CN115967111B - Energy storage conversion system and method based on energy storage bidirectional converter - Google Patents
Energy storage conversion system and method based on energy storage bidirectional converter Download PDFInfo
- Publication number
- CN115967111B CN115967111B CN202310252898.0A CN202310252898A CN115967111B CN 115967111 B CN115967111 B CN 115967111B CN 202310252898 A CN202310252898 A CN 202310252898A CN 115967111 B CN115967111 B CN 115967111B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- alternating current
- power grid
- power
- current power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/10—Flexible AC transmission systems [FACTS]
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to an energy storage conversion system and method based on an energy storage bidirectional converter, and relates to the field of AC power grid control and regulation, wherein the system comprises: the energy storage bidirectional converter is used for starting the self-adaptive charging processing of the battery energy storage system on the alternating current power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment when the received predicted load consumption power in the next time segment is greater than the maximum load power of the alternating current power grid; and the information prediction device is used for predicting the predicted load consumption power in the next time segment based on the total amount of power corresponding to each time segment in the past. According to the invention, the energy storage bidirectional converter can be adopted to determine whether the battery energy storage system needs to be started to execute self-adaptive charging processing on the alternating current power grid based on the predicted data of the load consumption power of the alternating current power grid in future time segments, so that the normal working performance of each load of the alternating current power grid is ensured.
Description
Technical Field
The invention relates to the field of control and regulation of alternating current power grids, in particular to an energy storage conversion system and method based on an energy storage bidirectional converter.
Background
Because the number of the loads and the load working lines of the AC power grid are changed in real time, and the working environment of the AC power grid is also changed in real time, the load consumption power of the AC power grid is also changed continuously, however, the load consumption power which can be supplied by a single AC power grid is limited, if the consumption power required by the currently mounted load of the AC power grid exceeds the maximum load power which can be supplied by the AC power grid, the currently mounted loads of the AC power grid cannot achieve the optimal working performance, meanwhile, the power is continuously supplied because the AC power grid is required to be always supplied, and the power failure and the equipment transformation are carried out on the AC power grid only when the consumption power required by the currently mounted load of the AC power grid exceeds the maximum load power which can be supplied by the AC power grid, so that the method is obviously not realistic.
It follows that the prior art has the disadvantages: the power required to be consumed by the load mounted on the alternating current power grid in the future time section cannot be predicted, so that whether the energy storage bidirectional converter needs to charge the alternating current power grid in the future time section and the specific charging power cannot be determined, the energy storage bidirectional converter cannot be configured in advance, and smooth and safe operation of the alternating current power grid is further affected.
Therefore, a prediction mechanism capable of predicting the power required to be consumed by the load mounted on the ac power grid in the future time section is needed, whether the energy storage bidirectional converter is required to start the battery energy storage system to charge the ac power grid or not is determined based on the prediction result, and specific charging power is determined, so that the normal working performance of each load in different load mounting scenes in each time section of the ac power grid is ensured, and the smooth and safe operation of the ac power grid is further ensured. Obviously, the technical solutions in the prior art cannot achieve the technical effects.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides an energy storage conversion system and method based on an energy storage bidirectional converter, which are used for providing a prediction mechanism for load consumption power in future time segments of an alternating current power grid by setting a custom structure for each alternating current power grid and completing a convolution neural network model of custom training, and adopting the energy storage bidirectional converter to determine whether a battery energy storage system needs to be started to execute self-adaptive charging treatment on the alternating current power grid based on the prediction power, so that the load consumption power which is insufficient in supply of the alternating current power grid is made up, and the normal working performance of each load of the alternating current power grid is ensured.
