Method for predicting power system new energy cluster output fault tolerance

Abstract

The invention discloses a prediction method for power system new energy cluster output fault tolerance, and belongs to the technical field of power system new energy clusters, and the method comprises the steps: obtaining a current measurement value of a parameter related to the operation of a power system according to a time sequence; determining a predicted value of the next time period based on an influence factor that the previous measured value has an influence on the value of the parameter of the next time period and the current measured value; and predicting a fault tolerance index of the next time period according to the predicted value, and adjusting the output of the new energy cluster of the target power system according to the predicted fault tolerance index of the next time period. According to the method, the output of the new energy cluster of the power system can be adjusted in real time according to the predicted fault tolerance index, and reliable power supply of the power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy clusters in power systems, and in particular to a method for predicting output fault tolerance of new energy clusters in power systems. Background Art

[0002] With the continuous advancement of new energy-related power system technologies, the output capacity of new energy clusters in power systems is gradually becoming an important focus of industry attention. This cluster output method can not only effectively integrate multiple renewable energy sources such as solar energy and wind energy, promote the diversification of energy structure, but also significantly reduce the dependence on traditional fossil energy and promote the development of a green and low-carbon economy. Among the existing technologies, through the application of intelligent management and dispatching technologies, the power system can more flexibly respond to fluctuations in power demand and changes in industrial production patterns, ensuring stable and reliable power supply under various circumstances.

[0003] But at the same time, as the proportion of new energy continues to increase, its own uncertainty affects the fault tolerance of the output of new energy clusters. Therefore, at this stage, it is necessary to accurately predict the fault tolerance of the output of new energy clusters. Summary of the invention

[0004] The present invention provides a method for predicting the output fault tolerance of a new energy cluster of an electric power system, so as to solve the defect in the prior art that the output fault tolerance of a new energy cluster of an electric power system cannot be predicted.

[0005] On the one hand, the present invention provides a method for predicting the output fault tolerance of a new energy cluster in an electric power system, the method comprising the following steps: obtaining current measured values ​​of parameters related to the operation of an electric power system of a target new energy cluster in time sequence; determining predicted values ​​of the parameters related to the operation of the electric power system in the next time period based on an influence factor of the current measured value on the value of the parameter related to the operation of the electric power system in the next time period, and the current measured value; predicting a fault tolerance index of the target new energy cluster in the next time period based on the determined predicted values ​​of the parameters related to the operation of the electric power system, the fault tolerance index being used to determine the fault tolerance of the output of the new energy cluster in the electric power system; and adjusting the output of the target new energy cluster in the electric power system based on the predicted fault tolerance index for the next time period to ensure reliable power supply to the electric power system.

[0006] Optionally, before obtaining the current measured values ​​of parameters related to the operation of the target power system new energy cluster according to the time sequence, the prediction method also includes: defining the fault tolerance index according to the fault tolerance characteristics of the output of the power system new energy cluster; and determining the parameters related to the operation of the power system that need to be measured based on the defined fault tolerance index.

[0007] Optionally, the parameters related to the operation of the power system include at least one of the following parameters: the total load power in the power system, the total output power of thermal power units in the power system, the total output power of wind power units in the power system, the total output power of photovoltaic power stations in the power system, and the total output power of energy storage in the power system.

[0008] Optionally, the impact factor is expressed by the following formula:

[0009]

[0010] Where i is the i-th time period, P' l,min is the total load power in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total load power in the power system in time period i, P' f,min is the total output power of thermal power units in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of thermal power units in the power system in time period i, P' w,min is the total output power of wind turbines in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of wind turbines in the power system in time period i, P' p,min is the total output power of the photovoltaic power station in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of the photovoltaic power station in the power system in the i-th time period, P' e,min is the total output power of energy storage in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, It is the measured value of the total output power of energy storage in the power system in time period i.

