Evaluation method and equipment for standby demand of power system, and medium

Abstract

The invention discloses a power system standby demand assessment method and device and a medium. The method comprises the following steps: determining a time scale level according to calling time of each standby resource and response time of each standby resource; obtaining a day-ahead net load prediction error curve; according to the time scale hierarchy, adopting an integrated empirical mode decomposition method to decompose the day-ahead net load prediction error curve to obtain a plurality of fluctuation components corresponding to the time scale hierarchy; and performing envelope reconstruction on each fluctuation component according to the time scale hierarchy to obtain a standby demand of each time scale hierarchy so as to obtain a standby demand of the power system. According to the method, the standby demand of the power system can be flexibly evaluated, cost waste can be avoided, and stable operation of the power system can be ensured.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a method, device, and medium for assessing the reserve requirements of power systems. Background Technology

[0002] In recent years, with the continuous advancement of new power system construction and the gradual increase in the penetration rate of new energy sources, the dual uncertainties of new energy sources and loads have increasingly highlighted the challenges to power system frequency security and real-time balance. The power grid faces multi-scale reserve regulation needs. Insufficient reserves may lead to reliability risks or a reduction in wind power generation, while excessive reserves will significantly increase operating costs. Therefore, accurate assessment of reserve needs and advance reservation of multi-timescale reserve capacity for all periods of the day based on the assessment are crucial to ensuring stable system operation.

[0003] Currently, scholars both domestically and internationally have conducted extensive research on the optimization of reserve demand assessment. There are three main research methods for reserve optimization: first, deterministic methods, which often allocate a certain percentage of capacity from a specific generating unit or a portion of the load as reserve. This reserve allocation method is widely used in power systems but lacks flexibility. Second, probabilistic methods based on probability density; and third, probabilistic methods based on scenario analysis. While these two methods are innovative, they fail to consider the time scale of reserve, and optimizing reserve capacity only for a single time scale leads to wasted costs. Therefore, current reserve demand assessment suffers from insufficient flexibility and wasted costs, resulting in an imbalance between system stability and cost reduction. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method, device, and medium for assessing the reserve requirements of a power system, which can flexibly assess the reserve requirements of a power system, avoiding waste of costs while ensuring the stable operation of the power system.

[0005] The present invention also proposes equipment and media for evaluating the aforementioned power system backup requirements.

[0006] A method for assessing power system reserve requirements according to a first aspect of the present invention includes:

[0007] The time scale level is determined based on the call time and response time of each backup resource;

[0008] Obtain the day-ahead net load forecast error curve;

[0009] The day-ahead net load forecast error curve is decomposed using the integrated empirical mode decomposition method based on the time scale level to obtain several fluctuation components corresponding to the time scale level.

[0010] The envelopes of each fluctuation component are reconstructed according to the time scale level to obtain the reserve requirements of each time scale level, thereby obtaining the reserve requirements of the power system.

[0011] According to an embodiment of the present invention, a method for assessing the reserve demand of a power system has at least the following beneficial effects: Since the fluctuations of renewable energy reserve resources exhibit significant time-varying and multi-scale characteristics, traditional methods cannot precisely match reserve demand according to time scales. Therefore, the present invention divides the entire day into time scales based on the characteristics of the time-varying nature of reserve resources; and employs an integrated empirical mode decomposition method to decompose the day-ahead net load forecast error curve into fluctuation components at different time scale levels, thereby extracting fluctuation characteristics of different frequency bands, quantifying reserve demand, and finally forming reserve demand based on the time-varying nature of reserve resources. This method can flexibly assess the reserve demand of the power system, avoiding cost waste and ensuring the stable operation of the power system.

[0012] According to some embodiments of the present invention, the step of decomposing the day-ahead net load forecast error curve using an integrated empirical mode decomposition method based on the time scale level to obtain several fluctuation components corresponding to the time scale level includes:

[0013] The pre-defined white noise sequence from the white noise set is added sequentially to the net load forecast error curve, and empirical mode decomposition is performed to obtain several modal components. These several modal components are then used as a modal residual set until all white noise sequences in the white noise set have been added. The several modal components correspond to a certain time scale, which belongs to a certain time scale level.

