CN116937569A - Intelligent energy storage method and device for photovoltaic power generation and electronic equipment - Google Patents
Intelligent energy storage method and device for photovoltaic power generation and electronic equipment Download PDFInfo
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- 238000010248 power generation Methods 0.000 title claims abstract description 226
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Classifications
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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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
Abstract
The application provides an intelligent energy storage method and device for photovoltaic power generation and electronic equipment, wherein the method is applied to a server and comprises the following steps: judging an abnormal frequency state of the power system, wherein the frequency state comprises an over-frequency state and an under-frequency state; determining predicted power generation capacity of a photovoltaic power generation system, wherein the photovoltaic power generation system is connected with a power system; determining a predicted electricity consumption of the power system; if the power system is in the over-frequency state, judging whether the predicted power generation amount is larger than or equal to the predicted power consumption amount; and if the predicted power generation amount is greater than or equal to the predicted power consumption amount, sending a power storage instruction to the energy storage system so that the energy storage system stores the power generation amount of the photovoltaic power generation system, and connecting the energy storage system with the photovoltaic power generation system. The application has the effect of maintaining the system frequency stability when the photovoltaic power generation system is connected into the power system.
Description
Technical Field
The application relates to the technical field of power systems, in particular to an intelligent energy storage method and device for photovoltaic power generation and electronic equipment.
Background
Photovoltaic power generation is a technology that uses the photovoltaic effect of a semiconductor interface to directly convert light energy into electrical energy. The photovoltaic power generation system at least comprises a solar panel and an inverter, wherein the solar panel converts sunlight into direct-current electric energy, and the inverter converts the direct-current electric energy into alternating-current electric energy. Because solar energy is an intermittent energy source, the output power of the photovoltaic power generation system also fluctuates under the influence of factors such as weather, seasons, sunshine duration and the like.
The system frequency of the power system refers to the frequency of the periodic variation of the voltage and current in the ac power system. The stability of the system frequency is very important for the normal operation of the power system, and abnormal changes in the frequency may cause the power system to malfunction or affect the normal operation of the equipment. When the load suddenly increases or the generator is shut down, the system frequency may drop; conversely, when the load suddenly decreases or a new generator is connected, the system frequency may rise. Abnormal changes in system frequency may cause power system failures or affect the proper operation of the equipment.
When a large-scale photovoltaic power generation system is connected into an electric power system, the output suddenly increases, and the consumption of a load is exceeded, so that the system frequency may be increased. Conversely, if the output of the solar power generation system suddenly decreases, it may result in a decrease in the system frequency. There is therefore a need for a method to maintain the stability of the system frequency of a photovoltaic power generation system when it is switched into the power system.
Disclosure of Invention
The application provides an intelligent energy storage method and device for photovoltaic power generation and electronic equipment, which have the effect of maintaining the stability of the system frequency of a power system when the photovoltaic power generation system is connected to the power system.
In a first aspect of the present application, there is provided a photovoltaic power generation intelligent energy storage method, the method being applied to a server and comprising:
judging an abnormal frequency state of the power system, wherein the frequency state comprises an over-frequency state and an under-frequency state;
determining a predicted power generation amount of a photovoltaic power generation system, wherein the photovoltaic power generation system is connected with the power system;
determining a predicted electricity consumption of the power system;
if the power system is in an over-frequency state, judging whether the predicted power generation amount is larger than or equal to the predicted power consumption amount;
and if the predicted generated energy is greater than or equal to the predicted used electric quantity, sending an electric storage instruction to an energy storage system so that the energy storage system stores the generated energy of the photovoltaic power generation system, wherein the energy storage system is connected with the photovoltaic power generation system.
By adopting the technical scheme, the server firstly determines the abnormal frequency state of the power system, and when the power system is in the over-frequency state, the server can judge that the power input quantity of the power system is larger than the power output quantity. When the photovoltaic power generation system is connected into the power system, and the predicted power generation amount of the photovoltaic power generation system is larger than the predicted power consumption amount of the power system, the server sends a power storage instruction to the energy storage system at the moment so that the energy storage system stores the power generation amount of the photovoltaic power generation system, thereby reducing the power input amount of the power system, further gradually stabilizing the system frequency of the power system, and achieving the effect of maintaining the stability of the system frequency of the power system.
Optionally, after the determining the abnormal frequency state of the power system, the method further includes:
if the power system is in the under-frequency state, judging whether the predicted generated energy is smaller than or equal to the predicted generated energy;
and if the predicted power generation amount is smaller than or equal to the predicted power consumption amount, sending a power supply instruction to the energy storage system so that the energy storage system supplies power to the power system, wherein the energy storage system is connected with the power system.
By adopting the technical scheme, when the power system is in the under-frequency state, the power input quantity of the power system is smaller than the power output quantity. And when the photovoltaic power generation system is connected to the power system, the predicted power generation amount is smaller than or equal to the predicted power consumption amount. In this case, by sending a power supply instruction to the energy storage system, the energy storage system may supply the stored power to the power system to fill the power gap, so that the frequency of the power system gradually tends to be stable.
Optionally, the determining the abnormal frequency state of the power system specifically includes:
acquiring a system frequency of the power system;
judging the magnitude relation between the system frequency and the preset frequency;
if the system frequency is smaller than the preset frequency, determining that the abnormal frequency state of the power system is an under-frequency state;
If the system frequency is larger than the preset frequency, determining that the abnormal frequency state of the power system is an over-frequency state.
