CN115907384A - Calculation Method of Power System Flexibility Demand Based on Net Load Decomposition - Google Patents

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

Calculation Method of Power System Flexibility Demand Based on Net Load Decomposition

technical field

The present application relates to a calculation method, in particular to a power system flexibility demand calculation method based on adaptive noise complete set empirical mode decomposition CEEMDAN net load decomposition, which belongs to the field of energy.

Background technique

In recent years, the grid-connected ratio of wind power and photovoltaics has been increasing, which puts forward higher requirements for the flexibility of the power system. In order to fully consider the fluctuation and peak-valley characteristics of wind power and photovoltaic output, the net load (load level minus wind, light and other renewable energy output) is often used to calculate the flexibility demand of the power system. However, flexibility resources have different adjustment rates, which correspond to the flexibility requirements of the power system on different time scales, but the existing research on the quantitative analysis of flexibility requirements in power systems rarely involves the distribution of flexibility requirements on different time scales feature. For example, the documents "Evaluation of power system flexibility", "Transmission, VariableGeneration, and power system flexibility" by scholars such as Lannoye E, Flynn D, and "Power System with High Proportion of Renewable Energy Connected to the Grid" by Lu Zongxiang, Li Haibo and other scholars Flexibility Evaluation and Balance Mechanism", "Evaluation of Power System Operation Flexibility for Large-Scale Wind Power Grid-connected" and Zhao J, Zheng T and other scholars' literature "A unified framework for defining and measuring flexibility in power system" quantifies the demand for flexibility Both are based on the first-order difference of the net load curve, which cannot reflect the difference in flexibility requirements of different time scales.

Flexible resources have different adjustment rates, which correspond to the flexibility requirements of the power system on different time scales. It is urgent to establish a method that can accurately calculate the flexibility requirements of the power system on different time scales.

Contents of the invention

In order to solve the defects in the prior art, the present invention regards the net load distribution curve as a section of non-stationary and violently fluctuating digital signal, and adopts complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) Algorithms and infinite impulse response (IIR) filters decompose the net load into high-frequency (<15min) components, intermediate-frequency (15-60min) components and low-frequency (>1h) components to calculate flexible capacity requirements, the technical solution is as follows:

The calculation method of power system flexibility demand based on CEEMDAN net load decomposition mainly includes the following steps:

Step 1: Construct net load distribution curve: collect wind power output photovoltaic output and load data, construct wind power generation curve, photovoltaic power generation curve and load distribution curve with a time scale of minutes, and then subtract wind power generation curve and photovoltaic power generation curve from the load distribution curve Generation curves to construct net load distribution curves;

Step 2: Net load multi-time scale decomposition: use the CEEMDAN algorithm to decompose the net load sequence into a series of fluctuation components with different frequency distributions, and then use the infinite impulse response (infinite impulse response, IIR) filter to analyze each fluctuation component Reconstruction, so as to obtain the high-frequency (<15min) component, medium-frequency (15-60min) component and low-frequency (>1h) component of the net load, which are used to calculate the flexibility requirements of the power system at different time scales;

Step 3: Calculate the flexibility requirements of different time scales: Based on the fluctuation components of the net load on different time scales, each fluctuation component is split into two climbing subsets, upward and downward, through waveform identification, so as to determine the power system in different time scales. The need for flexibility on timescales.

Preferably, each element in the climbing subset includes two variables, the climbing amplitude and the duration, the climbing amplitude represents the flexibility requirement of the climbing segment, and the duration represents the fluctuation cycle of the climbing segment.

Beneficial effect

The CEEMDAN algorithm can be used to decompose the net load into a series of IMFs with relatively regular distribution, and then use the IIR filter to reconstruct the IMFs into fluctuation components with different frequencies. Flexibility requirements on time scales; this method can effectively calculate the flexibility requirements of the power system on different time scales based on the difference in the distribution characteristics of net loads on different time scales, so as to provide electrochemical energy storage, virtual power plants, It provides a reference for the allocation of flexible resources with significantly different adjustment rates such as coal power flexible transformation.

Description of drawings

Fig. 1 is a flowchart of the method proposed by the present invention. This figure shows in detail the three steps of the method proposed by the present invention, namely: constructing net load distribution curve, multi-time scale decomposition of net load and calculating flexibility requirements of different time scales.

Figure 2 is the decomposition effect diagram of the CEEMDAN algorithm. The figure shows the IMF components obtained after the decomposition of the CEEMDAN algorithm, reflecting the decomposition effect of the CEEMDAN algorithm.

