Flexible distribution network control method and system based on multi-converter grid-connected coordinated control

Abstract

The present application discloses a flexible distribution network control method and system based on multi-converter grid-connected collaborative control, relates to the technical field of flexible distribution networks, and solves the technical problem of inaccurate load estimation of flexible distribution networks; a system coordination layer, wherein the system coordination layer is provided with a central processing unit, which is used to obtain the distribution network predicted load PY, external load and distribution network load at the current time in the next period T; an optimization scheduling layer, wherein the optimization scheduling layer includes several controllers, which are used to send adjustment signals to the instruction execution unit in the local control layer according to the distribution network predicted load PY, external load and distribution network load at the current time; the local control layer includes a power consumption module, a distributed power supply module, an energy storage module and a grid-connected commutation module, which are used to upload operation data to the optimization scheduling layer and execute the adjustment signals sent by the optimization scheduling layer; the present invention is reasonably designed to facilitate the control of flexible distribution networks based on multi-converter grid-connected collaborative control.

Description

Technical Field

[0001] The present application belongs to the field of flexible distribution network technology, and specifically to a flexible distribution network control method and system based on multi-converter grid-connected coordinated control. Background Art

[0002] A flexible distribution network is one that can achieve flexible closed-loop operation. Transforming distribution networks with flexible power electronics technology is a significant trend, effectively addressing some of the bottlenecks in the development of traditional distribution networks. Advanced power electronics technology can build flexible, reliable, and efficient distribution networks, improving the power quality, reliability, and operational efficiency of urban distribution systems while also addressing the volatility of traditional loads and the proportion of renewable energy.

[0003] In the existing technology, it is difficult to obtain relatively accurate data on the internal load of the flexible distribution network and the load request of the external grid at a certain time in the future. In real life, electricity consumption habits are closely related to meteorological data. The distribution network load at a certain time in the future can be comprehensively judged through meteorological data and historical operation data of the power grid. Based on this, a flexible distribution network control method and system based on multi-converter grid-connected collaborative control are proposed to solve the above technical problems. Summary of the Invention

[0004] The present application provides a flexible distribution network control method and system based on multi-converter grid-connected coordinated control, which is used to solve the technical problem of inaccurate load estimation in flexible distribution networks.

[0005] The purpose of this application can be achieved through the following technical solutions:

[0006] Flexible distribution network control system based on multi-converter grid-connected coordinated control, including:

[0007] The system coordination layer is provided with a central processing unit (CPU), which is in communication with the CPUs of other regional distribution networks. The CPU is used to obtain the distribution network predicted load PY for the next period T, the external load, and the distribution network load at the current time based on the historical data in the region and in combination with real-time meteorological data. The process of obtaining the predicted load PY includes:

[0008] Set a period T, and obtain meteorological data every set period T; obtain meteorological forecast data for the next set period T, then obtain a number of comparison data, and obtain judgment values ​​of the meteorological forecast data and the comparison data; obtain the difference between the judgment value of the meteorological forecast data and the judgment value of the comparison meteorological data, and take the load corresponding to the meteorological data with the smallest difference as the predicted load PY;

[0009] The optimization scheduling layer includes several controllers for sending adjustment signals to the instruction execution units in the local control layer based on the distribution network predicted load PY, external load and the distribution network load at the current time;

[0010] The local control layer, including the power consumption module, distributed power supply module, energy storage module, and grid-connected commutation module, is used to upload operating data to the optimization scheduling layer and execute the regulation signals issued by the optimization scheduling layer;

[0011] Furthermore, the process of obtaining the judgment value includes:

[0012] Filter several groups of comparative data from the historical data in the region, then de-dimensionalize the meteorological data, and then set the standard data; obtain the absolute value of each data minus the corresponding standard data, obtain the minimum and maximum values, and then the maximum and minimum values; calculate the correlation coefficient of each data through the formula; obtain the proportional coefficient based on the power consumption characteristics in the distribution network; obtain the sum of the product of the correlation coefficient and the corresponding proportional coefficient as the judgment value.

[0013] Furthermore, the process of obtaining the comparison data includes:

[0014] Obtain meteorological data for the previous period T, current meteorological data, and meteorological forecast data after period T; calculate the average value of the corresponding data in the three meteorological data respectively, and obtain meteorological data in the historical data of the region where all data are within the set range of the corresponding average value; then obtain several sets of data closest to the current time as comparison data;

[0015] Furthermore, the comparison data includes meteorological data of the previous period T and current meteorological data.

[0016] Furthermore, the external load acquisition process includes:

[0017] The load request of the external distribution network is obtained every set period T and recorded in the corresponding period. The time in advance for the external distribution network to send the load request is N times the period T, where N is a positive integer and greater than 1.

[0018] Furthermore, the process of acquiring the adjustment signal includes:

[0019] The predicted load PY, external load and distribution network load at the current time are input into the intelligent regulation model. The intelligent regulation model generates an adjustment signal based on the operation data of each module uploaded from the local layer and sends it to the instruction execution unit of the corresponding module.

[0020] Furthermore, the weather forecast data is obtained through a weather data grey model.

[0021] Furthermore, the meteorological data gray model is a gray model GM(1,1), which is constructed by using the meteorological data at the current moment and several previous sets of meteorological data as original data. After reaching the next set period, the first set of meteorological data is removed from the original data, and then the meteorological data of the new set period is added to the original data.

[0022] Furthermore, a flexible distribution network control method based on multi-converter grid-connected coordinated control is proposed:

[0023] Step 1: The central processing unit is used to obtain the distribution network forecast load PY, external load and current distribution network load for the next period T based on historical data in the region and combined with real-time meteorological data;

[0024] Step 2: The local control layer uploads the operating data to the corresponding controller in the optimization scheduling layer

[0025] Step 3: According to the distribution network predicted load PY, external load and the current distribution network load, an adjustment signal is sent to the instruction execution unit in the local control layer;

[0026] Step 4: The instruction execution units corresponding to the modules in the local control layer execute the adjustment signals.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] The present invention periodically collects meteorological data, estimates the meteorological data of the next period and the historical records of the distribution network to obtain the estimated load of the next period, so as to facilitate advance preparation and timely adjustment of various modules in the local control layer; by using the load and meteorological data of the current moment and the previous period as comparison data, the result of the estimated load is made more accurate; by periodically collecting load requests from the external power grid, the load requests are recorded in the corresponding period in a timely manner, and the reception of load requests for the corresponding period in the previous period before the corresponding period is stopped, so that the external load requests for the corresponding period are more accurate; by setting an intelligent adjustment model, the present invention makes the adjustment signal output more accurate, which is convenient for the control of the flexible distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 This is the principle block diagram of this application. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions of this application in conjunction with the embodiments. Obviously, the embodiments described are only a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0032] The terms used herein are used to describe embodiments and are not intended to restrict and / or limit the present disclosure; it should be noted that the singular forms "a", "an", and "the" also include plural forms unless the context clearly indicates otherwise; and, although terms "first", "second", etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.

[0033] like Figure 1 As shown in FIG, the flexible distribution network control system based on multi-converter grid-connected coordinated control includes:

[0034] The system coordination layer is provided with a central processing unit, which is in communication with the central processing units of the distribution networks in other regions. The central processing unit is used to obtain the distribution network predicted load PY for the next period T, the external load, and the distribution network load at the current time based on the historical data in the region, which is the meteorological data and its corresponding grid load; and the real-time meteorological conditions.

[0035] The process of obtaining the predicted load PY includes:

[0036] Set a period T, and obtain meteorological data every set period, wherein the meteorological data includes temperature QW, air pressure QY, humidity SD, and wind speed FS; obtain meteorological forecast data for the next set period T using a meteorological data gray model, then obtain a number of comparison data, and obtain judgment values ​​of the meteorological forecast data and the comparison data; obtain the difference between the judgment value of the meteorological forecast data and the judgment value of the comparison meteorological data, and take the load corresponding to the meteorological data with the smallest difference as the predicted load PY;

[0037] It should be noted that the predicted load PY is the load of the electrical equipment in the flexible distribution network itself.

[0038] The external load acquisition process includes:

[0039] Obtain the load request of other power grids at the current moment. When other power grids have load requests, add the corresponding load to the distribution network load of the corresponding period. It should be noted that the time when other distribution networks make load requests in advance is an integer multiple of the period T. Then obtain whether there is a load request from the external power grid in the next set period T. When there is no load request from the external distribution network, the distribution network load of the next period is PY. When there is a load request from the external distribution network, the distribution network load of the next period is PY+P 其他 ; Then get the current distribution network load P 当前 The load request of the external power grid can be greater than 0 or less than 0. When the load request of the external power grid is greater than 0, it means that the external power grid needs to output the load of the flexible power grid. When the load request is less than 0, it means that the external power grid has excess load that needs to be transferred to the flexible distribution network.

[0040] The meteorological data gray model is a gray model GM(1,1), which is constructed using the current meteorological data and several previous sets of meteorological data as raw data. It only predicts the meteorological data of the next set period T. After the next set period arrives, the first set of meteorological data is removed from the raw data, and then the meteorological data of the new set period is added to the raw data.

[0041] The process of obtaining the judgment value includes:

[0042] Several groups of comparison data are selected from historical data in the region, and the comparison data corresponds to the load within the distribution network. In this embodiment, the number of comparison data groups is ten. The comparison data includes current meteorological data and meteorological data of the previous period T. The meteorological data is then dimensioned, and the dimensioning process is a common technique known to those skilled in the art. Standard data is then set. The absolute value of each data item is subtracted from the corresponding standard data. The standard data is the median of each data item in the comparison data. The minimum and maximum values ​​are obtained, and then the maximum and minimum values ​​are added. The correlation coefficient of each data item is calculated using the formula:

[0043]

[0044] Where R is the correlation coefficient; Xmin is the minimum value; Xmax is the maximum value; Xi is the corresponding data; ρ is 0.5.

[0045] Set different proportional coefficients [α1, α2, α3, α4] for the air temperature QW, air pressure QY, air humidity SD, and wind speed FS, where α1, α2, α3, α4 are all greater than 0, and α1+α2+α3+α4=1;

[0046] The sum of the product of the correlation coefficient and the corresponding proportional coefficient is obtained as the judgment value; the comparison data is the meteorological data and the corresponding power grid load value.

[0047] The process of obtaining the comparative data includes:

[0048] Get the meteorological data of the previous period T [QWS, QYS, SDS, FSS], the current meteorological data [QWD, QYD, SDD, FSD] and the meteorological forecast data after period T [QWY, QYY, SDY, FSY]; calculate the average value of the corresponding data in the three meteorological data respectively First, obtain meteorological data from historical data in the region, where all data are within the set range of the corresponding average value; it should be noted that the set range corresponding to different data is different; then obtain the eight sets of data closest to the current time as comparison data;

[0049] The optimization scheduling layer includes several controllers for sending adjustment signals to the instruction execution units in the local control layer based on the distribution network predicted load PY, external load and the distribution network load at the current time;

[0050] The process of acquiring the adjustment signal includes:

[0051] PY、P 其他 and P 当前 The intelligent adjustment model is input, and the intelligent adjustment model generates an adjustment signal based on the operation data of each module uploaded by the local layer and sends it to the instruction execution unit of the corresponding module.

[0052] The intelligent adjustment model is a neural network model; PY, P 其他 、P 当前 , the operating data of each module and the corresponding adjustment signal are divided into training set, test set and verification set; the intelligent adjustment model is trained, tested and verified through the training set, test set and verification set; the trained artificial intelligence model is marked as an intelligent adjustment model.

[0053] Local control layer, including:

[0054] An electrical module, which includes various electrical appliances provided at the electrical terminal. The electrical module may be an AC appliance or a DC appliance.

[0055] In this embodiment, the flexible distribution network is a DC distribution network. When the AC appliance is connected to the distribution network, it needs to be connected to a DC / AC converter first. When the DC appliance is connected to the distribution network, it needs to be connected to a DC / DC converter first.

[0056] A distributed power supply module includes various power supply devices arranged at the power supply terminal. The distributed power supply module includes a photovoltaic array and a permanent magnet direct-drive wind turbine generator (PMSG). The photovoltaic array is connected to the power distribution network through a DC / DC converter, and the permanent magnet direct-drive wind turbine generator (PMSG) is connected to the power distribution network through an AC / DC converter.

[0057] Energy storage module, the energy storage module is a DC battery, the DC battery is connected to the DC distribution network through a DC / DC converter, and the converter of the DC battery is bidirectional commutation.

[0058] Grid-connected commutation module, the DC distribution network is connected to the AC distribution network through the grid-connected commutation module, and there are two grid-connected commutation modules, including a main grid-connected commutation module and a secondary grid-connected commutation module;

[0059] The power consumption module, distributed power supply module, energy storage module and grid-connected commutation module are all provided with a data acquisition unit and an instruction execution unit. The data acquisition unit is used to collect the operating data of the corresponding module, and the operating data includes the operating voltage, operating current and operating power. The operating data of the energy storage module also includes the current electric energy capacity; the instruction execution unit is used to execute the adjustment signal issued by the optimization scheduling layer.

[0060] On the other hand, the flexible distribution network control method based on multi-converter grid-connected coordinated control:

[0061] Step 1: The central processing unit is used to obtain the distribution network forecast load PY, external load and current distribution network load for the next period T based on historical data in the region and combined with real-time meteorological data;

[0062] Step 2: The local control layer uploads the operating data to the corresponding controller in the optimization scheduling layer

[0063] Step 3: According to the distribution network predicted load PY, external load and the current distribution network load, an adjustment signal is sent to the instruction execution unit in the local control layer;

[0064] Step 4: The instruction execution units corresponding to the modules in the local control layer execute the adjustment signals.

[0065] The data in the above formula are all dimensioned and calculated numerically. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data.

[0066] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0067] The above content is merely an example and explanation of the structure of the present application. Technicians in this technical field may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the application or exceed the scope defined by the claims, they should all fall within the scope of protection of the present application.

Claims

1. A flexible distribution network control system based on multi-converter grid-connected coordinated control, characterized in that: include: The system coordination layer is provided with a central processing unit (CPU), which is in communication with the CPUs of other regional distribution networks. The CPU is used to obtain the distribution network predicted load PY for the next period T, the external load, and the distribution network load at the current time based on the historical data in the region and in combination with real-time meteorological data. The process of obtaining the predicted load PY includes: Set a period T, and obtain meteorological data every set period T; obtain meteorological forecast data for the next set period T, then obtain a number of comparison data, and obtain a judgment value of the meteorological forecast data and the comparison data; obtain the difference between the judgment value of the meteorological forecast data and the judgment value of the comparison meteorological data, and take the load corresponding to the meteorological data with the smallest difference as the predicted load PY; the process of obtaining the judgment value includes: Filter several groups of comparison data from historical data in the region. The comparison data corresponds to the load in the distribution network. The comparison data includes the current meteorological data and the meteorological data of the previous period T. Then, the meteorological data is dimensionless. Then, standard data is set. Obtain the absolute value of each data item minus the corresponding standard data. The standard data is the median of each data item in the comparison data. Obtain the minimum and maximum values, and then add the maximum and minimum values. Calculate the correlation coefficient of each data item using the formula: ； Where R is the correlation coefficient; Xmin is the minimum value; Xmax is the maximum value; Xi is the corresponding data; ρ Take 0.5; Set different proportional coefficients [α1, α2, α3, α4] for air temperature QW, air pressure QY, air humidity SD, and wind speed FS, where α1, α2, α3, α4 are all greater than 0, and α1+α2+α3+α4=1; The sum of the product of the correlation coefficient and the corresponding proportional coefficient is obtained as the judgment value; The optimization scheduling layer includes several controllers for sending adjustment signals to the instruction execution units in the local control layer based on the distribution network predicted load PY, external load and the distribution network load at the current time; The local control layer, including the power consumption module, distributed power supply module, energy storage module and grid-connected commutation module, is used to upload operating data to the optimization scheduling layer and execute the regulation signals issued by the optimization scheduling layer.

2. The flexible distribution network control system based on multi-converter grid-connected coordinated control according to claim 1 is characterized in that: The process of obtaining the comparative data includes: Obtain the meteorological data of the previous period T, the current meteorological data, and the meteorological forecast data after period T; calculate the average value of the corresponding data in the three meteorological data respectively, and obtain meteorological data in which all data in the historical data in the region are within the set range of the corresponding average value; then obtain several groups of data closest to the current time as comparison data.

3. The flexible distribution network control system based on multi-converter grid-connected coordinated control according to claim 2 is characterized in that: The comparison data includes meteorological data of the previous period T and current meteorological data.

4. The flexible distribution network control system based on multi-converter grid-connected coordinated control according to claim 1, characterized in that: The external load acquisition process includes: The load request of the external distribution network is obtained every set period T and recorded in the corresponding period. The time in advance for the external distribution network to send the load request is N times the period T, where N is a positive integer and greater than 1.

5. The flexible distribution network control system based on multi-converter grid-connected coordinated control according to claim 1, characterized in that: The acquisition process of the adjustment signal includes: The predicted load PY, external load and distribution network load at the current time are input into the intelligent regulation model. The intelligent regulation model generates an adjustment signal based on the operation data of each module uploaded from the local layer and sends it to the instruction execution unit of the corresponding module.

6. The flexible distribution network control system based on multi-converter grid-connected coordinated control according to claim 1, characterized in that: The weather forecast data is obtained through a weather data grey model.

7. The flexible distribution network control system based on multi-converter grid-connected coordinated control according to claim 6, characterized in that: The meteorological data gray model is a gray model GM(1,1), which is constructed by using the current meteorological data and several previous sets of meteorological data as original data. After reaching the next set period, the first set of meteorological data is removed from the original data, and then the meteorological data of the new set period is added to the original data.

8. The control method of a flexible distribution network control system based on multi-converter grid-connected coordinated control according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: The central processing unit is used to obtain the distribution network forecast load PY, external load and current distribution network load for the next period T based on historical data in the region and combined with real-time meteorological data; The process of obtaining the predicted load PY includes: Set a period T, and obtain meteorological data every set period T; obtain meteorological forecast data for the next set period T, then obtain a number of comparison data, and obtain a judgment value of the meteorological forecast data and the comparison data; obtain the difference between the judgment value of the meteorological forecast data and the judgment value of the comparison meteorological data, and take the load corresponding to the meteorological data with the smallest difference as the predicted load PY; the process of obtaining the judgment value includes: Filter several groups of comparison data from historical data in the region. The comparison data corresponds to the load in the distribution network. The comparison data includes the current meteorological data and the meteorological data of the previous period T. Then, the meteorological data is dimensionless. Then, standard data is set. Obtain the absolute value of each data item minus the corresponding standard data. The standard data is the median of each data item in the comparison data. Obtain the minimum and maximum values, and then add the maximum and minimum values. Calculate the correlation coefficient of each data item using the formula: ； Where R is the correlation coefficient; Xmin is the minimum value; Xmax is the maximum value; Xi is the corresponding data; ρ Take 0.5; Set different proportional coefficients [α1, α2, α3, α4] for air temperature QW, air pressure QY, air humidity SD, and wind speed FS, where α1, α2, α3, α4 are all greater than 0, and α1+α2+α3+α4=1; The sum of the product of the correlation coefficient and the corresponding proportional coefficient is obtained as the judgment value; Step 2: The local control layer uploads the operating data to the corresponding controller in the optimization scheduling layer Step 3: According to the distribution network predicted load PY, external load and the distribution network load at the current time, an adjustment signal is sent to the instruction execution unit in the local control layer; Step 4: The instruction execution units corresponding to the modules in the local control layer execute the adjustment signals.

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