By adopting the technical scheme, the abnormal frequency state of the power system is accurately judged to be the under-frequency state or the over-frequency state according to the magnitude relation between the frequency of the actual power system and the preset frequency. By knowing the state of the power system, the operation of the energy storage system is conveniently regulated by adopting a corresponding control strategy subsequently so as to stabilize the system frequency of the power system.
Optionally, the determining the predicted power generation amount of the photovoltaic power generation system specifically includes:
acquiring a plurality of prestored photovoltaic power generation influence parameters;
acquiring pre-stored power generation data corresponding to each photovoltaic power generation influence parameter;
analyzing a plurality of photovoltaic power generation influence parameters and a plurality of power generation data to obtain a relation between the photovoltaic power generation influence parameters and the power generation data;
establishing a prediction model based on the relation between the photovoltaic power generation influence parameters and the power generation data;
acquiring a plurality of prediction parameters of photovoltaic power generation influence parameters in a preset time period;
and predicting the predicted power generation amount in the preset time period through the prediction model according to a plurality of prediction parameters.
By adopting the technical scheme, the generated energy of the photovoltaic power generation system in a preset time period is predicted according to the pre-stored photovoltaic power generation influence parameters, the corresponding power generation power data and the established prediction model. By utilizing the existing data and the relation model, the prediction accuracy of the photovoltaic power generation amount can be improved, so that the work scheduling of the energy storage system can be conveniently and well carried out.
Optionally, before the determining the predicted power consumption of the power system, the method further includes:
acquiring a plurality of historical electricity consumption data, wherein the electricity consumption data are prestored electricity consumption data of an electricity consumption load, and the electricity consumption load is connected with the power system;
performing fitting analysis on a plurality of historical electricity utilization data to determine an electricity utilization rule of the electricity utilization load;
acquiring power consumption influence factors corresponding to each historical power consumption data;
determining target electricity consumption data exceeding a preset electricity consumption range in a plurality of historical electricity consumption data according to the preset electricity consumption range;
and analyzing the electricity consumption influence factors corresponding to the target electricity consumption data, and determining the influence weight value of each electricity consumption influence factor.
By adopting the technical scheme, the accuracy and the reliability of electricity consumption prediction can be improved by acquiring historical electricity consumption data, analyzing electricity consumption rules, identifying influence factors and determining weight values. And meanwhile, a data basis is provided for the subsequent electricity consumption prediction.
Optionally, the determining the predicted electricity consumption of the electric power system specifically includes:
acquiring the basic electricity consumption of the electricity load in a preset time period based on the electricity consumption rule;
acquiring a predicted electricity consumption influence factor in the preset time period;
determining a power consumption fluctuation value corresponding to the predicted power consumption influence factor based on the influence weight value;
and determining the predicted electricity consumption according to the basic electricity consumption and the electricity consumption fluctuation value.
By adopting the technical scheme, the predicted electricity consumption in the predicted time period is determined according to the basic electricity consumption and the electricity consumption fluctuation value. And combining the basic electricity consumption with the fluctuation range of each influence factor to obtain the final predicted electricity consumption. Thereby providing a prediction of the electrical load during a future time period and providing a reference basis for the scheduling and planning of the energy storage system.
Optionally, the electricity consumption influence factors include human influence factors and weather influence factors;
the artificial influence factors comprise date factors, weather factors, holiday factors and social event factors;
the weather influencing factors include air temperature factors, humidity factors, rainfall factors and wind power factors.
By adopting the technical scheme, various factors influencing the electricity consumption can be more comprehensively captured by the prediction model by considering the artificial influence factors and the weather influence factors. This helps to improve the accuracy and reliability of the electricity consumption prediction, making the prediction result closer to the actual situation. Date factors and holiday factors among the human influence factors may reflect seasonal and periodic power usage changes. By considering the factors, the influence of a specific date, a special holiday or a social event on the electricity consumption can be predicted better, so that the prediction result is more accurate. Weather influencing factors include factors such as air temperature, humidity, rainfall, wind power, and the like. These factors have a significant impact on many electrical loads, such as air conditioning systems, heating systems, wind power generation, and the like. By considering weather influencing factors, the demand change of the electricity loads can be predicted better, and the prediction result of the electricity consumption can be adjusted better.
In a second aspect of the present application, an intelligent energy storage device for photovoltaic power generation is provided, the device is a server, and includes a judging module, a calculating module and a sending module, wherein:
the judging module is used for judging the abnormal frequency state of the power system, wherein the frequency state comprises an over-frequency state and an under-frequency state;
The calculation module is used for determining the predicted generated energy of a photovoltaic power generation system, and the photovoltaic power generation system is connected with the power system;
a calculation module for determining a predicted power consumption of the power system;
the judging module is used for judging whether the predicted power generation amount is larger than or equal to the predicted power consumption amount or not if the power system is in an over-frequency state;
and the sending module is used for sending a power storage instruction to an energy storage system if the predicted power generation amount is larger than or equal to the predicted power consumption amount, so that the energy storage system stores the power generation amount of the photovoltaic power generation system, and the energy storage system is connected with the photovoltaic power generation system.
In a third aspect the application provides an electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface, both for communicating with other devices, the processor being for executing instructions stored in the memory to cause the electronic device to perform a method as claimed in any one of the preceding claims.
In a fourth aspect of the application there is provided a computer readable storage medium storing instructions which, when executed, perform a method as claimed in any one of the preceding claims.