Fig. 3 is a schematic diagram of an IIR low-pass filter. This figure shows the process of IIR low-pass filter filtering the original curve (signal) and obtaining the low frequency part (signal) in the original curve.

Fig. 4 Schematic diagram of waveform identification to obtain flexibility requirements. This figure shows in detail the process of the present invention to obtain flexibility requirements by performing waveform identification on fluctuation components.

Figure 5 is a graph of net load distribution. The figure shows the payload distribution data generated by simulation in the embodiment of the present invention.

Figure 6 is a graph of net load components at different time scales. This figure shows the low-frequency (>1h) component, medium-frequency (15min-1h) component and high-frequency (<15min) component of the net load distribution in Fig. 5 decomposed by the net load multi-time scale decomposition method proposed by the present invention.

Figure 7 is a graph of flexibility requirements at different time scales. This figure shows the flexibility requirements on different time scales for waveform identification and determination of each fluctuation component in Fig. 6 .

Detailed ways

A method for calculating power system flexibility demand based on net load decomposition, the method mainly includes the following steps:

Step 1: Construct the net load distribution curve: A high proportion of wind and solar grid connection will be a typical feature of the new power system in the future, and it is necessary to consider the output of wind, light and other renewable energy sources and the fluctuation of load levels at the same time. Therefore, it is necessary to collect wind power output, photovoltaic output, load and other data, construct wind power generation curves, photovoltaic power generation curves, and load distribution curves with a time scale of minutes, and then subtract wind power generation curves and photovoltaic power generation curves from the load distribution curves to construct Net load distribution curve.

Step 2: Multi-time scale decomposition of net load: Different types of flexible resources have different adjustment rates, which correspond to the fluctuation components of net load at different frequencies. Therefore, the CEEMDAN algorithm is used to decompose the net load series into a series of fluctuation components with different frequency distributions. Then, use the infinite impulse response (infinite impulse response, IIR) filter to reconstruct each fluctuation component, so as to obtain the high frequency (<15min) component, intermediate frequency (15～60min) component and low frequency (>1h) component of the payload , which are used to calculate the flexibility requirements of the power system at different time scales.

Step 3: Calculate the flexibility requirements of different time scales: Based on the fluctuation components of the net load on different time scales, each fluctuation component is split into two climbing subsets, upward and downward, through waveform identification, so as to determine the power system in different time scales. The need for flexibility on timescales. Each element in the climbing subset includes two variables, the climbing amplitude and the duration. The climbing amplitude represents the flexibility requirement of the climbing segment, and the duration represents the fluctuation cycle of the climbing segment.

The specific flow chart of the method is shown in Fig. 1 .

The specific content of the method is as follows:

(1) Construct the net load distribution curve of the power system

Currently, load distribution curves are often used to analyze the flexibility requirements of power systems. The high proportion of renewable energy such as wind power and photovoltaics connected to the grid will have a greater impact on the stable operation of the power system. Therefore, it is difficult for the load distribution curve to reflect the flexible capacity demand of the power system. Not only the fluctuation of load level, but also the fluctuation of wind power, photovoltaic and other renewable energy generation should be considered. The net load (load minus renewable energy generation output) takes into account the dual effects of load fluctuations and renewable energy generation fluctuations, so it can better reflect the flexibility requirements of the power system. Therefore, it is necessary to construct the net load distribution curve of the power system, and the calculation formula is expressed as follows:

为在时刻t的总负荷需求。

表示风电在时刻t的预测出力，

表示光伏在时刻t的预测出力。In the formula: L _t is the net load demand at time t;

is the total load demand at time t.

Indicates the predicted output of wind power at time t,

Indicates the predicted output of PV at time t.

(2) Net load multi-time scale decomposition

The adjustment rate of flexible resources can generally be divided into fast (<15min), medium (15-60min) and slow (>1h), corresponding to the flexibility requirements on different time scales. Therefore, the present invention sees the net load distribution curve as a section of non-stationary and violently fluctuating digital signal, uses the fluctuation period to represent the frequency distribution equivalently, adjusts the rate distribution according to the flexible resources, and divides the net load fluctuation rate into high frequency (< 15min), medium frequency (15～60min) and low frequency (>1h). However, the net load has fluctuation characteristics in different frequency bands, and the overall distribution is relatively complex and irregular, so it is difficult to accurately decompose according to the above frequency bands. Therefore, aiming at the complexity of the net load fluctuation, the present invention adopts the CEEMDAN algorithm to decompose the net load into a series of relatively regular fluctuation components with different frequency distributions. Finally, each fluctuation component is reconstructed into high-frequency (<15min) component, intermediate-frequency (15-60min) component and low-frequency (>1h) component by using an infinite impulse response (infinite impulse response, IIR) filter, which is used to determine the The need for flexibility at scale. The low-frequency component can be regarded as the change trend of the net load within a day, the medium-frequency component can be regarded as the fluctuation of the net load in a long period of time, and the low-frequency component can be regarded as the sharp fluctuation of the net load in a short period of time.

