CN109888839B - Load balancing capability evaluation method and system for power plant unit - Google Patents
Load balancing capability evaluation method and system for power plant unit Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a system for evaluating load balancing capacity of a power plant unit, which are used for quantitatively evaluating the load balancing capacity of the power plant unit under three different time scales, namely a peak shaving scale, an ultra-short period scale and an AGC scale, by decomposing and comparing actual load data and output data on different time scales, so that technical blank in the prior art is made up, and an evaluation result is more objective. In addition, the scheduling unit can carry out reward and punishment on the relevant power plant unit according to the evaluation results under different time scales.
Description
Technical Field
The embodiment of the invention relates to the technical field of power dispatching of a power system, in particular to a method and a system for evaluating load balancing capacity of a power plant unit.
Background
Due to the fact that electric energy generated by power plant units in the power grid area is not easy to store in a large scale, real-time balance between power supply and load in the power grid area needs to be guaranteed.
At present, a scheduling unit generally realizes the supply and demand balance of power supply and load in a period of time in the future through daily planning, ultra-short-term adjustment and AGC processes, but the scheduling unit cannot know the load balance effect of each specific stage, cannot evaluate the load balance capacity of a power plant unit on different time scales, and further cannot realize reward and punishment on the power plant unit on different time scales.
Therefore, it is urgently needed to provide a method and a system for evaluating the load balancing capability of a power plant unit, so as to evaluate the load balancing capability of the power plant unit on different time scales and make up for the technical blank in the prior art.
Disclosure of Invention
In order to overcome the problems or at least partially solve the problems, embodiments of the present invention provide a method and a system for evaluating load balancing capability of a power plant unit.
In a first aspect, an embodiment of the present invention provides a method for evaluating load balancing capability of a power plant unit, including:
acquiring actual load data in a preset power grid area and output data of power plant units in the preset power grid area;
respectively performing frequency domain decomposition on the actual load data and the output data based on a peak shaving scale, an ultra-short period scale and an Automatic Generation Control (AGC) scale of the power plant unit, and determining frequency domain components of the actual load data under the peak shaving scale, the ultra-short period scale and the AGC scale and frequency domain components of the output data under the peak shaving scale, the ultra-short period scale and the AGC scale;
for each time scale of the peak shaver scale, the ultra-short term scale and the AGC scale, determining a correlation coefficient between the actual load data and the frequency domain component of the output data at each time scale;
and evaluating the load balancing capacity of the power plant unit under each time scale based on the actual load data and the correlation coefficient between the frequency domain components of the output data under each time scale.
In a second aspect, an embodiment of the present invention provides a system for evaluating load balancing capability of a power plant unit, including:
the data acquisition module is used for acquiring actual load data in a preset power grid area and output data of power plant units in the preset power grid area;
a frequency domain component determination module, configured to perform frequency domain decomposition on the actual load data and the output data respectively based on a peak shaving scale, an ultra-short term scale, and an automatic generation control AGC scale of the power plant unit, and determine frequency domain components of the actual load data under the peak shaving scale, the ultra-short term scale, and the AGC scale, and frequency domain components of the output data under the peak shaving scale, the ultra-short term scale, and the AGC scale;
a correlation coefficient determining module, configured to determine, for each time scale of the peak shaving scale, the ultra-short term scale, and the AGC scale, a correlation coefficient between the actual load data and the frequency-domain component of the output data at each time scale based on a result of frequency-domain decomposition;
and the evaluation module is used for evaluating the load balancing capacity of the power plant unit under each time scale based on the actual load data and the correlation coefficient between the frequency domain components of the output data under each time scale.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor, at least one memory, a communication interface, and a bus; wherein the content of the first and second substances,
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method for evaluating load balancing capability of a power plant unit provided by the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions cause the computer to execute the method for evaluating load balancing capability of a power plant unit provided in the first aspect.