In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. the server first determines an abnormal frequency state of the power system, and when the power system is in an over-frequency state, it can be determined that the power input amount of the power system is greater than the power output amount. When the photovoltaic power generation system is connected into the power system, and the predicted power generation amount of the photovoltaic power generation system is larger than the predicted power consumption amount of the power system, the server sends a power storage instruction to the energy storage system at the moment so that the energy storage system stores the power generation amount of the photovoltaic power generation system, thereby reducing the power input amount of the power system, further gradually stabilizing the system frequency of the power system, and achieving the effect of maintaining the stability of the system frequency of the power system.
2. When the power system is in the under-frequency state, the power input quantity of the power system is smaller than the power output quantity. And when the photovoltaic power generation system is connected to the power system, the predicted power generation amount is smaller than or equal to the predicted power consumption amount. In this case, by sending a power supply instruction to the energy storage system, the energy storage system may supply the stored power to the power system to fill the power gap, so that the frequency of the power system gradually tends to be stable.
3. And predicting the generated energy of the photovoltaic power generation system in a preset time period according to the pre-stored photovoltaic power generation influence parameters, the corresponding power generation data and the established prediction model. By utilizing the existing data and the relation model, the prediction accuracy of the photovoltaic power generation amount can be improved, so that the work scheduling of the energy storage system can be conveniently and well carried out.
4. And determining the predicted electricity consumption in the predicted time period according to the basic electricity consumption and the electricity consumption fluctuation value. And combining the basic electricity consumption with the fluctuation range of each influence factor to obtain the final predicted electricity consumption. Thereby providing a prediction of the electrical load during a future time period and providing a reference basis for the scheduling and planning of the energy storage system.
Drawings
Fig. 1 is a schematic flow chart of an intelligent energy storage method for photovoltaic power generation, which is disclosed in the embodiment of the application;
fig. 2 is a schematic diagram of an application scenario of the intelligent energy storage method for photovoltaic power generation disclosed in the embodiment of the present application;
fig. 3 is a schematic structural diagram of a photovoltaic power generation intelligent energy storage device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 201. a server; 202. an electric power system; 203. a photovoltaic power generation system; 204. an energy storage system; 205. an electrical load; 301. a judging module; 302. a computing module; 303. a transmitting module; 304. an acquisition module; 401. a processor; 402. a communication bus; 403. a user interface; 404. a network interface; 405. a memory.
Detailed Description
In order that those skilled in the art will better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.
In describing embodiments of the present application, words such as "for example" or "for example" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "such as" or "for example" in embodiments of the application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.
In the description of embodiments of the application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Photovoltaic power generation is a technology that uses the photovoltaic effect of a semiconductor interface to directly convert light energy into electrical energy. The photovoltaic power generation system 203 includes at least a solar panel that converts sunlight into direct current electric energy and an inverter that converts direct current electric energy into alternating current electric energy. Because solar energy is an intermittent energy source, the output power of the photovoltaic power generation system 203 also fluctuates due to factors such as weather, seasons, and sunshine duration.
The system frequency of the power system 202 refers to the frequency of the periodic variation of the voltage and current in the ac power system 202. Stability of the system frequency is important to the proper operation of the power system 202, and abnormal changes in frequency may cause the power system 202 to malfunction or affect the proper operation of the equipment. When the load suddenly increases or the generator is shut down, the system frequency may drop; conversely, when the load suddenly decreases or a new generator is connected, the system frequency may rise. Abnormal changes in system frequency may cause the power system 202 to malfunction or affect the proper operation of the device.
When the large-scale photovoltaic power generation system 203 is connected to the power system 202, the output suddenly increases, exceeding the consumption of the load, and when the power load of the power system 202 is unchanged, the system frequency may increase. Conversely, if the output of the photovoltaic power generation system 203 suddenly decreases, while the power load of the power system 202 is unchanged, the system frequency may be reduced. There is therefore a need for a solution to maintain the stability of the system frequency of the power system 202 when the photovoltaic power generation system 203 is connected to the power system 202.
The embodiment discloses an intelligent energy storage method for photovoltaic power generation, referring to fig. 1, comprising the following steps S110-S150:
s110, judging an abnormal frequency state of the power system 202.
Specifically, the photovoltaic power generation intelligent energy storage method disclosed in the embodiment is applied to the server 201, and the server 201 includes, but is not limited to, electronic devices such as a mobile phone, a tablet computer, a wearable device, a PC (Personal Computer, a personal computer) and the like, and may also be a background server 201 running the photovoltaic power generation intelligent energy storage method. The server 201 may be implemented as a stand-alone server 201 or as a cluster of servers 201 consisting of multiple servers 201. Referring to fig. 2, a server 201 is connected to a power system 202, the server 201 is also connected to a photovoltaic power generation system 203, and the photovoltaic power generation system 203 is connected to the power system 202. The photovoltaic power generation system 203 is a power generation system that converts light energy into electric energy by photovoltaic effect using energy of sunlight. In the embodiment of the present application, the power system 202 is dedicated to a complete power distribution and delivery system consisting of a power transmission network, a power distribution network, and the like. The power system 202 is also connected with an electricity load 205 of the user power grid, and is responsible for transmitting the electric energy generated by the photovoltaic power generation system 203 to the electricity load 205 through a power transmission line so as to meet the electricity demand of the electricity load 205.
Typically, the photovoltaic power generation system 203 is connected to the power system 202 through a site grid connection point, and the site grid connection point may be connected to a frequency measurement device, such as a frequency meter or a frequency meter, which uses a sensor or a network of sensors to monitor the voltage or current signals of the power system 202 and calculate the system frequency through a mathematical algorithm. Or the station grid connection point is connected with a phasor measurement device (Phasor Measurement Unit, PMU), and the phasor measurement device is mainly used for measuring the phasor and frequency of the high-precision and high-speed power system 202. The phasor measurement device can rapidly acquire important parameters such as phase angle and frequency of the power system 202 by collecting phasor information of each voltage and current in the power system 202 and performing data processing and calculation on the basis of high-speed sampling. Finally, a communication connection is established between the frequency measurement device and the server 201, so that the measured frequency of the power system 202 can be sent to the server 201.
