A control method for a power system involving new energy

Abstract

The present invention relates to the field of power system control technology, and specifically to a new power system control method involving new energy. The method includes: obtaining historical and predicted environmental data, power consumption data and power supply data; determining the stability coefficient according to the numerical fluctuation of the power supply data and the correlation of the environmental data; determining the predicted power supply accuracy index in combination with the numerical fluctuation of the predicted power supply data; determining the historical power consumption impact coefficient according to the numerical changes of the historical power consumption data; and determining the power storage reliability index at the current moment in combination with the numerical fluctuation of the predicted power consumption data within the predicted time period; and performing new energy access control at the current moment according to the power storage reliability index and the predicted power supply accuracy index. It can effectively combine the stability of power consumption and power supply, thereby ensuring the stable control of the overall power system and improving the stability of the power system in power planning.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and in particular to a new power system control method involving new energy. Background Art

[0002] Traditional power systems mainly rely on fossil energy such as coal and natural gas, which have the characteristics of stable supply, but will produce more greenhouse gases and other pollution when supplying electricity. New energy is mainly renewable energy such as wind energy and solar energy. These energy sources are clean and low-carbon, but intermittent and unstable. Adding new energy to the traditional power system can reduce the generation of greenhouse gases and other pollution while maintaining a stable power supply.

[0003] In the related technology, the control of the new power system is achieved directly by storing excess electricity. This method has high requirements for the power generation planning of traditional power plants. Under the current circumstances, due to the intermittent and unstable characteristics of new energy sources themselves, the overall power system control will be unstable, resulting in unstable new energy sources during peak power consumption periods or power fluctuations. Instead, it will increase the overall power supply pressure, and the stability of the power system in power planning is low. Summary of the invention

[0004] In order to solve the technical problems in the related art that the overall power system control is unstable, the unstable new energy will increase the overall power supply pressure during the peak power consumption period or power fluctuation, and the power system has low stability in power planning, the present invention provides a new power system control method involving new energy, and the technical scheme adopted is as follows:

[0005] The present invention proposes a novel power system control method involving new energy sources, taking a preset time period before the current moment as a historical time period, and taking a preset time period after the current moment as a predicted time period, the method comprises:

[0006] Obtain historical environmental data, historical power consumption data, and historical power supply data at each sampling moment in the historical time period; obtain predicted environmental data, predicted power consumption data, and predicted power supply data at each sampling moment in the predicted time period;

[0007] Determine the stability coefficient of the predicted power supply data according to the numerical fluctuations of the historical power supply data and the predicted power supply data, and the correlation between the historical environmental data and the predicted environmental data; determine the predicted power supply accuracy index for power supply based on environmental prediction in combination with the stability coefficient and the numerical fluctuations of the predicted power supply data;

[0008] The historical time period is evenly divided into different sub-segments, and in the sub-segments adjacent in time sequence, the power consumption mutation index of the sub-segment is determined according to the numerical change of the historical power consumption data, and the power consumption mutation index of all sub-segments is combined to determine the historical power consumption influence coefficient at the current moment;

[0009] Based on the historical power consumption impact coefficient and the numerical fluctuation of the predicted power consumption data in the predicted time period, the power storage reliability index at the current moment is determined; according to the power storage reliability index and the predicted power supply accuracy index, new energy access control is performed at the current moment.

[0010] Further, the determining of the stability coefficient of the predicted power supply data according to the numerical fluctuations of the historical power supply data and the predicted power supply data, and the correlation between the historical environment data and the predicted environment data, includes:

[0011] According to the predicted power supply data at each sampling time within the prediction time period, a predicted power supply mean is obtained; according to the difference between the predicted power supply data at each sampling time and the predicted power supply mean, a predicted fluctuation index of the predicted power supply is determined;

[0012] According to the historical power supply data at each sampling moment in the historical time period, the historical power supply average is obtained; according to the difference between the historical power supply data at each sampling moment and the historical power supply average, the historical fluctuation index of the historical power supply is determined;

[0013] Performing correlation analysis on the historical environmental data and the predicted environmental data to obtain a correlation index;

[0014] The historical fluctuation index and the predicted fluctuation index are weighted according to the correlation index to obtain a stability coefficient of the predicted power supply data.