[0014] The mean values ​​of the modal components in several modal residual sets are calculated to obtain several fluctuation components.

[0015] According to some embodiments of the present invention, the step of reconstructing the envelope of each fluctuation component according to the time scale hierarchy to obtain the backup requirements of each time scale hierarchy includes:

[0016] Perform a Fast Fourier Transform on each wave component to obtain the spectrum of each wave component, and then obtain the dominant period of each wave component.

[0017] Perform Hilbert transform on each dominant component to extract the envelope; wherein, the dominant component is the component in the fluctuation component whose period is the dominant period;

[0018] Based on the dominant period and envelope of each fluctuation component, the fluctuation components are mapped to a preset time scale level to obtain the backup demand range of each time scale level.

[0019] Based on the range of reserve requirements at each time scale level, the reserve requirements at each time scale level are obtained.

[0020] According to some embodiments of the present invention, the step of performing a Fast Fourier Transform on each wave component to obtain the spectrum of each wave component, thereby obtaining the dominant period of each wave component, includes:

[0021] Perform a Fast Fourier Transform on each wave component to obtain the spectrum of each wave component;

[0022] Based on the spectrum of each wave component, the power spectral density of each wave component at different frequencies is calculated.

[0023] Determine the maximum power spectral density of each fluctuation component;

[0024] The dominant period of each wave component is obtained based on the maximum power spectral density of each wave component.

[0025] According to some embodiments of the present invention, the step of mapping the fluctuation components to a preset time scale level based on the dominant period and envelope of each fluctuation component to obtain the reserve demand range of each time scale level includes:

[0026] For each fluctuation component, if the dominant period of the fluctuation component is determined to be within a certain time scale level, then the fluctuation component is mapped to that time scale level.

[0027] The envelopes of each fluctuation component mapped to the same time scale are superimposed to obtain the range of reserve demand at each time scale.

[0028] According to some embodiments of the present invention, the superposition of the envelopes of each fluctuation component mapped to the same time scale level to obtain the reserve requirement range for each time scale level includes:

[0029] The envelopes of each fluctuation component mapped to the same time scale are superimposed to obtain the envelope of the superimposed total component.

[0030] For the total component at each time scale level, within a preset scheduling period, the maximum upper and lower amplitude of the envelope of the total component is taken as the range of backup demand corresponding to the time scale level.

[0031] According to some embodiments of the present invention, determining the time scale hierarchy based on the call time and response time of each backup resource includes:

[0032] The complete response time of each backup resource is obtained based on the call time and response time of each backup resource.

[0033] The time scale is classified according to the complete response time of each backup resource to obtain a time scale hierarchy; wherein the complete response time is less than half of the time scale.

[0034] According to some embodiments of the present invention, the time scale hierarchy is a five-level time scale hierarchy, and the number of levels is proportional to the call time and response time of the backup resources;

[0035] The backup requirements of the power system include:

[0036] Based on the maximum single fault in the power system, the emergency reserve requirement is obtained;

[0037] The emergency reserve demand is superimposed onto the upward adjustment reserve demand at the first to third time scale levels to obtain the reserve demand of the power system.

[0038] An electronic device according to a second aspect of the present invention includes:

[0039] Memory, used to store programs;

[0040] A processor for executing a program stored in the memory, wherein when the processor executes the program stored in the memory, the processor is configured to perform the method as described in any one of the first aspects.

[0041] According to a third aspect of the present invention, a storage medium stores computer-executable instructions for performing the method as described in any one of the first aspects.

[0042] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0043] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0044] Figure 1 This is a flowchart of a method for assessing the reserve requirements of a power system according to an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram illustrating the effect of step S300 in a method for assessing the reserve requirements of a power system provided in another embodiment of the present invention;

[0046] Figure 3This is a schematic diagram illustrating the effect of step S400 in a method for assessing the reserve requirements of a power system provided in another embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] It should be understood that in the description of the embodiments of the present invention, "multiple" (or "amounts") means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. If "first," "second," etc., are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0049] like Figure 1 As shown, this embodiment of the invention provides a method for assessing the reserve requirement of a power system, including:

[0050] Step S100: Determine the time scale level based on the call time and response time of each backup resource;

[0051] Step S200: Obtain the day-ahead net load forecast error curve;

[0052] Step S300: Decompose the day-ahead net load forecast error curve according to the time scale level using the integrated empirical mode decomposition method to obtain several fluctuation components corresponding to the time scale level.

