CN116914781A - New energy rapid frequency response system and method - Google Patents
New energy rapid frequency response system and method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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Abstract
The application discloses a new energy rapid frequency response system and a method, which relate to the technical field of new energy power grids and comprise a historical data acquisition module: acquiring historical meteorological data, historical holiday data and historical power load; and a model building module: establishing a power grid load prediction model; and a future data acquisition module: acquiring future meteorological data and future holiday data; load/frequency prediction module: predicting the load and frequency change of the power grid according to the power grid load prediction model; the execution module: executing a frequency adjustment task; and a monitoring module: monitoring the frequency of the power grid and the load/frequency of the generator set to obtain the real-time frequency of the power grid and the generator set; and a feedback module: generating feedback comments according to the acquired data; and the self-adaptive adjusting module is used for: and generating an adaptive adjustment instruction according to the acquired data, and sending the adaptive adjustment instruction to an execution module. The application has the effect of improving the rapidity and the adaptivity of the power grid frequency adjustment.
Description
Technical Field
The application relates to the technical field of new energy power grids, in particular to a new energy rapid frequency response system and a new energy rapid frequency response method.
Background
The rapid frequency response of the new energy is that in the power system, when the power load is suddenly changed, the new energy power generation device can rapidly adjust the output power, so that the frequency stability of the power system is ensured.
The traditional power generation mode such as coal and gas power generation has stronger inertia, can quickly respond to the load change of the system, and keeps the frequency of the power system stable. However, as the proportion of new energy sources such as wind power, photovoltaic and the like connected into a power grid is continuously increased, the new energy sources gradually become important power sources in a future power system, but the problem of unstable power generation output exists in new energy power generation, renewable energy sources such as wind energy and solar energy have intermittence and volatility, the fluctuation of output power is large, and inertial response capability is lacking, so that the new energy power generation device needs to have rapid frequency response capability so as to rapidly adjust the output power when the load of the power system suddenly changes, and the system frequency is kept stable.
However, the frequency response in the current power system is generally adjusted according to the change condition of the generated power frequency after the power load in the power grid is changed, so that the response is slower during the frequency adjustment, and meanwhile, when the power load is changed faster, the frequency response is not in pace with the frequency change, so that the energy balance and the stability of the power system are abnormal.
Therefore, we propose a new energy rapid frequency response system and method for solving the above problems.
Disclosure of Invention
The application aims to provide a new energy rapid frequency response system and a new energy rapid frequency response method, so as to solve the problems in the background technology.
In a first aspect, the present application provides a new energy rapid frequency response system, which adopts the following technical scheme:
a historical data acquisition module: the method comprises the steps of acquiring historical meteorological data, historical holiday data and historical power loads;
and a model building module: the system is configured to be in data connection with the historical data acquisition module and is used for establishing a power grid load prediction model;
and a future data acquisition module: the method comprises the steps of acquiring future meteorological data and future holiday data;
load/frequency prediction module: the system comprises a model building module, a future data acquisition module, a power grid load prediction module and a power grid frequency prediction module, wherein the model building module is configured to be in data connection with the future data acquisition module and is used for predicting load and frequency changes of a power grid according to the power grid load prediction model and acquiring a prediction result;
the execution module: the system is configured to be in data connection with the load/frequency prediction module and is used for generating an adjustment target value according to the prediction result and executing a frequency adjustment task;
and a monitoring module: the method comprises the steps of monitoring the frequency of a power grid and the load/frequency of a generator set, and obtaining the real-time frequency of the power grid and the generator set;
and the self-adaptive adjusting module is used for: the power grid monitoring module is configured to be in data connection with the power grid monitoring module and the generator set monitoring module, generates an adaptive adjustment instruction according to the acquired data, and sends the adaptive adjustment instruction to the execution module;
and a feedback module: the power grid monitoring module is configured to be in data connection with the historical data acquisition module, the power grid monitoring module and the generator set monitoring module, and feedback comments are generated according to the acquired data.