According to a first aspect of the present invention, there is provided an energy storage conversion system based on an energy storage bidirectional converter, the system comprising:
the energy storage bidirectional converter is respectively connected with the battery energy storage system and the alternating current power grid and is used for starting the self-adaptive charging processing of the battery energy storage system on the alternating current power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment when the received predicted load consumption power in the next time segment is greater than the maximum load power of the alternating current power grid;
the battery energy storage system is connected with the energy storage bidirectional converter and is used for executing charging treatment or discharging treatment on the alternating current power grid under the control of the energy storage bidirectional converter;
the alternating current power grid is connected with the energy storage bidirectional converter and is used for providing power supply of less than or equal to maximum load power for the load of the alternating current power grid;
the power measuring device is connected with the alternating current power grid and is used for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past so as to obtain each total power corresponding to each time section in the past;
the information prediction device is respectively connected with the power measurement device and the energy storage bidirectional converter and is used for establishing a convolutional neural network model for the alternating current power grid, the model is trained for a set number of times, the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total power corresponding to each time section in the past are used as input contents of the model, and the model is operated to obtain predicted load consumption power in the next time section output by the model;
the method for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past to obtain each total power corresponding to each time section in the past comprises the following steps: before the next time segment, each time segment and the next time segment form a complete time length on a time axis, and the duration of each time segment in each time segment and the duration of each time segment in the next time segment are equal;
the method for obtaining the predicted load consumption power in the next time segment output by the model comprises the following steps of: the number of each time segment in the past is monotonically positively correlated with the maximum load power of the ac power grid.
According to a second aspect of the present invention, there is provided an energy storage conversion method based on an energy storage bidirectional converter, the method comprising using an energy storage conversion system based on an energy storage bidirectional converter as described above to resolve a predicted load consumption power of an ac grid for a future time segment based on a maximum load power of the ac grid, a coverage area of the ac grid and a total sum of supply load powers respectively corresponding to each time segment of the ac grid by using a convolutional neural network model, so as to provide power reference data for whether the energy storage bidirectional converter starts an adaptive charging process of a battery energy storage system for the ac grid within the future time segment.
It follows that compared with the prior art, the present invention at least needs to have the following significant technical improvements:
the method comprises the steps that a first place adopts an energy storage bidirectional converter which is respectively connected with a battery energy storage system and an alternating current power grid, and is used for starting the battery energy storage system to adaptively charge the alternating current power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment when the predicted load consumption power in the next time segment of the alternating current power grid is greater than the maximum load power of the alternating current power grid, so that the robustness and the stability of the alternating current power grid are ensured;
secondly, designing a convolution neural network model of a custom structure for each alternating current power grid to analyze the predicted load consumption power of the alternating current power grid of the next time segment based on the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total sum of the supply load power corresponding to each past time segment of the alternating current power grid, wherein the custom structure is characterized in that the number of each past time segment is monotonically and positively correlated with the maximum load power of the alternating current power grid, and the two personalized models of the maximum load power of the alternating current power grid and the coverage area of the alternating current power grid are input;
thirdly, in order to ensure the prediction reliability of the convolutional neural network model, model training is carried out on the model for set times before the model is used, and the wider the coverage area of the alternating current power grid is, the larger the value of the set times is.
Drawings
Embodiments of the present invention will be described below with reference to the accompanying drawings, in which:
fig. 1 is a technical flow chart of an energy storage converter system based on an energy storage bidirectional converter according to the invention.
Fig. 2 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 1 of the present invention.
Fig. 3 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 2 of the present invention.
Fig. 4 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 3 of the present invention.
Fig. 5 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 4 of the present invention.
Fig. 6 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 5 of the present invention.
Fig. 7 is a flowchart of the step of the energy storage conversion method based on the energy storage bidirectional converter according to embodiment 6 of the present invention.
Description of the embodiments
As shown in fig. 1, a technical flow chart of an energy storage conversion system and method based on an energy storage bidirectional converter is provided.
In fig. 1, the specific technical process of the present invention can be split into the following three main steps:
firstly, setting a custom structure for a current alternating current power grid by adopting a computer control system and completing a convolution neural network model of custom training, and providing a prediction mechanism for load consumption power in future time segments of the alternating current power grid, wherein the custom of the structure is represented by monotonically and positively correlating the number of each time segment in the past serving as model input content with the maximum load power of the alternating current power grid, and the model input content also comprises two personalized information, namely the maximum load power of the alternating current power grid and the coverage area of the alternating current power grid;
the computer control system can be a computer PC or a mobile terminal, and establishes a bidirectional communication link with the energy storage bidirectional converter through various communication modes including WIFI or GPRS;
secondly, measuring the power consumption of each corresponding load of the alternating current power grid in each past time segment before the next time segment serving as a future time segment from the current alternating current power grid, so as to provide basic data for the prediction of the power consumption of the load in the subsequent future time segment;
thirdly, adopting a convolutional neural network model running on the computer control system to predict the load consumption power of the alternating current power grid of the next time segment based on the load consumption power of each part corresponding to the alternating current power grid in each past time segment, the maximum load power of the alternating current power grid and the coverage area of the alternating current power grid;
and finally, aiming at the energy storage bidirectional converter built between the current alternating current power grid and the battery energy storage system, determining whether the battery energy storage system needs to be started to perform self-adaptive charging treatment on the alternating current power grid or not by utilizing the predicted load consumption power of the alternating current power grid in the next time section and the maximum load power which can be provided by the alternating current power grid, so as to make up for the insufficient load consumption power of the alternating current power grid.
