CN115693702B - Frequency modulation method and device for electric power system - Google Patents
Frequency modulation method and device for electric power systemInfo
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- CN115693702B CN115693702B CN202211111998.3A CN202211111998A CN115693702B CN 115693702 B CN115693702 B CN 115693702B CN 202211111998 A CN202211111998 A CN 202211111998A CN 115693702 B CN115693702 B CN 115693702B
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Abstract
本公开涉及一种电力系统的调频方法和装置，所述方法包括：获取第一实际输出功率数据，所述第一实际输出功率数据是储能系统在采集时刻以后的第一时段内的实际输出功率，所述储能系统包括功率型飞轮和能量型飞轮；根据所述第一实际输出功率数据进行经验模态分解，确定多个功率分量；根据多个所述功率分量确定多个功率分配策略，每个所述功率分配策略包括分配给所述功率型飞轮的第一功率分量，以及分配给所述能量型飞轮的第二功率分量；将多个所述功率分配策略输入第一预测模型，输出目标功率分配策略；根据所述目标功率分配策略，控制所述功率型飞轮和所述能量型飞轮进行充电或放电，以调整所述储能系统所属的电力系统的频率。
This disclosure relates to a frequency regulation method and apparatus for a power system. The method includes: acquiring first actual output power data, wherein the first actual output power data is the actual output power of an energy storage system during a first time period after the acquisition time, and the energy storage system includes a power flywheel and an energy flywheel; performing empirical mode decomposition based on the first actual output power data to determine multiple power components; determining multiple power allocation strategies based on the multiple power components, each power allocation strategy including a first power component allocated to the power flywheel and a second power component allocated to the energy flywheel; inputting the multiple power allocation strategies into a first prediction model to output a target power allocation strategy; and controlling the power flywheel and the energy flywheel to charge or discharge according to the target power allocation strategy to adjust the frequency of the power system to which the energy storage system belongs.
Description
Technical Field
The disclosure relates to the technical field of power generation, in particular to a frequency modulation method and device of a power system.
Background
Wind energy is taken as renewable energy, is environment-friendly and widely distributed, and is a main energy source for development and utilization of new energy. Wind power generation is an important way of wind energy utilization, and along with the continuous increase of the installed capacity of wind power, the problem of stable operation of a power system caused by strong randomness and weak controllability of wind power becomes an important factor for restricting the development of wind power. The flywheel energy storage is used as a mechanical energy storage mode with high response speed, high conversion efficiency, cleanness and no pollution, and is the best choice for realizing the frequency modulation of the power system by the wind storage combined system.
In the related art, a single energy storage device, such as a single flywheel, can be used to achieve the frequency modulation of the power system, however, the frequency modulation effect of the single flywheel is not good, and the cost of the single flywheel is high.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a frequency modulation method and apparatus for an electric power system.
According to a first aspect of an embodiment of the present disclosure, there is provided a frequency modulation method of an electric power system, including:
Acquiring first actual output power data, wherein the first actual output power data is the actual output power of an energy storage system in a first period after the acquisition time, and the energy storage system comprises a power flywheel and an energy flywheel;
performing empirical mode decomposition according to the first actual output power data to determine a plurality of power components;
determining a plurality of power distribution strategies according to a plurality of the power components, wherein each power distribution strategy comprises a first power component distributed to the power type flywheel and a second power component distributed to the energy type flywheel;
Inputting a plurality of power distribution strategies into a first prediction model, outputting a target power distribution strategy, wherein the first prediction model is pre-built based on a first constraint condition and a first target loss function, the first constraint condition is used for constraining the power distribution strategy, the first target loss function is used for determining the total charge state of the energy storage system in the first period and the target power distribution strategy enabling the actual total output power of the energy storage system in the first period to meet a first preset condition from the power distribution strategies meeting the first constraint condition;
and controlling the power flywheel and the energy flywheel to charge or discharge according to the target power distribution strategy so as to adjust the frequency of the power system to which the energy storage system belongs.
In some embodiments, the first constraint includes at least one of:
The first power component in the power distribution strategy is between a maximum discharge power and a maximum charge power of the power type flywheel, the second power component in the power distribution strategy is between a maximum discharge power and a maximum charge power of the energy type flywheel, a first state of charge of the power type flywheel obtained based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the power type flywheel, and a second state of charge of the energy type flywheel obtained based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the energy type flywheel.
In some embodiments, the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes:
And after carrying out weighted summation on a first difference between the first charge state and a target charge state of the first period, a second difference between the second charge state and the target charge state of the first period and a third difference between the actual total output power of the first period and the required total output power of the energy storage system of the first period, obtaining a value of a first target loss function which is minimum.
In some embodiments, the acquiring the first actual output power data includes:
Acquiring second required output power data, wherein the second required output power data is required output power of the energy storage system in a second period after the acquisition time, and the time length of the second period is longer than that of the first period;
Inputting the second required output power data into a second prediction model, outputting second actual output power data, the second actual output power data being an actual output power of the energy storage system in the second period, the second prediction model being pre-constructed based on a second constraint condition for constraining at least one of a total capacity of the energy storage system, the actual total output power, and the total state of charge, and a second target loss function for outputting the second actual output power data under the second constraint condition such that the total state of charge of the energy storage system in the second period, and the actual total output power of the energy storage system in the second period satisfy a second preset condition;
and acquiring the first actual output power data from the second actual output power data.
In some embodiments, the second constraint includes at least one of:
The total capacity is equal to the sum of the first capacity of the power flywheel and the second capacity of the energy flywheel, the total state of charge satisfies a system dynamics equation, the actual total output power is between a maximum total discharge power and a maximum total charge power of the energy storage system, the maximum total discharge power is equal to the sum of the maximum discharge power of the power flywheel and the maximum discharge power of the energy flywheel, the maximum total charge power is equal to the sum of the maximum charge power of the power flywheel and the maximum charge power of the energy flywheel, and the total state of charge is between a minimum total state of charge and a maximum total state of charge of the energy storage system.