[0011] Optionally, the predicted value of the parameter related to the operation of the power system in the next time period is determined by the following formula:

[0012]

[0013] in, T m+1 The predicted value of the total load power in the power system during the time period, T m+1 The predicted value of the total output power of thermal power units in the power system during the time period, T m+1 The predicted value of the total output power of wind turbines in the power system during the time period, T m+1 The predicted value of the total output power of the photovoltaic power station in the power system during the time period, T m+1 The predicted value of the total output power of energy storage in the power system during the time period, are the measured values ​​of the total load power in the power system in the 1st, 2nd, ..., i, ..., mth time periods, respectively. are the measured values ​​of the total output power of thermal power units in the power system in the 1st, 2nd, ..., i, ..., m time periods, respectively. are the measured values ​​of the total output power of wind turbines in the power system in the 1st, 2nd, ..., i, ..., m time periods, respectively. are the measured values ​​of the total output power of the photovoltaic power station in the power system in the 1st, 2nd, ..., i, ..., m time periods, respectively. are the measured values ​​of the total output power of energy storage in the power system in the 1st, 2nd, ..., i, ..., m time periods respectively.

[0014] Optionally, after determining the predicted value of the parameter related to the operation of the power system for the next time period, the prediction method also includes: normalizing the predicted value of the parameter related to the operation of the power system for the next time period to obtain a processed normalized value; and determining the fault tolerance index of the new energy cluster of the power system for the next time period based on the normalized value.

[0015] Optionally, the fault tolerance index of the next time period is expressed by the following formula:

[0016]

[0017] in, is the predicted fault tolerance index of the output of the new energy cluster of the power system in the next time period, i is the i-th time period, and is the time period of m time intervals. It is T m+1 The normalized value of the total load power forecast value in the power system during the time period, It is T m+1 The normalized value of the predicted total output power of thermal power units in the power system during the time period, It is T m+1The normalized value of the predicted total output power of wind turbines in the power system during the time period, It is T m+1 The normalized value of the predicted total output power of the photovoltaic power station in the power system during the time period, It is T m+1 Normalized value of the total output power forecast of energy storage in the power system during the time period.

[0018] Optionally, the output of the new energy cluster of the target power system is adjusted according to the predicted fault tolerance index of the next time period, including: when the predicted fault tolerance index of the next time period exceeds a preset threshold, adding new energy output; and when the predicted fault tolerance index of the next time period is less than the preset threshold, reducing the new energy output and increasing the output of conventional thermal power units and energy storage.

[0019] On the other hand, the present invention also provides a control device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned prediction methods.

[0020] On the other hand, the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions enable a machine to execute any one of the above prediction methods.

[0021] The method for predicting the output fault tolerance of a new energy cluster of an electric power system provided by the present invention obtains the current measured value of a parameter of a new energy cluster of a target electric power system related to the operation of the electric power system, and determines the influencing factor affecting the value of the parameter related to the operation of the electric power system based on the measured value; then, according to the influencing factor and the measured value, determines the predicted value of the parameter related to the operation of the electric power system for the next time period, and according to the predicted value, predicts the fault tolerance index of the new energy cluster of the target electric power system for the next time period; and finally adjusts the output of the new energy cluster of the target electric power system according to the fault tolerance index. Through the above method, the present invention establishes an effective method for predicting the output fault tolerance of the new energy cluster of the electric power system in the next stage, and realizes the beneficial effect of being able to adjust the output of the new energy cluster of the electric power system in real time according to the predicted fault tolerance index, thereby ensuring the reliable power supply of the electric power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0023] Figure 1 It is a flow chart of a method for predicting output fault tolerance of a new energy cluster in a power system provided by an embodiment of the present invention.

[0024] Figure 2 A flowchart of a method for predicting output fault tolerance of a new energy cluster in a power system provided in another embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] As mentioned above, as the proportion of new energy continues to increase, its own uncertainty affects the fault tolerance of the output of the new energy cluster. Therefore, at this stage, it is necessary to accurately predict the fault tolerance of the output of the new energy cluster, so as to adjust the output of the new energy cluster of the target power system and ensure the reliable power supply of the power system.