The key points of the invention are as follows: the method comprises the steps of designing convolutional neural network models of customized structures and customized training mechanisms for different alternating current power grids, acquiring prediction data of load consumption power of the alternating current power grid in a reliable future time section, and then utilizing the prediction data to realize self-adaptive charging control of a battery energy storage system by an energy storage bidirectional converter so as to ensure stable and smooth operation of the alternating current power grid and maintain normal working performance of each load mounted on the alternating current power grid; it can be seen that the custom design model and adaptive charge control mechanism described above are the most significant two-point difference between the present invention and the prior art.
The energy storage conversion system and method based on the energy storage bidirectional converter of the present invention will be specifically described by way of examples.
Examples
Fig. 2 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 1 of the present invention.
As shown in fig. 2, the energy storage converter system based on the energy storage bidirectional converter comprises the following components:
the energy storage bidirectional converter is respectively connected with the battery energy storage system and the alternating current power grid and is used for starting the self-adaptive charging processing of the battery energy storage system on the alternating current power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment when the received predicted load consumption power in the next time segment is greater than the maximum load power of the alternating current power grid;
by way of example, the ac power network here is not limited to various industrial ac power networks and various commercial ac power networks, but also includes some miniature ac power networks, for example, domestic ac power networks;
taking a house of a home as an example, the capacity of the maximum load carried by the home ac power grid is determined by the size of the section of the house lead-in, for example, when the house lead-in is two 4 square copper wires, namely, the voltage of single-phase 220 v, the safe current-carrying capacity of the 4 square copper wires is about 30 a, so that the maximum load power allowed by the home ac power grid is 220 x30=6600W;
thus, for a household ac power grid used by this household, starting the adaptive charging process of the battery energy storage system on the ac power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment comprises: starting the self-adaptive charging process of the battery energy storage system on the alternating current power grid based on the difference power of the predicted load consumption power exceeding 6600W in the next time segment;
the battery energy storage system is connected with the energy storage bidirectional converter and is used for executing charging treatment or discharging treatment on the alternating current power grid under the control of the energy storage bidirectional converter;
the alternating current power grid is connected with the energy storage bidirectional converter and is used for providing power supply of less than or equal to maximum load power for the load of the alternating current power grid;
the power measuring device is connected with the alternating current power grid and is used for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past so as to obtain each total power corresponding to each time section in the past;
the information prediction device is respectively connected with the power measurement device and the energy storage bidirectional converter and is used for establishing a convolutional neural network model for the alternating current power grid, the model is trained for a set number of times, the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total power corresponding to each time section in the past are used as input contents of the model, and the model is operated to obtain predicted load consumption power in the next time section output by the model;
the method for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past to obtain each total power corresponding to each time section in the past comprises the following steps: before the next time segment, each time segment and the next time segment form a complete time length on a time axis, and the duration of each time segment in each time segment and the duration of each time segment in the next time segment are equal;
illustratively, the duration of each of the previous time segments and the next time segment may be any one of 5 minutes to 30 minutes;
the method for obtaining the predicted load consumption power in the next time segment output by the model comprises the following steps of: the number of each time segment in the past is monotonically and positively correlated with the maximum load power of the alternating current power grid;
illustratively, when the maximum load power of the alternating current power grid is 6600W, the number of past time segments is selected to be 50, and when the maximum load power of the alternating current power grid is 8000W, the number of past time segments is selected to be 100; when the maximum load power of the alternating current power grid is 12000W, the number of each time segment in the past is selected to be 150 so as to maintain the numerical value corresponding relation of monotonic forward correlation of the two time segments;
the method for obtaining the predicted load consumption power in the next time segment output by the model comprises the following steps of: the wider the coverage area of the alternating current power grid is, the larger the value of the set times is;
