predicted power data, and compare the predicted power deviation value with the power deviation threshold range of multiple new energy sources to determine the fluctuation early warning level to which the predicted power deviation value belongs.

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