CN218678464U - Predictable light storage distributed energy management system based on error feedback - Google Patents
Predictable light storage distributed energy management system based on error feedback Download PDFInfo
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Abstract
The utility model provides a predictable light stores up distributed energy management system based on error feedback, this predictable light stores up distributed energy management system based on error feedback includes energy prediction system controlling means, electric energy data acquisition module, power data acquisition module, meteorological data acquisition module, power generation prediction module and controller, energy prediction system controlling means respectively with electric energy data acquisition module, power data acquisition module, meteorological data acquisition module, power generation prediction module and controller are connected, power data acquisition module is connected with power generation prediction module. According to the utility model discloses a system can carry out the matching of energy storage and electricity generation better.
Description
Technical Field
The utility model relates to an energy management technical field especially relates to a predictable light stores up distributed energy management system based on error feedback.
Background
The light energy storage source management system is a set of energy management and control integrated computer system. The system can realize centralized monitoring and unified scheduling of photovoltaic and various energy supply and energy utilization systems, for example, output of photovoltaic and energy storage is adjusted to peak clipping and valley filling. However, in the prior art, the stored energy of the energy storage system is fixed, and the generated energy of the photovoltaic system is variable, so that the energy storage and the power generation cannot be well matched.
SUMMERY OF THE UTILITY MODEL
The utility model discloses aim at solving one of above-mentioned technical problem at least.
Therefore, the first objective of the present invention is to provide a predictable light storage distributed energy management system based on error feedback, and the main objective is to better match the stored energy with the power generation.
In order to realize the above-mentioned purpose, the utility model discloses predictable light based on error feedback stores up distributed energy management system, including energy prediction system controlling means, power data acquisition module, meteorological data acquisition module, power generation prediction module and controller, energy prediction system controlling means is connected with power data acquisition module, meteorological data acquisition module, power generation prediction module and controller respectively, and power data acquisition module is connected with power generation prediction module.
According to the utility model discloses predictable light stores up distributed energy management system based on error feedback, this predictable light stores up distributed energy management system based on error feedback includes energy prediction system controlling means, electric energy data acquisition module, power data acquisition module, meteorological data acquisition module, power generation prediction module and controller, energy prediction system controlling means respectively with electric energy data acquisition module, power data acquisition module, meteorological data acquisition module, power generation prediction module and controller are connected, power data acquisition module is connected with power generation prediction module. Under this condition, synthesize and utilize electric energy data acquisition module, power data acquisition module, meteorological data acquisition module, generated power prediction module and energy prediction system controlling means to carry out the acquisition and the processing of data to match energy storage and electricity generation through the controller, from this, can carry out the matching of energy storage and electricity generation better.
In an embodiment of the present invention, the generated power prediction module is built-in with a neural network unit, and the neural network unit is one of an adaptive neural fuzzy system, an extreme learning machine, and a BP neural network.
In an embodiment of the present invention, the generated power prediction module is an AI chip.
The utility model discloses an in one embodiment, still include photovoltaic unit and energy storage unit, photovoltaic unit and energy storage unit are connected respectively to the controller.
In an embodiment of the present invention, the photovoltaic unit includes a photovoltaic module.
In an embodiment of the present invention, the energy storage unit includes a lithium battery energy storage or a super capacitor energy storage.
The utility model discloses an in one embodiment, still include the storage module, the storage module is connected with energy prediction system controlling means, electric energy data acquisition module, power data acquisition module, meteorological data acquisition module, power generation prediction module and controller respectively.
The utility model discloses an in one embodiment, still include photovoltaic electric energy module, energy storage unit electric energy module and load electric energy module, photovoltaic electric energy module energy storage unit electric energy module with load electric energy module respectively with electric energy data acquisition module connects.
The utility model discloses an embodiment, meteorological data acquisition module includes temperature sensor, pressure sensor, humidity transducer and sunlight illuminance sensor.
The utility model discloses an embodiment, meteorological data acquisition module still includes air velocity transducer, wind direction sensor and rainfall data acquisition instrument.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, of which,
fig. 1 is a block diagram of a predictable light storage distributed energy management system based on error feedback according to an embodiment of the present invention;
fig. 2 is a schematic connection diagram of a predictable light storage distributed energy management system based on error feedback according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an artificial neural network model according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with embodiments of the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of embodiments of the invention, as detailed in the appended claims.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. It should also be understood that the term "and/or" as used in this disclosure refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below by referring to the drawings are exemplary intended for explaining the present invention, and should not be construed as limiting the present invention.