illustratively, the set number of times is 30 times when the coverage area of the ac power grid is 100 square meters, the set number of times is 60 times when the coverage area of the ac power grid is 150 square meters, the set number of times is 120 times when the coverage area of the ac power grid is 200 square meters, and the set number of times is 180 times when the coverage area of the ac power grid is 400 square meters;
wherein starting the adaptive charging process of the battery energy storage system to the ac power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment comprises: driving the battery energy storage system to charge differential power into the ac power grid during a next time segment to supplement power supply to a load of the ac power grid;
for example, also for the household ac power grid used by the household, driving the battery energy storage system to charge the ac power grid with the difference power to supplement the power supply to the load of the ac power grid in the next time segment when the predicted load consumption power of each of the electric loads of the household is 7000W in the next time segment includes: and driving the battery energy storage system to charge 400W of power serving as difference power into the household alternating current power grid in the next time segment so as to supplement power supply to loads of the household alternating current power grid, and maintaining normal operation of each household electric load.
Examples
Fig. 3 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 2 of the present invention.
As shown in fig. 3, compared to embodiment 1 of the present invention, the energy storage converter system based on the energy storage bidirectional converter further includes:
model training means, connected to the information prediction means, for performing model training for a set number of times on the model before the information prediction means uses the model;
wherein performing model training for a set number of times on the model before the information predicting device uses the model includes: in each model training, taking the total power corresponding to a certain time segment as output content of the model, taking the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total power corresponding to each time segment before the certain time segment as input content of the model, and completing one-time model training;
wherein, CPLD chip, ASIC chip, FPGA chip or SOC chip can be selected to realize the model training device.
Examples
Fig. 4 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 3 of the present invention.
As shown in fig. 4, compared to embodiment 1 of the present invention, the energy storage converter system based on the energy storage bidirectional converter further includes:
the parameter storage device is connected with the information prediction device and used for storing various model parameters of the model after model training for set times is completed;
the parameter storage device is one of a FLASH memory chip, an MMC memory chip and a static memory chip, for example.
Examples
Fig. 5 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 4 of the present invention.
As shown in fig. 5, compared to embodiment 1 of the present invention, the energy storage converter system based on the energy storage bidirectional converter further includes:
the field timing device is respectively connected with the energy storage bidirectional converter and the power measuring device and is used for providing field timing service for the energy storage bidirectional converter and the power measuring device;
the on-site timing device can also be connected with a remote timing server for updating local timing data in real time.
Examples
Fig. 6 is a schematic structural diagram of an energy storage conversion system based on an energy storage bidirectional converter according to embodiment 5 of the present invention.
As shown in fig. 6, compared to embodiment 1 of the present invention, the energy storage converter system based on the energy storage bidirectional converter further includes:
the LCD display array is connected with the information prediction device and used for displaying the received predicted load consumption power in the next time segment;
the LCD display array can comprise a plurality of LCD display units and a synchronous control unit, wherein the LCD display units are arranged in a rectangular mode, and the synchronous control mechanism is respectively connected with the plurality of LCD display units and used for realizing synchronous display control of the plurality of LCD display units.
Next, various embodiments of the present invention will be described in further detail.
In an energy storage conversion system based on an energy storage bidirectional converter according to various embodiments of the present invention:
the performing of the charging process or the discharging process on the ac grid under the control of the energy storage bidirectional converter includes: and when the charging process is carried out on the alternating current power grid under the control of the energy storage bidirectional converter, the energy storage bidirectional converter determines the charging power of the battery energy storage system on the alternating current power grid.
In an energy storage conversion system based on an energy storage bidirectional converter according to various embodiments of the present invention:
the performing of the charging process or the discharging process on the ac grid under the control of the energy storage bidirectional converter includes: and when the discharge processing is carried out on the alternating current power grid under the control of the energy storage bidirectional converter, the energy storage bidirectional converter determines the discharge power of the battery energy storage system on the alternating current power grid.