By adopting the technical scheme, the historical data are acquired, the power grid load prediction model is established according to the historical data, the load and the frequency of the power grid are predicted by utilizing the power grid load model according to the acquired future data, a prediction result is acquired, an adjustment target value is generated according to the prediction result, the frequency of the power generator set is adjusted, the power grid and the power generator set are monitored, whether the adjustment is correct or not is judged, after the adjustment is found, the problem is analyzed, advice is generated, the power grid load prediction model is optimized, meanwhile, the self-adaptive adjustment is generated according to the problem, and the power generator set is further adjusted in frequency. The rapidity and the adaptability to the power grid frequency adjustment are improved.
Preferably, the model building module comprises a data processing unit and a model unit;
the data processing unit processes the data acquired by the historical data acquisition module and generates a data processing result;
and the model unit establishes a power grid load prediction model according to the data processing result generated by the data processing unit.
By adopting the technical scheme, the historical data are acquired and processed, the historical power grid load condition is judged according to the data processing result of the historical data, and a power grid load prediction model is built according to the historical power grid load condition. The standardization and rapidity of the prediction of the power grid load are improved.
Preferably, the load/frequency prediction module comprises a prediction unit and an instruction unit;
the prediction unit receives the future weather data and the future holiday data acquired by the future data acquisition module;
inputting the future weather data and the future holiday data into the power grid load prediction model, and generating a future curve based on the future weather data and the future holiday data;
the power grid load prediction model judges a curve change trend according to the curve change condition of the future curve, predicts the power grid load based on the curve change trend, and generates a power grid load prediction curve;
based on the power grid load prediction curve, acquiring the change trend of the power grid load prediction curve, judging the power grid frequency change trend based on the change trend, and processing the power grid frequency change trend to generate a power grid frequency change curve;
judging a curve segment of the power grid frequency curve exceeding a preset threshold value based on the power grid frequency curve, and generating a frequency prediction result based on the curve segment;
and the instruction unit acquires an adjustment target value according to the frequency prediction result, generates a frequency adjustment instruction based on the adjustment target value and outputs the frequency adjustment instruction.
By adopting the technical scheme, future data are acquired, the future data are processed to generate a future curve, the future curve is input into a power grid load prediction model to generate a power grid load prediction curve, the change trend of the curve is judged according to the power grid load prediction curve, a power grid frequency change curve is generated according to the change trend of the curve, a frequency prediction result is generated, the frequency prediction result is processed to acquire an adjustment target value of frequency adjustment, and a frequency adjustment instruction is generated and output. The accuracy of the frequency change prediction is improved.
Preferably, the monitoring module comprises a power grid monitoring unit and a generator set monitoring unit;
the power grid monitoring unit is used for monitoring the frequency and the load of the power grid in real time, acquiring real-time first load/frequency data on the power grid and generating a frequency signal;
the generator set monitoring unit is used for monitoring the frequency and the load of the generator set in real time and obtaining second load/frequency data of the generator set.
By adopting the technical scheme, the power grid and the generator set are monitored in real time, the frequency signal is generated according to the first load/frequency data obtained by detecting the power grid, the frequency and the load of the generator set are monitored, and the second load/frequency data is obtained. The real-time performance of load and frequency monitoring of the power grid and the generator set is improved.
Preferably, the execution module comprises an operation unit and a timing unit;
the operation unit issues the frequency adjustment instruction according to the frequency adjustment instruction generated by the load/frequency prediction module, and performs frequency adjustment operation on the generator set according to the frequency signal of the monitoring module;
the timing unit establishes a generator set in each different area, and after receiving the frequency adjustment instruction, the timing unit temporarily buckles the frequency adjustment instruction, sets adjustment time according to the frequency adjustment instruction and returns a ready signal;
and after the timing unit judges that all the generator sets receive the frequency adjustment instruction, sending an permission signal to each generator set, and after the generator sets receive the permission signals, sending the frequency adjustment instruction to the generator sets according to the adjustment time.