[0027] To address this problem, the present invention provides a method for predicting the output fault tolerance of a new energy cluster in an electric power system. By real-time monitoring of relevant operating parameters of the electric power system and predicting the predicted values ​​of the parameters for the next time period, the output fault tolerance index of the new energy cluster in the electric power system for the next time period is calculated, thereby establishing an effective method for predicting the output fault tolerance of the new energy cluster in the electric power system in the next stage, which solves the defects existing in the current technology.

[0028] Combine the following Figure 1 and Figure 2 The present invention is described in detail.

[0029] Figure 1 FIG. 1 is a flow chart of a method for predicting the output fault tolerance of a new energy cluster in a power system provided by an embodiment of the present invention. Figure 1 As shown, a method for predicting output fault tolerance of a new energy cluster of a power system provided by an embodiment of the present invention may be executed by a controller, and the method may include the following steps:

[0030] Step 101: acquiring current measured values ​​of parameters related to power system operation of a target power system new energy cluster according to a time sequence;

[0031] Step 102: determining a predicted value of the parameter related to the operation of the power system in the next time period based on an influence factor of the current measurement value on the value of the parameter related to the operation of the power system in the next time period and the current measurement value;

[0032] Step 103: predicting the fault tolerance index of the target power system new energy cluster in the next time period according to the determined predicted values ​​of the parameters related to the power system operation;

[0033] Step 104: According to the predicted fault tolerance index of the next time period, the output of the new energy cluster of the target power system is adjusted to ensure reliable power supply of the power system.

[0034] The method for predicting the output fault tolerance of a new energy cluster of an electric power system provided by the present invention obtains the current measured value of a parameter of a new energy cluster of a target electric power system related to the operation of the electric power system, and determines the influencing factor that affects the value of the parameter related to the operation of the electric power system based on the measured value; then, based on the influencing factor and the measured value, the predicted value of the parameter related to the operation of the electric power system for the next time period is determined, and based on the predicted value, the fault tolerance index of the new energy cluster of the target electric power system for the next time period is predicted, and based on the fault tolerance index, the output of the new energy cluster of the target electric power system is finally adjusted, thereby ensuring reliable power supply to the electric power system.

[0035] In order to refine and explain the steps of adjusting the output of the power system new energy cluster in real time according to the calculation results of the present invention, the present invention also provides a preferred embodiment. In a preferred embodiment of the present invention, the output of the power system new energy cluster includes but is not limited to the output of new energy units, the output of conventional standby units such as thermal power, and the output of energy storage devices, such as Figure 2 shown.

[0036] Before step 101, the prediction method may further include: defining the fault tolerance index according to the fault tolerance characteristics of the output of the new energy cluster of the power system, and determining the parameters related to the operation of the power system that need to be measured according to the defined fault tolerance index.

[0037] In a preferred embodiment of the present invention, the fault tolerance characteristics of the output of the new energy cluster of the power system can be analyzed by data and / or through expert experience, and the fault tolerance index can be defined as:

[0038]

[0039] Among them, EWSC er is the output fault tolerance index of the new energy cluster of the power system, T1, T2, ..., T i ,...,Tm is m time periods (hereinafter also referred to as time intervals), where m is a natural number, m∈1,2,L, i is the i-th time period, and i is a natural number, i∈{1,2,L,m}, It is T i Total load power in the power system during the time period, It is T i Total output power of thermal power units in the power system during the time period, It is T i Total output power of wind turbines in the power system during the time period, It is T i Total output power of photovoltaic power station in the power system during the time period, It is T i Total output power of energy storage in the power system during the time period, P l,max is T1,T2,...,T i ,...,T m The maximum value of the total power measured by the power system load in this m time period, P f,max is T1,T2,...,T i ,...,T m The maximum value of the total output power measured by the thermal power units in the power system during this m time period, P w,max is T1,T2,...,T i ,...,T m The maximum value of the total output power measured by the wind turbines in the power system during this m time period, P p,max is T1,T2,...,T i ,...,T m The maximum value of the total output power measured by the photovoltaic power station of the power system in this m time period, P e,max is T1,T2,...,T i ,...,T m The maximum value of the total energy storage output power measurement of the power system during these m time periods.