The following describes a predictable light storage distributed energy management system based on error feedback according to an embodiment of the present invention with reference to the drawings.
The utility model provides a predictable light stores up distributed energy management system based on error feedback, main aim at carries out the matching of energy storage and electricity generation better. The utility model discloses a predictable light stores up distributed energy management system based on error feedback can be referred to as light for short and store up distributed energy management system.
Fig. 1 is a block diagram of a predictable light storage distributed energy management system based on error feedback according to an embodiment of the present invention. Fig. 2 is a schematic connection diagram of a predictable light storage distributed energy management system based on error feedback according to an embodiment of the present invention.
As shown in fig. 1, the embodiment of the utility model provides a predictable light stores up distributed energy management system based on error feedback includes energy prediction system controlling means 1, electric energy data acquisition module 2, power data acquisition module 3, meteorological data acquisition module 4, power generation prediction module 5 and controller 6.
In this embodiment, the electric energy data acquisition module 2 is used for acquiring data such as voltage, current and power consumption of the photovoltaic unit, the energy storage unit and the load terminal.
Specifically, in this embodiment, the light storage distributed energy management system further includes a photovoltaic power module, and the power data acquisition module 2 is connected to the photovoltaic power module. The photovoltaic electric energy module is used for measuring data such as voltage, current and power consumption of the photovoltaic unit in real time. The photovoltaic electric energy module is, for example, a photovoltaic unit electric energy meter.
In this embodiment, the optical storage distributed energy management system further includes an energy storage unit electric energy module, and the electric energy data acquisition module 2 is connected to the energy storage unit electric energy module. The energy storage unit electric energy module is used for measuring data such as voltage, current and power consumption of the energy storage unit in real time. The energy storage unit electric energy module is, for example, an energy storage unit electric energy meter.
In this embodiment, the optical storage distributed energy management system further includes a load power module, and the power data acquisition module 2 is connected to the load power module. The load electric energy module is used for measuring data such as voltage, current and electricity consumption (namely, electric load) of a load end in real time. The load electric energy module is, for example, a load end electric energy meter.
In this embodiment, the power data acquisition module 3 is used for acquiring photovoltaic power generation power data. The collected photovoltaic power generation power data are sent to the energy prediction system control device 1 for storage, and are also sent to the power generation power prediction module 5 for caching.
In the present embodiment, the meteorological data acquisition module 4 is used for acquiring meteorological data. Specifically, the meteorological data acquisition module 4 includes a temperature sensor, a pressure sensor, a humidity sensor, and a sunlight illuminance sensor. The meteorological data acquisition module 4 further comprises a wind speed sensor, a wind direction sensor and a rainfall data acquisition instrument. The meteorological data acquired by the meteorological data acquisition module 4 includes: irradiance vector (i.e., radiometric vector), temperature vector, wind speed vector, wind direction vector, barometric pressure vector, humidity vector, rainfall vector, relative humidity vector.
In some embodiments, the weather data collection module 4 may be provided in a weather station, and the weather data collected by the weather data collection module 4 may be stored in a weather server. The meteorological server stores historical data of ground solar energy radiation amount corresponding to cloud layer movement conditions for at least more than one year.
In the present embodiment, the generated power prediction module 5 is connected to the power data acquisition module 3. The generated power prediction module 5 receives and caches the photovoltaic generated power data from the power data acquisition module 3. The cached data may be all photovoltaic power generation power data within the predicted day-ahead 15.
In the present embodiment, the generated power prediction module 5 incorporates a neural network unit.
In this embodiment, the neural Network unit is one of an Adaptive Network-based Fuzzy Inference System (ANFIS), an Extreme Learning Machine (ELM), and a BP neural Network.
In some embodiments, the generated power prediction module 5 is an AI chip.
In the present embodiment, the generated power prediction module 5 is configured to receive a predicted photovoltaic power generation power value (described in detail later) from the energy prediction system control device 1 and photovoltaic power generation power data (i.e., a measured photovoltaic power generation power value) from the power data acquisition module 3, and perform fuzzy calculation based on the predicted photovoltaic power generation power value and the measured photovoltaic power generation power value, so as to obtain an error improvement factor (also referred to as an error improvement coefficient). The error improvement factor may be denoted by the symbol δ.