[0015] Furthermore, the correlation analysis of the historical environmental data and the predicted environmental data is performed to obtain a correlation index, including:

[0016] The Pearson correlation coefficient of the historical environmental data and the predicted environmental data is calculated, and the maximum and minimum values ​​of the Pearson correlation coefficient are normalized to obtain a correlation index.

[0017] Furthermore, the historical fluctuation index and the predicted fluctuation index are weighted according to the correlation index to obtain a stability coefficient of the predicted power supply data, including:

[0018] Calculate the product of the correlation index and the historical volatility index, and use the sum of the product value and the predicted volatility index as the overall volatility index;

[0019] The opposite number of the overall fluctuation index is normalized to the maximum and minimum values ​​to obtain the stability coefficient of the predicted power supply data.

[0020] Further, combining the stability coefficient and the numerical fluctuation of the predicted power supply data, determining a predicted power supply accuracy index for power supply based on environmental prediction, including:

[0021] Calculating the numerical variance of all the predicted power supply data, and normalizing the opposite numbers of the numerical variance to the maximum and minimum values ​​to obtain the accurate influence coefficient;

[0022] The product value of the accurate influence coefficient and the stability coefficient is calculated as an indicator for predicting power supply accuracy.

[0023] Furthermore, according to the numerical changes of historical power consumption data, the power consumption mutation index within the historical time period is determined, including:

[0024] Calculate the range of historical power consumption data at all sampling moments in the same sub-segment to obtain the power consumption range of the corresponding sub-segment;

[0025] Determine two other sub-segments that are closest to any sub-segment in time sequence as adjacent sub-segments;

[0026] The absolute values ​​of the differences between the extreme differences in power consumption of a sub-segment and its adjacent sub-segments are averaged to obtain the power consumption mutation index of the sub-segment.

[0027] Furthermore, combining the power consumption mutation index of all sub-segments to determine the historical power consumption impact coefficient at the current moment includes:

[0028] The mean of the power consumption mutation index of all sub-segments is calculated as the historical power consumption impact coefficient at the current moment.

[0029] Further, based on the historical power consumption influence coefficient and the numerical fluctuation of the predicted power consumption data within the predicted time period, the power storage reliability index at the current moment is determined, including:

[0030] Taking the numerical standard deviation of the predicted power consumption data at all sampling moments within the predicted time period as the predicted fluctuation coefficient;

[0031] The product of the historical power consumption impact coefficient and the predicted fluctuation coefficient is calculated, and the opposite of the product value is normalized to the maximum and minimum values ​​to serve as the power storage reliability indicator at the current moment.

[0032] Further, according to the power storage reliability index and the predicted power supply accuracy index, new energy access control is performed at the current moment, including:

[0033] Calculate the product of the power storage reliability index and the predicted power supply accuracy index, and perform maximum and minimum value normalization processing to obtain the new energy access impact index at the current moment;

[0034] Whether to connect to new energy power generation is determined according to the new energy access impact index.

[0035] Further, determining whether to connect to the renewable energy power generation according to the renewable energy access impact index includes:

[0036] When the new energy access impact index is less than the preset access threshold, the new energy generation is not connected;

[0037] When the new energy access impact index is greater than or equal to a preset access threshold, new energy is connected for power generation.

[0038] The present invention has the following beneficial effects:

[0039] In summary, the embodiment of the present invention combines and analyzes historical data and predicted data; according to the numerical fluctuations of historical power supply data and predicted power supply data, the correlation between historical environmental data and predicted environmental data, and the numerical fluctuations of predicted power supply data, obtains the predicted power supply accuracy index for power supply for environmental prediction, and the predicted power supply accuracy index is an accuracy index combined with environmental changes for analysis, thereby improving the reliability of the predicted power supply analysis; determines the historical power consumption influence coefficient through the numerical changes of historical power consumption data, and determines the power storage reliability index at the current moment in combination with the numerical fluctuations of predicted power consumption data within the prediction time period, that is, analyzes the stable state of power storage itself from the power consumption dimension; so as to combine the power storage reliability index and the predicted power supply accuracy index to control the access of new energy at the current moment. In the embodiment of the present invention, the new energy access control method can effectively combine the stability of power consumption and power supply, thereby ensuring the stable control of the overall power system, avoiding the forced intervention of unstable new energy power generation during peak power consumption or power fluctuations, resulting in an increase in the overall power supply pressure, and improving the stability of power planning of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A flow chart of a new power system control method involving new energy sources provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a new power system control method involving new energy proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, 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 belongs.