[0040] Before step 101, the prediction method may further include: determining parameters related to the operation of the power system that need to be measured according to the defined fault tolerance index.

[0041] Based on this, in a preferred embodiment of the present invention, the parameters related to the operation of the power system include at least one of the following parameters: the total load power in the power system, the total output power of thermal power units in the power system, the total output power of wind power units in the power system, the total output power of photovoltaic power stations in the power system, and the total output power of energy storage in the power system.

[0042] In step 101, current measured values ​​of parameters related to power system operation of a target power system new energy cluster are obtained according to time sequence.

[0043] Based on this, a preferred embodiment of the present invention can time-order the current measured values ​​of the parameters related to the operation of the power system to obtain the time series of the parameters related to the operation of the power system, and the time series is described as follows:

[0044]

[0045] in, are the measured values ​​of the total load power in the power system in the 1st, 2nd, ..., zth, ..., mth time periods, respectively. are the measured values ​​of the total output power of thermal power units in the power system in the 1st, 2nd, ..., zth, ..., mth time periods, respectively. are the measured values ​​of the total output power of wind turbines in the power system in the 1st, 2nd, ..., zth, ..., mth time periods, respectively. are the measured values ​​of the total output power of the photovoltaic power station in the power system in the 1st, 2nd, ..., z, ..., mth time periods, respectively. are the measured values ​​of the total output power of energy storage in the power system in the 1st, 2nd, ..., z, ..., m time periods respectively.

[0046] With m=6 (i.e., T m =T6) as an example, it is determined that the current measured values ​​of the parameters related to the operation of the power system of the target power system new energy cluster in the time periods T1, T2, T3, T4, T5, and T6 need to be obtained, and they are sorted according to time, and the time series is obtained as follows:

[0047]

[0048] As an example, in a preferred embodiment of the present invention, the time interval in the measurement process can be set to 1h, and 6 measurement time periods are selected, namely, T1, T2, T3, T4, T5, and T6 time periods, and the total load power in the power system is measured in the time periods T1, T2, T3, T4, T5, and T6. Measured value of total output power of thermal power units Measured value of total output power Measured value of total output power of photovoltaic power station Measured value of total output power of energy storage

[0049] In step 102, the prediction method may further include: determining the predicted value of the parameter related to the operation of the power system in the next time period based on the influencing factor of the current measurement value on the value of the parameter related to the operation of the power system in the next time period, and the current measurement value.

[0050] Based on this, in a preferred embodiment of the present invention, the impact factor As shown below:

[0051]

[0052] Among them, P' l,min is the total load power in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total load power in the power system in time period i, P' f,min is the total output power of thermal power units in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of thermal power units in the power system in time period i, P' w,min is the total output power of wind turbines in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of wind turbines in the power system in time period i, P' p,min is the total output power of the photovoltaic power station in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of the photovoltaic power station in the power system in the i-th time period, P' e,min is the total output power of energy storage in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of energy storage in the power system in time period i.

[0053] As an example, in a preferred embodiment of the present invention, the impact factor can be obtained based on the above six time periods T1, T2, T3, T4, T5, and T6: The predicted value of the parameter related to the operation of the power system is obtained as shown in the following formula:

[0054]

[0055] in, T m+1 The predicted value of the total load power in the power system during the time period, T m+1 The predicted value of the total output power of thermal power units in the power system during the time period, T m+1 The predicted value of the total output power of wind turbines in the power system during the time period, T m+1 The predicted value of the total output power of the photovoltaic power station in the power system during the time period, T m+1 The predicted value of the total output power of energy storage in the power system during the time period.