And in an energy storage conversion system based on an energy storage bidirectional converter according to various embodiments of the present invention:
the energy storage bidirectional converter is further used for stopping the charging processing of the battery energy storage system on the alternating current power grid in the next time segment when the received predicted load consumption power in the next time segment is smaller than or equal to the maximum load power of the alternating current power grid.
Examples
In this embodiment, the invention sets up an energy storage conversion method based on an energy storage bidirectional converter, which includes using the energy storage conversion system based on the energy storage bidirectional converter to analyze the predicted load consumption power of the alternating current power grid in the future time segment based on the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total sum of the supply load power corresponding to each time segment of the alternating current power grid by adopting a convolutional neural network model, so as to provide power reference data for the self-adaptive charging process of the battery energy storage system on the alternating current power grid when the energy storage bidirectional converter starts in the future time segment.
Specifically, as shown in fig. 7, the energy storage current transformation method based on the energy storage bidirectional current transformer may include the following steps:
step S701: the method comprises the steps that an energy storage bidirectional converter is used, and is respectively connected with a battery energy storage system and an alternating current power grid, and is used for starting the battery energy storage system to adaptively charge the alternating current power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment when the received predicted load consumption power in the next time segment is greater than the maximum load power of the alternating current power grid;
by way of example, the ac power network here is not limited to various industrial ac power networks and various commercial ac power networks, but also includes some miniature ac power networks, for example, domestic ac power networks;
taking a house of a home as an example, the capacity of the maximum load carried by the home ac power grid is determined by the size of the section of the house lead-in, for example, when the house lead-in is two 4 square copper wires, namely, the voltage of single-phase 220 v, the safe current-carrying capacity of the 4 square copper wires is about 30 a, so that the maximum load power allowed by the home ac power grid is 220 x30=6600W;
thus, for a household ac power grid used by this household, starting the adaptive charging process of the battery energy storage system on the ac power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment comprises: starting the self-adaptive charging process of the battery energy storage system on the alternating current power grid based on the difference power of the predicted load consumption power exceeding 6600W in the next time segment;
step S702: the battery energy storage system is connected with the energy storage bidirectional converter and is used for executing charging treatment or discharging treatment on the alternating current power grid under the control of the energy storage bidirectional converter;
step S703: using an alternating current power grid, connecting with the energy storage bidirectional converter, and providing power supply smaller than or equal to maximum load power for the load of the alternating current power grid;
step S704: the power measuring device is connected with the alternating current power grid and is used for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past so as to obtain each total power corresponding to each time section in the past;
step S705: the information prediction device is respectively connected with the power measurement device and the energy storage bidirectional converter and is used for establishing a convolutional neural network model for the alternating current power grid, the model is trained for a set number of times, the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total amount of power corresponding to each time section in the past are used as input contents of the model, and the model is operated to obtain predicted load consumption power in the next time section output by the model;
the method for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past to obtain each total power corresponding to each time section in the past comprises the following steps: before the next time segment, each time segment and the next time segment form a complete time length on a time axis, and the duration of each time segment in each time segment and the duration of each time segment in the next time segment are equal;
illustratively, the duration of each of the previous time segments and the next time segment may be any one of 5 minutes to 30 minutes;
the method for obtaining the predicted load consumption power in the next time segment output by the model comprises the following steps of: the number of each time segment in the past is monotonically and positively correlated with the maximum load power of the alternating current power grid;
illustratively, when the maximum load power of the alternating current power grid is 6600W, the number of past time segments is selected to be 50, and when the maximum load power of the alternating current power grid is 8000W, the number of past time segments is selected to be 100; when the maximum load power of the alternating current power grid is 12000W, the number of each time segment in the past is selected to be 150 so as to maintain the numerical value corresponding relation of monotonic forward correlation of the two time segments;
the method for obtaining the predicted load consumption power in the next time segment output by the model comprises the following steps of: the wider the coverage area of the alternating current power grid is, the larger the value of the set times is;
illustratively, the set number of times is 30 times when the coverage area of the ac power grid is 100 square meters, the set number of times is 60 times when the coverage area of the ac power grid is 150 square meters, the set number of times is 120 times when the coverage area of the ac power grid is 200 square meters, and the set number of times is 180 times when the coverage area of the ac power grid is 400 square meters;
wherein starting the adaptive charging process of the battery energy storage system to the ac power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment comprises: driving the battery energy storage system to charge differential power into the ac power grid during a next time segment to supplement power supply to a load of the ac power grid;
for example, also for the household ac power grid used by the household, driving the battery energy storage system to charge the ac power grid with the difference power to supplement the power supply to the load of the ac power grid in the next time segment when the predicted load consumption power of each of the electric loads of the household is 7000W in the next time segment includes: and driving the battery energy storage system to charge 400W of power serving as difference power into the household alternating current power grid in the next time segment so as to supplement power supply to loads of the household alternating current power grid, and maintaining normal operation of each household electric load.