[0053] Step S400: Reconstruct the envelope of each fluctuation component according to the time scale level to obtain the reserve demand of each time scale level, thereby obtaining the reserve demand of the power system.

[0054] Because the fluctuations of renewable energy reserve resources exhibit significant time-varying and multi-scale characteristics, traditional methods cannot precisely match reserve demand according to time scales. Therefore, this invention divides the entire day into time scales based on the time-varying characteristics of reserve resources, thereby constructing a time scale hierarchy. Furthermore, it employs an integrated empirical mode decomposition method to decompose the day-ahead net load forecast error curve into fluctuation components at different time scale levels, thereby extracting fluctuation characteristics of different frequency bands, quantifying reserve demand, and ultimately forming reserve demand based on the time-varying fluctuations of reserve resources. This approach enables flexible assessment of power system reserve demand, avoiding cost waste while ensuring the stable operation of the power system.

[0055] like Figure 2As shown, in one embodiment, in step S300, the day-ahead net load forecast error curve is decomposed according to the time scale hierarchy using an integrated empirical mode decomposition method, resulting in several fluctuation components corresponding to the time scale hierarchy, including:

[0056] The white noise sequence from a preset white noise set is added sequentially to the net load forecast error curve, and empirical mode decomposition is performed to obtain several modal components and one residual component. The several modal components and one residual component are combined into a modal residual set until all white noise sequences in the white noise set have been added. Among them, several modal components and one residual component correspond to a certain time scale, and the time scale belongs to a certain time scale level.

[0057] The mean values ​​of the modal components in several modal residual sets are calculated to obtain several fluctuation components.

[0058] Specifically, the integrated empirical mode decomposition (IMD) method is a novel adaptive time-frequency signal processing method suitable for the analysis and processing of non-stationary signals. Its key feature is the addition of white noise to alter the extreme point characteristics of the signal, followed by overall averaging of the obtained intrinsic mode function (IMF) components to achieve automatic signal distribution at an appropriate time scale, effectively suppressing mode aliasing. Compared with low-pass filtering algorithms, wavelet packet decomposition, and empirical mode decomposition, the integrated EMD method offers advantages such as adaptability and intuitiveness. It avoids the limitations of traditional filters in terms of precise time constant determination and wavelet packet basis function selection, while also mitigating the mode aliasing effect generated by empirical mode decomposition.

[0059] It is easy to understand that a time scale is essentially a point in time, and a time scale hierarchy is essentially a period of time.

[0060] In this embodiment, white noise sequences from a preset white noise set are sequentially added to the day-ahead net load forecast error curve, and empirical mode decomposition is performed to obtain several modal components and one residual component. These modal components and the residual component are then combined into a modal residual set, and this process continues until all white noise sequences in the white noise set have been added. Specifically:

[0061] Step S310: Add a normally distributed white noise sequence from a preset white noise set to the day-ahead net load forecast error curve to form a new sequence x. i (t) can be represented as:

[0062] x i (t)=x(t)+n i (t)

[0063] Where x(t) represents the sequence of the day-ahead net load forecast error curve, n i(t) represents the i-th white noise sequence in the white noise set, x i (t) represents the additional noise signal, i.e., the new sequence, in the i-th experiment;

[0064] Step S320: Perform empirical mode decomposition on the additional noise signal to obtain K mode components and 1 residual component. The decomposition process can be expressed as follows:

[0065]

[0066] Among them, IMF k Represented as the k-th modal component obtained from empirical mode decomposition; r i (t) represents the i-th residual component, and EMD represents performing empirical mode decomposition.

[0067] Step S330: Repeat steps S310 and S320, and in step S310, add different white noise sequences from the white noise set to obtain N modal residual sets, which can be represented as...