[0044] It should be noted that the renewable energy power generation in the embodiments of the present invention mainly refers to wind power and hydropower, which are unstable power supplies. That is to say, directly connecting wind power and hydropower to the power system for power storage and supply will lead to the instability of the overall power supply effect. Therefore, renewable energy power generation is affected by environmental changes and has strong intermittent and instability. It is necessary to conduct a stable analysis of the current power supply and consumption to determine whether it is appropriate to participate in new energy.

[0045] The specific scheme of a new power system control method involving new energy provided by the present invention is described in detail below with reference to the accompanying drawings.

[0046] See also Figure 1 , which shows a flow chart of a new power system control method involving new energy provided by an embodiment of the present invention, the method comprising:

[0047] S101: Acquire historical environmental data, historical power consumption data and historical power supply data at each sampling moment in a historical time period; acquire predicted environmental data, predicted power consumption data and predicted power supply data at each sampling moment in a predicted time period.

[0048] The preset time period before the current moment is used as the historical time period, and the preset time period after the current moment is used as the predicted time period. The preset time period is specifically the time period for power analysis, and optionally, it is specifically 1 day, that is, the 24 hours before the current moment is used as the historical time period, and the 24 hours after the current moment is used as the predicted time period.

[0049] Among them, the sampling moment is the moment for data sampling and subsequent data prediction. The time interval between two adjacent sampling moments is the same. For example, a sampling moment can be obtained every 30 minutes. Thus, historical environmental data, historical power consumption data and historical power supply data are collected. At the same time, predicted environmental data, predicted power consumption data and predicted power supply data are predicted.

[0050] Among them, historical environmental data and predicted environmental data can be specifically, for example, data such as temperature and wind speed. It can be understood that temperature values ​​affect production and life. For example, if the temperature is too cold or too hot, it will increase the electricity consumption in daily life. Therefore, when the temperature is similar, the corresponding electricity consumption is also similar. Wind speed will affect the efficiency of wind power generation. In a power system that includes wind power generation, wind speed can be used as environmental data for analysis; of course, environmental data can be specifically, for example, other data, such as water flow velocity (affecting hydropower generation), which can be quantified according to actual environmental conditions, and there is no restriction on this.

[0051] Among them, the historical power consumption data, the historical power supply data, the predicted power consumption data and the predicted power supply data all represent continuous values ​​from the last sampling moment to the current sampling moment.

[0052] In the embodiment of the present invention, data prediction of predicted environmental data, predicted power consumption data and predicted power supply data is performed for the predicted time period at the current moment, which is specifically based on the existing data prediction method and is not limited thereto.

[0053] S102: Determine the stability coefficient of the predicted power supply data based on the numerical fluctuations of the historical power supply data and the predicted power supply data, and the correlation between the historical environmental data and the predicted environmental data; and determine the predicted power supply accuracy index for power supply based on environmental prediction by combining the stability coefficient and the numerical fluctuations of the predicted power supply data.

[0054] Among them, the analysis of power supply can conduct a specific analysis of the overall power supply situation, regardless of whether new energy is connected, and calculate data fluctuations and correlations based on historical power supply data in historical time periods and predicted power supply data in predicted time periods, so as to conduct a specific analysis of the stability coefficient.

[0055] The stability coefficient is the stability information of the predicted power supply data. The larger the value of the stability coefficient is, the greater the overall stability of the power supply after the current moment is.

[0056] Further, in some embodiments of the present invention, the stability coefficient of the predicted power supply data is determined based on the numerical fluctuations of historical power supply data and predicted power supply data, as well as the correlation between historical environmental data and predicted environmental data, including: obtaining a predicted power supply mean value based on the predicted power supply data at each sampling moment in the prediction time period; determining a predicted fluctuation index of the predicted power supply by averaging the difference between the predicted power supply data and the predicted power supply mean at each sampling moment; obtaining a historical power supply mean value based on the historical power supply data at each sampling moment in the historical time period; determining a historical fluctuation index of the historical power supply by averaging the difference between the historical power supply data and the historical power supply mean at each sampling moment; performing correlation analysis on the historical environmental data and the predicted environmental data to obtain a correlation index; and weighting the historical fluctuation index and the predicted fluctuation index according to the correlation index to obtain the stability coefficient of the predicted power supply data.

[0057] The specific calculation of the stability coefficient mainly includes two aspects: one is the stability of the historical power supply data, and the other is the stability of the predicted power supply data. By integrating the two data, a specific stability coefficient analysis is carried out.

[0058] In the embodiment of the present invention, the mean difference is calculated, and the mean of the mean difference is used as the historical fluctuation index of the historical power supply and the predicted fluctuation index of the predicted power supply.

[0059] That is to say, the absolute value of the difference between the predicted power supply data and the predicted power supply mean at each sampling time is calculated, and the absolute value of the difference at all sampling times is averaged to obtain the predicted fluctuation index of the predicted power supply. Similarly, the historical fluctuation index of the historical power supply is obtained. The larger the value of the predicted fluctuation index, the larger the mean difference of the power supply in the predicted time period, which means that the overall stability in the predicted time period is greater. The larger the value of the historical fluctuation index, the larger the mean difference of the power supply in the historical time period, which means that the overall stability in the historical time period is greater.

[0060] However, since there will be certain differences between the historical environment and the predicted environment, in order to conduct a specific analysis of the environmental differences, it is necessary to determine the correlation between the historical environment and the predicted environment.

[0061] Furthermore, in some embodiments of the present invention, a correlation analysis is performed on historical environmental data and predicted environmental data to obtain a correlation index, including: calculating the Pearson correlation coefficient of the historical environmental data and the predicted environmental data, and performing maximum and minimum value normalization processing on the Pearson correlation coefficient to obtain a correlation index.

[0062] Among them, the larger the value of the Pearson correlation coefficient is, the greater the positive correlation between the historical environmental data and the predicted environmental data is. The Pearson correlation coefficient is directly normalized to the maximum and minimum values ​​to obtain the correlation index.

[0063] The larger the value of the correlation index is, the more similar the historical environment is to the predicted environment, that is, the greater the reference significance of the historical fluctuation index is, and the predicted fluctuation index can be corrected according to the historical fluctuation index. Therefore, in an embodiment of the present invention, the historical fluctuation index and the predicted fluctuation index are weighted according to the correlation index to obtain the stability coefficient of the predicted power supply data, including: calculating the product of the correlation index and the historical fluctuation index, and taking the sum of the product value and the predicted fluctuation index as the overall fluctuation index; normalizing the opposite of the overall fluctuation index to the maximum and minimum values ​​to obtain the stability coefficient of the predicted power supply data.

[0064] Among them, the overall fluctuation index represents the amplitude information of the overall fluctuation. The larger the value of the overall fluctuation index is, the greater the fluctuation of the power supply at the current moment. That is, regardless of whether there is new energy access, the power supply at the current moment will produce a large fluctuation. By normalizing the maximum and minimum values ​​of the opposite number of the overall fluctuation index, the stability coefficient of the predicted power supply data is obtained.

[0065] The specific calculation of the overall volatility index is to directly weight the correlation index as the weight value of the historical volatility index, that is, to calculate the product of the correlation index and the historical volatility index as the weighted value, and then take the sum of the weighted value and the predicted volatility index as the overall volatility index.

[0066] It is possible to combine historical analysis with environmental data, thus avoiding the lack of practical reference significance caused by fluctuation analysis based solely on forecast data, and at the same time avoiding the low reference significance caused by large differences in the environment. The integrated overall fluctuation index has stronger reliability.

[0067] Furthermore, in some embodiments of the present invention, the stability coefficient and the numerical fluctuation of the predicted power supply data are combined to determine the accuracy index of the predicted power supply for environmental prediction, including: calculating the numerical variance of all predicted power supply data, normalizing the opposite of the numerical variance to the maximum and minimum values ​​as the accurate influence coefficient; calculating the product of the accurate influence coefficient and the stability coefficient as the predicted power supply accuracy index.

[0068] Among them, the variance is used for numerical fluctuation analysis, that is, the larger the variance value, the greater the overall fluctuation of the predicted power supply data, that is, the more unstable the predicted power supply data is, and the lower the accuracy is. Therefore, the opposite of the variance is normalized to the maximum and minimum values ​​to obtain the accurate influence coefficient. The larger the value of the accurate influence index is, the more stable the predicted power supply data is, and the higher the accuracy of the prediction is. Therefore, the product of the accurate influence coefficient and the stability coefficient is directly calculated as the predicted power supply accuracy index.

[0069] Among them, the predicted power supply accuracy index represents the indicator information for analyzing the predicted power supply. The larger the value of the predicted power supply accuracy index is, the greater the fluctuation of the predicted power supply is and the more unpredictable it is. At this time, connecting to the equally unstable new energy power generation project will lead to increased risks.

[0070] S103: Divide the historical time period into different sub-segments evenly, and in the sub-segments that are adjacent in time sequence, determine the power consumption mutation index of the sub-segment according to the numerical change of the historical power consumption data, and determine the historical power consumption impact coefficient at the current moment by combining the power consumption mutation indexes of all sub-segments.

[0071] Among them, the division of sub-segments is based on the actual situation. Every 2 hours can be regarded as a sub-segment, and the sub-segments adjacent in time sequence can specifically represent the sub-segments that are continuous in time sequence. For example, 2 o'clock to 4 o'clock is regarded as a sub-segment, and its adjacent sub-segments are the sub-segments from 0 o'clock to 2 o'clock and the sub-segments from 4 o'clock to 6 o'clock. Since electricity consumption is volatile and the electricity consumption varies greatly in different time periods, the reliability of the overall electricity consumption analysis can be improved by analyzing each sub-segment and determining the power consumption mutation index.

[0072] Furthermore, in some embodiments of the present invention, based on the numerical changes in historical power consumption data, a power consumption mutation index within a historical time period is determined, including: calculating the range of historical power consumption data at all sampling moments in the same sub-segment to obtain the power consumption range of the corresponding sub-segment; determining two other sub-segments that are closest in time sequence to any sub-segment as adjacent sub-segments; and averaging the absolute values ​​of the differences between the power consumption ranges of a sub-segment and its adjacent sub-segments to obtain the power consumption mutation index of the sub-segment.

[0073] The larger the value of the extreme difference of power consumption, the greater the fluctuation of power consumption in the sub-segment. The power consumption fluctuation of the sub-segment is analyzed with the power consumption fluctuation of the adjacent sub-segment to obtain the power consumption mutation index. In other words, the larger the value of the power consumption mutation index, the greater the fluctuation of the overall historical power consumption data of the sub-segment.

[0074] Therefore, in some embodiments of the present invention, the power consumption mutation index of all sub-segments is combined to determine the historical power consumption impact coefficient at the current moment, including: calculating the mean of the power consumption mutation index of all sub-segments as the historical power consumption impact coefficient at the current moment.

[0075] The historical electricity consumption impact coefficient indicates the fluctuation of the overall electricity consumption value in the historical time period. The larger the value of the historical electricity consumption impact coefficient is, the more unstable the numerical changes in electricity consumption in the historical time period are. This unstable factor will affect the overall stability effect after the new energy power supply.

[0076] S104: Determine the power storage reliability index at the current moment based on the historical power consumption impact coefficient and the numerical fluctuation of the predicted power consumption data within the predicted time period; and perform new energy access control at the current moment based on the power storage reliability index and the predicted power supply accuracy index.

[0077] Among them, the power storage reliability index is the reliability of power storage at the current moment. It can be understood that by predicting power consumption data and historical power consumption influence coefficients, a specific analysis of the power storage reliability index at the current moment is conducted to ensure the dynamic stability of power storage.

[0078] Furthermore, in some embodiments of the present invention, based on the historical power consumption impact coefficient and the numerical fluctuation of the predicted power consumption data in the predicted time period, the power storage reliability index at the current moment is determined, including: taking the numerical standard deviation of the predicted power consumption data at all sampling moments in the predicted time period as the predicted fluctuation coefficient; calculating the product of the historical power consumption impact coefficient and the predicted fluctuation coefficient, and normalizing the opposite of the product value to the maximum and minimum values ​​as the power storage reliability index at the current moment.

[0079] Among them, the predicted fluctuation coefficient is obtained by calculating the standard deviation, and the historical power consumption influence coefficient is integrated to obtain the product value. The larger the product value, the more unstable the predicted power consumption is and the worse the overall stability of the historical power consumption is. Therefore, the opposite of the product value is normalized to the maximum and minimum values ​​as the reliability indicator of power storage at the current moment.

[0080] Furthermore, in some embodiments of the present invention, new energy access control is performed at the current moment based on the power storage reliability index and the predicted power supply accuracy index, including: calculating the product of the power storage reliability index and the predicted power supply accuracy index, performing maximum and minimum value normalization processing to obtain the new energy access impact index at the current moment; determining whether to access new energy power generation based on the new energy access impact index.

[0081] Among them, the power storage reliability index is a reliability index obtained by analyzing historical power consumption and predicted power consumption changes. The power storage reliability index characterizes the stability of power consumption, that is, the larger the value of the power storage reliability index, the more stable the power consumption at the current moment; the predicted power supply accuracy index represents an analysis index for power supply after the current moment. The larger the value of the predicted power supply accuracy index, the more stable the subsequent power supply will be after combining the historical power supply and predicted power supply analysis.

[0082] Therefore, the product of the power storage reliability index and the predicted power supply accuracy index is directly calculated. The larger the product value is, the more stable the power supply and power consumption are at the current moment. The maximum and minimum values ​​are normalized to obtain the new energy access impact index at the current moment, so that the new energy access impact index can accurately represent the power stability at the corresponding moment.

[0083] Furthermore, in some embodiments of the present invention, whether to connect to new energy power generation is determined based on the new energy access impact index, including: when the new energy access impact index is less than a preset access threshold, not connecting to new energy power generation; when the new energy access impact index is greater than or equal to the preset access threshold, connecting to new energy power generation.

[0084] In the embodiment of the present invention, the preset access threshold is the threshold value of the new energy access impact index. Specifically, the preset access threshold may be 0.5, that is, when the new energy access impact index is less than 0.5, the new energy power generation is not connected; when the new energy access impact index is greater than or equal to 0.5, the new energy power generation is connected. It can be understood that since the new energy access impact index characterizes the power stability at the corresponding moment, that is, the larger the value of the new energy access index, the greater the stability of the overall power supply and power consumption. At this time, it is convenient to access the new energy power generation project to ensure the cleanliness of energy. After the unstable impact occurs, the new energy power generation project needs to be shut down in time to improve the stability of power supply and power consumption.

[0085] In summary, the embodiment of the present invention combines and analyzes historical data and predicted data; according to the numerical fluctuations of historical power supply data and predicted power supply data, the correlation between historical environmental data and predicted environmental data, and the numerical fluctuations of predicted power supply data, obtains the predicted power supply accuracy index for environmental prediction, and the predicted power supply accuracy index is an accuracy index combined with environmental changes for analysis, thereby improving the reliability of the predicted power supply analysis; determines the historical power consumption influence coefficient through the numerical changes of historical power consumption data, and determines the power storage reliability index at the current moment in combination with the numerical fluctuations of predicted power consumption data within the prediction time period, that is, analyzes the stable state of power storage itself from the power consumption dimension; so as to combine the power storage reliability index and the predicted power supply accuracy index to control the access of new energy at the current moment. In the embodiment of the present invention, the new energy access control can effectively combine the stability of power consumption and power supply, thereby ensuring the stable control of the overall power system, avoiding the forced intervention of unstable new energy generation during peak power consumption or power fluctuations, resulting in an increase in the overall power supply pressure, and improving the stability of power planning of the power system.

[0086] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for controlling a power system involving new energy, characterized in that: The preset time period before the current moment is used as the historical time period, and the preset time period after the current moment is used as the predicted time period, the method includes: Obtain historical environmental data, historical power consumption data, and historical power supply data at each sampling moment in the historical time period; obtain predicted environmental data, predicted power consumption data, and predicted power supply data at each sampling moment in the predicted time period; Determine the stability coefficient of the predicted power supply data according to the numerical fluctuations of the historical power supply data and the predicted power supply data, and the correlation between the historical environmental data and the predicted environmental data; determine the predicted power supply accuracy index for power supply based on environmental prediction in combination with the stability coefficient and the numerical fluctuations of the predicted power supply data; The historical time period is evenly divided into different sub-segments, and in the sub-segments adjacent in time sequence, the power consumption mutation index of the sub-segment is determined according to the numerical change of the historical power consumption data, and the power consumption mutation index of all sub-segments is combined to determine the historical power consumption influence coefficient at the current moment; Based on the historical power consumption impact coefficient and the numerical fluctuation of the predicted power consumption data in the predicted time period, the power storage reliability index at the current moment is determined; according to the power storage reliability index and the predicted power supply accuracy index, new energy access control is performed at the current moment.

2. A method for controlling a power system involving new energy sources as claimed in claim 1, characterized in that: Determining the stability coefficient of the predicted power supply data according to the numerical fluctuations of the historical power supply data and the predicted power supply data, and the correlation between the historical environment data and the predicted environment data, includes: According to the predicted power supply data at each sampling time within the prediction time period, a predicted power supply mean is obtained; according to the difference between the predicted power supply data at each sampling time and the predicted power supply mean, a predicted fluctuation index of the predicted power supply is determined; According to the historical power supply data at each sampling moment in the historical time period, the historical power supply average is obtained; according to the difference between the historical power supply data at each sampling moment and the historical power supply average, the historical fluctuation index of the historical power supply is determined; Performing correlation analysis on the historical environmental data and the predicted environmental data to obtain a correlation index; The historical fluctuation index and the predicted fluctuation index are weighted according to the correlation index to obtain a stability coefficient of the predicted power supply data.

3. A method for controlling a power system involving new energy as claimed in claim 2, characterized in that: The correlation analysis of the historical environmental data and the predicted environmental data is performed to obtain a correlation index, including: The Pearson correlation coefficient of the historical environmental data and the predicted environmental data is calculated, and the maximum and minimum values ​​of the Pearson correlation coefficient are normalized to obtain a correlation index.

4. A method for controlling a power system involving new energy as claimed in claim 3, characterized in that: The historical fluctuation index and the predicted fluctuation index are weighted according to the correlation index to obtain a stability coefficient of the predicted power supply data, including: Calculate the product of the correlation index and the historical volatility index, and use the sum of the product value and the predicted volatility index as the overall volatility index; The opposite number of the overall fluctuation index is normalized to the maximum and minimum values ​​to obtain the stability coefficient of the predicted power supply data.

5. A method for controlling a power system involving new energy sources as claimed in claim 1, characterized in that: Combining the stability coefficient and the numerical fluctuation of the predicted power supply data, determining the predicted power supply accuracy index for power supply based on environmental prediction, including: Calculating the numerical variance of all the predicted power supply data, and normalizing the opposite of the numerical variance to the maximum and minimum values ​​to obtain an accurate influence coefficient; The product value of the accurate influence coefficient and the stability coefficient is calculated as an indicator for predicting power supply accuracy.

6. A method for controlling a power system involving new energy sources as claimed in claim 1, characterized in that: According to the numerical changes of historical power consumption data, determine the power consumption mutation indicators within the historical time period, including: Calculate the range of historical power consumption data at all sampling moments in the same sub-segment to obtain the power consumption range of the corresponding sub-segment; Determine two other sub-segments that are closest to any sub-segment in time sequence as adjacent sub-segments; The absolute values ​​of the differences between the extreme differences in power consumption of a sub-segment and its adjacent sub-segments are averaged to obtain the power consumption mutation index of the sub-segment.

7. A method for controlling a power system involving new energy sources as claimed in claim 1, characterized in that: Combining the power consumption mutation index of all sub-segments, determining the historical power consumption impact coefficient at the current moment, including: The mean of the power consumption mutation index of all sub-segments is calculated as the historical power consumption impact coefficient at the current moment.

8. A method for controlling a power system involving new energy sources as claimed in claim 1, characterized in that: Based on the historical power consumption impact coefficient and the numerical fluctuation of the predicted power consumption data within the predicted time period, determining the power storage reliability index at the current moment, including: Taking the numerical standard deviation of the predicted power consumption data at all sampling moments within the predicted time period as the predicted fluctuation coefficient; The product of the historical power consumption impact coefficient and the predicted fluctuation coefficient is calculated, and the opposite of the product value is normalized to the maximum and minimum values ​​to serve as the power storage reliability indicator at the current moment.

9. A method for controlling a power system involving new energy sources as claimed in claim 1, characterized in that: According to the power storage reliability index and the predicted power supply accuracy index, new energy access control is performed at the current moment, including: Calculate the product of the power storage reliability index and the predicted power supply accuracy index, and perform maximum and minimum value normalization processing to obtain the new energy access impact index at the current moment; Whether to connect to new energy power generation is determined according to the new energy access impact index.

10. A method for controlling a power system involving new energy sources as claimed in claim 9, characterized in that: Determining whether to connect to new energy power generation based on the new energy access impact index includes: When the new energy access impact index is less than the preset access threshold, the new energy power generation is not connected; When the new energy access impact index is greater than or equal to a preset access threshold, new energy is connected for power generation.

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