CN115907384A - Electric power system flexibility demand calculation method based on net load decomposition - Google Patents
Electric power system flexibility demand calculation method based on net load decomposition Download PDFInfo
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Abstract
The method for calculating the flexibility requirement of the power system based on the net load decomposition comprises the following steps: constructing a net load distribution curve: payload multi-time scale decomposition; calculating the flexibility requirements of different time scales. The CEEMDAN algorithm can be adopted to better decompose the net load into a series of IMFs with more regular distribution, then an IIR filter is used to reconstruct the IMFs into fluctuation components with different frequencies, and finally the flexibility requirements on different time scales are calculated by carrying out waveform identification on each fluctuation component; the method starts from the difference of the distribution characteristics of the net loads on different time scales, and can effectively calculate the flexibility requirements of the power system on different time scales, so that reference is provided for the configuration of flexibility resources with obvious difference in regulation rates, such as electrochemical energy storage, virtual power plants, coal-electricity flexibility transformation and the like.
Description
Technical Field
The application relates to a calculation method, in particular to a calculation method for flexibility requirements of a power system based on adaptive noise complete set empirical mode decomposition CEEMDAN net load decomposition, and belongs to the field of energy.
Background
In recent years, the grid-connected ratio of wind power and photovoltaic is continuously improved, and higher requirements are put forward on the flexibility of a power system. To fully account for the volatility and peak-to-valley characteristics of wind and photovoltaic outputs, the net load (load level minus wind, light, etc. renewable energy outputs) is often used to calculate the flexibility requirements of the power system. However, the flexibility resources have different adjustment rates, which respectively correspond to the flexibility requirements of the power system on different time scales, but the existing research on quantitative analysis of the flexibility requirements of the power system is less related to the flexibility requirement distribution characteristics on different time scales. For example, the literature of students such as Lannoye E, flynn D, and the like, "Evaluation of power system flexibility", the literature of students such as Luzhou and Lihebo, and the literature of students such as "Evaluation and balance mechanism of power system flexibility for high-proportion renewable energy grid connection", "Evaluation of power system operation flexibility for large-scale wind power grid connection", and the literature of students such as Zhao J, and Zheng T "quantification of flexibility requirements" are all based on first-order difference of net load curves, and cannot reflect the difference of flexibility requirements of different time scales.
The flexibility resources have different regulation rates, and correspond to the flexibility requirements of the power system on different time scales respectively, so that a method capable of accurately calculating the flexibility requirements of the power system on different time scales needs to be established urgently.
Disclosure of Invention
In order to solve the defects in the prior art, the invention considers a net load distribution curve as a section of non-stationary digital signal with severe fluctuation, and adopts a self-adaptive noise complete set empirical mode decomposition (CEEMDAN) algorithm and an Infinite Impulse Response (IIR) filter to decompose the net load into a high-frequency (< 15 min) component, an intermediate-frequency (15-60 min) component and a low-frequency (> 1 h) component, so as to calculate the flexibility capacity requirements under different time scales, and the technical scheme is as follows:
the method for calculating the flexibility requirement of the power system based on CEEMDAN net load decomposition mainly comprises the following steps:
step 1: constructing a net load distribution curve: collecting wind power output photovoltaic output and load data, constructing a wind power generation curve, a photovoltaic power generation curve and a load distribution curve with a time scale of minute level, and then subtracting the wind power generation curve and the photovoltaic power generation curve from the load distribution curve so as to construct a net load distribution curve;
step 2: payload multi-time scale decomposition: decomposing a net load sequence into a series of fluctuation components with different frequency distributions by adopting a CEEMDAN algorithm, and then reconstructing each fluctuation component by utilizing an Infinite Impulse Response (IIR) filter, thereby obtaining a high-frequency (< 15 min) component, a medium-frequency (15-60 min) component and a low-frequency (> 1 h) component of the net load, wherein the high-frequency component, the medium-frequency (15-60 min) component and the low-frequency (1 h) component are respectively used for calculating the flexibility requirements of the power system under different time scales;
and step 3: calculating the flexibility requirements of different time scales: based on the fluctuation components of the net load on different time scales, each fluctuation component is divided into 2 climbing subsets which are upward and downward through waveform identification, and therefore the flexibility requirements of the power system on different time scales are determined.