[0056] As an example, in a preferred embodiment of the present invention, based on formula (5), the following parameter prediction values ​​can be obtained according to the above example values:

[0057]

[0058] in, is the predicted value of the total load power in the power system during the T7 period, is the predicted value of the total output power of thermal power units in the power system during the T7 period, is the predicted value of the total output power of wind turbines in the power system during the T7 period, is the predicted value of the total output power of the photovoltaic power station in the power system during the T7 period, It is the predicted value of the total output power of energy storage in the power system during the T7 period.

[0059] In a preferred embodiment of the present invention, after step 102, the prediction method may further include: normalizing the predicted values ​​of the parameters related to the operation of the power system for the determined next time period to obtain processed normalized values; and determining the fault tolerance index of the new energy cluster of the power system for the next time period based on the normalized values.

[0060] Based on this, in a preferred embodiment of the present invention, the parameters related to the operation of the power system can be normalized by the following formula:

[0061]

[0062] in, T m+1 Normalized value of the total load power forecast value in the power system during the time period, P' l,max is T1,T2,...,T z,...,T m The maximum value of the total load power measurement value in the power system during these m time periods, T m+1 Normalized value of the total output power forecast of thermal power units in the power system during the time period, P' f,max is T1,T2,...,T z ,...,T m The maximum value of the total output power measured by the thermal power units in the power system during this m time period, T m+1 Normalized value of the predicted total output power of wind turbines in the power system during the time period, P' w,max is T1,T2,...,T z ,...,T m The maximum value of the total output power measured by wind turbines in the power system during this m time period, T m+1 Normalized value of the total output power forecast of the photovoltaic power station in the power system during the time period, P' pmax is T1,T2,...,T z ,...,T m The maximum value of the total output power measured by the photovoltaic power station in the power system during this m time period, T m+1 Normalized value of the total output power forecast of energy storage in the power system during the time period, P' e,max is T1,T2,...,T z ,...,T m The maximum value of the total energy storage output power measured in the power system during these m time periods.

[0063] As an example, in a preferred embodiment of the present invention, according to the above example values, since T m =T6, then T m+1 =T7, thus the predicted value of the parameter related to the operation of the power system in the determined time period T7 is normalized, and the formula for obtaining the processed normalized value is as follows:

[0064]

[0065] in, is the normalized value of the total load power forecast value in the power system during the T7 period, is the normalized value of the predicted total output power of thermal power units in the power system during the T7 period, is the normalized value of the predicted total output power of wind turbines in the power system during the T7 period, is the normalized value of the predicted total output power of the photovoltaic power station in the power system during the T7 period, It is the normalized value of the predicted total output power of energy storage in the power system during the T7 period.

[0066] In step 103, the prediction method may further include: determining a fault tolerance index of the new energy cluster of the power system in the next time period.

[0067] Based on this, in a preferred embodiment of the present invention, the process of determining the predicted fault tolerance index of the output of the new energy cluster of the power system in the next time period is shown as follows:

[0068]

[0069] It is the predicted fault tolerance index of the output of the new energy cluster of the power system in the next time period.

[0070] As an example, in a preferred embodiment of the present invention, the fault tolerance index of the T7 time period is:

[0071]

[0072] After step 104, the prediction method may further include: when the predicted fault tolerance index of the next time period exceeds a preset threshold, adding new renewable energy output. When the predicted fault tolerance index of the next time period is less than the preset threshold, reducing the renewable energy output and increasing the output of conventional thermal power units and energy storage.

[0073] By way of example, the preset threshold (e.g., 0.5) can be determined by data analysis and / or by expert experience. For example, if the predicted value of the output fault tolerance index of the power system new energy cluster exceeds 0.5, it is determined that the output fault tolerance capability of the power system new energy cluster is strong in the next time period, and the output of new energy can be increased at this time; if the predicted value of the output fault tolerance index of the power system new energy cluster is less than 0.5, it is determined that the output fault tolerance capability of the power system new energy cluster is poor in the next time period, and the output of new energy can be reduced, and the output of conventional thermal power units and energy storage can be increased.