In the present embodiment, the error improvement factor generated by the generated power prediction module 5 is sent to the energy prediction system control device 1 to participate in the calculation of the predicted photovoltaic generated power value.
In this embodiment, the energy prediction system control device 1 is connected to the electric energy data acquisition module 2, the power data acquisition module 3, the meteorological data acquisition module 4, the generated power prediction module 5, and the controller 6, respectively.
Specifically, the energy prediction system control device 1 is provided with an artificial neural network model, the input of the artificial neural network model is meteorological data and an error improvement factor, and the output of the artificial neural network model is a photovoltaic power generation power prediction value.
Fig. 3 is a schematic structural diagram of an artificial neural network model according to an embodiment of the present invention. As shown in fig. 3, the input layer of the artificial neural network model includes radiance, temperature, wind speed, wind direction, air pressure, humidity, rainfall, relative humidity and error improvement factor, and the output layer is the predicted photovoltaic power generation power value P. The artificial neural network model shown in fig. 3 includes multiple intermediate layers, wherein the intermediate layers apply summation (Σ) and transfer function (f 1, f2, f 3) operations.
In this embodiment, the artificial neural network model is trained using a training set to obtain a trained power prediction model. The input of the trained power prediction model is meteorological data and an error improvement factor, and the output of the trained power prediction model is a photovoltaic power generation power prediction value.
In the present embodiment, the energy prediction system control device 1 receives and stores data from the power data acquisition module 3, the meteorological data acquisition module 4, and the generated power prediction module 5. The training set includes meteorological data, error improvement factors, and photovoltaic power generation power. The meteorological data and the error improvement factor are used as the input of the model, the photovoltaic power generation power is used as the label, and the meteorological data, the error improvement factor and the photovoltaic power generation power can come from the data cached in the energy prediction system control device 1.
In this embodiment, when the trained power prediction model is used for power prediction, the input error improvement factor is affected by the predicted photovoltaic power generation power value output by the previous power prediction model, as shown in fig. 3, the predicted photovoltaic power generation power value output by the previous power prediction model is sent to the power generation power prediction module 5 to calculate the error improvement factor, and then the power generation power prediction module 5 sends the calculated error improvement factor to the energy prediction system control device 1 to participate in power prediction.
In this embodiment, on the predicted day, the meteorological data of the predicted day is acquired by the meteorological data acquisition module 4, and the meteorological data of the predicted day is sent to the power prediction model of the energy prediction system control device 1 to obtain the predicted value of the photovoltaic power generation power of the predicted day.
In this embodiment, the energy prediction system control device 1 calculates a photovoltaic power generation power predicted value based on the photovoltaic power generation power predicted value. The calculation formula for predicting the power generation capacity on the internet on the day is as follows:
and E is the predicted on-line power generation amount on the same day, and the unit is kW.h. t represents time in min. P is a predicted value of photovoltaic power generation power, and the unit is kW.
In the present embodiment, the energy prediction system control device 1 also receives and stores data from the electric energy data collection module 2. The energy prediction system control device 1 calculates the average power consumption in the daytime and the battery residual capacity based on the voltage, the current and the power consumption of the photovoltaic unit, the energy storage unit and the load end from the electric energy data acquisition module 2, and calculates the predicted energy storage night charging amount on the predicted day based on the average power consumption in the daytime, the battery residual capacity and the predicted on-line power generation amount on the same day.
The estimated night charging amount of the stored energy calculation formula satisfies:
E y ＝w e -E-E s
in the formula, E y The unit is kW.h for the estimated energy storage night charge. w is a e The unit is kW.h, which is the average power consumption in daytime. E s The unit is kW.h, which is the residual electric quantity of the battery.
In the present embodiment, the energy prediction system control device 1 transmits the calculated predicted on-line power generation amount on the same day and the predicted energy storage nighttime charge amount to the controller 6, thereby controlling the photovoltaic unit and the energy storage unit.
In this embodiment, the light storage distributed energy management system further includes a photovoltaic unit and an energy storage unit, and the controller 6 is connected to the photovoltaic unit and the energy storage unit respectively.
In some embodiments, the photovoltaic unit comprises a photovoltaic assembly. The energy storage unit comprises a lithium battery energy storage unit or a super capacitor energy storage unit.
In this embodiment, the controller 6 receives the night charging amount of the stored energy, and charges the corresponding amount of electricity to the energy storage unit through the utility power during the night electricity price valley period.
In this embodiment, the energy prediction system control device 1 further compares the photovoltaic power generation power data collected by the power data collection module 3 with the power load of the electric energy data collection module 2 in real time, and generates a corresponding operation instruction through the controller to control the photovoltaic unit and the energy storage unit.
Specifically, the controller controls the photovoltaic unit to start to generate power in the peak-average time period with high electricity price in the day on the next day, if the electricity utilization load is smaller than the power generation power (namely photovoltaic power generation power data) of the photovoltaic unit, the controller stores the redundant photovoltaic power generation amount in the energy storage unit, and if the energy storage unit is full of electricity, the power generation power of the photovoltaic unit is reduced to match the load; if the electricity load is larger than the power generation power of the photovoltaic unit, the controller is supplemented by the energy storage unit in a discharging mode, and if the electricity in the energy storage unit is used up and cannot meet the electricity consumption, the electricity is supplemented by the commercial power.
In some embodiments, the optical storage distributed energy management system further includes a storage module, and the storage module is respectively connected with the energy prediction system control device 1, the electric energy data acquisition module 2, the power data acquisition module 3, the meteorological data acquisition module 4, the power generation prediction module 5, and the controller 6.
The embodiment of the utility model provides a predictable light stores up distributed energy management system based on error feedback, this predictable light stores up distributed energy management system based on error feedback includes energy prediction system controlling means, the electric energy data acquisition module, the power data acquisition module, meteorological data acquisition module, power generation power prediction module and controller, energy prediction system controlling means respectively with the electric energy data acquisition module, the power data acquisition module, meteorological data acquisition module, power generation power prediction module and controller are connected, the power data acquisition module is connected with power generation power prediction module. Under the condition, the electric energy data acquisition module, the power data acquisition module, the meteorological data acquisition module, the power generation power prediction module and the energy prediction system control device are comprehensively utilized to acquire and process data, so that the energy storage and the power generation are matched through the controller, and therefore the energy storage and the power generation can be better matched.
It is to be understood that the components shown in the present disclosure, the connections and relationships of the components, and the functions of the components are exemplary only, and are not intended to limit implementations of the disclosure described and/or claimed in the present disclosure. Steps may be reordered, added, or deleted using the various forms of flow shown above. For example, the steps described in the present invention may be executed in parallel or sequentially or in different orders, as long as the desired result of the technical solution disclosed in the present invention can be achieved, and the present invention is not limited herein.
The above detailed description does not limit the scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The predictable light storage distributed energy management system based on error feedback is characterized by comprising an energy forecasting system control device, an electric energy data acquisition module, a power data acquisition module, a meteorological data acquisition module, a power generation power forecasting module and a controller, wherein the energy forecasting system control device is respectively connected with the electric energy data acquisition module, the power data acquisition module, the meteorological data acquisition module, the power generation power forecasting module and the controller, and the power data acquisition module is connected with the power generation power forecasting module.
2. The system of claim 1, wherein the power generation power prediction module is embedded with a neural network unit, and the neural network unit is one of an adaptive neural fuzzy system, an extreme learning machine and a BP neural network.
3. The system of claim 1, wherein the power generation power prediction module is an AI chip.
4. The system according to claim 1, further comprising a photovoltaic unit and an energy storage unit, wherein the controller is connected to the photovoltaic unit and the energy storage unit respectively.
5. The system of claim 4, wherein the photovoltaic unit comprises a photovoltaic module.
6. The system of claim 4, wherein the energy storage unit comprises a lithium battery energy storage or a super capacitor energy storage.
7. The error feedback-based predictable light storage distributed energy management system of claim 1, further comprising a storage module connected to the energy prediction system control, the electrical energy data collection module, the power data collection module, the meteorological data collection module, the power generation prediction module, and the controller, respectively.
8. The error feedback-based predictable light storage distributed energy management system according to claim 1, further comprising a photovoltaic power module, a storage unit power module, and a load power module, wherein the photovoltaic power module, the storage unit power module, and the load power module are respectively connected to the power data collection module.
9. The system of claim 1, wherein the meteorological data acquisition module comprises a temperature sensor, a pressure sensor, a humidity sensor, and a solar light illumination sensor.
10. The system of claim 9, wherein the meteorological data acquisition module further comprises a wind speed sensor, a wind direction sensor, and a rain data acquirer.
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