In addition, in the present invention, more specifically, driving the battery energy storage system to charge the ac power grid with differential power for a next time segment to supplement power supply to a load of the ac power grid includes: driving the battery energy storage system to disperse the differential power into equal power at each evenly spaced point in time within the next time segment to time-share charge the ac grid to supplement the power supply to the load of the ac grid;
and in the present invention, more specifically, driving the battery energy storage system to disperse differential power into equal amounts of power at respective evenly spaced points in time within a next time segment to time-share charge into the ac power grid to supplement power supply to a load of the ac power grid comprises: the number of each evenly spaced point in time within the next time segment multiplied by the equi-power equals the difference power.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present disclosure, and not for limiting the same; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the embodiments of the disclosure, and are intended to be included within the scope of the claims and specification of the present disclosure.
Claims (10)
1. An energy storage conversion system based on an energy storage bidirectional converter, the system comprising:
the energy storage bidirectional converter is respectively connected with the battery energy storage system and the alternating current power grid and is used for starting the self-adaptive charging processing of the battery energy storage system on the alternating current power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment when the received predicted load consumption power in the next time segment is greater than the maximum load power of the alternating current power grid;
the battery energy storage system is connected with the energy storage bidirectional converter and is used for executing charging treatment or discharging treatment on the alternating current power grid under the control of the energy storage bidirectional converter;
the alternating current power grid is connected with the energy storage bidirectional converter and is used for providing power supply of less than or equal to maximum load power for the load of the alternating current power grid;
the power measuring device is connected with the alternating current power grid and is used for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past so as to obtain each total power corresponding to each time section in the past;
the information prediction device is respectively connected with the power measurement device and the energy storage bidirectional converter and is used for establishing a convolutional neural network model for the alternating current power grid, the model is trained for a set number of times, the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total power corresponding to each time section in the past are used as input contents of the model, and the model is operated to obtain predicted load consumption power in the next time section output by the model;
the method for measuring the total power provided by the alternating current power grid to the load of the alternating current power grid in each time section in the past to obtain each total power corresponding to each time section in the past comprises the following steps: before the next time segment, each time segment and the next time segment form a complete time length on a time axis, and the duration of each time segment in each time segment and the duration of each time segment in the next time segment are equal;
the method for obtaining the predicted load consumption power in the next time segment output by the model comprises the following steps of: the number of each time segment in the past is monotonically positively correlated with the maximum load power of the ac power grid.
2. The energy storage conversion system based on energy storage bi-directional converter according to claim 1, wherein:
establishing a convolutional neural network model for the alternating current power grid, wherein the model has completed model training for a set number of times, taking the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total power corresponding to each time segment in the past as the input content of the model, and running the model to obtain the predicted load consumption power in the next time segment output by the model further comprises: the wider the coverage area of the alternating current power grid is, the larger the value of the set times is;
wherein starting the adaptive charging process of the battery energy storage system to the ac power grid based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment comprises: the battery energy storage system is driven to charge the ac power grid with differential power during a next time segment to supplement the power supply to the load of the ac power grid.
3. The energy storage conversion system based on an energy storage bi-directional converter according to claim 2, wherein said system further comprises:
model training means, connected to the information prediction means, for performing model training for a set number of times on the model before the information prediction means uses the model;
wherein performing model training for a set number of times on the model before the information predicting device uses the model includes: in each model training, taking the total power corresponding to a certain time segment as the output content of the model, taking the maximum load power of the alternating current power grid, the coverage area of the alternating current power grid and the total power corresponding to each time segment before the certain time segment as the input content of the model, and completing one-time model training.