According to the method and the system for evaluating the load balancing capacity of the power plant unit, provided by the embodiment of the invention, the load balancing capacity of the power plant unit under three different time scales, namely a peak shaving scale, an ultra-short period scale and an AGC scale, is quantitatively evaluated by decomposing and comparing actual load data and output data on different time scales, so that the technical blank in the prior art is made up, and the evaluation result is more objective. In addition, the scheduling unit can carry out reward and punishment on the relevant power plant unit according to the evaluation results under different time scales.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for evaluating load balancing capability of a power plant unit according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a load balancing capability evaluation system of a power plant unit according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the embodiments of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the embodiments of the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have specific orientations, be configured in specific orientations, and operate, and thus, should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the embodiments of the present invention, it should be noted that, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. Specific meanings of the above terms in the embodiments of the present invention can be understood in specific cases by those of ordinary skill in the art.
In order to make up for a technical gap in the prior art that evaluation of load balancing capability of a power plant unit on different time scales cannot be achieved, as shown in fig. 1, an embodiment of the present invention provides a method for evaluating load balancing capability of a power plant unit, including:
s1, acquiring actual load data in a preset power grid area and output data of power plant units in the preset power grid area;
s2, respectively performing frequency domain decomposition on the actual load data and the output data based on a peak shaving scale, an ultra-short period scale and an Automatic Generation Control (AGC) scale of the power plant unit, and determining frequency domain components of the actual load data under the peak shaving scale, the ultra-short period scale and the AGC scale and frequency domain components of the output data under the peak shaving scale, the ultra-short period scale and the AGC scale;
s3, determining a correlation coefficient between the actual load data and the frequency domain components of the output data at each time scale for each time scale of the peak shaving scale, the ultra-short term scale and the AGC scale;
s4, evaluating the load balancing capacity of the power plant unit under each time scale based on the actual load data and the correlation coefficient between the frequency domain components of the output data under each time scale.
Specifically, according to the method for evaluating the load balancing capability of the power plant unit provided by the embodiment of the invention, firstly, actual load data in a preset power grid area and output data of the power plant unit in the preset power grid area need to be acquired. The preset power grid area refers to a power grid area researched in the embodiment of the invention, a load and a plurality of power plant units are arranged in the preset power grid area, and the specific number of the power plant units can be one or more. The load balancing capability evaluation method of the power plant unit provided by the embodiment of the invention is used for determining the capability of the power plant unit for realizing supply and demand balance in the whole preset power grid area. The actual load data refers to the power consumption of the load in the preset power grid area on the electric energy generated by the power plant unit, and the output data of the power plant unit refers to the power generation power of the power plant unit. The actual load data and the output data are both power data at each time point, and therefore the acquired actual load data and the acquired output data are both power data in a time domain. In the embodiment of the invention, the actual load data can be expressed as LoadData, and the output data of the power plant unit can be expressed as Gendata.
After the actual load data and the output data are obtained, frequency domain decomposition is respectively carried out on the actual load data and the output data based on a peak regulation scale, an ultra-short period scale and an Automatic Generation Control (AGC) scale of a power plant unit, namely, Fourier transform is respectively carried out on the actual load data and the output data, the actual load data and the output data are respectively converted into frequency domain data, then, frequency domain decomposition is carried out on the frequency domain data according to the peak regulation scale, the ultra-short period scale and the AGC scale of the power plant unit, and frequency domain components of the actual load data under the peak regulation scale, the ultra-short period scale and the AGC scale and frequency domain components of the output data under the peak regulation scale, the ultra-short period scale and the AGC scale are determined. The frequency domain components of the actual load data under the peak-shaving scale, the ultra-short-term scale and the AGC scale can be sequentially expressed as LoadData1, LoadData2 and LoadData3, and the frequency domain components of the output data of the power plant unit under the peak-shaving scale, the ultra-short-term scale and the AGC scale can be sequentially expressed as Gendata1, Gendata2 and Gendata 3.
The specific operation may be to convert the peak shaving scale, the ultra-short term scale, and the AGC scale from the time domain interval to the frequency domain interval, where the frequency domain data in the frequency domain interval corresponding to the peak shaving scale is the frequency domain component under the peak shaving scale, the frequency domain data in the frequency domain interval corresponding to the ultra-short term scale is the frequency domain component under the ultra-short term scale, and the frequency domain data in the frequency domain interval corresponding to the AGC scale is the frequency domain component under the AGC scale. For example: the frequency domain data of the actual load data in the frequency domain interval corresponding to the peak shaving scale, namely, the frequency domain component of the actual load data in the peak shaving scale, the frequency domain data of the actual load data in the frequency domain interval corresponding to the ultra-short term scale, namely, the frequency domain component of the actual load data in the ultra-short term scale, and the frequency domain data of the actual load data in the frequency domain interval corresponding to the AGC scale, namely, the frequency domain component of the actual load data in the AGC scale.
