Method and system for dividing wind power quality matched with power grid load time sequence
By establishing a wind power quality classification model and analyzing wind power prediction errors and time-series volatility, the wind power generation plan was optimized, which solved the problem of wind power mismatch with load and improved the economic efficiency and system flexibility of the power grid operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID NINGXIA ELECTRIC POWER CO LTD ECO TECH RES INST
- Filing Date
- 2022-12-09
- Publication Date
- 2026-05-29
AI Technical Summary
Existing methods for allocating wind power capacity cannot accurately evaluate the quality of wind power, resulting in a mismatch between wind power output and peak load times. This increases grid connection costs, reduces system flexibility, and leads to poor economic and environmental performance.
By analyzing wind power prediction errors and time-series volatility, a wind power quality classification model is established. The quality factors are used to evaluate wind power, determine the power generation plan that matches the load time series, and optimize the wind farm power generation plan.
It has enabled quantitative assessment of wind power quality, increased the proportion of electricity traded by wind power companies, reduced grid connection costs, and improved the economic efficiency and system flexibility of grid operation.
Smart Images
Figure CN115864387B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power quality assessment technology, and relates to a method and system for classifying wind power generation power quality that matches the timing of grid load. Background Technology
[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.
[0003] New energy power generation, represented by wind and solar power, is highly uncertain and volatile. The quality of wind power is often used as a measure of the quality of wind farm grid connection. Wind power output is characterized by strong volatility, strong uncertainty, and anti-peak shaving. Wind power output is greatly affected by factors such as weather, time, and environment, resulting in large output fluctuations and frequent grid connection correction requirements.
[0004] Based on the relationship between wind power generation and load temporal fluctuations, wind power can be categorized into "high-quality" and "low-quality" power. Taking a certain region's wind power generation as an example, the installed capacity is very large. With the rapid expansion of wind power capacity and the strong "anti-peak shaving" capability of wind power (which cannot fully meet grid dispatch), wind power output often does not match peak load times. Wind farms generate a large amount of "low-quality" power, which is opposite to load fluctuations, significantly increasing grid connection costs. The output of a large amount of "low-quality" wind power reduces system flexibility, weakens peak shaving and valley filling capabilities, increases grid connection costs, and negatively impacts economic and environmental performance. Therefore, the grid side needs a method to classify wind power generation to assess the power generation of each wind farm and formulate corresponding power generation plans.
[0005] However, current classification methods mostly focus on a specific characteristic of wind power, such as wind power conversion rate or wind power utilization hours. These methods emphasize a single aspect without corresponding quantification, making it impossible to accurately evaluate the quality of wind power. Summary of the Invention
[0006] To address the aforementioned problems, this invention proposes a method and system for classifying wind power generation quality in accordance with grid load timing. This invention can effectively quantify and evaluate the wind power generation quality at wind farms, ensure the proportion of wind power generated by wind farm operators participating in guaranteed transactions and market transactions, and rationally arrange power generation plans.
[0007] According to some embodiments, the present invention adopts the following technical solution:
[0008] A method for classifying wind power generation power quality to match grid load timing includes the following steps:
[0009] Based on historical wind power prediction data, historical wind power actual generation data, and historical load power, wind power prediction error analysis and wind power time series fluctuation analysis are conducted.
[0010] Based on historical load demand, a wind power generation quality classification model matching the time sequence of grid load is established.
[0011] The first quality factor of wind power generated by wind farms within the generation cycle is established from two dimensions: wind power prediction error and time series volatility. The second quality factor is established by considering the proportion of high-quality wind power with similar load fluctuations to the total wind power.
[0012] The wind power generation quality classification model is used to solve the wind power curves of each wind power operator. The two established quality factors are used to judge them respectively, and the power generation plan is determined based on the judgment results.
[0013] As an alternative implementation method, the process of wind power prediction error analysis includes analyzing the historical wind power prediction power and the actual wind power generated based on historical wind farm data, and obtaining the parameters of the wind power prediction error probability density function by assuming that the wind power prediction error follows a normal distribution, and establishing the probability density function of the wind power prediction error.