[0074] As an example, in a preferred embodiment of the present invention, if the predicted value of the output fault tolerance index of the new energy cluster of the power system in the T7 period is obtained If the fault tolerance index of the power system's new energy cluster output is greater than or equal to 0.5, it is considered that the power system's new energy cluster output fault tolerance capability is good during the T8 period, and the new energy output is increased at this time. If it is less than 0.5, it is considered that the fault tolerance of the power system's new energy cluster output is poor during the T8 period. At this time, the output of new energy should be reduced, and the output of conventional thermal power units and energy storage should be increased.

[0075] According to the prediction method provided in this embodiment, by real-time monitoring and predicting the predicted values ​​of parameters for the next time period for the total load power in the power system, the total output power of thermal power units in the power system, the total output power of wind power units in the power system, the total output power of photovoltaic power stations in the power system, and the total output power of energy storage in the power system, and calculating the output fault tolerance of the new energy cluster of the power system based on the obtained monitoring parameters and the predicted values ​​of the parameters for the next time period, an effective method for predicting the output fault tolerance of the new energy cluster of the power system in the next stage is established, which solves the defect of the current lack of a method for predicting the output fault tolerance of the new energy cluster of the power system, and achieves the beneficial effect of adjusting the output of new energy units, conventional standby units such as thermal power, and the output of energy storage devices in real time according to the calculation results to ensure reliable power supply of the power system.

[0076] Based on the same general inventive concept, the present invention also protects a control device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement any one of the above-mentioned embodiments for predicting the output fault tolerance of a new energy cluster of a power system.

[0077] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0078] On the other hand, the present invention also protects a machine-readable storage medium having instructions stored thereon, the instructions enabling a machine to execute any one of the above-mentioned methods for predicting the output fault tolerance of a new energy cluster of a power system.

[0079] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0080] In addition, the terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0081] It should be understood that in the embodiment of the present invention, "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0082] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0084] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0085] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0086] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0087] Through the description of the above embodiments, it is clear to those skilled in the art that the present invention can be implemented in hardware, firmware, or a combination thereof. When software is used for implementation, the above functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a computer can access. Taking this as an example but not limited to: a computer-readable medium can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer. In addition. Any connection can be appropriately a computer-readable medium. For example, if the software is transmitted from a website, a server or other remote source using a coaxial cable, an optical fiber cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, then the coaxial cable, optical fiber cable, twisted pair, DSL, or wireless technologies such as infrared, wireless, and microwaves are included in the fixing of the medium. As used in the present invention, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blue-ray disc, wherein disk usually reproduces data magnetically, while disc uses laser to reproduce data optically. The above combination should also be included in the scope of protection of computer-readable media.

[0088] In short, the above is only a preferred embodiment of the technical solution of the present invention, and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A prediction method for the fault tolerance of the output of a new energy cluster in a power system, characterized in that: The prediction method comprises the following steps: Obtaining current measured values ​​of parameters related to power system operation of the target power system new energy cluster according to the time sequence; Determine a predicted value of the parameter related to the operation of the power system in the next time period based on an influence factor of the current measurement value on the value of the parameter related to the operation of the power system in the next time period and the current measurement value; According to the predicted values ​​of the parameters related to the operation of the power system, the fault tolerance index of the new energy cluster of the target power system in the next time period is predicted. The fault tolerance index is used to determine the fault tolerance of the output of the new energy cluster of the power system; and According to the predicted fault tolerance index of the next time period, the output of the new energy cluster of the target power system is adjusted to ensure the reliable power supply of the power system.

2. The prediction method according to claim 1, characterized in that: Before obtaining the current measured values ​​of the parameters related to the operation of the power system of the target power system new energy cluster according to the time sequence, the prediction method further includes: Defining the fault tolerance index according to the fault tolerance characteristics of the output of the new energy cluster of the power system; and According to the defined fault tolerance index, parameters related to the operation of the power system that need to be measured are determined.