Preferably: each element in the climbing subset comprises two variables, namely a climbing amplitude and a duration, wherein the climbing amplitude represents the flexibility requirement of the climbing section, and the duration represents the fluctuation period of the climbing section.
Advantageous effects
The CEEMDAN algorithm can be adopted to better decompose the net load into a series of IMFs with more regular distribution, then an IIR filter is used to reconstruct the IMFs into fluctuation components with different frequencies, and finally the flexibility requirements on different time scales are calculated by carrying out waveform identification on each fluctuation component; the method starts from the difference of distribution characteristics of the net loads on different time scales, and can effectively calculate the flexibility requirements of the power system on different time scales, so that reference is provided for the configuration of flexibility resources with obvious difference in regulation rates such as electrochemical energy storage, virtual power plants, coal-electricity flexibility transformation and the like.
Drawings
FIG. 1 is a flow chart of the proposed method of the present invention. The figure shows in detail 3 steps of the method proposed by the invention, which are respectively: and constructing a net load distribution curve, decomposing the net load by multiple time scales and calculating the flexibility requirements of different time scales.
Fig. 2 is a decomposition effect diagram of the CEEMDAN algorithm. The figure shows IMF components obtained after decomposition of the CEEMDAN algorithm, and the decomposition effect of the CEEMDAN algorithm is reflected.
Fig. 3 is a schematic diagram of an IIR low-pass filter. The figure shows the process of filtering the original curve (signal) by the IIR low-pass filter and obtaining the low-frequency part (signal) in the original curve.
FIG. 4 is a schematic diagram of waveform identification for flexibility requirements. The figure shows the process of the invention for finding the flexibility requirement by waveform identification of the fluctuation component in detail.
Fig. 5 is a net load distribution graph. The graphical image illustrates the simulated payload distribution data generated in an embodiment of the present invention.
Fig. 6 is a graph of net load components over different time scales. This figure shows the low frequency (> 1 h), mid frequency (15 min-1 h) and high frequency (< 15 min) components of the payload distribution of figure 5 decomposed by the proposed payload multi-time scale decomposition method.
FIG. 7 is a graph of different time scale flexibility requirements. This figure illustrates the flexibility required on different time scales for waveform identification for each of the fluctuating components of figure 6.
Detailed Description
A method for calculating flexibility requirement of an electric power system based on net load decomposition mainly comprises the following steps:
step 1: constructing a net load distribution curve: wind-light high-proportion grid connection is a typical characteristic of a novel power system in the future, and the output of renewable energy sources such as wind and light and the fluctuation of load level need to be considered simultaneously. Therefore, data such as wind power output photovoltaic output, load and the like need to be collected, a wind power generation curve, a photovoltaic power generation curve and a load distribution curve with a time scale of minute level are constructed, and then the wind power generation curve and the photovoltaic power generation curve are subtracted through the load distribution curve, so that a net load distribution curve is constructed.
And 2, step: payload multi-time scale decomposition: different types of flexible resources have different adjustment rates, corresponding to the fluctuating components of the payload at different frequencies, respectively. Thus, the CEEMDAN algorithm is used to decompose the payload sequence into a series of fluctuating components with different frequency distributions. Then, reconstructing each fluctuation component by using an Infinite Impulse Response (IIR) filter, thereby obtaining a high frequency (< 15 min) component, a medium frequency (15-60 min) component and a low frequency (> 1 h) component of the payload, which are respectively used for calculating flexibility requirements of the power system under different time scales.