By adopting the technical scheme, the generated frequency adjustment instruction is issued, the frequency of the generator set is adjusted according to the frequency signal of the monitoring module, meanwhile, a timing device is arranged on the generator sets in different areas, the frequency adjustment instruction is sent to the timing device in advance, a signal for continuing is returned, the adjustment time is set, the timing device confirms that all the generator sets receive the frequency adjustment instruction and then sends an allowable instruction, and the frequency adjustment instruction is issued to the generator set according to the preset adjustment time to adjust the frequency. And the prejudgement and uniformity of the power grid frequency adjustment are improved.
Preferably, the adaptive adjustment module comprises a data analysis unit and an adjustment unit;
the data analysis unit is used for analyzing the load/frequency of the power grid and the generator set according to the first load/frequency data and the second load/frequency data output by the monitoring module, acquiring an analysis result and judging the frequency stability difference based on the analysis result;
the adjusting unit is used for judging whether the frequency stability difference exceeds a preset threshold value, if not, generating an adaptive adjusting instruction based on the analysis result and the frequency stability difference, and sending the adjusting instruction to the executing module.
By adopting the technical scheme, the load and the frequency of the power grid and the generator set are analyzed according to the acquired first load/frequency data and second load/frequency data, the stability of the power grid and the generator set is judged, and if the stability is poor, a self-adaptive adjustment instruction is generated according to the difference value of the stability, and the frequency of the generator set is adjusted. The timeliness of error frequency correction and the adaptability in the frequency adjustment process are improved.
Preferably, the feedback module comprises an effect judging unit and a problem suggesting unit;
the effect judging unit judges and generates frequency adjusting effects of the power grid and the generator set according to the first load/frequency data and the second load/frequency data output by the monitoring module;
the problem suggestion unit judges whether the frequency adjustment effect reaches an expected value, and if not, judges a problem point based on the first load/frequency data and the second load/frequency data;
tracing the problem points based on the problem points, and judging the problem sources in the power grid load prediction model;
and carrying out analysis and verification on the problem source, obtaining an analysis and verification result, generating a problem suggestion list based on the analysis and verification result, and sending the problem suggestion list to the model building module to optimize the power grid load model.
By adopting the technical scheme, whether the effect of frequency adjustment reaches the expected value is judged according to the acquired first load/frequency data and second load/frequency data, if not, the problem point is traced according to the difference between the actual adjustment effect and the expected effect, the problem source in the power grid load prediction model is judged, the problem source is analyzed and verified, a problem list is generated, an improvement suggestion is generated according to the problem on the problem list, and the power grid load prediction model is optimized. The self-correcting capability and the correcting timeliness of the power grid load prediction model are improved.
In a second aspect, the present application provides a new energy rapid frequency response method, which adopts the following technical scheme:
acquiring a historical power load of a power grid, and judging and acquiring a load rule based on the historical power load;
acquiring historical meteorological data and historical holiday data, and establishing a power grid load prediction model by combining the load rules;
predicting the power load of the power grid based on the power grid load prediction model, generating a prediction result, and judging predicted frequency change data of the power grid based on the prediction result;
generating an adjustment target value based on the predicted frequency change data, and transmitting the adjustment target value to a generator set, wherein the generator set adjusts the generated power after receiving the adjustment target value;
the frequency change of the power grid is monitored in real time, real-time frequency change data are obtained, the real-time frequency change data are compared with the predicted frequency change data, and whether the frequency change data are consistent or not is judged;
and if the real-time frequency variation data are inconsistent, acquiring the data difference between the real-time frequency variation data and the predicted frequency variation data, and optimizing the power grid load prediction model based on the data difference.
Preferably, the step of obtaining historical meteorological data and historical holiday data and establishing a power grid load prediction model by combining the load rule includes:
acquiring historical meteorological data, processing the historical meteorological data, establishing a time axis, and generating a historical meteorological curve;
acquiring historical festival data, generating a historical festival curve on the time axis based on the time axis, and fusing the historical festival curve with the historical meteorological curve to generate a historical curve;
comparing the history curve with the load rule to obtain a comparison value, and judging that the history curve accords with the load rule if the comparison value exceeds a preset threshold value;
and establishing a power grid load prediction model based on the predicted load rule.