In some embodiments, the second preset condition includes:
And carrying out weighted summation on a fourth difference between the total charge state of the second period and the target charge state and a fifth difference between the actual total output power of the second period and the required total output power of the energy storage system of the second period, wherein the obtained value of the second target loss function is minimum.
In some embodiments, the obtaining the second required output power data includes:
acquiring wind power fluctuation data and frequency fluctuation data of the power system in a historical period before the acquisition time;
processing the wind power fluctuation data and the frequency fluctuation data in the historical period according to a pre-trained machine learning model, and outputting predicted wind power fluctuation data and predicted frequency fluctuation data in the second period after the acquisition time;
And determining second required output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, wherein the second required output power data is the required output power of the energy storage system in the second period after the acquisition time.
In some embodiments, the acquisition time instants comprise a plurality of acquisition time instants determined at preset time intervals, the method further comprising:
and executing the step of acquiring the first actual output power data to the frequency of the power system to which the energy storage system belongs for each acquisition time until the acquisition time is the last acquisition time in a plurality of acquisition times.
According to a second aspect of the embodiments of the present disclosure, there is provided a frequency modulation device of an electric power system, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is configured to acquire first actual output power data, the first actual output power data is the actual output power of an energy storage system in a first period after an acquisition time, and the energy storage system comprises a power flywheel and an energy flywheel;
the decomposition module is configured to perform empirical mode decomposition according to the first actual output power data and determine a plurality of power components;
A determining module configured to determine a plurality of power allocation policies based on a plurality of the power components, each of the power allocation policies including a first power component allocated to the power flywheel and a second power component allocated to the energy flywheel;
an output module configured to input a plurality of the power distribution strategies into a first prediction model, and output a target power distribution strategy, the first prediction model being pre-built based on a first constraint condition for constraining the power distribution strategy and a first target loss function for determining, from the power distribution strategies that meet the first constraint condition, a total state of charge of the energy storage system for the first period of time, and the target power distribution strategy that enables an actual total output power of the energy storage system for the first period of time to meet a first preset condition;
And the control module is configured to control the power flywheel and the energy flywheel to charge or discharge according to the target power distribution strategy so as to adjust the frequency of the power system to which the energy storage system belongs.
According to a third aspect of the embodiments of the present disclosure, there is provided a frequency modulation apparatus of an electric power system, including:
A processor;
A memory for storing processor-executable instructions;
Wherein the processor is configured to:
Acquiring first actual output power data, wherein the first actual output power data is the actual output power of an energy storage system in a first period after the acquisition time, and the energy storage system comprises a power flywheel and an energy flywheel;
performing empirical mode decomposition according to the first actual output power data to determine a plurality of power components;
determining a plurality of power distribution strategies according to a plurality of the power components, wherein each power distribution strategy comprises a first power component distributed to the power type flywheel and a second power component distributed to the energy type flywheel;
Inputting a plurality of power distribution strategies into a first prediction model, outputting a target power distribution strategy, wherein the first prediction model is pre-built based on a first constraint condition and a first target loss function, the first constraint condition is used for constraining the power distribution strategy, the first target loss function is used for determining the total charge state of the energy storage system in the first period and the target power distribution strategy enabling the actual total output power of the energy storage system in the first period to meet a first preset condition from the power distribution strategies meeting the first constraint condition;
and controlling the power flywheel and the energy flywheel to charge or discharge according to the target power distribution strategy so as to adjust the frequency of the power system to which the energy storage system belongs.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the frequency modulation method of the power system provided by the first aspect of the present disclosure.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects that the energy storage system comprises the power flywheel and the energy flywheel, and the power flywheel has high response speed, small capacity and high capacity, and the energy flywheel has low response speed and large capacity. By combining the power flywheel and the energy flywheel with complementary relation, compared with the situation that only one flywheel is used in the related art, the energy storage system has better effect of realizing frequency modulation of the power system, and the power required by the two flywheels is not higher than the power required by one flywheel, so that the cost of the energy storage system is low under the condition of ensuring the same energy storage efficiency. In addition, the power components respectively distributed to the power flywheel and the energy flywheel can be dynamically determined according to the acquisition time, namely, the target power distribution strategy is dynamically determined, so that the two flywheels in the energy storage system can be better controlled to charge or discharge, and the frequency modulation effect of the power system where the energy storage system is located is further improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart illustrating a method of frequency modulation of an electrical power system according to an exemplary embodiment.
Fig. 2 is a schematic diagram of a power system according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating the acquisition of first actual output power data according to an exemplary embodiment.
Fig. 4 is a block diagram of a frequency modulation device of a power system according to an exemplary embodiment.
Fig. 5 is a block diagram illustrating an apparatus for frequency modulation of a power system according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Fig. 1 is a flow chart illustrating a method of frequency modulation of an electrical power system, as shown in fig. 1, according to an exemplary embodiment, which may include the following steps.
Step 110, obtaining first actual output power data, where the first actual output power data is actual output power of the energy storage system in a first period after the acquisition time, and the energy storage system includes a power flywheel and an energy flywheel.
In some embodiments, the acquisition time may be specifically determined according to the actual situation. In some embodiments, the acquisition time may be the current time or a time determined at preset time intervals starting from the current time. For example, taking the current time as 0:00am and the preset time interval as 1h as an example, the 1 st acquisition time is 0:00am, the 2 nd acquisition time is 1:00am, the 3 rd acquisition time is 2:00am, and so on.
In some embodiments, the first period may be specifically determined according to the actual situation, and the first period may be 1h. For example, with an acquisition time of 0:00am, the first actual output power data may be the actual output power of the energy storage system within 1h after 0:00 am.