[0068] {c 1,k (t),r1,c 2,k (t),r2…,c N,k (t),r k}, k=1,2,…,K

[0069] Among them, c N,k (t) represents the set of the Nth modal components, k represents the kth modal component, and each set c N,k (t) all have K modal components;

[0070] By averaging the modal components in several sets of modal residuals, several fluctuation components are obtained, including:

[0071] The mean values ​​of the modal components and residual components in several modal residual sets are calculated respectively to obtain the final components of the integrated empirical mode decomposition, denoted as:

[0072]

[0073] Among them, c k r(t) is the k-th IMF component obtained by integrated empirical mode decomposition, i.e., the fluctuation component, and r(t) is the residual component.

[0074] like Figure 3 As shown, in one embodiment, in step S400, the envelope reconstruction of each fluctuation component is performed according to the time scale level to obtain the backup requirements for each time scale level, including:

[0075] Perform a Fast Fourier Transform on each wave component to obtain the spectrum of each wave component, and then obtain the dominant period of each wave component.

[0076] Perform Hilbert transform on each dominant component to extract the envelope; wherein, the dominant component is the component in the fluctuation component whose period is the dominant period;

[0077] Based on the dominant period and envelope of each fluctuation component, the fluctuation components are mapped to a preset time scale level to obtain the backup demand range of each time scale level.

[0078] Based on the range of reserve requirements at each time scale level, the reserve requirements at each time scale level are obtained.

[0079] Fast Fourier Transform (FFT) is an efficient frequency domain analysis method used to extract the dominant period and frequency features of a signal. Through FFT, we can convert a time-domain signal to the frequency domain, thereby extracting its frequency components and further calculating its dominant period.

[0080] In one embodiment, a Fast Fourier Transform is performed on each wave component to obtain the spectrum of each wave component, thereby obtaining the dominant period of each wave component, including:

[0081] Performing a Fast Fourier Transform on each wave component yields its spectrum, which is expressed as follows:

[0082]

[0083] Where IMF(f) is the amplitude (i.e., spectrum) at frequency point f in the frequency domain, IMF(n) is the sampled value of the IMF component in the time domain, N is the total number of sampling points of the data, i.e. the number of modal residual sets, f is the frequency index, and j is the imaginary unit;

[0084] Based on the spectrum of each wave component, the power spectral density of each wave component at different frequencies is calculated and expressed as:

[0085]

[0086] In the formula, P(f) is the power spectral density at frequency f, which reflects the energy distribution of the signal at different frequencies, and IMF(f) is the spectrum mentioned above;

[0087] Determine the maximum power spectral density of each fluctuation component; since the dominant period usually refers to the period corresponding to the frequency point where the energy is most concentrated in the frequency domain, the dominant period can be obtained by finding the frequency corresponding to the maximum power spectral density.

[0088] Based on the maximum power spectral density of each wave component, the dominant period corresponding to each wave component is obtained, specifically:

[0089] f peak =argmax f p k (f)

[0090]

[0091] In the formula, f peak It is the frequency corresponding to the maximum value of the power spectral density, T d It is the dominant cycle.

[0092] In one embodiment, based on the dominant period and envelope of each fluctuation component, the fluctuation components are mapped to a preset time scale level, resulting in the backup demand range for each time scale level, including:

[0093] For each fluctuation component, if the dominant period of the fluctuation component is determined to be within a certain time scale level, then the fluctuation component is mapped to that time scale level.

[0094] The envelopes of each fluctuation component mapped to the same time scale are superimposed to obtain the range of reserve demand at each time scale.

[0095] In one embodiment, the envelopes of the fluctuation components mapped to the same time scale are superimposed to obtain the reserve demand range for each time scale, including:

[0096] The envelopes of each fluctuation component mapped to the same time scale are superimposed to obtain the envelope of the superimposed total component.

[0097] For the total component at each time scale level, within the preset scheduling period, the maximum upper and lower amplitude of the envelope of the total component is selected as the range of backup demand corresponding to the time scale level; the preset scheduling period is 15 minutes.