It should be noted that, the peak shaving scale in the embodiment of the present invention may specifically be: the time range is greater than 15min, the ultra-short term scale may be specifically greater than 5min and less than or equal to 15min, and the AGC scale may be specifically greater than 0 and less than or equal to 5 min.
For each time scale in the peak shaving scale, the ultra-short term scale and the AGC scale, determining a correlation coefficient between the actual load data and the frequency domain components of the output data under each time scale, namely determining a correlation coefficient between the actual load data and the frequency domain components of the output data under the peak shaving scale, determining a correlation coefficient between the actual load data and the frequency domain components of the output data under the ultra-short term scale, and determining a correlation coefficient between the actual load data and the frequency domain components of the output data under the AGC scale. Symbolically, this is: determine the correlation coefficient between LoadData1 and Gendata1, determine the correlation coefficient between LoadData2 and Gendata2, and determine the correlation coefficient between LoadData3 and Gendata 3.
After the correlation coefficients under the three conditions are determined, the load balancing capacities of the power plant units under different conditions can be respectively evaluated according to the correlation coefficients, the value of the correlation coefficients is generally 0-1, the larger the correlation coefficient is, the better the load balancing capacity of the power plant unit is, the larger the contribution to the load balancing in the whole preset power grid area is, and otherwise, the smaller the correlation coefficient is, the worse the load balancing capacity of the power plant unit is, and the smaller the contribution to the load balancing in the whole preset power grid area is.
Furthermore, the scheduling unit can also reward the power plant unit according to the correlation coefficient, the power plant unit with the correlation coefficient larger than a first preset value can reward, and the power plant unit with the correlation coefficient smaller than a second preset value can punish. The first preset value and the second preset value can be set according to needs, and the first preset value and the second preset value can be equal. This is not particularly limited in the embodiments of the present invention.
According to the method for evaluating the load balancing capacity of the power plant unit, provided by the embodiment of the invention, the load balancing capacity of the power plant unit under three different time scales, namely a peak shaving scale, an ultra-short period scale and an AGC scale, is quantitatively evaluated by decomposing and comparing actual load data and output data on different time scales, so that the technical blank in the prior art is made up, and the evaluation result is more objective. In addition, the scheduling unit can carry out reward and punishment on the relevant power plant unit according to the evaluation results under different time scales.
On the basis of the foregoing embodiment, the method for evaluating load balancing capability of a power plant unit according to the embodiment of the present invention performs frequency domain decomposition on the actual load data and the output data, respectively, based on a peak shaving scale, an ultra-short term scale, and an automatic generation control AGC scale of the power plant unit, and specifically includes:
and performing FFT frequency domain decomposition on the actual load data and the output data respectively based on the peak regulation scale, the ultra-short term scale and the automatic generation control AGC scale of the power plant unit.
Specifically, according to the three time scales, the FFT may be adopted to perform frequency domain decomposition on the actual load data and the output data of the power plant unit, that is, the FFT is adopted to perform fast fourier transform, the actual load data and the output data of the power plant unit are converted into frequency domain data, and the frequency domain data is subjected to frequency domain decomposition.
In the embodiment of the invention, because the FFT can easily realize inverse transformation, the FFT is adopted for frequency domain decomposition, so that the feasibility of the subsequent evaluation method can be more conveniently and quickly verified.
On the basis of the above embodiment, the method for evaluating load balancing capability of a power plant unit provided in the embodiment of the present invention for acquiring actual load data in a preset power grid area and output data of the power plant unit in the preset power grid area specifically includes:
and setting a sampling period, and acquiring the actual load data and the output data based on the sampling period.
Specifically, when the actual load data and the output data are obtained, a sampling period may be set first, and then the actual load data and the output data of the power plant unit may be sampled according to the set sampling period.