After the server 201 obtains the system frequency of the power system 202, it determines the relationship between the system frequency and the preset frequency, where the preset frequency is the standard frequency of the power system 202, usually 50Hz, and some countries are 60Hz. When the system frequency is less than the preset frequency and less than the standard frequency, the abnormal frequency state of the power system 202 is an under-frequency state. When the system frequency is greater than the preset frequency and higher than the standard frequency, the abnormal frequency state of the power system 202 is an over-frequency state. Whether the power system 202 is in an underfrequency state or an overfrequency state, the stable operation of the power system 202 is affected. According to the magnitude relation between the frequency of the actual power system 202 and the preset frequency, whether the abnormal frequency state of the power system 202 is an under-frequency state or an over-frequency state is accurately judged. By knowing the state of the power system 202, a corresponding control strategy is facilitated to be subsequently adopted to regulate the operation of the energy storage system 204 to stabilize the system frequency of the power system 202.
S120, determining a predicted power generation amount of the photovoltaic power generation system 203.
Since photovoltaic power generation is greatly affected by the environment, for example, when the weather is sunny, the power generation power of the solar power generation group is high, and the power generation amount per unit time of the photovoltaic power generation system 203 is large. In contrast, when the weather is overcast and rainy, the power generation of the solar power generation group is low, and the power generation amount per unit time of the photovoltaic power generation system 203 is small. Therefore, when determining the predicted power generation amount of the photovoltaic power generation system 203, it is necessary to predict from a large amount of pre-stored historical power generation data in combination with future weather data.
Specifically, a large amount of historical power generation data including photovoltaic power generation influence parameters and power generation data corresponding to the photovoltaic power generation influence parameters is first retrieved. And during data acquisition, related data can be acquired through monitoring equipment provided with the photovoltaic power generation system 203, wherein the data comprise solar irradiance sensors, temperature sensors and monitoring equipment for photovoltaic power generation, so that data such as solar irradiance, temperature and output power of photovoltaic power generation can be acquired in real time. After the data is collected, the collected data needs to be processed and cleaned so as to ensure the accuracy and the integrity of the data. This may include removing outliers, smoothing data, filling in missing data, etc. For example, if solar irradiance data is not acquired at a certain point in time, interpolation methods may be used to fill in the missing values.
And then, the relation among solar irradiance, temperature and photovoltaic power generation output power is explored by analyzing the photovoltaic power generation influence parameters and power generation data corresponding to the photovoltaic power generation influence parameters. This can be achieved by statistical analysis, trend graph plotting, correlation analysis, etc. For example, a scatter plot and regression analysis may be used to study the linear relationship between solar irradiance and photovoltaic power generation output. And then, according to the result of data analysis, determining key characteristics influencing the photovoltaic power generation amount. This may include solar irradiance, temperature, weather conditions, and the like. For example, by analyzing the history data, it can be found that the solar irradiance has the most significant effect on the photovoltaic power generation power, and thus the solar irradiance can be taken as a main feature.
And then, model building and training are carried out, and a proper prediction model is selected for building and training according to the data and the characteristics. Common predictive models include regression models, time series models, machine learning algorithms, and the like. For example, a multiple linear regression model may be used to model the relationship between solar irradiance and photovoltaic power generation.
When predicting the predicted power generation amount of the photovoltaic power generation system 203, it is necessary to acquire weather data, i.e., a plurality of predicted parameters of photovoltaic power generation influence parameters, within a predetermined period of time in the future. And predicting according to solar irradiance, temperature and other data in a future period by using the trained model to obtain the predicted power generation amount of the photovoltaic power generation system 203 in a future preset period. The prediction may be at a time-by-time, day-by-day, or month-by-month granularity, depending on the need and availability of data.
And predicting the generated energy of the photovoltaic power generation system 203 in a preset time period according to the pre-stored photovoltaic power generation influence parameters, the corresponding power generation data and the established prediction model. By utilizing existing data and relational models, the accuracy of the prediction of photovoltaic power generation may be improved, thereby facilitating better scheduling of the operation of the energy storage system 204.
For example, assume that the server 201 collects data on solar irradiance, temperature, and photovoltaic power generation for the past year. By processing and cleaning the data. And then analyzing the historical data to find that the solar irradiance and the photovoltaic power generation amount are in positive correlation. Next, a multiple linear regression model may be selected to model the relationship between solar irradiance and photovoltaic power generation. Using the trained model, the user may input solar irradiance data for a future period of time and predict to obtain a predicted power generation of the photovoltaic power generation system 203 during the period of time.
It should be noted that the prediction of photovoltaic power generation is affected by a number of factors, such as weather forecast accuracy, model selection and training quality, data acquisition and processing accuracy, etc. Therefore, in the implementation of the prediction scheme, comprehensive consideration and optimization are required according to practical situations, so as to improve the prediction accuracy.
S130, determining the predicted power consumption of the power system 202.
The power system 202 is connected with an electric load 205, the electric load 205 is the electric demand of various electrical devices, machines and electric equipment connected to the power system 202 on the power system 202, and the electric consumption of the electric load 205 is the sum of the electric energy actually consumed by the power system 202. Because the electricity consumption of the electricity load 205 is not completely the same under different time conditions, the accurate electricity consumption of the electricity load 205 in the future time period cannot be directly calculated, and the electricity consumption change rule can only be calculated according to the historical electricity consumption data of the electricity load 205, so as to predict the future electricity consumption.