[0098] This IMF component envelope reconstruction technique systematically quantifies the fluctuation amplitude of reserve demand across different time periods, ensuring a more accurate reserve resource allocation strategy for power system dispatch while considering the characteristics of fluctuations at multiple time scales. The approach involves first extracting the envelope of each IMF component individually to obtain local extremum information within each frequency band, and then summing the envelope signals belonging to the same time scale. The advantage of this method is that even if the peaks within different IMF components do not appear simultaneously, their respective envelopes can reflect their maximum possible amplitude. This allows for a more conservative reflection of the potential reserve demand at each level when aggregated to the same time scale. Furthermore, the phase difference between the oscillations within each IMF component may partially cancel each other out when directly added; by extracting the envelope first, the phase cancellation problem is avoided.

[0099] Since the upper and lower reserve requirements are approximately symmetrical, this embodiment uses a 15-minute scheduling cycle. Within each scheduling interval, the maximum absolute value of the envelope (i.e., the maximum upper and lower amplitude of the envelope) is selected as the upper and lower reserve capacity reservation result (i.e., the reserve requirement range) for that cycle. It should be noted that the above analysis method is an evaluation method for net load reserve requirements that takes into account the prediction error of new energy power generation.

[0100] It is easy to understand that reserve demand includes reserve demand adjusted upwards and reserve demand adjusted downwards; adjusting reserves upwards solves the problem of "supply falling short of demand", while adjusting reserves downwards solves the problem of "supply exceeding demand".

[0101] In one embodiment, in step S100, determining the time scale level based on the call time and response time of each backup resource includes:

[0102] The complete response time of each backup resource is obtained based on the call time and response time of each backup resource.

[0103] Based on the full response time of each backup resource, the time scale is classified into time scale levels; among them, the full response time is less than half of the time scale.

[0104] It should be noted that the power grid automatically responds to rapid changes in demand on a minute-by-minute basis using frequency regulation control; the regulating resources for dealing with relatively slow changes in demand are called reserve resources; the dispatch time refers to the time period from when the power system needs to adjust until the resource begins to execute the regulation command, and the response time refers to the time period from when the resource receives the regulation command to when the regulation effect is achieved.

[0105] In one embodiment, the backup resources can be divided into "primary frequency modulation backup", "secondary frequency modulation backup" and "tertiary frequency modulation backup" according to the order of their operation and backup call time;

[0106] "Primary frequency regulation reserve" refers to the active power reserve capacity that is automatically dispatched within 3-60 seconds to maintain the frequency stability of the power system.

[0107] "Secondary frequency regulation reserve" refers to the active power reserve capacity that can be automatically retrieved and restored to the grid frequency and tie-line power target within 1 to 5 minutes during the generator set's spinning reserve after the primary frequency regulation reserve is retrieved.

[0108] "Tertiary frequency regulation reserve" refers to the reserve capacity that can be brought under load within a specified time (e.g., within 30 minutes).

[0109] On the other hand, standby resources can be divided into load standby and contingency standby according to their purpose. The response time for contingency standby is 10 minutes, and the response time for load standby is 30 minutes.

[0110] The complete response time of each backup resource is obtained based on its call time and response time. This includes dividing the backup resources (i.e., frequency modulation resources) into five levels of regulation resources based on their complete response times. The five levels of regulation resources are as follows:

[0111] The first-level regulation resource refers to the primary frequency regulation resource with a response time of less than 1 minute;

[0112] The second-level regulation resources refer to secondary frequency regulation resources with a response time of less than five minutes but more than one minute.

[0113] Level 3 adjustment resources refer to standby resources with a response time of less than 10 minutes but greater than 5 minutes;

[0114] Level 4 adjustment resources refer to standby resources with a response time of less than 30 minutes but more than 10 minutes;

[0115] Level 5 regulating resources refer to standby resources with a response time exceeding 30 minutes. However, due to their excessively long response times, Level 5 regulating resources are of little significance in actual scheduling and are typically not considered. Since response time is the time required for power supply regulation to be in place, for fluctuations in a net load forecast error curve, the complete response time of the regulating resources required by the system to smooth out the fluctuations must be less than half the fluctuation period.