In the embodiment of the invention, the sampling period is preferably as small as possible. Preferably, the sampling period may be set to 1min or less, but is not limited thereto.
Taking the sampling period of 1min as an example for explanation, the frequency domain interval corresponding to the peak regulation scale is: greater than 0 and less than 1/15, the frequency domain interval corresponding to the ultra-short term scale is: 1/15 and 1/5, the frequency domain interval corresponding to the AGC scale is: greater than 1/5.
On the basis of the above embodiment, in the method for evaluating load balancing capability of a power plant unit provided in the embodiment of the present invention, the correlation coefficient specifically includes: pearson correlation coefficient.
Specifically, in the embodiment of the invention, the linear correlation between the frequency domain component of the actual load data and the frequency domain component of the output data in each time scale can be determined by determining the actual load data and the pearson correlation coefficient between the frequency domain components of the output data in each time scale, and the load balancing capability of the power plant unit in each time scale can be evaluated by determining the pearson correlation coefficient. The larger the Pearson correlation coefficient is, the better the linear correlation between the frequency domain component of the actual load data under the corresponding time scale and the frequency domain component of the output data under the corresponding time scale is, and the better the load balancing capability of the power plant unit under the corresponding time scale is. It should be noted that, in the embodiment of the present invention, only the determination of the pearson correlation coefficient is taken as an example for description, and other correlation coefficients between the actual load data and the frequency domain component of the output data in each time scale may also be determined, as long as the linear correlation between the frequency domain component of the actual load data in each time scale and the frequency domain component of the output data in each time scale can be determined.
On the basis of the above embodiment, the method for evaluating the load balancing capability of the power plant unit provided in the embodiment of the present invention further includes:
converting the frequency domain components of the actual load data and the output data under each time scale into time domain components;
verifying the feasibility of the evaluation method based on the time domain component.
Specifically, in the embodiment of the invention, the frequency domain components of the actual load data and the output data of the power plant unit under each time scale are converted into the time domain components, whether the original data before frequency domain decomposition can be formed after the time domain components obtained after conversion are synthesized is judged, and if the original data before frequency domain decomposition can be formed or the error between the formed data and the original data before frequency domain decomposition is within a preset range, the load balancing capability evaluation method of the power plant unit provided in the embodiment of the invention is considered to be feasible, namely, the feasibility of the evaluation method is verified.
As shown in fig. 2, on the basis of the foregoing embodiment, an embodiment of the present invention further provides a system for evaluating load balancing capability of a power plant unit, including: a data acquisition module 21, a frequency domain component determination module 22, a correlation coefficient determination module 23 and an evaluation module 24. Wherein the content of the first and second substances,
the data acquisition module 21 is configured to acquire actual load data in a preset power grid area and output data of a power plant unit in the preset power grid area;
the frequency domain component determining module 22 is configured to perform frequency domain decomposition on the actual load data and the output data respectively based on a peak shaving scale, an ultra-short term scale and an automatic generation control AGC scale of the power plant unit, and determine frequency domain components of the actual load data under the peak shaving scale, the ultra-short term scale and the AGC scale and frequency domain components of the output data under the peak shaving scale, the ultra-short term scale and the AGC scale;
the correlation coefficient determining module 23 is configured to determine, for each time scale of the peak shaving scale, the ultra-short term scale, and the AGC scale, a correlation coefficient between the actual load data and the frequency-domain component of the output data at each time scale based on a result of frequency-domain decomposition;
the evaluation module 24 is configured to evaluate the load balancing capability of the power plant unit at each time scale based on the actual load data and a correlation coefficient between the frequency domain components of the output data at each time scale.
Specifically, the functions of the modules in the load balancing capability evaluation system of the power plant unit provided in the embodiment of the present invention correspond to the processing flows of the steps in the above method embodiments one to one, and the realized technical effects are also consistent, which is not described again in the embodiment of the present invention.