[0014] As an alternative implementation method, the specific process of conducting wind power time-series volatility analysis includes constructing the actual wind power curve and load power curve of the wind farm. When the wind power curve and the load power curve have the same or similar volatility trends, it is considered high-quality wind power, and the rest are considered low-quality wind power.
[0015] As an alternative implementation, the wind power generation quality classification model uses the maximum wind power with the same fluctuation trend as the load power curve as the objective function, where the power with the same trend is the high-quality power.
[0016] As an alternative implementation, a first quality factor is constructed based on the parameters in the probability density function of wind power prediction error, the correlation coefficient between wind power and load power.
[0017] As an alternative implementation, a second quality factor is constructed based on the actual wind power generated by the wind farm during the target scheduling period and the high-quality wind power generated during the target scheduling period.
[0018] As an alternative implementation method, based on the wind power curve and load curve, the high-quality power contained in the actual wind power generation is selected. The conditions for different wind power operators to simultaneously consider the wind power prediction error and wind power time series fluctuation are obtained. Through chance constraints, under a certain confidence level, the high-quality wind power can be maximized while ensuring that the wind farm can generate wind power. Using the first quality factor and the second quality factor, the power generation plan with the optimal quality factor is determined as the final plan.
[0019] A wind power generation power quality classification system that matches the time sequence of grid load includes:
[0020] The analysis module is configured to perform wind power prediction error analysis and wind power time series fluctuation analysis based on historical wind power prediction power data, historical wind power actual generation power data and historical load power of wind farms.
[0021] The model building module is configured to establish a wind power generation quality classification model that matches the time sequence of grid load based on historical load demand.
[0022] The quality factor establishment module is configured to consider the inherent attributes of wind power, establish the first quality factor of wind power generated by wind farms within the generation cycle from two dimensions: wind power prediction error and time series volatility, and establish the second quality factor by considering the proportion of high-quality wind power similar to load fluctuations in the total wind power.
[0023] The planning module is configured to use a wind power generation quality classification model to solve the wind power curves of each wind power company, judge them by two established quality factors, and determine the power generation plan based on the judgment results.
[0024] A computer-readable storage medium storing a plurality of instructions adapted for loading by a processor of a terminal device and executing steps in the method.
[0025] A terminal device includes a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer-readable storage medium being configured to store a plurality of instructions adapted to be loaded by the processor and executed in accordance with the steps of the method described therein.
[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0027] This invention can effectively classify the quality of wind power. By quantitatively analyzing the quality of wind power levels, it can effectively assess and guide the wind power quality of each wind farm, reduce the system's need for flexible resources to accommodate wind power, and increase the power generation of high-quality wind farms by formulating appropriate power generation plans. High-quality wind power is output according to the load fluctuation trend, which also reduces the operating cost of the power system when connected to the grid. It has good economic efficiency and certain versatility and practicality. Attached Figure Description
[0028] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.
[0029] Figure 1This is a flowchart illustrating the process of this embodiment;
[0030] Figure 2 This is a schematic diagram of wind power fluctuations;
[0031] Figure 3 This is a schematic diagram of wind power quality grading;
[0032] Figure 4 Today's load forecast curve;
[0033] Figure 5 The wind power forecast curve for today;
[0034] Figure 6 This is a schematic diagram of the W1 wind power quality classification by the wind power e-commerce platform.
[0035] Figure 7 This is a schematic diagram of the wind power quality classification of the W2 wind power system. Detailed Implementation
[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0037] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0038] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0039] Example 1
[0040] like Figure 1 As shown, a method for classifying wind power generation power quality to match grid load time sequence includes the following steps:
[0041] Based on historical wind power prediction data, historical wind power actual generation data, and historical load power, wind power prediction error analysis and wind power time series fluctuation analysis are conducted.
[0042] Based on historical load demand, a wind power generation quality classification model matching the time sequence of grid load is established.
[0043] The first quality factor of wind power generated by wind farms within the generation cycle is established from two dimensions: wind power prediction error and time series volatility. The second quality factor is established by considering the proportion of high-quality wind power with similar load fluctuations to the total wind power.