[0116] Therefore, in this embodiment, the time scale level of the fluctuation component (actually the fluctuation period range of the fluctuation component) is divided into five levels, namely the first time scale level (0, 2min), the second time scale level (2min, 10min), the third time scale level (10min, 20min), the fourth time scale level (20min, 60min), and the fifth time scale level greater than 60 minutes. Taking the fluctuation component x in the day-ahead net load forecast error curve y as an example, the fluctuation period (i.e. the dominant period) of x obtained by the above method is 15min, and its time scale is 15min, which is within the third time scale level (10min, 20min). Therefore, the fluctuation component x is mapped to the third time scale level.

[0117] In one embodiment, the method further includes: in step S400, obtaining the power system's reserve requirement includes:

[0118] Based on the maximum single fault of the power system, the emergency backup requirements are obtained; the maximum single fault of the power system includes power losses caused by single-pole blocking of tie line channels, unit failure, wind farm disconnection, etc.

[0119] The emergency reserve demand is superimposed onto the upward adjustment reserve demand at the first to third time levels to obtain the reserve demand of the power system.

[0120] In one embodiment, obtaining the day-ahead net load forecast error curve includes:

[0121] Load day forecast data P′ l,t Subtract the day-ahead wind power forecast data P′ respectively w,t Photovoltaic day-ahead forecast data P′ w,t The current net load forecast curve P′ is obtained. t Specifically, P′ t =P′ l,t -P′ w,t -P′ pv,t ;

[0122] The actual data P before the load date l,t Subtract the actual day-ahead data P from each w,t Actual data for P in the photoelectric field pv,t The actual net load curve P was obtained. t Specifically, P t =P l,t -P w,t -P pv,t ;

[0123] The actual net load curve P of the day before t Net load forecast curve P′ t The difference is used to obtain the day-ahead net load forecast error curve ΔP. t Specifically, ΔP t =P t -P′ t .

[0124] The flexibility requirements of power systems mainly stem from factors such as net load forecasting errors and fluctuations within the forecasting range of renewable energy sources. The difference between active load and renewable energy output is termed the net load curve. With increasing renewable energy penetration, the power grid needs a more accurate understanding of the forecasting error characteristics of the net load curve and the establishment of reserve demand assessment techniques adapted to renewable energy development. In the multi-timescale analysis of net load forecasting errors, multi-scale decomposition aims to decompose the net load forecasting error curve into fluctuation components at different time scales. By using integrated empirical mode decomposition to decompose the net load error curve, fluctuation characteristics at different frequency bands can be extracted, quantifying reserve demand.

[0125] This invention also provides an electronic device, which includes, but is not limited to:

[0126] Memory, used to store programs;

[0127] The processor is used to execute programs stored in memory. When the processor executes the programs stored in memory, it is used to perform the aforementioned method for assessing the reserve requirements of a power system.

[0128] The processor and memory can be connected via a bus or other means.

[0129] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the method described in the embodiments of the present invention. The processor implements the above method by running the non-transitory software program and instructions stored in the memory.

[0130] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and application programs required for at least one function; the data storage area may store data for executing the methods described above. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0131] The non-transitory software program and instructions required to implement the above terminal selection method are stored in memory and are executed by one or more processors.

[0132] This invention also provides a storage medium storing computer-executable instructions for performing the above-described methods.

[0133] In one embodiment, the storage medium stores computer-executable instructions that are executed by one or more control processors.

[0134] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0136] This document describes embodiments of the invention, including preferred embodiments known to the inventors for carrying out the invention. Variations of these embodiments will become apparent to those skilled in the art upon reading the foregoing description. The inventors encourage those skilled in the art to adopt such variations as appropriate, and the inventors intend to practice embodiments of the invention in ways other than those specifically described herein. Therefore, the scope of the invention includes all modifications and equivalents of the subject matter set forth in the appended claims, as permitted by applicable law. Furthermore, the scope of the invention covers any combination of the foregoing elements in all possible variations thereof, unless otherwise indicated herein or otherwise clearly contradicted by the context.

Claims

1. A method for assessing the reserve demand of a power system, characterized in that, include: The time scale level is determined based on the call time and response time of each backup resource; Obtain the day-ahead net load forecast error curve; The day-ahead net load forecast error curve is decomposed using the integrated empirical mode decomposition method based on the time scale level to obtain several fluctuation components corresponding to the time scale level. The envelopes of each fluctuation component are reconstructed according to the time scale level to obtain the reserve requirements of each time scale level, thereby obtaining the reserve requirements of the power system.

2. The method for assessing power system reserve requirements according to claim 1, characterized in that, The step of decomposing the day-ahead net load forecast error curve using an integrated empirical mode decomposition method based on the time scale level to obtain several fluctuation components corresponding to the time scale level includes: The pre-defined white noise sequence from the white noise set is added sequentially to the net load forecast error curve, and empirical mode decomposition is performed to obtain several modal components. These several modal components are then used as a modal residual set until all white noise sequences in the white noise set have been added. The several modal components correspond to a certain time scale, which belongs to a certain time scale level. The mean values ​​of the modal components in several modal residual sets are calculated to obtain several fluctuation components.

3. The method for assessing power system reserve requirements according to claim 1, characterized in that, The step of reconstructing the envelope of each fluctuation component according to the time scale level to obtain the backup requirements for each time scale level includes: Perform a Fast Fourier Transform on each wave component to obtain the spectrum of each wave component, and then obtain the dominant period of each wave component. Perform Hilbert transform on each dominant component to extract the envelope; wherein, the dominant component is the component in the fluctuation component whose period is the dominant period; Based on the dominant period and envelope of each fluctuation component, the fluctuation components are mapped to a preset time scale level to obtain the backup demand range of each time scale level. Based on the range of reserve requirements at each time scale level, the reserve requirements at each time scale level are obtained.

4. The method for assessing power system reserve requirements according to claim 3, characterized in that, The step of performing a Fast Fourier Transform on each wave component to obtain the spectrum of each wave component, and thus obtaining the dominant period of each wave component, includes: Perform a Fast Fourier Transform on each wave component to obtain the spectrum of each wave component; Based on the spectrum of each wave component, the power spectral density of each wave component at different frequencies is calculated. Determine the maximum power spectral density of each fluctuation component; The dominant period of each wave component is obtained based on the maximum power spectral density of each wave component.

5. The method for assessing the reserve requirement of a power system according to claim 3, characterized in that, The process of mapping the fluctuation components to a preset time scale level based on the dominant period and envelope of each fluctuation component yields the reserve demand range for each time scale level, including: For each fluctuation component, if the dominant period of the fluctuation component is determined to be within a certain time scale level, then the fluctuation component is mapped to that time scale level. The envelopes of each fluctuation component mapped to the same time scale are superimposed to obtain the range of reserve demand at each time scale.

6. The method for assessing power system reserve requirements according to claim 3, characterized in that, The method of superimposing the envelopes of each fluctuation component mapped to the same time scale level to obtain the reserve demand range for each time scale level includes: The envelopes of each fluctuation component mapped to the same time scale are superimposed to obtain the envelope of the superimposed total component. For the total component at each time scale level, within a preset scheduling period, the maximum upper and lower amplitude of the envelope of the total component is taken as the range of backup demand corresponding to the time scale level.

7. The method for assessing the reserve demand of a power system according to claim 1, characterized in that, The determination of the time scale hierarchy based on the call time and response time of each backup resource includes: The complete response time of each backup resource is obtained based on the call time and response time of each backup resource. The time scale is classified according to the complete response time of each backup resource to obtain a time scale hierarchy; wherein the complete response time is less than half of the time scale.

8. The method for assessing the reserve demand of a power system according to claim 1, characterized in that, The time scale hierarchy is a five-level time scale hierarchy, and the level is proportional to the time of call and response of backup resources; The backup requirements of the power system include: Based on the maximum single fault in the power system, the emergency reserve requirement is obtained; The emergency reserve demand is superimposed onto the upward adjustment reserve demand at the first to third time scale levels to obtain the reserve demand of the power system.

9. An electronic device, characterized in that, include: Memory, used to store programs; A processor for executing a program stored in the memory, wherein when the processor executes the program stored in the memory, the processor is configured to perform the method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 8.

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