As shown in fig. 3, on the basis of the above embodiment, an embodiment of the present invention further provides an electronic device, including: a processor (processor)301, a memory (memory)302, a communication Interface (Communications Interface)303, and a bus 304; wherein the content of the first and second substances,
the processor 301, the memory 302 and the communication interface 303 complete communication with each other through the bus 304. The memory 302 stores program instructions executable by the processor 301, and the processor 301 is configured to call the program instructions in the memory 302 to perform the methods provided by the above-mentioned method embodiments, for example, including: s1, acquiring actual load data in a preset power grid area and output data of power plant units in the preset power grid area; s2, respectively performing frequency domain decomposition on the actual load data and the output data based on a peak shaving scale, an ultra-short period scale and an Automatic Generation Control (AGC) scale of the power plant unit, and determining frequency domain components of the actual load data under the peak shaving scale, the ultra-short period scale and the AGC scale and frequency domain components of the output data under the peak shaving scale, the ultra-short period scale and the AGC scale; s3, determining a correlation coefficient between the actual load data and the frequency domain components of the output data at each time scale for each time scale of the peak shaving scale, the ultra-short term scale and the AGC scale; s4, evaluating the load balancing capacity of the power plant unit under each time scale based on the actual load data and the correlation coefficient between the frequency domain components of the output data under each time scale.
The logic instructions in memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone article of manufacture. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
On the basis of the foregoing embodiments, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions that cause the computer to execute the method provided by the foregoing method embodiments, including: s1, acquiring actual load data in a preset power grid area and output data of power plant units in the preset power grid area; s2, respectively performing frequency domain decomposition on the actual load data and the output data based on a peak shaving scale, an ultra-short period scale and an Automatic Generation Control (AGC) scale of the power plant unit, and determining frequency domain components of the actual load data under the peak shaving scale, the ultra-short period scale and the AGC scale and frequency domain components of the output data under the peak shaving scale, the ultra-short period scale and the AGC scale; s3, determining a correlation coefficient between the actual load data and the frequency domain components of the output data at each time scale for each time scale of the peak shaving scale, the ultra-short term scale and the AGC scale; s4, evaluating the load balancing capacity of the power plant unit under each time scale based on the actual load data and the correlation coefficient between the frequency domain components of the output data under each time scale.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for evaluating load balancing capability of a power plant unit is characterized by comprising the following steps:
acquiring actual load data in a preset power grid area and output data of power plant units in the preset power grid area;
respectively performing frequency domain decomposition on the actual load data and the output data based on a peak shaving scale, an ultra-short period scale and an Automatic Generation Control (AGC) scale of the power plant unit, and determining frequency domain components of the actual load data under three different time scales of the peak shaving scale, the ultra-short period scale and the AGC scale and frequency domain components of the output data under the peak shaving scale, the ultra-short period scale and the AGC scale;
for each time scale of the peak shaver scale, the ultra-short term scale and the AGC scale, determining a correlation coefficient between the actual load data and the frequency domain component of the output data at each time scale;
and evaluating the load balancing capacity of the power plant unit under each time scale based on the actual load data and the correlation coefficient between the frequency domain components of the output data under each time scale.
2. The method for evaluating the load balancing capability of the power plant unit according to claim 1, wherein the frequency domain decomposition is performed on the actual load data and the output data respectively based on a peak shaving scale, an ultra-short term scale and an Automatic Generation Control (AGC) scale of the power plant unit, and specifically comprises:
and performing FFT frequency domain decomposition on the actual load data and the output data respectively based on the peak regulation scale, the ultra-short term scale and the automatic generation control AGC scale of the power plant unit.
3. The method for evaluating the load balancing capability of the power plant unit according to claim 1, wherein the acquiring actual load data in a preset power grid area and the output data of the power plant unit in the preset power grid area specifically comprises:
and setting a sampling period, and acquiring the actual load data and the output data based on the sampling period.
4. The method for evaluating the load balancing capability of the power plant unit according to claim 3, wherein the sampling period is less than or equal to 1 min.
5. The method for evaluating the load balancing capability of the power plant unit according to claim 1, wherein the correlation coefficient specifically comprises: pearson correlation coefficient.
6. The method for evaluating the load balancing capability of the power plant unit according to claim 1, wherein the peak shaving scale specifically comprises: a time range greater than 15min, specifically, a time range greater than 5min and less than or equal to 15min, and specifically, a time range greater than 0 and less than or equal to 5 min.