[0044] The wind power generation quality classification model is used to solve the wind power curves of each wind power operator. The two established quality factors are used to judge them respectively, and the power generation plan is determined based on the judgment results.
[0045] This method analyzes wind power prediction errors and time-series volatility based on historical wind power prediction and actual wind power generation data. The "anti-peak-shaving" characteristic of wind power generation results in a significant amount of "low-quality" power in the generated output. Furthermore, both prediction errors and time-series volatility increase system operating costs. Therefore, this method incorporates prediction errors and volatility into the wind power quality classification model. While ensuring the wind farm can generate wind power, a wind power curve with the same volatility trend as the load power curve is constructed. This curve represents high-quality wind power, while the remaining wind power is considered low-quality. Two wind power quality factor indices are determined based on the ratio of "high-quality" wind power absorption to total generated power, and considering prediction errors and time-series volatility.
[0046] This embodiment can effectively quantify and assess the quality of wind power generated at wind farms, ensuring the proportion of electricity generated by wind power companies participating in guaranteed and market transactions, and rationally arranging power generation plans. This promotes the consumption of renewable energy while improving the economic efficiency of grid operation.
[0047] Specifically, wind power prediction error analysis: Based on historical wind farm data, the historical predicted wind power and actual wind power generation are analyzed. Since the wind power prediction error follows a normal distribution, the parameters of the probability density function of the wind power prediction error are obtained, and the probability density function of the wind power prediction error is established. This invention uses a normal distribution to represent the probability density function of the wind power prediction error for each wind farm.
[0048] A. Probability density function of wind farm prediction error:
[0049]
[0050] In the formula Let be the probability density function of wind power prediction error; The power of wind power prediction error; This represents the mean error in wind power prediction. The variance of the wind power prediction error;
[0051] Wind power time-series volatility analysis: Wind power volatility refers to the change in wind power output over time, resulting from the motion patterns of various particles in the atmosphere as wind force changes. To reduce the impact of wind power volatility on the system, a certain amount of spindle reserve is usually required, which increases the system operating cost.
[0052] The actual wind power curve and load power curve of a wind farm can be roughly divided into three cases, such as... Figure 2 As shown.
[0053] Wind power curve 3, due to its similarity to the load power curve's fluctuation trend, indirectly reduces load fluctuation costs; wind power curve 1, due to its anti-peak-shaving characteristic with the load power curve, invisibly increases load fluctuation costs; wind power curve 2 falls between the two scenarios. Considering the impact of wind power volatility on system costs, wind power curve 3 has the best quality, followed by wind power curve 2, and wind power curve 1 has the worst quality.
[0054] Criteria and Methods for Classifying Wind Power Quality: The actual output power of a wind farm often fluctuates around the predicted value. A Gaussian distribution is used to characterize the wind power prediction error. While ensuring the wind farm can generate wind power, a wind power curve with the same fluctuation trend as the load power curve is constructed. This curve is considered high-quality wind power, and the remaining wind power is considered low-quality wind power. The objective function is to maximize the wind power with the same fluctuation trend as the load power curve, with constraints including the similarity of the fluctuation trend between the high-quality wind power curve and the load power curve, and the stable output power of high-quality wind power. Finally, the wind farm quality factor considering wind power prediction error and temporal volatility, as well as the total amount of high-quality wind power, are derived.
[0055] The objective function is:
[0056]
[0057] In the formula For high-quality wind power volume; For the number of scheduling dates; Number of daily scheduling periods; For high-quality wind power Heaven is Power during a given time period.
[0058] Figure 3 The high-quality wind power trend in the right figure is based on the formula in the constraints. Based on the historical load curve, a high-quality wind power curve with the same trend as the load curve is obtained using the above formula. Then, the wind power prediction error is maximized under different confidence level constraints.
[0059] The specific constraints are as follows:
[0060]
[0061]
[0062]
[0063] In the formula The confidence level for the conditional constraints; This represents the predicted power output of the wind farm during time period t. For the system in Load power during a given time period.