Specifically, first, the server 201 needs to acquire a plurality of historical electricity usage data of the predicted electricity usage load 205, and is the historical electricity usage data of different times, that is, actual electricity usage situations of the electricity usage load 205 in different time periods. It is also desirable to clean and process the collected data, including handling missing data, outliers and noise, to ensure that the data is accurate, complete and in a usable format.
And then carrying out linear fitting analysis on the plurality of historical electricity utilization data, wherein a statistical method or a machine learning algorithm such as regression analysis, time sequence analysis or a neural network can be used for fitting the historical electricity utilization data and extracting electricity utilization rules, and the historical electricity utilization data can be visually displayed through a user electricity utilization rule curve. And analyzing the electricity consumption influence factors, wherein the electricity consumption influence factors comprise, but are not limited to, artificial influence factors such as dates, weather, holidays, social events and the like, and weather factors such as air temperature, humidity, rainfall/snow, wind power and the like. Therefore, when collecting data, a correspondence relationship between each historical electricity consumption data and the electricity consumption influencing factors needs to be established, for example, 2014, 4, 16, 14: the power consumption of 00-19:00 is 15046kWh, the weekend, the weather is clear, and the temperature is 27 ℃.
By considering human influence factors and weather influence factors, the prediction model can more comprehensively capture various factors influencing the electricity consumption. This helps to improve the accuracy and reliability of the electricity consumption prediction, making the prediction result closer to the actual situation. Date factors and holiday factors among the human influence factors may reflect seasonal and periodic power usage changes. By considering the factors, the influence of a specific date, a special holiday or a social event on the electricity consumption can be predicted better, so that the prediction result is more accurate. Weather influencing factors include factors such as air temperature, humidity, rainfall, wind power, and the like. These factors have an important influence on many electric loads 205, such as an air conditioning system, a heating system, wind power generation, and the like. By taking into account weather-influencing factors, the demand changes of these electricity loads 205 can be better predicted and the prediction results of the electricity consumption can be better adjusted.
And determining target electricity consumption data exceeding the preset electricity consumption range in the plurality of historical electricity consumption data according to the preset electricity consumption range. The preset electricity consumption fluctuation range can be analyzed according to a large amount of historical electricity consumption data, and the fluctuation range of the historical electricity consumption is calculated, namely the preset electricity consumption fluctuation range. It can be understood that when the power consumption corresponding to the historical power consumption data is greater than the maximum value of the preset power consumption dynamic range, or the power consumption corresponding to the historical power consumption data is less than the minimum value of the preset power consumption dynamic range, the historical power consumption data is the target power consumption data. And analyzing the power consumption influence factors corresponding to the target power consumption data to determine the influence weight value of each power consumption influence factor. Expert knowledge, statistical methods or machine learning algorithms can be used to determine the weight value of each electricity consumption influencing factor according to the association relationship and importance in the historical data. By acquiring historical electricity utilization data, analyzing electricity utilization rules, identifying influence factors and determining weight values, the accuracy and reliability of electricity utilization quantity prediction can be improved. And meanwhile, a data basis is provided for the subsequent electricity consumption prediction.
And finally, predicting the electricity consumption according to the data, and calculating the basic electricity consumption of the electricity consumption load 205 in a future preset time period based on the electricity consumption rule and the historical data. The basic electricity consumption is the electricity consumption predicted according to the electricity consumption rule under the condition of not considering influence factors. And acquiring the electric influence factors for prediction in a preset time period, namely weather forecast data, calendar events and the like in a future time period. And according to the factor values in the prediction time period, taking the factor values as input of the prediction power consumption. And determining the power consumption fluctuation value corresponding to the predicted power consumption influence factor based on the influence weight value. And determining the fluctuation range of each influence factor on the electricity consumption in the prediction time period according to the weight value and the influence range of the influence factors in the historical data. And calculating the predicted electricity consumption in the predicted time period according to the basic electricity consumption and the electricity consumption fluctuation value. And combining the basic electricity consumption with the fluctuation range of each influence factor to obtain the final predicted electricity consumption.
For example, assume that a certain electricity load 205 is the electricity consumption of a commercial building. The historical data shows the electricity consumption change of the building under different seasons and different weather conditions. From these historical data, factors related to electricity consumption, including seasonal, temperature and special activities, are determined through fitting analysis and influence factor analysis. And screening out target electricity utilization data exceeding the range in the historical data by setting a preset electricity utilization fluctuation range. And then analyzing according to the influence factors corresponding to the target electricity consumption data to obtain the weight value of each influence factor. And in the prediction stage, weather forecast data and calendar events in a prediction time period are obtained, and the predicted electricity consumption is calculated according to the weight value and the influence range. And finally, obtaining a power consumption prediction result of the commercial building in the prediction time period.
And determining the predicted electricity consumption in the predicted time period according to the basic electricity consumption and the electricity consumption fluctuation value. And combining the basic electricity consumption with the fluctuation range of each influence factor to obtain the final predicted electricity consumption. Thereby providing a prediction of the electrical load 205 for a predetermined time period in the future, providing a reference basis for the scheduling and planning of the energy storage system 204.
S140, if the power system 202 is in the over-frequency state, it is determined whether the predicted power generation amount is greater than or equal to the predicted power consumption amount.
There are different processing methods according to the abnormal frequency state of the power system 202 and the magnitude relation between the predicted power generation amount and the predicted power consumption amount. When the power system 202 is in the over-frequency state, it is indicated that the power input amount of the power system 202 is larger than the power output amount, and in the case where the power consumption amount of the power consuming load 205 is unchanged, it is necessary to reduce the power input amount of the power system 202. When the power system 202 is in the underfrequency state, it is indicated that the power input amount of the power system 202 is smaller than the power output amount, and in the case where the power consumption amount of the power consuming load 205 is unchanged, it is necessary to increase the power input amount of the power system 202.
Since the photovoltaic power generation system 203 is different from the conventional thermal power generation system, the generation power is not controllable. For example, when solar radiation is strong, the generated power is large, the generated energy is large, and when the solar radiation is weak, the generated power is small, and the generated energy is small. Accordingly, in order to maintain the balance of the power input and the power output of the power system 202, such that the system frequency is in a normal state, the server 201 may discharge the power system 202 by controlling the energy storage system 204, or charge the energy storage system 204 with the photovoltaic power generation system 203, to maintain the balance of the power input and the power output of the power system 202.
And S150, if the predicted power generation amount is greater than or equal to the predicted power consumption amount, sending a power storage instruction to the energy storage system 204 to enable the energy storage system 204 to store the power generation amount of the photovoltaic power generation system 203, wherein the energy storage system 204 is connected with the photovoltaic power generation system 203.
Specifically, when the power system 202 is in an over-frequency state, it indicates that the power input amount of the power system 202 is greater than the power output amount. And the predicted power generation amount is greater than or equal to the predicted power consumption amount, the power input amount of the power system 202 will be further greater than the power output amount, and the server 201 sends a power storage instruction to the energy storage system 204, so that the energy storage system 204 stores the power generation amount of part of the photovoltaic power generation system 203. The electricity generated by the photovoltaic power generation system 203 flows partially to the power system 202 and partially to the energy storage system 204. And gradually increases the amount of power flowing to the energy storage system 204 until the system frequency of the power system 202 is equal to the preset frequency.
When the power system 202 is in an over-frequency state, it indicates that the power input of the power system 202 is greater than the power output. And the predicted power generation amount is smaller than the predicted power consumption amount, the working state of the energy storage system 204 needs to be determined according to the first difference value between the power input amount and the power output amount and the second difference value between the predicted power consumption amount and the predicted power generation amount. When the first difference is greater than the second difference, the server 201 sends a power storage command to the energy storage system 204, so that the energy storage system 204 stores the power generation amount of a part of the photovoltaic power generation system 203. When the first difference is equal to the second difference, the energy storage system 204 is not operating. When the first difference is smaller than the second difference, the server 201 sends a power supply command to the energy storage system 204, so that the energy storage system 204 supplies power to the power system 202 until the system frequency of the power system 202 is equal to the preset frequency.
When the power system 202 is in an underfrequency state, it is indicated that the power input of the power system 202 is less than the power output. And the predicted power generation amount is less than or equal to the predicted power consumption amount, the server 201 sends a power supply instruction to the energy storage system 204, so that the energy storage system 204 supplies power to the power system 202 until the system frequency of the power system 202 is equal to the preset frequency to stop power supply.
When the power system 202 is in the underfrequency state and the predicted power consumption is greater than the predicted power consumption, the working state of the energy storage system 204 needs to be determined according to the first difference between the power input and the power output of the power system 202 and the third difference between the predicted power consumption and the predicted power consumption. When the first difference is greater than the third difference, the server 201 sends a power storage command to the energy storage system 204, so that the energy storage system 204 stores the power generation amount of a part of the photovoltaic power generation system 203. When the first difference is equal to the third difference, the energy storage system 204 is not operating. When the first difference is smaller than the third difference, the server 201 sends a power supply command to the energy storage system 204, so that the energy storage system 204 supplies power to the power system 202 until the system frequency of the power system 202 is equal to the preset frequency.
When the power system 202 is in an underfrequency state, it is indicated that the power input of the power system 202 is less than the power output. When the photovoltaic power generation system 203 is connected to the power system 202, the predicted power generation amount is less than or equal to the predicted power consumption amount. In this case, by sending a power supply command to the energy storage system 204, the energy storage system 204 may supply the stored power to the power system 202 to fill the power gap, so that the frequency of the power system 202 gradually tends to be stable.
By adopting the above-described technical solution, the server 201 first determines the abnormal frequency state of the power system 202, and when the power system 202 is in the over-frequency state, it can be determined that the power input amount of the power system 202 is greater than the power output amount. When the photovoltaic power generation system 203 is connected to the power system 202, and the predicted power generation amount of the photovoltaic power generation system 203 is larger than the predicted power consumption amount of the power system 202, the server 201 sends a power storage instruction to the energy storage system 204 at this time, so that the energy storage system 204 stores the power generation amount of the photovoltaic power generation system 203, thereby reducing the power input amount of the power system 202, further gradually stabilizing the system frequency of the power system 202, and achieving the effect of maintaining the stability of the system frequency of the power system 202.
The embodiment also discloses a photovoltaic power generation intelligent energy storage device, which is a server 201, referring to fig. 3, and includes a judging module 301, a calculating module 302 and a sending module 303, wherein:
the judging module 301 is configured to judge an abnormal frequency state of the power system 202, where the frequency state includes an over-frequency state and an under-frequency state.
The calculation module 302 is configured to determine a predicted power generation amount of the photovoltaic power generation system 203, where the photovoltaic power generation system 203 is connected to the power system 202.
The calculation module 302 is further configured to determine a predicted power consumption of the power system 202.