7. The method for evaluating the load balancing capability of a power plant unit according to any one of claims 1 to 6, further comprising:
converting the frequency domain components of the actual load data and the output data under each time scale into time domain components;
verifying the feasibility of the evaluation method based on the time domain component.
8. A load balancing ability evaluation system of a power plant unit is characterized by comprising:
the data acquisition module is used for acquiring actual load data in a preset power grid area and output data of power plant units in the preset power grid area;
a frequency domain component determination module, configured to perform frequency domain decomposition on the actual load data and the output data respectively based on a peak shaving scale, an ultra-short term scale, and an automatic generation control AGC scale of the power plant unit, and determine frequency domain components of the actual load data in three different time scales, namely, the peak shaving scale, the ultra-short term scale, and the AGC scale, and frequency domain components of the output data in the peak shaving scale, the ultra-short term scale, and the AGC scale;
a correlation coefficient determining module, configured to determine, for each time scale of the peak shaving scale, the ultra-short term scale, and the AGC scale, a correlation coefficient between the actual load data and the frequency-domain component of the output data at each time scale based on a result of frequency-domain decomposition;
and the evaluation module is used for evaluating the load balancing capacity of the power plant unit under each time scale based on the actual load data and the correlation coefficient between the frequency domain components of the output data under each time scale.
9. An electronic device, comprising:
at least one processor, at least one memory, a communication interface, and a bus; wherein the content of the first and second substances,
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor to perform the method of load balancing capability evaluation of a power plant unit of any of claims 1 to 7.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method for load balancing capability evaluation of a power plant unit of any of claims 1-7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102075014A (en) * | 2011-01-06 | 2011-05-25 | 清华大学 | Large grid real-time scheduling method for accepting access of wind power |
| CN103078319A (en) * | 2013-01-11 | 2013-05-01 | 华中电网有限公司 | Real-time plan balancing capability evaluation method and real-time plan balancing capability evaluation system for power grids |
| CN103474991A (en) * | 2013-09-18 | 2013-12-25 | 国电南瑞科技股份有限公司 | Power distribution network global optimization scheduling method based on time scale |
| CN104181895A (en) * | 2014-08-28 | 2014-12-03 | 国家电网公司 | Strategy for optimizing short-term and ultra-short-term coordination rolling schedules adapting to access of new energy resources |
| CN104268800A (en) * | 2014-09-30 | 2015-01-07 | 清华大学 | Wind power integration peak-load regulating balance judgment method based on scene library |
| CN104933516A (en) * | 2015-05-27 | 2015-09-23 | 华南理工大学 | Multi-time-scale power system robustness scheduling system design method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10379586B2 (en) * | 2016-07-21 | 2019-08-13 | International Business Machines Corporation | System and method for a data center to provide energy elasticity services at different time scales |
-
2019
- 2019-01-22 CN CN201910057482.7A patent/CN109888839B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102075014A (en) * | 2011-01-06 | 2011-05-25 | 清华大学 | Large grid real-time scheduling method for accepting access of wind power |
| CN103078319A (en) * | 2013-01-11 | 2013-05-01 | 华中电网有限公司 | Real-time plan balancing capability evaluation method and real-time plan balancing capability evaluation system for power grids |
| CN103474991A (en) * | 2013-09-18 | 2013-12-25 | 国电南瑞科技股份有限公司 | Power distribution network global optimization scheduling method based on time scale |
| CN104181895A (en) * | 2014-08-28 | 2014-12-03 | 国家电网公司 | Strategy for optimizing short-term and ultra-short-term coordination rolling schedules adapting to access of new energy resources |
| CN104268800A (en) * | 2014-09-30 | 2015-01-07 | 清华大学 | Wind power integration peak-load regulating balance judgment method based on scene library |
| CN104933516A (en) * | 2015-05-27 | 2015-09-23 | 华南理工大学 | Multi-time-scale power system robustness scheduling system design method |
Non-Patent Citations (2)
| Title |
|---|
| 多时间尺度协调的柔性负荷互动响应调度模型与策略;杨胜春 等;《中国电机工程学报》;20140805;第34卷(第22期);第3664-3673页 * |
| 多时间尺度的扰动控制标准及备用协调;鄢发齐 等;《电网技术》;20170331;第41卷(第3期);第901-906页 * |
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