[0064] The model obtained in this embodiment has the following quality factor:
[0065]
[0066]
[0067] In the formula To account for wind power prediction errors and time-series fluctuations, the wind farm quality factor is designed to account for wind power prediction errors and time-series fluctuations. The wind farm quality factor is used to account for the total amount of high-quality wind power. and These represent the weights of wind power prediction error and wind power fluctuation on the quality factor, respectively. The normalized wind power prediction error index; This is the correlation coefficient between normalized wind power and load power. For the first wind farm Heaven is The actual wind power generated during a given time period.
[0068] Calculate the wind power generation prediction error using historical statistical data of wind power e-commerce companies. As a quantitative indicator characterizing the uncertainty of wind power, the correlation between wind power and load fluctuations is used to characterize the degree of impact of wind power fluctuations on system operation. This indicator is specifically expressed as...
[0069]
[0070]
[0071] In the formula: The correlation coefficient between wind power and active load for wind power generators (W); and These represent the changes in wind power and load between adjacent time periods.
[0072] By using historical data from different wind farms, the wind farm quality factor at different confidence levels is solved through objective functions and constraints. This quantitative model of quality factor is then used to compare the quality of wind power in different wind farms over a period of time.
[0073] The total amount of high-quality wind power and the proportion of total wind power generated by a wind farm are only one of the quality factors (one of the indicators for evaluating the quality of wind power generated by a wind farm).
[0074] By comprehensively comparing the quality factors of different wind farms, wind farms with better quality factors can be prioritized for power generation plans.
[0075] The quality factor is a quantitative value that reflects the relative quality of wind power generated by a wind farm. It is derived from historical data. By comparing these values, wind farms with better quality factors are prioritized for power generation plans.
[0076] Depend on Figure 4 — Figure 7 It can be seen that different wind power operators simultaneously consider both wind power prediction errors and wind power time-series volatility. Under a certain confidence level, they can construct a wind power curve with the same fluctuation trend as the load power curve, maximizing high-quality wind power while ensuring the wind farm can generate wind power. The confidence level should be selected based on the prediction errors of each wind farm; different confidence levels will affect the quality factor. k The size of 2.
[0077] Example 2
[0078] A wind power generation power quality classification system that matches the time sequence of grid load includes:
[0079] The analysis module is configured to perform wind power prediction error analysis and wind power time series fluctuation analysis based on historical wind power prediction power data, historical wind power actual generation power data and historical load power of wind farms.
[0080] The model building module is configured to establish a wind power generation quality classification model that matches the time sequence of grid load based on historical load demand.
[0081] The quality factor establishment module is configured to consider the inherent attributes of wind power, establish the first quality factor of wind power generated by wind farms within the generation cycle from two dimensions: wind power prediction error and time series volatility, and establish the second quality factor by considering the proportion of high-quality wind power similar to load fluctuations in the total wind power.
[0082] The planning module is configured to use a wind power generation quality classification model to solve the wind power curves of each wind power company, judge them by two established quality factors, and determine the power generation plan based on the judgment results.
[0083] Example 3
[0084] A computer-readable storage medium storing a plurality of instructions adapted for loading by a processor of a terminal device and executing the steps of the method provided in Embodiment 1.
[0085] Example 4
[0086] A terminal device includes a processor and a computer-readable storage medium, the processor being configured to implement various instructions; the computer-readable storage medium being configured to store a plurality of instructions adapted to be loaded by the processor and executed in the method provided in Embodiment 1.
[0087] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.