3. The prediction method according to claim 1 or 2, characterized in that: The parameters related to the operation of the power system include at least one of the following parameters: the total load power in the power system, the total output power of thermal power units in the power system, the total output power of wind power units in the power system, the total output power of photovoltaic power stations in the power system, and the total output power of energy storage in the power system.

4. The prediction method according to claim 1, characterized in that: The impact factor is expressed by the following formula: Among them, i is the i-th time period, P' l,min is the total load power in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total load power in the power system in time period i, P' f,min is the total output power of thermal power units in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of thermal power units in the power system in time period i, P' w,min is the total output power of wind turbines in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of wind turbines in the power system in time period i, P' p,min is the total output power of the photovoltaic power station in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, is the measured value of the total output power of the photovoltaic power station in the power system in the i-th time period, P' e,min is the total output power of energy storage in the power system at T1, T2, ..., T i ,...,T m The minimum value of the measured value in these m time periods, It is the measured value of the total output power of energy storage in the power system in time period i.

5. The prediction method according to claim 1 or 4, characterized in that: The predicted value of the parameter related to the operation of the power system in the next time period is determined by the following formula: in, T m+1 The predicted value of the total load power in the power system during the time period, T m+1 The predicted value of the total output power of thermal power units in the power system during the time period, T m+1 The predicted value of the total output power of wind turbines in the power system during the time period, T m+1 The predicted value of the total output power of the photovoltaic power station in the power system during the time period, T m+1 The predicted value of the total output power of energy storage in the power system during the time period, are the measured values ​​of the total load power in the power system in the 1st, 2nd, ..., i, ..., m time periods, respectively. are the measured values ​​of the total output power of thermal power units in the power system in the 1st, 2nd, ..., i, ..., m time periods, respectively. are the measured values ​​of the total output power of wind turbines in the power system in the 1st, 2nd, ..., i, ..., m time periods, respectively. are the measured values ​​of the total output power of the photovoltaic power station in the power system in the 1st, 2nd, ..., i, ..., m time periods, respectively. are the measured values ​​of the total output power of energy storage in the power system in the 1st, 2nd, ..., i, ..., m time periods respectively.

6. The prediction method according to claim 1, characterized in that: After determining the predicted value of the parameter related to the power system operation in the next time period, the prediction method further includes: Normalizing the predicted value of the parameter related to the operation of the power system in the determined next time period to obtain a processed normalized value; and According to the normalized value, a fault tolerance index of the new energy cluster of the power system for the next time period is determined.

7. The prediction method according to claim 1, characterized in that: The fault tolerance index of the next time period is expressed by the following formula: in, is the predicted fault tolerance index of the output of the new energy cluster of the power system in the next time period, i is the i-th time period, and is the time period of m time intervals. It is T m+1 The normalized value of the total load power forecast value in the power system during the time period, It is T m+1 The normalized value of the predicted total output power of thermal power units in the power system during the time period, It is T m+1 The normalized value of the predicted total output power of wind turbines in the power system during the time period, It is T m+1 Normalized value of the predicted total output power of the photovoltaic power station in the power system during the time period, It is T m+1 Normalized value of the predicted total output power of energy storage in the power system during the time period.

8. The prediction method according to claim 1, characterized in that: The step of adjusting the output of the target power system new energy cluster according to the predicted fault tolerance index of the next time period includes: When the predicted fault tolerance index of the next time period exceeds a preset threshold, adding new energy output; and When the predicted fault tolerance index of the next time period is less than the preset threshold, the output of new energy is reduced, and the output of conventional thermal power units and energy storage is increased.

9. A control device, characterized in that: The control device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the prediction method according to any one of claims 1-8.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions, which enable the machine to execute the prediction method according to any one of claims 1-8.

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