In the case where the acquisition time is the current time, since the first actual output power data is the actual output power in the first period after the acquisition time, the first actual output power data can be predicted. The specific details of the first actual output power may be found in fig. 2 and the related description, which are not described herein. In some embodiments, the first actual output power may also be obtained from a memory having the first actual output power previously obtained stored therein.
The flywheel may be one type of energy storage device and the energy storage system of the present disclosure may include a variety of energy storage devices. In some embodiments, the energy storage system may include a power flywheel and an energy flywheel, where the power flywheel has a fast response speed, a small capacity, and the energy flywheel has a slow response speed and a large capacity. By combining the power flywheel and the energy flywheel with complementary relation, compared with the situation that only one flywheel is used in the related art, the energy storage system has better effect of realizing frequency modulation of the power system, and the power required by the two flywheels is not higher than the power required by one flywheel, so that the cost of the energy storage system is low under the condition of ensuring the same energy storage efficiency.
Step 120, empirical mode decomposition is performed according to the first actual output power data to determine a plurality of power components.
The empirical mode decomposition (EMPIRICAL MODE DECOMPOSITION, abbreviated as EMD) is to perform signal decomposition according to the time scale characteristics of the data, and has obvious advantages in processing non-stationary and non-linear data without presetting a basis function, and is suitable for analyzing non-linear and non-stationary signal sequences.
EMD decomposition of the data may result in an eigenmode function (INTRINSIC MODE FUNCTION, IMF for short) component and a residual component. In some embodiments, the plurality of power components may include a plurality of IMF components and one residual component.
Step 130, determining a plurality of power distribution strategies based on the plurality of power components, each power distribution strategy including a first power component assigned to the power flywheel and a second power component assigned to the energy flywheel.
In some embodiments, the plurality of power components may be randomly combined to determine a plurality of power allocation policies. In some embodiments, a high frequency component of the plurality of power components may also be assigned to a power flywheel and a low frequency component of the plurality of power components may be assigned to an energy flywheel to derive a plurality of power distribution strategies. Because the power type flywheel responds to the frequency fluctuation of high frequency, and the energy type flywheel responds to the frequency fluctuation of low frequency, the efficiency of the energy storage system can be improved to the greatest extent by distributing the high frequency component to the power type flywheel and the low frequency component to the energy type flywheel, and therefore the frequency modulation effect of the power system is improved.
By way of example, where the plurality of power components includes IMFs 1-9 and residual components obtained in order from high frequency to low frequency, the plurality of power distribution strategies may include 8 strategies, namely, power distribution strategy 1:imf1 is assigned to the power type flywheel, IMFs 2-IMF8 and residual components are assigned to the energy type flywheel, power distribution strategies 2:imf1 and IMF2 are assigned to the power type flywheel, IMFs 3-IMF8 and residual components are assigned to the energy type flywheel, power distribution strategies 3:imf1-IMF3 are assigned to the power type flywheel, IMFs 4-IMF8 and residual components are assigned to the energy type flywheel, power distribution strategies 4:imf1-IMF4 are assigned to the power type flywheel, IMFs 5-IMF8 and residual components are assigned to the energy type flywheel, power distribution strategies 5:imf1-IMF5 are assigned to the power type flywheel, IMF6-IMF8 and residual components are assigned to the energy type flywheel, power distribution strategies 6:imf1-IMF6 are assigned to the power type flywheel, IMF7-IMF8 and residual components are assigned to the energy type flywheel, power distribution strategies 3:imf 7-IMF8 are assigned to the power distribution strategies and residual components are assigned to the energy type flywheel, and residual components are assigned to the power distribution strategies 1-IMF8 are assigned to the energy type flywheel, and residual components are assigned to the residual components.
And 140, inputting the plurality of power distribution strategies into a first prediction model, and outputting a target power distribution strategy, wherein the first prediction model is pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition is used for constraining the power distribution strategy, the first target loss function is used for determining a total charge state of the energy storage system in a first time period and a target power distribution strategy for enabling the actual total output power of the energy storage system in the first time period to meet a first preset condition from the power distribution strategies meeting the first constraint condition.
In some embodiments, the first constraint may include at least one of a first power component in the power distribution strategy being between a maximum discharge power and a maximum charge power of the power flywheel, a second power component in the power distribution strategy being between a maximum discharge power and a maximum charge power of the energy flywheel, a first state of charge of the power flywheel being between a minimum state of charge and a maximum state of charge of the power flywheel being based on the power distribution strategy, and a second state of charge of the energy flywheel being between a minimum state of charge and a maximum state of charge of the energy flywheel being based on the power distribution strategy.
In some embodiments, the first constraint may be represented by the following formulas (1) - (4):
umin,1≤u1(k,i)≤umax,1,i＝0,1,……,n1 (1)
Where u min,1 represents the maximum discharge power of the power flywheel, u max,1 represents the maximum charge power of the power flywheel, u 1 (k, i) represents the power component allocated to the power flywheel at the i-th time, u 1 (k, i) may be obtained according to the first power component in the power allocation policy, n 1 represents the total time included in the first period obtained in seconds, for example, when the first period is 1h, for example, n 1 =1×60×60=3600, and for example, when the first period is 15min, for example, n 1 =15×60=900.
umin,2≤u2(k,i)≤umax,2,i＝0,1,……,n1 (2)
Where u min,2 represents the maximum discharge power of the energy flywheel, u max,2 represents the maximum charge power of the energy flywheel, u 2 (k, i) represents the power component allocated to the energy flywheel at the i-th time, u 2 (k, i) can be obtained according to the second power component in the power allocation strategy, and n 1 can be referred to the related description in formula (1) and will not be repeated here.
ymin,1≤y1(k,i)≤ymax,1,i＝0,1,……,n1 (3)
Wherein y min,1 represents the minimum state of charge of the power flywheel, y max,1 represents the maximum state of charge of the power flywheel, y 1 (k, i) represents the state of charge of the power flywheel obtained at the i-th moment, y 1 (k, i) can be determined according to the first state of charge of the power flywheel obtained based on the power distribution strategy, and n 1 can be referred to the related description in formula (1) and will not be repeated herein.
ymin,2≤y2(k,i)≤ymax,2,i＝0,1,……,n1 (4)
Wherein y min,2 represents the minimum state of charge of the energy flywheel, y max,2 represents the maximum state of charge of the energy flywheel, y 2 (k, i) represents the state of charge of the energy flywheel obtained at the i-th moment, and y 2 (k, i) can be determined according to the second state of charge of the energy flywheel obtained based on the power distribution strategy, and n 1 can be referred to the related description in formula (1) and will not be repeated herein.
In some embodiments, y min,1 and y max,1 may be specifically determined according to actual needs, for example, y min,1 may be 0.1, y max,1 may be 0.9, y min,2 may be 0.2, y max,2 may be 0.8, etc.
In some embodiments, the first state of charge may be derived based on the following equation (5):
Wherein y 1 (k, i+1) represents the state of charge of the power flywheel obtained at the i+1 th moment, y 1 (k, i) represents the state of charge of the power flywheel obtained at the i th moment, u (k, i) represents the actual output power obtained by the energy storage system at the i th moment, and u (k, i) may be the sum of the actual output power obtained by the power flywheel at the i th moment and the actual output power obtained by the energy flywheel at the i th moment, t=1s, and c 1 is the rated capacity of the power flywheel, and n 1 may be referred to the related description in the formula (1) and will not be repeated herein.
In some embodiments, the second state of charge may be derived based on the following equation (6):
where k 2 (k, i+1) represents the state of charge of the energy flywheel obtained at time i+1, y 2 (k, i) represents the state of charge of the energy flywheel obtained at time i, u (k, i) represents the actual output power obtained by the energy storage system at time i, where u (k, i) may be the sum of the actual output power obtained by the energy flywheel at time i and the actual output power obtained by the energy flywheel at time i, t=1s, and c 2 is the rated capacity of the energy flywheel, and n 1 may be referred to the related description in formula (1) and will not be repeated herein.
In some embodiments, u (k, i) may be derived based on the first power component and the second power component.
In some embodiments, the total state of charge comprises a first state of charge and a second state of charge, and the first preset condition comprises that a value of a first target loss function obtained after weighted summation of a first difference between the first state of charge and the target state of charge for the first period, a second difference between the second state of charge and the target state of charge for the first period, and a third difference between an actual total output power for the first period and a required total output power of the energy storage system for the first period is minimum.
In some embodiments, the value of the first objective loss function may be obtained by the following equation (7):
J=a1J1+a2J2+a3J3 (7)
Where a 1、a2 and a 3 may represent weight coefficients. J 1 may represent the value of the first difference, J 2 the value of the second difference, and J 3 the value of the third difference.
In some embodiments, the target state of charge may be 0.5, and the corresponding J 1 and J 2 may be obtained by the following equations (8) and (9), respectively:
Where J 1 denotes the value of the first difference, y 1 (k) denotes the first state of charge at time k, and n 1 denotes the total time comprised by the first period in seconds.
In some embodiments, J 2 can be derived by the following equation (9):
Where J 2 denotes the value of the second difference, y 2 (k) denotes the second state of charge at time k, and n 1 denotes the total time comprised by the first period in seconds.
In some embodiments, J 3 can be derived by the following equation (10):
Where J 3 represents the value of the third difference, u 1 (k) represents the actual output power of the power flywheel at time k, u 2 (k) represents the actual output power of the energy flywheel at time k, u 1(k)+u2 (k) represents the actual total output power of the energy storage system at time k, p (k) represents the required total output power of the energy storage system at time k, and n 1 represents the total time involved in the first period obtained in seconds.
In some embodiments, the power allocation policy that minimizes the value of the first target loss function (i.e., the function value of the J function) may be determined as the target power allocation policy, and for example, the power allocation policy that minimizes the value of the first target loss function among the 8 power allocation policies may be determined as the target power allocation policy. In some embodiments, the total required output power of the energy storage system in the first period may be preset, or may be obtained from the second required output power, and the specific details regarding obtaining the second required output power may be referred to as step 210 and the related description thereof, which are not described herein.
In the embodiment of the disclosure, the power flywheel and the energy flywheel form the energy storage system, and since the constraint of the first constraint condition may cause the actual total output power of the energy storage system to be smaller than the required total output power, the third difference is constructed in the first objective loss function, so that the actual total output power is equal to the required total output power as much as possible. In this way, the rationality and accuracy of the target power allocation strategy may be improved.
And step 150, controlling the power flywheel and the energy flywheel to charge or discharge according to the target power distribution strategy so as to adjust the frequency of the power system to which the energy storage system belongs.
In some embodiments, it may be determined to charge or discharge the power flywheel and the energy flywheel based on the total required output power of the energy storage system for the first period of time. For example, when the required total output power is >0, the power flywheel and the energy flywheel are controlled to charge, and when the required total output power is <0, the power flywheel and the energy flywheel are controlled to discharge.
As shown in fig. 2, the power system to which the energy storage system belongs may include a wind turbine, an AC/DC converter (i.e., an AC-to-DC converter), an energy management system, a DC/DC converter (i.e., a DC-to-DC converter), a DC/AC converter (i.e., a DC-to-AC converter), a power flywheel, an energy flywheel, and a power grid. The power flywheel and the energy flywheel form an energy storage system. The functions and connection relationships of other devices in fig. 2 except the power flywheel and the energy flywheel may be referred to in the related art, and will not be described herein.
In some embodiments, frequency adjustment, also known as frequency control, is a primary measure in the power system to maintain the active power supply-demand balance, and is used to ensure the frequency stability of the power system. By controlling the flywheel in the power system for charging and discharging, the load and thus the frequency of the power system can be adjusted. The power system may be a wind-powered cogeneration system.
According to the embodiment of the disclosure, the frequency of the power system is adjusted by controlling the charge and discharge of the flywheel included in the power system, so that resources included in the power system can be utilized as much as possible, and resource waste is avoided.
Fig. 3 is a flowchart illustrating the acquisition of first actual output power data according to an exemplary embodiment, which may include the following steps, as illustrated in fig. 3.
Step 310, obtaining second required output power data, where the second required output power data is required output power of the energy storage system in a second period after the acquisition time, and the time length of the second period is greater than that of the first period.
In some embodiments, the second period may be specifically determined according to the actual situation, for example, taking the foregoing first period as an example of 1h, then the second period may be 4h.
In some embodiments, obtaining the second required output power data may include obtaining wind power fluctuation data and frequency fluctuation data of the power system in a historical period before the collection time, processing the wind power fluctuation data and the frequency fluctuation data in the historical period according to a pre-trained machine learning model, outputting predicted wind power fluctuation data and predicted frequency fluctuation data in a second period after the collection time, and determining the second required output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, wherein the second required output power data is required output power of the energy storage system in the second period after the collection time.
In some embodiments, the history period may be specifically determined according to the actual situation, for example, the history period may be 24 hours before the acquisition time. In some embodiments, a pre-trained machine learning model may be used to process wind power fluctuation data and frequency fluctuation data in a historical period of time before the acquisition time, and output wind power fluctuation data and frequency fluctuation data in a second period of time after the acquisition time.
In some embodiments, wind power fluctuation data and frequency fluctuation data over a historical period may be obtained by an energy management system as shown in FIG. 2. In some embodiments, the second required output power data may be obtained by the following equation (11):
Wherein, P represents the second required output power, k f represents the active frequency modulation coefficient, Δf represents the power system frequency deviation, namely frequency fluctuation data, f N represents the power system rated power, and P t represents the wind farm active power, namely wind power fluctuation data. The wind power plant refers to a group of power stations consisting of wind turbine generators or wind motor groups, collecting lines, main step-up transformers and other devices.
In some embodiments, the active frequency modulation factor k f and the power system rated power f N may be specifically set according to actual requirements, for example, k f may be selected from 10-50, and f N may be 50Hz. From the above, the second required output power data in the second period can be obtained according to the wind power fluctuation data and the frequency fluctuation data in the second period obtained by prediction and the above formula (11).
In some embodiments, the machine learning model may be trained from a plurality of training sample data, each training sample data including actual wind power fluctuation data and actual frequency fluctuation data for a sample period. For training details of the machine learning model, reference may be made to an end-to-end training method in the related art, which is not described herein.
In some embodiments, the second required output power data may also be obtained by other manners, for example, in some embodiments, obtaining the second required output power data may include obtaining actual required output power data of the energy storage system in a history period before the collection time, processing the actual required output power data in the history period according to a pre-trained machine learning model, and outputting the second required output power data, where the second required output power data is required output power of the energy storage system in a second period after the collection time.
In this case, the pre-trained machine learning model may be used to process the actual required output power data in the history period before the acquisition time, and output the required output power in the second period after the acquisition time, that is, output the second actual output power data, which may be predicted by the machine learning model. Correspondingly, the machine learning model can be trained according to a plurality of training sample data, and each training sample data comprises actual required output power data of a sample period. For training details of the machine learning model, reference may be made to an end-to-end training method in the related art, which is not described herein.
Step 320, inputting the second required output power data into a second prediction model, outputting second actual output power data, where the second actual output power data is an actual output power of the energy storage system in a second period, and the second prediction model is pre-built based on a second constraint condition and a second target loss function, where the second constraint condition is used to constrain at least one of a total capacity, an actual total output power, and a total state of charge of the energy storage system, and the second target loss function is used to output the second actual output power data under the second constraint condition so that the total state of charge of the energy storage system in the second period, and the actual total output power of the energy storage system in the second period satisfies a second preset condition.
In some embodiments, the second constraint may include at least one of a total capacity equal to a sum of a first capacity of the power flywheel and a second capacity of the energy flywheel, a total state of charge satisfying a system dynamics equation, an actual total output power between a maximum total discharge power and a maximum total charge power of the energy storage system, a maximum total discharge power equal to a sum of a maximum discharge power of the power flywheel and a maximum discharge power of the energy flywheel, a maximum total charge power equal to a sum of a maximum charge power of the power flywheel and a maximum charge power of the energy flywheel, and a total state of charge between a minimum total state of charge and a maximum total state of charge of the energy storage system.
In some embodiments, the second constraint may be derived by the following formulas (12) - (17):
C=C1+C2 (12)
Wherein, C represents the total capacity of the energy storage system, C 1 represents the first capacity, namely the rated capacity of the power type flywheel, and C 2 represents the second capacity, namely the rated capacity of the energy type flywheel.
Where y (k+i+1) represents the total state of charge of the energy storage system at time i+1, y (k+i) represents the total state of charge of the energy storage system at time i, u (k+i) represents the actual total output power of the energy storage system at time i, t=1s, c represents the total capacity of the energy storage system, n 2 represents the total time involved in the second period obtained in seconds, e.g. for example for 1 hour of the second period, n 2 =1x60=3600.
umin≤u(k+i)≤umax,i＝0,1,……,n2-1 (14)
Where u (k+i) represents the actual total output power of the energy storage system at the time i, u min represents the maximum discharge power of the energy storage system, u max represents the maximum charge power of the energy storage system, and n 2 is referred to in the related description and will not be repeated here.
umin＝umin,1+umin,2 (15)
Where u min represents the maximum discharge power of the energy storage system, u min,1 represents the maximum discharge power of the power flywheel, and u min,2 represents the maximum discharge power of the energy flywheel.
umax＝umax,1+umax,2 (16)
Where u max represents the maximum charge power of the energy storage system, u max,1 represents the maximum charge power of the power flywheel, and u max,2 represents the maximum charge power of the energy flywheel.
ymin≤y(k+i)≤ymax,i＝0,1,……,n2-1 (17)
Where y (k+i) represents the total state of charge at time i, y min represents the minimum state of charge of the energy storage system, and y max represents the maximum state of charge of the energy storage system. See the relevant description above for n 2, and are not repeated here. In some embodiments, y min and y max may be specifically determined according to the actual situation, for example, y min may be 0.1 and y max may be 0.9. In some embodiments, the total state of charge may be satisfied by equation (13) above to satisfy the system dynamics equation.
In some embodiments, the second preset condition may include that a value of the second target loss function obtained after weighted summation of a fourth difference between the total state of charge of the second period and the target state of charge and a fifth difference between the actual total output power of the second period and the required total output power of the energy storage system of the second period is minimum.
In some embodiments, the value of the second objective loss function may be obtained by the following equation (18):
J′=a4J4+a5J5 (18)
where J' represents the value of the second objective loss function, a 4 and a 5 may represent the weight coefficients. J 4 may represent the value of the fourth difference and J 5 the value of the fifth difference.
As previously described, the target state of charge may be 0.5, and the corresponding J 4 and J 5 may be obtained by the following equations (19) and (20), respectively:
Where J 4 denotes the value of the fourth difference, y (k) denotes the total state of charge at time k, and n 2 denotes the total time included in the second period obtained in seconds. By summing the differences between the total state of charge and the target state of charge at each time k, a fourth difference between the total state of charge and the target state of charge for the second period of time may be obtained.
Where J 5 represents the value of the fifth difference, u (k) represents the actual total output power of the energy storage system at time k, p (k) represents the required total output power of the energy storage system at time k, and n 2 represents the total time involved in the second period obtained in seconds. By summing the difference between the actual total output power and the required total output power for each time k, a fifth difference between the actual total output power for the second period and the required total output power of the energy storage system for the second period may be obtained.
Step 330, obtain the first actual output power data from the second actual output power data.
As described above, the second actual output power data is the actual output power of the energy storage system in the second period after the acquisition time, and the first actual output power data is the actual output power of the energy storage system in the first period after the acquisition time, where the time length of the second period is longer than the first period.
In some embodiments, the actual output power data for a first period of time in the second actual output power data may be determined as the first actual output power data. For example, taking the example that the acquisition time is 0:00am and the second actual output power data is the actual output power of the energy storage system of 0:00-4:00am, the first actual output power data is the actual output power of the energy storage system of 0:00-1:00 am.
As previously described, in some embodiments, the acquisition time comprises a plurality of acquisition times determined at preset time intervals, and the method further comprises, for each acquisition time, performing the step of obtaining the first actual output power data to adjust the frequency of the power system to which the energy storage system belongs until the acquisition time is the last acquisition time of the plurality of acquisition times. Thus, the frequency modulation method of the power system of the present disclosure may be cyclic frequency modulation.
For example, the foregoing step 310-320-330-120-130-140-150 may be sequentially performed at each acquisition time, where the target power allocation strategy output in step 150 is used to control the charging and discharging of the power-type flywheel and the energy-type flywheel at a preset time interval between the current acquisition time and the next acquisition time to adjust the frequency of the power system at the preset time interval. And then, when the acquisition time is not the last acquisition time, sequentially executing the step 310-320-330-120-130-140-150 at the next acquisition time until the last acquisition time stops executing the step, so as to complete the frequency modulation method of the power system.
Fig. 4 is a block diagram of a frequency modulation device of a power system according to an exemplary embodiment. Referring to fig. 4, the apparatus 400 includes an acquisition module 410, a decomposition module 420, a determination module 430, an output module 440, and a control module 450.
The acquisition module 410 is configured to acquire first actual output power data, the first actual output power data being an actual output power of an energy storage system during a first period of time after an acquisition time, the energy storage system including a power flywheel and an energy flywheel;
The decomposition module 420 is configured to perform empirical mode decomposition based on the first actual output power data, determining a plurality of power components;
the determination module 430 is configured to determine a plurality of power allocation policies based on a plurality of the power components, each of the power allocation policies including a first power component allocated to the power flywheel and a second power component allocated to the energy flywheel;
The output module 440 is configured to input a plurality of the power distribution strategies into a first prediction model, and output a target power distribution strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used for constraining the power distribution strategy, the first target loss function being used for determining a total state of charge of the energy storage system in the first period and the target power distribution strategy that enables an actual total output power of the energy storage system in the first period to meet a first preset condition from the power distribution strategies meeting the first constraint condition;
the control module 450 is configured to control the power flywheel and the energy flywheel to charge or discharge according to the target power distribution strategy, so as to adjust the frequency of the power system to which the energy storage system belongs.
In some embodiments, the first constraint includes at least one of:
The first power component in the power distribution strategy is between a maximum discharge power and a maximum charge power of the power type flywheel, the second power component in the power distribution strategy is between a maximum discharge power and a maximum charge power of the energy type flywheel, a first state of charge of the power type flywheel obtained based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the power type flywheel, and a second state of charge of the energy type flywheel obtained based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the energy type flywheel.
In some embodiments, the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes:
And after carrying out weighted summation on a first difference between the first charge state and a target charge state of the first period, a second difference between the second charge state and the target charge state of the first period and a third difference between the actual total output power of the first period and the required total output power of the energy storage system of the first period, obtaining a value of a first target loss function which is minimum.
In some embodiments, the acquisition module 310 is further configured to:
Acquiring second required output power data, wherein the second required output power data is required output power of the energy storage system in a second period after the acquisition time, and the time length of the second period is longer than that of the first period;
Inputting the second required output power data into a second prediction model, outputting second actual output power data, the second actual output power data being an actual output power of the energy storage system in the second period, the second prediction model being pre-constructed based on a second constraint condition for constraining at least one of a total capacity of the energy storage system, the actual total output power, and the total state of charge, and a second target loss function for outputting the second actual output power data under the second constraint condition such that the total state of charge of the energy storage system in the second period, and the actual total output power of the energy storage system in the second period satisfy a second preset condition;
and acquiring the first actual output power data from the second actual output power data.
In some embodiments, the second constraint includes at least one of:
The total capacity is equal to the sum of the first capacity of the power flywheel and the second capacity of the energy flywheel, the total state of charge satisfies a system dynamics equation, the actual total output power is between a maximum total discharge power and a maximum total charge power of the energy storage system, the maximum total discharge power is equal to the sum of the maximum discharge power of the power flywheel and the maximum discharge power of the energy flywheel, the maximum total charge power is equal to the sum of the maximum charge power of the power flywheel and the maximum charge power of the energy flywheel, and the total state of charge is between a minimum total state of charge and a maximum total state of charge of the energy storage system.
In some embodiments, the second preset condition includes:
And carrying out weighted summation on a fourth difference between the total charge state of the second period and the target charge state and a fifth difference between the actual total output power of the second period and the required total output power of the energy storage system of the second period, wherein the obtained value of the second target loss function is minimum.
In some embodiments, the acquisition module 410 is further configured to:
Acquiring actual demand output power data of the energy storage system in a historical period before the acquisition time;
And processing the actual demand output power data in the historical period according to a pre-trained machine learning model, and outputting second demand output power data, wherein the second demand output power data is the demand output power of the energy storage system in the second period after the acquisition time.
In some embodiments, the acquisition module 310 is further configured to:
acquiring wind power fluctuation data and frequency fluctuation data of the power system in a historical period before the acquisition time;
processing the wind power fluctuation data and the frequency fluctuation data in the historical period according to a pre-trained machine learning model, and outputting predicted wind power fluctuation data and predicted frequency fluctuation data in the second period after the acquisition time;
And determining second required output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, wherein the second required output power data is the required output power of the energy storage system in the second period after the acquisition time.
In some embodiments, the acquisition time instants comprise a plurality of acquisition time instants determined at preset time intervals, the apparatus further comprising:
The execution module is configured to execute the step of acquiring the first actual output power data to the frequency of the power system to which the adjustment energy storage system belongs for each acquisition time until the acquisition time is the last acquisition time in a plurality of acquisition times.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the frequency modulation method of the power system provided by the present disclosure.
Fig. 5 is a block diagram illustrating an apparatus 500 for frequency modulation of a power system, according to an exemplary embodiment. For example, the apparatus 500 may be provided as a server. Referring to fig. 5, apparatus 500 includes a processing component 522 that further includes one or more processors and memory resources represented by memory 532 for storing instructions, such as applications, executable by processing component 522. The application programs stored in the memory 532 may include one or more modules each corresponding to a set of instructions. Further, the processing component 522 is configured to execute instructions to perform the frequency modulation method of the power system described above.
The apparatus 500 may also include a power component 526 configured to perform power management of the apparatus 500, a wired or wireless network interface 550 configured to connect the apparatus 500 to a network, and an input/output interface 558. The apparatus 500 may operate based on an operating system stored in the memory 532, such as Windows Server TM,Mac OS XTM,UnixTM,LinuxTM,FreeBSDTM or the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. A method of frequency modulation of an electrical power system, comprising:
Acquiring first actual output power data, wherein the first actual output power data is the actual output power of an energy storage system in a first period after the acquisition time, and the energy storage system comprises a power flywheel and an energy flywheel;
performing empirical mode decomposition according to the first actual output power data to determine a plurality of power components;
determining a plurality of power distribution strategies according to a plurality of the power components, wherein each power distribution strategy comprises a first power component distributed to the power type flywheel and a second power component distributed to the energy type flywheel;
Inputting a plurality of power distribution strategies into a first prediction model, outputting a target power distribution strategy, wherein the first prediction model is pre-built based on a first constraint condition and a first target loss function, the first constraint condition is used for constraining the power distribution strategy, the first target loss function is used for determining the total charge state of the energy storage system in the first period and the target power distribution strategy enabling the actual total output power of the energy storage system in the first period to meet a first preset condition from the power distribution strategies meeting the first constraint condition;
according to the target power distribution strategy, controlling the power flywheel and the energy flywheel to charge or discharge so as to adjust the frequency of a power system to which the energy storage system belongs;
the first constraint includes at least one of:
The first power component in the power distribution strategy is between a maximum discharge power and a maximum charge power of the power type flywheel, the second power component in the power distribution strategy is between a maximum discharge power and a maximum charge power of the energy type flywheel, a first state of charge of the power type flywheel obtained based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the power type flywheel, and a second state of charge of the energy type flywheel obtained based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the energy type flywheel;
the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes:
And after carrying out weighted summation on a first difference between the first charge state and a target charge state of the first period, a second difference between the second charge state and the target charge state of the first period and a third difference between the actual total output power of the first period and the required total output power of the energy storage system of the first period, obtaining a value of a first target loss function which is minimum.
2. The method of claim 1, wherein the obtaining the first actual output power data comprises:
Acquiring second required output power data, wherein the second required output power data is required output power of the energy storage system in a second period after the acquisition time, and the time length of the second period is longer than that of the first period;
Inputting the second required output power data into a second prediction model, outputting second actual output power data, the second actual output power data being an actual output power of the energy storage system in the second period, the second prediction model being pre-constructed based on a second constraint condition for constraining at least one of a total capacity of the energy storage system, the actual total output power, and the total state of charge, and a second target loss function for outputting the second actual output power data under the second constraint condition such that the total state of charge of the energy storage system in the second period, and the actual total output power of the energy storage system in the second period satisfy a second preset condition;
and acquiring the first actual output power data from the second actual output power data.
3. The method of claim 2, wherein the second constraint comprises at least one of:
The total capacity is equal to the sum of the first capacity of the power flywheel and the second capacity of the energy flywheel, the total state of charge satisfies a system dynamics equation, the actual total output power is between a maximum total discharge power and a maximum total charge power of the energy storage system, the maximum total discharge power is equal to the sum of the maximum discharge power of the power flywheel and the maximum discharge power of the energy flywheel, the maximum total charge power is equal to the sum of the maximum charge power of the power flywheel and the maximum charge power of the energy flywheel, and the total state of charge is between a minimum total state of charge and a maximum total state of charge of the energy storage system.
4. The method of claim 2, wherein the second preset condition comprises:
And carrying out weighted summation on a fourth difference between the total charge state of the second period and the target charge state and a fifth difference between the actual total output power of the second period and the required total output power of the energy storage system of the second period, wherein the obtained value of the second target loss function is minimum.
5. The method of claim 2, wherein the obtaining the second required output power data comprises:
acquiring wind power fluctuation data and frequency fluctuation data of the power system in a historical period before the acquisition time;
processing the wind power fluctuation data and the frequency fluctuation data in the historical period according to a pre-trained machine learning model, and outputting predicted wind power fluctuation data and predicted frequency fluctuation data in the second period after the acquisition time;
And determining second required output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, wherein the second required output power data is the required output power of the energy storage system in the second period after the acquisition time.
6. The method of claim 1, wherein the acquisition time instants comprise a plurality of acquisition time instants determined at preset time intervals, the method further comprising:
And executing the step of acquiring the first actual output power data to adjust the frequency of the power system to which the energy storage system belongs for each acquisition time until the acquisition time is the last acquisition time in a plurality of acquisition times.
7. A frequency modulation device for an electrical power system, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is configured to acquire first actual output power data, the first actual output power data is the actual output power of an energy storage system in a first period after an acquisition time, and the energy storage system comprises a power flywheel and an energy flywheel;
the decomposition module is configured to perform empirical mode decomposition according to the first actual output power data and determine a plurality of power components;
A determining module configured to determine a plurality of power allocation policies based on a plurality of the power components, each of the power allocation policies including a first power component allocated to the power flywheel and a second power component allocated to the energy flywheel;
an output module configured to input a plurality of the power distribution strategies into a first prediction model, and output a target power distribution strategy, the first prediction model being pre-built based on a first constraint condition for constraining the power distribution strategy and a first target loss function for determining, from the power distribution strategies that meet the first constraint condition, a total state of charge of the energy storage system for the first period of time, and the target power distribution strategy that enables an actual total output power of the energy storage system for the first period of time to meet a first preset condition;
The control module is configured to control the power flywheel and the energy flywheel to charge or discharge according to the target power distribution strategy so as to adjust the frequency of the power system to which the energy storage system belongs;
The first constraint condition comprises at least one of the first power component in the power distribution strategy being between maximum discharge power and maximum charge power of the power type flywheel, the second power component in the power distribution strategy being between maximum discharge power and maximum charge power of the energy type flywheel, the first state of charge of the power type flywheel being between minimum state of charge and maximum state of charge of the power type flywheel being obtained based on the power distribution strategy, and the second state of charge of the energy type flywheel being between minimum state of charge and maximum state of charge of the energy type flywheel being obtained based on the power distribution strategy;
the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes that a value of a first target loss function obtained after weighted summation of a first difference between the first state of charge and a target state of charge in the first period, a second difference between the second state of charge and the target state of charge in the first period, and a third difference between the actual total output power in the first period and a required total output power of the energy storage system in the first period is minimum.
8. A frequency modulation device for an electrical power system, comprising:
A processor;
A memory for storing processor-executable instructions;
Wherein the processor is configured to:
Acquiring first actual output power data, wherein the first actual output power data is the actual output power of an energy storage system in a first period after the acquisition time, and the energy storage system comprises a power flywheel and an energy flywheel;
performing empirical mode decomposition according to the first actual output power data to determine a plurality of power components;
determining a plurality of power distribution strategies according to a plurality of the power components, wherein each power distribution strategy comprises a first power component distributed to the power type flywheel and a second power component distributed to the energy type flywheel;
Inputting a plurality of power distribution strategies into a first prediction model, outputting a target power distribution strategy, wherein the first prediction model is pre-built based on a first constraint condition and a first target loss function, the first constraint condition is used for constraining the power distribution strategy, the first target loss function is used for determining the total charge state of the energy storage system in the first period and the target power distribution strategy enabling the actual total output power of the energy storage system in the first period to meet a first preset condition from the power distribution strategies meeting the first constraint condition;
according to the target power distribution strategy, controlling the power flywheel and the energy flywheel to charge or discharge so as to adjust the frequency of a power system to which the energy storage system belongs;
The first constraint condition comprises at least one of the first power component in the power distribution strategy being between maximum discharge power and maximum charge power of the power type flywheel, the second power component in the power distribution strategy being between maximum discharge power and maximum charge power of the energy type flywheel, the first state of charge of the power type flywheel being between minimum state of charge and maximum state of charge of the power type flywheel being obtained based on the power distribution strategy, and the second state of charge of the energy type flywheel being between minimum state of charge and maximum state of charge of the energy type flywheel being obtained based on the power distribution strategy;
the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes that a value of a first target loss function obtained after weighted summation of a first difference between the first state of charge and a target state of charge in the first period, a second difference between the second state of charge and the target state of charge in the first period, and a third difference between the actual total output power in the first period and a required total output power of the energy storage system in the first period is minimum.
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