Claims
1. A method for classifying wind power generation power quality in accordance with grid load time sequence, characterized in that, Includes the following steps: Based on historical wind power prediction data, historical wind power actual generation data, and historical load power, wind power prediction error analysis and wind power time series fluctuation analysis are conducted. Based on historical load demand, a wind power generation quality classification model matching the time sequence of grid load is established. The wind power generation quality classification model takes the maximum wind power with the same trend of load power curve fluctuation as the objective function, where the power with the same trend is the high quality power. The first quality factor of wind power generated by wind farms within the generation cycle is established from two dimensions: wind power prediction error and time series volatility. The second quality factor is established by considering the proportion of high-quality wind power with similar load fluctuations to the total wind power. ； ； In the formula To account for wind power prediction errors and time-series fluctuations, the wind farm quality factor is designed to account for wind power prediction errors and time-series fluctuations. The wind farm quality factor is used to account for the total amount of high-quality wind power. and These represent the weights of wind power prediction error and wind power fluctuation on the quality factor, respectively. The normalized wind power prediction error index; This is the correlation coefficient between normalized wind power and load power. For the first wind farm Heaven is Actual wind power generation during the specified time period; For the number of scheduling dates; Number of daily scheduling periods; For high-quality wind power Heaven is Power during a given time period; The wind power generation quality classification model is used to solve the wind power curves of each wind power operator. The two established quality factors are used to judge them respectively, and the power generation plan is determined based on the judgment results.
2. The wind power generation power quality classification method matching grid load time sequence as described in claim 1, characterized in that, The process of wind power prediction error analysis includes analyzing the historical wind power prediction power and the actual wind power generated based on historical wind farm data. The wind power prediction error follows a normal distribution, and the parameters of the wind power prediction error probability density function are obtained to establish the probability density function of the wind power prediction error.
3. The wind power generation power quality classification method matching grid load time sequence as described in claim 1, characterized in that, The specific process of conducting wind power time-series volatility analysis includes constructing the actual wind power curve and load power curve of the wind farm. When the wind power curve and the load power curve have the same or similar volatility trends, it is considered high-quality wind power, and the rest are considered low-quality wind power.
4. The wind power generation power quality classification method matching grid load time sequence as described in claim 1, characterized in that, Based on the wind power curve and load curve, high-quality power contained in the actual wind power generation is selected. Under the condition that different wind power operators simultaneously consider the wind power prediction error and wind power time series fluctuation, and through chance constraints at a certain confidence level, the high-quality wind power can be maximized while ensuring that the wind farm can generate wind power. Using the first quality factor and the second quality factor, the power generation plan with the optimal quality factor is determined as the final plan.
5. A wind power generation power quality classification system that matches the time sequence of grid load, characterized in that, include: The analysis module is configured to perform wind power prediction error analysis and wind power time series fluctuation analysis based on historical wind power prediction power data, historical wind power actual generation power data and historical load power of wind farms. The model building module is configured to establish a wind power generation quality classification model that matches the time sequence of grid load based on historical load demand. The wind power generation quality classification model takes the maximum wind power with the same trend of load power curve fluctuation as the objective function, where the power with the same trend is the high quality power. The quality factor establishment module is configured to consider the inherent attributes of wind power, establish the first quality factor of wind power generated by wind farms within the generation cycle from two dimensions: wind power prediction error and time series volatility, and establish the second quality factor by considering the proportion of high-quality wind power similar to load fluctuations in the total wind power. ； ； In the formula To account for wind power prediction errors and time-series fluctuations, the wind farm quality factor is designed to account for wind power prediction errors and time-series fluctuations. The wind farm quality factor is used to account for the total amount of high-quality wind power. and These represent the weights of wind power prediction error and wind power fluctuation on the quality factor, respectively. The normalized wind power prediction error index; This is the correlation coefficient between normalized wind power and load power. For the first wind farm Heaven is Actual wind power generation during the specified time period; For the number of scheduling dates; Number of daily scheduling periods; For high-quality wind power Heaven is Power during a given time period; The planning module is configured to use a wind power generation quality classification model to solve the wind power curves of each wind power company, judge them by two established quality factors, and determine the power generation plan based on the judgment results.
6. A computer-readable storage medium, characterized in that, It stores multiple instructions adapted for loading by the processor of a terminal device and executing the steps of the method according to any one of claims 1-4.
7. A terminal device, characterized in that, It includes a processor and a computer-readable storage medium, the processor being used to implement various instructions; the computer-readable storage medium being used to store a plurality of instructions adapted to be loaded by the processor and executed in the steps of the method of any one of claims 1-4.