And 3, step 3: calculating the flexibility requirements of different time scales: based on the fluctuation components of the net load on different time scales, each fluctuation component is divided into an upward slope subset and a downward slope subset through waveform identification, and therefore the flexibility requirements of the power system on different time scales are determined. Each element in the climbing subset comprises two variables, namely a climbing amplitude and a duration, wherein the climbing amplitude represents the flexibility requirement of the climbing section, and the duration represents the fluctuation period of the climbing section.
The detailed flow chart of the method is shown in fig. 1.
The specific content of the method is as follows:
(1) Constructing a net load distribution curve for an electric power system
Currently, load distribution curves are often used to analyze the flexibility requirements of a power system. And the high proportion of renewable energy sources such as wind power, photovoltaic and the like is combined into a grid, so that great impact is brought to the stable operation of the power system. Therefore, the load distribution curve is difficult to reflect the flexible capacity requirement of the power system, and not only the fluctuation of the load level but also the fluctuation of the power generation of renewable energy sources such as wind power and photovoltaic are considered. And the net load (the load minus the power generation output of the renewable energy source) simultaneously considers the dual effects of the load fluctuation and the power generation fluctuation of the renewable energy source, so the flexibility demand of the power system can be better reflected. Therefore, it is necessary to construct a net load distribution curve of the power system, and the calculation formula is expressed as follows:
in the formula: l is a radical of an alcohol t Is the net load demand at time t;
is the total load demand at time t. />
Represents the predicted power output of the wind power at the time t>
Representing the predicted output of the photovoltaic at time t.
(2) Payload multi-time scale decomposition
The adjustment rate of the flexibility resource can be generally divided into a fast speed (< 15 min), a medium speed (15-60 min) and a slow speed (> 1 h), and corresponds to the flexibility requirement on different time scales. Therefore, the invention shows a section of non-stable digital signal with severe fluctuation from the net load distribution curve, equivalently represents the frequency distribution by the fluctuation period, adjusts the rate distribution according to the flexible resource, and divides the net load fluctuation rate into high frequency (< 15 min), intermediate frequency (15-60 min) and low frequency (> 1 h). However, the net load has the fluctuation characteristics in different frequency bands, and the overall distribution is complex and irregular, so that the net load is difficult to be accurately decomposed according to the frequency bands. Thus, for the complexity of the payload fluctuation, the present invention employs the CEEMDAN algorithm to decompose the payload into a series of more regular fluctuation components with different frequency distributions. And finally, reconstructing each fluctuation component into a high-frequency (< 15 min) component, a medium-frequency (15-60 min) component and a low-frequency (> 1 h) component by using an Infinite Impulse Response (IIR) filter, and determining flexibility requirements under different time scales. The low frequency component can be regarded as the variation trend of the net load in one day, the medium frequency component can be regarded as the fluctuation of the net load in a longer time, and the low frequency component can be regarded as the severe fluctuation of the net load in a shorter time.
The CEEMDAN algorithm is an improved method of Empirical Mode Decomposition (EMD). The essence of the EMD method is to decompose the original signal according to different fluctuation scales to obtain a series of Intrinsic Mode Functions (IMFs) with different amplitudes. The CEEMDAN algorithm can reduce the reconstruction error to 0 almost under fewer average times by adding the limited self-adaptive white noise at each stage, and effectively avoids the modal aliasing problem of the EMD method. The specific decomposition steps of the CEEMDAN algorithm can be referred to the literature of the scholars of the army, the Yue-Yong et al, "short-term power load prediction based on CEEMDAN-SE and DBN". The decomposition effect graph of the CEEMDAN algorithm is shown in FIG. 2. As can be seen from fig. 2, the CEEMDAN algorithm can effectively extract the fluctuation characteristics of the original curve at different frequencies, so as to decompose the original curve into IMF components with different fluctuation frequencies.
Now define F as the original curve of the net load, IMF i (1, 2, L, n) and res are n fluctuation components and residuals respectively obtained by decomposition of CEEMDAN algorithm, and can be represented by the following formula:
in the formula: res is orders of magnitude smaller and negligible.
However, the number of IMF components obtained by CEEMDAN decomposition is too large, and the frequency distribution of each IMF component does not strictly conform to the time scale of <15min, 15min-1h, and >1h, so that each fluctuation component also needs to be reconstructed. Since the original payload curve is relatively irregular, it is difficult to decompose it on the above time scale. As can be seen from fig. 2, the cemdan algorithm decomposes the obtained IMF components relatively regularly, and has obvious periodicity, so that the decomposition can be performed more accurately according to the time scale. Therefore, the present invention further utilizes an Infinite Impulse Response (IIR) filter to filter and reconstruct each IMF component according to the above 3 frequency bands, so as to obtain the fluctuation components of the payload at 3 time scales of high frequency (< 15 min), intermediate frequency (15-60 min), and low frequency (> 1 h). Fig. 3 is a schematic diagram of an IIR low-pass filter. As can be seen from fig. 3, the IIR low-pass filter can filter the input original curve (signal) to leave only the low-frequency part (signal) of the original curve. The principle of IIR high-pass filters is similar. At present, the function of an IIR filter can be easily realized by utilizing data output software such as matlab and the like. The method comprises the following specific steps of filtering and reconstructing IMF components by using an IIR filter:
1) Firstly, IMF is sequentially processed by IIR low-pass filter i (1, 2, L, n) are filtered, whereby an IMF can be obtained i Low frequency part IMF of i low Subtracting the two to obtain IMF i Is IMF of i rest I.e., the part filtered by the IIR low-pass filter, as shown in the following equation:
IMF i rest ＝IMF i -IMF i low (3)
2) Then, the IMF is filtered by IIR high-pass filter i rest Filtering is carried out to obtain IMF i High frequency part IMF of i high . After filtering out the high and low frequency parts, the IMF remains i Intermediate frequency part IMF of i mid As shown in the following formula:
IMF i mid ＝IMF i rest -IMF i high (4)
3) Finally, the components are superposed according to different frequency distributions, so that a high-frequency component, a medium-frequency component and a low-frequency component of the net load are obtained, as shown in the following formula:
in the formula: f high 、F mid And F low I.e. the high frequency component of the net load (<15 min) intermediate frequency component (15-60 min) and low frequency component (C>1h)。
(3) Calculating different time scale flexibility requirements
After the net load sequence is decomposed in multiple time scales, the fluctuation components of different frequency distributions correspond to the flexible capacity requirements of the power system in different time scales. The flexibility requirement of the power system comprises 2 adjustment directions which are upward and downward, so the invention divides each fluctuation component into 2 climbing subsets which are upward and downward by waveform identification, thereby representing the flexibility requirement under different time scales. The schematic diagram of waveform identification is shown in fig. 4. As can be seen from fig. 4, the waveform identification mainly splits the fluctuation component into several ascending or descending slope segments, and the flexibility requirement of the slope segment is described by the ascending or descending flexibility requirement and the fluctuation period. All climbing sections in the same direction form a climbing subset, which is specifically expressed as follows:
CA＝{C 1 (L 1 ,T 1 ),C 2 (L 2 ,T 2 ),L,C m (L m ,T m )} (6)
in the formula: CA denotes a subset of climbs, C j (L j ,T j ) (j =1,2,l, m) is a climbing element; l is j Representing the flexible capacity requirement of the climbing section for the climbing section amplitude; t is a unit of j The duration represents the period of fluctuation of the uphill segment. The length of the fluctuation period under different fluctuation components should correspond to the time scale defined by the fluctuation period.
Furthermore, if the period of fluctuation is much larger than the time scale (1 h or 15 min) in the medium and low frequency components of the payload, the climbing section can be divided into multiple small climbing sections to refine the flexible capacity requirement on the time scale, but the duration of each small climbing section should be larger than 1h or 15min. Suppose to climb slope segment C k (L k ,T k ) Divided into n small climbing sections
Then the following relationship holds:
in the formula:
and &>
Representing the magnitude and duration of the climbing section of the ith small climbing section.
Examples
Step 1: and (4) generating minute-level data of wind power, photovoltaic and load in a simulation mode, wherein the data emphasizes that the fluctuation of the net load on the minute level is reflected. Then, the payload distribution is calculated according to equation (1), as shown in fig. 5. As can be seen from fig. 5, the net load distribution has the fluctuation characteristics of different frequencies, has the variation trend on a longer time scale, and has the complex fluctuation characteristics on a shorter time scale.
Step 2: by adopting the net load multi-time scale decomposition method based on the CEEMDAN algorithm and the IIR filter, the net load distribution curve in the graph 5 is decomposed into a low-frequency component (> 1 h), a medium-frequency component (15 min-1 h) and a high-frequency component (< 15 min), and the method is specifically shown in the graph 6. As can be seen from fig. 6, the low frequency component reflects the rough trend of the change of the payload in the scheduling period, the medium frequency component reflects the fluctuation of the payload on a longer time scale, and the high frequency component reflects the severe fluctuation of the payload on a shorter time scale.
And 3, step 3: the flexibility requirement of the power system on different time scales is calculated by performing waveform identification on each fluctuation component in fig. 6, which is specifically shown in fig. 7. As can be seen in FIG. 7, for the >1h timescale, downward flexibility requirements are mainly focused on periods 8-12 and 22-24, and most cases are within 200MW, with some periods exceeding 200MW, such as periods 9-11; the upward flexibility requirements are mainly concentrated in periods 6-7 and 16-21, which are mostly within 200MW, with some periods exceeding 200MW, such as periods 18-19. For the time scale of 15min to 1h, the upward and downward flexibility requirements are integrally low, because the net load minute-level data obtained by simulation in this section is randomly generated by taking 1min as the time scale, and the fluctuation component on the time scale of 15min to 1h is not obvious. For the <15min time scale, the up and down flexibility requirements are both high, varying dramatically between approximately-50 MW to 50 MW. The above results fully illustrate that the method for calculating the flexibility capacity demand provided by the invention can effectively depict the distribution characteristics of the flexibility demand of the power system on different time scales, and respectively calculate the corresponding flexibility demand.
The invention provides a flexibility demand calculation method of an electric power system based on CEEMDAN (computer aided design) payload decomposition aiming at the distribution difference of flexibility demands on different time scales. The method starts from the difference of distribution characteristics of the net loads on different time scales, and can effectively calculate the flexibility requirements of the power system on different time scales, so that reference is provided for the configuration of flexibility resources with obvious difference in regulation rates such as electrochemical energy storage, virtual power plants, coal-electricity flexibility transformation and the like.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A method for calculating the flexibility requirement of a power system based on payload decomposition, in particular to a method for calculating the flexibility requirement of a power system based on adaptive noise complete set empirical mode decomposition CEEMDAN payload decomposition, which is characterized in that: the method comprises the following steps:
step 1: constructing a net load distribution curve: collecting wind power output, photovoltaic output and load data, constructing a wind power generation curve, a photovoltaic power generation curve and a load distribution curve with time scale of minute level, and then subtracting the wind power generation curve and the photovoltaic power generation curve from the load distribution curve so as to construct a net load distribution curve;
step 2: payload multi-time scale decomposition: decomposing a net load sequence into a series of fluctuation components with different frequency distributions by adopting a CEEMDAN algorithm, and then reconstructing each fluctuation component by utilizing an Infinite Impulse Response (IIR) filter so as to obtain a high-frequency component, a medium-frequency component and a low-frequency component of the net load, wherein the high-frequency component, the medium-frequency component and the low-frequency component are respectively used for calculating the flexibility requirements of the power system under different time scales;
and 3, step 3: calculating the flexibility requirements of different time scales: based on the fluctuation components of the net load on different time scales, each fluctuation component is divided into an upward slope subset and a downward slope subset through waveform identification, and therefore the flexibility requirements of the power system on different time scales are determined.