Preferably, if the real-time frequency variation data and the predicted frequency variation data are inconsistent, the step of obtaining a data difference between the real-time frequency variation data and the predicted frequency variation data, and optimizing the power grid load prediction model based on the data difference includes:
generating a prediction curve based on the predicted frequency change data, and placing a real-time curve generated by the real-time frequency change data on the prediction curve by taking the prediction curve as an axis;
judging an intersection point of the prediction curve and the real-time curve, and calculating the area between the real-time curve and the prediction curve based on the intersection point;
judging the area size and the area position of each area based on the intersection point, judging the data difference between the real-time frequency change data and the predicted frequency change data based on the area size, and judging the time period of occurrence of the difference based on the area position;
and optimizing and adjusting the power grid load prediction model based on the data difference and the time period.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method comprises the steps of obtaining historical data, building a power grid load prediction model according to the historical data, predicting the load and the frequency of a power grid by using the power grid load model according to the obtained future data, obtaining a prediction result, generating an adjustment target value according to the prediction result, adjusting the frequency of a generator set, monitoring the power grid and the generator set, judging whether the adjustment is correct, analyzing problems after the adjustment is found to be problematic, generating suggestions, optimizing the power grid load prediction model, generating adaptive adjustment according to the problems, and performing further frequency adjustment on the generator set. The rapidity and the adaptivity of the frequency adjustment are improved.
2. And sending the generated frequency adjustment instruction down, adjusting the frequency of the generator set according to the frequency signal of the monitoring module, setting a timing device on the generator sets in different areas, sending the frequency adjustment instruction to the timing device in advance, returning a signal for preparing to continue, setting the adjustment time, and sending an permission instruction after the timing device confirms that all the generator sets receive the frequency adjustment instruction, sending the frequency adjustment instruction to the generator set according to the preset adjustment time, and adjusting the frequency. And the prejudgement and uniformity of the power grid frequency adjustment are improved.
3. Judging whether the effect of frequency adjustment reaches the expected value according to the acquired first load/frequency data and second load/frequency data, if not, tracing the problem point according to the difference between the actual adjustment effect and the expected effect, judging the problem source in the power grid load prediction model, analyzing and verifying the problem source, generating a problem list, generating an improvement suggestion according to the problem on the problem list, and optimizing the power grid load prediction model. The self-correcting capability and the correcting timeliness of the power grid load prediction model are improved.
Drawings
FIG. 1 is a block diagram of a new energy fast frequency response system of the present application;
fig. 2 is a flow chart of steps of a new energy rapid frequency response method of the present application.
Reference numerals illustrate: 1. a historical data acquisition module; 2. a model building module; 3. a future data acquisition module; 4. a load/frequency prediction module; 5. an execution module; 6. a monitoring module; 7. an adaptive adjustment module; 8. and a feedback module.
Detailed Description
The present application will be described in further detail with reference to fig. 1-2, but embodiments of the present application are not limited thereto.
The embodiment of the application discloses a new energy rapid frequency response system and a method.
In this embodiment, referring to fig. 1, a new energy rapid frequency response system includes:
historical data acquisition module 1: the method comprises the steps of acquiring historical meteorological data, historical holiday data and historical power loads;
model building module 2: the system is configured to be in data connection with the historical data acquisition module 1 and is used for establishing a power grid load prediction model;
future data acquisition module 3: the method comprises the steps of acquiring future meteorological data and future holiday data;
load/frequency prediction module 4: the system is configured to be in data connection with a model building module 2 and a future data acquisition module 3, and is used for predicting the load and frequency change of the power grid according to a power grid load prediction model and acquiring a prediction result;
and an execution module 5: is configured to be in data connection with the load/frequency prediction module 4, and is used for generating an adjustment target value according to a prediction result and executing a frequency adjustment task;
monitoring module 6: the method comprises the steps of monitoring the frequency of a power grid and the load/frequency of a generator set, and obtaining the real-time frequency of the power grid and the generator set;
adaptive adjustment module 7: the system is configured to be in data connection with the power grid monitoring module 6 and the generator set monitoring module 6, generates an adaptive adjustment instruction according to the acquired data, and sends the adaptive adjustment instruction to the execution module 5;
feedback module 8: the system is configured to be in data connection with the historical data acquisition module 1, the power grid monitoring module 6 and the generator set monitoring module 6, and generates feedback comments according to the acquired data.
