CN115967111B - Energy storage and conversion system and method based on energy storage bidirectional converter - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及交流电网控制调节领域，尤其涉及一种基于储能双向变流器的储能变流系统及方法。The invention relates to the field of AC power grid control and regulation, in particular to an energy storage and conversion system and method based on an energy storage bidirectional converter.
背景技术Background technique
因为交流电网挂载的各个负荷的数量以及负荷工作线在实时发生变化，同时交流电网的工作环境也在发生实时变化，导致交流电网的负荷消耗功率也在不断变化，然而单个交流电网能够供应的负荷消耗功率有限，如果交流电网当前挂载的负荷需要的消耗功率超过交流电网能够供应的最大负荷功率，则交流电网当前挂载的各个负荷无法实现各自最佳的工作性能，同时由于交流电网需要一直保持持续供电，在交流电网当前挂载的负荷需要的消耗功率超过交流电网能够供应的最大负荷功率时才对交流电网进行停电以及设备改造，显然不够现实。Because the number of loads mounted on the AC grid and the load working line are changing in real time, and the working environment of the AC grid is also changing in real time, resulting in constant changes in the load power consumption of the AC grid. However, a single AC grid can supply Load consumption power is limited. If the power consumption required by the loads currently mounted on the AC grid exceeds the maximum load power that the AC grid can supply, each load currently mounted on the AC grid cannot achieve their respective optimal performance. It is obviously unrealistic to maintain continuous power supply, and to cut off the AC grid and perform equipment transformation when the power consumption required by the current load on the AC grid exceeds the maximum load power that the AC grid can supply.
由此可见，现有技术存在的缺点是：无法预测未来时间分段内的交流电网挂载的负荷需要消耗的功率，导致无法确定未来时间分段内储能双向变流器是否需要为交流电网进行充电以及无法确定具体的充电功率，使得储能双向变流器无法完成提前配置，进而影响了交流电网的平滑、安全运行。It can be seen that the disadvantage of the existing technology is that it is impossible to predict the power consumed by the loads mounted on the AC grid in the future time segment, resulting in the inability to determine whether the energy storage bidirectional converter needs to be used for the AC grid in the future time segment. Charging and the inability to determine the specific charging power make it impossible for the energy storage bidirectional converter to complete the configuration in advance, which in turn affects the smooth and safe operation of the AC grid.
因此，需要一种能够预测未来时间分段内的交流电网挂载的负荷需要消耗的功率的预测机制，基于预测结果确定是否需要储能双向变流器启动电池储能系统为交流电网进行充电以及确定具体的充电功率，从而保证了交流电网各个时间分段内不同负荷挂载场景下的各个负荷的正常工作性能，进一步保证交流电网的平滑、安全运行。显然，上述现有技术中的各个技术方案无法达到上述技术效果。Therefore, there is a need for a prediction mechanism that can predict the power consumed by the loads mounted on the AC grid in the future time segment, and based on the prediction results, determine whether the energy storage bidirectional converter is required to start the battery energy storage system to charge the AC grid and Determine the specific charging power, thereby ensuring the normal working performance of each load under different load loading scenarios in each time segment of the AC grid, and further ensuring the smooth and safe operation of the AC grid. Apparently, each technical solution in the above-mentioned prior art cannot achieve the above-mentioned technical effect.
发明内容Contents of the invention
为了解决现有技术中的技术问题，本发明提供了一种基于储能双向变流器的储能变流系统及方法，通过为每一交流电网设置定制结构且完成定制训练的卷积神经网络模型，为交流电网未来时间分段内的负荷消耗功率提供预测机制，并采用储能双向变流器以基于预测功率确定是否需要启动电池储能系统对所述交流电网执行自适应充电处理，从而弥补所述交流电网本身供应不足的负荷消耗功率，保证所述交流电网的各个负荷的正常工作性能。In order to solve the technical problems in the prior art, the present invention provides an energy storage and conversion system and method based on an energy storage bidirectional converter, by setting a customized structure for each AC grid and completing a customized training convolutional neural network The model provides a prediction mechanism for the load consumption power of the AC grid in the future time segment, and uses the energy storage bidirectional converter to determine whether it is necessary to start the battery energy storage system to perform adaptive charging processing on the AC grid based on the predicted power, so that Make up for the power consumption of loads that are insufficiently supplied by the AC grid itself, and ensure the normal working performance of each load of the AC grid.
根据本发明的第一方面，提供了一种基于储能双向变流器的储能变流系统，所述系统包括：According to the first aspect of the present invention, there is provided an energy storage conversion system based on an energy storage bidirectional converter, the system comprising:
储能双向变流器，分别与电池储能系统以及交流电网连接，用于在接收到的下一时间分段内的预测负荷消耗功率大于所述交流电网的最大负荷功率时，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理；The energy storage bidirectional converter is respectively connected to the battery energy storage system and the AC power grid, and is used for receiving the predicted load consumption power in the next time segment that is greater than the maximum load power of the AC grid in the next time segment. Starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power in which the predicted load consumption power exceeds the maximum load power in the segment;
电池储能系统，与所述储能双向变流器连接，用于在所述储能双向变流器的控制下对所述交流电网执行充电处理或者放电处理；A battery energy storage system, connected to the energy storage bidirectional converter, configured to perform charging or discharging processing on the AC grid under the control of the energy storage bidirectional converter;
交流电网，与所述储能双向变流器连接，用于为所述交流电网的负荷提供小于等于最大负荷功率的功率供应；An AC power grid, connected to the energy storage bidirectional converter, for providing a power supply less than or equal to the maximum load power for the load of the AC power grid;
功率测量器件，与所述交流电网连接，用于测量过往每一时间分段内所述交流电网向其负荷提供的功率总额，以获得过往各个时间分段分别对应的各个功率总额；A power measuring device, connected to the AC grid, used to measure the total amount of power provided by the AC grid to its load in each time segment in the past, so as to obtain the total amount of power corresponding to each time segment in the past;
信息预测器件，分别与所述功率测量器件以及所述储能双向变流器连接，用于为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率；The information prediction device is respectively connected with the power measurement device and the energy storage bidirectional converter, and is used to establish a convolutional neural network model for the AC power grid, and the model has completed a set number of model trainings, and the The maximum load power of the AC grid, the coverage area of the AC grid, and the total power values corresponding to each time segment in the past are used as the input content of the model, and the model is run to obtain the next time segment output by the model. The predicted load consumption power in the segment;
其中，测量过往每一时间分段内所述交流电网向其负荷提供的功率总额，以获得过往各个时间分段分别对应的各个功率总额包括：过往各个时间分段在下一时间分段之前，且过往各个时间分段以及下一时间分段在时间轴上构成一个完整的时间长度，以及过往各个时间分段以及下一时间分段中每一时间分段的持续时长相等；Wherein, measuring the total amount of power provided by the AC grid to its load in each past time segment, so as to obtain the respective total power amounts corresponding to each past time segment includes: each past time segment is before the next time segment, and Each past time segment and the next time segment form a complete time length on the time axis, and the duration of each time segment in the past time segment and the next time segment is equal;
其中，为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率包括：过往各个时间分段的数量与所述交流电网的最大负荷功率单调正向关联。Wherein, a convolutional neural network model is established for the AC grid, and the model has completed model training for a set number of times, and the maximum load power of the AC grid, the coverage area of the AC grid, and the past time segments are respectively The corresponding total amount of power is used as the input content of the model, and the predicted load consumption power in the next time segment obtained by running the model to obtain the output of the model includes: the number of each time segment in the past and the The maximum load power is monotonically positively correlated.
根据本发明的第二方面，提供了一种基于储能双向变流器的储能变流方法，所述方法包括使用如上述的基于储能双向变流器的储能变流系统以采用卷积神经网络模型基于交流电网的最大负荷功率、交流电网的覆盖面积以及交流电网过往各个时间分段分别对应的各个供应负荷功率总额解析未来时间分段所述交流电网的预测负荷消耗功率，从而为储能双向变流器在未来时间分段内是否启动电池储能系统对所述交流电网的自适应充电处理提供功率参考数据。According to the second aspect of the present invention, there is provided an energy storage conversion method based on an energy storage bidirectional converter, the method includes using the above energy storage bidirectional converter based energy storage conversion system to adopt coil The product neural network model is based on the maximum load power of the AC grid, the coverage area of the AC grid, and the total supply load power corresponding to each time segment of the AC grid in the past to analyze the predicted load consumption power of the AC grid in the future time segment, so as to be Whether the energy storage bidirectional converter starts the battery energy storage system in the future time segment provides power reference data for adaptive charging processing of the AC grid.
由此可见，相比较于现有技术，本发明至少需要具备以下几处显著的技术进步：It can be seen that, compared with the prior art, the present invention at least needs to possess the following significant technological advances:
第一处、采用分别与电池储能系统以及交流电网连接的储能双向变流器，用于在所述交流电网下一时间分段内的预测负荷消耗功率大于所述交流电网的最大负荷功率时，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理，从而保证了交流电网的健壮性和稳定性；In the first place, energy storage bidirectional converters connected to the battery energy storage system and the AC power grid are used to predict the power consumption of the load in the next time segment of the AC power grid to be greater than the maximum load power of the AC power grid , start the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment, thereby ensuring the robustness and stability of the AC grid ;
第二处、为每一交流电网设计定制结构的卷积神经网络模型，以基于所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及所述交流电网过往各个时间分段分别对应的各个供应负荷功率总额解析下一时间分段所述交流电网的预测负荷消耗功率，其中，结构的定制表现为过往各个时间分段的数量与交流电网的最大负荷功率单调正向关联，以及交流电网的最大负荷功率和交流电网的覆盖面积这两项个性化的模型输入内容；In the second place, a convolutional neural network model with a custom structure is designed for each AC grid, based on the maximum load power of the AC grid, the coverage area of the AC grid, and the corresponding time periods of the AC grid in the past. The total amount of each supply load power analyzes the predicted load consumption power of the AC power grid in the next time segment, wherein the customization of the structure shows that the number of each time segment in the past is monotonously positively correlated with the maximum load power of the AC grid, and the AC grid The two personalized model inputs are the maximum load power and the coverage area of the AC grid;
第三处、为保证卷积神经网络模型的预测可靠性，在模型使用前对模型执行设定次数的模型训练，所述交流电网的覆盖面积越广，所述设定次数的取值越大。In the third place, in order to ensure the prediction reliability of the convolutional neural network model, the model is trained for a set number of times before the model is used. The wider the coverage area of the AC power grid, the greater the value of the set number of times .
附图说明Description of drawings
以下将结合附图对本发明的实施方案进行描述，其中：Embodiments of the present invention will be described below in conjunction with the accompanying drawings, wherein:
图1为根据本发明的基于储能双向变流器的储能变流系统的技术流程图。Fig. 1 is a technical flow chart of an energy storage conversion system based on an energy storage bidirectional converter according to the present invention.
图2为根据本发明的实施例1示出的基于储能双向变流器的储能变流系统的结构示意图。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.
图3为根据本发明的实施例2示出的基于储能双向变流器的储能变流系统的结构示意图。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.
图4为根据本发明的实施例3示出的基于储能双向变流器的储能变流系统的结构示意图。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.
图5为根据本发明的实施例4示出的基于储能双向变流器的储能变流系统的结构示意图。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.
图6为根据本发明的实施例5示出的基于储能双向变流器的储能变流系统的结构示意图。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.
图7为根据本发明的实施例6示出的基于储能双向变流器的储能变流方法的步骤流程图。Fig. 7 is a flow chart showing the steps of an energy storage conversion method based on an energy storage bidirectional converter according to Embodiment 6 of the present invention.
实施方式Implementation
如图1所示，给出了根据本发明示出的基于储能双向变流器的储能变流系统及方法的技术流程图。As shown in FIG. 1 , a technical flow chart of the energy storage conversion system and method based on the energy storage bidirectional converter shown in the present invention is given.
在图1中，本发明的具体的技术流程可以拆分成以下三个主要步骤：In Fig. 1, the specific technical process of the present invention can be split into the following three main steps:
首先，采用计算机控制系统为当前的交流电网设置定制结构且完成定制训练的卷积神经网络模型，为交流电网未来时间分段内的负荷消耗功率提供预测机制，其中，结构的定制表现为作为模型输入内容的过往各个时间分段的数量与交流电网的最大负荷功率单调正向关联，以及所述模型输入内容还包括交流电网的最大负荷功率和交流电网的覆盖面积这两项个性化信息；First of all, the convolutional neural network model that uses the computer control system to set a customized structure for the current AC grid and completes the customized training provides a prediction mechanism for the load consumption power of the AC grid in the future time segment, where the customization of the structure is represented as a model The number of each past time segment of the input content is monotonically positively correlated with the maximum load power of the AC grid, and the input content of the model also includes two personalized information of the maximum load power of the AC grid and the coverage area of the AC grid;
其中，所述计算机控制系统可以是计算机PC或者移动终端，所述计算机控制系统通过包括WIFI或者GPRS的各种通信模式与储能双向变流器之间建立双向通信链路；Wherein, the computer control system may be a computer PC or a mobile terminal, and the computer control system establishes a bidirectional communication link with the energy storage bidirectional converter through various communication modes including WIFI or GPRS;
其次，从当前的交流电网处测量作为未来时间分段的下一时间分段之前的各个过往时间分段中，交流电网分别对应的各份负荷消耗功率，从而为后续的未来时间分段内的负荷消耗功率的预测提供基础数据；Secondly, measure the power consumption of each load corresponding to the AC grid in each past time segment before the next time segment as the future time segment from the current AC grid, so as to provide the power consumption of each load in the subsequent future time segment. The prediction of load power consumption provides basic data;
再次，采用运行在所述计算机控制系统上的卷积神经网络模型，以基于各个过往时间分段中交流电网分别对应的各份负荷消耗功率以及交流电网的最大负荷功率、交流电网的覆盖面积，预测下一时间分段交流电网的负荷消耗功率；Again, using the convolutional neural network model running on the computer control system, based on the power consumption of each load corresponding to the AC grid in each past time segment, the maximum load power of the AC grid, and the coverage area of the AC grid, Predict the load power consumption of the AC grid in the next time segment;
最后，针对搭建在当前的交流电网以及电池储能系统之间的储能双向变流器，利用预测的下一时间分段交流电网的负荷消耗功率以及交流电网能够提供的最大负荷功率，确定是否需要启动电池储能系统对所述交流电网执行自适应充电处理，从而弥补所述交流电网本身供应不足的负荷消耗功率。Finally, for the energy storage bidirectional converter built between the current AC grid and the battery energy storage system, use the predicted load consumption power of the AC grid in the next time segment and the maximum load power that the AC grid can provide to determine whether It is necessary to start the battery energy storage system to perform adaptive charging processing on the AC grid, so as to make up for the power consumption of loads that are insufficiently supplied by the AC grid itself.
本发明的关键点在于：为不同交流电网设计了定制结构和定制训练机制的卷积神经网络模型，用于获取可靠的未来时间分段的交流电网的负荷消耗功率的预测数据，随后，储能双向变流器利用上述预测数据实现对电池储能系统的自适应充电控制，以保证交流电网的稳定、平滑运行，维护了交流电网挂载的各个负荷的正常工作性能；由此可见，上述的定制设计模型以及自适应充电控制机制是本发明与现有技术之间最为显著的两处区别点。The key point of the present invention is: a convolutional neural network model with a customized structure and a customized training mechanism is designed for different AC grids, which is used to obtain reliable prediction data of the load consumption power of the AC grid in future time segments, and then, the energy storage The bidirectional converter uses the above prediction data to realize the adaptive charging control of the battery energy storage system, so as to ensure the stable and smooth operation of the AC grid and maintain the normal performance of each load on the AC grid; thus, the above The custom designed model and the adaptive charging control mechanism are the two most significant points of difference between the present invention and the prior art.
下面，将对本发明的基于储能双向变流器的储能变流系统及方法以实施例的方式进行具体说明。In the following, 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 embodiments.
实施例Example
图2为根据本发明的实施例1示出的基于储能双向变流器的储能变流系统的结构示意图。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.
如图2所示，所述基于储能双向变流器的储能变流系统包括以下部件：As shown in Figure 2, the energy storage and conversion system based on the energy storage bidirectional converter includes the following components:
储能双向变流器，分别与电池储能系统以及交流电网连接，用于在接收到的下一时间分段内的预测负荷消耗功率大于所述交流电网的最大负荷功率时，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理；The energy storage bidirectional converter is respectively connected to the battery energy storage system and the AC power grid, and is used for receiving the predicted load consumption power in the next time segment that is greater than the maximum load power of the AC grid in the next time segment. Starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power in which the predicted load consumption power exceeds the maximum load power in the segment;
示例地，这里的交流电网不仅仅限于各种工业交流电网和各种商用交流电网，也包括一些微型交流电网，例如家用的交流电网；Exemplarily, the AC grid here is not limited to various industrial AC grids and various commercial AC grids, but also includes some micro AC grids, such as household AC grids;
以一个家庭所在房屋为例，家用交流电网所带的最大负荷的容量，由进户线的截面大小来决定，例如，当进户线是两根4平方铜线的话，也就是单相220伏的电压，4平方铜线的安全载流量大约是30安，这样，这个家用交流电网允许带的最大负荷功率就是220ⅹ30=6600W；Taking a house where a family lives as an example, the capacity of the maximum load carried by the household AC power grid is determined by the cross-sectional size of the incoming line. For example, if the incoming line is two 4 square copper wires, that is, single-phase 220 volts voltage, the safe carrying capacity of 4 square copper wires is about 30 amps, so the maximum load power allowed by this household AC power grid is 220ⅹ30=6600W;
因此，针对这个家庭所使用的家用交流电网，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理包括：在下一时间分段内基于预测负荷消耗功率超出6600W的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理；Therefore, for the household AC grid used by this family, starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment includes: Starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding 6600W in the next time segment;
电池储能系统，与所述储能双向变流器连接，用于在所述储能双向变流器的控制下对所述交流电网执行充电处理或者放电处理；A battery energy storage system, connected to the energy storage bidirectional converter, configured to perform charging or discharging processing on the AC grid under the control of the energy storage bidirectional converter;
交流电网，与所述储能双向变流器连接，用于为所述交流电网的负荷提供小于等于最大负荷功率的功率供应；An AC power grid, connected to the energy storage bidirectional converter, for providing a power supply less than or equal to the maximum load power for the load of the AC power grid;
功率测量器件，与所述交流电网连接，用于测量过往每一时间分段内所述交流电网向其负荷提供的功率总额，以获得过往各个时间分段分别对应的各个功率总额；A power measuring device, connected to the AC grid, used to measure the total amount of power provided by the AC grid to its load in each time segment in the past, so as to obtain the total amount of power corresponding to each time segment in the past;
信息预测器件，分别与所述功率测量器件以及所述储能双向变流器连接，用于为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率；The information prediction device is respectively connected with the power measurement device and the energy storage bidirectional converter, and is used to establish a convolutional neural network model for the AC power grid, and the model has completed a set number of model trainings, and the The maximum load power of the AC grid, the coverage area of the AC grid, and the total power values corresponding to each time segment in the past are used as the input content of the model, and the model is run to obtain the next time segment output by the model. The predicted load consumption power in the segment;
其中，测量过往每一时间分段内所述交流电网向其负荷提供的功率总额，以获得过往各个时间分段分别对应的各个功率总额包括：过往各个时间分段在下一时间分段之前，且过往各个时间分段以及下一时间分段在时间轴上构成一个完整的时间长度，以及过往各个时间分段以及下一时间分段中每一时间分段的持续时长相等；Wherein, measuring the total amount of power provided by the AC grid to its load in each past time segment, so as to obtain the respective total power amounts corresponding to each past time segment includes: each past time segment is before the next time segment, and Each past time segment and the next time segment form a complete time length on the time axis, and the duration of each time segment in the past time segment and the next time segment is equal;
示例地，过往各个时间分段以及下一时间分段中每一时间分段的持续时长可以为5分钟到30分钟中的任一取值；For example, the duration of each time segment in the past and each time segment in the next time segment can be any value from 5 minutes to 30 minutes;
其中，为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率包括：过往各个时间分段的数量与所述交流电网的最大负荷功率单调正向关联；Wherein, a convolutional neural network model is established for the AC grid, and the model has completed model training for a set number of times, and the maximum load power of the AC grid, the coverage area of the AC grid, and the past time segments are respectively The corresponding total amount of power is used as the input content of the model, and the predicted load consumption power in the next time segment obtained by running the model to obtain the output of the model includes: the number of each time segment in the past and the The maximum load power is monotonously positively correlated;
示例地，所述交流电网的最大负荷功率为6600W时，过往各个时间分段的数量选择为50，所述交流电网的最大负荷功率为8000W时，过往各个时间分段的数量选择为100；以及所述交流电网的最大负荷功率为12000W时，过往各个时间分段的数量选择为150，以保持二者的单调正向关联的数值对应关系；For example, when the maximum load power of the AC power grid is 6600W, the number of past time segments is selected as 50, and when the maximum load power of the AC power grid is 8000W, the number of past time segments is selected as 100; and When the maximum load power of the AC power grid is 12000W, the number of each time segment in the past is selected as 150, so as to maintain the numerical correspondence between the two monotone positive correlations;
其中，为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率还包括：所述交流电网的覆盖面积越广，所述设定次数的取值越大；Wherein, a convolutional neural network model is established for the AC grid, and the model has completed model training for a set number of times, and the maximum load power of the AC grid, the coverage area of the AC grid, and the past time segments are respectively The corresponding total amount of power is used 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 also includes: the wider the coverage area of the AC power grid, the The larger the value of the set times is;
示例地，所述交流电网的覆盖面积为100平方米时，所述设定次数的取值为30次，所述交流电网的覆盖面积为150平方米时，所述设定次数的取值为60次，所述交流电网的覆盖面积为200平方米时，所述设定次数的取值为120次，以及所述交流电网的覆盖面积为400平方米时，所述设定次数的取值为180次；For example, when the coverage area of the AC grid is 100 square meters, the value of the set number of times is 30; when the coverage area of the AC grid is 150 square meters, the value of the set number of times is 60 times, when the coverage area of the AC grid is 200 square meters, the value of the set number of times is 120 times, and when the coverage area of the AC grid is 400 square meters, the value of the set number of times is 180 times;
其中，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理包括：在下一时间分段内驱动所述电池储能系统将差值功率充入所述交流电网以补充对所述交流电网的负荷的功率供应；Wherein, starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment includes: driving the battery in the next time segment The energy storage system charges differential power into the AC grid to supplement power supply to loads of the AC grid;
示例地，还是针对上述家庭所使用的家用交流电网，当预测的下一时间分段内上述家庭各个用电负荷的负荷消耗功率为7000W时，在下一时间分段内驱动所述电池储能系统将差值功率充入所述交流电网以补充对所述交流电网的负荷的功率供应包括：在下一时间分段内驱动所述电池储能系统将作为差值功率的400W的功率充入所述家用交流电网以补充对所述家用交流电网的负荷的功率供应，维护上述家庭各个用电负荷的正常工作。For example, for the household AC power grid used by the above-mentioned family, when the power consumption of each electric load in the above-mentioned household is predicted to be 7000W in the next time segment, the battery energy storage system will be driven in the next time segment Charging the difference power into the AC grid to supplement the power supply to the load of the AC grid includes: driving the battery energy storage system to charge 400W of power as the difference power into the The household AC power grid supplements the power supply to the loads of the household AC grid, and maintains the normal operation of the above-mentioned household power loads.
实施例Example
图3为根据本发明的实施例2示出的基于储能双向变流器的储能变流系统的结构示意图。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.
如图3所示，相比较于本发明的实施例1，所述基于储能双向变流器的储能变流系统还包括：As shown in Figure 3, compared with Embodiment 1 of the present invention, the energy storage and conversion system based on the energy storage bidirectional converter further includes:
模型训练器件，与所述信息预测器件连接，用于在所述信息预测器件使用所述模型之前，对所述模型执行设定次数的模型训练；A model training device, connected to the information prediction device, for performing model training on the model for a set number of times before the information prediction device uses the model;
其中，在所述信息预测器件使用所述模型之前，对所述模型执行设定次数的模型训练包括：每一次模型训练中，将某一时间分段对应的功率总额作为所述模型的输出内容，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及所述某一时间分段之前各个时间分段分别对应的各个功率总额作为所述模型的输入内容，完成对一次模型训练；Wherein, before the information prediction device uses the model, performing a set number of model training on the model includes: in each model training, using the total power corresponding to a certain time segment as the output content of the model , using the maximum load power of the AC grid, the coverage area of the AC grid, and the total power corresponding to each time segment before the certain time segment as the input content of the model to complete a model training ;
其中，可以选择采用CPLD芯片、ASIC芯片、FPGA芯片或者SOC芯片来实现所述模型训练器件。Wherein, a CPLD chip, an ASIC chip, an FPGA chip or an SOC chip can be selected to implement the model training device.
实施例Example
图4为根据本发明的实施例3示出的基于储能双向变流器的储能变流系统的结构示意图。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.
如图4所示，相比较于本发明的实施例1，所述基于储能双向变流器的储能变流系统还包括：As shown in Figure 4, compared with Embodiment 1 of the present invention, the energy storage and conversion system based on the energy storage bidirectional converter further includes:
参数存储器件，与所述信息预测器件连接，用于存储已完成设定次数的模型训练后的模型的各项模型参数；The parameter storage device is connected with the information prediction device, and is used to store various model parameters of the model after the model training of the set number of times has been completed;
示例地，所述参数存储器件为FLASH存储芯片、MMC存储芯片以及静态存储芯片中的一种。Exemplarily, the parameter storage device is one of a FLASH memory chip, an MMC memory chip and a static memory chip.
实施例Example
图5为根据本发明的实施例4示出的基于储能双向变流器的储能变流系统的结构示意图。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.
如图5所示，相比较于本发明的实施例1，所述基于储能双向变流器的储能变流系统还包括：As shown in Figure 5, compared with Embodiment 1 of the present invention, the energy storage and conversion system based on the energy storage bidirectional converter further includes:
现场计时器件，分别与所述储能双向变流器以及所述功率测量器件连接，用于分别为所述储能双向变流器以及所述功率测量器件提供现场计时服务；An on-site timing device is respectively connected to the energy storage bidirectional converter and the power measurement device, and is used to provide on-site timing services for the energy storage bidirectional converter and the power measurement device respectively;
其中，所述现场计时器件还可以与远端的计时服务器连接，用于实时更新本地的计时数据。Wherein, the on-site timing device can also be connected to a remote timing server for updating local timing data in real time.
实施例Example
图6为根据本发明的实施例5示出的基于储能双向变流器的储能变流系统的结构示意图。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.
如图6所示，相比较于本发明的实施例1，所述基于储能双向变流器的储能变流系统还包括：As shown in Figure 6, compared with Embodiment 1 of the present invention, the energy storage and conversion system based on the energy storage bidirectional converter further includes:
LCD显示阵列，与所述信息预测器件连接，用于显示接收到的下一时间分段内的预测负荷消耗功率；LCD display array, connected with the information prediction device, used to display the received predicted load power consumption in the next time segment;
其中，所述LCD显示阵列可以包括以矩形方式布置的多个LCD显示单元以及同步控制单元，所述同步控制机构分别与所述多个LCD显示单元连接，用于实现对所述多个LCD显示单元的同步显示控制。Wherein, the LCD display array may include a plurality of LCD display units arranged in a rectangular manner and a synchronous control unit, and the synchronous control mechanism is respectively connected with the plurality of LCD display units for realizing the display of the plurality of LCDs. Synchronous display control of units.
接着，将对本发明的各个实施例进行进一步的具体说明。Next, various embodiments of the present invention will be further described in detail.
在根据本发明的各个实施例的基于储能双向变流器的储能变流系统中：In the energy storage conversion system based on the energy storage bidirectional converter according to various embodiments of the present invention:
在所述储能双向变流器的控制下对所述交流电网执行充电处理或者放电处理包括：在所述储能双向变流器的控制下对所述交流电网执行充电处理时所述储能双向变流器确定所述电池储能系统对所述交流电网的充电功率。Performing charging or discharging processing on the AC grid under the control of the energy storage bidirectional converter includes: when performing charging processing on the AC grid under the control of the energy storage bidirectional converter, the energy storage The bidirectional converter determines the charging power of the battery energy storage system to the AC grid.
在根据本发明的各个实施例的基于储能双向变流器的储能变流系统中：In the energy storage conversion system based on the energy storage bidirectional converter according to various embodiments of the present invention:
在所述储能双向变流器的控制下对所述交流电网执行充电处理或者放电处理包括：在所述储能双向变流器的控制下对所述交流电网执行放电处理时所述储能双向变流器确定所述电池储能系统对所述交流电网的放电功率。Performing charging or discharging processing on the AC power grid under the control of the energy storage bidirectional converter includes: when performing discharging processing on the AC power grid under the control of the energy storage bidirectional converter, the energy storage The bidirectional converter determines the discharge power of the battery energy storage system to the AC grid.
以及在根据本发明的各个实施例的基于储能双向变流器的储能变流系统中：And in the energy storage conversion system based on the energy storage bidirectional converter according to various embodiments of the present invention:
所述储能双向变流器还用于在接收到的下一时间分段内的预测负荷消耗功率小于或者等于所述交流电网的最大负荷功率时，在下一时间分段内停止所述电池储能系统对所述交流电网的充电处理。The energy storage bidirectional converter is also used to stop the battery storage in the next time segment when the received predicted load consumption power in the next time segment is less than or equal to the maximum load power of the AC grid. The energy system charges the AC power grid.
实施例Example
在本实施例中，本发明搭建了一种基于储能双向变流器的储能变流方法，所述方法包括使用如上述的基于储能双向变流器的储能变流系统以采用卷积神经网络模型基于交流电网的最大负荷功率、交流电网的覆盖面积以及交流电网过往各个时间分段分别对应的各个供应负荷功率总额解析未来时间分段所述交流电网的预测负荷消耗功率，从而为储能双向变流器在未来时间分段内是否启动电池储能系统对所述交流电网的自适应充电处理提供功率参考数据。In this embodiment, the present invention builds an energy storage conversion method based on an energy storage bidirectional converter, the method includes using the above-mentioned energy storage conversion system based on an energy storage bidirectional converter to adopt a coil The product neural network model is based on the maximum load power of the AC grid, the coverage area of the AC grid, and the total supply load power corresponding to each time segment of the AC grid in the past to analyze the predicted load consumption power of the AC grid in the future time segment, so as to be Whether the energy storage bidirectional converter starts the battery energy storage system in the future time segment provides power reference data for adaptive charging processing of the AC grid.
具体地，如图7所示，所述基于储能双向变流器的储能变流方法可以包括以下步骤：Specifically, as shown in FIG. 7, the energy storage and conversion method based on the energy storage bidirectional converter may include the following steps:
步骤S701：使用储能双向变流器，分别与电池储能系统以及交流电网连接，用于在接收到的下一时间分段内的预测负荷消耗功率大于所述交流电网的最大负荷功率时，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理；Step S701: Use the energy storage bidirectional converter to connect to the battery energy storage system and the AC grid respectively, for when the received predicted load consumption power in the next time segment is greater than the maximum load power of the AC grid, Starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment;
示例地，这里的交流电网不仅仅限于各种工业交流电网和各种商用交流电网，也包括一些微型交流电网，例如家用的交流电网；Exemplarily, the AC grid here is not limited to various industrial AC grids and various commercial AC grids, but also includes some micro AC grids, such as household AC grids;
以一个家庭所在房屋为例，家用交流电网所带的最大负荷的容量，由进户线的截面大小来决定，例如，当进户线是两根4平方铜线的话，也就是单相220伏的电压，4平方铜线的安全载流量大约是30安，这样，这个家用交流电网允许带的最大负荷功率就是220ⅹ30=6600W；Taking a house where a family lives as an example, the capacity of the maximum load carried by the household AC power grid is determined by the cross-sectional size of the incoming line. For example, if the incoming line is two 4 square copper wires, that is, single-phase 220 volts voltage, the safe carrying capacity of 4 square copper wires is about 30 amps, so the maximum load power allowed by this household AC power grid is 220ⅹ30=6600W;
因此，针对这个家庭所使用的家用交流电网，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理包括：在下一时间分段内基于预测负荷消耗功率超出6600W的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理；Therefore, for the household AC grid used by this family, starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment includes: Starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding 6600W in the next time segment;
步骤S702：使用电池储能系统，与所述储能双向变流器连接，用于在所述储能双向变流器的控制下对所述交流电网执行充电处理或者放电处理；Step S702: using a battery energy storage system, connected to the energy storage bidirectional converter, for performing charging or discharging processing on the AC grid under the control of the energy storage bidirectional converter;
步骤S703：使用交流电网，与所述储能双向变流器连接，用于为所述交流电网的负荷提供小于等于最大负荷功率的功率供应；Step S703: using an AC grid, connected to the energy storage bidirectional converter, for providing power supply less than or equal to the maximum load power for the load of the AC grid;
步骤S704：使用功率测量器件，与所述交流电网连接，用于测量过往每一时间分段内所述交流电网向其负荷提供的功率总额，以获得过往各个时间分段分别对应的各个功率总额；Step S704: use a power measuring device connected to the AC grid to measure the total amount of power provided by the AC grid to its load in each past time segment, so as to obtain the respective total power values corresponding to each past time segment ;
步骤S705：使用信息预测器件，分别与所述功率测量器件以及所述储能双向变流器连接，用于为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率；Step S705: Use information prediction devices to connect with the power measurement device and the energy storage bidirectional converter respectively, to establish a convolutional neural network model for the AC power grid, and the model has completed a set number of models Training, using the maximum load power of the AC grid, the coverage area of the AC grid, and the total power values corresponding to each time segment in the past as the input content of the model, and running the model to obtain the output of the model The predicted load power consumption in the next time segment;
其中，测量过往每一时间分段内所述交流电网向其负荷提供的功率总额，以获得过往各个时间分段分别对应的各个功率总额包括：过往各个时间分段在下一时间分段之前，且过往各个时间分段以及下一时间分段在时间轴上构成一个完整的时间长度，以及过往各个时间分段以及下一时间分段中每一时间分段的持续时长相等；Wherein, measuring the total amount of power provided by the AC grid to its load in each past time segment, so as to obtain the respective total power amounts corresponding to each past time segment includes: each past time segment is before the next time segment, and Each past time segment and the next time segment form a complete time length on the time axis, and the duration of each time segment in the past time segment and the next time segment is equal;
示例地，过往各个时间分段以及下一时间分段中每一时间分段的持续时长可以为5分钟到30分钟中的任一取值；For example, the duration of each time segment in the past and each time segment in the next time segment can be any value from 5 minutes to 30 minutes;
其中，为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率包括：过往各个时间分段的数量与所述交流电网的最大负荷功率单调正向关联；Wherein, a convolutional neural network model is established for the AC grid, and the model has completed model training for a set number of times, and the maximum load power of the AC grid, the coverage area of the AC grid, and the past time segments are respectively The corresponding total amount of power is used as the input content of the model, and the predicted load consumption power in the next time segment obtained by running the model to obtain the output of the model includes: the number of each time segment in the past and the The maximum load power is monotonously positively correlated;
示例地，所述交流电网的最大负荷功率为6600W时，过往各个时间分段的数量选择为50，所述交流电网的最大负荷功率为8000W时，过往各个时间分段的数量选择为100；以及所述交流电网的最大负荷功率为12000W时，过往各个时间分段的数量选择为150，以保持二者的单调正向关联的数值对应关系；For example, when the maximum load power of the AC power grid is 6600W, the number of past time segments is selected as 50, and when the maximum load power of the AC power grid is 8000W, the number of past time segments is selected as 100; and When the maximum load power of the AC power grid is 12000W, the number of each time segment in the past is selected as 150, so as to maintain the numerical correspondence between the two monotone positive correlations;
其中，为所述交流电网建立卷积神经网络模型，所述模型已完成设定次数的模型训练，将所述交流电网的最大负荷功率、所述交流电网的覆盖面积以及过往各个时间分段分别对应的各个功率总额作为所述模型的输入内容，运行所述模型以获得所述模型输出的下一时间分段内的预测负荷消耗功率还包括：所述交流电网的覆盖面积越广，所述设定次数的取值越大；Wherein, a convolutional neural network model is established for the AC grid, and the model has completed model training for a set number of times, and the maximum load power of the AC grid, the coverage area of the AC grid, and the past time segments are respectively The corresponding total amount of power is used 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 also includes: the wider the coverage area of the AC power grid, the The larger the value of the set times is;
示例地，所述交流电网的覆盖面积为100平方米时，所述设定次数的取值为30次，所述交流电网的覆盖面积为150平方米时，所述设定次数的取值为60次，所述交流电网的覆盖面积为200平方米时，所述设定次数的取值为120次，以及所述交流电网的覆盖面积为400平方米时，所述设定次数的取值为180次；For example, when the coverage area of the AC grid is 100 square meters, the value of the set number of times is 30; when the coverage area of the AC grid is 150 square meters, the value of the set number of times is 60 times, when the coverage area of the AC grid is 200 square meters, the value of the set number of times is 120 times, and when the coverage area of the AC grid is 400 square meters, the value of the set number of times is 180 times;
其中，在下一时间分段内基于预测负荷消耗功率超出最大负荷功率的差值功率启动所述电池储能系统对所述交流电网的自适应充电处理包括：在下一时间分段内驱动所述电池储能系统将差值功率充入所述交流电网以补充对所述交流电网的负荷的功率供应；Wherein, starting the adaptive charging process of the AC grid by the battery energy storage system based on the difference power of the predicted load consumption power exceeding the maximum load power in the next time segment includes: driving the battery in the next time segment The energy storage system charges differential power into the AC grid to supplement power supply to loads of the AC grid;
示例地，还是针对上述家庭所使用的家用交流电网，当预测的下一时间分段内上述家庭各个用电负荷的负荷消耗功率为7000W时，在下一时间分段内驱动所述电池储能系统将差值功率充入所述交流电网以补充对所述交流电网的负荷的功率供应包括：在下一时间分段内驱动所述电池储能系统将作为差值功率的400W的功率充入所述家用交流电网以补充对所述家用交流电网的负荷的功率供应，维护上述家庭各个用电负荷的正常工作。For example, for the household AC power grid used by the above-mentioned family, when the power consumption of each electric load in the above-mentioned household is predicted to be 7000W in the next time segment, the battery energy storage system will be driven in the next time segment Charging the difference power into the AC grid to supplement the power supply to the load of the AC grid includes: driving the battery energy storage system to charge 400W of power as the difference power into the The household AC power grid supplements the power supply to the loads of the household AC grid, and maintains the normal operation of the above-mentioned household power loads.
另外，在本发明中，更具体地，在下一时间分段内驱动所述电池储能系统将差值功率充入所述交流电网以补充对所述交流电网的负荷的功率供应包括：在下一时间分段内的各个均匀间隔的时间点驱动所述电池储能系统将差值功率分散成等额功率分时充入所述交流电网以补充对所述交流电网的负荷的功率供应；In addition, in the present invention, more specifically, driving the battery energy storage system to charge the difference power into the AC grid to supplement the power supply to the load of the AC grid in the next time segment includes: Each evenly spaced time point in the time segment drives the battery energy storage system to disperse the differential power into equal power and charge it to the AC grid in time-sharing to supplement the power supply to the load of the AC grid;
以及在本发明中，更具体地，在下一时间分段内的各个均匀间隔的时间点驱动所述电池储能系统将差值功率分散成等额功率分时充入所述交流电网以补充对所述交流电网的负荷的功率供应包括：在下一时间分段内的各个均匀间隔的时间点的数量乘以所述等额功率等于所述差值功率。And in the present invention, more specifically, at each evenly spaced time point in the next time segment, the battery energy storage system is driven to disperse the differential power into equal power and charge it to the AC grid in time-sharing to supplement the power of all The power supply to the load of the AC power grid includes: multiplying the number of uniformly spaced time points in the next time segment by the equivalent power equals the differential power.
最后应说明的是：以上各实施例仅用以说明本公开的技术方案，而非对其限制；尽管参照前述各实施例对本公开进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本公开各实施例技术方案的范围，其均应涵盖在本公开的权利要求和说明书的范围当中。Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the various embodiments of the present disclosure. All of them should fall within the scope of the claims and description of the present disclosure.
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