CEEMDAN algorithm is an improved method of empirical mode decomposition (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 components (intrinsic mode function, IMF) with different amplitudes. The CEEMDAN algorithm can reduce the reconstruction error to almost zero with a small number of averaging times by adding a limited number of adaptive white noises in each stage, and effectively avoid the modal aliasing problem of the EMD method. For the specific decomposition steps of the CEEMDAN algorithm, please refer to the literature "Short-term Power Load Forecasting Based on CEEMDAN-SE and DBN" by Yue Youjun and other scholars. The decomposition effect diagram of the CEEMDAN algorithm is shown in Figure 2. It can be seen from Figure 2 that the CEEMDAN algorithm can effectively extract the fluctuation characteristics of the original curve at different frequencies, thereby decomposing 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 the n fluctuation components and residuals obtained from the decomposition of the CEEMDAN algorithm respectively, which can be expressed by the following formula:

In the formula: the order of magnitude of res is small and can be ignored.

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 it is necessary to reconstruct each fluctuation component. Due to the irregular nature of the raw payload curve, it is difficult to decompose it on the above time scale. From Figure 2, it can be seen that the relative regularity of each IMF component obtained by CEEMDAN algorithm decomposition has obvious periodicity, and can be more accurately decomposed according to the above time scale. Therefore, the present invention uses the infinite impulse response (infinite impulse response, IIR) filter to filter and reconstruct each IMF component according to the above-mentioned 3 frequency bands, thereby obtaining the net load at high frequency (<15min), intermediate frequency (15～60min) ) and low frequency (>1h) fluctuation components on three time scales. FIG. 3 is a schematic diagram of an IIR low-pass filter. It can be seen from Figure 3 that the IIR low-pass filter can filter the input original curve (signal), leaving only the low-frequency part (signal) in the original curve. The principle of IIR high-pass filter is similar. At present, the function of the IIR filter can be realized relatively easily by using data output software such as matlab. The present invention utilizes the IIR filter to filter and reconstruct the specific steps of each IMF component as follows:

1) First, use the IIR low-pass filter to filter IMF _i (1,2,L,n) sequentially, so that the low-frequency part IMF _i ^low of IMF _i can be obtained, and the remaining part IMF _i of IMF _i can be obtained by subtracting the two ^rest , which is the part filtered by the IIR low-pass filter, as shown in the following formula:

IMF _i ^rest =IMF _i -IMF _i ^low (3)

2) Then, the IMF _i ^rest is filtered by an IIR high-pass filter, so as to obtain the high-frequency part IMF _i ^high of the IMF _i . After filtering out the high frequency part and the low frequency part, what remains is the intermediate frequency part IMF _i ^{mid of} IMF _i , as shown in the following formula:

IMF _i ^mid = IMF _i ^rest - IMF _i ^high (4)

3) Finally, the components are superimposed according to different frequency distributions to obtain the high-frequency components, intermediate-frequency components and low-frequency components of the net load, as shown in the following formula:

In the formula: F _high , F _mid and F _low are the high-frequency component (<15min), medium-frequency component (15-60min) and low-frequency component (>1h) of the net load.

(3) Calculation of flexibility requirements for different time scales

After the net load sequence is decomposed into multiple time scales, the fluctuation components of different frequency distributions correspond to the flexible capacity requirements of the power system at different time scales. The flexibility requirements of the power system include two adjustment directions, upward and downward, so the present invention splits each fluctuation component into two climbing subsets, upward and downward, through waveform identification, so as to represent the flexibility under different time scales need. The schematic diagram of waveform identification is shown in Figure 4. It can be seen from Figure 4 that the waveform identification mainly splits the fluctuation component into several upward or downward climbing segments, and describes the flexibility requirement of the climbing segment through the upward or downward flexibility requirements and the fluctuation cycle. All climbing segments in the same direction form a climbing subset, 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 the climbing subset, C _j (L _j , T _j ) (j=1,2,L,m) is the climbing element; L _j is the amplitude of the climbing section, which represents the climbing section The flexible capacity demand of T _j is the duration, which represents the fluctuation period of the climbing section. The length of the fluctuation cycle under different fluctuation components should correspond to the designated time scale.

则具有以下关系：In addition, if the fluctuation period is much longer than the time scale (1h or 15min) in the middle and low frequency components of the payload, the climbing section can be divided into several small climbing sections to refine the flexibility on the time scale Capacity requirements, but the duration of each small climbing section should be greater than 1h or 15min. Assume that the climbing section C _k (L _k , T _k ) is divided into n small climbing sections

then has the following relationship:

和

表示第i个小爬坡段的爬坡段幅值和持续时间。In the formula:

and

Indicates the amplitude and duration of the climbing segment of the i-th small climbing segment.

Example

Step 1: Simulate and generate minute-level data of wind power, photovoltaic and load, which focuses on reflecting the fluctuation of net load at the minute level. Then, the net load distribution is calculated according to the formula (1), as shown in Fig. 5 . It can be seen from Figure 5 that the net load distribution has fluctuation characteristics of different frequencies at the same time, has a change trend on a longer time scale, and has complex fluctuation characteristics on a shorter time scale.

Step 2: adopt the net load multi-time scale decomposition method based on CEEMDAN algorithm and IIR filter proposed by the present invention, decompose the net load distribution curve in Fig. 5 into low frequency (>1h) component, intermediate frequency (15min-1h) component and High-frequency (<15min) components, as shown in Figure 6. It can be seen from Figure 6 that the low-frequency components reflect the general trend of net load changes in the dispatching period, the medium-frequency components reflect the fluctuations of net loads on a longer time scale, and the high-frequency components reflect the changes in net loads on a shorter time scale. violent fluctuations.

Step 3: Calculate the flexibility requirements of the power system on different time scales by performing waveform identification on each fluctuation component in Figure 6, as shown in Figure 7. It can be seen from Figure 7 that for the time scale >1h, the demand for downward flexibility is mainly concentrated in period 8-12 and period 22-24, and most cases are within 200MW, and some periods will exceed 200MW, such as period 9- 11. The demand for upward flexibility is mainly concentrated in period 6-7 and period 16-21, most of which are within 200MW, and some periods will exceed 200MW, such as period 18-19. For the time scale of 15min-1h, the demand for upward and downward flexibility is generally low, because the minute-level data of the net load simulated in this section is randomly generated on the time scale of 1min, resulting in the fluctuation component on the time scale of 15min-1h Not obvious. For the time scale of <15min, both upward and downward flexibility requirements are high, roughly changing drastically between -50MW and 50MW. The above results fully demonstrate that the flexible capacity demand calculation method proposed by the present invention can effectively describe the distribution characteristics of the flexibility demand of the power system on different time scales, and calculate the corresponding flexibility demand respectively.

Aiming at the differences in the distribution of flexibility requirements on different time scales, the present invention proposes a calculation method for power system flexibility requirements based on CEEMDAN net load decomposition. Since the fluctuation of net load is relatively complex and irregular, the present invention adopts CEEMDAN algorithm to be able to Decompose the payload into a series of IMFs with regular distribution, and then use the IIR filter to reconstruct the IMFs into fluctuation components with different frequencies. Flexibility needs. Starting from the differences in the distribution characteristics of net loads on different time scales, this method can effectively calculate the flexibility requirements of the power system on different time scales, so as to adjust the rate for electrochemical energy storage, virtual power plants, and coal-fired power flexibility transformation. Provides a reference for the configuration of flexible resources with significant differences.

The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments. What are described in the above-mentioned embodiments and the description are only the principles of the present invention. Variations and improvements, which fall within the scope of the claimed invention. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims (8)

1. The calculation method of power system flexibility demand based on net load decomposition, especially relates to a kind of power system flexibility demand calculation method based on adaptive noise complete set empirical mode decomposition CEEMDAN net load decomposition, which is characterized by: comprising the following steps : Step 1: Construct the net load distribution curve: collect wind power output, photovoltaic output, and load data, construct the wind power generation curve, photovoltaic power generation curve and load distribution curve with a time scale of minutes, and then subtract the wind power generation curve and the load distribution curve from the load distribution curve Photovoltaic power generation curve to construct net load distribution curve; Step 2: Net load multi-time scale decomposition: use the CEEMDAN algorithm to decompose the net load sequence into a series of fluctuation components with different frequency distributions, and then use the infinite impulse response (infinite impulse response, IIR) filter to analyze each fluctuation component Reconstruction, so as to obtain the high-frequency components, intermediate frequency components and low-frequency components of the net load, which are used to calculate the flexibility requirements of the power system at different time scales; Step 3: Calculate the flexibility requirements of different time scales: Based on the fluctuation components of the net load on different time scales, each fluctuation component is split into two climbing subsets, upward and downward, through waveform identification, so as to determine the power system in different time scales. The need for flexibility on timescales. 2. The power system flexibility demand calculation method based on net load decomposition according to claim 1, characterized in that: each element in the ramp subset includes two variables of ramp amplitude and duration, and ramp The amplitude represents the flexibility requirement of the climbing segment, and the duration represents the fluctuation cycle of the climbing segment. 3. The power system flexibility demand calculation method based on net load decomposition according to claim 1, characterized in that: said step 1 further includes the following content: the calculation formula of net load is expressed as follows:

In the formula: L _t is the net load demand at time t;

is the total load demand at time t;

Indicates the predicted output of wind power at time t,

Indicates the predicted output of PV at time t. 4. The power system flexibility demand calculation method based on net load decomposition according to claim 1, characterized in that: said step 2 further comprises the following content: Define L as the original curve of the net load, IMF _i (1,2,L,n) and res are the n fluctuation components and residuals obtained from the decomposition of the CEEMDAN algorithm, and have the following relationship:

In the formula: the order of magnitude of res is small and can be ignored; The adjustment rate of flexible resources, that is, the response time is often divided into three types: <15min, 15min-1h and >1h, so it is regarded as the fluctuation period, which is equivalent to the three frequency bands of net load fluctuations, and then, using infinite impulse response The (infinite impulse response, IIR) filter filters and reconstructs each IMF component according to the above three frequency bands, so as to obtain the fluctuation components of the payload at the three time scales of high frequency, intermediate frequency and low frequency. The specific steps are as follows: (1) First, use the IIR low-pass filter to filter IMF _i (1,2,L,n) sequentially, so that the low-frequency part IMF _i ^low of IMF _i can be obtained, and the remaining part IMF of IMF _i can be obtained by subtracting the two _i ^rest is the part filtered by the IIR low-pass filter, as shown in the following formula: IMF _i ^rest =IMF _i -IMF _i ^low (3) (2) Then, use the IIR high-pass filter to filter the IMF _i ^rest to obtain the high frequency part IMF _i ^high of IMF _i ; after filtering out the high frequency part and the low frequency part, the rest is the intermediate frequency part IMF _i 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 superimposed according to different frequency distributions to obtain the high-frequency components, intermediate-frequency components and low-frequency components of the net load, as shown in the following formula:

In the formula: F _high , F _mid and F _low are high frequency components, intermediate frequency components and low frequency components of the payload. 5. The power system flexibility demand calculation method based on net load decomposition according to claim 1, characterized in that: said step 3 further includes the following content: each element in the climbing subset includes the climbing amplitude and duration There are two variables of time, 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 the climbing subset, C _j (L _j , T _j ) (j=1,2,L,m) is the climbing element; L _j is the amplitude of the climbing section, which represents the climbing section The flexibility capacity demand of the slope; T _j is the duration, which represents the fluctuation cycle of the climbing section; the length of the fluctuation cycle under different fluctuation components should correspond to the designated time scale. 6. The calculation method for power system flexibility demand based on net load decomposition according to claim 5, characterized in that: if the fluctuation cycle is much larger than the time scale in the intermediate frequency component and low frequency component of the net load, the ramp can be The section is divided into several small climbing sections to refine the flexible capacity requirements on this time scale, but the duration of each small climbing section should be longer than 1h or 15min; assuming that the climbing section C _k (L _k ,T _k ) is divided into n small climbing sections

then has the following relationship:

In the formula:

and

Indicates the amplitude and duration of the climbing segment of the i-th small climbing segment. 7. A non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute claims 1 to 1. The method described in any one of 6. 8. An electronic device, characterized in that it comprises a processor and a memory; computer-readable instructions are stored in the memory, and the processor is used to run the computer-readable instructions, wherein the computer-readable instructions run When performing the method described in any one of claims 1 to 6.

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