It should be noted that the above modules are merely basic modules of the present embodiment, and in the implementation process, some modules may be added, reduced or modified appropriately without affecting the overall implementation effect.
Referring to fig. 1, the model creation module 2 includes a data processing unit and a model unit;
the data processing unit processes the data acquired by the historical data acquisition module 1 and generates a data processing result;
and the model unit establishes a power grid load prediction model according to the data processing result generated by the data processing unit.
In the application, the data processing unit and the model unit respectively process the acquired historical data, and a power grid load prediction model is built according to the processing result. For example, the historical data in the past 3 years are obtained, the historical data are classified, combed and cleaned, the historical data are sorted, preliminary analysis is carried out on the historical data, the change condition of the data in the past three years is judged, a data processing result is generated, and a model unit establishes a power grid load prediction model according to the change condition of the historical data in the past three years.
Referring to fig. 1, the load/frequency prediction module 4 includes a prediction unit and an instruction unit;
the prediction unit receives the future weather data and the future holiday data acquired by the future data acquisition module 3;
inputting future weather data and future holiday data into a power grid load prediction model, and generating a future curve based on the future weather data and the future holiday data;
the power grid load prediction model judges a curve change trend according to the curve change condition of a future curve, predicts the power grid load based on the curve change trend, and generates a power grid load prediction curve;
based on the power grid load prediction curve, acquiring the change trend of the power grid load prediction curve, judging the power grid frequency change trend based on the change trend, and processing the power grid frequency change trend to generate a power grid frequency change curve;
judging a curve segment of the power grid frequency curve exceeding a preset threshold value based on the power grid frequency curve, and generating a frequency prediction result based on the curve segment;
the instruction unit obtains an adjustment target value according to the frequency prediction result, generates a frequency adjustment instruction based on the adjustment target value, and outputs the frequency adjustment instruction.
In the application, future data are acquired, the power grid frequency is predicted according to a power grid load prediction model, and a frequency adjustment instruction is generated according to a prediction result. For example, the acquired future data are processed, the weather condition and the holiday condition in a future period are judged, the power grid load prediction model is processed according to the weather condition and the holiday condition in the future period to generate a future curve, then the power grid load is predicted according to the curve change trend of the future curve to acquire a power grid load prediction curve, the power grid load prediction curve indicates that the power grid load fluctuation in the future period is larger, the fluctuation of the frequency of the power grid is judged to be larger according to the result, the power grid frequency change curve is generated, line segments exceeding a threshold value on the curve are judged, the frequency prediction result is generated according to the line segments, an adjustment target value is generated, and a frequency adjustment instruction is generated according to the adjustment target value.
Referring to fig. 1, the monitoring module 6 includes a grid monitoring unit and a generator set monitoring unit;
the power grid monitoring unit is used for monitoring the frequency and the load of the power grid in real time, acquiring real-time first load/frequency data on the power grid and generating a frequency signal;
the generator set monitoring unit is used for monitoring the frequency and the load of the generator set in real time and obtaining second load/frequency data of the generator set.
In the application, the load and the frequency of the power grid and the generator set are monitored in real time, and meanwhile, a frequency signal is generated according to the monitoring condition of the power grid. For example, in the process of monitoring the frequency and load of the power grid and the generator set, the frequency of the power grid is found to start to fluctuate, and a frequency signal is generated and output according to the fluctuation condition.
Referring to fig. 1, the execution module 5 includes an operation unit and a timing unit;
the operation unit issues a frequency adjustment instruction according to the frequency adjustment instruction generated by the load/frequency prediction module 4, and performs frequency adjustment operation on the generator set according to the frequency signal of the monitoring module 6;
the timing unit establishes the generator sets in each different area, after receiving the frequency adjustment instruction, the timing unit temporarily deducts the frequency adjustment instruction, sets the adjustment time according to the frequency adjustment instruction, and returns a ready signal;
when the timing unit judges that all the generator sets receive the frequency adjustment instruction, an permission signal is sent to each generator set, and after the generator sets receive the permission signals, the frequency adjustment instruction is sent to the generator sets according to the adjustment time.
In application, the operation unit issues the frequency adjustment instruction, adjusts the frequency of the generator set, establishes a timing device at the same time, and ensures the uniformity of the frequency adjustment. For example, when a certain prediction is performed, a frequency signal is sent to an operation unit after finding that the frequency of the generator set needs to be adjusted for one hour, the operation unit sends a frequency adjustment instruction to the generator set, the new energy generator set is provided with a plurality of areas, certain communication delay exists between the different areas, a timing device is arranged on the generator set in the different areas, when the timing device receives the frequency adjustment instruction, countdown is arranged according to the adjustment time in the frequency adjustment instruction, and a ready signal is returned to indicate that the generator set is ready, after the timing unit receives the signal, an permission signal is sent to the generator set in the different areas, and the frequency of the generator set is uniformly adjusted according to the set countdown time.
Referring to fig. 1, the adaptive adjustment module 7 includes a data analysis unit and an adjustment unit;
the data analysis unit analyzes the load/frequency of the power grid and the generator set according to the first load/frequency data and the second load/frequency data output by the monitoring module 6, obtains an analysis result, and judges the frequency stability difference based on the analysis result;
the adjusting unit is configured to determine whether the frequency stability difference exceeds a preset threshold, and if not, generate an adaptive adjustment instruction based on the analysis result and the frequency stability difference, and send the adjustment instruction to the execution module 5.
In the application, the stability of the frequency of the adjusted power grid is judged according to the data obtained by monitoring the power grid and the generator set, and the self-adaptive frequency adjustment is carried out on the generator set. For example, after the generator set is regulated for a certain time, if the frequency of the power grid does not reach the stable state, the frequency stability difference between the power grid and the standard state is judged according to the current state, and according to the frequency stability difference, the generator set is judged to need to carry out frequency regulation within a small range, and then an adaptive regulation command is generated and sent to the generator set.
Referring to fig. 1, the feedback module 8 includes an effect judging unit and a problem suggesting unit;
the effect judging unit judges and generates the frequency adjusting effect of the power grid and the generator set according to the first load/frequency data and the second load/frequency data output by the monitoring module 6;
the problem suggestion unit judges whether the frequency adjustment effect reaches the expected value, if not, the problem point is judged based on the first load/frequency data and the second load/frequency data;
tracing the problem points based on the problem points, and judging the problem sources in the power grid load prediction model;
and analyzing and verifying the problem source, obtaining an analysis and verification result, generating a problem suggestion list based on the analysis and verification result, and sending the problem suggestion list to the model building module 2 to optimize the power grid load model.
In the application, the effect of the frequency adjustment is judged according to the data obtained by monitoring the power grid and the generator set, and the power grid load prediction model is optimized according to the effect. For example, after the frequency of the power grid is regulated for a certain time, the power grid frequency regulation effect is found to be poor, the fact that the power grid load prediction model generates a larger error in the power grid load prediction, so that the power grid load prediction model carries out self-adaptive regulation for a plurality of times later is mainly shown, the error is taken as a cause point, tracing is carried out in the power grid load prediction model according to the cause point, the travel development condition of the power utilization region is found to be not recorded in the power grid load prediction model, in the past months, the power utilization region explodes, a large number of tourists are surging into, and the prediction is caused to have a problem, and then the development condition of the power utilization region is taken as a record object according to the problem, and the power grid prediction model is optimized according to the development condition.