4. The energy storage conversion system based on an energy storage bi-directional converter according to claim 2, wherein said system further comprises:
and the parameter storage device is connected with the information prediction device and used for storing various model parameters of the model after model training for set times.
5. The energy storage conversion system based on an energy storage bi-directional converter according to claim 2, wherein said system further comprises:
and the field timing device is respectively connected with the energy storage bidirectional converter and the power measuring device and is used for respectively providing field timing service for the energy storage bidirectional converter and the power measuring device.
6. The energy storage conversion system based on an energy storage bi-directional converter according to claim 2, wherein said system further comprises:
and the LCD display array is connected with the information prediction device and used for displaying the received predicted load consumption power in the next time segment.
7. An energy storage conversion system based on an energy storage bi-directional converter according to any of claims 3-6, characterized in that:
the performing of the charging process or the discharging process on the ac grid under the control of the energy storage bidirectional converter includes: and when the charging process is carried out on the alternating current power grid under the control of the energy storage bidirectional converter, the energy storage bidirectional converter determines the charging power of the battery energy storage system on the alternating current power grid.
8. An energy storage conversion system based on an energy storage bi-directional converter according to any of claims 3-6, characterized in that:
the performing of the charging process or the discharging process on the ac grid under the control of the energy storage bidirectional converter includes: and when the discharge processing is carried out on the alternating current power grid under the control of the energy storage bidirectional converter, the energy storage bidirectional converter determines the discharge power of the battery energy storage system on the alternating current power grid.
9. An energy storage conversion system based on an energy storage bi-directional converter according to any of claims 3-6, characterized in that:
the energy storage bidirectional converter is further used for stopping the charging processing of the battery energy storage system on the alternating current power grid in the next time segment when the received predicted load consumption power in the next time segment is smaller than or equal to the maximum load power of the alternating current power grid.
10. An energy storage conversion method based on an energy storage bidirectional converter, the method comprising using the energy storage conversion system based on the energy storage bidirectional converter according to any one of claims 1-9 to analyze predicted load consumption power of an alternating current power grid in a future time segment based on maximum load power of the alternating current power grid, coverage area of the alternating current power grid and total sum of supply load power respectively corresponding to each time segment of the alternating current power grid by adopting a convolutional neural network model, so as to provide power reference data for whether the energy storage bidirectional converter starts self-adaptive charging treatment of a battery energy storage system to the alternating current power grid in the future time segment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310252898.0A CN115967111B (en) | 2023-03-16 | 2023-03-16 | Energy storage conversion system and method based on energy storage bidirectional converter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310252898.0A CN115967111B (en) | 2023-03-16 | 2023-03-16 | Energy storage conversion system and method based on energy storage bidirectional converter |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115967111A CN115967111A (en) | 2023-04-14 |
| CN115967111B true CN115967111B (en) | 2023-06-30 |
Family
ID=85894727
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310252898.0A Active CN115967111B (en) | 2023-03-16 | 2023-03-16 | Energy storage conversion system and method based on energy storage bidirectional converter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115967111B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115498623A (en) * | 2022-06-28 | 2022-12-20 | 广东电网有限责任公司 | Energy storage configuration optimization method, device, equipment and storage medium for multiple micro-grids |
| CN115564108A (en) * | 2022-09-30 | 2023-01-03 | 杨迦迤 | Deep peak regulation oriented optimal scheduling method for light storage and load in virtual power plant |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130088900A1 (en) * | 2011-10-10 | 2013-04-11 | Jong-Ho Park | Energy storage system and controlling method of the same |