The determining module 301 is further configured to determine whether the predicted power generation amount is greater than or equal to the predicted power consumption amount if the power system 202 is in the over-frequency state.
And the sending module 303 is configured to send a power storage instruction to the energy storage system 204 if the predicted power generation amount is greater than or equal to the predicted power consumption amount, so that the energy storage system 204 stores the power generation amount of the photovoltaic power generation system 203, and the energy storage system 204 is connected with the photovoltaic power generation system 203.
In one possible implementation, the determining module 301 is configured to determine whether the predicted power generation amount is less than or equal to the predicted power generation amount if the power system 202 is in the underfrequency state.
And the sending module 303 is configured to send a power supply instruction to the energy storage system 204 if the predicted power generation amount is less than or equal to the predicted power consumption amount, so that the energy storage system 204 supplies power to the power system 202, and the energy storage system 204 is connected to the power system 202.
In one possible implementation, the apparatus further includes an acquisition module 304.
An acquisition module 304 is configured to acquire a system frequency of the power system 202.
The judging module 301 is configured to judge a magnitude relation between a system frequency and a preset frequency.
The determining module 301 is further configured to determine that the abnormal frequency status of the power system 202 is an under-frequency status if the system frequency is less than a preset frequency.
The determining module 301 is further configured to determine that the abnormal frequency state of the power system 202 is an over-frequency state if the system frequency is greater than a preset frequency.
In one possible implementation, the obtaining module 304 is configured to obtain a plurality of pre-stored photovoltaic power generation influencing parameters.
The obtaining module 304 is further configured to obtain power generation data corresponding to the pre-stored photovoltaic power generation influencing parameters.
The calculation module 302 is configured to analyze the plurality of photovoltaic power generation influence parameters and the plurality of power generation data, and obtain a relationship between the photovoltaic power generation influence parameters and the power generation data.
The calculation module 302 is further configured to establish a prediction model based on a relationship between the photovoltaic power generation influence parameter and the generated power data.
The obtaining module 304 is further configured to obtain a plurality of predicted parameters of the photovoltaic power generation influence parameter in a preset period of time.
The calculation module 302 is further configured to predict a predicted power generation amount within a preset period of time through a prediction model according to the plurality of prediction parameters.
In one possible implementation, the obtaining module 304 is configured to obtain a plurality of historical electricity usage data, where the electricity usage data is pre-stored electricity usage data of the electricity usage load 205, and the electricity usage load 205 is connected to the power system 202.
The calculation module 302 is configured to perform fitting analysis on the plurality of historical electricity consumption data, and determine an electricity consumption rule of the electricity consumption load 205.
The obtaining module 304 is further configured to obtain power consumption influencing factors corresponding to each historical power consumption data.
The calculation module 302 is further configured to determine, according to the preset electricity consumption range, target electricity consumption data exceeding the preset electricity consumption range from the plurality of historical electricity consumption data.
The calculation module 302 is further configured to analyze the power consumption influencing factors corresponding to the target power consumption data, and determine an influence weight value of each power consumption influencing factor.
In a possible implementation manner, the obtaining module 304 is configured to obtain the basic electricity consumption of the electricity load 205 in a preset period of time based on the electricity consumption rule.
The obtaining module 304 is further configured to obtain a predicted electricity consumption influencing factor in a preset time period.
The calculation module 302 is configured to determine a power consumption fluctuation value corresponding to the predicted power consumption influence factor based on the influence weight value.
The calculation module 302 is further configured to determine a predicted power consumption according to the base power consumption and the power consumption fluctuation value.
In one possible implementation, the electricity usage influencing factors include human influencing factors and weather influencing factors.
The artificial influence factors include date factors, weather factors, holiday factors, and social event factors.
Weather influencing factors include air temperature factors, humidity factors, rainfall factors, and wind power factors.
It should be noted that: in the device provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.
The embodiment also discloses an electronic device, referring to fig. 4, the electronic device may include: at least one processor 401, at least one communication bus 402, a user interface 403, a network interface 404, at least one memory 405.
Wherein communication bus 402 is used to enable connected communications between these components.
The user interface 403 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 403 may further include a standard wired interface and a standard wireless interface.
The network interface 404 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 401 may include one or more processing cores. The processor 401 connects the various parts within the entire server 201 using various interfaces and lines, performs various functions of the server 201 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and invoking data stored in the memory 405. Alternatively, the processor 401 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 401 may integrate one or a combination of several of a central processor 401 (Central Processing Unit, CPU), an image processor 401 (Graphics Processing Unit, GPU), a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 401 and may be implemented by a single chip.
The Memory 405 may include a random access Memory 405 (Random Access Memory, RAM) or a Read-Only Memory 405 (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 405 may be used to store instructions, programs, code sets, or instruction sets. The memory 405 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described various method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 405 may also optionally be at least one storage device located remotely from the aforementioned processor 401. As shown, an operating system, a network communication module, a user interface 403 module, and applications of the photovoltaic power generation intelligent energy storage method may be included in the memory 405, which is a computer storage medium.
In the electronic device shown in fig. 4, the user interface 403 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 401 may be used to invoke an application in the memory 405 that stores photovoltaic power generation intelligent energy storage methods, which when executed by the one or more processors 401, cause the electronic device to perform the methods as in one or more of the embodiments described above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory 405. Based on such understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory 405, and includes several instructions for causing a computer device (which may be a personal computer, a server 201, a network device, or the like) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned memory 405 includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. 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 variations, 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 scope and spirit of the disclosure being indicated by the claims.
Claims (10)
1. The intelligent energy storage method for photovoltaic power generation is characterized in that the method is applied to a server (201) and comprises the following steps:
judging abnormal frequency states of the power system (202), wherein the frequency states comprise an over-frequency state and an under-frequency state;
determining a predicted power generation amount of a photovoltaic power generation system (203), the photovoltaic power generation system (203) being connected to the power system (202);
determining a predicted power usage of the power system (202);
if the power system (202) is in an over-frequency state, judging whether the predicted power generation amount is larger than or equal to the predicted power consumption amount;
And if the predicted power generation amount is larger than or equal to the predicted power consumption amount, sending a power storage instruction to an energy storage system (204) so that the energy storage system (204) stores the power generation amount of the photovoltaic power generation system (203), wherein the energy storage system (204) is connected with the photovoltaic power generation system (203).
2. The intelligent energy storage method of photovoltaic power generation of claim 1, wherein after said determining an abnormal frequency condition of the power system (202), the method further comprises:
if the power system (202) is in an underfrequency state, judging whether the predicted generated energy is smaller than or equal to the predicted generated energy;
and if the predicted power generation amount is smaller than or equal to the predicted power consumption amount, sending a power supply instruction to the energy storage system (204) so that the energy storage system (204) supplies power to the power system (202), and connecting the energy storage system (204) with the power system (202).
3. The intelligent energy storage method for photovoltaic power generation according to claim 1, wherein the determining of the abnormal frequency state of the power system (202) specifically comprises:
-acquiring a system frequency of the power system (202);
judging the magnitude relation between the system frequency and the preset frequency;
If the system frequency is smaller than the preset frequency, determining that the abnormal frequency state of the power system (202) is an under-frequency state;
and if the system frequency is greater than the preset frequency, determining that the abnormal frequency state of the power system (202) is an over-frequency state.
4. The intelligent energy storage method for photovoltaic power generation according to claim 1, wherein the determining of the predicted power generation amount of the photovoltaic power generation system (203) specifically comprises:
acquiring a plurality of prestored photovoltaic power generation influence parameters;
acquiring pre-stored power generation data corresponding to each photovoltaic power generation influence parameter;
analyzing a plurality of photovoltaic power generation influence parameters and a plurality of power generation data to obtain a relation between the photovoltaic power generation influence parameters and the power generation data;
establishing a prediction model based on the relation between the photovoltaic power generation influence parameters and the power generation data;
acquiring a plurality of prediction parameters of photovoltaic power generation influence parameters in a preset time period;
and predicting the predicted power generation amount in the preset time period through the prediction model according to a plurality of prediction parameters.
5. The intelligent energy storage method of photovoltaic power generation of claim 1, wherein prior to said determining the predicted amount of power usage of the power system (202), the method further comprises:
Acquiring a plurality of historical electricity consumption data, wherein the electricity consumption data are prestored electricity consumption data of an electricity consumption load (205), and the electricity consumption load (205) is connected with the power system (202);
fitting analysis is carried out on a plurality of historical electricity utilization data, and electricity utilization rules of the electricity utilization load (205) are determined;
acquiring power consumption influence factors corresponding to each historical power consumption data;
determining target electricity consumption data exceeding a preset electricity consumption range in a plurality of historical electricity consumption data according to the preset electricity consumption range;
and analyzing the electricity consumption influence factors corresponding to the target electricity consumption data, and determining the influence weight value of each electricity consumption influence factor.
6. The intelligent energy storage method for photovoltaic power generation according to claim 5, wherein said determining the predicted power consumption of the power system (202) specifically comprises:
acquiring basic electricity consumption of the electricity load (205) in a preset time period based on the electricity consumption rule;
acquiring a predicted electricity consumption influence factor in the preset time period;
determining a power consumption fluctuation value corresponding to the predicted power consumption influence factor based on the influence weight value;
and determining the predicted electricity consumption according to the basic electricity consumption and the electricity consumption fluctuation value.
7. The intelligent energy storage method for photovoltaic power generation according to claim 5, wherein the electricity consumption influencing factors comprise human influencing factors and weather influencing factors;
the artificial influence factors comprise date factors, weather factors, holiday factors and social event factors;
the weather influencing factors include air temperature factors, humidity factors, rainfall factors and wind power factors.
8. The intelligent energy storage device for photovoltaic power generation is characterized by comprising a server (201), a judging module (301), a calculating module (302) and a sending module (303), wherein:
a judging module (301) for judging an abnormal frequency state of the power system (202), the frequency state including an over-frequency state and an under-frequency state;
a calculation module (302) for determining a predicted power generation amount of a photovoltaic power generation system (203), the photovoltaic power generation system (203) being connected to the power system (202);
-a calculation module (302) for determining a predicted power consumption of the power system (202);
a judging module (301) configured to judge whether the predicted power generation amount is greater than or equal to the predicted power consumption amount if the power system (202) is in an over-frequency state;
and the sending module (303) is used for sending a power storage instruction to the energy storage system (204) if the predicted power generation amount is greater than or equal to the predicted power consumption amount, so that the energy storage system (204) stores the power generation amount of the photovoltaic power generation system (203), and the energy storage system (204) is connected with the photovoltaic power generation system (203).
9. An electronic device comprising a processor (401), a memory (405), a user interface (403) and a network interface (404), the memory (405) being configured to store instructions, the user interface (403) and the network interface (404) being configured to communicate with other devices, the processor (401) being configured to execute the instructions stored in the memory (405) to cause the electronic device to perform the method of any of claims 1-7.
10. A computer readable storage medium storing instructions which, when executed, perform the method of any one of claims 1-7.
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