Referring to fig. 2, a new energy rapid frequency response method includes the steps of:
s100: acquiring a historical power load of a power grid, and judging and acquiring a load rule based on the historical power load;
s200: acquiring historical meteorological data and historical holiday data, and establishing a power grid load prediction model by combining a load rule;
s300: based on a power grid load prediction model, predicting the power load of the power grid, generating a prediction result, and judging predicted frequency change data of the power grid based on the prediction result;
s400: generating an adjustment target value based on the predicted frequency change data, transmitting the adjustment target value to a generator set, and adjusting the generated power after the generator set receives the adjustment target value;
s500: the frequency change of the power grid is monitored in real time, real-time frequency change data are obtained, the real-time frequency change data are compared with predicted frequency change data, and whether the frequency change data are consistent or not is judged;
s600: and if the real-time frequency variation data are inconsistent, acquiring the data difference between the real-time frequency variation data and the predicted frequency variation data, and optimizing the power grid load prediction model based on the data difference.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (10)
1. A new energy rapid frequency response system, comprising:
historical data acquisition module (1): the method comprises the steps of acquiring historical meteorological data, historical holiday data and historical power loads;
model building module (2): the system is configured to be in data connection with the historical data acquisition module (1) and is used for establishing a power grid load prediction model;
future data acquisition module (3): the method comprises the steps of acquiring future meteorological data and future holiday data;
load/frequency prediction module (4): the system is configured to be in data connection with the model building module (2) and the future data acquisition module (3) and is used for predicting the load and frequency change of the power grid according to the power grid load prediction model and acquiring a prediction result;
execution module (5): is configured to be in data connection with the load/frequency prediction module (4) for generating an adjustment target value according to the prediction result and performing a frequency adjustment task;
monitoring module (6): the method comprises the steps of monitoring the frequency of a power grid and the load/frequency of a generator set, and obtaining the real-time frequency of the power grid and the generator set;
adaptive adjustment module (7): the system is configured to be in data connection with the power grid monitoring module (6) and the generator set monitoring module (6), generates an adaptive adjustment instruction according to the acquired data, and sends the adaptive adjustment instruction to the execution module (5);
feedback module (8): the system is configured to be in data connection with the historical data acquisition module (1), the power grid monitoring module (6) and the generator set monitoring module (6), and generate feedback comments according to the acquired data.
2. The new energy rapid frequency response system of claim 1, comprising:
the model building module (2) comprises a data processing unit and a model unit;
the data processing unit processes the data acquired by the historical data acquisition module (1) and generates a data processing result;
and the model unit establishes a power grid load prediction model according to the data processing result generated by the data processing unit.
3. The new energy rapid frequency response system of claim 1, comprising:
the load/frequency prediction module (4) comprises a prediction unit and an instruction unit;
the prediction unit receives future weather data and future holiday data acquired by the future data acquisition module (3);
inputting the future weather data and the future holiday data into the power grid load prediction model, and generating a future curve based on the future weather data and the future holiday data;
the power grid load prediction model judges a curve change trend according to the curve change condition of the future curve, predicts the power grid load based on the curve change trend, and generates a power grid load prediction curve;
based on the power grid load prediction curve, acquiring the change trend of the power grid load prediction curve, judging the power grid frequency change trend based on the change trend, and processing the power grid frequency change trend to generate a power grid frequency change curve;
judging a curve segment of the power grid frequency curve exceeding a preset threshold value based on the power grid frequency curve, and generating a frequency prediction result based on the curve segment;
and the instruction unit acquires an adjustment target value according to the frequency prediction result, generates a frequency adjustment instruction based on the adjustment target value and outputs the frequency adjustment instruction.
4. The new energy rapid frequency response system of claim 1, comprising:
the monitoring module (6) comprises a power grid monitoring unit and a generator set monitoring unit;
the power grid monitoring unit is used for monitoring the frequency and the load of the power grid in real time, acquiring real-time first load/frequency data on the power grid and generating a frequency signal;
the generator set monitoring unit is used for monitoring the frequency and the load of the generator set in real time and obtaining second load/frequency data of the generator set.
5. The new energy rapid frequency response system of claim 1, comprising:
the execution module (5) comprises an operation unit and a timing unit;
the operation unit issues the frequency adjustment instruction according to the frequency adjustment instruction generated by the load/frequency prediction module (4), and performs frequency adjustment operation on the generator set according to the frequency signal of the monitoring module (6);
the timing unit establishes a generator set in each different area, and after receiving the frequency adjustment instruction, the timing unit temporarily buckles the frequency adjustment instruction, sets adjustment time according to the frequency adjustment instruction and returns a ready signal;
and after the timing unit judges that all the generator sets receive the frequency adjustment instruction, sending an permission signal to each generator set, and after the generator sets receive the permission signals, sending the frequency adjustment instruction to the generator sets according to the adjustment time.
6. The new energy rapid frequency response system of claim 1, comprising:
the self-adaptive adjusting module (7) comprises a data analysis unit and an adjusting unit;
the data analysis unit is used for analyzing the load/frequency of the power grid and the generator set according to the first load/frequency data and the second load/frequency data output by the monitoring module (6), acquiring an analysis result and judging the frequency stability difference based on the analysis result;
the adjusting unit is used for judging whether the frequency stability difference exceeds a preset threshold value, if not, generating an adaptive adjusting instruction based on the analysis result and the frequency stability difference, and sending the adjusting instruction to the executing module (5).
7. The new energy rapid frequency response system of claim 1, comprising:
the feedback module (8) comprises an effect judging unit and a problem suggesting unit;
the effect judging unit judges and generates a frequency adjusting effect of the power grid and the generator set according to the first load/frequency data and the second load/frequency data output by the monitoring module (6);
the problem suggestion unit judges whether the frequency adjustment effect reaches an expected value, and if not, judges a problem point based on the first load/frequency data and the second load/frequency data;
tracing the problem points based on the problem points, and judging the problem sources in the power grid load prediction model;
and carrying out analysis and verification on the problem source, obtaining an analysis and verification result, generating a problem suggestion list based on the analysis and verification result, and sending the problem suggestion list to the model building module (2) to optimize the power grid load model.
8. A new energy rapid frequency response method using the new energy rapid response system according to any one of claims 1 to 7, comprising the steps of:
acquiring a historical power load of a power grid, and judging and acquiring a load rule based on the historical power load;
acquiring historical meteorological data and historical holiday data, and establishing a power grid load prediction model by combining the load rules;
predicting the power load of the power grid based on the power grid load prediction model, generating a prediction result, and judging predicted frequency change data of the power grid based on the prediction result;
generating an adjustment target value based on the predicted frequency change data, and transmitting the adjustment target value to a generator set, wherein the generator set adjusts the generated power after receiving the adjustment target value;
the frequency change of the power grid is monitored in real time, real-time frequency change data are obtained, the real-time frequency change data are compared with the predicted frequency change data, and whether the frequency change data are consistent or not is judged;
and if the real-time frequency variation data are inconsistent, acquiring the data difference between the real-time frequency variation data and the predicted frequency variation data, and optimizing the power grid load prediction model based on the data difference.
9. The method of claim 8, wherein the step of obtaining historical meteorological data and historical holiday data and combining the load law to build a power grid load prediction model comprises the steps of:
acquiring historical meteorological data, processing the historical meteorological data, establishing a time axis, and generating a historical meteorological curve;
acquiring historical festival data, generating a historical festival curve on the time axis based on the time axis, and fusing the historical festival curve with the historical meteorological curve to generate a historical curve;
comparing the history curve with the load rule to obtain a comparison value, and judging that the history curve accords with the load rule if the comparison value exceeds a preset threshold value;
and establishing a power grid load prediction model based on the predicted load rule.
10. The method according to claim 8, wherein if the real-time frequency variation data and the predicted frequency variation data are inconsistent, the step of obtaining a data difference between the real-time frequency variation data and the predicted frequency variation data, and optimizing the power grid load prediction model based on the data difference comprises:
generating a prediction curve based on the predicted frequency change data, and placing a real-time curve generated by the real-time frequency change data on the prediction curve by taking the prediction curve as an axis;
judging an intersection point of the prediction curve and the real-time curve, and calculating the area between the real-time curve and the prediction curve based on the intersection point;
judging the area size and the area position of each area based on the intersection point, judging the data difference between the real-time frequency change data and the predicted frequency change data based on the area size, and judging the time period of occurrence of the difference based on the area position;
and optimizing and adjusting the power grid load prediction model based on the data difference and the time period.
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