2. The method of calculating a flexibility requirement for a power system based on payload decomposition of claim 1, wherein: each element in the climbing subset comprises two variables, namely a climbing amplitude and a duration, wherein the climbing amplitude represents the flexibility requirement of the climbing section, and the duration represents the fluctuation period of the climbing section.
3. The method of calculating a power system flexibility requirement based on payload decomposition of claim 1, wherein: the step 1 further comprises the following steps: the calculation formula of the payload is as follows:
in the formula: l is t Is the net load demand at time t;
is the total load demand at time t; />
Represents the predicted power output of the wind power at the time t>
Representing the predicted output of the photovoltaic at time t.
4. The method of calculating a power system flexibility requirement based on payload decomposition of claim 1, wherein: the step 2 further comprises the following steps:
defining L as the original curve of the payload, IMF i (1, 2, L, n) and res are n fluctuation components and residual errors obtained by decomposition of the CEEMDAN algorithm, and then have the following relations:
in the formula: the magnitude of res is small and can be ignored;
the adjustment rate of the flexibility resource, that is, the response time is often divided into three types of <15min, 15min-1h and >1h, so that the adjustment rate is taken as a fluctuation period and is equivalent to 3 frequency bands of the fluctuation of the payload, and then, each IMF component is filtered and reconstructed according to the 3 frequency bands by using an Infinite Impulse Response (IIR) filter, so that the fluctuation components of the payload under 3 time scales of high frequency, intermediate frequency and low frequency are obtained, and the method specifically comprises the following steps:
(1) Firstly, IMF is sequentially paired by using IIR low-pass filter i (1, 2, L, n) are filtered, whereby an IMF can be obtained i Low frequency part IMF of i low Subtracting the two to obtain IMF i Is IMF of i rest I.e., the part filtered by the IIR low-pass filter, as shown in the following equation:
IMF i rest ＝IMF i -IMF i low (3)
(2) Then, the IMF is subjected to IIR high-pass filter i rest Filtering is carried out to obtain IMF i High frequency part IMF of i high (ii) a After filtering out the high and low frequency parts, the IMF remains i Intermediate frequency part of (IMF) i mid As shown in the following formula:
IMF i mid ＝IMF i rest -IMF i high (4)
(3) Finally, the components are superposed according to different frequency distributions, so that a high-frequency component, a medium-frequency component and a low-frequency component of the net load are obtained, as shown in the following formula:
in the formula: f high 、F mid And F low I.e., high frequency components, mid frequency components, and low frequency components of the payload.
5. The method of calculating a power system flexibility requirement based on payload decomposition of claim 1, wherein: the step 3 further comprises the following steps: each element in the climbing subset includes two variables of a climbing amplitude and a climbing duration, which are specifically expressed as follows:
CA＝{C 1 (L 1 ,T 1 ),C 2 (L 2 ,T 2 ),L,C m (L m ,T m )} (6)
in the formula: cA represents a climbing subset, C j (L j ,T j ) (j =1,2,l,m) is a climbing element; l is j The amplitude of the climbing section represents the flexible capacity requirement of the climbing section; t is a unit of j The duration represents the fluctuation period of the climbing section; the length of the fluctuation period under different fluctuation components should correspond to the time scale defined by the fluctuation period.
6. The method of calculating a flexibility requirement for a power system based on payload decomposition of claim 5, wherein: if the fluctuation period is far larger than the time scale in the medium-frequency component and the low-frequency component of the net load, the climbing section can be divided into a plurality of small climbing sections so as to refine the flexible capacity requirement on the time scale, but the duration of each small climbing section is larger than 1h or 15min; suppose that the slope will be climbed C k (L k ,T k ) Divided into n small climbing sections
Then the following relationship holds:
in the formula:
and &>
Representing the magnitude and duration of the climbing section of the ith small climbing section.
7. A non-volatile storage medium, comprising a stored program, wherein the program when executed controls an apparatus in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 6.
8. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 6.
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