| CN103236718B (en) * | 2013-03-26 | 2014-12-31 | 东北大学 | Source-network-load automatic control system and method for intelligent microgrid |
| CN104600731B (en) * | 2015-02-06 | 2017-02-22 | 山东理工大学 | Energy storage system control method of optical storage system for peak shifting |
| CN107240925B (en) * | 2017-05-12 | 2021-03-16 | 国家电网公司 | Control method of energy storage battery system in micro-grid |
| CN107332266A (en) * | 2017-08-25 | 2017-11-07 | 浙江南都电源动力股份有限公司 | User side energy-accumulating power station and its application model |
| CN108258695B (en) * | 2018-02-28 | 2019-08-27 | 东南大学 | A kind of random robust coupled mode Optimization Scheduling of alternating current-direct current mixed connection microgrid |
| CN108899921B (en) * | 2018-07-04 | 2022-03-29 | 国电南瑞科技股份有限公司 | Energy-storage-oriented multi-port energy router energy management strategy |
| AU2020102245A4 (en) * | 2019-01-08 | 2020-10-29 | Nanjing Institute Of Technology | A grid hybrid rolling dispatching method considering congestion and energy storage tou price |
| CN112564144A (en) * | 2019-09-26 | 2021-03-26 | 株洲中车时代电气股份有限公司 | Super-capacitor energy storage auxiliary power supply system, power supply system and power supply method |
| CN113783216A (en) * | 2021-08-13 | 2021-12-10 | 上海电享信息科技有限公司 | Charging and discharging control method of energy storage device |
| CN113922402B (en) * | 2021-09-26 | 2024-07-12 | 北京能高自动化技术股份有限公司 | Energy storage converter control method based on highway traffic load prediction |
| CN115001149A (en) * | 2022-06-13 | 2022-09-02 | 阳光新能源开发股份有限公司 | Energy storage control method and device and microgrid |
| CN114844114B (en) * | 2022-06-20 | 2022-11-04 | 北京信云筑科技有限责任公司 | Distributed optical storage-based remote safe operation and maintenance method for power distribution network |
-
2023
- 2023-03-16 CN CN202310252898.0A patent/CN115967111B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115498623A (en) * | 2022-06-28 | 2022-12-20 | 广东电网有限责任公司 | Energy storage configuration optimization method, device, equipment and storage medium for multiple micro-grids |
| CN115564108A (en) * | 2022-09-30 | 2023-01-03 | 杨迦迤 | Deep peak regulation oriented optimal scheduling method for light storage and load in virtual power plant |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115967111A (en) | 2023-04-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3249768B1 (en) | Load management in hybrid electrical systems | |
| CN102598454B (en) | Control device and control method | |
| EP2506387B1 (en) | Charge control device, charge control method, program, and system | |
| CN103762624A (en) | Battery management method and device | |
| CN102820679B (en) | A kind of charge control method and electronic equipment | |
| CN116565926B (en) | Intelligent energy scheduling method and device based on scene activity energy consumption prediction | |
| EP4167417A1 (en) | Device power supply method, system, and related device | |
| Hamedi et al. | Explicit degradation modelling in optimal lead–acid battery use for photovoltaic systems | |
| CN108181590B (en) | Battery power display circuit and method, chip, remote controller and air conditioner | |
| Cui et al. | An optimal energy co-scheduling framework for smart buildings | |
| CN114217530B (en) | Hybrid energy storage control system based on lithium battery power estimation | |
| CN117578617A (en) | New energy station electric quantity control method, device, equipment and medium | |
| CN116111656A (en) | Micro-grid dispatching method and device | |
| Wijesingha et al. | Smart residential energy management system (rems) using machine learning | |
| CN115967111B (en) | Energy storage conversion system and method based on energy storage bidirectional converter | |
| CN116826814B (en) | Electric energy management method based on battery cluster, energy manager and related medium | |
| CN116404683A (en) | Energy regulation and control method, device, terminal and medium of flexible-direct interconnection system | |
| JP7489623B2 (en) | Charging control system, charging control method and program | |
| CN107925244A (en) | Based on the horizontal definite control load of future energy and energy source | |
| CN217307277U (en) | Intelligent grouping management device for power supply of multi-branch battery pack | |
| CN114123430A (en) | Intelligent grouping management device powered by multi-branch battery pack and control method | |
| CN114912722B (en) | Operation arrangement method and device of battery energy storage device and electronic equipment | |
| CN117277596B (en) | Power supply control system and method based on energy storage equipment | |
| CN117117926B (en) | Power distribution network energy storage configuration method and system | |
| CN116700126B (en) | Energy device control method, device, electronic device and computer readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |